






"""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       __A		:

					def __init__(			self  :      Union[str, Any]     ,		__snake_case  :      Union[str, Any]     ,		__snake_case  :      Optional[int]=1_3     ,		__snake_case  :      Tuple=7     ,		__snake_case  :      Optional[Any]=True     ,		__snake_case  :      List[str]=True     ,		__snake_case  :      List[Any]=True     ,		__snake_case  :      List[str]=True     ,		__snake_case  :      Dict=9_9     ,		__snake_case  :      Dict=3_2     ,		__snake_case  :      Optional[int]=2     ,		__snake_case  :      List[Any]=4     ,		__snake_case  :      List[str]=3_7     ,		__snake_case  :      str="gelu"     ,		__snake_case  :      List[str]=0.1     ,		__snake_case  :      Tuple=0.1     ,		__snake_case  :      str=5_1_2     ,		__snake_case  :      Optional[int]=1_6     ,		__snake_case  :      List[str]=2     ,		__snake_case  :      str=0.02     ,		__snake_case  :      Optional[int]=3     ,		__snake_case  :      List[str]=4     ,		__snake_case  :      Any=None     ,		__snake_case  :      int=0     ,		)			->							int:
											__magic_name__:	int							=       parent
											__magic_name__:	Tuple							=       batch_size
											__magic_name__:	List[Any]							=       seq_length
											__magic_name__:	str							=       is_training
											__magic_name__:	Union[str, Any]							=       use_input_mask
											__magic_name__:	Any							=       use_token_type_ids
											__magic_name__:	Tuple							=       use_labels
											__magic_name__:	Tuple							=       vocab_size
											__magic_name__:	Tuple							=       hidden_size
											__magic_name__:	str							=       num_hidden_layers
											__magic_name__:	List[Any]							=       num_attention_heads
											__magic_name__:	Any							=       intermediate_size
											__magic_name__:	List[Any]							=       hidden_act
											__magic_name__:	Optional[int]							=       hidden_dropout_prob
											__magic_name__:	Dict							=       attention_probs_dropout_prob
											__magic_name__:	Union[str, Any]							=       max_position_embeddings
											__magic_name__:	Tuple							=       type_vocab_size
											__magic_name__:	List[str]							=       type_sequence_label_size
											__magic_name__:	Union[str, Any]							=       initializer_range
											__magic_name__:	List[str]							=       num_labels
											__magic_name__:	List[Any]							=       num_choices
											__magic_name__:	str							=       scope
											__magic_name__:	Dict							=       projection_dim

					def   lowerCamelCase__   (			self  :      List[Any]					)			->							int:
											__magic_name__:	str							=       ids_tensor([self.batch_size, self.seq_length]     ,		self.vocab_size					)

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

											__magic_name__:	Dict							=       None
											if self.use_token_type_ids:
																	__magic_name__:	Tuple							=       ids_tensor([self.batch_size, self.seq_length]     ,		self.type_vocab_size					)

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

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

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

					def   lowerCamelCase__   (			self  :      Optional[int]     ,		__snake_case  :      Optional[int]     ,		__snake_case  :      Optional[int]     ,		__snake_case  :      str     ,		__snake_case  :      str     ,		__snake_case  :      List[str]     ,		__snake_case  :      Any     ,		__snake_case  :      int					)			->							List[Any]:
											__magic_name__:	List[str]							=       TFDPRReader(config=__snake_case					)
											__magic_name__:	Dict							=       model(__snake_case     ,		attention_mask=__snake_case					)

											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   lowerCamelCase__   (			self  :      Dict					)			->							Tuple:
											__magic_name__:	int							=       self.prepare_config_and_inputs()
											(
											    (
											    __magic_name__
											),			(
											    __magic_name__
											),			(
											    __magic_name__
											),			(
											    __magic_name__
											),			(
											    __magic_name__
											),			(
											    __magic_name__
											),			(
											    __magic_name__
											),			
											):	Optional[int]							=       config_and_inputs
											__magic_name__:	List[str]							=       {"""input_ids""": input_ids}
											return config, inputs_dict



