

'''simple docstring'''


import json
import os
import re
import unicodedata
from typing import Dict, List, Optional, Tuple

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


lowerCAmelCase__      				=			logging.get_logger(__name__)

lowerCAmelCase__      				=			{
    "src_vocab_file": "vocab-src.json",
    "tgt_vocab_file": "vocab-tgt.json",
    "merges_file": "merges.txt",
}

lowerCAmelCase__      				=			{
    "src_vocab_file": {
        "stas/tiny-wmt19-en-de": "https://huggingface.co/stas/tiny-wmt19-en-de/resolve/main/vocab-src.json"
    },
    "tgt_vocab_file": {
        "stas/tiny-wmt19-en-de": "https://huggingface.co/stas/tiny-wmt19-en-de/resolve/main/vocab-tgt.json"
    },
    "merges_file": {"stas/tiny-wmt19-en-de": "https://huggingface.co/stas/tiny-wmt19-en-de/resolve/main/merges.txt"},
}

lowerCAmelCase__      				=			{"stas/tiny-wmt19-en-de": 1024}
lowerCAmelCase__      				=			{
    "stas/tiny-wmt19-en-de": {
        "langs": ["en", "de"],
        "model_max_length": 1024,
        "special_tokens_map_file": None,
        "full_tokenizer_file": None,
    }
}







def     SCREAMING_SNAKE_CASE(	UpperCamelCase	)     ->     Optional[int]:
	UpperCAmelCase_		:   int													=       set()
	UpperCAmelCase_		:   Dict													=       word[0]
	for char in word[1:]:
		pairs.add((prev_char, char)	)
		UpperCAmelCase_		:   Dict													=       char
	return pairs







def     SCREAMING_SNAKE_CASE(	UpperCamelCase	)     ->     Optional[Any]:
	UpperCAmelCase_		:   List[str]													=       text.replace('，' ,','	)
	UpperCAmelCase_		:   List[Any]													=       re.sub(r'。\s*' ,'. ' ,UpperCamelCase	)
	UpperCAmelCase_		:   List[Any]													=       text.replace('、' ,','	)
	UpperCAmelCase_		:   Any													=       text.replace('”' ,'"'	)
	UpperCAmelCase_		:   List[Any]													=       text.replace('“' ,'"'	)
	UpperCAmelCase_		:   Tuple													=       text.replace('∶' ,':'	)
	UpperCAmelCase_		:   str													=       text.replace('：' ,':'	)
	UpperCAmelCase_		:   Union[str, Any]													=       text.replace('？' ,'?'	)
	UpperCAmelCase_		:   int													=       text.replace('《' ,'"'	)
	UpperCAmelCase_		:   List[str]													=       text.replace('》' ,'"'	)
	UpperCAmelCase_		:   str													=       text.replace('）' ,')'	)
	UpperCAmelCase_		:   int													=       text.replace('！' ,'!'	)
	UpperCAmelCase_		:   Optional[int]													=       text.replace('（' ,'('	)
	UpperCAmelCase_		:   int													=       text.replace('；' ,';'	)
	UpperCAmelCase_		:   Dict													=       text.replace('１' ,'1'	)
	UpperCAmelCase_		:   Optional[int]													=       text.replace('」' ,'"'	)
	UpperCAmelCase_		:   Any													=       text.replace('「' ,'"'	)
	UpperCAmelCase_		:   Union[str, Any]													=       text.replace('０' ,'0'	)
	UpperCAmelCase_		:   Any													=       text.replace('３' ,'3'	)
	UpperCAmelCase_		:   List[str]													=       text.replace('２' ,'2'	)
	UpperCAmelCase_		:   Optional[int]													=       text.replace('５' ,'5'	)
	UpperCAmelCase_		:   List[str]													=       text.replace('６' ,'6'	)
	UpperCAmelCase_		:   Dict													=       text.replace('９' ,'9'	)
	UpperCAmelCase_		:   Any													=       text.replace('７' ,'7'	)
	UpperCAmelCase_		:   int													=       text.replace('８' ,'8'	)
	UpperCAmelCase_		:   Optional[int]													=       text.replace('４' ,'4'	)
	UpperCAmelCase_		:   Optional[int]													=       re.sub(r'．\s*' ,'. ' ,UpperCamelCase	)
	UpperCAmelCase_		:   int													=       text.replace('～' ,'~'	)
	UpperCAmelCase_		:   Optional[int]													=       text.replace('’' ,'\''	)
	UpperCAmelCase_		:   int													=       text.replace('…' ,'...'	)
	UpperCAmelCase_		:   int													=       text.replace('━' ,'-'	)
	UpperCAmelCase_		:   List[Any]													=       text.replace('〈' ,'<'	)
	UpperCAmelCase_		:   int													=       text.replace('〉' ,'>'	)
	UpperCAmelCase_		:   Tuple													=       text.replace('【' ,'['	)
	UpperCAmelCase_		:   List[str]													=       text.replace('】' ,']'	)
	UpperCAmelCase_		:   Union[str, Any]													=       text.replace('％' ,'%'	)
	return text







