



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_					)




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)