code: 
  
  
  
  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  				=					{
      """facebook/mbart-large-cc25""": """https://huggingface.co/facebook/mbart-large-cc25/resolve/main/config.json""",
      # See all MBART models at https://huggingface.co/models?filter=mbart
  }
  
  
  
  class 		_snake_case      (			__UpperCAmelCase				):
  
  
  
  
  
   """simple docstring"""
  
  
  
   lowerCamelCase_          =			'''mbart'''
   lowerCamelCase_          =			['''past_key_values''']
   lowerCamelCase_          =			{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}
   def __init__(						self	,    a=5_0_2_6_5	,    a=1_0_2_4	,    a=1_2	,    a=4_0_9_6	,    a=1_6	,    a=1_2	,    a=4_0_9_6	,    a=1_6	,    a=0.0	,    a=0.0	,    a=True	,    a=True	,    a="gelu"	,    a=1_0_2_4	,    a=0.1	,    a=0.0	,    a=0.0	,    a=0.02	,    a=0.0	,    a=False	,    a=1	,    a=0	,    a=2	,    a=2	,    **a	,    )	->							Optional[Any]:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _A           =    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           =    classifier_dropout
     _A           =    use_cache
     _A           =    encoder_layers
     _A           =    scale_embedding  # scale factor will be sqrt(d_model) if True
     super().__init__(
         pad_token_id=lowercase_	,    bos_token_id=lowercase_	,    eos_token_id=lowercase_	,    is_encoder_decoder=lowercase_	,    forced_eos_token_id=lowercase_	,    **lowercase_	,    )
  
  
  
  
  
  
  
  class 		_snake_case      (			__UpperCAmelCase				):
  
  
  
  
  
   """simple docstring"""
  
  
  
   @property
   def lowercase_		(						self					)	->							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_(lowercase_	,    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           =    self.num_layers
         for i in range(lowercase_					):
           _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
   def lowercase_		(						self					)	->							Mapping[str, Mapping[int, str]]:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     if self.task in ["default", "seq2seq-lm"]:
       _A           =    super().outputs
     else:
       _A           =    super(lowercase_	,    self					).outputs
       if self.use_past:
         _A           =    self.num_layers
         for i in range(lowercase_					):
           _A           =    {0: '''batch''', 2: '''past_sequence + sequence'''}
           _A           =    {0: '''batch''', 2: '''past_sequence + sequence'''}
     return common_outputs
   def lowercase_		(						self	,    a	,    a = -1	,    a = -1	,    a = False	,    a = None	,    )	->							Mapping[str, Any]:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _A           =    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
         lowercase_	,    lowercase_	,    lowercase_	,    lowercase_	,    lowercase_					)
  
     # Generate decoder inputs
     _A           =    seq_length if not self.use_past else 1
     _A           =    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
         lowercase_	,    lowercase_	,    lowercase_	,    lowercase_	,    lowercase_					)
     _A           =    {f'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
     _A           =    dict(**lowercase_	,    **lowercase_					)
  
     if self.use_past:
       if not is_torch_available():
         raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''					)
       else:
         import torch
       _A           =    common_inputs['''input_ids'''].shape
       _A           =    common_inputs['''decoder_input_ids'''].shape[1]
       _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(lowercase_	,    lowercase_					)]	,    dim=1					)
  
       _A           =    []
       # If the number of encoder and decoder layers are present in the model configuration, both are considered
       _A           =    self.num_layers
       _A           =    min(lowercase_	,    lowercase_					)
       _A           =    max(lowercase_	,    lowercase_					) - min_num_layers
       _A           =    '''encoder''' if num_encoder_layers > num_decoder_layers else '''decoder'''
  
