




'''simple docstring'''
import json
import os
import re
import warnings
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


A				: str					      =			logging.get_logger(__name__)

A				: int					      =			{
    """source_spm""": """source.spm""",
    """target_spm""": """target.spm""",
    """vocab""": """vocab.json""",
    """target_vocab_file""": """target_vocab.json""",
    """tokenizer_config_file""": """tokenizer_config.json""",
}

A				: Dict					      =			{
    """source_spm""": {
        """Helsinki-NLP/opus-mt-en-de""": """https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/source.spm"""
    },
    """target_spm""": {
        """Helsinki-NLP/opus-mt-en-de""": """https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/target.spm"""
    },
    """vocab""": {
        """Helsinki-NLP/opus-mt-en-de""": """https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/vocab.json"""
    },
    """tokenizer_config_file""": {
        """Helsinki-NLP/opus-mt-en-de""": (
            """https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/tokenizer_config.json"""
        )
    },
}

A				: Optional[Any]					      =			{"""Helsinki-NLP/opus-mt-en-de""": 512}
A				: Dict					      =			{}






class 			SCREAMING_SNAKE_CASE(							__A	):
       snake_case_					:	Union[str, Any]       =   VOCAB_FILES_NAMES
       snake_case_					:	Tuple       =   PRETRAINED_VOCAB_FILES_MAP
       snake_case_					:	List[str]       =   PRETRAINED_INIT_CONFIGURATION
       snake_case_					:	str       =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       snake_case_					:	Optional[int]       =   ["""input_ids""", """attention_mask"""]
       snake_case_					:	Optional[int]       =   re.compile(""">>.+<<"""	)  # type: re.Pattern

       def __init__(       self   ,						lowerCamelCase__   ,						lowerCamelCase__   ,						lowerCamelCase__   ,						lowerCamelCase__=None   ,						lowerCamelCase__=None   ,						lowerCamelCase__=None   ,						lowerCamelCase__="<unk>"   ,						lowerCamelCase__="</s>"   ,						lowerCamelCase__="<pad>"   ,						lowerCamelCase__=512   ,						lowerCamelCase__ = None   ,						lowerCamelCase__=False   ,						**lowerCamelCase__   ,						)       ->   None:

           """simple docstring"""



           __lowercase     							=		{} if sp_model_kwargs is None else sp_model_kwargs

           super().__init__(
               # bos_token=bos_token,  unused. Start decoding with config.decoder_start_token_id
               source_lang=lowerCamelCase__   ,						target_lang=lowerCamelCase__   ,						unk_token=lowerCamelCase__   ,						eos_token=lowerCamelCase__   ,						pad_token=lowerCamelCase__   ,						model_max_length=lowerCamelCase__   ,						sp_model_kwargs=self.sp_model_kwargs   ,						target_vocab_file=lowerCamelCase__   ,						separate_vocabs=lowerCamelCase__   ,						**lowerCamelCase__   ,						)
           assert Path(lowerCamelCase__	).exists(), F'cannot find spm source {source_spm}'

           __lowercase     							=		separate_vocabs
           __lowercase     							=		load_json(lowerCamelCase__	)
           if self.unk_token not in self.encoder:
               raise KeyError("""<unk> token must be in vocab"""	)
           assert self.pad_token in self.encoder

           if separate_vocabs:
               __lowercase     							=		load_json(lowerCamelCase__	)
               __lowercase     							=		{v: k for k, v in self.target_encoder.items()}
               __lowercase     							=		[]
           else:
               __lowercase     							=		{v: k for k, v in self.encoder.items()}
               __lowercase     							=		[k for k in self.encoder if k.startswith(""">>"""	) and k.endswith("""<<"""	)]

           __lowercase     							=		source_lang
           __lowercase     							=		target_lang
           __lowercase     							=		[source_spm, target_spm]

