



import timeit
from typing import Callable, Optional

from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_auto import MODEL_MAPPING, MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_torch_available, logging
from .benchmark_utils import (
    Benchmark,
    Memory,
    MemorySummary,
    measure_peak_memory_cpu,
    start_memory_tracing,
    stop_memory_tracing,
)


if is_torch_available():
	import torch

	from .benchmark_args import PyTorchBenchmarkArguments


if is_pyanvml_available():
	import pyanvml.pyanvml as nvml


UpperCAmelCase							=       logging.get_logger(__name__)

class 			__snake_case   ( SCREAMING_SNAKE_CASE):







							'''simple docstring'''






							UpperCamelCase__					:   PyTorchBenchmarkArguments
							UpperCamelCase__					:   PretrainedConfig
							UpperCamelCase__					:   str       =       "PyTorch"


							@property
							def _a   (				self		):
											return torch.__version__


							def _a   (				self	,	a_	,	a_	,	a_		):
											a__					   =		self._prepare_inference_func(a_	,	a_	,	a_		)
											return self._measure_speed(_inference		)


							def _a   (				self	,	a_	,	a_	,	a_		):
											a__					   =		self._prepare_inference_func(a_	,	a_	,	a_		)
											return self._measure_memory(_inference		)


							def _a   (				self	,	a_	,	a_	,	a_		):
											a__					   =		self._prepare_train_func(a_	,	a_	,	a_		)
											return self._measure_speed(_train		)


							def _a   (				self	,	a_	,	a_	,	a_		):
											a__					   =		self._prepare_train_func(a_	,	a_	,	a_		)
											return self._measure_memory(_train		)


							def _a   (				self	,	a_	,	a_	,	a_		):
											a__					   =		self.config_dict[model_name]

											if self.args.torchscript:
															a__					   =		True

											a__					   =		(
											    hasattr(a_	,	"""architectures"""		)
											    and isinstance(config.architectures	,	a_		)
											    and len(config.architectures		) > 0
											)
											if not self.args.only_pretrain_model and has_model_class_in_config:
															try:
																			a__					   =		config.architectures[0]
																			a__					   =		__import__("""transformers"""	,	fromlist=[model_class]		)
																			a__					   =		getattr(a_	,	a_		)
																			a__					   =		model_cls(a_		)
															except ImportError:
																			raise ImportError(
																			    F'''{model_class} does not exist. If you just want to test the pretrained model, you might want to'''
																			    """ set `--only_pretrain_model` or `args.only_pretrain_model=True`."""		)
											else:
															a__					   =		MODEL_MAPPING[config.__class__](a_		)

											model.eval()
											model.to(self.args.device		)

											# encoder-decoder has vocab size saved differently
											a__					   =		config.vocab_size if hasattr(a_	,	"""vocab_size"""		) else config.encoder.vocab_size
											a__					   =		torch.randint(a_	,	(batch_size, sequence_length)	,	dtype=torch.long	,	device=self.args.device		)

											if self.args.fpaa:
															logger.info("""Running training in Mixed Precision..."""		)
															if not self.args.is_gpu:
																			raise ValueError("""Mixed precision is possible only for GPU."""		)
															# amp seems to have memory leaks so that memory usage
															# is measured using .half() for now https://github.com/NVIDIA/apex/issues/439
															model.half()

											if self.args.torchscript:
															with torch.no_grad():
																			a__					   =		torch.jit.trace(a_	,	a_		)
											else:
															a__					   =		model

											def encoder_decoder_forward():
															with torch.no_grad():
																			a__					   =		inference_model(a_	,	decoder_input_ids=a_		)
															return outputs

											def encoder_forward():
															with torch.no_grad():
																			a__					   =		inference_model(a_		)
															return outputs

											a__					   =		encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
											return _forward


							def _a   (				self	,	a_	,	a_	,	a_		):
											a__					   =		self.config_dict[model_name]

