

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


__lowercase				      =						logging.get_logger(__name__)

__lowercase				      =						{
    """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    _lowercase				(					snake_case__			):
   _lowercase     :       Optional[Any]  					=    'marian'
   _lowercase     :       List[Any]  					=    ['past_key_values']
   _lowercase     :       Optional[int]  					=    {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}

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





         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=_UpperCamelCase			,						eos_token_id=_UpperCamelCase			,						is_encoder_decoder=_UpperCamelCase			,						decoder_start_token_id=_UpperCamelCase			,						forced_eos_token_id=_UpperCamelCase			,						**_UpperCamelCase			,						)



class    _lowercase				(					snake_case__			):

   @property
   # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
   def     UpperCamelCase					(				self	:    Optional[int]       ) ->			Mapping[str, Mapping[int, str]]:
         """simple docstring"""





         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_(_UpperCamelCase			,						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(_UpperCamelCase       ):
                           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	:    List[str]       ) ->			Mapping[str, Mapping[int, str]]:
         """simple docstring"""





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

   def     UpperCamelCase					(				self	:    Optional[int]			,						lowerCamelCase__	:    Dict			,						lowerCamelCase__	:    Any = -1			,						lowerCamelCase__	:    str = -1			,						lowerCamelCase__	:    Optional[Any] = False			,						lowerCamelCase__	:    Optional[Any] = None			,						) ->			Mapping[str, Any]:
         """simple docstring"""





         A_							       =		self._generate_dummy_inputs_for_encoder_and_decoder(
             _UpperCamelCase			,						_UpperCamelCase			,						_UpperCamelCase			,						_UpperCamelCase			,						_UpperCamelCase       )

         # Generate decoder inputs
         A_							       =		seq_length if not self.use_past else 1
         A_							       =		self._generate_dummy_inputs_for_encoder_and_decoder(
             _UpperCamelCase			,						_UpperCamelCase			,						_UpperCamelCase			,						_UpperCamelCase			,						_UpperCamelCase       )
         A_							       =		{F"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
         A_							       =		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
               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(_UpperCamelCase			,						_UpperCamelCase       )]			,						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(_UpperCamelCase			,						_UpperCamelCase       )
               A_							       =		max(_UpperCamelCase			,						_UpperCamelCase       ) - min_num_layers
               A_							       =		'''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.
               A_							       =		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     UpperCamelCase					(				self	:    Union[str, Any]			,						lowerCamelCase__	:    Optional[int]			,						lowerCamelCase__	:    int = -1			,						lowerCamelCase__	:    List[str] = -1			,						lowerCamelCase__	:    Dict = False			,						lowerCamelCase__	:    Any = None			,						) ->			Mapping[str, Any]:
         """simple docstring"""





         A_							       =		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
               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(_UpperCamelCase			,						_UpperCamelCase			,						dtype=_UpperCamelCase       )]			,						dim=1       )
               A_							       =		[
                   (torch.zeros(_UpperCamelCase       ), torch.zeros(_UpperCamelCase       )) for _ in range(_UpperCamelCase       )
               ]
         return common_inputs

   def     UpperCamelCase					(				self	:    Tuple			,						lowerCamelCase__	:    Any			,						lowerCamelCase__	:    Optional[int] = -1			,						lowerCamelCase__	:    Dict = -1			,						lowerCamelCase__	:    str = False			,						lowerCamelCase__	:    Optional[int] = None			,						) ->			Mapping[str, Any]:
         """simple docstring"""





         A_							       =		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
         A_							       =		tokenizer.num_special_tokens_to_add(_UpperCamelCase       )
         A_							       =		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
         A_							       =		[''' '''.join([tokenizer.unk_token]       ) * seq_length] * batch_size
         A_							       =		dict(tokenizer(_UpperCamelCase			,						return_tensors=_UpperCamelCase       )       )
         return common_inputs

   def     UpperCamelCase					(				self	:    str			,						lowerCamelCase__	:    Optional[Any]			,						lowerCamelCase__	:    List[str] = -1			,						lowerCamelCase__	:    int = -1			,						lowerCamelCase__	:    Optional[int] = False			,						lowerCamelCase__	:    List[str] = None			,						) ->			Mapping[str, Any]:
         """simple docstring"""





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

         else:
               A_							       =		self._generate_dummy_inputs_for_causal_lm(
                   _UpperCamelCase			,						batch_size=_UpperCamelCase			,						seq_length=_UpperCamelCase			,						is_pair=_UpperCamelCase			,						framework=_UpperCamelCase       )

         return common_inputs

   def     UpperCamelCase					(				self	:    List[Any]			,						lowerCamelCase__	:    Tuple			,						lowerCamelCase__	:    int			,						lowerCamelCase__	:    Any			,						lowerCamelCase__	:    Optional[int]       ) ->			str:
         """simple docstring"""





         if self.task in ["default", "seq2seq-lm"]:
               A_							       =		super()._flatten_past_key_values_(_UpperCamelCase			,						_UpperCamelCase			,						_UpperCamelCase			,						_UpperCamelCase       )
         else:
               A_							       =		super(_UpperCamelCase			,						self       )._flatten_past_key_values_(
                   _UpperCamelCase			,						_UpperCamelCase			,						_UpperCamelCase			,						_UpperCamelCase       )





   @property
   def     UpperCamelCase					(				self	:    Optional[int]       ) ->			float:
         """simple docstring"""





         return 1e-4








from PIL import Image







def 						lowerCamelCase__      ( __A    :Image		):


       """simple docstring"""




       __snake_case	,    __snake_case       	=							image.size
       __snake_case       	=							0
       __snake_case       	=							image.load()
       for i in range(__A		):
              for j in range(__A		):
                     __snake_case       	=							pixels[j, i]
                     mean += pixel
       mean //= width * height

       for j in range(__A		):
              for i in range(__A		):
                     __snake_case       	=							2_5_5 if pixels[i, j] > mean else 0
       return image


if __name__ == "__main__":
    UpperCamelCase__		 =     mean_threshold(Image.open('''path_to_image''').convert('''L'''))
    image.save('''output_image_path''')

