



'''simple docstring'''






import unittest

import numpy as np

from transformers import DistilBertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
     import jax.numpy as jnp

     from transformers.models.distilbert.modeling_flax_distilbert import (
         FlaxDistilBertForMaskedLM,
         FlaxDistilBertForMultipleChoice,
         FlaxDistilBertForQuestionAnswering,
         FlaxDistilBertForSequenceClassification,
         FlaxDistilBertForTokenClassification,
         FlaxDistilBertModel,
     )

class 							a__( unittest.TestCase     ):







    def __init__(				self			:  Optional[int]						,      __snake_case			:  List[Any]						,      __snake_case			:  Optional[int]=13						,      __snake_case			:  Optional[int]=7						,      __snake_case			:  Tuple=True						,      __snake_case			:  Optional[Any]=True						,      __snake_case			:  str=True						,      __snake_case			:  Optional[int]=True						,      __snake_case			:  List[Any]=99						,      __snake_case			:  Optional[int]=32						,      __snake_case			:  str=5						,      __snake_case			:  Optional[int]=4						,      __snake_case			:  List[Any]=37						,      __snake_case			:  Tuple="gelu"						,      __snake_case			:  Dict=0.1						,      __snake_case			:  Optional[int]=0.1						,      __snake_case			:  Optional[Any]=5_12						,      __snake_case			:  Union[str, Any]=16						,      __snake_case			:  str=2						,      __snake_case			:  List[Any]=0.02						,      __snake_case			:  Optional[int]=4						,      ):
      a						: Any					       =					parent
      a						: int					       =					batch_size
      a						: Optional[Any]					       =					seq_length
      a						: Union[str, Any]					       =					is_training
      a						: str					       =					use_attention_mask
      a						: int					       =					use_token_type_ids
      a						: Tuple					       =					use_labels
      a						: Optional[int]					       =					vocab_size
      a						: str					       =					hidden_size
      a						: Optional[Any]					       =					num_hidden_layers
      a						: Optional[int]					       =					num_attention_heads
      a						: Tuple					       =					intermediate_size
      a						: str					       =					hidden_act
      a						: List[str]					       =					hidden_dropout_prob
      a						: List[Any]					       =					attention_probs_dropout_prob
      a						: List[Any]					       =					max_position_embeddings
      a						: List[Any]					       =					type_vocab_size
      a						: Dict					       =					type_sequence_label_size
      a						: List[str]					       =					initializer_range
      a						: str					       =					num_choices







    def       lowercase_	(				self			:  int     ):
      a						: int					       =					ids_tensor([self.batch_size, self.seq_length]						,      self.vocab_size     )

      a						: Tuple					       =					None
      if self.use_attention_mask:
        a						: Tuple					       =					random_attention_mask([self.batch_size, self.seq_length]     )

      a						: int					       =					DistilBertConfig(
          vocab_size=self.vocab_size						,      dim=self.hidden_size						,      n_layers=self.num_hidden_layers						,      n_heads=self.num_attention_heads						,      hidden_dim=self.intermediate_size						,      hidden_act=self.hidden_act						,      dropout=self.hidden_dropout_prob						,      attention_dropout=self.attention_probs_dropout_prob						,      max_position_embeddings=self.max_position_embeddings						,      initializer_range=self.initializer_range						,      tie_weights_=lowerCamelCase__						,      )

      return config, input_ids, attention_mask







    def       lowercase_	(				self			:  int     ):
      a						: Optional[int]					       =					self.prepare_config_and_inputs()
      a       ,     a       ,     a						: Any					       =					config_and_inputs
      a						: Optional[int]					       =					{'input_ids': input_ids, 'attention_mask': attention_mask}
      return config, inputs_dict

@require_flax
class 							a__( A__		,			unittest.TestCase     ):
    lowercase__			=   (
        (
            FlaxDistilBertModel,
            FlaxDistilBertForMaskedLM,
            FlaxDistilBertForMultipleChoice,
            FlaxDistilBertForQuestionAnswering,
            FlaxDistilBertForSequenceClassification,
            FlaxDistilBertForTokenClassification,
            FlaxDistilBertForQuestionAnswering,
        )
        if is_flax_available()
        else ()
    )







    def       lowercase_	(				self			:  List[Any]     ):
      a						: Dict					       =					FlaxDistilBertModelTester(self     )