       for _ in range(lowercase_					):
         common_inputs["past_key_values"].append(
             (
                 torch.zeros(lowercase_					),
                 torch.zeros(lowercase_					),
                 torch.zeros(lowercase_					),
                 torch.zeros(lowercase_					),
             )					)
       # TODO: test this.
       _A           =    encoder_shape if remaining_side_name == '''encoder''' else decoder_shape
       for _ in range(lowercase_	,    lowercase_					):
         common_inputs["past_key_values"].append((torch.zeros(lowercase_					), torch.zeros(lowercase_					))					)
     return common_inputs
   def lowercase_		(						self	,    a	,    a = -1	,    a = -1	,    a = False	,    a = None	,    )	->							Mapping[str, Any]:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _A           =    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
         lowercase_	,    lowercase_	,    lowercase_	,    lowercase_	,    lowercase_					)
  
     if self.use_past:
       if not is_torch_available():
         raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''					)
       else:
         import torch
       _A           =    common_inputs['''input_ids'''].shape
       # Not using the same length for past_key_values
       _A           =    seqlen + 2
       _A           =    self.num_layers
       _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(lowercase_	,    lowercase_	,    dtype=lowercase_					)]	,    dim=1					)
       _A           =    [
           (torch.zeros(lowercase_					), torch.zeros(lowercase_					)) for _ in range(lowercase_					)
       ]
     return common_inputs
   def lowercase_		(						self	,    a	,    a = -1	,    a = -1	,    a = False	,    a = None	,    )	->							Mapping[str, Any]:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _A           =    compute_effective_axis_dimension(
         lowercase_	,    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(lowercase_					)
     _A           =    compute_effective_axis_dimension(
         lowercase_	,    fixed_dimension=OnnxConfig.default_fixed_sequence	,    num_token_to_add=lowercase_					)
  
     # Generate dummy inputs according to compute batch and sequence
     _A           =    [''' '''.join([tokenizer.unk_token]					) * seq_length] * batch_size
     _A           =    dict(tokenizer(lowercase_	,    return_tensors=lowercase_					)					)
     return common_inputs
   def lowercase_		(						self	,    a	,    a = -1	,    a = -1	,    a = False	,    a = None	,    )	->							Mapping[str, Any]:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     if self.task in ["default", "seq2seq-lm"]:
       _A           =    self._generate_dummy_inputs_for_default_and_seqaseq_lm(
           lowercase_	,    batch_size=lowercase_	,    seq_length=lowercase_	,    is_pair=lowercase_	,    framework=lowercase_					)
  
     elif self.task == "causal-lm":
       _A           =    self._generate_dummy_inputs_for_causal_lm(
           lowercase_	,    batch_size=lowercase_	,    seq_length=lowercase_	,    is_pair=lowercase_	,    framework=lowercase_					)
     else:
       _A           =    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
           lowercase_	,    batch_size=lowercase_	,    seq_length=lowercase_	,    is_pair=lowercase_	,    framework=lowercase_					)
  
     return common_inputs
  
  
  
   def lowercase_		(						self	,    a	,    a	,    a	,    a					)	->							Dict:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     if self.task in ["default", "seq2seq-lm"]:
       _A           =    super()._flatten_past_key_values_(lowercase_	,    lowercase_	,    lowercase_	,    lowercase_					)
     else:
       _A           =    super(lowercase_	,    self					)._flatten_past_key_values_(
           lowercase_	,    lowercase_	,    lowercase_	,    lowercase_					)
style_context: 
  
  
  
  import cva
  import numpy as np
  
  from digital_image_processing.filters.convolve import img_convolve
  from digital_image_processing.filters.sobel_filter import sobel_filter
  
  __lowerCamelCase  				=					1_8_0
  
  
  
  
  def     UpperCAmelCase__ (							__snake_case						,				__snake_case							)   -> Dict:
    _A           =    k_size // 2
    _A    ,   _A           =    np.mgrid[0 - center : k_size - center, 0 - center : k_size - center]
    _A           =    (
        1
        / (2 * np.pi * sigma)
        * np.exp(-(np.square(__snake_case							) + np.square(__snake_case							)) / (2 * np.square(__snake_case							))							)
    )
    return g
  
