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 				a							(			__UpperCAmelCase       ):







 """simple docstring"""






 lowerCamelCase	:Dict    			=		'''bloom'''
 lowerCamelCase	:Optional[Any]    			=		['''past_key_values''']
 lowerCamelCase	:Any    			=		{
     '''num_hidden_layers''': '''n_layer''',
     '''num_attention_heads''': '''n_head''',
 }



 def __init__(					self		,				lowerCAmelCase_=25_08_80		,				lowerCAmelCase_=64		,				lowerCAmelCase_=2		,				lowerCAmelCase_=8		,				lowerCAmelCase_=1E-5		,				lowerCAmelCase_=0.02		,				lowerCAmelCase_=True		,				lowerCAmelCase_=1		,				lowerCAmelCase_=2		,				lowerCAmelCase_=False		,				lowerCAmelCase_=0.0		,				lowerCAmelCase_=0.0		,				lowerCAmelCase_=1		,				lowerCAmelCase_=False		,				**lowerCAmelCase_		,				)  ->					Union[str, Any]:
        _A        =  vocab_size
        # Backward compatibility with n_embed kwarg
        _A        =  kwargs.pop("""n_embed"""		,				UpperCamelCase__							)
        _A        =  hidden_size if n_embed is None else n_embed
        _A        =  n_layer
        _A        =  n_head
        _A        =  layer_norm_epsilon
        _A        =  initializer_range
        _A        =  use_cache
        _A        =  pretraining_tp
        _A        =  apply_residual_connection_post_layernorm
        _A        =  hidden_dropout
        _A        =  attention_dropout

        _A        =  bos_token_id
        _A        =  eos_token_id
        _A        =  slow_but_exact

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



class 				a							(			__UpperCAmelCase       ):







 """simple docstring"""






 lowerCamelCase	:List[str]    			=		version.parse('''1.12'''       )



 def __init__(					self		,				lowerCAmelCase_		,				lowerCAmelCase_ = "default"		,				lowerCAmelCase_ = None		,				lowerCAmelCase_ = False		,				)  ->					int:
        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?
               _A        =  0



 @property
 def      UpperCAmelCase							(					self							)  ->					List[str]:
        _A        =  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__							)
               _A        =  {0: """batch""", 1: """past_sequence + sequence"""}
        else:
               _A        =  {0: """batch""", 1: """sequence"""}

        return common_inputs



 @property
 def      UpperCAmelCase							(					self							)  ->					List[str]:
        return self._config.n_layer



 @property
 def      UpperCAmelCase							(					self							)  ->					Any:
        return self._config.n_head



 @property
 def      UpperCAmelCase							(					self							)  ->					List[str]:
        return 1E-3



 def      UpperCAmelCase							(					self		,				lowerCAmelCase_		,				lowerCAmelCase_ = -1		,				lowerCAmelCase_ = -1		,				lowerCAmelCase_ = False		,				lowerCAmelCase_ = None		,				)  ->					int:
        _A        =  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()
        _A        =  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        =  common_inputs["""input_ids"""].shape
                      # Not using the same length for past_key_values
                      _A        =  seqlen + 2
                      _A        =  self._config.hidden_size // self.num_attention_heads
                      _A        =  (
                          batch * self.num_attention_heads,
                          head_dim,
                          past_key_values_length,
                      )
                      _A        =  (
                          batch * self.num_attention_heads,
                          past_key_values_length,
                          head_dim,
                      )
                      _A        =  [
                          (torch.zeros(UpperCamelCase__							), torch.zeros(UpperCamelCase__							)) for _ in range(self.num_layers							)
                      ]

        _A        =  common_inputs["""attention_mask"""]
        if self.use_past:
               _A        =  ordered_inputs["""attention_mask"""].dtype
               _A        =  torch.cat(
                   [ordered_inputs["""attention_mask"""], torch.ones(UpperCamelCase__		,				UpperCamelCase__		,				dtype=UpperCamelCase__							)]		,				dim=1							)

        return ordered_inputs



 @property
 def      UpperCAmelCase							(					self							)  ->					str:
        return 13










import warnings

from ...utils import logging
from .image_processing_beit import BeitImageProcessor


_A     =			logging.get_logger(__name__)


class  A		(  __UpperCAmelCase     ):



		def __init__(     self,	*UpperCamelCase__,	**UpperCamelCase__   ):







								"""simple docstring"""



								warnings.warn(
								    '''The class BeitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
								    ''' use BeitImageProcessor instead.''',	UpperCamelCase__,	)
								super().__init__(*UpperCamelCase__,	**UpperCamelCase__   )




