






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


lowerCamelCase_		  =						logging.get_logger(__name__)

lowerCamelCase_		  =						{
    """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 							a_				(		a_	):


       '''simple docstring'''






       __a:			Union[str, Any]									=					'''m2m_100'''
       __a:			Any									=					['''past_key_values''']
       __a:			int									=					{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}

       def __init__(	self    ,    lowercase_=1_2_8_1_1_2    ,    lowercase_=1_0_2_4    ,    lowercase_=1_2    ,    lowercase_=4_0_9_6    ,    lowercase_=1_6    ,    lowercase_=1_2    ,    lowercase_=4_0_9_6    ,    lowercase_=1_6    ,    lowercase_=0.05    ,    lowercase_=0.05    ,    lowercase_=True    ,    lowercase_=True    ,    lowercase_="relu"    ,    lowercase_=1_0_2_4    ,    lowercase_=0.1    ,    lowercase_=0.1    ,    lowercase_=0.0    ,    lowercase_=0.02    ,    lowercase_=2    ,    lowercase_=True    ,    lowercase_=1    ,    lowercase_=0    ,    lowercase_=2    ,    **lowercase_    ,    )      ->      int:
              '''simple docstring'''

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

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




class 							a_				(		a_	):


       '''simple docstring'''






       @property
       def   _lowercase (	self     )      ->      Mapping[str, Mapping[int, str]]:
              '''simple docstring'''

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

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

              if self.use_past:
                     self.fill_with_past_key_values_(lowercase_    ,    direction='inputs'     )
              return common_inputs

       def   _lowercase (	self    ,    lowercase_    ,    lowercase_ = -1    ,    lowercase_ = -1    ,    lowercase_ = False    ,    lowercase_ = None    ,    )      ->      Mapping[str, Any]:
              '''simple docstring'''

              lowerCAmelCase_       		=  compute_effective_axis_dimension(
                  lowercase_    ,    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
              lowerCAmelCase_       		=  tokenizer.num_special_tokens_to_add(lowercase_     )
              lowerCAmelCase_       		=  compute_effective_axis_dimension(
                  lowercase_    ,    fixed_dimension=OnnxConfig.default_fixed_sequence    ,    num_token_to_add=lowercase_     )

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

       def   _lowercase (	self    ,    lowercase_    ,    lowercase_ = -1    ,    lowercase_ = -1    ,    lowercase_ = False    ,    lowercase_ = None    ,    )      ->      Mapping[str, Any]:
              '''simple docstring'''

              lowerCAmelCase_       		=  self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                  lowercase_    ,    lowercase_    ,    lowercase_    ,    lowercase_    ,    lowercase_     )

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

              if self.use_past:
                     if not is_torch_available():
                            raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'     )
                     else:
                            import torch
                     lowerCAmelCase_		,     lowerCAmelCase_       		=  common_inputs['input_ids'].shape
                     lowerCAmelCase_       		=  common_inputs['decoder_input_ids'].shape[1]
                     lowerCAmelCase_		,     lowerCAmelCase_       		=  self.num_attention_heads
                     lowerCAmelCase_       		=  (
                         batch,
                         num_encoder_attention_heads,
                         encoder_seq_length,
                         self._config.hidden_size // num_encoder_attention_heads,
                     )
                     lowerCAmelCase_       		=  decoder_seq_length + 3
                     lowerCAmelCase_       		=  (
                         batch,
                         num_decoder_attention_heads,
                         decoder_past_length,
                         self._config.hidden_size // num_decoder_attention_heads,
                     )

                     lowerCAmelCase_       		=  torch.cat(
                         [common_inputs['decoder_attention_mask'], torch.ones(lowercase_    ,    lowercase_     )]    ,    dim=1     )

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

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






       __a:			Optional[int]									=					_generate_dummy_inputs_for_default_and_seqaseq_lm











from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase_		  =						{
    """configuration_prophetnet""": ["""PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ProphetNetConfig"""],
    """tokenization_prophetnet""": ["""ProphetNetTokenizer"""],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCamelCase_		  =						[
		    """PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """ProphetNetDecoder""",
		    """ProphetNetEncoder""",
		    """ProphetNetForCausalLM""",
		    """ProphetNetForConditionalGeneration""",
		    """ProphetNetModel""",
		    """ProphetNetPreTrainedModel""",
		]


if TYPE_CHECKING:
		from .configuration_prophetnet import PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ProphetNetConfig
		from .tokenization_prophetnet import ProphetNetTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_prophetnet import (
				    PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST,
				    ProphetNetDecoder,
				    ProphetNetEncoder,
				    ProphetNetForCausalLM,
				    ProphetNetForConditionalGeneration,
				    ProphetNetModel,
				    ProphetNetPreTrainedModel,
				)

else:
		import sys

		lowerCamelCase_		  =						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