def     SCREAMING_SNAKE_CASE(	UpperCamelCase	)     ->     Union[str, Any]:
	UpperCAmelCase_		:   Tuple													=       []
	for char in text:
		UpperCAmelCase_		:   Tuple													=       unicodedata.category(UpperCamelCase	)
		if cat.startswith('C'	):
			continue
		output.append(UpperCamelCase	)
	return "".join(UpperCamelCase	)







class        lowercase    (	a_			):
							_lowerCamelCase		:   Dict= VOCAB_FILES_NAMES
							_lowerCamelCase		:   Dict= PRETRAINED_VOCAB_FILES_MAP
							_lowerCamelCase		:   Any= PRETRAINED_INIT_CONFIGURATION
							_lowerCamelCase		:   Optional[int]= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							_lowerCamelCase		:   Tuple= ["input_ids", "attention_mask"]


							def __init__(      self     ,      _snake_case=None     ,      _snake_case=None     ,      _snake_case=None     ,      _snake_case=None     ,      _snake_case=False     ,      _snake_case="<unk>"     ,      _snake_case="<s>"     ,      _snake_case="</s>"     ,      _snake_case="<pad>"     ,      **_snake_case     ,      )				->					List[str]:
								super().__init__(
								    langs=_snake_case     ,      src_vocab_file=_snake_case     ,      tgt_vocab_file=_snake_case     ,      merges_file=_snake_case     ,      do_lower_case=_snake_case     ,      unk_token=_snake_case     ,      bos_token=_snake_case     ,      sep_token=_snake_case     ,      pad_token=_snake_case     ,      **_snake_case     ,      )

								try:
									import sacremoses
								except ImportError:
									raise ImportError(
									    'You need to install sacremoses to use XLMTokenizer. '
									    'See https://pypi.org/project/sacremoses/ for installation.')

								UpperCAmelCase_		:   Dict													=       sacremoses

								UpperCAmelCase_		:   int													=       src_vocab_file
								UpperCAmelCase_		:   Dict													=       tgt_vocab_file
								UpperCAmelCase_		:   Optional[Any]													=       merges_file
								UpperCAmelCase_		:   Tuple													=       do_lower_case

								# cache of sm.MosesPunctNormalizer instance
								UpperCAmelCase_		:   Dict													=       {}
								# cache of sm.MosesTokenizer instance
								UpperCAmelCase_		:   Optional[int]													=       {}
								UpperCAmelCase_		:   str													=       {}

								if langs and len(_snake_case) == 2:
									UpperCAmelCase_						,     UpperCAmelCase_		:   Dict													=       langs
								else:
									raise ValueError(
									    F"""arg `langs` needs to be a list of 2 langs, e.g. ['en', 'ru'], but got {langs}. """
									    'Usually that means that tokenizer can\'t find a mapping for the given model path '
									    'in PRETRAINED_VOCAB_FILES_MAP, and other maps of this tokenizer.')

