




from collections import OrderedDict
from dataclasses import fields
from typing import Any, Tuple

import numpy as np

from .import_utils import is_torch_available

def 	lowerCAmelCase_	(     lowerCamelCase:				int  )    ->	Tuple:


      '''simple docstring'''




      if is_torch_available():
            import torch

            if isinstance(lowerCamelCase   , torch.Tensor  ):
                  return True

      return isinstance(lowerCamelCase   , np.ndarray  )






class 	A	(						_lowerCAmelCase						):
     """simple docstring"""

     def        __snake_case      (			self     :      Optional[int]	) ->				List[Any]:
           lowerCamelCase__					     = fields(self	)

           # Safety and consistency checks
           if not len(A__	):
                 raise ValueError(F"""{self.__class__.__name__} has no fields."""	)

           lowerCamelCase__					     = getattr(self		,     class_fields[0].name	)
           lowerCamelCase__					     = all(getattr(self		,     field.name	) is None for field in class_fields[1:]	)

           if other_fields_are_none and isinstance(A__		,     A__	):
                 for key, value in first_field.items():
                       lowerCamelCase__					     = value
           else:
                 for field in class_fields:
                       lowerCamelCase__					     = getattr(self		,     field.name	)
                       if v is not None:
                             lowerCamelCase__					     = v




     def __delitem__(			self     :      List[Any]		,     *A__     :      str		,     **A__     :      List[Any]	) ->				List[str]:
           raise Exception(F"""You cannot use ``__delitem__`` on a {self.__class__.__name__} instance."""	)




     def        __snake_case      (			self     :      Optional[Any]		,     *A__     :      Union[str, Any]		,     **A__     :      Optional[int]	) ->				Optional[Any]:
           raise Exception(F"""You cannot use ``setdefault`` on a {self.__class__.__name__} instance."""	)




     def        __snake_case      (			self     :      str		,     *A__     :      Union[str, Any]		,     **A__     :      Any	) ->				Optional[int]:
           raise Exception(F"""You cannot use ``pop`` on a {self.__class__.__name__} instance."""	)




     def        __snake_case      (			self     :      List[Any]		,     *A__     :      List[Any]		,     **A__     :      Union[str, Any]	) ->				Optional[Any]:
           raise Exception(F"""You cannot use ``update`` on a {self.__class__.__name__} instance."""	)




     def __getitem__(			self     :      Optional[Any]		,     A__     :      Union[str, Any]	) ->				Optional[int]:
           if isinstance(A__		,     A__	):
                 lowerCamelCase__					     = dict(self.items()	)
                 return inner_dict[k]
           else:
                 return self.to_tuple()[k]




     def __setattr__(			self     :      Dict		,     A__     :      int		,     A__     :      Union[str, Any]	) ->				Any:
           if name in self.keys() and value is not None:
                 # Don't call self.__setitem__ to avoid recursion errors
                 super().__setitem__(A__		,     A__	)
           super().__setattr__(A__		,     A__	)




     def __setitem__(			self     :      int		,     A__     :      str		,     A__     :      Optional[Any]	) ->				List[Any]:
           # Will raise a KeyException if needed
           super().__setitem__(A__		,     A__	)
           # Don't call self.__setattr__ to avoid recursion errors
           super().__setattr__(A__		,     A__	)




     def        __snake_case      (			self     :      List[Any]	) ->				Tuple[Any]:
           return tuple(self[k] for k in self.keys()	)






"""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


_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 			snake_case		(   __UpperCAmelCase   ):
      lowerCamelCase__						= '''m2m_100'''
      lowerCamelCase__						= ['''past_key_values''']
      lowerCamelCase__						= {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}

      def __init__(			self   :int						,							_lowerCamelCase   :Union[str, Any]=1_2_8_1_1_2						,							_lowerCamelCase   :str=1_0_2_4						,							_lowerCamelCase   :Any=1_2						,							_lowerCamelCase   :Dict=4_0_9_6						,							_lowerCamelCase   :List[str]=1_6						,							_lowerCamelCase   :Union[str, Any]=1_2						,							_lowerCamelCase   :Any=4_0_9_6						,							_lowerCamelCase   :str=1_6						,							_lowerCamelCase   :Union[str, Any]=0.0_5						,							_lowerCamelCase   :Any=0.0_5						,							_lowerCamelCase   :int=True						,							_lowerCamelCase   :Dict=True						,							_lowerCamelCase   :List[str]="relu"						,							_lowerCamelCase   :List[Any]=1_0_2_4						,							_lowerCamelCase   :Dict=0.1						,							_lowerCamelCase   :List[Any]=0.1						,							_lowerCamelCase   :Any=0.0						,							_lowerCamelCase   :Tuple=0.0_2						,							_lowerCamelCase   :Dict=2						,							_lowerCamelCase   :List[str]=True						,							_lowerCamelCase   :Any=1						,							_lowerCamelCase   :Dict=0						,							_lowerCamelCase   :List[Any]=2						,							**_lowerCamelCase   :Optional[Any]						,							):
       __SCREAMING_SNAKE_CASE :			Any												=       vocab_size
       __SCREAMING_SNAKE_CASE :			Any												=       max_position_embeddings
       __SCREAMING_SNAKE_CASE :			int												=       d_model
       __SCREAMING_SNAKE_CASE :			List[Any]												=       encoder_ffn_dim
       __SCREAMING_SNAKE_CASE :			Dict												=       encoder_layers
       __SCREAMING_SNAKE_CASE :			int												=       encoder_attention_heads
       __SCREAMING_SNAKE_CASE :			Dict												=       decoder_ffn_dim
       __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       decoder_layers
       __SCREAMING_SNAKE_CASE :			Optional[Any]												=       decoder_attention_heads
       __SCREAMING_SNAKE_CASE :			Optional[Any]												=       dropout
       __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       attention_dropout
       __SCREAMING_SNAKE_CASE :			List[str]												=       activation_dropout
       __SCREAMING_SNAKE_CASE :			List[Any]												=       activation_function
       __SCREAMING_SNAKE_CASE :			Any												=       init_std
       __SCREAMING_SNAKE_CASE :			List[Any]												=       encoder_layerdrop
       __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       decoder_layerdrop
       __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       use_cache
       __SCREAMING_SNAKE_CASE :			List[str]												=       encoder_layers
       __SCREAMING_SNAKE_CASE :			str												=       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 			snake_case		(   __UpperCAmelCase   ):

