



"""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


lowerCAmelCase__        =				logging.get_logger(__name__)

lowerCAmelCase__        =				{
    "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        __snake_case       (	_a):
  snake_case__				:   str  			=						"marian"
  snake_case__				:   str  			=						["past_key_values"]
  snake_case__				:   int  			=						{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}




  def __init__( self     :						List[str]						,					__lowerCAmelCase     :						Optional[Any]=5_8_1_0_1						,					__lowerCAmelCase     :						Dict=None						,					__lowerCAmelCase     :						Any=1_0_2_4						,					__lowerCAmelCase     :						List[str]=1_2						,					__lowerCAmelCase     :						Dict=4_0_9_6						,					__lowerCAmelCase     :						Tuple=1_6						,					__lowerCAmelCase     :						Any=1_2						,					__lowerCAmelCase     :						List[str]=4_0_9_6						,					__lowerCAmelCase     :						int=1_6						,					__lowerCAmelCase     :						str=0.0						,					__lowerCAmelCase     :						Optional[Any]=0.0						,					__lowerCAmelCase     :						Optional[int]=True						,					__lowerCAmelCase     :						List[Any]=True						,					__lowerCAmelCase     :						Optional[int]="gelu"						,					__lowerCAmelCase     :						List[Any]=1_0_2_4						,					__lowerCAmelCase     :						str=0.1						,					__lowerCAmelCase     :						Dict=0.0						,					__lowerCAmelCase     :						Tuple=0.0						,					__lowerCAmelCase     :						List[str]=0.02						,					__lowerCAmelCase     :						List[str]=5_8_1_0_0						,					__lowerCAmelCase     :						Dict=False						,					__lowerCAmelCase     :						Optional[int]=5_8_1_0_0						,					__lowerCAmelCase     :						Union[str, Any]=0						,					__lowerCAmelCase     :						List[str]=0						,					__lowerCAmelCase     :						int=True						,					**__lowerCAmelCase     :						Optional[Any]						,					):






         """simple docstring"""






         _lowerCamelCase						:				int    			= vocab_size
         _lowerCamelCase						:				List[Any]    			= decoder_vocab_size or vocab_size
         _lowerCamelCase						:				Union[str, Any]    			= max_position_embeddings
         _lowerCamelCase						:				Optional[Any]    			= d_model
         _lowerCamelCase						:				Optional[int]    			= encoder_ffn_dim
         _lowerCamelCase						:				str    			= encoder_layers
         _lowerCamelCase						:				Optional[int]    			= encoder_attention_heads
         _lowerCamelCase						:				Union[str, Any]    			= decoder_ffn_dim
         _lowerCamelCase						:				Dict    			= decoder_layers
         _lowerCamelCase						:				Tuple    			= decoder_attention_heads
         _lowerCamelCase						:				Dict    			= dropout
         _lowerCamelCase						:				Optional[Any]    			= attention_dropout
         _lowerCamelCase						:				Union[str, Any]    			= activation_dropout
         _lowerCamelCase						:				str    			= activation_function
         _lowerCamelCase						:				int    			= init_std
         _lowerCamelCase						:				Dict    			= encoder_layerdrop
         _lowerCamelCase						:				Optional[int]    			= decoder_layerdrop
         _lowerCamelCase						:				Optional[int]    			= use_cache
         _lowerCamelCase						:				Optional[Any]    			= encoder_layers
         _lowerCamelCase						:				Any    			= scale_embedding  # scale factor will be sqrt(d_model) if True
         _lowerCamelCase						:				str    			= share_encoder_decoder_embeddings
         super().__init__(
             pad_token_id=UpperCamelCase__						,					eos_token_id=UpperCamelCase__						,					is_encoder_decoder=UpperCamelCase__						,					decoder_start_token_id=UpperCamelCase__						,					forced_eos_token_id=UpperCamelCase__						,					**UpperCamelCase__						,					)






class        __snake_case       (	_a):




  @property
  # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
  def 						SCREAMING_SNAKE_CASE				( self     :						List[str]		):






         """simple docstring"""






         if self.task in ["default", "seq2seq-lm"]:
                _lowerCamelCase						:				str    			= OrderedDict(
                    [
                        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
                    ]		)

