'''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'''




import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple, Union

import numpy as np
import regex as re

from ...utils import is_tf_available, is_torch_available, logging


if TYPE_CHECKING:
						if is_torch_available():
												import torch
						if is_tf_available():
												import tensorflow as tf

from ...tokenization_utils import AddedToken, PreTrainedTokenizer


__lowercase				   =  logging.get_logger(__name__)

__lowercase				   =  {
    '''vocab_file''': '''vocab.json''',
    '''merges_file''': '''merges.txt''',
}

__lowercase				   =  {
    '''vocab_file''': {
        '''Salesforce/codegen-350M-mono''': '''https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/vocab.json''',
    },
    '''merges_file''': {
        '''Salesforce/codegen-350M-mono''': '''https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/merges.txt''',
    },
}

__lowercase				   =  {
    '''Salesforce/codegen-350M-mono''': 2_0_4_8,
}






@lru_cache()
def     snake_case__							(      )   ->     int:

		'''simple docstring'''


		lowerCAmelCase        =  (
		    list(range(ord("""!"""			)				,       ord("""~"""			) + 1			)			) + list(range(ord("""¡"""			)				,       ord("""¬"""			) + 1			)			) + list(range(ord("""®"""			)				,       ord("""ÿ"""			) + 1			)			)
		)
		lowerCAmelCase        =  bs[:]
		lowerCAmelCase        =  0
		for b in range(2**8			):
				if b not in bs:
						bs.append(_A			)
						cs.append(2**8 + n			)
						n += 1
		lowerCAmelCase        =  [chr(_A			) for n in cs]
		return dict(zip(_A				,       _A			)			)






def     snake_case__							(      _A: int			)   ->     int:

		'''simple docstring'''


		lowerCAmelCase        =  set()
		lowerCAmelCase        =  word[0]
		for char in word[1:]:
				pairs.add((prev_char, char)			)
				lowerCAmelCase        =  char
		return pairs






class     a__( lowerCAmelCase__     ):


			'''simple docstring'''






			UpperCAmelCase_      :					Tuple								=	VOCAB_FILES_NAMES
			UpperCAmelCase_      :					List[str]								=	PRETRAINED_VOCAB_FILES_MAP
			UpperCAmelCase_      :					Any								=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			UpperCAmelCase_      :					str								=	['''input_ids''', '''attention_mask''']



			def __init__(       self	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase="replace"	,   __lowerCAmelCase="<|endoftext|>"	,   __lowerCAmelCase="<|endoftext|>"	,   __lowerCAmelCase="<|endoftext|>"	,   __lowerCAmelCase=None	,   __lowerCAmelCase=False	,   __lowerCAmelCase=False	,   **__lowerCAmelCase	,   ):




					"""simple docstring"""


					lowerCAmelCase        =  AddedToken(__lowerCAmelCase	,   lstrip=__lowerCAmelCase	,   rstrip=__lowerCAmelCase) if isinstance(__lowerCAmelCase	,   __lowerCAmelCase) else bos_token
					lowerCAmelCase        =  AddedToken(__lowerCAmelCase	,   lstrip=__lowerCAmelCase	,   rstrip=__lowerCAmelCase) if isinstance(__lowerCAmelCase	,   __lowerCAmelCase) else eos_token
					lowerCAmelCase        =  AddedToken(__lowerCAmelCase	,   lstrip=__lowerCAmelCase	,   rstrip=__lowerCAmelCase) if isinstance(__lowerCAmelCase	,   __lowerCAmelCase) else unk_token
					lowerCAmelCase        =  AddedToken(__lowerCAmelCase	,   lstrip=__lowerCAmelCase	,   rstrip=__lowerCAmelCase) if isinstance(__lowerCAmelCase	,   __lowerCAmelCase) else pad_token
					super().__init__(
					    errors=__lowerCAmelCase	,   unk_token=__lowerCAmelCase	,   bos_token=__lowerCAmelCase	,   eos_token=__lowerCAmelCase	,   pad_token=__lowerCAmelCase	,   add_prefix_space=__lowerCAmelCase	,   add_bos_token=__lowerCAmelCase	,   **__lowerCAmelCase	,   )
					lowerCAmelCase        =  add_bos_token

