import argparse
import logging
import os
from datetime import datetime

import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, SequentialSampler, Subset
from torch.utils.data.distributed import DistributedSampler
from tqdm import tqdm

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    GlueDataset,
    default_data_collator,
    glue_compute_metrics,
    glue_output_modes,
    glue_processors,
    set_seed,
)
from transformers.trainer_utils import is_main_process


_SCREAMING_SNAKE_CASE:						int    	=       logging.getLogger(__name__)






def    UpperCAmelCase__(						lowerCamelCase_  ):



			"""simple docstring"""





			_snake_case              =      p * torch.log(lowerCamelCase_  )
			_snake_case              =      0
			return -plogp.sum(dim=-1  )






def    UpperCAmelCase__(						lowerCamelCase_  ):



			"""simple docstring"""





			logger.info("lv, h >\t" + "\t".join(f'{x + 1}' for x in range(len(lowerCamelCase_  )  )  )  )
			for row in range(len(lowerCamelCase_  )  ):
						if tensor.dtype != torch.long:
									logger.info(f'layer {row + 1}:\t' + "\t".join(f'{x:.5f}' for x in tensor[row].cpu().data  )  )
						else:
									logger.info(f'layer {row + 1}:\t' + "\t".join(f'{x:d}' for x in tensor[row].cpu().data  )  )






def    UpperCAmelCase__(						lowerCamelCase_							,	lowerCamelCase_							,	lowerCamelCase_							,	lowerCamelCase_=True							,	lowerCamelCase_=True							,	lowerCamelCase_=None							,	lowerCamelCase_=False  ):



			"""simple docstring"""





			_snake_case       , _snake_case              =      model.config.num_hidden_layers, model.config.num_attention_heads
			_snake_case              =      torch.zeros(lowerCamelCase_							,	lowerCamelCase_  ).to(args.device  )
			_snake_case              =      torch.zeros(lowerCamelCase_							,	lowerCamelCase_  ).to(args.device  )

			if head_mask is None:
						_snake_case              =      torch.ones(lowerCamelCase_							,	lowerCamelCase_  ).to(args.device  )

			head_mask.requires_grad_(requires_grad=lowerCamelCase_  )
			# If actually pruned attention multi-head, set head mask to None to avoid shape mismatch
			if actually_pruned:
						_snake_case              =      None

			_snake_case              =      None
			_snake_case              =      None
			_snake_case              =      0.0

			for step, inputs in enumerate(tqdm(lowerCamelCase_							,	desc="Iteration"							,	disable=args.local_rank not in [-1, 0]  )  ):
						for k, v in inputs.items():
									_snake_case              =      v.to(args.device  )

						# Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
						_snake_case              =      model(**lowerCamelCase_							,	head_mask=lowerCamelCase_  )
						_snake_case       , _snake_case       , _snake_case              =      (
						    outputs[0],
						    outputs[1],
						    outputs[-1],
						)  # Loss and logits are the first, attention the last
						loss.backward()  # Backpropagate to populate the gradients in the head mask

						if compute_entropy:
									for layer, attn in enumerate(lowerCamelCase_  ):
												_snake_case              =      entropy(attn.detach()  ) * inputs["attention_mask"].float().unsqueeze(1  )
												attn_entropy[layer] += masked_entropy.sum(-1  ).sum(0  ).detach()

						if compute_importance:
									head_importance += head_mask.grad.abs().detach()

						# Also store our logits/labels if we want to compute metrics afterwards
						if preds is None:
									_snake_case              =      logits.detach().cpu().numpy()
									_snake_case              =      inputs["labels"].detach().cpu().numpy()
						else:
									_snake_case              =      np.append(lowerCamelCase_							,	logits.detach().cpu().numpy()							,	axis=0  )
									_snake_case              =      np.append(lowerCamelCase_							,	inputs["labels"].detach().cpu().numpy()							,	axis=0  )

						tot_tokens += inputs["attention_mask"].float().detach().sum().data

			# Normalize
			attn_entropy /= tot_tokens
			head_importance /= tot_tokens
			# Layerwise importance normalization
			if not args.dont_normalize_importance_by_layer:
						_snake_case              =      2
						_snake_case              =      torch.pow(torch.pow(lowerCamelCase_							,	lowerCamelCase_  ).sum(-1  )							,	1 / exponent  )
						head_importance /= norm_by_layer.unsqueeze(-1  ) + 1E-20

			if not args.dont_normalize_global_importance:
						_snake_case              =      (head_importance - head_importance.min()) / (head_importance.max() - head_importance.min())

