'''simple docstring'''




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     snake_case__							(      _A: int			)   ->     str:

		'''simple docstring'''


		lowerCAmelCase        =  """https://storage.googleapis.com/scenic-bucket/vivit/kinetics_400/vivit_base_16x2_unfactorized/checkpoint"""

		with open(_A				,       """wb"""			) as f:
				with requests.get(_A				,       stream=_A			) as req:
						for chunk in req.iter_content(chunk_size=2048			):
								f.write(_A			)






def     snake_case__							(      )   ->     VivitConfig:

		'''simple docstring'''


		lowerCAmelCase        =  VivitConfig()

		lowerCAmelCase        =  400
		lowerCAmelCase        =  """huggingface/label-files"""
		lowerCAmelCase        =  """kinetics400-id2label.json"""

		lowerCAmelCase        =  json.load(open(hf_hub_download(_A				,       _A				,       repo_type="""dataset"""			)				,       """r"""			)			)
		lowerCAmelCase        =  {int(_A			): v for k, v in idalabel.items()}
		lowerCAmelCase        =  idalabel
		lowerCAmelCase        =  {v: k for k, v in idalabel.items()}
		return config






def     snake_case__							(      )   ->     str:

		'''simple docstring'''


		lowerCAmelCase        =  hf_hub_download(
		    repo_id="""hf-internal-testing/spaghetti-video"""				,       filename="""eating_spaghetti_32_frames.npy"""				,       repo_type="""dataset"""			)
		lowerCAmelCase        =  np.load(_A			)
		return list(_A			)






def     snake_case__							(      _A: np.ndarray			)   ->     str:

		'''simple docstring'''


		if np.ndim(_A			) == 2:
				return transform_attention_bias(_A			)

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

		else:
				raise Exception(f"Invalid number of dimesions: {np.ndim(_A			)}"			)






def     snake_case__							(      _A: np.ndarray			)   ->     Any:

		'''simple docstring'''


		return current.flatten()






def     snake_case__							(      _A: np.ndarray			)   ->     str:

		'''simple docstring'''


		return np.reshape(_A				,       (current.shape[0], current.shape[1] * current.shape[2])			).T






def     snake_case__							(      _A: np.ndarray			)   ->     int:

		'''simple docstring'''


		return np.reshape(_A				,       (current.shape[0] * current.shape[1], current.shape[2])			).T






def     snake_case__							(      _A: Dict				,       _A: Optional[Any]			)   ->     int:

		'''simple docstring'''


		lowerCAmelCase        =  state_dict["""optimizer"""]["""target"""]["""Transformer"""][f"encoderblock_{i}"]

		lowerCAmelCase        =  f"encoder.layer.{i}."
		lowerCAmelCase        =  {
		    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     snake_case__							(      _A: Dict			)   ->     int:

		'''simple docstring'''


		return sum([1 if """encoderblock_""" in k else 0 for k in state_dict["""optimizer"""]["""target"""]["""Transformer"""].keys()]			)






def     snake_case__							(      _A: List[str]				,       _A: str=False			)   ->     Optional[Any]:

		'''simple docstring'''


		lowerCAmelCase        =  get_n_layers(_A			)

		lowerCAmelCase        =  OrderedDict()

		lowerCAmelCase        =  state_dict["""optimizer"""]["""target"""]["""Transformer"""]["""encoder_norm"""]["""bias"""]
		lowerCAmelCase        =  state_dict["""optimizer"""]["""target"""]["""Transformer"""]["""encoder_norm"""]["""scale"""]

		lowerCAmelCase        =  np.transpose(
		    state_dict["""optimizer"""]["""target"""]["""embedding"""]["""kernel"""]				,       (4, 3, 0, 1, 2)			)
		lowerCAmelCase        =  state_dict["""optimizer"""]["""target"""]["""embedding"""]["""bias"""]

		lowerCAmelCase        =  state_dict["""optimizer"""]["""target"""]["""cls"""]
		lowerCAmelCase        =  state_dict["""optimizer"""]["""target"""]["""Transformer"""]["""posembed_input"""][
		    """pos_embedding"""
		]

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

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

		return {k: torch.tensor(_A			) for k, v in new_state.items()}






def     snake_case__							(      )   ->     VivitImageProcessor:

		'''simple docstring'''


		lowerCAmelCase        =  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     snake_case__							(      _A: str			)   ->     Tuple:

		'''simple docstring'''


		lowerCAmelCase        =  """checkpoint"""

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

		lowerCAmelCase        =  restore_checkpoint(_A				,       _A			)
		lowerCAmelCase        =  transform_state(_A				,       classification_head=_A			)

		lowerCAmelCase        =  get_vivit_config()

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

		lowerCAmelCase        =  VivitForVideoClassification(_A			)
		model.load_state_dict(_A			)
		model.eval()

		lowerCAmelCase        =  get_processor()

		lowerCAmelCase        =  prepare_video()
		lowerCAmelCase        =  extractor(_A				,       return_tensors="""pt"""			)

		lowerCAmelCase        =  model(**_A			)

		lowerCAmelCase        =  torch.Size([1, 400]			)
		lowerCAmelCase        =  torch.tensor([-1.0_543, 2.0_764, -0.2_104, 0.4_439, -0.9_658]			)

