"""simple docstring"""






from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
       import numpy
       import tensorflow as tf

       from transformers import (
           TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
           TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
           TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
           BertConfig,
           DPRConfig,
           TFDPRContextEncoder,
           TFDPRQuestionEncoder,
           TFDPRReader,
       )


class        UpperCAmelCase		:



    """simple docstring"""

    def __init__(  self       ,     _UpperCAmelCase       ,     _UpperCAmelCase=13       ,     _UpperCAmelCase=7       ,     _UpperCAmelCase=True       ,     _UpperCAmelCase=True       ,     _UpperCAmelCase=True       ,     _UpperCAmelCase=True       ,     _UpperCAmelCase=99       ,     _UpperCAmelCase=32       ,     _UpperCAmelCase=2       ,     _UpperCAmelCase=4       ,     _UpperCAmelCase=37       ,     _UpperCAmelCase="gelu"       ,     _UpperCAmelCase=0.1       ,     _UpperCAmelCase=0.1       ,     _UpperCAmelCase=512       ,     _UpperCAmelCase=16       ,     _UpperCAmelCase=2       ,     _UpperCAmelCase=0.02       ,     _UpperCAmelCase=3       ,     _UpperCAmelCase=4       ,     _UpperCAmelCase=None       ,     _UpperCAmelCase=0       ,     ):
       lowercase__:  Optional[Any]   = parent
       lowercase__:  Optional[int]   = batch_size
       lowercase__:  Optional[int]   = seq_length
       lowercase__:  Optional[int]   = is_training
       lowercase__:  List[Any]   = use_input_mask
       lowercase__:  Dict   = use_token_type_ids
       lowercase__:  int   = use_labels
       lowercase__:  List[Any]   = vocab_size
       lowercase__:  Any   = hidden_size
       lowercase__:  Any   = num_hidden_layers
       lowercase__:  int   = num_attention_heads
       lowercase__:  str   = intermediate_size
       lowercase__:  Optional[Any]   = hidden_act
       lowercase__:  Union[str, Any]   = hidden_dropout_prob
       lowercase__:  Union[str, Any]   = attention_probs_dropout_prob
       lowercase__:  str   = max_position_embeddings
       lowercase__:  str   = type_vocab_size
       lowercase__:  Tuple   = type_sequence_label_size
       lowercase__:  Any   = initializer_range
       lowercase__:  List[str]   = num_labels
       lowercase__:  List[Any]   = num_choices
       lowercase__:  Dict   = scope
       lowercase__:  int   = projection_dim






    def _snake_case     (  self				):
       lowercase__:  int   = ids_tensor([self.batch_size, self.seq_length]       ,     self.vocab_size				)

       lowercase__:  List[str]   = None
       if self.use_input_mask:
          # follow test_modeling_tf_ctrl.py
          lowercase__:  Optional[int]   = random_attention_mask([self.batch_size, self.seq_length]				)

       lowercase__:  Union[str, Any]   = None
       if self.use_token_type_ids:
          lowercase__:  Union[str, Any]   = ids_tensor([self.batch_size, self.seq_length]       ,     self.type_vocab_size				)

       lowercase__:  List[Any]   = None
       lowercase__:  Tuple   = None
       lowercase__:  Union[str, Any]   = None
       if self.use_labels:
          lowercase__:  Union[str, Any]   = ids_tensor([self.batch_size]       ,     self.type_sequence_label_size				)
          lowercase__:  Any   = ids_tensor([self.batch_size, self.seq_length]       ,     self.num_labels				)
          lowercase__:  List[Any]   = ids_tensor([self.batch_size]       ,     self.num_choices				)

       lowercase__:  List[Any]   = BertConfig(
           vocab_size=self.vocab_size       ,     hidden_size=self.hidden_size       ,     num_hidden_layers=self.num_hidden_layers       ,     num_attention_heads=self.num_attention_heads       ,     intermediate_size=self.intermediate_size       ,     hidden_act=self.hidden_act       ,     hidden_dropout_prob=self.hidden_dropout_prob       ,     attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,     max_position_embeddings=self.max_position_embeddings       ,     type_vocab_size=self.type_vocab_size       ,     is_decoder=A_       ,     initializer_range=self.initializer_range       ,     )
       lowercase__:  Dict   = DPRConfig(projection_dim=self.projection_dim       ,     **config.to_dict()				)

       return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels






    def _snake_case     (  self       ,     _UpperCAmelCase       ,     _UpperCAmelCase       ,     _UpperCAmelCase       ,     _UpperCAmelCase       ,     _UpperCAmelCase       ,     _UpperCAmelCase       ,     _UpperCAmelCase				):
       lowercase__:  int   = TFDPRContextEncoder(config=A_				)
       lowercase__:  Tuple   = model(A_       ,     attention_mask=A_       ,     token_type_ids=A_				)
       lowercase__:  Optional[Any]   = model(A_       ,     token_type_ids=A_				)
       lowercase__:  Optional[int]   = model(A_				)
       self.parent.assertEqual(result.pooler_output.shape       ,     (self.batch_size, self.projection_dim or self.hidden_size)				)