					with open(__lowerCAmelCase	,   encoding="""utf-8""") as vocab_handle:
							lowerCAmelCase        =  json.load(__lowerCAmelCase)
					lowerCAmelCase        =  {v: k for k, v in self.encoder.items()}
					lowerCAmelCase        =  errors  # how to handle errors in decoding
					lowerCAmelCase        =  bytes_to_unicode()
					lowerCAmelCase        =  {v: k for k, v in self.byte_encoder.items()}
					with open(__lowerCAmelCase	,   encoding="""utf-8""") as merges_handle:
							lowerCAmelCase        =  merges_handle.read().split("""\n""")[1:-1]
					lowerCAmelCase        =  [tuple(merge.split()) for merge in bpe_merges]
					lowerCAmelCase        =  dict(zip(__lowerCAmelCase	,   range(len(__lowerCAmelCase))))
					lowerCAmelCase        =  {}
					lowerCAmelCase        =  add_prefix_space

					# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
					lowerCAmelCase        =  re.compile(r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+""")



			@property
			def 							a_	(       self):




					"""simple docstring"""


					return len(self.encoder)



			def 							a_	(       self):




					"""simple docstring"""


					return dict(self.encoder	,   **self.added_tokens_encoder)



			def 							a_	(       self	,   __lowerCAmelCase):




					"""simple docstring"""


					if token in self.cache:
							return self.cache[token]
					lowerCAmelCase        =  tuple(__lowerCAmelCase)
					lowerCAmelCase        =  get_pairs(__lowerCAmelCase)

					if not pairs:
							return token

					while True:
							lowerCAmelCase        =  min(__lowerCAmelCase	,   key=lambda __lowerCAmelCase: self.bpe_ranks.get(__lowerCAmelCase	,   float("""inf""")))
							if bigram not in self.bpe_ranks:
									break
							lowerCAmelCase ,					lowerCAmelCase        =  bigram
							lowerCAmelCase        =  []
							lowerCAmelCase        =  0
							while i < len(__lowerCAmelCase):
									try:
											lowerCAmelCase        =  word.index(__lowerCAmelCase	,   __lowerCAmelCase)
									except ValueError:
											new_word.extend(word[i:])
											break
									else:
											new_word.extend(word[i:j])
											lowerCAmelCase        =  j

									if word[i] == first and i < len(__lowerCAmelCase) - 1 and word[i + 1] == second:
											new_word.append(first + second)
											i += 2
									else:
											new_word.append(word[i])
											i += 1
							lowerCAmelCase        =  tuple(__lowerCAmelCase)
							lowerCAmelCase        =  new_word
							if len(__lowerCAmelCase) == 1:
									break
							else:
									lowerCAmelCase        =  get_pairs(__lowerCAmelCase)
					lowerCAmelCase        =  """ """.join(__lowerCAmelCase)
					lowerCAmelCase        =  word
					return word



			def 							a_	(       self	,   __lowerCAmelCase	,   __lowerCAmelCase=None):




					"""simple docstring"""


					if self.add_bos_token:
							lowerCAmelCase        =  [self.bos_token_id]
					else:
							lowerCAmelCase        =  []

					lowerCAmelCase        =  bos_token_ids + token_ids_a

					if token_ids_a is None:
							return output

					return output + bos_token_ids + token_ids_a



			def 							a_	(       self	,   __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase        =  []
					for token in re.findall(self.pat	,   __lowerCAmelCase):
							lowerCAmelCase        =  """""".join(
							    self.byte_encoder[b] for b in token.encode("""utf-8"""))  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
							bpe_tokens.extend(bpe_token for bpe_token in self.bpe(__lowerCAmelCase).split(""" """))
					return bpe_tokens



			def 							a_	(       self	,   __lowerCAmelCase):




					"""simple docstring"""


					return self.encoder.get(__lowerCAmelCase	,   self.encoder.get(self.unk_token))



			def 							a_	(       self	,   __lowerCAmelCase):




