




'''simple docstring'''

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

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


a__         =     logging.get_logger(__name__)

a__         =     {
    '''facebook/m2m100_418M''': '''https://huggingface.co/facebook/m2m100_418M/resolve/main/config.json''',
    # See all M2M100 models at https://huggingface.co/models?filter=m2m_100
}





class __magic_name__(						__lowerCAmelCase      ):
						UpperCAmelCase_		:					Optional[Any]    =  """m2m_100"""
						UpperCAmelCase_		:					Any    =  ["""past_key_values"""]
						UpperCAmelCase_		:					Optional[Any]    =  {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}






						def __init__(   self						:						int					,   __UpperCamelCase						:						List[str]=1_2_8_1_1_2					,   __UpperCamelCase						:						List[str]=1_0_2_4					,   __UpperCamelCase						:						List[str]=1_2					,   __UpperCamelCase						:						int=4_0_9_6					,   __UpperCamelCase						:						Optional[Any]=1_6					,   __UpperCamelCase						:						Optional[Any]=1_2					,   __UpperCamelCase						:						int=4_0_9_6					,   __UpperCamelCase						:						Dict=1_6					,   __UpperCamelCase						:						List[Any]=0.05					,   __UpperCamelCase						:						List[str]=0.05					,   __UpperCamelCase						:						List[str]=True					,   __UpperCamelCase						:						int=True					,   __UpperCamelCase						:						List[Any]="relu"					,   __UpperCamelCase						:						Tuple=1_0_2_4					,   __UpperCamelCase						:						Dict=0.1					,   __UpperCamelCase						:						List[str]=0.1					,   __UpperCamelCase						:						List[Any]=0.0					,   __UpperCamelCase						:						str=0.02					,   __UpperCamelCase						:						Any=2					,   __UpperCamelCase						:						Any=True					,   __UpperCamelCase						:						Dict=1					,   __UpperCamelCase						:						Tuple=0					,   __UpperCamelCase						:						List[Any]=2					,   **__UpperCamelCase						:						Union[str, Any]					,   ):



									'''simple docstring'''






									snake_case__          =						vocab_size
									snake_case__          =						max_position_embeddings
									snake_case__          =						d_model
									snake_case__          =						encoder_ffn_dim
									snake_case__          =						encoder_layers
									snake_case__          =						encoder_attention_heads
									snake_case__          =						decoder_ffn_dim
									snake_case__          =						decoder_layers
									snake_case__          =						decoder_attention_heads
									snake_case__          =						dropout
									snake_case__          =						attention_dropout
									snake_case__          =						activation_dropout
									snake_case__          =						activation_function
									snake_case__          =						init_std
									snake_case__          =						encoder_layerdrop
									snake_case__          =						decoder_layerdrop
									snake_case__          =						use_cache
									snake_case__          =						encoder_layers
									snake_case__          =						scale_embedding  # scale factor will be sqrt(d_model) if True

									super().__init__(
									    pad_token_id=__UpperCamelCase					,   bos_token_id=__UpperCamelCase					,   eos_token_id=__UpperCamelCase					,   is_encoder_decoder=__UpperCamelCase					,   decoder_start_token_id=__UpperCamelCase					,   **__UpperCamelCase					,   )







class __magic_name__(						__lowerCAmelCase      ):






						@property
						def       __lowerCAmelCase(   self						:						Any       ):



									'''simple docstring'''






									snake_case__          =						OrderedDict(
									    [
									        ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
									        ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
									    ]       )

									if self.use_past:
												snake_case__          =						{0: """batch"""}
												snake_case__          =						{0: """batch""", 1: """past_decoder_sequence + sequence"""}
									else:
												snake_case__          =						{0: """batch""", 1: """decoder_sequence"""}
												snake_case__          =						{0: """batch""", 1: """decoder_sequence"""}

									if self.use_past:
												self.fill_with_past_key_values_(__UpperCamelCase					,   direction="""inputs"""       )
									return common_inputs






						def       __lowerCAmelCase(   self						:						int					,   __UpperCamelCase						:						PreTrainedTokenizer					,   __UpperCamelCase						:						int = -1					,   __UpperCamelCase						:						int = -1					,   __UpperCamelCase						:						bool = False					,   __UpperCamelCase						:						Optional[TensorType] = None					,   ):



									'''simple docstring'''






									snake_case__          =						compute_effective_axis_dimension(
									    __UpperCamelCase					,   fixed_dimension=OnnxConfig.default_fixed_batch					,   num_token_to_add=0       )

									# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
									snake_case__          =						tokenizer.num_special_tokens_to_add(__UpperCamelCase       )
									snake_case__          =						compute_effective_axis_dimension(
									    __UpperCamelCase					,   fixed_dimension=OnnxConfig.default_fixed_sequence					,   num_token_to_add=__UpperCamelCase       )

									# Generate dummy inputs according to compute batch and sequence
									snake_case__          =						[""" """.join([tokenizer.unk_token]       ) * seq_length] * batch_size
									snake_case__          =						dict(tokenizer(__UpperCamelCase					,   return_tensors=__UpperCamelCase       )       )
									return common_inputs






						def       __lowerCAmelCase(   self						:						int					,   __UpperCamelCase						:						PreTrainedTokenizer					,   __UpperCamelCase						:						int = -1					,   __UpperCamelCase						:						int = -1					,   __UpperCamelCase						:						bool = False					,   __UpperCamelCase						:						Optional[TensorType] = None					,   ):



									'''simple docstring'''






									snake_case__          =						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
									    __UpperCamelCase					,   __UpperCamelCase					,   __UpperCamelCase					,   __UpperCamelCase					,   __UpperCamelCase       )

									# Generate decoder inputs
									snake_case__          =						seq_length if not self.use_past else 1
									snake_case__          =						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
									    __UpperCamelCase					,   __UpperCamelCase					,   __UpperCamelCase					,   __UpperCamelCase					,   __UpperCamelCase       )
									snake_case__          =						{f"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
									snake_case__          =						dict(**__UpperCamelCase					,   **__UpperCamelCase       )

									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
												snake_case__          =						common_inputs["""decoder_input_ids"""].shape[1]
												snake_case__    ,			snake_case__          =						self.num_attention_heads
												snake_case__          =						(
												    batch,
												    num_encoder_attention_heads,
												    encoder_seq_length,
												    self._config.hidden_size // num_encoder_attention_heads,
												)
												snake_case__          =						decoder_seq_length + 3
												snake_case__          =						(
												    batch,
												    num_decoder_attention_heads,
												    decoder_past_length,
												    self._config.hidden_size // num_decoder_attention_heads,
												)

												snake_case__          =						torch.cat(
												    [common_inputs["""decoder_attention_mask"""], torch.ones(__UpperCamelCase					,   __UpperCamelCase       )]					,   dim=1       )

												snake_case__          =						[]
												# If the number of encoder and decoder layers are present in the model configuration, both are considered
												snake_case__    ,			snake_case__          =						self.num_layers
												snake_case__          =						min(__UpperCamelCase					,   __UpperCamelCase       )
												snake_case__          =						max(__UpperCamelCase					,   __UpperCamelCase       ) - min_num_layers
												snake_case__          =						"""encoder""" if num_encoder_layers > num_decoder_layers else """decoder"""

												for _ in range(__UpperCamelCase       ):
															common_inputs["past_key_values"].append(
															    (
															        torch.zeros(__UpperCamelCase       ),
															        torch.zeros(__UpperCamelCase       ),
															        torch.zeros(__UpperCamelCase       ),
															        torch.zeros(__UpperCamelCase       ),
															    )       )
												# TODO: test this.
												snake_case__          =						encoder_shape if remaining_side_name == """encoder""" else decoder_shape
												for _ in range(__UpperCamelCase					,   __UpperCamelCase       ):
															common_inputs["past_key_values"].append((torch.zeros(__UpperCamelCase       ), torch.zeros(__UpperCamelCase       ))       )
									return common_inputs







						UpperCAmelCase_		:					Optional[Any]    =  _generate_dummy_inputs_for_default_and_seqaseq_lm

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a__				:      List[Any]    		=     {
    '''configuration_decision_transformer''': [
        '''DECISION_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''DecisionTransformerConfig''',
    ],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      a__				:      List[str]    		=     [
          '''DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''DecisionTransformerGPT2Model''',
          '''DecisionTransformerGPT2PreTrainedModel''',
          '''DecisionTransformerModel''',
          '''DecisionTransformerPreTrainedModel''',
      ]


if TYPE_CHECKING:
      from .configuration_decision_transformer import (
          DECISION_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
          DecisionTransformerConfig,
      )

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_decision_transformer import (
                DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                DecisionTransformerGPTaModel,
                DecisionTransformerGPTaPreTrainedModel,
                DecisionTransformerModel,
                DecisionTransformerPreTrainedModel,
            )


else:
      import sys

      a__				:      str    		=     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