    def _snake_case     (  self       ,     _UpperCAmelCase       ,     _UpperCAmelCase       ,     _UpperCAmelCase       ,     _UpperCAmelCase       ,     _UpperCAmelCase       ,     _UpperCAmelCase       ,     _UpperCAmelCase				):
       lowercase__:  str   = TFDPRQuestionEncoder(config=A_				)
       lowercase__:  List[str]   = model(A_       ,     attention_mask=A_       ,     token_type_ids=A_				)
       lowercase__:  Tuple   = model(A_       ,     token_type_ids=A_				)
       lowercase__:  str   = model(A_				)
       self.parent.assertEqual(result.pooler_output.shape       ,     (self.batch_size, self.projection_dim or self.hidden_size)				)






    def _snake_case     (  self       ,     _UpperCAmelCase       ,     _UpperCAmelCase       ,     _UpperCAmelCase       ,     _UpperCAmelCase       ,     _UpperCAmelCase       ,     _UpperCAmelCase       ,     _UpperCAmelCase				):
       lowercase__:  Tuple   = TFDPRReader(config=A_				)
       lowercase__:  Union[str, Any]   = model(A_       ,     attention_mask=A_				)

       self.parent.assertEqual(result.start_logits.shape       ,     (self.batch_size, self.seq_length)				)
       self.parent.assertEqual(result.end_logits.shape       ,     (self.batch_size, self.seq_length)				)
       self.parent.assertEqual(result.relevance_logits.shape       ,     (self.batch_size,)				)






    def _snake_case     (  self				):
       lowercase__:  Optional[Any]   = self.prepare_config_and_inputs()
       (
           lowercase__
       ):  Tuple   = config_and_inputs
       lowercase__:  int   = {"""input_ids""": input_ids}
       return config, inputs_dict



@require_tf
class        UpperCAmelCase		(_UpperCAmelCase       ,_UpperCAmelCase       ,unittest.TestCase    ):



    """simple docstring"""

    _UpperCAmelCase      :Dict		       =					(
        (
            TFDPRContextEncoder,
            TFDPRQuestionEncoder,
            TFDPRReader,
        )
        if is_tf_available()
        else ()
    )
    _UpperCAmelCase      :Dict		       =					{"feature-extraction": TFDPRQuestionEncoder} if is_tf_available() else {}

    _UpperCAmelCase      :List[str]		       =					False
    _UpperCAmelCase      :List[Any]		       =					False
    _UpperCAmelCase      :Any		       =					False
    _UpperCAmelCase      :List[str]		       =					False
    _UpperCAmelCase      :Dict		       =					False






    def _snake_case     (  self				):
       lowercase__:  Union[str, Any]   = TFDPRModelTester(self				)
       lowercase__:  Optional[Any]   = ConfigTester(self       ,     config_class=A_       ,     hidden_size=37				)






    def _snake_case     (  self				):
       self.config_tester.run_common_tests()






    def _snake_case     (  self				):
       lowercase__:  Any   = self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_dpr_context_encoder(*A_				)






    def _snake_case     (  self				):
       lowercase__:  Dict   = self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_dpr_question_encoder(*A_				)






    def _snake_case     (  self				):
       lowercase__:  List[Any]   = self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_dpr_reader(*A_				)






    @slow
    def _snake_case     (  self				):
       for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          lowercase__:  Tuple   = TFDPRContextEncoder.from_pretrained(A_				)
          self.assertIsNotNone(A_				)

       for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          lowercase__:  str   = TFDPRContextEncoder.from_pretrained(A_				)
          self.assertIsNotNone(A_				)

       for model_name in TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          lowercase__:  int   = TFDPRQuestionEncoder.from_pretrained(A_				)
          self.assertIsNotNone(A_				)

       for model_name in TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          lowercase__:  Dict   = TFDPRReader.from_pretrained(A_				)
          self.assertIsNotNone(A_				)



@require_tf
class        UpperCAmelCase		(unittest.TestCase    ):



    """simple docstring"""

    @slow
    def _snake_case     (  self				):
       lowercase__:  Optional[int]   = TFDPRQuestionEncoder.from_pretrained('''facebook/dpr-question_encoder-single-nq-base'''				)

       lowercase__:  Optional[int]   = tf.constant(
           [[101, 7592, 1010, 2003, 2026, 3899, 10140, 1029, 102]]				)  # [CLS] hello, is my dog cute? [SEP]
       lowercase__:  Tuple   = model(A_				)[0]  # embedding shape = (1, 768)
       # compare the actual values for a slice.
       lowercase__:  Union[str, Any]   = tf.constant(
           [
               [
                   0.03_236_253,
                   0.12_753_335,
                   0.16_818_509,
                   0.00_279_786,
                   0.3_896_933,
                   0.24_264_945,
                   0.2_178_971,
                   -0.02_335_227,
                   -0.08_481_959,
                   -0.14_324_117,
               ]
           ]				)
       self.assertTrue(numpy.allclose(output[:, :10].numpy()       ,     expected_slice.numpy()       ,     atol=1e-4				)				)




from PIL import Image




def    a__    (		snake_case__					:							Image						,   snake_case__					:							int     ):
  _UpperCAmelCase		:							Optional[Any]							=     (259 * (level + 255)) / (255 * (259 - level))

  def contrast(snake_case__					:							int     ) -> int:
    return int(128 + factor * (c - 128)     )

  return img.point(snake_case__     )


if __name__ == "__main__":
 # Load image
 with Image.open('image_data/lena.jpg') as img:
  # Change contrast to 170
  SCREAMING_SNAKE_CASE__					:     int       =     change_contrast(img, 170)
  cont_img.save('image_data/lena_high_contrast.png', format='png')





