



'''simple docstring'''






from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging


lowerCAmelCase: str							=						logging.get_logger(__name__)

lowerCAmelCase: List[Any]							=						{
    'EleutherAI/gpt-neo-1.3B': 'https://huggingface.co/EleutherAI/gpt-neo-1.3B/resolve/main/config.json',
    # See all GPTNeo models at https://huggingface.co/models?filter=gpt_neo
}

class 							a__( lowerCamelCase__     ):
    lowercase__			=   """gpt_neo"""
    lowercase__			=   ["""past_key_values"""]
    lowercase__			=   {"""num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}







    def __init__(				self			:  Dict						,      __snake_case			:  Optional[int]=5_02_57						,      __snake_case			:  Optional[int]=20_48						,      __snake_case			:  Union[str, Any]=20_48						,      __snake_case			:  Dict=24						,      __snake_case			:  Union[str, Any]=[[["global", "local"], 12]]						,      __snake_case			:  List[Any]=16						,      __snake_case			:  Any=None						,      __snake_case			:  Optional[Any]=2_56						,      __snake_case			:  Optional[Any]="gelu_new"						,      __snake_case			:  Optional[int]=0.0						,      __snake_case			:  str=0.0						,      __snake_case			:  Union[str, Any]=0.0						,      __snake_case			:  Optional[int]=0.1						,      __snake_case			:  Optional[int]=1e-5						,      __snake_case			:  Tuple=0.02						,      __snake_case			:  List[Any]=True						,      __snake_case			:  Optional[Any]=5_02_56						,      __snake_case			:  str=5_02_56						,      **__snake_case			:  str						,      ):
      a						: Optional[int]					       =					vocab_size
      a						: str					       =					max_position_embeddings
      a						: Dict					       =					hidden_size
      a						: str					       =					num_layers
      a						: Union[str, Any]					       =					num_heads
      a						: str					       =					intermediate_size
      a						: Optional[int]					       =					window_size
      a						: Union[str, Any]					       =					activation_function
      a						: Any					       =					resid_dropout
      a						: Optional[Any]					       =					embed_dropout
      a						: int					       =					attention_dropout
      a						: List[str]					       =					classifier_dropout
      a						: Tuple					       =					layer_norm_epsilon
      a						: Tuple					       =					initializer_range
      a						: List[str]					       =					use_cache

      a						: List[str]					       =					bos_token_id
      a						: Optional[int]					       =					eos_token_id

      a						: Optional[Any]					       =					attention_types
      a						: Any					       =					self.expand_attention_types_params(__snake_case     )

      if len(self.attention_layers     ) != self.num_layers:
        raise ValueError(
            'Configuration for convolutional module is incorrect. '
            'It is required that `len(config.attention_layers)` == `config.num_layers` '
            F"""but is `len(config.attention_layers) = {len(self.attention_layers     )}`, """
            F"""`config.num_layers = {self.num_layers}`. """
            '`config.attention_layers` is prepared using `config.attention_types`. '
            'Please verify the value of `config.attention_types` argument.'     )

      super().__init__(bos_token_id=__snake_case						,      eos_token_id=__snake_case						,      **__snake_case     )







    @staticmethod
    def       lowercase_	(				__snake_case			:  Optional[Any]     ):
      a						: Tuple					       =					[]
      for item in attention_types:
        for _ in range(item[1]     ):
          attentions.extend(item[0]     )
      return attentions
def 				lowerCamelCase__	(						_A			,      _A			,      _A			,      _A    ):
  import torch

  a						: int					       =					input.size()
  a						: Tuple					       =					len(_A    )
  a						: List[str]					       =					shape[dimension]

  a						: Any					       =					torch.arange(0			,      _A			,      _A    )
  a						: Union[str, Any]					       =					torch.div(sizedim - size			,      _A			,      rounding_mode='floor'    ) + 1
  a						: str					       =					torch.arange(_A    ) + low_indices[:min_length][:, None]

  a						: Any					       =					[slice(_A    )] * rank
  a						: str					       =					indices
  a						: Dict					       =					input[s]

  a						: str					       =					list(range(0			,      rank + 1    )    )
  perm.append(perm.pop(dimension + 1    )    )

  return sliced.permute(_A    )
def 				lowerCamelCase__	(						_A			,      _A    ):
  import torch

  a						: Union[str, Any]					       =					torch.arange(1			,      _A    )
  a						: List[Any]					       =					torch.remainder(_A			,      _A    )
  a						: List[str]					       =					remainders == 0
  a						: int					       =					candidates[divisor_indices]
  a						: int					       =					torch.max(_A    )
  return largest_divisor, torch.div(_A			,      _A			,      rounding_mode='floor'    )

class 							a__( lowerCamelCase__     ):