@require_tf
class       __A		(   SCREAMING_SNAKE_CASE_     ,SCREAMING_SNAKE_CASE_     ,unittest.TestCase			):
					UpperCAmelCase__								=				(
					    (
					        TFDPRContextEncoder,
					        TFDPRQuestionEncoder,
					        TFDPRReader,
					    )
					    if is_tf_available()
					    else ()
					)
					UpperCAmelCase__								=				{"feature-extraction": TFDPRQuestionEncoder} if is_tf_available() else {}

					UpperCAmelCase__								=				False
					UpperCAmelCase__								=				False
					UpperCAmelCase__								=				False
					UpperCAmelCase__								=				False
					UpperCAmelCase__								=				False

					def   lowerCamelCase__   (			self  :      Any					)			->							List[Any]:
											__magic_name__:	List[Any]							=       TFDPRModelTester(self					)
											__magic_name__:	Optional[Any]							=       ConfigTester(self     ,		config_class=__snake_case     ,		hidden_size=3_7					)

					def   lowerCamelCase__   (			self  :      List[str]					)			->							str:
											self.config_tester.run_common_tests()

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

					def   lowerCamelCase__   (			self  :      str					)			->							Optional[Any]:
											__magic_name__:	int							=       self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_dpr_question_encoder(*__snake_case					)

					def   lowerCamelCase__   (			self  :      Dict					)			->							List[str]:
											__magic_name__:	Tuple							=       self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_dpr_reader(*__snake_case					)






					@slow
					def   lowerCamelCase__   (			self  :      Optional[int]					)			->							int:
											for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	__magic_name__:	Tuple							=       TFDPRContextEncoder.from_pretrained(__snake_case					)
																	self.assertIsNotNone(__snake_case					)

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

											for model_name in TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	__magic_name__:	Optional[Any]							=       TFDPRQuestionEncoder.from_pretrained(__snake_case					)
																	self.assertIsNotNone(__snake_case					)

											for model_name in TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	__magic_name__:	Optional[int]							=       TFDPRReader.from_pretrained(__snake_case					)
																	self.assertIsNotNone(__snake_case					)



@require_tf
class       __A		(   unittest.TestCase			):

					@slow
					def   lowerCamelCase__   (			self  :      Optional[Any]					)			->							Optional[int]:
											__magic_name__:	int							=       TFDPRQuestionEncoder.from_pretrained("""facebook/dpr-question_encoder-single-nq-base"""					)

											__magic_name__:	Optional[Any]							=       tf.constant(
											    [[1_0_1, 7_5_9_2, 1_0_1_0, 2_0_0_3, 2_0_2_6, 3_8_9_9, 1_0_1_4_0, 1_0_2_9, 1_0_2]]					)  # [CLS] hello, is my dog cute? [SEP]
											__magic_name__:	Optional[Any]							=       model(__snake_case					)[0]  # embedding shape = (1, 768)
											# compare the actual values for a slice.
											__magic_name__:	List[Any]							=       tf.constant(
											    [
											        [
											            0.03236253,
											            0.12753335,
											            0.16818509,
											            0.00279786,
											            0.3896933,
											            0.24264945,
											            0.2178971,
											            -0.02335227,
											            -0.08481959,
											            -0.14324117,
											        ]
											    ]					)
											self.assertTrue(numpy.allclose(output[:, :1_0].numpy()     ,		expected_slice.numpy()     ,		atol=1E-4					)					)











"""simple docstring"""





def   a    (     __UpperCAmelCase      :    list[int]  )    ->							float:
						if not nums:  # Makes sure that the list is not empty
												raise ValueError("""List is empty"""  )

						__magic_name__:	Dict							=       sum(__UpperCAmelCase  ) / len(__UpperCAmelCase  )  # Calculate the average
						return sum(abs(x - average  ) for x in nums  ) / len(__UpperCAmelCase  )


if __name__ == "__main__":
							import doctest

							doctest.testmod()