			# Print/save matrices
			np.save(os.path.join(args.output_dir							,	"attn_entropy.npy"  )							,	attn_entropy.detach().cpu().numpy()  )
			np.save(os.path.join(args.output_dir							,	"head_importance.npy"  )							,	head_importance.detach().cpu().numpy()  )

			logger.info("Attention entropies"  )
			print_ad_tensor(lowerCamelCase_  )
			logger.info("Head importance scores"  )
			print_ad_tensor(lowerCamelCase_  )
			logger.info("Head ranked by importance scores"  )
			_snake_case              =      torch.zeros(head_importance.numel()							,	dtype=torch.long							,	device=args.device  )
			_snake_case              =      torch.arange(
			    head_importance.numel()							,	device=args.device  )
			_snake_case              =      head_ranks.view_as(lowerCamelCase_  )
			print_ad_tensor(lowerCamelCase_  )

			return attn_entropy, head_importance, preds, labels






def    UpperCAmelCase__(						lowerCamelCase_							,	lowerCamelCase_							,	lowerCamelCase_  ):



			"""simple docstring"""





			_snake_case       , _snake_case       , _snake_case       , _snake_case              =      compute_heads_importance(lowerCamelCase_							,	lowerCamelCase_							,	lowerCamelCase_							,	compute_entropy=lowerCamelCase_  )
			_snake_case              =      np.argmax(lowerCamelCase_							,	axis=1  ) if args.output_mode == "classification" else np.squeeze(lowerCamelCase_  )
			_snake_case              =      glue_compute_metrics(args.task_name							,	lowerCamelCase_							,	lowerCamelCase_  )[args.metric_name]
			logger.info("Pruning: original score: %f, threshold: %f"							,	lowerCamelCase_							,	original_score * args.masking_threshold  )

			_snake_case              =      torch.ones_like(lowerCamelCase_  )
			_snake_case              =      max(1							,	int(new_head_mask.numel() * args.masking_amount  )  )

			_snake_case              =      original_score
			while current_score >= original_score * args.masking_threshold:
						_snake_case              =      new_head_mask.clone()  # save current head mask
						# heads from least important to most - keep only not-masked heads
						_snake_case              =      float("Inf"  )
						_snake_case              =      head_importance.view(-1  ).sort()[1]

						if len(lowerCamelCase_  ) <= num_to_mask:
									break

						# mask heads
						_snake_case              =      current_heads_to_mask[:num_to_mask]
						logger.info("Heads to mask: %s"							,	str(current_heads_to_mask.tolist()  )  )
						_snake_case              =      new_head_mask.view(-1  )
						_snake_case              =      0.0
						_snake_case              =      new_head_mask.view_as(lowerCamelCase_  )
						_snake_case              =      new_head_mask.clone().detach()
						print_ad_tensor(lowerCamelCase_  )

						# Compute metric and head importance again
						_snake_case       , _snake_case       , _snake_case       , _snake_case              =      compute_heads_importance(
						    lowerCamelCase_							,	lowerCamelCase_							,	lowerCamelCase_							,	compute_entropy=lowerCamelCase_							,	head_mask=lowerCamelCase_  )
						_snake_case              =      np.argmax(lowerCamelCase_							,	axis=1  ) if args.output_mode == "classification" else np.squeeze(lowerCamelCase_  )
						_snake_case              =      glue_compute_metrics(args.task_name							,	lowerCamelCase_							,	lowerCamelCase_  )[args.metric_name]
						logger.info(
						    "Masking: current score: %f, remaining heads %d (%.1f percents)"							,	lowerCamelCase_							,	new_head_mask.sum()							,	new_head_mask.sum() / new_head_mask.numel() * 100							,	)

			logger.info("Final head mask"  )
			print_ad_tensor(lowerCamelCase_  )
			np.save(os.path.join(args.output_dir							,	"head_mask.npy"  )							,	head_mask.detach().cpu().numpy()  )

			return head_mask






def    UpperCAmelCase__(						lowerCamelCase_							,	lowerCamelCase_							,	lowerCamelCase_							,	lowerCamelCase_  ):



			"""simple docstring"""





			_snake_case              =      datetime.now()
			_snake_case       , _snake_case       , _snake_case       , _snake_case              =      compute_heads_importance(
			    lowerCamelCase_							,	lowerCamelCase_							,	lowerCamelCase_							,	compute_entropy=lowerCamelCase_							,	compute_importance=lowerCamelCase_							,	head_mask=lowerCamelCase_  )
			_snake_case              =      np.argmax(lowerCamelCase_							,	axis=1  ) if args.output_mode == "classification" else np.squeeze(lowerCamelCase_  )
			_snake_case              =      glue_compute_metrics(args.task_name							,	lowerCamelCase_							,	lowerCamelCase_  )[args.metric_name]
			_snake_case              =      datetime.now() - before_time

			_snake_case              =      sum(p.numel() for p in model.parameters()  )
			_snake_case              =      {
			    layer: (1 - head_mask[layer].long()).nonzero().squeeze().tolist() for layer in range(len(lowerCamelCase_  )  )
			}

			assert sum(len(lowerCamelCase_  ) for h in heads_to_prune.values()  ) == (1 - head_mask.long()).sum().item()
			model.prune_heads(lowerCamelCase_  )
			_snake_case              =      sum(p.numel() for p in model.parameters()  )