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

		model.save_pretrained(_A			)
		extractor.save_pretrained(_A			)


if __name__ == "__main__":
			__lowercase				   =  argparse.ArgumentParser()

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

			__lowercase				   =  parser.parse_args()
			convert(args.output_model_name)





'''simple docstring'''




from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__lowercase				   =  logging.get_logger(__name__)

__lowercase				   =  {
    '''flaubert/flaubert_small_cased''': '''https://huggingface.co/flaubert/flaubert_small_cased/resolve/main/config.json''',
    '''flaubert/flaubert_base_uncased''': '''https://huggingface.co/flaubert/flaubert_base_uncased/resolve/main/config.json''',
    '''flaubert/flaubert_base_cased''': '''https://huggingface.co/flaubert/flaubert_base_cased/resolve/main/config.json''',
    '''flaubert/flaubert_large_cased''': '''https://huggingface.co/flaubert/flaubert_large_cased/resolve/main/config.json''',
}






class     a__( lowerCAmelCase__     ):


			'''simple docstring'''






			UpperCAmelCase_      :					int								=	'''flaubert'''
			UpperCAmelCase_      :					Optional[Any]								=	{
			    '''hidden_size''': '''emb_dim''',
			    '''num_attention_heads''': '''n_heads''',
			    '''num_hidden_layers''': '''n_layers''',
			    '''n_words''': '''vocab_size''',  # For backward compatibility
			}



			def __init__(       self	,   __lowerCAmelCase=False	,   __lowerCAmelCase=0.0	,   __lowerCAmelCase=30145	,   __lowerCAmelCase=2048	,   __lowerCAmelCase=12	,   __lowerCAmelCase=16	,   __lowerCAmelCase=0.1	,   __lowerCAmelCase=0.1	,   __lowerCAmelCase=True	,   __lowerCAmelCase=False	,   __lowerCAmelCase=False	,   __lowerCAmelCase=False	,   __lowerCAmelCase=1	,   __lowerCAmelCase=True	,   __lowerCAmelCase=512	,   __lowerCAmelCase=2048**-0.5	,   __lowerCAmelCase=1E-1_2	,   __lowerCAmelCase=0.02	,   __lowerCAmelCase=0	,   __lowerCAmelCase=1	,   __lowerCAmelCase=2	,   __lowerCAmelCase=3	,   __lowerCAmelCase=5	,   __lowerCAmelCase=True	,   __lowerCAmelCase="first"	,   __lowerCAmelCase=True	,   __lowerCAmelCase=None	,   __lowerCAmelCase=True	,   __lowerCAmelCase=0.1	,   __lowerCAmelCase=5	,   __lowerCAmelCase=5	,   __lowerCAmelCase=0	,   __lowerCAmelCase=0	,   __lowerCAmelCase=2	,   __lowerCAmelCase=0	,   **__lowerCAmelCase	,   ):




					"""simple docstring"""


					lowerCAmelCase        =  pre_norm
					lowerCAmelCase        =  layerdrop
					lowerCAmelCase        =  vocab_size
					lowerCAmelCase        =  emb_dim
					lowerCAmelCase        =  n_layers
					lowerCAmelCase        =  n_heads
					lowerCAmelCase        =  dropout
					lowerCAmelCase        =  attention_dropout
					lowerCAmelCase        =  gelu_activation
					lowerCAmelCase        =  sinusoidal_embeddings
					lowerCAmelCase        =  causal
					lowerCAmelCase        =  asm
					lowerCAmelCase        =  n_langs
					lowerCAmelCase        =  use_lang_emb
					lowerCAmelCase        =  layer_norm_eps
					lowerCAmelCase        =  bos_index
					lowerCAmelCase        =  eos_index
					lowerCAmelCase        =  pad_index
					lowerCAmelCase        =  unk_index
					lowerCAmelCase        =  mask_index
					lowerCAmelCase        =  is_encoder
					lowerCAmelCase        =  max_position_embeddings
					lowerCAmelCase        =  embed_init_std
					lowerCAmelCase        =  init_std
					lowerCAmelCase        =  summary_type
					lowerCAmelCase        =  summary_use_proj
					lowerCAmelCase        =  summary_activation
					lowerCAmelCase        =  summary_proj_to_labels
					lowerCAmelCase        =  summary_first_dropout
					lowerCAmelCase        =  start_n_top
					lowerCAmelCase        =  end_n_top
					lowerCAmelCase        =  mask_token_id
					lowerCAmelCase        =  lang_id

					if "n_words" in kwargs:
							lowerCAmelCase        =  kwargs["""n_words"""]

					super().__init__(pad_token_id=__lowerCAmelCase	,   bos_token_id=__lowerCAmelCase	,   **__lowerCAmelCase)







class     a__( lowerCAmelCase__     ):


			'''simple docstring'''






			@property
			def 							a_	(       self):




					"""simple docstring"""


					if self.task == "multiple-choice":
							lowerCAmelCase        =  {0: """batch""", 1: """choice""", 2: """sequence"""}
					else:
							lowerCAmelCase        =  {0: """batch""", 1: """sequence"""}
					return OrderedDict(
					    [
					        ("""input_ids""", dynamic_axis),
					        ("""attention_mask""", dynamic_axis),
					    ])