											a__					   =		(
											    hasattr(a_	,	"""architectures"""		)
											    and isinstance(config.architectures	,	a_		)
											    and len(config.architectures		) > 0
											)
											if not self.args.only_pretrain_model and has_model_class_in_config:
															try:
																			a__					   =		config.architectures[0]
																			a__					   =		__import__("""transformers"""	,	fromlist=[model_class]		)
																			a__					   =		getattr(a_	,	a_		)
																			a__					   =		model_cls(a_		)
															except ImportError:
																			raise ImportError(
																			    F'''{model_class} does not exist. If you just want to test the pretrained model, you might want to'''
																			    """ set `--only_pretrain_model` or `args.only_pretrain_model=True`."""		)
											else:
															a__					   =		MODEL_WITH_LM_HEAD_MAPPING[config.__class__](a_		)

											if self.args.torchscript:
															raise NotImplementedError("""Training for torchscript is currently not implemented"""		)
											else:
															a__					   =		model

											model.train()
											model.to(self.args.device		)

											# encoder-decoder has vocab size saved differently
											a__					   =		config.vocab_size if hasattr(a_	,	"""vocab_size"""		) else config.encoder.vocab_size
											a__					   =		torch.randint(a_	,	(batch_size, sequence_length)	,	dtype=torch.long	,	device=self.args.device		)

											if self.args.fpaa:
															logger.info("""Running training in Mixed Precision..."""		)
															if not self.args.is_gpu:
																			raise ValueError("""Mixed precision is possible only for GPU."""		)

															# amp seems to have memory leaks so that memory usage
															# is measured using .half() for now https://github.com/NVIDIA/apex/issues/439
															model.half()

											def compute_loss_and_backprob_encoder():
															a__					   =		train_model(a_	,	labels=a_		)[0]
															loss.backward()
															return loss

											def compute_loss_and_backprob_encoder_decoder():
															a__					   =		train_model(a_	,	decoder_input_ids=a_	,	labels=a_		)[0]
															loss.backward()
															return loss

											a__					   =		(
											    compute_loss_and_backprob_encoder_decoder
											    if config.is_encoder_decoder
											    else compute_loss_and_backprob_encoder
											)
											return _train


							def _a   (				self	,	a_		):
											try:
															if self.args.is_tpu or self.args.torchscript:
																			# run additional 10 times to stabilize compilation for tpu and torchscript
																			logger.info("""Do inference on TPU or torchscript. Running model 5 times to stabilize compilation"""		)
																			timeit.repeat(
																			    a_	,	repeat=1	,	number=5	,	)

															# as written in https://docs.python.org/2/library/timeit.html#timeit.Timer.repeat, min should be taken rather than the average
															a__					   =		timeit.repeat(
															    a_	,	repeat=self.args.repeat	,	number=10	,	)

															if self.args.is_tpu and self.args.torch_xla_tpu_print_metrics:
																			import torch_xla.debug.metrics as met

																			self.print_fn(met.metrics_report()		)

															return min(a_		) / 10.0
											except RuntimeError as e:
															self.print_fn(F'''Doesn\'t fit on GPU. {e}'''		)
															return "N/A"







							def _a   (				self	,	a_		):
											try:
															if self.args.trace_memory_line_by_line:
																			a__					   =		start_memory_tracing("""transformers"""		)

															if self.args.is_tpu:
																			# tpu
																			raise NotImplementedError(
																			    """Memory Benchmarking is currently not implemented for TPU. Please disable memory benchmarking with"""
																			    """ `--no-memory` or `args.memory=False`"""		)
															elif self.args.is_gpu:
																			if not is_pyanvml_available():
																							logger.warning(
																							    """py3nvml not installed, we won't log GPU memory usage. """
																							    """Install py3nvml (pip install py3nvml) to log information about GPU."""		)
																							a__					   =		"""N/A"""
																			else:
																							logger.info(
																							    """Measuring total GPU usage on GPU device. Make sure to not have additional processes running"""
																							    """ on the same GPU."""		)
																							# init nvml
																							nvml.nvmlInit()
																							func()
																							a__					   =		nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx		)
																							a__					   =		nvml.nvmlDeviceGetMemoryInfo(a_		)
																							a__					   =		meminfo.used
																							a__					   =		Memory(a_		)
																							# shutdown nvml
																							nvml.nvmlShutdown()
															else:
																			# cpu
																			a__					   =		measure_peak_memory_cpu(a_		)
																			a__					   =		Memory(a_		) if isinstance(a_	,	a_		) else memory_bytes

															if self.args.trace_memory_line_by_line:
																			a__					   =		stop_memory_tracing(a_		)
															else:
																			a__					   =		None

															return memory, summary
											except RuntimeError as e:
															self.print_fn(F'''Doesn\'t fit on GPU. {e}'''		)
															return "N/A", None






import argparse
import os
import pathlib

import fairseq
import torch
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import (
    DataaVecTextConfig,
    DataaVecTextForMaskedLM,
    DataaVecTextForSequenceClassification,
    DataaVecTextModel,
)
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)