    @property
    def       lowercase_	(				self			:  List[Any]     ):
      a						: List[str]					       =					OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}}     )
      if self.use_past:
        self.fill_with_past_key_values_(__snake_case						,      direction='inputs'     )
        a						: Union[str, Any]					       =					{0: 'batch', 1: 'past_sequence + sequence'}
      else:
        a						: Any					       =					{0: 'batch', 1: 'sequence'}

      return common_inputs







    @property
    def       lowercase_	(				self			:  Any     ):
      return self._config.num_heads







    def       lowercase_	(				self			:  Union[str, Any]						,      __snake_case			:  PreTrainedTokenizer						,      __snake_case			:  int = -1						,      __snake_case			:  int = -1						,      __snake_case			:  bool = False						,      __snake_case			:  Optional[TensorType] = None						,      ):
      a						: Dict					       =					super(__snake_case						,      self     ).generate_dummy_inputs(
          __snake_case						,      batch_size=__snake_case						,      seq_length=__snake_case						,      is_pair=__snake_case						,      framework=__snake_case     )

      # We need to order the input in the way they appears in the forward()
      a						: List[Any]					       =					OrderedDict({'input_ids': common_inputs['input_ids']}     )

      # Need to add the past_keys
      if self.use_past:
        if not is_torch_available():
          raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'     )
        else:
          import torch

          a       ,     a						: List[Any]					       =					common_inputs['input_ids'].shape
          # Not using the same length for past_key_values
          a						: Any					       =					seqlen + 2
          a						: str					       =					(
              batch,
              self.num_attention_heads,
              past_key_values_length,
              self._config.hidden_size // self.num_attention_heads,
          )
          a						: Dict					       =					[
              (torch.zeros(__snake_case     ), torch.zeros(__snake_case     )) for _ in range(self.num_layers     )
          ]

      a						: int					       =					common_inputs['attention_mask']
      if self.use_past:
        a						: Optional[int]					       =					ordered_inputs['attention_mask'].dtype
        a						: int					       =					torch.cat(
            [ordered_inputs['attention_mask'], torch.ones(__snake_case						,      __snake_case						,      dtype=__snake_case     )]						,      dim=1     )

      return ordered_inputs







    @property
    def       lowercase_	(				self			:  str     ):
      return 13




'''simple docstring'''






import argparse
import os
import re

import packaging.version


lowerCAmelCase: List[str]							=						'examples/'
lowerCAmelCase: List[Any]							=						{
    'examples': (re.compile(r'^check_min_version\("[^"]+"\)\s*$', re.MULTILINE), 'check_min_version("VERSION")\n'),
    'init': (re.compile(r'^__version__\s+=\s+"([^"]+)"\s*$', re.MULTILINE), '__version__ = "VERSION"\n'),
    'setup': (re.compile(r'^(\s*)version\s*=\s*"[^"]+",', re.MULTILINE), r'\1version="VERSION",'),
    'doc': (re.compile(r'^(\s*)release\s*=\s*"[^"]+"$', re.MULTILINE), 'release = "VERSION"\n'),
}
lowerCAmelCase: str							=						{
    'init': 'src/transformers/__init__.py',
    'setup': 'setup.py',
}
lowerCAmelCase: str							=						'README.md'
def 				lowerCamelCase__	(						_A			,      _A			,      _A    ):
  with open(_A			,      'r'			,      encoding='utf-8'			,      newline='\n'    ) as f:
    a						: Tuple					       =					f.read()
  a       ,     a						: Tuple					       =					REPLACE_PATTERNS[pattern]
  a						: Dict					       =					replace.replace('VERSION'			,      _A    )
  a						: Dict					       =					re_pattern.sub(_A			,      _A    )
  with open(_A			,      'w'			,      encoding='utf-8'			,      newline='\n'    ) as f:
    f.write(_A    )
def 				lowerCamelCase__	(						_A    ):
  for folder, directories, fnames in os.walk(_A    ):
    # Removing some of the folders with non-actively maintained examples from the walk
    if "research_projects" in directories:
      directories.remove('research_projects'    )
    if "legacy" in directories:
      directories.remove('legacy'    )
    for fname in fnames:
      if fname.endswith('.py'    ):
        update_version_in_file(os.path.join(_A			,      _A    )			,      _A			,      pattern='examples'    )
def 				lowerCamelCase__	(						_A			,      _A=False    ):
  for pattern, fname in REPLACE_FILES.items():
    update_version_in_file(_A			,      _A			,      _A    )
  if not patch:
    update_version_in_examples(_A    )
def 				lowerCamelCase__	(						):
  a						: Tuple					       =					'🤗 Transformers currently provides the following architectures'
  a						: Any					       =					'1. Want to contribute a new model?'
  with open(_A			,      'r'			,      encoding='utf-8'			,      newline='\n'    ) as f:
    a						: Tuple					       =					f.readlines()