			_snake_case              =      datetime.now()
			_snake_case       , _snake_case       , _snake_case       , _snake_case              =      compute_heads_importance(
			    lowerCamelCase_							,	lowerCamelCase_							,	lowerCamelCase_							,	compute_entropy=lowerCamelCase_							,	compute_importance=lowerCamelCase_							,	head_mask=lowerCamelCase_							,	actually_pruned=lowerCamelCase_							,	)
			_snake_case              =      np.argmax(lowerCamelCase_							,	axis=1  ) if args.output_mode == "classification" else np.squeeze(lowerCamelCase_  )
			_snake_case              =      glue_compute_metrics(args.task_name							,	lowerCamelCase_							,	lowerCamelCase_  )[args.metric_name]
			_snake_case              =      datetime.now() - before_time

			logger.info(
			    "Pruning: original num of params: %.2e, after pruning %.2e (%.1f percents)"							,	lowerCamelCase_							,	lowerCamelCase_							,	pruned_num_params / original_num_params * 100							,	)
			logger.info("Pruning: score with masking: %f score with pruning: %f"							,	lowerCamelCase_							,	lowerCamelCase_  )
			logger.info("Pruning: speed ratio (new timing / original timing): %f percents"							,	original_time / new_time * 100  )






def    UpperCAmelCase__(						):



			"""simple docstring"""





			_snake_case              =      argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    "--data_dir"							,	default=lowerCamelCase_							,	type=lowerCamelCase_							,	required=lowerCamelCase_							,	help="The input data dir. Should contain the .tsv files (or other data files) for the task."							,	)
			parser.add_argument(
			    "--model_name_or_path"							,	default=lowerCamelCase_							,	type=lowerCamelCase_							,	required=lowerCamelCase_							,	help="Path to pretrained model or model identifier from huggingface.co/models"							,	)
			parser.add_argument(
			    "--task_name"							,	default=lowerCamelCase_							,	type=lowerCamelCase_							,	required=lowerCamelCase_							,	help="The name of the task to train selected in the list: " + ", ".join(glue_processors.keys()  )							,	)
			parser.add_argument(
			    "--output_dir"							,	default=lowerCamelCase_							,	type=lowerCamelCase_							,	required=lowerCamelCase_							,	help="The output directory where the model predictions and checkpoints will be written."							,	)

			# Other parameters
			parser.add_argument(
			    "--config_name"							,	default=""							,	type=lowerCamelCase_							,	help="Pretrained config name or path if not the same as model_name_or_path"							,	)
			parser.add_argument(
			    "--tokenizer_name"							,	default=""							,	type=lowerCamelCase_							,	help="Pretrained tokenizer name or path if not the same as model_name_or_path"							,	)
			parser.add_argument(
			    "--cache_dir"							,	default=lowerCamelCase_							,	type=lowerCamelCase_							,	help="Where do you want to store the pre-trained models downloaded from huggingface.co"							,	)
			parser.add_argument(
			    "--data_subset"							,	type=lowerCamelCase_							,	default=-1							,	help="If > 0: limit the data to a subset of data_subset instances."  )
			parser.add_argument(
			    "--overwrite_output_dir"							,	action="store_true"							,	help="Whether to overwrite data in output directory"  )
			parser.add_argument(
			    "--overwrite_cache"							,	action="store_true"							,	help="Overwrite the cached training and evaluation sets"  )

			parser.add_argument(
			    "--dont_normalize_importance_by_layer"							,	action="store_true"							,	help="Don't normalize importance score by layers"  )
			parser.add_argument(
			    "--dont_normalize_global_importance"							,	action="store_true"							,	help="Don't normalize all importance scores between 0 and 1"							,	)

			parser.add_argument(
			    "--try_masking"							,	action="store_true"							,	help="Whether to try to mask head until a threshold of accuracy."  )
			parser.add_argument(
			    "--masking_threshold"							,	default=0.9							,	type=lowerCamelCase_							,	help="masking threshold in term of metrics (stop masking when metric < threshold * original metric value)."							,	)
			parser.add_argument(
			    "--masking_amount"							,	default=0.1							,	type=lowerCamelCase_							,	help="Amount to heads to masking at each masking step."  )
			parser.add_argument("--metric_name"							,	default="acc"							,	type=lowerCamelCase_							,	help="Metric to use for head masking."  )

			parser.add_argument(
			    "--max_seq_length"							,	default=128							,	type=lowerCamelCase_							,	help=(
			        "The maximum total input sequence length after WordPiece tokenization. \n"
			        "Sequences longer than this will be truncated, sequences shorter padded."
			    )							,	)
			parser.add_argument("--batch_size"							,	default=1							,	type=lowerCamelCase_							,	help="Batch size."  )