# IMPORTANT: In order for this script to run, please make sure to download the dictionary: `dict.txt` from wget https://dl.fbaipublicfiles.com/fairseq/models/roberta.large.tar.gz
# File copied from https://github.com/pytorch/fairseq/blob/main/examples/data2vec/models/data2vec_text.py
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse("""0.9.0"""):
	raise Exception("""requires fairseq >= 0.9.0""")


logging.set_verbosity_info()
UpperCAmelCase							=       logging.get_logger(__name__)

UpperCAmelCase							=       """Hello world! cécé herlolip"""

def       A_   (						__a  :					str			,		__a  :					str			,		__a  :					bool		):

				"""simple docstring"""
				a__   ,      a__					   =		os.path.split(__a		)
				a__					   =		DataaVecTextModel.from_pretrained(
				    __a			,		checkpoint_file=__a		)
				dataavec.eval()  # disable dropout
				a__					   =		dataavec.models[0]
				a__					   =		dataavec_model.encoder.sentence_encoder
				a__					   =		DataaVecTextConfig(
				    vocab_size=dataavec_sent_encoder.embed_tokens.num_embeddings			,		hidden_size=dataavec_model.args.encoder_embed_dim			,		num_hidden_layers=dataavec_model.args.encoder_layers			,		num_attention_heads=dataavec_model.args.encoder_attention_heads			,		intermediate_size=dataavec_model.args.encoder_ffn_embed_dim			,		max_position_embeddings=514			,		type_vocab_size=1			,		layer_norm_eps=1e-5			,		)
				if classification_head:
								a__					   =		dataavec.model.classification_heads["""mnli"""].out_proj.weight.shape[0]
				print("""Our BERT config:"""			,		__a		)

				a__					   =		DataaVecTextForSequenceClassification(__a		) if classification_head else DataaVecTextForMaskedLM(__a		)
				model.eval()

				# Now let's copy all the weights.
				# Embeddings
				a__					   =		dataavec_sent_encoder.embed_tokens.weight
				a__					   =		dataavec_sent_encoder.embed_positions.weight
				a__					   =		torch.zeros_like(
				    model.dataavec_text.embeddings.token_type_embeddings.weight		)  # just zero them out b/c data2vec doesn't use them.
				a__					   =		dataavec_sent_encoder.layernorm_embedding.weight
				a__					   =		dataavec_sent_encoder.layernorm_embedding.bias

				for i in range(config.num_hidden_layers		):
								# Encoder: start of layer
								a__					   =		model.dataavec_text.encoder.layer[i]
								a__					   =		dataavec_sent_encoder.layers[i]

								# self attention
								a__					   =		layer.attention.self
								assert dataavec_layer.self_attn.k_proj.weight.data.shape == torch.Size(
								    (config.hidden_size, config.hidden_size)		), (
								    "Shape for data2vec_layer.self_attn.k_proj.weight.data should be"
								    F''' {torch.Size((config.hidden_size, config.hidden_size)		)}'''
								)
								assert dataavec_layer.self_attn.q_proj.weight.data.shape == torch.Size(
								    (config.hidden_size, config.hidden_size)		), (
								    "Shape for data2vec_layer.self_attn.q_proj.weight.data should be"
								    F''' {torch.Size((config.hidden_size, config.hidden_size)		)}'''
								)
								assert dataavec_layer.self_attn.v_proj.weight.data.shape == torch.Size(
								    (config.hidden_size, config.hidden_size)		), (
								    "Shape for data2vec_layer.self_attn.v_proj.weight.data should be"
								    F''' {torch.Size((config.hidden_size, config.hidden_size)		)}'''
								)

								a__					   =		dataavec_layer.self_attn.q_proj.weight
								a__					   =		dataavec_layer.self_attn.q_proj.bias
								a__					   =		dataavec_layer.self_attn.k_proj.weight
								a__					   =		dataavec_layer.self_attn.k_proj.bias
								a__					   =		dataavec_layer.self_attn.v_proj.weight
								a__					   =		dataavec_layer.self_attn.v_proj.bias

