



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 				__A					:





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





			def        _snake_case    (self				):
						lowerCamelCase__ :      str								=		ids_tensor([self.batch_size, self.seq_length]							,    self.vocab_size				)

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

						lowerCamelCase__ :      List[Any]								=		None
						if self.use_token_type_ids:
									lowerCamelCase__ :      Tuple								=		ids_tensor([self.batch_size, self.seq_length]							,    self.type_vocab_size				)

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

						lowerCamelCase__ :      Dict								=		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=__magic_name__							,    initializer_range=self.initializer_range							,    )
						lowerCamelCase__ :      Tuple								=		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							,    __magic_name__							,    __magic_name__							,    __magic_name__							,    __magic_name__							,    __magic_name__							,    __magic_name__							,    __magic_name__				):
						lowerCamelCase__ :      Optional[Any]								=		TFDPRContextEncoder(config=__magic_name__				)
						lowerCamelCase__ :      Tuple								=		model(__magic_name__							,    attention_mask=__magic_name__							,    token_type_ids=__magic_name__				)
						lowerCamelCase__ :      Any								=		model(__magic_name__							,    token_type_ids=__magic_name__				)
						lowerCamelCase__ :      Optional[Any]								=		model(__magic_name__				)
						self.parent.assertEqual(result.pooler_output.shape							,    (self.batch_size, self.projection_dim or self.hidden_size)				)





			def        _snake_case    (self							,    __magic_name__							,    __magic_name__							,    __magic_name__							,    __magic_name__							,    __magic_name__							,    __magic_name__							,    __magic_name__				):
						lowerCamelCase__ :      Optional[Any]								=		TFDPRQuestionEncoder(config=__magic_name__				)
						lowerCamelCase__ :      Optional[int]								=		model(__magic_name__							,    attention_mask=__magic_name__							,    token_type_ids=__magic_name__				)
						lowerCamelCase__ :      Union[str, Any]								=		model(__magic_name__							,    token_type_ids=__magic_name__				)
						lowerCamelCase__ :      str								=		model(__magic_name__				)
						self.parent.assertEqual(result.pooler_output.shape							,    (self.batch_size, self.projection_dim or self.hidden_size)				)





			def        _snake_case    (self							,    __magic_name__							,    __magic_name__							,    __magic_name__							,    __magic_name__							,    __magic_name__							,    __magic_name__							,    __magic_name__				):
						lowerCamelCase__ :      Dict								=		TFDPRReader(config=__magic_name__				)
						lowerCamelCase__ :      Tuple								=		model(__magic_name__							,    attention_mask=__magic_name__				)

						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				):
						lowerCamelCase__ :      Optional[Any]								=		self.prepare_config_and_inputs()
						(
						    lowerCamelCase__
						) :      List[Any]								=		config_and_inputs
						lowerCamelCase__ :      Optional[Any]								=		{"""input_ids""": input_ids}
						return config, inputs_dict







@require_tf
class 				__A					( A_					, A_					, unittest.TestCase      ):
			UpperCamelCase					:Union[str, Any]     =			(
			    (
			        TFDPRContextEncoder,
			        TFDPRQuestionEncoder,
			        TFDPRReader,
			    )
			    if is_tf_available()
			    else ()
			)
			UpperCamelCase					:Optional[int]     =			{'''feature-extraction''': TFDPRQuestionEncoder} if is_tf_available() else {}

			UpperCamelCase					:int     =			False
			UpperCamelCase					:Dict     =			False
			UpperCamelCase					:Tuple     =			False
			UpperCamelCase					:Optional[int]     =			False
			UpperCamelCase					:str     =			False





			def        _snake_case    (self				):
						lowerCamelCase__ :      List[Any]								=		TFDPRModelTester(self				)
						lowerCamelCase__ :      List[Any]								=		ConfigTester(self							,    config_class=__magic_name__							,    hidden_size=37				)





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





			def        _snake_case    (self				):
						lowerCamelCase__ :      List[str]								=		self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_dpr_context_encoder(*__magic_name__				)





			def        _snake_case    (self				):
						lowerCamelCase__ :      List[str]								=		self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_dpr_question_encoder(*__magic_name__				)





			def        _snake_case    (self				):
						lowerCamelCase__ :      Tuple								=		self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_dpr_reader(*__magic_name__				)





