import argparse
import json
import os.path
from collections import OrderedDict

import numpy as np
import requests
import torch
from flax.training.checkpoints import restore_checkpoint
from huggingface_hub import hf_hub_download

from transformers import VivitConfig, VivitForVideoClassification, VivitImageProcessor
from transformers.image_utils import PILImageResampling
def 					UpperCamelCase__		(							A__       )  ->   Optional[Any]:
				snake_case__   :      List[str]   				= '''https://storage.googleapis.com/scenic-bucket/vivit/kinetics_400/vivit_base_16x2_unfactorized/checkpoint'''

				with open(__UpperCAmelCase     ,					'wb'       ) as f:
								with requests.get(__UpperCAmelCase     ,					stream=__UpperCAmelCase       ) as req:
												for chunk in req.iter_content(chunk_size=2048       ):
																f.write(__UpperCAmelCase       )
def 					UpperCamelCase__		(							)  ->   VivitConfig:
				snake_case__   :      int   				= VivitConfig()

				snake_case__   :      str   				= 400
				snake_case__   :      Optional[Any]   				= '''huggingface/label-files'''
				snake_case__   :      Optional[Any]   				= '''kinetics400-id2label.json'''

				snake_case__   :      Tuple   				= json.load(open(hf_hub_download(__UpperCAmelCase     ,					__UpperCAmelCase     ,					repo_type='dataset'       )     ,					'r'       )       )
				snake_case__   :      List[Any]   				= {int(__UpperCAmelCase       ): v for k, v in idalabel.items()}
				snake_case__   :      Union[str, Any]   				= idalabel
				snake_case__   :      Optional[Any]   				= {v: k for k, v in idalabel.items()}
				return config
def 					UpperCamelCase__		(							)  ->   Optional[int]:
				snake_case__   :      str   				= hf_hub_download(
				    repo_id='hf-internal-testing/spaghetti-video'     ,					filename='eating_spaghetti_32_frames.npy'     ,					repo_type='dataset'       )
				snake_case__   :      Union[str, Any]   				= np.load(__UpperCAmelCase       )
				return list(__UpperCAmelCase       )
def 					UpperCamelCase__		(							A__       )  ->   Dict:
				if np.ndim(__UpperCAmelCase       ) == 2:
								return transform_attention_bias(__UpperCAmelCase       )

				elif np.ndim(__UpperCAmelCase       ) == 3:
								return transform_attention_kernel(__UpperCAmelCase       )

				else:
								raise Exception(F"""Invalid number of dimesions: {np.ndim(__UpperCAmelCase       )}"""       )
def 					UpperCamelCase__		(							A__       )  ->   Optional[int]:
				return current.flatten()
def 					UpperCamelCase__		(							A__       )  ->   Optional[Any]:
				return np.reshape(__UpperCAmelCase     ,					(current.shape[0], current.shape[1] * current.shape[2])       ).T
def 					UpperCamelCase__		(							A__       )  ->   Dict:
				return np.reshape(__UpperCAmelCase     ,					(current.shape[0] * current.shape[1], current.shape[2])       ).T
def 					UpperCamelCase__		(							A__     ,					A__       )  ->   int:
				snake_case__   :      str   				= state_dict['''optimizer''']['''target''']['''Transformer'''][F"""encoderblock_{i}"""]

				snake_case__   :      Optional[int]   				= F"""encoder.layer.{i}."""
				snake_case__   :      Tuple   				= {
				    prefix + '''intermediate.dense.bias''': state['''MlpBlock_0''']['''Dense_0''']['''bias'''],
				    prefix + '''intermediate.dense.weight''': np.transpose(state['MlpBlock_0']['Dense_0']['kernel']       ),
				    prefix + '''output.dense.bias''': state['''MlpBlock_0''']['''Dense_1''']['''bias'''],
				    prefix + '''output.dense.weight''': np.transpose(state['MlpBlock_0']['Dense_1']['kernel']       ),
				    prefix + '''layernorm_before.bias''': state['''LayerNorm_0''']['''bias'''],
				    prefix + '''layernorm_before.weight''': state['''LayerNorm_0''']['''scale'''],
				    prefix + '''layernorm_after.bias''': state['''LayerNorm_1''']['''bias'''],
				    prefix + '''layernorm_after.weight''': state['''LayerNorm_1''']['''scale'''],
				    prefix
				    + '''attention.attention.query.bias''': transform_attention(
				        state['MultiHeadDotProductAttention_0']['query']['bias']       ),
				    prefix
				    + '''attention.attention.query.weight''': transform_attention(
				        state['MultiHeadDotProductAttention_0']['query']['kernel']       ),
				    prefix
				    + '''attention.attention.key.bias''': transform_attention(state['MultiHeadDotProductAttention_0']['key']['bias']       ),
				    prefix
				    + '''attention.attention.key.weight''': transform_attention(
				        state['MultiHeadDotProductAttention_0']['key']['kernel']       ),
				    prefix
				    + '''attention.attention.value.bias''': transform_attention(
				        state['MultiHeadDotProductAttention_0']['value']['bias']       ),
				    prefix
				    + '''attention.attention.value.weight''': transform_attention(
				        state['MultiHeadDotProductAttention_0']['value']['kernel']       ),
				    prefix + '''attention.output.dense.bias''': state['''MultiHeadDotProductAttention_0''']['''out''']['''bias'''],
				    prefix
				    + '''attention.output.dense.weight''': transform_attention_output_weight(
				        state['MultiHeadDotProductAttention_0']['out']['kernel']       ),
				}

