


'''simple docstring'''






from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


a_   :      List[str]				    =      logging.get_logger(__name__)

a_   :      List[str]				    =      {
    "Helsinki-NLP/opus-mt-en-de": "https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/config.json",
    # See all Marian models at https://huggingface.co/models?filter=marian
}




class 	a       (       _SCREAMING_SNAKE_CASE    ):
 _lowerCAmelCase	   =		"""marian"""
 _lowerCAmelCase	   =		["""past_key_values"""]
 _lowerCAmelCase	   =		{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}
 def __init__(	self ,			__magic_name__=5_81_01 ,			__magic_name__=None ,			__magic_name__=10_24 ,			__magic_name__=12 ,			__magic_name__=40_96 ,			__magic_name__=16 ,			__magic_name__=12 ,			__magic_name__=40_96 ,			__magic_name__=16 ,			__magic_name__=0.0 ,			__magic_name__=0.0 ,			__magic_name__=True ,			__magic_name__=True ,			__magic_name__="gelu" ,			__magic_name__=10_24 ,			__magic_name__=0.1 ,			__magic_name__=0.0 ,			__magic_name__=0.0 ,			__magic_name__=0.0_2 ,			__magic_name__=5_81_00 ,			__magic_name__=False ,			__magic_name__=5_81_00 ,			__magic_name__=0 ,			__magic_name__=0 ,			__magic_name__=True ,			**__magic_name__ ,			)   ->							int:
      _a      =							vocab_size
      _a      =							decoder_vocab_size or vocab_size
      _a      =							max_position_embeddings
      _a      =							d_model
      _a      =							encoder_ffn_dim
      _a      =							encoder_layers
      _a      =							encoder_attention_heads
      _a      =							decoder_ffn_dim
      _a      =							decoder_layers
      _a      =							decoder_attention_heads
      _a      =							dropout
      _a      =							attention_dropout
      _a      =							activation_dropout
      _a      =							activation_function
      _a      =							init_std
      _a      =							encoder_layerdrop
      _a      =							decoder_layerdrop
      _a      =							use_cache
      _a      =							encoder_layers
      _a      =							scale_embedding  # scale factor will be sqrt(d_model) if True
      _a      =							share_encoder_decoder_embeddings
      super().__init__(
          pad_token_id=__magic_name__ ,			eos_token_id=__magic_name__ ,			is_encoder_decoder=__magic_name__ ,			decoder_start_token_id=__magic_name__ ,			forced_eos_token_id=__magic_name__ ,			**__magic_name__ ,			)





class 	a       (       _SCREAMING_SNAKE_CASE    ):
 @property
 # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
 def 							__UpperCAmelCase						(	self  )   ->							Mapping[str, Mapping[int, str]]:
      if self.task in ["default", "seq2seq-lm"]:
           _a      =							OrderedDict(
               [
                   ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                   ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
               ]  )

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

           if self.use_past:
                self.fill_with_past_key_values_(__magic_name__ ,			direction='inputs'  )
      elif self.task == "causal-lm":
           # TODO: figure this case out.
           _a      =							OrderedDict(
               [
                   ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                   ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
               ]  )
           if self.use_past:
                _a ,				_a      =							self.num_layers
                for i in range(__magic_name__  ):
                     _a      =							{0: 'batch', 2: 'past_sequence + sequence'}
                     _a      =							{0: 'batch', 2: 'past_sequence + sequence'}
      else:
           _a      =							OrderedDict(
               [
                   ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                   ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                   ('decoder_input_ids', {0: 'batch', 1: 'decoder_sequence'}),
                   ('decoder_attention_mask', {0: 'batch', 1: 'decoder_sequence'}),
               ]  )

      return common_inputs
 @property
 # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
 def 							__UpperCAmelCase						(	self  )   ->							Mapping[str, Mapping[int, str]]:
      if self.task in ["default", "seq2seq-lm"]:
           _a      =							super().outputs
      else:
           _a      =							super(__magic_name__ ,			self  ).outputs
           if self.use_past:
                _a ,				_a      =							self.num_layers
                for i in range(__magic_name__  ):
                     _a      =							{0: 'batch', 2: 'past_sequence + sequence'}
                     _a      =							{0: 'batch', 2: 'past_sequence + sequence'}
      return common_outputs
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__ = -1 ,			__magic_name__ = -1 ,			__magic_name__ = False ,			__magic_name__ = None ,			)   ->							Mapping[str, Any]:
      _a      =							self._generate_dummy_inputs_for_encoder_and_decoder(
          __magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__  )