			parser.add_argument("--seed"							,	type=lowerCamelCase_							,	default=42  )
			parser.add_argument("--local_rank"							,	type=lowerCamelCase_							,	default=-1							,	help="local_rank for distributed training on gpus"  )
			parser.add_argument("--no_cuda"							,	action="store_true"							,	help="Whether not to use CUDA when available"  )
			parser.add_argument("--server_ip"							,	type=lowerCamelCase_							,	default=""							,	help="Can be used for distant debugging."  )
			parser.add_argument("--server_port"							,	type=lowerCamelCase_							,	default=""							,	help="Can be used for distant debugging."  )
			_snake_case              =      parser.parse_args()

			if args.server_ip and args.server_port:
						# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
						import ptvsd

						print("Waiting for debugger attach"  )
						ptvsd.enable_attach(address=(args.server_ip, args.server_port)							,	redirect_output=lowerCamelCase_  )
						ptvsd.wait_for_attach()

			# Setup devices and distributed training
			if args.local_rank == -1 or args.no_cuda:
						_snake_case              =      torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu"  )
						_snake_case              =      0 if args.no_cuda else torch.cuda.device_count()
			else:
						torch.cuda.set_device(args.local_rank  )
						_snake_case              =      torch.device("cuda"							,	args.local_rank  )
						_snake_case              =      1
						torch.distributed.init_process_group(backend="nccl"  )  # Initializes the distributed backend

			# Setup logging
			logging.basicConfig(level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN  )
			logger.info("device: {} n_gpu: {}, distributed: {}".format(args.device							,	args.n_gpu							,	bool(args.local_rank != -1  )  )  )
			# Set the verbosity to info of the Transformers logger (on main process only):
			if is_main_process(args.local_rank  ):
						transformers.utils.logging.set_verbosity_info()
						transformers.utils.logging.enable_default_handler()
						transformers.utils.logging.enable_explicit_format()

			# Set seeds
			set_seed(args.seed  )

			# Prepare GLUE task
			_snake_case              =      args.task_name.lower()
			if args.task_name not in glue_processors:
						raise ValueError("Task not found: %s" % (args.task_name)  )
			_snake_case              =      glue_processors[args.task_name]()
			_snake_case              =      glue_output_modes[args.task_name]
			_snake_case              =      processor.get_labels()
			_snake_case              =      len(lowerCamelCase_  )

			# Load pretrained model and tokenizer
			#
			# Distributed training:
			# The .from_pretrained methods guarantee that only one local process can concurrently
			# download model & vocab.

			_snake_case              =      AutoConfig.from_pretrained(
			    args.config_name if args.config_name else args.model_name_or_path							,	num_labels=lowerCamelCase_							,	finetuning_task=args.task_name							,	output_attentions=lowerCamelCase_							,	cache_dir=args.cache_dir							,	)
			_snake_case              =      AutoTokenizer.from_pretrained(
			    args.tokenizer_name if args.tokenizer_name else args.model_name_or_path							,	cache_dir=args.cache_dir							,	)
			_snake_case              =      AutoModelForSequenceClassification.from_pretrained(
			    args.model_name_or_path							,	from_tf=bool(".ckpt" in args.model_name_or_path  )							,	config=lowerCamelCase_							,	cache_dir=args.cache_dir							,	)

			# Distributed and parallel training
			model.to(args.device  )
			if args.local_rank != -1:
						_snake_case              =      nn.parallel.DistributedDataParallel(
						    lowerCamelCase_							,	device_ids=[args.local_rank]							,	output_device=args.local_rank							,	find_unused_parameters=lowerCamelCase_  )
			elif args.n_gpu > 1:
						_snake_case              =      nn.DataParallel(lowerCamelCase_  )

			# Print/save training arguments
			os.makedirs(args.output_dir							,	exist_ok=lowerCamelCase_  )
			torch.save(lowerCamelCase_							,	os.path.join(args.output_dir							,	"run_args.bin"  )  )
			logger.info("Training/evaluation parameters %s"							,	lowerCamelCase_  )

			# Prepare dataset for the GLUE task
			_snake_case              =      GlueDataset(lowerCamelCase_							,	tokenizer=lowerCamelCase_							,	mode="dev"  )
			if args.data_subset > 0:
						_snake_case              =      Subset(lowerCamelCase_							,	list(range(min(args.data_subset							,	len(lowerCamelCase_  )  )  )  )  )
			_snake_case              =      SequentialSampler(lowerCamelCase_  ) if args.local_rank == -1 else DistributedSampler(lowerCamelCase_  )
			_snake_case              =      DataLoader(
			    lowerCamelCase_							,	sampler=lowerCamelCase_							,	batch_size=args.batch_size							,	collate_fn=lowerCamelCase_  )

			# Compute head entropy and importance score
			compute_heads_importance(lowerCamelCase_							,	lowerCamelCase_							,	lowerCamelCase_  )