				return new_state
def 					UpperCamelCase__		(							A__       )  ->   int:
				return sum([1 if 'encoderblock_' in k else 0 for k in state_dict['optimizer']['target']['Transformer'].keys()]       )
def 					UpperCamelCase__		(							A__     ,					A__=False       )  ->   Optional[Any]:
				snake_case__   :      str   				= get_n_layers(__UpperCAmelCase       )

				snake_case__   :      Union[str, Any]   				= OrderedDict()

				snake_case__   :      str   				= state_dict['''optimizer''']['''target''']['''Transformer''']['''encoder_norm''']['''bias''']
				snake_case__   :      Tuple   				= state_dict['''optimizer''']['''target''']['''Transformer''']['''encoder_norm''']['''scale''']

				snake_case__   :      Optional[Any]   				= np.transpose(
				    state_dict['optimizer']['target']['embedding']['kernel']     ,					(4, 3, 0, 1, 2)       )
				snake_case__   :      Optional[Any]   				= state_dict['''optimizer''']['''target''']['''embedding''']['''bias''']

				snake_case__   :      Tuple   				= state_dict['''optimizer''']['''target''']['''cls''']
				snake_case__   :      List[Any]   				= state_dict['''optimizer''']['''target''']['''Transformer''']['''posembed_input'''][
				    '''pos_embedding'''
				]

				for i in range(__UpperCAmelCase       ):
								new_state.update(transform_state_encoder_block(__UpperCAmelCase     ,					__UpperCAmelCase       )       )

				if classification_head:
								snake_case__   :      Dict   				= {'''vivit.''' + k: v for k, v in new_state.items()}
								snake_case__   :      List[Any]   				= np.transpose(state_dict['optimizer']['target']['output_projection']['kernel']       )
								snake_case__   :      Any   				= np.transpose(state_dict['optimizer']['target']['output_projection']['bias']       )

				return {k: torch.tensor(__UpperCAmelCase       ) for k, v in new_state.items()}
def 					UpperCamelCase__		(							)  ->   VivitImageProcessor:
				snake_case__   :      List[str]   				= VivitImageProcessor()

				assert extractor.do_resize is True
				assert extractor.size == {"shortest_edge": 256}
				assert extractor.do_center_crop is True
				assert extractor.crop_size == {"width": 224, "height": 224}
				assert extractor.resample == PILImageResampling.BILINEAR

				# here: https://github.com/deepmind/dmvr/blob/master/dmvr/modalities.py
				# one can seen that add_image has default values for normalization_mean and normalization_std set to 0 and 1
				# which effectively means no normalization (and ViViT does not overwrite those when calling this func)
				assert extractor.do_normalize is False
				assert extractor.do_rescale is True
				assert extractor.rescale_factor == 1 / 255

				# zero-centering = True in original implementation
				assert extractor.do_zero_centering is True

				return extractor
def 					UpperCamelCase__		(							A__       )  ->   int:
				snake_case__   :      Any   				= '''checkpoint'''

				if not os.path.exists(__UpperCAmelCase       ):
								download_checkpoint(__UpperCAmelCase       )

				snake_case__   :      List[Any]   				= restore_checkpoint(__UpperCAmelCase     ,					__UpperCAmelCase       )
				snake_case__   :      int   				= transform_state(__UpperCAmelCase     ,					classification_head=__UpperCAmelCase       )

				snake_case__   :      str   				= get_vivit_config()