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

                if self.use_past:
                       self.fill_with_past_key_values_(UpperCamelCase__						,					direction='''inputs'''		)
         elif self.task == "causal-lm":
                # TODO: figure this case out.
                _lowerCamelCase						:				Optional[int]    			= OrderedDict(
                    [
                        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
                    ]		)
                if self.use_past:
                       _lowerCamelCase			,    _lowerCamelCase						:				List[str]    			= self.num_layers
                       for i in range(UpperCamelCase__		):
                              _lowerCamelCase						:				Tuple    			= {0: '''batch''', 2: '''past_sequence + sequence'''}
                              _lowerCamelCase						:				List[Any]    			= {0: '''batch''', 2: '''past_sequence + sequence'''}
         else:
                _lowerCamelCase						:				Tuple    			= 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 						SCREAMING_SNAKE_CASE				( self     :						Dict		):






         """simple docstring"""






         if self.task in ["default", "seq2seq-lm"]:
                _lowerCamelCase						:				Any    			= super().outputs
         else:
                _lowerCamelCase						:				Dict    			= super(UpperCamelCase__						,					self		).outputs
                if self.use_past:
                       _lowerCamelCase			,    _lowerCamelCase						:				Tuple    			= self.num_layers
                       for i in range(UpperCamelCase__		):
                              _lowerCamelCase						:				Optional[Any]    			= {0: '''batch''', 2: '''past_sequence + sequence'''}
                              _lowerCamelCase						:				str    			= {0: '''batch''', 2: '''past_sequence + sequence'''}
         return common_outputs




  def 						SCREAMING_SNAKE_CASE				( self     :						List[str]						,					__lowerCAmelCase     :						PreTrainedTokenizer						,					__lowerCAmelCase     :						int = -1						,					__lowerCAmelCase     :						int = -1						,					__lowerCAmelCase     :						bool = False						,					__lowerCAmelCase     :						Optional[TensorType] = None						,					):






         """simple docstring"""






         _lowerCamelCase						:				Dict    			= self._generate_dummy_inputs_for_encoder_and_decoder(
             UpperCamelCase__						,					UpperCamelCase__						,					UpperCamelCase__						,					UpperCamelCase__						,					UpperCamelCase__		)

         # Generate decoder inputs
         _lowerCamelCase						:				Optional[Any]    			= seq_length if not self.use_past else 1
         _lowerCamelCase						:				Optional[Any]    			= self._generate_dummy_inputs_for_encoder_and_decoder(
             UpperCamelCase__						,					UpperCamelCase__						,					UpperCamelCase__						,					UpperCamelCase__						,					UpperCamelCase__		)
         _lowerCamelCase						:				List[str]    			= {f'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
         _lowerCamelCase						:				List[str]    			= dict(**UpperCamelCase__						,					**UpperCamelCase__		)

         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						:				Dict    			= common_inputs['''input_ids'''].shape
                _lowerCamelCase						:				Optional[Any]    			= common_inputs['''decoder_input_ids'''].shape[1]
                _lowerCamelCase			,    _lowerCamelCase						:				Tuple    			= self.num_attention_heads
                _lowerCamelCase						:				Optional[Any]    			= (
                    batch,
                    num_encoder_attention_heads,
                    encoder_seq_length,
                    self._config.hidden_size // num_encoder_attention_heads,
                )
                _lowerCamelCase						:				List[str]    			= decoder_seq_length + 3
                _lowerCamelCase						:				str    			= (
                    batch,
                    num_decoder_attention_heads,
                    decoder_past_length,
                    self._config.hidden_size // num_decoder_attention_heads,
                )

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

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

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




  def 						SCREAMING_SNAKE_CASE				( self     :						Tuple						,					__lowerCAmelCase     :						PreTrainedTokenizer						,					__lowerCAmelCase     :						int = -1						,					__lowerCAmelCase     :						int = -1						,					__lowerCAmelCase     :						bool = False						,					__lowerCAmelCase     :						Optional[TensorType] = None						,					):






         """simple docstring"""






         _lowerCamelCase						:				int    			= self._generate_dummy_inputs_for_encoder_and_decoder(
             UpperCamelCase__						,					UpperCamelCase__						,					UpperCamelCase__						,					UpperCamelCase__						,					UpperCamelCase__		)

