code: 
  
  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
  
  
  snake_case_    						=				logging.get_logger(__name__)
  
  snake_case_    						=				{
      '''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    SCREAMING_SNAKE_CASE__      (__snake_case  ):
      __lowerCamelCase		: str														=  """m2m_100"""
      __lowerCamelCase		: str														=  ["""past_key_values"""]
      __lowerCamelCase		: Optional[Any]														=  {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}
  
  
  
  
  
      def __init__(   self		,     a=12_8112		,     a=1024		,     a=12		,     a=4096		,     a=16		,     a=12		,     a=4096		,     a=16		,     a=0.05		,     a=0.05		,     a=True		,     a=True		,     a="relu"		,     a=1024		,     a=0.1		,     a=0.1		,     a=0.0		,     a=0.02		,     a=2		,     a=True		,     a=1		,     a=0		,     a=2		,     **a		,     ):
           lowercase__					:     str   = vocab_size
           lowercase__					:     Optional[Any]   = max_position_embeddings
           lowercase__					:     Optional[Any]   = d_model
           lowercase__					:     Tuple   = encoder_ffn_dim
           lowercase__					:     Optional[int]   = encoder_layers
           lowercase__					:     Optional[int]   = encoder_attention_heads
           lowercase__					:     List[str]   = decoder_ffn_dim
           lowercase__					:     List[Any]   = decoder_layers
           lowercase__					:     Union[str, Any]   = decoder_attention_heads
           lowercase__					:     Dict   = dropout
           lowercase__					:     Dict   = attention_dropout
           lowercase__					:     str   = activation_dropout
           lowercase__					:     Any   = activation_function
           lowercase__					:     str   = init_std
           lowercase__					:     Tuple   = encoder_layerdrop
           lowercase__					:     List[Any]   = decoder_layerdrop
           lowercase__					:     List[str]   = use_cache
           lowercase__					:     Union[str, Any]   = encoder_layers
           lowercase__					:     Dict   = scale_embedding  # scale factor will be sqrt(d_model) if True
  
           super().__init__(
               pad_token_id=a		,     bos_token_id=a		,     eos_token_id=a		,     is_encoder_decoder=a		,     decoder_start_token_id=a		,     **a		,     )
  
  
  
  class    SCREAMING_SNAKE_CASE__      (__snake_case  ):
  
  
  
  
  
      @property
      def    snake_case_       (   self):
           lowercase__					:     str   = OrderedDict(
               [
                   ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                   ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
               ])
  
           if self.use_past:
                lowercase__					:     Union[str, Any]   = {0: 'batch'}
                lowercase__					:     List[str]   = {0: 'batch', 1: 'past_decoder_sequence + sequence'}
           else:
                lowercase__					:     List[str]   = {0: 'batch', 1: 'decoder_sequence'}
                lowercase__					:     Dict   = {0: 'batch', 1: 'decoder_sequence'}
  
           if self.use_past:
                self.fill_with_past_key_values_(a		,     direction='inputs')
           return common_inputs
  
  
  
  
  
      def    snake_case_       (   self		,     a		,     a = -1		,     a = -1		,     a = False		,     a = None		,     ):
           # Copied from OnnxConfig.generate_dummy_inputs
           # Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
           # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
           lowercase__					:     Optional[int]   = compute_effective_axis_dimension(
               a		,     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
           lowercase__					:     Optional[Any]   = tokenizer.num_special_tokens_to_add(a)
           lowercase__					:     Union[str, Any]   = compute_effective_axis_dimension(
               a		,     fixed_dimension=OnnxConfig.default_fixed_sequence		,     num_token_to_add=a)
  
           # Generate dummy inputs according to compute batch and sequence
           lowercase__					:     Tuple   = [' '.join([tokenizer.unk_token]) * seq_length] * batch_size
           lowercase__					:     Optional[Any]   = dict(tokenizer(a		,     return_tensors=a))
           return common_inputs
  
  
  
  
  
      def    snake_case_       (   self		,     a		,     a = -1		,     a = -1		,     a = False		,     a = None		,     ):
           lowercase__					:     str   = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
               a		,     a		,     a		,     a		,     a)
  