			@slow
			def        _snake_case    (self				):
						for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									lowerCamelCase__ :      List[str]								=		TFDPRContextEncoder.from_pretrained(__magic_name__				)
									self.assertIsNotNone(__magic_name__				)

						for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									lowerCamelCase__ :      Dict								=		TFDPRContextEncoder.from_pretrained(__magic_name__				)
									self.assertIsNotNone(__magic_name__				)

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

						for model_name in TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									lowerCamelCase__ :      List[Any]								=		TFDPRReader.from_pretrained(__magic_name__				)
									self.assertIsNotNone(__magic_name__				)







@require_tf
class 				__A					( unittest.TestCase      ):





			@slow
			def        _snake_case    (self				):
						lowerCamelCase__ :      Any								=		TFDPRQuestionEncoder.from_pretrained("""facebook/dpr-question_encoder-single-nq-base"""				)

						lowerCamelCase__ :      List[Any]								=		tf.constant(
						    [[101, 7592, 1010, 2003, 2026, 3899, 10140, 1029, 102]]				)  # [CLS] hello, is my dog cute? [SEP]
						lowerCamelCase__ :      Optional[Any]								=		model(__magic_name__				)[0]  # embedding shape = (1, 768)
						# compare the actual values for a slice.
						lowerCamelCase__ :      Union[str, Any]								=		tf.constant(
						    [
						        [
						            0.03_23_62_53,
						            0.12_75_33_35,
						            0.16_81_85_09,
						            0.00_27_97_86,
						            0.3_89_69_33,
						            0.24_26_49_45,
						            0.2_17_89_71,
						            -0.02_33_52_27,
						            -0.08_48_19_59,
						            -0.14_32_41_17,
						        ]
						    ]				)
						self.assertTrue(numpy.allclose(output[:, :10].numpy()							,    expected_slice.numpy()							,    atol=1E-4				)				)




from pathlib import Path

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






def      _A				(UpperCamelCase		:      np.ndarray      ,			UpperCamelCase		:      np.ndarray      ,			UpperCamelCase		:      np.ndarray      ,			UpperCamelCase		:      int      ,			UpperCamelCase		:      int )  ->np.ndarray:



			'''simple docstring'''







			lowerCamelCase__ :      Union[str, Any]								=		cva.getAffineTransform(UpperCamelCase      ,			UpperCamelCase )
			return cva.warpAffine(UpperCamelCase      ,			UpperCamelCase      ,			(rows, cols) )


if __name__ == "__main__":
				# read original image
				_lowercase     							=						cva.imread(
				    str(Path(__file__).resolve().parent.parent / '''image_data''' / '''lena.jpg''')
				)
				# turn image in gray scale value
				_lowercase     							=						cva.cvtColor(image, cva.COLOR_BGR2GRAY)
				# get image shape
				_lowercase							,				_lowercase     							=						gray_img.shape

				# set different points to rotate image
				_lowercase     							=						np.array([[50, 50], [2_00, 50], [50, 2_00]], np.floataa)
				_lowercase     							=						np.array([[10, 1_00], [2_00, 50], [1_00, 2_50]], np.floataa)
				_lowercase     							=						np.array([[50, 50], [1_50, 50], [1_20, 2_00]], np.floataa)
				_lowercase     							=						np.array([[10, 1_00], [80, 50], [1_80, 2_50]], np.floataa)

				# add all rotated images in a list
				_lowercase     							=						[
				    gray_img,
				    get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
				    get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
				    get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
				]

				# plot different image rotations
				_lowercase     							=						plt.figure(1)
				_lowercase     							=						['''Original''', '''Rotation 1''', '''Rotation 2''', '''Rotation 3''']
				for i, image in enumerate(images):
								plt.subplot(2, 2, i + 1), plt.imshow(image, '''gray''')
								plt.title(titles[i])
								plt.axis('''off''')
								plt.subplots_adjust(left=0.0, bottom=0.05, right=1.0, top=0.95)
				plt.show()