           # load SentencePiece model for pre-processing
           __lowercase     							=		load_spm(lowerCamelCase__   ,						self.sp_model_kwargs	)
           __lowercase     							=		load_spm(lowerCamelCase__   ,						self.sp_model_kwargs	)
           __lowercase     							=		self.spm_source
           __lowercase     							=		self.encoder

           # Multilingual target side: default to using first supported language code.

           self._setup_normalizer()

       def        snake_case__     (       self	)       ->   int:

           """simple docstring"""



           try:
               from sacremoses import MosesPunctNormalizer

               __lowercase     							=		MosesPunctNormalizer(self.source_lang	).normalize
           except (ImportError, FileNotFoundError):
               warnings.warn("""Recommended: pip install sacremoses."""	)
               __lowercase     							=		lambda lowerCamelCase__	: x

       def        snake_case__     (       self   ,						lowerCamelCase__	)       ->   str:

           """simple docstring"""



           return self.punc_normalizer(lowerCamelCase__	) if x else ""

       def        snake_case__     (       self   ,						lowerCamelCase__	)       ->   Optional[int]:

           """simple docstring"""



           return self.current_encoder.get(lowerCamelCase__   ,						self.current_encoder[self.unk_token]	)

       def        snake_case__     (       self   ,						lowerCamelCase__	)       ->   List[str]:

           """simple docstring"""



           __lowercase     							=		self.language_code_re.match(lowerCamelCase__	)
           __lowercase     							=		[match.group(0	)] if match else []
           return code, self.language_code_re.sub(""""""   ,						lowerCamelCase__	)

       def        snake_case__     (       self   ,						lowerCamelCase__	)       ->   List[str]:

           """simple docstring"""



           __lowercase							,__lowercase     							=		self.remove_language_code(lowerCamelCase__	)
           __lowercase     							=		self.current_spm.encode(lowerCamelCase__   ,						out_type=lowerCamelCase__	)
           return code + pieces

       def        snake_case__     (       self   ,						lowerCamelCase__	)       ->   str:

           """simple docstring"""



           return self.decoder.get(lowerCamelCase__   ,						self.unk_token	)

       def        snake_case__     (       self   ,						lowerCamelCase__   ,						**lowerCamelCase__	)       ->   Optional[int]:

           """simple docstring"""



           return super().batch_decode(lowerCamelCase__   ,						**lowerCamelCase__	)

       def        snake_case__     (       self   ,						lowerCamelCase__   ,						**lowerCamelCase__	)       ->   Union[str, Any]:

           """simple docstring"""



           return super().decode(lowerCamelCase__   ,						**lowerCamelCase__	)

       def        snake_case__     (       self   ,						lowerCamelCase__	)       ->   str:

           """simple docstring"""



           __lowercase     							=		self.spm_source if self._decode_use_source_tokenizer else self.spm_target
           __lowercase     							=		[]
           __lowercase     							=		""""""
           for token in tokens:
               # make sure that special tokens are not decoded using sentencepiece model
               if token in self.all_special_tokens:
                   out_string += sp_model.decode_pieces(lowerCamelCase__	) + token + " "
                   __lowercase     							=		[]
               else:
                   current_sub_tokens.append(lowerCamelCase__	)
           out_string += sp_model.decode_pieces(lowerCamelCase__	)
           return out_string.strip()

       def        snake_case__     (       self   ,						lowerCamelCase__   ,						lowerCamelCase__=None	)       ->   List[int]:

           """simple docstring"""



           if token_ids_a is None:
               return token_ids_a + [self.eos_token_id]
           # We don't expect to process pairs, but leave the pair logic for API consistency
           return token_ids_a + token_ids_a + [self.eos_token_id]

       def        snake_case__     (       self	)       ->   str:

           """simple docstring"""



           __lowercase     							=		self.spm_source
           __lowercase     							=		self.encoder

       def        snake_case__     (       self	)       ->   List[str]:

           """simple docstring"""



           __lowercase     							=		self.spm_target
           if self.separate_vocabs:
               __lowercase     							=		self.target_encoder

