
"""simple docstring"""

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

from packaging import version


if TYPE_CHECKING:
   from ... import PreTrainedTokenizer, TensorType

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import is_torch_available, logging


SCREAMING_SNAKE_CASE_    					=						logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_    					=						{
    '''bigscience/bloom''': '''https://huggingface.co/bigscience/bloom/resolve/main/config.json''',
    '''bigscience/bloom-560m''': '''https://huggingface.co/bigscience/bloom-560m/blob/main/config.json''',
    '''bigscience/bloom-1b1''': '''https://huggingface.co/bigscience/bloom-1b1/blob/main/config.json''',
    '''bigscience/bloom-1b7''': '''https://huggingface.co/bigscience/bloom-1b7/blob/main/config.json''',
    '''bigscience/bloom-3b''': '''https://huggingface.co/bigscience/bloom-3b/blob/main/config.json''',
    '''bigscience/bloom-7b1''': '''https://huggingface.co/bigscience/bloom-7b1/blob/main/config.json''',
}
class      _UpperCAmelCase							(		__UpperCAmelCase							):
  __SCREAMING_SNAKE_CASE   :							Union[str, Any]							      =	"""bloom"""
  __SCREAMING_SNAKE_CASE   :							Any							      =	["""past_key_values"""]
  __SCREAMING_SNAKE_CASE   :							str							      =	{
      """num_hidden_layers""": """n_layer""",
      """num_attention_heads""": """n_head""",
  }




  def __init__(							self   ,       lowercase_=2_5_0_8_8_0   ,       lowercase_=6_4   ,       lowercase_=2   ,       lowercase_=8   ,       lowercase_=1E-5   ,       lowercase_=0.0_2   ,       lowercase_=True   ,       lowercase_=1   ,       lowercase_=2   ,       lowercase_=False   ,       lowercase_=0.0   ,       lowercase_=0.0   ,       lowercase_=1   ,       lowercase_=False   ,       **lowercase_   ,       )						->					Tuple:
      UpperCAmelCase									=					vocab_size
      # Backward compatibility with n_embed kwarg
      UpperCAmelCase									=					kwargs.pop('n_embed'   ,       UpperCAmelCase_							)
      UpperCAmelCase									=					hidden_size if n_embed is None else n_embed
      UpperCAmelCase									=					n_layer
      UpperCAmelCase									=					n_head
      UpperCAmelCase									=					layer_norm_epsilon
      UpperCAmelCase									=					initializer_range
      UpperCAmelCase									=					use_cache
      UpperCAmelCase									=					pretraining_tp
      UpperCAmelCase									=					apply_residual_connection_post_layernorm
      UpperCAmelCase									=					hidden_dropout
      UpperCAmelCase									=					attention_dropout

      UpperCAmelCase									=					bos_token_id
      UpperCAmelCase									=					eos_token_id
      UpperCAmelCase									=					slow_but_exact

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

class      _UpperCAmelCase							(		__UpperCAmelCase							):
  __SCREAMING_SNAKE_CASE   :							List[Any]							      =	version.parse("1.12"							)




  def __init__(							self   ,       lowercase_   ,       lowercase_ = "default"   ,       lowercase_ = None   ,       lowercase_ = False   ,       )						->					Tuple:
      super().__init__(UpperCAmelCase_   ,       task=UpperCAmelCase_   ,       patching_specs=UpperCAmelCase_   ,       use_past=UpperCAmelCase_							)
      if not getattr(self._config   ,       'pad_token_id'   ,       UpperCAmelCase_							):
          # TODO: how to do that better?
          UpperCAmelCase									=					0




  @property
  def     a_      (							self							)						->					Any:
      UpperCAmelCase									=					OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}}							)
      if self.use_past:
          # BLOOM stores values on dynamic axis 2. For more details see: https://github.com/huggingface/transformers/pull/18344
          self.fill_with_past_key_values_(UpperCAmelCase_   ,       direction='inputs'   ,       inverted_values_shape=UpperCAmelCase_							)
          UpperCAmelCase									=					{0: 'batch', 1: 'past_sequence + sequence'}
      else:
          UpperCAmelCase									=					{0: 'batch', 1: 'sequence'}

      return common_inputs




  @property
  def     a_      (							self							)						->					Union[str, Any]:
      return self._config.n_layer




