



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


lowerCAmelCase:      List[str]		 =		logging.get_logger(__name__)

lowerCAmelCase:      int		 =		{
    '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 							a__(      lowerCamelCase__			):
    lowercase__				=					"""bloom"""
    lowercase__				=					["""past_key_values"""]
    lowercase__				=					{
        """num_hidden_layers""": """n_layer""",
        """num_attention_heads""": """n_head""",
    }







    def __init__(   self				:						Any		,    __snake_case				:						Tuple=25_08_80		,    __snake_case				:						Tuple=64		,    __snake_case				:						Union[str, Any]=2		,    __snake_case				:						Any=8		,    __snake_case				:						List[Any]=1e-5		,    __snake_case				:						Tuple=0.02		,    __snake_case				:						Any=True		,    __snake_case				:						Optional[int]=1		,    __snake_case				:						Tuple=2		,    __snake_case				:						List[Any]=False		,    __snake_case				:						Any=0.0		,    __snake_case				:						List[Any]=0.0		,    __snake_case				:						Any=1		,    __snake_case				:						List[str]=False		,    **__snake_case				:						Tuple		,    ):
      a		:      Union[str, Any]       =  vocab_size
      # Backward compatibility with n_embed kwarg
      a		:      List[Any]       =  kwargs.pop('n_embed'		,    __snake_case			)
      a		:      Optional[Any]       =  hidden_size if n_embed is None else n_embed
      a		:      Dict       =  n_layer
      a		:      List[str]       =  n_head
      a		:      str       =  layer_norm_epsilon
      a		:      Dict       =  initializer_range
      a		:      Dict       =  use_cache
      a		:      Optional[int]       =  pretraining_tp
      a		:      Optional[Any]       =  apply_residual_connection_post_layernorm
      a		:      str       =  hidden_dropout
      a		:      Any       =  attention_dropout

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

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

class 							a__(      lowerCamelCase__			):
    lowercase__				=					version.parse("""1.12"""			)







    def __init__(   self				:						str		,    __snake_case				:						PretrainedConfig		,    __snake_case				:						str = "default"		,    __snake_case				:						List[PatchingSpec] = None		,    __snake_case				:						bool = False		,    ):
      super().__init__(__snake_case		,    task=__snake_case		,    patching_specs=__snake_case		,    use_past=__snake_case			)
      if not getattr(self._config		,    'pad_token_id'		,    __snake_case			):
        # TODO: how to do that better?
        a		:      str       =  0







    @property
    def     lowercase_ (   self				:						Dict			):
      a		:      str       =  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_(__snake_case		,    direction='inputs'		,    inverted_values_shape=__snake_case			)
        a		:      Union[str, Any]       =  {0: 'batch', 1: 'past_sequence + sequence'}
      else:
        a		:      Optional[Any]       =  {0: 'batch', 1: 'sequence'}

      return common_inputs







    @property
    def     lowercase_ (   self				:						Tuple			):
      return self._config.n_layer







    @property
    def     lowercase_ (   self				:						Dict			):
      return self._config.n_head







    @property
    def     lowercase_ (   self				:						List[str]			):
      return 1e-3







    def     lowercase_ (   self				:						List[Any]		,    __snake_case				:						"PreTrainedTokenizer"		,    __snake_case				:						int = -1		,    __snake_case				:						int = -1		,    __snake_case				:						bool = False		,    __snake_case				:						Optional["TensorType"] = None		,    ):
      a		:      Optional[int]       =  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		:      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		:      List[Any]       =  common_inputs['input_ids'].shape
          # Not using the same length for past_key_values
          a		:      Any       =  seqlen + 2
          a		:      List[Any]       =  self._config.hidden_size // self.num_attention_heads
          a		:      Any       =  (
              batch * self.num_attention_heads,
              head_dim,
              past_key_values_length,
          )
          a		:      Dict       =  (
              batch * self.num_attention_heads,
              past_key_values_length,
              head_dim,
          )
          a		:      List[str]       =  [
              (torch.zeros(__snake_case			), torch.zeros(__snake_case			)) for _ in range(self.num_layers			)
          ]

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

      return ordered_inputs







    @property
    def     lowercase_ (   self				:						List[str]			):
      return 13




'''simple docstring'''






import warnings

from ...utils import logging
from .image_processing_chinese_clip import ChineseCLIPImageProcessor


lowerCAmelCase:      Optional[Any]		 =		logging.get_logger(__name__)

class 							a__(      lowerCamelCase__			):







    def __init__(   self				:						Union[str, Any]		,    *__snake_case				:						List[str]		,    **__snake_case				:						Union[str, Any]			):
      warnings.warn(
          'The class ChineseCLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
          ' Please use ChineseCLIPImageProcessor instead.'		,    __snake_case		,    )
      super().__init__(*__snake_case		,    **__snake_case			)