			# Try head masking (set heads to zero until the score goes under a threshole)
			# and head pruning (remove masked heads and see the effect on the network)
			if args.try_masking and args.masking_threshold > 0.0 and args.masking_threshold < 1.0:
						_snake_case              =      mask_heads(lowerCamelCase_							,	lowerCamelCase_							,	lowerCamelCase_  )
						prune_heads(lowerCamelCase_							,	lowerCamelCase_							,	lowerCamelCase_							,	lowerCamelCase_  )


if __name__ == "__main__":
			main()


import functools
import math

import flax.linen as nn
import jax
import jax.numpy as jnp






def    UpperCAmelCase__(						lowerCamelCase_							,	lowerCamelCase_							,	lowerCamelCase_							,	lowerCamelCase_							,	lowerCamelCase_ = 4096  ):



			"""simple docstring"""





			_snake_case       , _snake_case       , _snake_case              =      key.shape[-3:]
			_snake_case              =      value.shape[-1]
			_snake_case              =      min(lowerCamelCase_							,	lowerCamelCase_  )
			_snake_case              =      query / jnp.sqrt(lowerCamelCase_  )

			@functools.partial(jax.checkpoint							,	prevent_cse=lowerCamelCase_  )
			def summarize_chunk(lowerCamelCase_							,	lowerCamelCase_							,	lowerCamelCase_  ):
						_snake_case              =      jnp.einsum("...qhd,...khd->...qhk"							,	lowerCamelCase_							,	lowerCamelCase_							,	precision=lowerCamelCase_  )

						_snake_case              =      jnp.max(lowerCamelCase_							,	axis=-1							,	keepdims=lowerCamelCase_  )
						_snake_case              =      jax.lax.stop_gradient(lowerCamelCase_  )
						_snake_case              =      jnp.exp(attn_weights - max_score  )

						_snake_case              =      jnp.einsum("...vhf,...qhv->...qhf"							,	lowerCamelCase_							,	lowerCamelCase_							,	precision=lowerCamelCase_  )
						_snake_case              =      jnp.einsum("...qhk->...qh"							,	lowerCamelCase_  )

						return (exp_values, exp_weights.sum(axis=-1  ), max_score)

			def chunk_scanner(lowerCamelCase_  ):
						# julienne key array
						_snake_case              =      jax.lax.dynamic_slice(
						    operand=lowerCamelCase_							,	start_indices=[0] * (key.ndim - 3) + [chunk_idx, 0, 0]							,	slice_sizes=list(key.shape[:-3]  ) + [key_chunk_size, num_heads, k_features]							,	)

						# julienne value array
						_snake_case              =      jax.lax.dynamic_slice(
						    operand=lowerCamelCase_							,	start_indices=[0] * (value.ndim - 3) + [chunk_idx, 0, 0]							,	slice_sizes=list(value.shape[:-3]  ) + [key_chunk_size, num_heads, v_features]							,	)

						return summarize_chunk(lowerCamelCase_							,	lowerCamelCase_							,	lowerCamelCase_  )

			_snake_case       , _snake_case       , _snake_case              =      jax.lax.map(f=lowerCamelCase_							,	xs=jnp.arange(0							,	lowerCamelCase_							,	lowerCamelCase_  )  )

			_snake_case              =      jnp.max(lowerCamelCase_							,	axis=0							,	keepdims=lowerCamelCase_  )
			_snake_case              =      jnp.exp(chunk_max - global_max  )

			chunk_values *= jnp.expand_dims(lowerCamelCase_							,	axis=-1  )
			chunk_weights *= max_diffs

			_snake_case              =      chunk_values.sum(axis=0  )
			_snake_case              =      jnp.expand_dims(lowerCamelCase_							,	-1  ).sum(axis=0  )

			return all_values / all_weights






def    UpperCAmelCase__(						lowerCamelCase_							,	lowerCamelCase_							,	lowerCamelCase_							,	lowerCamelCase_=jax.lax.Precision.HIGHEST							,	lowerCamelCase_ = 1024							,	lowerCamelCase_ = 4096  ):



			"""simple docstring"""





			_snake_case       , _snake_case       , _snake_case              =      query.shape[-3:]

			def chunk_scanner(lowerCamelCase_							,	lowerCamelCase_  ):
						# julienne query array
						_snake_case              =      jax.lax.dynamic_slice(
						    operand=lowerCamelCase_							,	start_indices=([0] * (query.ndim - 3)) + [chunk_idx, 0, 0]							,	slice_sizes=list(query.shape[:-3]  ) + [min(lowerCamelCase_							,	lowerCamelCase_  ), num_heads, q_features]							,	)

						return (
						    chunk_idx + query_chunk_size,  # unused ignore it
						    _query_chunk_attention(
						        query=lowerCamelCase_							,	key=lowerCamelCase_							,	value=lowerCamelCase_							,	precision=lowerCamelCase_							,	key_chunk_size=lowerCamelCase_  ),
						)

