




'''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


lowercase	:      Optional[int]				=       logging.get_logger(__name__)

lowercase	:      List[Any]				=       {
    '''google/bigbird-pegasus-large-arxiv''': (
        '''https://huggingface.co/google/bigbird-pegasus-large-arxiv/resolve/main/config.json'''
    ),
    '''google/bigbird-pegasus-large-pubmed''': (
        '''https://huggingface.co/google/bigbird-pegasus-large-pubmed/resolve/main/config.json'''
    ),
    '''google/bigbird-pegasus-large-bigpatent''': (
        '''https://huggingface.co/google/bigbird-pegasus-large-bigpatent/resolve/main/config.json'''
    ),
    # See all BigBirdPegasus models at https://huggingface.co/models?filter=bigbird_pegasus
}

class   SCREAMING_SNAKE_CASE_		(       lowerCamelCase_				):
 __snake_case	:     Union[str, Any]			   =      "bigbird_pegasus"
 __snake_case	:     str			   =      ["past_key_values"]
 __snake_case	:     Dict			   =      {
     "num_attention_heads": "encoder_attention_heads",
     "hidden_size": "d_model",
     "attention_probs_dropout_prob": "attention_dropout",
 }



 def __init__(  self   ,		A=96103   ,		A=4096   ,		A=16   ,		A=4096   ,		A=16   ,		A=16   ,		A=4096   ,		A=16   ,		A=0.0   ,		A=0.0   ,		A=True   ,		A=True   ,		A="gelu_new"   ,		A=1024   ,		A=0.1   ,		A=0.0   ,		A=0.0   ,		A=0.0_2   ,		A=2   ,		A=0.0   ,		A=True   ,		A=0   ,		A=2   ,		A=1   ,		A="block_sparse"   ,		A=64   ,		A=3   ,		A=False   ,		**A   ,		):






        '''simple docstring'''




        lowercase  :		Optional[Any]						=      vocab_size
        lowercase  :		Optional[int]						=      max_position_embeddings
        lowercase  :		List[str]						=      d_model
        lowercase  :		Tuple						=      encoder_ffn_dim
        lowercase  :		Union[str, Any]						=      encoder_layers
        lowercase  :		Optional[int]						=      encoder_attention_heads
        lowercase  :		Union[str, Any]						=      decoder_ffn_dim
        lowercase  :		Dict						=      decoder_layers
        lowercase  :		Union[str, Any]						=      decoder_attention_heads
        lowercase  :		Tuple						=      dropout
        lowercase  :		str						=      attention_dropout
        lowercase  :		str						=      activation_dropout
        lowercase  :		Union[str, Any]						=      activation_function
        lowercase  :		str						=      init_std
        lowercase  :		Dict						=      encoder_layerdrop
        lowercase  :		Dict						=      decoder_layerdrop
        lowercase  :		Tuple						=      classifier_dropout
        lowercase  :		Any						=      use_cache
        lowercase  :		Dict						=      encoder_layers
        lowercase  :		List[Any]						=      scale_embedding  # scale factor will be sqrt(d_model) if True

        # extra config
        lowercase  :		str						=      attention_type
        lowercase  :		Optional[int]						=      block_size
        lowercase  :		List[Any]						=      num_random_blocks
        lowercase  :		Dict						=      use_bias

        super().__init__(
            pad_token_id=A   ,		bos_token_id=A   ,		eos_token_id=A   ,		is_encoder_decoder=A   ,		decoder_start_token_id=A   ,		**A   ,		)






class   SCREAMING_SNAKE_CASE_		(       lowerCamelCase_				):



 @property
 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




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

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

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






        '''simple docstring'''




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



 def    SCREAMING_SNAKE_CASE(  self   ,		A   ,		A = -1   ,		A = -1   ,		A = False   ,		A = None   ,		):






        '''simple docstring'''




        lowercase  :		Optional[int]						=      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
            A   ,		A   ,		A   ,		A   ,		A )

        # Generate decoder inputs
        lowercase  :		int						=      seq_length if not self.use_past else 1
        lowercase  :		Dict						=      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
            A   ,		A   ,		A   ,		A   ,		A )
        lowercase  :		Union[str, Any]						=      {f"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
        lowercase  :		List[Any]						=      dict(**A   ,		**A )

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

               lowercase  :		List[str]						=      torch.cat(
                   [common_inputs['''decoder_attention_mask'''], torch.ones(A   ,		A )]   ,		dim=1 )