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

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

           _a      =							torch.cat(
               [common_inputs['decoder_attention_mask'], torch.ones(__magic_name__ ,			__magic_name__  )] ,			dim=1  )

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

           for _ in range(__magic_name__  ):
                common_inputs["past_key_values"].append(
                    (
                        torch.zeros(__magic_name__  ),
                        torch.zeros(__magic_name__  ),
                        torch.zeros(__magic_name__  ),
                        torch.zeros(__magic_name__  ),
                    )  )
           # TODO: test this.
           _a      =							encoder_shape if remaining_side_name == 'encoder' else decoder_shape
           for _ in range(__magic_name__ ,			__magic_name__  ):
                common_inputs["past_key_values"].append((torch.zeros(__magic_name__  ), torch.zeros(__magic_name__  ))  )
      return common_inputs
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__ = -1 ,			__magic_name__ = -1 ,			__magic_name__ = False ,			__magic_name__ = None ,			)   ->							Mapping[str, Any]:
      _a      =							self._generate_dummy_inputs_for_encoder_and_decoder(
          __magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__  )

      if self.use_past:
           if not is_torch_available():
                raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'  )
           else:
                import torch
           _a ,				_a      =							common_inputs['input_ids'].shape
           # Not using the same length for past_key_values
           _a      =							seqlen + 2
           _a ,				_a      =							self.num_layers
           _a ,				_a      =							self.num_attention_heads
           _a      =							(
               batch,
               num_encoder_attention_heads,
               past_key_values_length,
               self._config.hidden_size // num_encoder_attention_heads,
           )

           _a      =							common_inputs['attention_mask'].dtype
           _a      =							torch.cat(
               [common_inputs['attention_mask'], torch.ones(__magic_name__ ,			__magic_name__ ,			dtype=__magic_name__  )] ,			dim=1  )
           _a      =							[
               (torch.zeros(__magic_name__  ), torch.zeros(__magic_name__  )) for _ in range(__magic_name__  )
           ]
      return common_inputs
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__ = -1 ,			__magic_name__ = -1 ,			__magic_name__ = False ,			__magic_name__ = None ,			)   ->							Mapping[str, Any]:
      # 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
      _a      =							compute_effective_axis_dimension(
          __magic_name__ ,			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
      _a      =							tokenizer.num_special_tokens_to_add(__magic_name__  )
      _a      =							compute_effective_axis_dimension(
          __magic_name__ ,			fixed_dimension=OnnxConfig.default_fixed_sequence ,			num_token_to_add=__magic_name__  )

      # Generate dummy inputs according to compute batch and sequence
      _a      =							[' '.join([tokenizer.unk_token]  ) * seq_length] * batch_size
      _a      =							dict(tokenizer(__magic_name__ ,			return_tensors=__magic_name__  )  )
      return common_inputs
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__ = -1 ,			__magic_name__ = -1 ,			__magic_name__ = False ,			__magic_name__ = None ,			)   ->							Mapping[str, Any]:
      if self.task in ["default", "seq2seq-lm"]:
           _a      =							self._generate_dummy_inputs_for_default_and_seqaseq_lm(
               __magic_name__ ,			batch_size=__magic_name__ ,			seq_length=__magic_name__ ,			is_pair=__magic_name__ ,			framework=__magic_name__  )

      else:
           _a      =							self._generate_dummy_inputs_for_causal_lm(
               __magic_name__ ,			batch_size=__magic_name__ ,			seq_length=__magic_name__ ,			is_pair=__magic_name__ ,			framework=__magic_name__  )

      return common_inputs
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__  )   ->							List[Any]:
      if self.task in ["default", "seq2seq-lm"]:
           _a      =							super()._flatten_past_key_values_(__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__  )
      else:
           _a      =							super(__magic_name__ ,			self  )._flatten_past_key_values_(
               __magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__  )




 @property
 def 							__UpperCAmelCase						(	self  )   ->							float:
      return 1e-4





'''simple docstring'''






import warnings

from ...utils import logging
from .image_processing_clip import CLIPImageProcessor


a_   :      Optional[Any]				    =      logging.get_logger(__name__)




class 	a       (       _SCREAMING_SNAKE_CASE    ):
 def __init__(	self ,			*__magic_name__ ,			**__magic_name__  )   ->							None:
      warnings.warn(
          'The class CLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
          ' use CLIPImageProcessor instead.' ,			__magic_name__ ,			)
      super().__init__(*__magic_name__ ,			**__magic_name__  )