			_snake_case       , _snake_case              =      jax.lax.scan(
			    f=lowerCamelCase_							,	init=0							,	xs=lowerCamelCase_							,	length=math.ceil(num_q / query_chunk_size  )  )

			return jnp.concatenate(lowerCamelCase_							,	axis=-3  )  # fuse the chunked result back

class 	_SCREAMING_SNAKE_CASE							(     nn.Module							):






		"""simple docstring"""







		A_						 =      42
		A_						 =      8
		A_						 =      64
		A_						 =      0.0
		A_						 =      False
		A_						 =      jnp.floataa



		def 				_a				(			self    :				Union[str, Any]   ):






					"""simple docstring"""
					_snake_case              =      self.dim_head * self.heads
					_snake_case              =      self.dim_head**-0.5

					# Weights were exported with old names {to_q, to_k, to_v, to_out}
					_snake_case              =      nn.Dense(__SCREAMING_SNAKE_CASE  ,					use_bias=__SCREAMING_SNAKE_CASE  ,					dtype=self.dtype  ,					name="to_q"   )
					_snake_case              =      nn.Dense(__SCREAMING_SNAKE_CASE  ,					use_bias=__SCREAMING_SNAKE_CASE  ,					dtype=self.dtype  ,					name="to_k"   )
					_snake_case              =      nn.Dense(__SCREAMING_SNAKE_CASE  ,					use_bias=__SCREAMING_SNAKE_CASE  ,					dtype=self.dtype  ,					name="to_v"   )

					_snake_case              =      nn.Dense(self.query_dim  ,					dtype=self.dtype  ,					name="to_out_0"   )
					_snake_case              =      nn.Dropout(rate=self.dropout   )



		def 				_a				(			self    :				Optional[int]  ,					__SCREAMING_SNAKE_CASE    :				Any   ):






					"""simple docstring"""
					_snake_case       , _snake_case       , _snake_case              =      tensor.shape
					_snake_case              =      self.heads
					_snake_case              =      tensor.reshape(__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE  ,					dim // head_size   )
					_snake_case              =      jnp.transpose(__SCREAMING_SNAKE_CASE  ,					(0, 2, 1, 3)   )
					_snake_case              =      tensor.reshape(batch_size * head_size  ,					__SCREAMING_SNAKE_CASE  ,					dim // head_size   )
					return tensor



		def 				_a				(			self    :				str  ,					__SCREAMING_SNAKE_CASE    :				Tuple   ):






					"""simple docstring"""
					_snake_case       , _snake_case       , _snake_case              =      tensor.shape
					_snake_case              =      self.heads
					_snake_case              =      tensor.reshape(batch_size // head_size  ,					__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE   )
					_snake_case              =      jnp.transpose(__SCREAMING_SNAKE_CASE  ,					(0, 2, 1, 3)   )
					_snake_case              =      tensor.reshape(batch_size // head_size  ,					__SCREAMING_SNAKE_CASE  ,					dim * head_size   )
					return tensor







		def __call__(			self    :				int  ,					__SCREAMING_SNAKE_CASE    :				int  ,					__SCREAMING_SNAKE_CASE    :				Tuple=None  ,					__SCREAMING_SNAKE_CASE    :				List[Any]=True   ):






					"""simple docstring"""
					_snake_case              =      hidden_states if context is None else context

					_snake_case              =      self.query(__SCREAMING_SNAKE_CASE   )
					_snake_case              =      self.key(__SCREAMING_SNAKE_CASE   )
					_snake_case              =      self.value(__SCREAMING_SNAKE_CASE   )

					_snake_case              =      self.reshape_heads_to_batch_dim(__SCREAMING_SNAKE_CASE   )
					_snake_case              =      self.reshape_heads_to_batch_dim(__SCREAMING_SNAKE_CASE   )
					_snake_case              =      self.reshape_heads_to_batch_dim(__SCREAMING_SNAKE_CASE   )

					if self.use_memory_efficient_attention:
								_snake_case              =      query_states.transpose(1  ,					0  ,					2   )
								_snake_case              =      key_states.transpose(1  ,					0  ,					2   )
								_snake_case              =      value_states.transpose(1  ,					0  ,					2   )

								# this if statement create a chunk size for each layer of the unet
								# the chunk size is equal to the query_length dimension of the deepest layer of the unet

								_snake_case              =      query_states.shape[-3]
								if flatten_latent_dim % 64 == 0:
											_snake_case              =      int(flatten_latent_dim / 64   )
								elif flatten_latent_dim % 16 == 0:
											_snake_case              =      int(flatten_latent_dim / 16   )
								elif flatten_latent_dim % 4 == 0:
											_snake_case              =      int(flatten_latent_dim / 4   )
								else:
											_snake_case              =      int(__SCREAMING_SNAKE_CASE   )