								with open(_snake_case     ,      encoding='utf-8') as src_vocab_handle:
									UpperCAmelCase_		:   str													=       json.load(_snake_case)
								with open(_snake_case     ,      encoding='utf-8') as tgt_vocab_handle:
									UpperCAmelCase_		:   str													=       json.load(_snake_case)
									UpperCAmelCase_		:   str													=       {v: k for k, v in tgt_vocab.items()}
								with open(_snake_case     ,      encoding='utf-8') as merges_handle:
									UpperCAmelCase_		:   Optional[Any]													=       merges_handle.read().split('\n')[:-1]
								UpperCAmelCase_		:   Tuple													=       [tuple(merge.split()[:2]) for merge in merges]
								UpperCAmelCase_		:   Tuple													=       dict(zip(_snake_case     ,      range(len(_snake_case))))
								UpperCAmelCase_		:   Optional[int]													=       {}


							def     _snake_case				(      self)				->					Dict[str, int]:
								return self.get_src_vocab()


							@property
							def     _snake_case				(      self)				->					int:
								return self.src_vocab_size


							def     _snake_case				(      self     ,      _snake_case     ,      _snake_case)				->					str:
								if lang not in self.cache_moses_punct_normalizer:
									UpperCAmelCase_		:   str													=       self.sm.MosesPunctNormalizer(lang=_snake_case)
									UpperCAmelCase_		:   Optional[int]													=       punct_normalizer
								return self.cache_moses_punct_normalizer[lang].normalize(_snake_case)


							def     _snake_case				(      self     ,      _snake_case     ,      _snake_case)				->					int:
								if lang not in self.cache_moses_tokenizer:
									UpperCAmelCase_		:   int													=       self.sm.MosesTokenizer(lang=_snake_case)
									UpperCAmelCase_		:   Dict													=       moses_tokenizer
								return self.cache_moses_tokenizer[lang].tokenize(
								    _snake_case     ,      aggressive_dash_splits=_snake_case     ,      return_str=_snake_case     ,      escape=_snake_case)


							def     _snake_case				(      self     ,      _snake_case     ,      _snake_case)				->					Tuple:
								if lang not in self.cache_moses_detokenizer:
									UpperCAmelCase_		:   Optional[Any]													=       self.sm.MosesDetokenizer(lang=_snake_case)
									UpperCAmelCase_		:   List[str]													=       moses_detokenizer
								return self.cache_moses_detokenizer[lang].detokenize(_snake_case)


							def     _snake_case				(      self     ,      _snake_case     ,      _snake_case)				->					Optional[Any]:
								UpperCAmelCase_		:   List[Any]													=       replace_unicode_punct(_snake_case)
								UpperCAmelCase_		:   Optional[int]													=       self.moses_punct_norm(_snake_case     ,      _snake_case)
								UpperCAmelCase_		:   Union[str, Any]													=       remove_non_printing_char(_snake_case)
								return text


							@property
							def     _snake_case				(      self)				->					Tuple:
								return len(self.encoder)


							@property
							def     _snake_case				(      self)				->					List[str]:
								return len(self.decoder)


							def     _snake_case				(      self)				->					Optional[Any]:
								return dict(self.encoder     ,      **self.added_tokens_encoder)


							def     _snake_case				(      self)				->					int:
								return dict(self.decoder     ,      **self.added_tokens_decoder)


							def     _snake_case				(      self     ,      _snake_case)				->					List[Any]:
								UpperCAmelCase_		:   List[str]													=       tuple(token[:-1]) + (token[-1] + '</w>',)
								if token in self.cache:
									return self.cache[token]
								UpperCAmelCase_		:   Optional[Any]													=       get_pairs(_snake_case)