				assert config.image_size == 224
				assert config.num_frames == 32

				snake_case__   :      List[str]   				= VivitForVideoClassification(__UpperCAmelCase       )
				model.load_state_dict(__UpperCAmelCase       )
				model.eval()

				snake_case__   :      Dict   				= get_processor()

				snake_case__   :      List[str]   				= prepare_video()
				snake_case__   :      int   				= extractor(__UpperCAmelCase     ,					return_tensors='pt'       )

				snake_case__   :      Optional[Any]   				= model(**__UpperCAmelCase       )

				snake_case__   :      List[Any]   				= torch.Size([1, 400]       )
				snake_case__   :      Tuple   				= torch.tensor([-1.0_5_4_3, 2.0_7_6_4, -0.2_1_0_4, 0.4_4_3_9, -0.9_6_5_8]       )

				assert outputs.logits.shape == expected_shape
				assert torch.allclose(outputs.logits[0, :5]     ,					__UpperCAmelCase     ,					atol=1e-4       ), outputs.logits[0, :5]

				model.save_pretrained(__UpperCAmelCase       )
				extractor.save_pretrained(__UpperCAmelCase       )


if __name__ == "__main__":
				lowerCAmelCase__	:  Dict						=  argparse.ArgumentParser()

				parser.add_argument('''--output_model_name''', '''-o''', type=str, help='''Output path for the converted HuggingFace model''')

				lowerCAmelCase__	:  Tuple						=  parser.parse_args()
				convert(args.output_model_name)



'''simple docstring'''
import unittest
from functools import partial

import torch

from accelerate import Accelerator, debug_launcher
from accelerate.state import AcceleratorState, GradientState
from accelerate.test_utils import require_cpu, require_huggingface_suite
from accelerate.utils import GradientAccumulationPlugin







def 				__UpperCamelCase (					__UpperCAmelCase  :					List[Any]=2 ,   __UpperCAmelCase  :					Dict=True ,   __UpperCAmelCase  :					str=False			)							->	List[Any]:
  lowercase				:							Tuple									=   Accelerator(step_scheduler_with_optimizer=__UpperCAmelCase ,   split_batches=__UpperCAmelCase			)
  lowercase				:							Optional[int]									=   torch.nn.Linear(2 ,   4			)
  lowercase				:							Dict									=   torch.optim.AdamW(model.parameters() ,   lr=1.0			)
  lowercase				:							List[Any]									=   torch.optim.lr_scheduler.OneCycleLR(__UpperCAmelCase ,   max_lr=0.01 ,   steps_per_epoch=2 ,   epochs=1			)
  lowercase				,      lowercase				,      lowercase				:							Optional[Any]									=   accelerator.prepare(__UpperCAmelCase ,   __UpperCAmelCase ,   __UpperCAmelCase			)

  # Optimizer has stepped
  scheduler.step()
  if step_scheduler_with_optimizer or (num_processes == 1):
    assert (
        scheduler.scheduler.last_epoch == num_processes
    ), f"""Last Epoch ({scheduler.scheduler.last_epoch}) != Num Processes ({num_processes})"""
  else:
    assert (
        scheduler.scheduler.last_epoch != num_processes
    ), f"""Last Epoch ({scheduler.scheduler.last_epoch}) == Num Processes ({num_processes})"""







def 				__UpperCamelCase (					__UpperCAmelCase  :					str=2 ,   __UpperCAmelCase  :					str=True ,   __UpperCAmelCase  :					Tuple=False			)							->	str:
  lowercase				:							List[Any]									=   Accelerator(step_scheduler_with_optimizer=__UpperCAmelCase ,   split_batches=__UpperCAmelCase			)
  lowercase				:							Optional[Any]									=   torch.nn.Linear(2 ,   4			)
  lowercase				:							List[str]									=   torch.optim.AdamW(model.parameters() ,   lr=1.0			)
  lowercase				:							Dict									=   torch.optim.lr_scheduler.LambdaLR(__UpperCAmelCase ,   lr_lambda=lambda __UpperCAmelCase			: 1 - n / 10			)
  lowercase				,      lowercase				,      lowercase				:							int									=   accelerator.prepare(__UpperCAmelCase ,   __UpperCAmelCase ,   __UpperCAmelCase			)

  # Optimizer has stepped
  lowercase				:							Any									=   False
  scheduler.step()
  lowercase				:							Optional[Any]									=   1 - (num_processes if (step_scheduler_with_optimizer and not split_batches) else 1) / 10
  assert (
      scheduler.get_last_lr()[0] == expected_lr
  ), f"""Wrong lr found at first step, expected {expected_lr}, got {scheduler.get_last_lr()[0]}"""