  # Find the start of the list.
  a						: Optional[int]					       =					0
  while not lines[start_index].startswith(_start_prompt    ):
    start_index += 1
  start_index += 1

  a						: Optional[int]					       =					start_index
  # Update the lines in the model list.
  while not lines[index].startswith(_end_prompt    ):
    if lines[index].startswith('1.'    ):
      a						: List[Any]					       =					lines[index].replace(
          'https://huggingface.co/docs/transformers/main/model_doc'			,      'https://huggingface.co/docs/transformers/model_doc'			,      )
    index += 1

  with open(_A			,      'w'			,      encoding='utf-8'			,      newline='\n'    ) as f:
    f.writelines(_A    )
def 				lowerCamelCase__	(						):
  with open(REPLACE_FILES['init']			,      'r'    ) as f:
    a						: Union[str, Any]					       =					f.read()
  a						: Tuple					       =					REPLACE_PATTERNS['init'][0].search(_A    ).groups()[0]
  return packaging.version.parse(_A    )
def 				lowerCamelCase__	(						_A=False    ):
  a						: int					       =					get_version()
  if patch and default_version.is_devrelease:
    raise ValueError('Can\'t create a patch version from the dev branch, checkout a released version!'    )
  if default_version.is_devrelease:
    a						: Any					       =					default_version.base_version
  elif patch:
    a						: Dict					       =					f"""{default_version.major}.{default_version.minor}.{default_version.micro + 1}"""
  else:
    a						: Union[str, Any]					       =					f"""{default_version.major}.{default_version.minor + 1}.0"""

  # Now let's ask nicely if that's the right one.
  a						: List[Any]					       =					input(f"""Which version are you releasing? [{default_version}]"""    )
  if len(_A    ) == 0:
    a						: Union[str, Any]					       =					default_version

  print(f"""Updating version to {version}."""    )
  global_version_update(_A			,      patch=_A    )
  if not patch:
    print('Cleaning main README, don\'t forget to run `make fix-copies`.'    )
    clean_main_ref_in_model_list()
def 				lowerCamelCase__	(						):
  a						: int					       =					get_version()
  a						: Any					       =					f"""{current_version.major}.{current_version.minor + 1}.0.dev0"""
  a						: int					       =					current_version.base_version

  # Check with the user we got that right.
  a						: Tuple					       =					input(f"""Which version are we developing now? [{dev_version}]"""    )
  if len(_A    ) == 0:
    a						: Optional[int]					       =					dev_version

  print(f"""Updating version to {version}."""    )
  global_version_update(_A    )
  print('Cleaning main README, don\'t forget to run `make fix-copies`.'    )
  clean_main_ref_in_model_list()


if __name__ == "__main__":
     lowerCAmelCase: Tuple							=						argparse.ArgumentParser()
     parser.add_argument('--post_release', action='store_true', help='Whether this is pre or post release.')
     parser.add_argument('--patch', action='store_true', help='Whether or not this is a patch release.')
     lowerCAmelCase: Optional[Any]							=						parser.parse_args()
     if not args.post_release:
          pre_release_work(patch=args.patch)
     elif args.patch:
          print('Nothing to do after a patch :-)')
     else:
          post_release_work()