
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


lowercase__				:				Tuple						=      logging.get_logger(__name__)

lowercase__				:				List[str]						=      {
    """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     ):

							"""simple docstring"""






							_snake_case  		=	'm2m_100'
							_snake_case  		=	['past_key_values']
							_snake_case  		=	{'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}



							def __init__(	self  ,						SCREAMING_SNAKE_CASE_=128112  ,						SCREAMING_SNAKE_CASE_=1024  ,						SCREAMING_SNAKE_CASE_=12  ,						SCREAMING_SNAKE_CASE_=4096  ,						SCREAMING_SNAKE_CASE_=16  ,						SCREAMING_SNAKE_CASE_=12  ,						SCREAMING_SNAKE_CASE_=4096  ,						SCREAMING_SNAKE_CASE_=16  ,						SCREAMING_SNAKE_CASE_=0.0_5  ,						SCREAMING_SNAKE_CASE_=0.0_5  ,						SCREAMING_SNAKE_CASE_=True  ,						SCREAMING_SNAKE_CASE_=True  ,						SCREAMING_SNAKE_CASE_="relu"  ,						SCREAMING_SNAKE_CASE_=1024  ,						SCREAMING_SNAKE_CASE_=0.1  ,						SCREAMING_SNAKE_CASE_=0.1  ,						SCREAMING_SNAKE_CASE_=0.0  ,						SCREAMING_SNAKE_CASE_=0.0_2  ,						SCREAMING_SNAKE_CASE_=2  ,						SCREAMING_SNAKE_CASE_=True  ,						SCREAMING_SNAKE_CASE_=1  ,						SCREAMING_SNAKE_CASE_=0  ,						SCREAMING_SNAKE_CASE_=2  ,						**SCREAMING_SNAKE_CASE_  ,						)-> str:





													'''simple docstring'''






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

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






class 							SCREAMING_SNAKE_CASE__     (				__snake_case     ):

							"""simple docstring"""






							@property
							def      A__		(	self      )-> Mapping[str, Mapping[int, str]]:





													'''simple docstring'''






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

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

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



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_ = -1  ,						SCREAMING_SNAKE_CASE_ = -1  ,						SCREAMING_SNAKE_CASE_ = False  ,						SCREAMING_SNAKE_CASE_ = None  ,						)-> Mapping[str, Any]:





													'''simple docstring'''






													__UpperCamelCase  	=      compute_effective_axis_dimension(
													    lowerCAmelCase__  ,						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
													__UpperCamelCase  	=      tokenizer.num_special_tokens_to_add(lowerCAmelCase__      )
													__UpperCamelCase  	=      compute_effective_axis_dimension(
													    lowerCAmelCase__  ,						fixed_dimension=OnnxConfig.default_fixed_sequence  ,						num_token_to_add=lowerCAmelCase__      )

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



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_ = -1  ,						SCREAMING_SNAKE_CASE_ = -1  ,						SCREAMING_SNAKE_CASE_ = False  ,						SCREAMING_SNAKE_CASE_ = None  ,						)-> Mapping[str, Any]:





													'''simple docstring'''






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

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

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

																			__UpperCamelCase  	=      torch.cat(
																			    [common_inputs['''decoder_attention_mask'''], torch.ones(lowerCAmelCase__  ,						lowerCAmelCase__      )]  ,						dim=1      )

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

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





'''simple docstring'''

from typing import Optional

from ..utils.logging import get_logger
from .audio_classificiation import AudioClassification
from .automatic_speech_recognition import AutomaticSpeechRecognition
from .base import TaskTemplate
from .image_classification import ImageClassification
from .language_modeling import LanguageModeling
from .question_answering import QuestionAnsweringExtractive
from .summarization import Summarization
from .text_classification import TextClassification


_lowerCamelCase :				int														= [
    """AutomaticSpeechRecognition""",
    """AudioClassification""",
    """ImageClassification""",
    """LanguageModeling""",
    """QuestionAnsweringExtractive""",
    """Summarization""",
    """TaskTemplate""",
    """TextClassification""",
]

_lowerCamelCase :				int														= get_logger(__name__)


_lowerCamelCase :				List[str]														= {
    AutomaticSpeechRecognition.task: AutomaticSpeechRecognition,
    AudioClassification.task: AudioClassification,
    ImageClassification.task: ImageClassification,
    LanguageModeling.task: LanguageModeling,
    QuestionAnsweringExtractive.task: QuestionAnsweringExtractive,
    Summarization.task: Summarization,
    TextClassification.task: TextClassification,
}



def  _lowerCAmelCase				(				__a      )  ->      Optional[TaskTemplate]:
     '''simple docstring'''







     _UpperCamelCase  :List[str]       =task_template_dict.get("""task"""      )
     if task_name is None:
          logger.warning(F'''Couldn\'t find template for task \'{task_name}\'. Available templates: {list(__a      )}'''      )
          return None
     _UpperCamelCase  :List[str]       =NAMEaTEMPLATE.get(__a      )
     return template.from_dict(__a      )