  # Optimizer has not stepped
  lowercase				:							List[Any]									=   True
  scheduler.step()
  if not step_scheduler_with_optimizer:
    lowercase				:							List[Any]									=   1 - 2 / 10
  assert (
      scheduler.get_last_lr()[0] == expected_lr
  ), f"""Wrong lr found at second step, expected {expected_lr}, got {scheduler.get_last_lr()[0]}"""







def 				__UpperCamelCase (					__UpperCAmelCase  :					int = 2			)							->	Dict:
  from transformers import get_linear_schedule_with_warmup

  lowercase				:							Optional[Any]									=   [1, 2, 4]
  for num_steps in steps:
    lowercase				:							Dict									=   GradientAccumulationPlugin(num_steps=__UpperCAmelCase ,   adjust_scheduler=num_steps > 1			)
    lowercase				:							Tuple									=   Accelerator(gradient_accumulation_plugin=__UpperCAmelCase			)
    lowercase				:							str									=   torch.nn.Linear(2 ,   4			)
    lowercase				:							Dict									=   torch.optim.AdamW(model.parameters() ,   lr=10.0			)
    lowercase				:							List[str]									=   get_linear_schedule_with_warmup(optimizer=__UpperCAmelCase ,   num_warmup_steps=0 ,   num_training_steps=20			)

    lowercase				,      lowercase				,      lowercase				:							Dict									=   accelerator.prepare(__UpperCAmelCase ,   __UpperCAmelCase ,   __UpperCAmelCase			)

    for i in range(10 * num_steps			):
      with accelerator.accumulate(__UpperCAmelCase			):
        optimizer.step()
        scheduler.step()

      if i == (10 * num_steps - 2):
        assert (
            scheduler.get_last_lr()[0] != 0
        ), f"""Wrong lr found at second-to-last step, expected non-zero, got {scheduler.get_last_lr()[0]}. num_steps: {num_steps}"""
    assert (
        scheduler.get_last_lr()[0] == 0
    ), f"""Wrong lr found at last step, expected 0, got {scheduler.get_last_lr()[0]}"""
    GradientState._reset_state()






@require_cpu
class _lowerCAmelCase			(	unittest.TestCase					):







      def      lowerCamelCase__      (  self     ):
        debug_launcher(partial(lowercase  ,    num_processes=1     )  ,    num_processes=1     )
        debug_launcher(partial(lowercase  ,    num_processes=1  ,    split_batches=lowercase     )  ,    num_processes=1     )







      def      lowerCamelCase__      (  self     ):
        debug_launcher(partial(lowercase  ,    num_processes=1     )  ,    num_processes=1     )
        debug_launcher(partial(lowercase  ,    num_processes=1  ,    split_batches=lowercase     )  ,    num_processes=1     )







      def      lowerCamelCase__      (  self     ):
        debug_launcher(partial(lowercase  ,    num_processes=1  ,    step_scheduler_with_optimizer=lowercase     )  ,    num_processes=1     )







      def      lowerCamelCase__      (  self     ):
        debug_launcher(partial(lowercase  ,    num_processes=1  ,    step_scheduler_with_optimizer=lowercase     )  ,    num_processes=1     )







      def      lowerCamelCase__      (  self     ):
        AcceleratorState._reset_state(lowercase     )
        debug_launcher(lowercase     )
        debug_launcher(partial(lowercase  ,    num_processes=1  ,    split_batches=lowercase     )  ,    num_processes=1     )







      def      lowerCamelCase__      (  self     ):
        AcceleratorState._reset_state(lowercase     )
        debug_launcher(lowercase     )
        debug_launcher(partial(lowercase  ,    num_processes=1  ,    split_batches=lowercase     )  ,    num_processes=1     )







      def      lowerCamelCase__      (  self     ):
        AcceleratorState._reset_state(lowercase     )
        debug_launcher(partial(lowercase  ,    step_scheduler_with_optimizer=lowercase     )     )







      def      lowerCamelCase__      (  self     ):
        AcceleratorState._reset_state(lowercase     )
        debug_launcher(partial(lowercase  ,    step_scheduler_with_optimizer=lowercase     )     )







      @require_huggingface_suite
      def      lowerCamelCase__      (  self     ):
        AcceleratorState._reset_state(lowercase     )
        debug_launcher(partial(lowercase  ,    num_processes=1     )     )
        debug_launcher(lowercase     )