								# self-attention output
								a__					   =		layer.attention.output
								assert (
								    self_output.dense.weight.shape == dataavec_layer.self_attn.out_proj.weight.shape
								), F'''Shape for self_output.dense.weight should be {dataavec_layer.self_attn.out_proj.weight.shape}'''
								a__					   =		dataavec_layer.self_attn.out_proj.weight
								a__					   =		dataavec_layer.self_attn.out_proj.bias
								a__					   =		dataavec_layer.self_attn_layer_norm.weight
								a__					   =		dataavec_layer.self_attn_layer_norm.bias

								# intermediate
								a__					   =		layer.intermediate
								assert (
								    intermediate.dense.weight.shape == dataavec_layer.fca.weight.shape
								), F'''Shape for intermediate.dense.weight should be {dataavec_layer.fca.weight.shape}'''
								a__					   =		dataavec_layer.fca.weight
								a__					   =		dataavec_layer.fca.bias

								# output
								a__					   =		layer.output
								assert (
								    bert_output.dense.weight.shape == dataavec_layer.fca.weight.shape
								), F'''Shape for bert_output.dense.weight should be {dataavec_layer.fca.weight.shape}'''
								a__					   =		dataavec_layer.fca.weight
								a__					   =		dataavec_layer.fca.bias
								a__					   =		dataavec_layer.final_layer_norm.weight
								a__					   =		dataavec_layer.final_layer_norm.bias
								# end of layer

				if classification_head:
								a__					   =		dataavec.model.classification_heads["""mnli"""].dense.weight
								a__					   =		dataavec.model.classification_heads["""mnli"""].dense.bias
								a__					   =		dataavec.model.classification_heads["""mnli"""].out_proj.weight
								a__					   =		dataavec.model.classification_heads["""mnli"""].out_proj.bias
				else:
								# LM Head
								a__					   =		dataavec_model.encoder.lm_head.dense.weight
								a__					   =		dataavec_model.encoder.lm_head.dense.bias
								a__					   =		dataavec_model.encoder.lm_head.layer_norm.weight
								a__					   =		dataavec_model.encoder.lm_head.layer_norm.bias
								a__					   =		dataavec_model.encoder.lm_head.weight
								a__					   =		dataavec_model.encoder.lm_head.bias

				# Let's check that we get the same results.
				a__					   =		dataavec.encode(__a		).unsqueeze(0		)  # batch of size 1

				a__					   =		model(__a		)[0]
				if classification_head:
								a__					   =		dataavec.model.classification_heads["""mnli"""](dataavec.extract_features(__a		)		)
				else:
								a__					   =		dataavec_model(__a		)[0]
				print(our_output.shape			,		their_output.shape		)
				a__					   =		torch.max(torch.abs(our_output - their_output		)		).item()
				print(F'''max_absolute_diff = {max_absolute_diff}'''		)  # ~ 1e-7
				a__					   =		torch.allclose(__a			,		__a			,		atol=1e-3		)
				print("""Do both models output the same tensors?"""			,		"""🔥""" if success else """💩"""		)
				if not success:
								raise Exception("""Something went wRoNg"""		)

				pathlib.Path(__a		).mkdir(parents=__a			,		exist_ok=__a		)
				print(F'''Saving model to {pytorch_dump_folder_path}'''		)
				model.save_pretrained(__a		)


if __name__ == "__main__":
	UpperCAmelCase							=       argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    """--checkpoint_path""", default=None, type=str, required=True, help="""Path the official PyTorch dump."""
	)
	parser.add_argument(
	    """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
	)
	parser.add_argument(
	    """--classification_head""", action="""store_true""", help="""Whether to convert a final classification head."""
	)
	UpperCAmelCase							=       parser.parse_args()
	convert_dataavec_checkpoint_to_pytorch(
	    args.checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
	)