    @slow
    def       lowercase_	(				self			:  Optional[Any]     ):
      for model_class_name in self.all_model_classes:
        a						: Optional[int]					       =					model_class_name.from_pretrained('distilbert-base-uncased'     )
        a						: Tuple					       =					model(np.ones((1, 1)     )     )
        self.assertIsNotNone(lowerCamelCase__     )

@require_flax
class 							a__( unittest.TestCase     ):







    @slow
    def       lowercase_	(				self			:  Any     ):
      a						: Optional[Any]					       =					FlaxDistilBertModel.from_pretrained('distilbert-base-uncased'     )
      a						: List[Any]					       =					np.array([[0, 3_45, 2_32, 3_28, 7_40, 1_40, 16_95, 69, 60_78, 15_88, 2]]     )
      a						: int					       =					np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]     )
      a						: List[Any]					       =					model(lowerCamelCase__						,      attention_mask=lowerCamelCase__     )[0]
      a						: List[Any]					       =					(1, 11, 7_68)
      self.assertEqual(output.shape						,      lowerCamelCase__     )
      a						: List[str]					       =					np.array([[[-0.1639, 0.3299, 0.1648], [-0.1746, 0.3289, 0.1710], [-0.1884, 0.3357, 0.1810]]]     )

      self.assertTrue(jnp.allclose(output[:, 1:4, 1:4]						,      lowerCamelCase__						,      atol=1e-4     )     )




'''simple docstring'''






import copy
import os

import cva
import numpy as np
from matplotlib import pyplot as plt

class 							a__:







    def __init__(				self			:  Optional[int]     ):
      a						: int					       =					''
      a						: List[str]					       =					''
      a						: int					       =					[]
      a						: Optional[Any]					       =					0
      a						: Optional[Any]					       =					2_56
      a						: int					       =					0
      a						: Optional[int]					       =					0
      a						: str					       =					0
      a						: int					       =					0







    def       lowercase_	(				self			:  List[str]						,      __snake_case			:  int     ):
      a						: Optional[Any]					       =					cva.imread(__snake_case						,      0     )
      a						: int					       =					copy.deepcopy(self.img     )
      a       ,     a       ,     a						: Optional[int]					       =					plt.hist(self.img.ravel()						,      2_56						,      [0, 2_56]						,      label='x'     )
      a						: str					       =					np.sum(__snake_case     )
      for i in range(len(__snake_case     )     ):
        a						: List[str]					       =					x[i] / self.k
        self.sk += prk
        a						: List[Any]					       =					(self.L - 1) * self.sk
        if self.rem != 0:
          a						: Union[str, Any]					       =					int(last % last     )
        a						: int					       =					int(last + 1 if self.rem >= 0.5 else last     )
        self.last_list.append(__snake_case     )
        a						: int					       =					int(np.ma.count(self.img     ) / self.img[1].size     )
        a						: Dict					       =					self.img[1].size
      for i in range(self.number_of_cols     ):
        for j in range(self.number_of_rows     ):
          a						: Tuple					       =					self.img[j][i]
          if num != self.last_list[num]:
            a						: Union[str, Any]					       =					self.last_list[num]
      cva.imwrite('output_data/output.jpg'						,      self.img     )







    def       lowercase_	(				self			:  Union[str, Any]     ):
      plt.hist(self.img.ravel()						,      2_56						,      [0, 2_56]     )







    def       lowercase_	(				self			:  Any     ):
      cva.imshow('Output-Image'						,      self.img     )
      cva.imshow('Input-Image'						,      self.original_image     )
      cva.waitKey(50_00     )
      cva.destroyAllWindows()


if __name__ == "__main__":
     lowerCAmelCase: Dict							=						os.path.join(os.path.basename(__file__), 'image_data/input.jpg')
     lowerCAmelCase: Optional[Any]							=						ConstantStretch()
     stretcher.stretch(file_path)
     stretcher.plot_histogram()
     stretcher.show_image()