								if not pairs:
									return token + "</w>"

								while True:
									UpperCAmelCase_		:   Optional[Any]													=       min(_snake_case     ,      key=lambda _snake_case: self.bpe_ranks.get(_snake_case     ,      float('inf')))
									if bigram not in self.bpe_ranks:
										break
									UpperCAmelCase_						,     UpperCAmelCase_		:   List[Any]													=       bigram
									UpperCAmelCase_		:   Any													=       []
									UpperCAmelCase_		:   int													=       0
									while i < len(_snake_case):
										try:
											UpperCAmelCase_		:   List[str]													=       word.index(_snake_case     ,      _snake_case)
										except ValueError:
											new_word.extend(word[i:])
											break
										else:
											new_word.extend(word[i:j])
											UpperCAmelCase_		:   Tuple													=       j

										if word[i] == first and i < len(_snake_case) - 1 and word[i + 1] == second:
											new_word.append(first + second)
											i += 2
										else:
											new_word.append(word[i])
											i += 1
									UpperCAmelCase_		:   str													=       tuple(_snake_case)
									UpperCAmelCase_		:   Tuple													=       new_word
									if len(_snake_case) == 1:
										break
									else:
										UpperCAmelCase_		:   int													=       get_pairs(_snake_case)
								UpperCAmelCase_		:   Tuple													=       ' '.join(_snake_case)
								if word == "\n  </w>":
									UpperCAmelCase_		:   str													=       '\n</w>'
								UpperCAmelCase_		:   Optional[int]													=       word
								return word


							def     _snake_case				(      self     ,      _snake_case     ,      _snake_case="en"     ,      _snake_case=False)				->					str:
								UpperCAmelCase_		:   Dict													=       self.src_lang

								if self.do_lower_case:
									UpperCAmelCase_		:   Union[str, Any]													=       text.lower()

								if bypass_tokenizer:
									UpperCAmelCase_		:   List[str]													=       text.split()
								else:
									UpperCAmelCase_		:   List[Any]													=       self.moses_pipeline(_snake_case     ,      lang=_snake_case)
									UpperCAmelCase_		:   Optional[int]													=       self.moses_tokenize(_snake_case     ,      lang=_snake_case)

								UpperCAmelCase_		:   Dict													=       []
								for token in text:
									if token:
										split_tokens.extend(list(self.bpe(_snake_case).split(' ')))

								return split_tokens


							def     _snake_case				(      self     ,      _snake_case)				->					int:
								return self.encoder.get(_snake_case     ,      self.encoder.get(self.unk_token))


							def     _snake_case				(      self     ,      _snake_case)				->					Tuple:
								return self.decoder.get(_snake_case     ,      self.unk_token)


							def     _snake_case				(      self     ,      _snake_case)				->					Union[str, Any]:
								UpperCAmelCase_		:   List[Any]													=       [t.replace(' '     ,      '').replace('</w>'     ,      ' ') for t in tokens]
								UpperCAmelCase_		:   Optional[int]													=       ''.join(_snake_case).split()
								# detokenize
								UpperCAmelCase_		:   Dict													=       self.moses_detokenize(_snake_case     ,      self.tgt_lang)
								return text


							def     _snake_case				(      self     ,      _snake_case     ,      _snake_case = None)				->					List[int]:
								UpperCAmelCase_		:   Optional[Any]													=       [self.sep_token_id]

								# no bos used in fairseq
								if token_ids_a is None:
									return token_ids_a + sep
								return token_ids_a + sep + token_ids_a + sep


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

								if already_has_special_tokens:
									return super().get_special_tokens_mask(
									    token_ids_a=_snake_case     ,      token_ids_a=_snake_case     ,      already_has_special_tokens=_snake_case)
								# no bos used in fairseq
								if token_ids_a is not None:
									return ([0] * len(_snake_case)) + [1] + ([0] * len(_snake_case)) + [1]
								return ([0] * len(_snake_case)) + [1]


							def     _snake_case				(      self     ,      _snake_case     ,      _snake_case = None)				->					List[int]:
								UpperCAmelCase_		:   Dict													=       [self.sep_token_id]