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

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



 def    SCREAMING_SNAKE_CASE(  self   ,		A   ,		A = -1   ,		A = -1   ,		A = False   ,		A = None   ,		):






        '''simple docstring'''




        lowercase  :		int						=      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
            A   ,		A   ,		A   ,		A   ,		A )

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

               lowercase  :		List[Any]						=      common_inputs['''attention_mask'''].dtype
               lowercase  :		Dict						=      torch.cat(
                   [common_inputs['''attention_mask'''], torch.ones(A   ,		A   ,		dtype=A )]   ,		dim=1 )
               lowercase  :		Optional[int]						=      [
                   (torch.zeros(A ), torch.zeros(A )) for _ in range(A )
               ]
        return common_inputs



 def    SCREAMING_SNAKE_CASE(  self   ,		A   ,		A = -1   ,		A = -1   ,		A = False   ,		A = None   ,		):






        '''simple docstring'''




        lowercase  :		Optional[int]						=      compute_effective_axis_dimension(
            A   ,		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
        lowercase  :		Optional[Any]						=      tokenizer.num_special_tokens_to_add(A )
        lowercase  :		Union[str, Any]						=      compute_effective_axis_dimension(
            A   ,		fixed_dimension=OnnxConfig.default_fixed_sequence   ,		num_token_to_add=A )

        # Generate dummy inputs according to compute batch and sequence
        lowercase  :		Optional[Any]						=      [''' '''.join([tokenizer.unk_token] ) * seq_length] * batch_size
        lowercase  :		Union[str, Any]						=      dict(tokenizer(A   ,		return_tensors=A ) )
        return common_inputs



 def    SCREAMING_SNAKE_CASE(  self   ,		A   ,		A = -1   ,		A = -1   ,		A = False   ,		A = None   ,		):






        '''simple docstring'''




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

        elif self.task == "causal-lm":
               lowercase  :		Optional[int]						=      self._generate_dummy_inputs_for_causal_lm(
                   A   ,		batch_size=A   ,		seq_length=A   ,		is_pair=A   ,		framework=A )
        else:
               lowercase  :		str						=      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                   A   ,		batch_size=A   ,		seq_length=A   ,		is_pair=A   ,		framework=A )

        return common_inputs





 def    SCREAMING_SNAKE_CASE(  self   ,		A   ,		A   ,		A   ,		A ):






        '''simple docstring'''




        if self.task in ["default", "seq2seq-lm"]:
               lowercase  :		Optional[int]						=      super()._flatten_past_key_values_(A   ,		A   ,		A   ,		A )
        else:
               lowercase  :		Optional[int]						=      super(A   ,		self )._flatten_past_key_values_(
                   A   ,		A   ,		A   ,		A )










'''simple docstring'''






import numpy as np
from PIL import Image




def 		a_( lowerCamelCase      )				->np.array:






       '''simple docstring'''




       lowercase,     lowercase,     lowercase  :		Union[str, Any]						=      rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
       return 0.29_89 * r + 0.58_70 * g + 0.11_40 * b




def 		a_( lowerCamelCase      )				->np.array:






       '''simple docstring'''




       return (gray > 127) & (gray <= 255)




def 		a_( lowerCamelCase   ,				lowerCamelCase      )				->np.array:






       '''simple docstring'''




       lowercase  :		Optional[Any]						=      np.zeros_like(lowerCamelCase      )
       lowercase  :		Any						=      np.zeros(
           (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)      )

       # Copy image to padded image
       lowercase  :		List[str]						=      image

       # Iterate over image & apply kernel
       for x in range(image.shape[1]      ):
              for y in range(image.shape[0]      ):
                     lowercase  :		Union[str, Any]						=      (
                         kernel * image_padded[y : y + kernel.shape[0], x : x + kernel.shape[1]]
                     ).sum()
                     lowercase  :		Any						=      int(summation == 5      )
       return output


# kernel to be applied
lowercase	:      List[Any]				=       np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])

if __name__ == "__main__":
      # read original image
      lowercase	:      Dict				=       np.array(Image.open(r'''..\image_data\lena.jpg'''))
      # Apply erosion operation to a binary image
      lowercase	:      List[str]				=       erosion(grayabinary(rgbagray(image)), structuring_element)
      # Save the output image
      lowercase	:      Optional[Any]				=       Image.fromarray(output).convert('''RGB''')
      pil_img.save('''result_erosion.png''')




