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       __snake_case (  lowerCamelCase__ ):
 __lowerCamelCase     : Optional[int] 	=   """m2m_100"""
 __lowerCamelCase     : List[str] 	=   ["""past_key_values"""]
 __lowerCamelCase     : Tuple 	=   {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}
 def __init__(	self		, snake_case__=12_8112		, snake_case__=1024		, snake_case__=12		, snake_case__=4096		, snake_case__=16		, snake_case__=12		, snake_case__=4096		, snake_case__=16		, snake_case__=0.05		, snake_case__=0.05		, snake_case__=True		, snake_case__=True		, snake_case__="relu"		, snake_case__=1024		, snake_case__=0.1		, snake_case__=0.1		, snake_case__=0.0		, snake_case__=0.02		, snake_case__=2		, snake_case__=True		, snake_case__=1		, snake_case__=0		, snake_case__=2		, **snake_case__		, )						->				Optional[Any]:
       '''simple docstring'''





       UpperCAmelCase     :			Tuple     	=vocab_size
       UpperCAmelCase     :			Optional[int]     	=max_position_embeddings
       UpperCAmelCase     :			Tuple     	=d_model
       UpperCAmelCase     :			Union[str, Any]     	=encoder_ffn_dim
       UpperCAmelCase     :			Union[str, Any]     	=encoder_layers
       UpperCAmelCase     :			Any     	=encoder_attention_heads
       UpperCAmelCase     :			Tuple     	=decoder_ffn_dim
       UpperCAmelCase     :			Tuple     	=decoder_layers
       UpperCAmelCase     :			Any     	=decoder_attention_heads
       UpperCAmelCase     :			Tuple     	=dropout
       UpperCAmelCase     :			Optional[int]     	=attention_dropout
       UpperCAmelCase     :			Union[str, Any]     	=activation_dropout
       UpperCAmelCase     :			Optional[Any]     	=activation_function
       UpperCAmelCase     :			List[Any]     	=init_std
       UpperCAmelCase     :			str     	=encoder_layerdrop
       UpperCAmelCase     :			List[str]     	=decoder_layerdrop
       UpperCAmelCase     :			Any     	=use_cache
       UpperCAmelCase     :			Optional[int]     	=encoder_layers
       UpperCAmelCase     :			List[str]     	=scale_embedding  # scale factor will be sqrt(d_model) if True

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







class       __snake_case (  lowerCamelCase__ ):
 @property
 def 						UpperCAmelCase__							(	self    )						->				Mapping[str, Mapping[int, str]]:
       '''simple docstring'''





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

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

       if self.use_past:
             self.fill_with_past_key_values_(snake_case__		, direction='''inputs'''    )
       return common_inputs
 def 						UpperCAmelCase__							(	self		, snake_case__		, snake_case__ = -1		, snake_case__ = -1		, snake_case__ = False		, snake_case__ = None		, )						->				Mapping[str, Any]:
       '''simple docstring'''





       UpperCAmelCase     :			str     	=compute_effective_axis_dimension(
           snake_case__		, 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     :			List[str]     	=tokenizer.num_special_tokens_to_add(snake_case__    )
       UpperCAmelCase     :			Any     	=compute_effective_axis_dimension(
           snake_case__		, fixed_dimension=OnnxConfig.default_fixed_sequence		, num_token_to_add=snake_case__    )

       # Generate dummy inputs according to compute batch and sequence
       UpperCAmelCase     :			int     	=[''' '''.join([tokenizer.unk_token]    ) * seq_length] * batch_size
       UpperCAmelCase     :			Optional[int]     	=dict(tokenizer(snake_case__		, return_tensors=snake_case__    )    )
       return common_inputs
 def 						UpperCAmelCase__							(	self		, snake_case__		, snake_case__ = -1		, snake_case__ = -1		, snake_case__ = False		, snake_case__ = None		, )						->				Mapping[str, Any]:
       '''simple docstring'''





       UpperCAmelCase     :			Union[str, Any]     	=self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
           snake_case__		, snake_case__		, snake_case__		, snake_case__		, snake_case__    )

       # Generate decoder inputs
       UpperCAmelCase     :			Union[str, Any]     	=seq_length if not self.use_past else 1
       UpperCAmelCase     :			str     	=self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
           snake_case__		, snake_case__		, snake_case__		, snake_case__		, snake_case__    )
       UpperCAmelCase     :			Dict     	={f'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
       UpperCAmelCase     :			Tuple     	=dict(**snake_case__		, **snake_case__    )

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

             UpperCAmelCase     :			Dict     	=torch.cat(
                 [common_inputs['''decoder_attention_mask'''], torch.ones(snake_case__		, snake_case__    )]		, dim=1    )

             UpperCAmelCase     :			List[str]     	=[]
             # If the number of encoder and decoder layers are present in the model configuration, both are considered
             UpperCAmelCase	,    UpperCAmelCase     :			Optional[Any]     	=self.num_layers
             UpperCAmelCase     :			Union[str, Any]     	=min(snake_case__		, snake_case__    )
             UpperCAmelCase     :			Optional[Any]     	=max(snake_case__		, snake_case__    ) - min_num_layers
             UpperCAmelCase     :			List[str]     	='''encoder''' if num_encoder_layers > num_decoder_layers else '''decoder'''

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






 __lowerCamelCase     : Dict 	=   _generate_dummy_inputs_for_default_and_seqaseq_lm

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
    is_vision_available,
)


__snake_case         =     {
    '''configuration_mobilevitv2''': [
        '''MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''MobileViTV2Config''',
        '''MobileViTV2OnnxConfig''',
    ],
}


try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __snake_case         =     [
         '''MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''MobileViTV2ForImageClassification''',
         '''MobileViTV2ForSemanticSegmentation''',
         '''MobileViTV2Model''',
         '''MobileViTV2PreTrainedModel''',
     ]

if TYPE_CHECKING:
     from .configuration_mobilevitva import (
         MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
         MobileViTVaConfig,
         MobileViTVaOnnxConfig,
     )

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_mobilevitva import (
              MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
              MobileViTVaForImageClassification,
              MobileViTVaForSemanticSegmentation,
              MobileViTVaModel,
              MobileViTVaPreTrainedModel,
          )

else:
     import sys

     __snake_case         =     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
