



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


_lowercase    	=		logging.get_logger(__name__)

_lowercase    	=		{
    "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      (							A__  ):
     UpperCamelCase__		     =					'''bloom'''
     UpperCamelCase__		     =					['''past_key_values''']
     UpperCamelCase__		     =					{
         '''num_hidden_layers''': '''n_layer''',
         '''num_attention_heads''': '''n_head''',
     }




     def __init__(					self			,    a__=2_5_0_8_8_0			,    a__=6_4			,    a__=2			,    a__=8			,    a__=1e-5			,    a__=0.0_2			,    a__=True			,    a__=1			,    a__=2			,    a__=False			,    a__=0.0			,    a__=0.0			,    a__=1			,    a__=False			,    **a__			,    ):
         A__					=       vocab_size
         # Backward compatibility with n_embed kwarg
         A__					=       kwargs.pop('''n_embed'''			,    a__)
         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=a__			,    eos_token_id=a__			,    **a__)







class      _UpperCAmelCase      (							A__  ):
     UpperCamelCase__		     =					version.parse('''1.12'''  )




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




     @property
     def 		snake_case_		(					self):
         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_(a__			,    direction='''inputs'''			,    inverted_values_shape=a__)
             A__					=       {0: '''batch''', 1: '''past_sequence + sequence'''}
         else:
             A__					=       {0: '''batch''', 1: '''sequence'''}

         return common_inputs




     @property
     def 		snake_case_		(					self):
         return self._config.n_layer




     @property
     def 		snake_case_		(					self):
         return self._config.n_head




     @property
     def 		snake_case_		(					self):
         return 1e-3




     def 		snake_case_		(					self			,    a__			,    a__ = -1			,    a__ = -1			,    a__ = False			,    a__ = None			,    ):
         A__					=       super(a__			,    self).generate_dummy_inputs(
             a__			,    batch_size=a__			,    seq_length=a__			,    is_pair=a__			,    framework=a__)

         # 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(a__), torch.zeros(a__)) 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(a__			,    a__			,    dtype=a__)]			,    dim=1)

         return ordered_inputs




     @property
     def 		snake_case_		(					self):
         return 1_3





from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowercase    	=		logging.get_logger(__name__)



class      _UpperCAmelCase      (							A__  ):
     UpperCamelCase__		     =					'''timm_backbone'''




     def __init__(					self			,    a__=None			,    a__=3			,    a__=True			,    a__=True			,    a__=None			,    **a__			,    ):
         super().__init__(**a__)
         A__					=       backbone
         A__					=       num_channels
         A__					=       features_only
         A__					=       use_pretrained_backbone
         A__					=       True
         A__					=       out_indices if out_indices is not None else (-1,)