  @property
  def     a_      (							self							)						->					Optional[int]:
      return self._config.n_head




  @property
  def     a_      (							self							)						->					Dict:
      return 1E-3




  def     a_      (							self   ,       lowercase_   ,       lowercase_ = -1   ,       lowercase_ = -1   ,       lowercase_ = False   ,       lowercase_ = None   ,       )						->					int:
      UpperCAmelCase									=					super(UpperCAmelCase_   ,       self							).generate_dummy_inputs(
          UpperCAmelCase_   ,       batch_size=UpperCAmelCase_   ,       seq_length=UpperCAmelCase_   ,       is_pair=UpperCAmelCase_   ,       framework=UpperCAmelCase_							)

      # We need to order the input in the way they appears in the forward()
      UpperCAmelCase									=					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

              UpperCAmelCase			,							UpperCAmelCase									=					common_inputs['input_ids'].shape
              # Not using the same length for past_key_values
              UpperCAmelCase									=					seqlen + 2
              UpperCAmelCase									=					self._config.hidden_size // self.num_attention_heads
              UpperCAmelCase									=					(
                  batch * self.num_attention_heads,
                  head_dim,
                  past_key_values_length,
              )
              UpperCAmelCase									=					(
                  batch * self.num_attention_heads,
                  past_key_values_length,
                  head_dim,
              )
              UpperCAmelCase									=					[
                  (torch.zeros(UpperCAmelCase_							), torch.zeros(UpperCAmelCase_							)) for _ in range(self.num_layers							)
              ]

      UpperCAmelCase									=					common_inputs['attention_mask']
      if self.use_past:
          UpperCAmelCase									=					ordered_inputs['attention_mask'].dtype
          UpperCAmelCase									=					torch.cat(
              [ordered_inputs['attention_mask'], torch.ones(UpperCAmelCase_   ,       UpperCAmelCase_   ,       dtype=UpperCAmelCase_							)]   ,       dim=1							)

      return ordered_inputs




  @property
  def     a_      (							self							)						->					Tuple:
      return 1_3





"""simple docstring"""

import os
from collections.abc import Iterator




def     lowercase__						(					lowerCAmelCase				:					str = "." ) ->      Iterator[str]:






    """simple docstring"""




    for dir_path, dir_names, filenames in os.walk(lowerCAmelCase ):
        UpperCAmelCase									=					[d for d in dir_names if d != 'scripts' and d[0] not in '._']
        for filename in filenames:
            if filename == "__init__.py":
                continue
            if os.path.splitext(lowerCAmelCase )[1] in (".py", ".ipynb"):
                yield os.path.join(lowerCAmelCase ,				lowerCAmelCase ).lstrip('./' )




def     lowercase__						(					lowerCAmelCase				:					Union[str, Any] ) ->      Any:






    """simple docstring"""




    return F"{i * '  '}*" if i else "\n##"




def     lowercase__						(					lowerCAmelCase				:					str ,				lowerCAmelCase				:					str ) ->      str:






    """simple docstring"""




    UpperCAmelCase									=					old_path.split(os.sep )
    for i, new_part in enumerate(new_path.split(os.sep ) ):
        if (i + 1 > len(lowerCAmelCase ) or old_parts[i] != new_part) and new_part:
            print(F"{md_prefix(lowerCAmelCase )} {new_part.replace('_' ,				' ' ).title()}" )
    return new_path




def     lowercase__						(					lowerCAmelCase				:					str = "." ) ->      None:






    """simple docstring"""




    UpperCAmelCase									=					''
    for filepath in sorted(good_file_paths(lowerCAmelCase ) ):
        UpperCAmelCase			,							UpperCAmelCase									=					os.path.split(lowerCAmelCase )
        if filepath != old_path:
            UpperCAmelCase									=					print_path(lowerCAmelCase ,				lowerCAmelCase )
        UpperCAmelCase									=					(filepath.count(os.sep ) + 1) if filepath else 0
        UpperCAmelCase									=					F"{filepath}/{filename}".replace(' ' ,				'%20' )
        UpperCAmelCase									=					os.path.splitext(filename.replace('_' ,				' ' ).title() )[0]
        print(F"{md_prefix(lowerCAmelCase )} [{filename}]({url})" )


if __name__ == "__main__":
   print_directory_md('''.''')