  
  
  
  def     UpperCAmelCase__ (							__snake_case						,				__snake_case						,				__snake_case							)   -> int:
    _A           =    np.zeros(__snake_case							)
  
    for row in range(1						,				image_shape[0] - 1							):
      for col in range(1						,				image_shape[1] - 1							):
        _A           =    gradient_direction[row, col]
  
        if (
            0 <= direction < PI / 8
            or 15 * PI / 8 <= direction <= 2 * PI
            or 7 * PI / 8 <= direction <= 9 * PI / 8
        ):
          _A           =    sobel_grad[row, col - 1]
          _A           =    sobel_grad[row, col + 1]
          if sobel_grad[row, col] >= w and sobel_grad[row, col] >= e:
            _A           =    sobel_grad[row, col]
  
        elif (
            PI / 8 <= direction < 3 * PI / 8
            or 9 * PI / 8 <= direction < 11 * PI / 8
        ):
          _A           =    sobel_grad[row + 1, col - 1]
          _A           =    sobel_grad[row - 1, col + 1]
          if sobel_grad[row, col] >= sw and sobel_grad[row, col] >= ne:
            _A           =    sobel_grad[row, col]
  
        elif (
            3 * PI / 8 <= direction < 5 * PI / 8
            or 11 * PI / 8 <= direction < 13 * PI / 8
        ):
          _A           =    sobel_grad[row - 1, col]
          _A           =    sobel_grad[row + 1, col]
          if sobel_grad[row, col] >= n and sobel_grad[row, col] >= s:
            _A           =    sobel_grad[row, col]
  
        elif (
            5 * PI / 8 <= direction < 7 * PI / 8
            or 13 * PI / 8 <= direction < 15 * PI / 8
        ):
          _A           =    sobel_grad[row - 1, col - 1]
          _A           =    sobel_grad[row + 1, col + 1]
          if sobel_grad[row, col] >= nw and sobel_grad[row, col] >= se:
            _A           =    sobel_grad[row, col]
  
    return destination
  
  
  
  
  def     UpperCAmelCase__ (							__snake_case						,				__snake_case						,				__snake_case						,				__snake_case						,				__snake_case						,				__snake_case							)   -> Optional[int]:
    for row in range(1						,				image_shape[0] - 1							):
      for col in range(1						,				image_shape[1] - 1							):
        if destination[row, col] >= threshold_high:
          _A           =    strong
        elif destination[row, col] <= threshold_low:
          _A           =    0
        else:
          _A           =    weak
  
  
  
  
  def     UpperCAmelCase__ (							__snake_case						,				__snake_case						,				__snake_case						,				__snake_case							)   -> List[Any]:
    for row in range(1						,				image_shape[0]							):
      for col in range(1						,				image_shape[1]							):
        if destination[row, col] == weak:
          if 255 in (
              destination[row, col + 1],
              destination[row, col - 1],
              destination[row - 1, col],
              destination[row + 1, col],
              destination[row - 1, col - 1],
              destination[row + 1, col - 1],
              destination[row - 1, col + 1],
              destination[row + 1, col + 1],
          ):
            _A           =    strong
          else:
            _A           =    0
  
  
  
  
  def     UpperCAmelCase__ (							__snake_case						,				__snake_case=15						,				__snake_case=30						,				__snake_case=128						,				__snake_case=255							)   -> List[Any]:
    # gaussian_filter
    _A           =    img_convolve(__snake_case						,				gen_gaussian_kernel(9						,				sigma=1.4							)							)
    # get the gradient and degree by sobel_filter
    _A    ,   _A           =    sobel_filter(__snake_case							)
    _A           =    PI + np.radadeg(__snake_case							)
  
    _A           =    suppress_non_maximum(image.shape						,				__snake_case						,				__snake_case							)
  
    detect_high_low_threshold(
        image.shape						,				__snake_case						,				__snake_case						,				__snake_case						,				__snake_case						,				__snake_case							)
  
    track_edge(image.shape						,				__snake_case						,				__snake_case						,				__snake_case							)
  
    return destination
  
  
  if __name__ == "__main__":
      # read original image in gray mode
      __lowerCamelCase  				=					cva.imread(R"""../image_data/lena.jpg""", 0)
      # canny edge detection
      __lowerCamelCase  				=					canny(lena)
      cva.imshow("""canny""", canny_destination)
      cva.waitKey(0)