



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       A_   (						__a  :					Tuple		):

				"""simple docstring"""
				a__					   =		"""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=2_048		):
																f.write(__a		)

def       A_   (						):

				"""simple docstring"""
				a__					   =		VivitConfig()

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

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

def       A_   (						):

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

def       A_   (						__a  :					np.ndarray		):

				"""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       A_   (						__a  :					np.ndarray		):

				"""simple docstring"""
				return current.flatten()

def       A_   (						__a  :					np.ndarray		):

				"""simple docstring"""
				return np.reshape(__a			,		(current.shape[0], current.shape[1] * current.shape[2])		).T

def       A_   (						__a  :					np.ndarray		):

				"""simple docstring"""
				return np.reshape(__a			,		(current.shape[0] * current.shape[1], current.shape[2])		).T

def       A_   (						__a  :					List[Any]			,		__a  :					int		):

				"""simple docstring"""
				a__					   =		state_dict["""optimizer"""]["""target"""]["""Transformer"""][F'''encoderblock_{i}''']

				a__					   =		F'''encoder.layer.{i}.'''
				a__					   =		{
				    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       A_   (						__a  :					str		):

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

def       A_   (						__a  :					Dict			,		__a  :					Union[str, Any]=False		):

				"""simple docstring"""
				a__					   =		get_n_layers(__a		)

				a__					   =		OrderedDict()

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

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

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

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

def       A_   (						):

				"""simple docstring"""
				a__					   =		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       A_   (						__a  :					str		):

				"""simple docstring"""
				a__					   =		"""checkpoint"""

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

				a__					   =		restore_checkpoint(__a			,		__a		)
				a__					   =		transform_state(__a			,		classification_head=__a		)

				a__					   =		get_vivit_config()

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

				a__					   =		VivitForVideoClassification(__a		)
				model.load_state_dict(__a		)
				model.eval()

				a__					   =		get_processor()

				a__					   =		prepare_video()
				a__					   =		extractor(__a			,		return_tensors="""pt"""		)

				a__					   =		model(**__a		)

				a__					   =		torch.Size([1, 400]		)
				a__					   =		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]			,		__a			,		atol=1e-4		), outputs.logits[0, :5]

				model.save_pretrained(__a		)
				extractor.save_pretrained(__a		)


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

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

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






'''simple docstring'''




import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import AddedToken, PreTrainedTokenizerFast
from ...utils import logging


a  :     Any       =       logging.get_logger(__name__)

a  :     int       =       {"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

a  :     List[Any]       =       {
    """vocab_file""": {
        """TsinghuaAI/CPM-Generate""": """https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model""",
    },
    """tokenizer_file""": {
        """TsinghuaAI/CPM-Generate""": """https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/tokenizer.json""",
    },
}

class      UpperCamelCase_					(				__magic_name__     ):





	def __init__(    self						,						A=None						,						A=None						,						A=False						,						A=True						,						A=False						,						A="<s>"						,						A="</s>"						,						A="<unk>"						,						A="<sep>"						,						A="<pad>"						,						A="<cls>"						,						A="<mask>"						,						A=["<eop>", "<eod>"]						,						**A						,						)     ->     Tuple:
								UpperCAmelCase	:						str					=						AddedToken(A						,						lstrip=A						,						rstrip=A       ) if isinstance(A						,						A       ) else mask_token

								super().__init__(
								    vocab_file=A						,						tokenizer_file=A						,						do_lower_case=A						,						remove_space=A						,						keep_accents=A						,						bos_token=A						,						eos_token=A						,						unk_token=A						,						sep_token=A						,						pad_token=A						,						cls_token=A						,						mask_token=A						,						additional_special_tokens=A						,						**A						,						)

								UpperCAmelCase	:						List[Any]					=						3
								UpperCAmelCase	:						Tuple					=						do_lower_case
								UpperCAmelCase	:						Union[str, Any]					=						remove_space
								UpperCAmelCase	:						Tuple					=						keep_accents
								UpperCAmelCase	:						Any					=						vocab_file
								UpperCAmelCase	:						Tuple					=						False if not self.vocab_file else True

								try:
															import jieba
								except ModuleNotFoundError as error:
															raise error.__class__(
															    """You need to install jieba to use CpmTokenizer or CpmTokenizerFast. """
															    """See https://pypi.org/project/jieba/ for installation."""       )
								UpperCAmelCase	:						int					=						jieba
								UpperCAmelCase	:						Optional[int]					=						str.maketrans(""" \n"""						,						"""\u2582\u2583"""       )





	def 					_lowercase(    self						,						A						,						A = None       )     ->     List[int]:
								UpperCAmelCase	:						Optional[int]					=						[self.sep_token_id]
								UpperCAmelCase	:						str					=						[self.cls_token_id]
								if token_ids_a is None:
															return token_ids_a + sep + cls
								return token_ids_a + sep + token_ids_a + sep + cls





	def 					_lowercase(    self						,						A						,						A = None       )     ->     List[int]:
								UpperCAmelCase	:						Union[str, Any]					=						[self.sep_token_id]
								UpperCAmelCase	:						str					=						[2]

								if token_ids_a is None:
															return len(token_ids_a + sep       ) * [0] + cls_segment_id
								return len(token_ids_a + sep       ) * [0] + len(token_ids_a + sep       ) * [1] + cls_segment_id





	def 					_lowercase(    self						,						A						,						A = None       )     ->     Tuple[str]:
								if not self.can_save_slow_tokenizer:
															raise ValueError(
															    """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
															    """tokenizer."""       )

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

								if os.path.abspath(self.vocab_file       ) != os.path.abspath(A       ):
															copyfile(self.vocab_file						,						A       )

								return (out_vocab_file,)





	def 					_lowercase(    self						,						A						,						*A						,						**A       )     ->     List[Any]:
								UpperCAmelCase	:						Optional[Any]					=						[
								    """ """.join([x.translate(self.translator       ) for x in self.jieba.cut(A						,						cut_all=A       )]       )
								    for text in batch_text_or_text_pairs
								]
								return super()._batch_encode_plus(A						,						*A						,						**A       )






	def 					_lowercase(    self						,						*A						,						**A       )     ->     str:
								UpperCAmelCase	:						Optional[int]					=						super()._decode(*A						,						**A       )
								UpperCAmelCase	:						int					=						text.replace(""" """						,						""""""       ).replace("""\u2582"""						,						""" """       ).replace("""\u2583"""						,						"""\n"""       )
								return text