           # Generate decoder inputs
           lowercase__					:     Tuple   = seq_length if not self.use_past else 1
           lowercase__					:     List[Any]   = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
               a		,     a		,     a		,     a		,     a)
           lowercase__					:     int   = {f"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
           lowercase__					:     Optional[int]   = dict(**a		,     **a)
  
           if self.use_past:
                if not is_torch_available():
                     raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.')
                else:
                     import torch
                lowercase__   ,				lowercase__					:     Optional[Any]   = common_inputs['input_ids'].shape
                lowercase__					:     Optional[int]   = common_inputs['decoder_input_ids'].shape[1]
                lowercase__   ,				lowercase__					:     Dict   = self.num_attention_heads
                lowercase__					:     Tuple   = (
                    batch,
                    num_encoder_attention_heads,
                    encoder_seq_length,
                    self._config.hidden_size // num_encoder_attention_heads,
                )
                lowercase__					:     List[str]   = decoder_seq_length + 3
                lowercase__					:     List[Any]   = (
                    batch,
                    num_decoder_attention_heads,
                    decoder_past_length,
                    self._config.hidden_size // num_decoder_attention_heads,
                )
  
                lowercase__					:     Optional[Any]   = torch.cat(
                    [common_inputs['decoder_attention_mask'], torch.ones(a		,     a)]		,     dim=1)
  
                lowercase__					:     Optional[int]   = []
                # If the number of encoder and decoder layers are present in the model configuration, both are considered
                lowercase__   ,				lowercase__					:     Tuple   = self.num_layers
                lowercase__					:     Dict   = min(a		,     a)
                lowercase__					:     str   = max(a		,     a) - min_num_layers
                lowercase__					:     Union[str, Any]   = 'encoder' if num_encoder_layers > num_decoder_layers else 'decoder'
  
                for _ in range(a):
                     common_inputs["past_key_values"].append(
                         (
                             torch.zeros(a),
                             torch.zeros(a),
                             torch.zeros(a),
                             torch.zeros(a),
                         ))
                # TODO: test this.
                lowercase__					:     Union[str, Any]   = encoder_shape if remaining_side_name == 'encoder' else decoder_shape
                for _ in range(a		,     a):
                     common_inputs["past_key_values"].append((torch.zeros(a), torch.zeros(a)))
           return common_inputs
  
      __lowerCamelCase		: List[Any]														=  _generate_dummy_inputs_for_default_and_seqaseq_lm
  
  
style_context: 
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_flax_available,
      is_tf_available,
      is_torch_available,
  )
  
  
  snake_case_    						=				{'''configuration_gptj''': ['''GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTJConfig''', '''GPTJOnnxConfig''']}
  
  try:
         if not is_torch_available():
                raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
         pass
  else:
         snake_case_    						=				[
             '''GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST''',
             '''GPTJForCausalLM''',
             '''GPTJForQuestionAnswering''',
             '''GPTJForSequenceClassification''',
             '''GPTJModel''',
             '''GPTJPreTrainedModel''',
         ]
  
  try:
         if not is_tf_available():
                raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
         pass
  else:
         snake_case_    						=				[
             '''TFGPTJForCausalLM''',
             '''TFGPTJForQuestionAnswering''',
             '''TFGPTJForSequenceClassification''',
             '''TFGPTJModel''',
             '''TFGPTJPreTrainedModel''',
         ]
  
  try:
         if not is_flax_available():
                raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
         pass
  else:
         snake_case_    						=				[
             '''FlaxGPTJForCausalLM''',
             '''FlaxGPTJModel''',
             '''FlaxGPTJPreTrainedModel''',
         ]
  
  
  if TYPE_CHECKING:
         from .configuration_gptj import GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTJConfig, GPTJOnnxConfig
  
         try:
                if not is_torch_available():
                       raise OptionalDependencyNotAvailable()
         except OptionalDependencyNotAvailable:
                pass
         else:
                from .modeling_gptj import (
                    GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST,
                    GPTJForCausalLM,
                    GPTJForQuestionAnswering,
                    GPTJForSequenceClassification,
                    GPTJModel,
                    GPTJPreTrainedModel,
                )
  
         try:
                if not is_tf_available():
                       raise OptionalDependencyNotAvailable()
         except OptionalDependencyNotAvailable:
                pass
         else:
                from .modeling_tf_gptj import (
                    TFGPTJForCausalLM,
                    TFGPTJForQuestionAnswering,
                    TFGPTJForSequenceClassification,
                    TFGPTJModel,
                    TFGPTJPreTrainedModel,
                )
  
         try:
                if not is_flax_available():
                       raise OptionalDependencyNotAvailable()
         except OptionalDependencyNotAvailable:
                pass
         else:
                from .modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel, FlaxGPTJPreTrainedModel
  
  else:
         import sys
  
         snake_case_    						=				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
  
  