

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




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__)