								# no bos used in fairseq
								if token_ids_a is None:
									return len(token_ids_a + sep) * [0]
								return len(token_ids_a + sep) * [0] + len(token_ids_a + sep) * [1]


							def     _snake_case				(      self     ,      _snake_case     ,      _snake_case = None)				->					Tuple[str]:
								if not os.path.isdir(_snake_case):
									logger.error(F"""Vocabulary path ({save_directory}) should be a directory""")
									return

								UpperCAmelCase_		:   Tuple													=       os.path.join(
								    _snake_case     ,      (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['src_vocab_file'])
								UpperCAmelCase_		:   Any													=       os.path.join(
								    _snake_case     ,      (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['tgt_vocab_file'])
								UpperCAmelCase_		:   List[Any]													=       os.path.join(
								    _snake_case     ,      (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file'])

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

								with open(_snake_case     ,      'w'     ,      encoding='utf-8') as f:
									UpperCAmelCase_		:   str													=       {v: k for k, v in self.decoder.items()}
									f.write(json.dumps(_snake_case     ,      indent=2     ,      sort_keys=_snake_case     ,      ensure_ascii=_snake_case) + '\n')

								UpperCAmelCase_		:   Optional[int]													=       0
								with open(_snake_case     ,      'w'     ,      encoding='utf-8') as writer:
									for bpe_tokens, token_index in sorted(self.bpe_ranks.items()     ,      key=lambda _snake_case: kv[1]):
										if index != token_index:
											logger.warning(
											    F"""Saving vocabulary to {merges_file}: BPE merge indices are not consecutive."""
											    ' Please check that the tokenizer is not corrupted!')
											UpperCAmelCase_		:   List[str]													=       token_index
										writer.write(' '.join(_snake_case) + '\n')
										index += 1

								return src_vocab_file, tgt_vocab_file, merges_file


							def __getstate__(      self)				->					Dict:
								UpperCAmelCase_		:   List[Any]													=       self.__dict__.copy()
								UpperCAmelCase_		:   int													=       None
								return state





							def __setstate__(      self     ,      _snake_case)				->					List[str]:
								UpperCAmelCase_		:   Any													=       d

								try:
									import sacremoses
								except ImportError:
									raise ImportError(
									    'You need to install sacremoses to use XLMTokenizer. '
									    'See https://pypi.org/project/sacremoses/ for installation.')

								UpperCAmelCase_		:   Optional[Any]													=       sacremoses









'''simple docstring'''


import inspect
from typing import Callable, List, Optional, Set, Tuple, Union

import torch
from packaging import version
from safetensors.torch import storage_ptr, storage_size
from torch import nn

from .utils import logging


lowerCAmelCase__      				=			[nn.LayerNorm]

lowerCAmelCase__      				=			logging.get_logger(__name__)

lowerCAmelCase__      				=			version.parse(version.parse(torch.__version__).base_version)

lowerCAmelCase__      				=			parsed_torch_version_base >= version.parse("2.0")
lowerCAmelCase__      				=			parsed_torch_version_base >= version.parse("1.12")
lowerCAmelCase__      				=			parsed_torch_version_base >= version.parse("1.11")
lowerCAmelCase__      				=			parsed_torch_version_base < version.parse("1.11")







def     SCREAMING_SNAKE_CASE(	UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,self	)     ->     List[Any]:
	from torch import _softmax_backward_data

	if is_torch_less_than_1_11:
		return _softmax_backward_data(UpperCamelCase ,UpperCamelCase ,parent.dim ,self	)
	else:
		return _softmax_backward_data(UpperCamelCase ,UpperCamelCase ,parent.dim ,self.dtype	)