       @property
       def        snake_case__     (       self	)       ->   int:

           """simple docstring"""



           return len(self.encoder	)

       def        snake_case__     (       self   ,						lowerCamelCase__   ,						lowerCamelCase__ = None	)       ->   Tuple[str]:

           """simple docstring"""



           if not os.path.isdir(lowerCamelCase__	):
               logger.error(F'Vocabulary path ({save_directory}) should be a directory'	)
               return
           __lowercase     							=		[]

           if self.separate_vocabs:
               __lowercase     							=		os.path.join(
                   lowerCamelCase__   ,						(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab"""]   ,						)
               __lowercase     							=		os.path.join(
                   lowerCamelCase__   ,						(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""target_vocab_file"""]   ,						)
               save_json(self.encoder   ,						lowerCamelCase__	)
               save_json(self.target_encoder   ,						lowerCamelCase__	)
               saved_files.append(lowerCamelCase__	)
               saved_files.append(lowerCamelCase__	)
           else:
               __lowercase     							=		os.path.join(
                   lowerCamelCase__   ,						(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab"""]	)
               save_json(self.encoder   ,						lowerCamelCase__	)
               saved_files.append(lowerCamelCase__	)

           for spm_save_filename, spm_orig_path, spm_model in zip(
               [VOCAB_FILES_NAMES["""source_spm"""], VOCAB_FILES_NAMES["""target_spm"""]]   ,						self.spm_files   ,						[self.spm_source, self.spm_target]   ,						):
               __lowercase     							=		os.path.join(
                   lowerCamelCase__   ,						(filename_prefix + """-""" if filename_prefix else """""") + spm_save_filename	)
               if os.path.abspath(lowerCamelCase__	) != os.path.abspath(lowerCamelCase__	) and os.path.isfile(lowerCamelCase__	):
                   copyfile(lowerCamelCase__   ,						lowerCamelCase__	)
                   saved_files.append(lowerCamelCase__	)
               elif not os.path.isfile(lowerCamelCase__	):
                   with open(lowerCamelCase__   ,						"""wb"""	) as fi:
                       __lowercase     							=		spm_model.serialized_model_proto()
                       fi.write(lowerCamelCase__	)
                   saved_files.append(lowerCamelCase__	)

           return tuple(lowerCamelCase__	)

       def        snake_case__     (       self	)       ->   Dict:

           """simple docstring"""



           return self.get_src_vocab()

       def        snake_case__     (       self	)       ->   Optional[Any]:

           """simple docstring"""



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

       def        snake_case__     (       self	)       ->   Tuple:

           """simple docstring"""



           return dict(self.target_encoder   ,						**self.added_tokens_decoder	)

       def __getstate__(       self	)       ->   Dict:

           """simple docstring"""



           __lowercase     							=		self.__dict__.copy()
           state.update(
               {k: None for k in ["""spm_source""", """spm_target""", """current_spm""", """punc_normalizer""", """target_vocab_file"""]}	)
           return state

       def __setstate__(       self   ,						lowerCamelCase__	)       ->   None:

           """simple docstring"""



           __lowercase     							=		d

           # for backward compatibility
           if not hasattr(self   ,						"""sp_model_kwargs"""	):
               __lowercase     							=		{}

           __lowercase							,__lowercase     							=		(load_spm(lowerCamelCase__   ,						self.sp_model_kwargs	) for f in self.spm_files)
           __lowercase     							=		self.spm_source
           self._setup_normalizer()

       def        snake_case__     (       self   ,						*lowerCamelCase__   ,						**lowerCamelCase__	)       ->   int:

           """simple docstring"""



           return 1

       def        snake_case__     (       self   ,						lowerCamelCase__	)       ->   Optional[Any]:

           """simple docstring"""



           __lowercase     							=		set(self.all_special_ids	)  # call it once instead of inside list comp
           all_special_ids.remove(self.unk_token_id	)  # <unk> is only sometimes special
           return [1 if x in all_special_ids else 0 for x in seq]




       def        snake_case__     (       self   ,						lowerCamelCase__   ,						lowerCamelCase__ = None   ,						lowerCamelCase__ = False	)       ->   List[int]:

           """simple docstring"""



           if already_has_special_tokens:
               return self._special_token_mask(lowerCamelCase__	)
           elif token_ids_a is None:
               return self._special_token_mask(lowerCamelCase__	) + [1]
           else:
               return self._special_token_mask(token_ids_a + token_ids_a	) + [1]

def 							snake_case_    (  a__						:      str       ,a__						:      Dict[str, Any]     ):



    """simple docstring"""




    __lowercase     							=		sentencepiece.SentencePieceProcessor(**a__     )
    spm.Load(a__     )
    return spm
def 							snake_case_    (  a__						:      str       ,a__						:      str     ):



    """simple docstring"""




    with open(a__       ,"""w"""     ) as f:
        json.dump(a__       ,a__       ,indent=2     )



def 							snake_case_    (  a__						:      str     ):



    """simple docstring"""




    with open(a__       ,"""r"""     ) as f:
        return json.load(a__     )










'''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__						:      Any     ):



    """simple docstring"""




    __lowercase     							=		"""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=20_48     ):
                f.write(a__     )
def 							snake_case_    (  ):



    """simple docstring"""




    __lowercase     							=		VivitConfig()

    __lowercase     							=		4_00
    __lowercase     							=		"""huggingface/label-files"""
    __lowercase     							=		"""kinetics400-id2label.json"""

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



    """simple docstring"""




    __lowercase     							=		hf_hub_download(
        repo_id="""hf-internal-testing/spaghetti-video"""       ,filename="""eating_spaghetti_32_frames.npy"""       ,repo_type="""dataset"""     )
    __lowercase     							=		np.load(a__     )
    return list(a__     )
def 							snake_case_    (  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 							snake_case_    (  a__						:      np.ndarray     ):



    """simple docstring"""




    return current.flatten()
def 							snake_case_    (  a__						:      np.ndarray     ):



    """simple docstring"""




    return np.reshape(a__       ,(current.shape[0], current.shape[1] * current.shape[2])     ).T
def 							snake_case_    (  a__						:      np.ndarray     ):



    """simple docstring"""




    return np.reshape(a__       ,(current.shape[0] * current.shape[1], current.shape[2])     ).T
def 							snake_case_    (  a__						:      Tuple       ,a__						:      Dict     ):



    """simple docstring"""




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

    __lowercase     							=		f'encoder.layer.{i}.'
    __lowercase     							=		{
        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__						:      str     ):



    """simple docstring"""




    return sum([1 if """encoderblock_""" in k else 0 for k in state_dict["""optimizer"""]["""target"""]["""Transformer"""].keys()]     )
def 							snake_case_    (  a__						:      Any       ,a__						:      int=False     ):



    """simple docstring"""




    __lowercase     							=		get_n_layers(a__     )

    __lowercase     							=		OrderedDict()

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

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

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

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



    """simple docstring"""




    __lowercase     							=		VivitImageProcessor()

    assert extractor.do_resize is True
    assert extractor.size == {"shortest_edge": 2_56}
    assert extractor.do_center_crop is True
    assert extractor.crop_size == {"width": 2_24, "height": 2_24}
    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 / 2_55

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

    return extractor
def 							snake_case_    (  a__						:      str     ):



    """simple docstring"""




    __lowercase     							=		"""checkpoint"""

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

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

    __lowercase     							=		get_vivit_config()

    assert config.image_size == 2_24
    assert config.num_frames == 32

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

    __lowercase     							=		get_processor()

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

    __lowercase     							=		model(**a__     )

    __lowercase     							=		torch.Size([1, 4_00]     )
    __lowercase     							=		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__":
  A				: Union[str, Any]					      =			argparse.ArgumentParser()

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

  A				: Optional[Any]					      =			parser.parse_args()
  convert(args.output_model_name)