         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						:				Optional[Any]    			= common_inputs['''input_ids'''].shape
                # Not using the same length for past_key_values
                _lowerCamelCase						:				Any    			= seqlen + 2
                _lowerCamelCase			,    _lowerCamelCase						:				Dict    			= self.num_layers
                _lowerCamelCase			,    _lowerCamelCase						:				List[Any]    			= self.num_attention_heads
                _lowerCamelCase						:				Dict    			= (
                    batch,
                    num_encoder_attention_heads,
                    past_key_values_length,
                    self._config.hidden_size // num_encoder_attention_heads,
                )

                _lowerCamelCase						:				Tuple    			= common_inputs['''attention_mask'''].dtype
                _lowerCamelCase						:				Any    			= torch.cat(
                    [common_inputs['''attention_mask'''], torch.ones(UpperCamelCase__						,					UpperCamelCase__						,					dtype=UpperCamelCase__		)]						,					dim=1		)
                _lowerCamelCase						:				Optional[int]    			= [
                    (torch.zeros(UpperCamelCase__		), torch.zeros(UpperCamelCase__		)) for _ in range(UpperCamelCase__		)
                ]
         return common_inputs




  def 						SCREAMING_SNAKE_CASE				( self     :						int						,					__lowerCAmelCase     :						PreTrainedTokenizer						,					__lowerCAmelCase     :						int = -1						,					__lowerCAmelCase     :						int = -1						,					__lowerCAmelCase     :						bool = False						,					__lowerCAmelCase     :						Optional[TensorType] = None						,					):






         """simple docstring"""






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

         # Generate dummy inputs according to compute batch and sequence
         _lowerCamelCase						:				List[str]    			= [''' '''.join([tokenizer.unk_token]		) * seq_length] * batch_size
         _lowerCamelCase						:				Dict    			= dict(tokenizer(UpperCamelCase__						,					return_tensors=UpperCamelCase__		)		)
         return common_inputs




  def 						SCREAMING_SNAKE_CASE				( self     :						Dict						,					__lowerCAmelCase     :						PreTrainedTokenizer						,					__lowerCAmelCase     :						int = -1						,					__lowerCAmelCase     :						int = -1						,					__lowerCAmelCase     :						bool = False						,					__lowerCAmelCase     :						Optional[TensorType] = None						,					):






         """simple docstring"""






         if self.task in ["default", "seq2seq-lm"]:
                _lowerCamelCase						:				int    			= self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                    UpperCamelCase__						,					batch_size=UpperCamelCase__						,					seq_length=UpperCamelCase__						,					is_pair=UpperCamelCase__						,					framework=UpperCamelCase__		)

         else:
                _lowerCamelCase						:				List[str]    			= self._generate_dummy_inputs_for_causal_lm(
                    UpperCamelCase__						,					batch_size=UpperCamelCase__						,					seq_length=UpperCamelCase__						,					is_pair=UpperCamelCase__						,					framework=UpperCamelCase__		)

         return common_inputs




  def 						SCREAMING_SNAKE_CASE				( self     :						List[str]						,					__lowerCAmelCase     :						Any						,					__lowerCAmelCase     :						List[str]						,					__lowerCAmelCase     :						Tuple						,					__lowerCAmelCase     :						Optional[int]		):






         """simple docstring"""






         if self.task in ["default", "seq2seq-lm"]:
                _lowerCamelCase						:				List[Any]    			= super()._flatten_past_key_values_(UpperCamelCase__						,					UpperCamelCase__						,					UpperCamelCase__						,					UpperCamelCase__		)
         else:
                _lowerCamelCase						:				List[Any]    			= super(UpperCamelCase__						,					self		)._flatten_past_key_values_(
                    UpperCamelCase__						,					UpperCamelCase__						,					UpperCamelCase__						,					UpperCamelCase__		)




  @property
  def 						SCREAMING_SNAKE_CASE				( self     :						Dict		):






         """simple docstring"""






         return 1E-4







'''simple docstring'''


from PIL import Image







def 				__lowerCamelCase				(   A__    ,						A__    )       ->	Image:






   """simple docstring"""

   def brightness(A__    ) -> float:
      return 128 + level + (c - 128)

   if not -255.0 <= level <= 255.0:
      raise ValueError('level must be between -255.0 (black) and 255.0 (white)'    )
   return img.point(A__    )


if __name__ == "__main__":
   # Load image
   with Image.open("image_data/lena.jpg") as img:
      # Change brightness to 100
      _lowerCamelCase	:       List[str]      	=	change_brightness(img, 100)
      brigt_img.save("image_data/lena_brightness.png", format="png")