								_snake_case              =      jax_memory_efficient_attention(
								    __SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE  ,					query_chunk_size=__SCREAMING_SNAKE_CASE  ,					key_chunk_size=4_096 * 4   )

								_snake_case              =      hidden_states.transpose(1  ,					0  ,					2   )
					else:
								# compute attentions
								_snake_case              =      jnp.einsum("b i d, b j d->b i j"  ,					__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE   )
								_snake_case              =      attention_scores * self.scale
								_snake_case              =      nn.softmax(__SCREAMING_SNAKE_CASE  ,					axis=2   )

								# attend to values
								_snake_case              =      jnp.einsum("b i j, b j d -> b i d"  ,					__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE   )

					_snake_case              =      self.reshape_batch_dim_to_heads(__SCREAMING_SNAKE_CASE   )
					_snake_case              =      self.proj_attn(__SCREAMING_SNAKE_CASE   )
					return self.dropout_layer(__SCREAMING_SNAKE_CASE  ,					deterministic=__SCREAMING_SNAKE_CASE   )

class 	_SCREAMING_SNAKE_CASE							(     nn.Module							):






		"""simple docstring"""







		A_						 =      42
		A_						 =      42
		A_						 =      42
		A_						 =      0.0
		A_						 =      False
		A_						 =      jnp.floataa
		A_						 =      False



		def 				_a				(			self    :				Any   ):






					"""simple docstring"""
					# self attention (or cross_attention if only_cross_attention is True)
					_snake_case              =      FlaxAttention(
					    self.dim  ,					self.n_heads  ,					self.d_head  ,					self.dropout  ,					self.use_memory_efficient_attention  ,					dtype=self.dtype   )
					# cross attention
					_snake_case              =      FlaxAttention(
					    self.dim  ,					self.n_heads  ,					self.d_head  ,					self.dropout  ,					self.use_memory_efficient_attention  ,					dtype=self.dtype   )
					_snake_case              =      FlaxFeedForward(dim=self.dim  ,					dropout=self.dropout  ,					dtype=self.dtype   )
					_snake_case              =      nn.LayerNorm(epsilon=1e-5  ,					dtype=self.dtype   )
					_snake_case              =      nn.LayerNorm(epsilon=1e-5  ,					dtype=self.dtype   )
					_snake_case              =      nn.LayerNorm(epsilon=1e-5  ,					dtype=self.dtype   )
					_snake_case              =      nn.Dropout(rate=self.dropout   )







		def __call__(			self    :				List[Any]  ,					__SCREAMING_SNAKE_CASE    :				Any  ,					__SCREAMING_SNAKE_CASE    :				List[str]  ,					__SCREAMING_SNAKE_CASE    :				Any=True   ):






					"""simple docstring"""
					# self attention
					_snake_case              =      hidden_states
					if self.only_cross_attention:
								_snake_case              =      self.attna(self.norma(__SCREAMING_SNAKE_CASE   )  ,					__SCREAMING_SNAKE_CASE  ,					deterministic=__SCREAMING_SNAKE_CASE   )
					else:
								_snake_case              =      self.attna(self.norma(__SCREAMING_SNAKE_CASE   )  ,					deterministic=__SCREAMING_SNAKE_CASE   )
					_snake_case              =      hidden_states + residual

					# cross attention
					_snake_case              =      hidden_states
					_snake_case              =      self.attna(self.norma(__SCREAMING_SNAKE_CASE   )  ,					__SCREAMING_SNAKE_CASE  ,					deterministic=__SCREAMING_SNAKE_CASE   )
					_snake_case              =      hidden_states + residual

					# feed forward
					_snake_case              =      hidden_states
					_snake_case              =      self.ff(self.norma(__SCREAMING_SNAKE_CASE   )  ,					deterministic=__SCREAMING_SNAKE_CASE   )
					_snake_case              =      hidden_states + residual

					return self.dropout_layer(__SCREAMING_SNAKE_CASE  ,					deterministic=__SCREAMING_SNAKE_CASE   )

class 	_SCREAMING_SNAKE_CASE							(     nn.Module							):






		"""simple docstring"""







		A_						 =      42
		A_						 =      42
		A_						 =      42
		A_						 =      1
		A_						 =      0.0
		A_						 =      False
		A_						 =      False
		A_						 =      jnp.floataa
		A_						 =      False



		def 				_a				(			self    :				Union[str, Any]   ):






					"""simple docstring"""
					_snake_case              =      nn.GroupNorm(num_groups=32  ,					epsilon=1e-5   )

					_snake_case              =      self.n_heads * self.d_head
					if self.use_linear_projection:
								_snake_case              =      nn.Dense(__SCREAMING_SNAKE_CASE  ,					dtype=self.dtype   )
					else:
								_snake_case              =      nn.Conv(
								    __SCREAMING_SNAKE_CASE  ,					kernel_size=(1, 1)  ,					strides=(1, 1)  ,					padding="VALID"  ,					dtype=self.dtype  ,					)

