

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


__A      		=     logging.get_logger(__name__)

__A      		=     {
    '''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   lowercase_   (  __lowercase					):
      UpperCamelCase_      :		Union[str, Any]										=    "bloom"
      UpperCamelCase_      :		str										=    ["past_key_values"]
      UpperCamelCase_      :		Dict										=    {
          "num_hidden_layers": "n_layer",
          "num_attention_heads": "n_head",
      }



      def __init__(      self				:    str      , A__				:    Dict=250880      , A__				:    Optional[int]=64      , A__				:    Optional[int]=2      , A__				:    Dict=8      , A__				:    str=1e-5      , A__				:    Tuple=0.02      , A__				:    Dict=True      , A__				:    List[str]=1      , A__				:    Tuple=2      , A__				:    List[str]=False      , A__				:    int=0.0      , A__				:    List[Any]=0.0      , A__				:    Optional[int]=1      , A__				:    Tuple=False      , **A__				:    Union[str, Any]      , )   ->					Optional[int]:
        _snake_case						=							vocab_size
        # Backward compatibility with n_embed kwarg
        _snake_case						=							kwargs.pop('''n_embed'''      , A__		)
        _snake_case						=							hidden_size if n_embed is None else n_embed
        _snake_case						=							n_layer
        _snake_case						=							n_head
        _snake_case						=							layer_norm_epsilon
        _snake_case						=							initializer_range
        _snake_case						=							use_cache
        _snake_case						=							pretraining_tp
        _snake_case						=							apply_residual_connection_post_layernorm
        _snake_case						=							hidden_dropout
        _snake_case						=							attention_dropout

        _snake_case						=							bos_token_id
        _snake_case						=							eos_token_id
        _snake_case						=							slow_but_exact

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



class   lowercase_   (  __lowercase					):
      UpperCamelCase_      :		Tuple										=    version.parse("1.12"					)



      def __init__(      self				:    str      , A__				:    PretrainedConfig      , A__				:    str = "default"      , A__				:    List[PatchingSpec] = None      , A__				:    bool = False      , )   ->					Tuple:
        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?
          _snake_case						=							0



      @property
      def 						UpperCamelCase_		(      self				:    Tuple		)   ->					Mapping[str, Mapping[int, str]]:
        _snake_case						=							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__		)
          _snake_case						=							{0: '''batch''', 1: '''past_sequence + sequence'''}
        else:
          _snake_case						=							{0: '''batch''', 1: '''sequence'''}

        return common_inputs



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



      @property
      def 						UpperCamelCase_		(      self				:    Tuple		)   ->					int:
        return self._config.n_head



      @property
      def 						UpperCamelCase_		(      self				:    int		)   ->					float:
        return 1e-3



      def 						UpperCamelCase_		(      self				:    Dict      , A__				:    "PreTrainedTokenizer"      , A__				:    int = -1      , A__				:    int = -1      , A__				:    bool = False      , A__				:    Optional["TensorType"] = None      , )   ->					Mapping[str, Any]:
        _snake_case						=							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()
        _snake_case						=							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

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

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

        return ordered_inputs







      @property
      def 						UpperCamelCase_		(      self				:    Union[str, Any]		)   ->					int:
        return 13




# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__A      		=     {'''configuration_mra''': ['''MRA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MraConfig''']}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A      		=     [
     '''MRA_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''MraForMaskedLM''',
     '''MraForMultipleChoice''',
     '''MraForQuestionAnswering''',
     '''MraForSequenceClassification''',
     '''MraForTokenClassification''',
     '''MraLayer''',
     '''MraModel''',
     '''MraPreTrainedModel''',
 ]


if TYPE_CHECKING:
 from .configuration_mra import MRA_PRETRAINED_CONFIG_ARCHIVE_MAP, MraConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_mra import (
      MRA_PRETRAINED_MODEL_ARCHIVE_LIST,
      MraForMaskedLM,
      MraForMultipleChoice,
      MraForQuestionAnswering,
      MraForSequenceClassification,
      MraForTokenClassification,
      MraLayer,
      MraModel,
      MraPreTrainedModel,
  )
else:
 import sys

 __A      		=     _LazyModule(__name__, globals()['''__file__'''], _import_structure)