def     SCREAMING_SNAKE_CASE(	UpperCamelCase ,UpperCamelCase ,UpperCamelCase = 0	)     ->     nn.Linear:
	UpperCAmelCase_		:   Tuple													=       index.to(layer.weight.device	)
	UpperCAmelCase_		:   Tuple													=       layer.weight.index_select(UpperCamelCase ,UpperCamelCase	).clone().detach()
	if layer.bias is not None:
		if dim == 1:
			UpperCAmelCase_		:   List[Any]													=       layer.bias.clone().detach()
		else:
			UpperCAmelCase_		:   Tuple													=       layer.bias[index].clone().detach()
	UpperCAmelCase_		:   Optional[Any]													=       list(layer.weight.size()	)
	UpperCAmelCase_		:   int													=       len(UpperCamelCase	)
	UpperCAmelCase_		:   str													=       nn.Linear(new_size[1] ,new_size[0] ,bias=layer.bias is not None	).to(layer.weight.device	)
	UpperCAmelCase_		:   str													=       False
	new_layer.weight.copy_(W.contiguous()	)
	UpperCAmelCase_		:   Tuple													=       True
	if layer.bias is not None:
		UpperCAmelCase_		:   Union[str, Any]													=       False
		new_layer.bias.copy_(b.contiguous()	)
		UpperCAmelCase_		:   Any													=       True
	return new_layer



class        lowercase    (	nn.Module			):


							def __init__(      self     ,      _snake_case     ,      _snake_case)				->					int:
								super().__init__()
								UpperCAmelCase_		:   List[Any]													=       nf
								UpperCAmelCase_		:   List[Any]													=       nn.Parameter(torch.empty(_snake_case     ,      _snake_case))
								UpperCAmelCase_		:   List[Any]													=       nn.Parameter(torch.zeros(_snake_case))
								nn.init.normal_(self.weight     ,      std=0.02)





							def     _snake_case				(      self     ,      _snake_case)				->					Optional[int]:
								UpperCAmelCase_		:   Dict													=       x.size()[:-1] + (self.nf,)
								UpperCAmelCase_		:   Optional[Any]													=       torch.addmm(self.bias     ,      x.view(-1     ,      x.size(-1))     ,      self.weight)
								UpperCAmelCase_		:   List[str]													=       x.view(_snake_case)
								return x







def     SCREAMING_SNAKE_CASE(	UpperCamelCase ,UpperCamelCase ,UpperCamelCase = 1	)     ->     ConvaD:
	UpperCAmelCase_		:   List[str]													=       index.to(layer.weight.device	)
	UpperCAmelCase_		:   Optional[Any]													=       layer.weight.index_select(UpperCamelCase ,UpperCamelCase	).clone().detach()
	if dim == 0:
		UpperCAmelCase_		:   Tuple													=       layer.bias.clone().detach()
	else:
		UpperCAmelCase_		:   str													=       layer.bias[index].clone().detach()
	UpperCAmelCase_		:   str													=       list(layer.weight.size()	)
	UpperCAmelCase_		:   List[str]													=       len(UpperCamelCase	)
	UpperCAmelCase_		:   List[Any]													=       ConvaD(new_size[1] ,new_size[0]	).to(layer.weight.device	)
	UpperCAmelCase_		:   Tuple													=       False
	new_layer.weight.copy_(W.contiguous()	)
	UpperCAmelCase_		:   Optional[Any]													=       True
	UpperCAmelCase_		:   Any													=       False
	new_layer.bias.copy_(b.contiguous()	)
	UpperCAmelCase_		:   Union[str, Any]													=       True
	return new_layer