					_snake_case              =      [
					    FlaxBasicTransformerBlock(
					        __SCREAMING_SNAKE_CASE  ,					self.n_heads  ,					self.d_head  ,					dropout=self.dropout  ,					only_cross_attention=self.only_cross_attention  ,					dtype=self.dtype  ,					use_memory_efficient_attention=self.use_memory_efficient_attention  ,					)
					    for _ in range(self.depth   )
					]

					if self.use_linear_projection:
								_snake_case              =      nn.Dense(__SCREAMING_SNAKE_CASE  ,					dtype=self.dtype   )
					else:
								_snake_case              =      nn.Conv(
								    __SCREAMING_SNAKE_CASE  ,					kernel_size=(1, 1)  ,					strides=(1, 1)  ,					padding="VALID"  ,					dtype=self.dtype  ,					)

					_snake_case              =      nn.Dropout(rate=self.dropout   )







		def __call__(			self    :				Optional[int]  ,					__SCREAMING_SNAKE_CASE    :				List[str]  ,					__SCREAMING_SNAKE_CASE    :				Union[str, Any]  ,					__SCREAMING_SNAKE_CASE    :				Union[str, Any]=True   ):






					"""simple docstring"""
					_snake_case       , _snake_case       , _snake_case       , _snake_case              =      hidden_states.shape
					_snake_case              =      hidden_states
					_snake_case              =      self.norm(__SCREAMING_SNAKE_CASE   )
					if self.use_linear_projection:
								_snake_case              =      hidden_states.reshape(__SCREAMING_SNAKE_CASE  ,					height * width  ,					__SCREAMING_SNAKE_CASE   )
								_snake_case              =      self.proj_in(__SCREAMING_SNAKE_CASE   )
					else:
								_snake_case              =      self.proj_in(__SCREAMING_SNAKE_CASE   )
								_snake_case              =      hidden_states.reshape(__SCREAMING_SNAKE_CASE  ,					height * width  ,					__SCREAMING_SNAKE_CASE   )

					for transformer_block in self.transformer_blocks:
								_snake_case              =      transformer_block(__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE  ,					deterministic=__SCREAMING_SNAKE_CASE   )

					if self.use_linear_projection:
								_snake_case              =      self.proj_out(__SCREAMING_SNAKE_CASE   )
								_snake_case              =      hidden_states.reshape(__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE   )
					else:
								_snake_case              =      hidden_states.reshape(__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE   )
								_snake_case              =      self.proj_out(__SCREAMING_SNAKE_CASE   )

					_snake_case              =      hidden_states + residual
					return self.dropout_layer(__SCREAMING_SNAKE_CASE  ,					deterministic=__SCREAMING_SNAKE_CASE   )

class 	_SCREAMING_SNAKE_CASE							(     nn.Module							):






		"""simple docstring"""







		A_						 =      42
		A_						 =      0.0
		A_						 =      jnp.floataa



		def 				_a				(			self    :				Optional[int]   ):






					"""simple docstring"""
					# The second linear layer needs to be called
					# net_2 for now to match the index of the Sequential layer
					_snake_case              =      FlaxGEGLU(self.dim  ,					self.dropout  ,					self.dtype   )
					_snake_case              =      nn.Dense(self.dim  ,					dtype=self.dtype   )







		def __call__(			self    :				Tuple  ,					__SCREAMING_SNAKE_CASE    :				List[str]  ,					__SCREAMING_SNAKE_CASE    :				List[Any]=True   ):






					"""simple docstring"""
					_snake_case              =      self.net_a(__SCREAMING_SNAKE_CASE  ,					deterministic=__SCREAMING_SNAKE_CASE   )
					_snake_case              =      self.net_a(__SCREAMING_SNAKE_CASE   )
					return hidden_states

class 	_SCREAMING_SNAKE_CASE							(     nn.Module							):






		"""simple docstring"""







		A_						 =      42
		A_						 =      0.0
		A_						 =      jnp.floataa



		def 				_a				(			self    :				Optional[Any]   ):






					"""simple docstring"""
					_snake_case              =      self.dim * 4
					_snake_case              =      nn.Dense(inner_dim * 2  ,					dtype=self.dtype   )
					_snake_case              =      nn.Dropout(rate=self.dropout   )







		def __call__(			self    :				Optional[int]  ,					__SCREAMING_SNAKE_CASE    :				Union[str, Any]  ,					__SCREAMING_SNAKE_CASE    :				List[str]=True   ):






					"""simple docstring"""
					_snake_case              =      self.proj(__SCREAMING_SNAKE_CASE   )
					_snake_case       , _snake_case              =      jnp.split(__SCREAMING_SNAKE_CASE  ,					2  ,					axis=2   )
					return self.dropout_layer(hidden_linear * nn.gelu(__SCREAMING_SNAKE_CASE   )  ,					deterministic=__SCREAMING_SNAKE_CASE   )