      @property
      def 						SCREAMING_SNAKE_CASE_			(			self   :int						):
       __SCREAMING_SNAKE_CASE :			str												=       OrderedDict(
           [
               ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
               ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
           ]						)

       if self.use_past:
        __SCREAMING_SNAKE_CASE :			Dict												=       {0: '''batch'''}
        __SCREAMING_SNAKE_CASE :			Any												=       {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
       else:
        __SCREAMING_SNAKE_CASE :			int												=       {0: '''batch''', 1: '''decoder_sequence'''}
        __SCREAMING_SNAKE_CASE :			List[Any]												=       {0: '''batch''', 1: '''decoder_sequence'''}

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

      def 						SCREAMING_SNAKE_CASE_			(			self   :Tuple						,							_lowerCamelCase   :PreTrainedTokenizer						,							_lowerCamelCase   :int = -1						,							_lowerCamelCase   :int = -1						,							_lowerCamelCase   :bool = False						,							_lowerCamelCase   :Optional[TensorType] = 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
       __SCREAMING_SNAKE_CASE :			List[Any]												=       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
       __SCREAMING_SNAKE_CASE :			List[str]												=       tokenizer.num_special_tokens_to_add(_lowerCamelCase						)
       __SCREAMING_SNAKE_CASE :			int												=       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
       __SCREAMING_SNAKE_CASE :			int												=       [''' '''.join([tokenizer.unk_token]						) * seq_length] * batch_size
       __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       dict(tokenizer(_lowerCamelCase						,							return_tensors=_lowerCamelCase						)						)
       return common_inputs

      def 						SCREAMING_SNAKE_CASE_			(			self   :str						,							_lowerCamelCase   :PreTrainedTokenizer						,							_lowerCamelCase   :int = -1						,							_lowerCamelCase   :int = -1						,							_lowerCamelCase   :bool = False						,							_lowerCamelCase   :Optional[TensorType] = None						,							):
       __SCREAMING_SNAKE_CASE :			Dict												=       self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
           _lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase						)

       # Generate decoder inputs
       __SCREAMING_SNAKE_CASE :			Dict												=       seq_length if not self.use_past else 1
       __SCREAMING_SNAKE_CASE :			Any												=       self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
           _lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase						)
       __SCREAMING_SNAKE_CASE :			Optional[int]												=       {f'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
       __SCREAMING_SNAKE_CASE :			Optional[Any]												=       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
        __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE :			int												=       common_inputs['''input_ids'''].shape
        __SCREAMING_SNAKE_CASE :			Tuple												=       common_inputs['''decoder_input_ids'''].shape[1]
        __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE :			Optional[Any]												=       self.num_attention_heads
        __SCREAMING_SNAKE_CASE :			List[str]												=       (
            batch,
            num_encoder_attention_heads,
            encoder_seq_length,
            self._config.hidden_size // num_encoder_attention_heads,
        )
        __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       decoder_seq_length + 3
        __SCREAMING_SNAKE_CASE :			List[str]												=       (
            batch,
            num_decoder_attention_heads,
            decoder_past_length,
            self._config.hidden_size // num_decoder_attention_heads,
        )

        __SCREAMING_SNAKE_CASE :			Any												=       torch.cat(
            [common_inputs['''decoder_attention_mask'''], torch.ones(_lowerCamelCase						,							_lowerCamelCase						)]						,							dim=1						)

        __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       []
        # If the number of encoder and decoder layers are present in the model configuration, both are considered
        __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       self.num_layers
        __SCREAMING_SNAKE_CASE :			Any												=       min(_lowerCamelCase						,							_lowerCamelCase						)
        __SCREAMING_SNAKE_CASE :			Tuple												=       max(_lowerCamelCase						,							_lowerCamelCase						) - min_num_layers
        __SCREAMING_SNAKE_CASE :			Optional[int]												=       '''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.
        __SCREAMING_SNAKE_CASE :			Any												=       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



      lowerCamelCase__						= _generate_dummy_inputs_for_default_and_seqaseq_lm