def     SCREAMING_SNAKE_CASE(	UpperCamelCase ,UpperCamelCase ,UpperCamelCase = None	)     ->     Union[nn.Linear, ConvaD]:
	if isinstance(UpperCamelCase ,nn.Linear	):
		return prune_linear_layer(UpperCamelCase ,UpperCamelCase ,dim=0 if dim is None else dim	)
	elif isinstance(UpperCamelCase ,UpperCamelCase	):
		return prune_convad_layer(UpperCamelCase ,UpperCamelCase ,dim=1 if dim is None else dim	)
	else:
		raise ValueError(f"""Can't prune layer of class {layer.__class__}"""	)







def     SCREAMING_SNAKE_CASE(	UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,*UpperCamelCase	)     ->     torch.Tensor:
	assert len(UpperCamelCase	) > 0, f"""{input_tensors} has to be a tuple/list of tensors"""

	# inspect.signature exist since python 3.5 and is a python method -> no problem with backward compatibility
	UpperCAmelCase_		:   str													=       len(inspect.signature(UpperCamelCase	).parameters	)
	if num_args_in_forward_chunk_fn != len(UpperCamelCase	):
		raise ValueError(
		    f"""forward_chunk_fn expects {num_args_in_forward_chunk_fn} arguments, but only {len(UpperCamelCase	)} input """
		    'tensors are given'	)

	if chunk_size > 0:
		UpperCAmelCase_		:   Optional[int]													=       input_tensors[0].shape[chunk_dim]
		for input_tensor in input_tensors:
			if input_tensor.shape[chunk_dim] != tensor_shape:
				raise ValueError(
				    f"""All input tenors have to be of the same shape: {tensor_shape}, """
				    f"""found shape {input_tensor.shape[chunk_dim]}"""	)

		if input_tensors[0].shape[chunk_dim] % chunk_size != 0:
			raise ValueError(
			    f"""The dimension to be chunked {input_tensors[0].shape[chunk_dim]} has to be a multiple of the chunk """
			    f"""size {chunk_size}"""	)

		UpperCAmelCase_		:   Any													=       input_tensors[0].shape[chunk_dim] // chunk_size

		# chunk input tensor into tuples
		UpperCAmelCase_		:   int													=       tuple(input_tensor.chunk(UpperCamelCase ,dim=UpperCamelCase	) for input_tensor in input_tensors	)
		# apply forward fn to every tuple
		UpperCAmelCase_		:   List[str]													=       tuple(forward_fn(*UpperCamelCase	) for input_tensors_chunk in zip(*UpperCamelCase	)	)
		# concatenate output at same dimension
		return torch.cat(UpperCamelCase ,dim=UpperCamelCase	)

	return forward_fn(*UpperCamelCase	)







def     SCREAMING_SNAKE_CASE(	UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase	)     ->     Tuple[Set[int], torch.LongTensor]:
	UpperCAmelCase_		:   List[Any]													=       torch.ones(UpperCamelCase ,UpperCamelCase	)
	UpperCAmelCase_		:   Optional[int]													=       set(UpperCamelCase	) - already_pruned_heads  # Convert to set and remove already pruned heads
	for head in heads:
		# Compute how many pruned heads are before the head and move the index accordingly
		UpperCAmelCase_		:   Any													=       head - sum(1 if h < head else 0 for h in already_pruned_heads	)
		UpperCAmelCase_		:   List[Any]													=       0
	UpperCAmelCase_		:   str													=       mask.view(-1	).contiguous().eq(1	)
	UpperCAmelCase_		:   torch.LongTensor													=       torch.arange(len(UpperCamelCase	)	)[mask].long()
	return heads, index







def     SCREAMING_SNAKE_CASE(	*UpperCamelCase ,UpperCamelCase = None	)     ->     Tuple[torch.Tensor, ...]:
	return torch.meshgrid(*UpperCamelCase ,indexing=UpperCamelCase	)







def     SCREAMING_SNAKE_CASE(	UpperCamelCase	)     ->     Tuple[torch.device, int, int]:
	return tensor.device, storage_ptr(UpperCamelCase	), storage_size(UpperCamelCase	)