					"""simple docstring"""


					return self.decoder.get(__lowerCAmelCase)



			def 							a_	(       self	,   __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase        =  """""".join(__lowerCAmelCase)
					lowerCAmelCase        =  bytearray([self.byte_decoder[c] for c in text]).decode("""utf-8"""	,   errors=self.errors)
					return text



			def 							a_	(       self	,   __lowerCAmelCase	,   __lowerCAmelCase = None):




					"""simple docstring"""


					if not os.path.isdir(__lowerCAmelCase):
							logger.error(f"Vocabulary path ({save_directory}) should be a directory")
							return
					lowerCAmelCase        =  os.path.join(
					    __lowerCAmelCase	,   (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""])
					lowerCAmelCase        =  os.path.join(
					    __lowerCAmelCase	,   (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""])

					with open(__lowerCAmelCase	,   """w"""	,   encoding="""utf-8""") as f:
							f.write(json.dumps(self.encoder	,   indent=2	,   sort_keys=__lowerCAmelCase	,   ensure_ascii=__lowerCAmelCase) + """\n""")

					lowerCAmelCase        =  0
					with open(__lowerCAmelCase	,   """w"""	,   encoding="""utf-8""") as writer:
							writer.write("""#version: 0.2\n""")
							for bpe_tokens, token_index in sorted(self.bpe_ranks.items()	,   key=lambda __lowerCAmelCase: kv[1]):
									if index != token_index:
											logger.warning(
											    f"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."
											    """ Please check that the tokenizer is not corrupted!""")
											lowerCAmelCase        =  token_index
									writer.write(""" """.join(__lowerCAmelCase) + """\n""")
									index += 1

					return vocab_file, merge_file



			def 							a_	(       self	,   __lowerCAmelCase	,   __lowerCAmelCase=False	,   **__lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase        =  kwargs.pop("""add_prefix_space"""	,   self.add_prefix_space)
					if is_split_into_words or add_prefix_space:
							lowerCAmelCase        =  """ """ + text
					return (text, kwargs)



			def 							a_	(       self	,   __lowerCAmelCase	,   __lowerCAmelCase = False	,   __lowerCAmelCase = None	,   __lowerCAmelCase = None	,   **__lowerCAmelCase	,   ):




					"""simple docstring"""


					lowerCAmelCase        =  super()._decode(
					    token_ids=__lowerCAmelCase	,   skip_special_tokens=__lowerCAmelCase	,   clean_up_tokenization_spaces=__lowerCAmelCase	,   **__lowerCAmelCase	,   )

					if truncate_before_pattern is not None and len(__lowerCAmelCase) > 0:
							lowerCAmelCase        =  self.truncate(__lowerCAmelCase	,   __lowerCAmelCase)

					return decoded_text







			def 							a_	(       self	,   __lowerCAmelCase	,   __lowerCAmelCase):




					"""simple docstring"""


					def find_re(__lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase):
							lowerCAmelCase        =  pattern.search(__lowerCAmelCase	,   __lowerCAmelCase)
							return m.start() if m else -1

					lowerCAmelCase        =  [re.compile(__lowerCAmelCase	,   re.MULTILINE) for pattern in truncate_before_pattern]

					lowerCAmelCase        =  list(re.finditer("""^print"""	,   __lowerCAmelCase	,   re.MULTILINE))

					if len(__lowerCAmelCase) > 1:
							lowerCAmelCase        =  completion[: prints[1].start()]

					lowerCAmelCase        =  list(re.finditer("""^def"""	,   __lowerCAmelCase	,   re.MULTILINE))

					if len(__lowerCAmelCase) > 1:
							lowerCAmelCase        =  completion[: defs[1].start()]

					lowerCAmelCase        =  0

					lowerCAmelCase        =  [
					    pos for pos in [find_re(__lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase) for terminal in terminals] if pos != -1
					]

					if len(__lowerCAmelCase) > 0:
							return completion[: min(__lowerCAmelCase)]
					else:
							return completion




