




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 			__lowerCamelCase	:




    """simple docstring"""

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


    def        a				(	self					:					Any       )				->	List[Any]:
         lowerCAmelCase__          =			ids_tensor([self.batch_size, self.seq_length]       ,     self.vocab_size       )

         lowerCAmelCase__          =			None
         if self.use_input_mask:
              # follow test_modeling_tf_ctrl.py
              lowerCAmelCase__          =			random_attention_mask([self.batch_size, self.seq_length]       )

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

         lowerCAmelCase__          =			None
         lowerCAmelCase__          =			None
         lowerCAmelCase__          =			None
         if self.use_labels:
              lowerCAmelCase__          =			ids_tensor([self.batch_size]       ,     self.type_sequence_label_size       )
              lowerCAmelCase__          =			ids_tensor([self.batch_size, self.seq_length]       ,     self.num_labels       )
              lowerCAmelCase__          =			ids_tensor([self.batch_size]       ,     self.num_choices       )

         lowerCAmelCase__          =			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       ,     )
         lowerCAmelCase__          =			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        a				(	self					:					Union[str, Any]       ,     SCREAMING_SNAKE_CASE__					:					Union[str, Any]       ,     SCREAMING_SNAKE_CASE__					:					str       ,     SCREAMING_SNAKE_CASE__					:					Any       ,     SCREAMING_SNAKE_CASE__					:					Dict       ,     SCREAMING_SNAKE_CASE__					:					Union[str, Any]       ,     SCREAMING_SNAKE_CASE__					:					int       ,     SCREAMING_SNAKE_CASE__					:					List[str]       )				->	str:
         lowerCAmelCase__          =			TFDPRContextEncoder(config=_a       )
         lowerCAmelCase__          =			model(_a       ,     attention_mask=_a       ,     token_type_ids=_a       )
         lowerCAmelCase__          =			model(_a       ,     token_type_ids=_a       )
         lowerCAmelCase__          =			model(_a       )
         self.parent.assertEqual(result.pooler_output.shape       ,     (self.batch_size, self.projection_dim or self.hidden_size)       )


    def        a				(	self					:					Any       ,     SCREAMING_SNAKE_CASE__					:					int       ,     SCREAMING_SNAKE_CASE__					:					Dict       ,     SCREAMING_SNAKE_CASE__					:					Optional[Any]       ,     SCREAMING_SNAKE_CASE__					:					List[str]       ,     SCREAMING_SNAKE_CASE__					:					Tuple       ,     SCREAMING_SNAKE_CASE__					:					int       ,     SCREAMING_SNAKE_CASE__					:					Dict       )				->	Optional[Any]:
         lowerCAmelCase__          =			TFDPRQuestionEncoder(config=_a       )
         lowerCAmelCase__          =			model(_a       ,     attention_mask=_a       ,     token_type_ids=_a       )
         lowerCAmelCase__          =			model(_a       ,     token_type_ids=_a       )
         lowerCAmelCase__          =			model(_a       )
         self.parent.assertEqual(result.pooler_output.shape       ,     (self.batch_size, self.projection_dim or self.hidden_size)       )


    def        a				(	self					:					Any       ,     SCREAMING_SNAKE_CASE__					:					Optional[int]       ,     SCREAMING_SNAKE_CASE__					:					int       ,     SCREAMING_SNAKE_CASE__					:					int       ,     SCREAMING_SNAKE_CASE__					:					int       ,     SCREAMING_SNAKE_CASE__					:					Dict       ,     SCREAMING_SNAKE_CASE__					:					Optional[int]       ,     SCREAMING_SNAKE_CASE__					:					str       )				->	str:
         lowerCAmelCase__          =			TFDPRReader(config=_a       )
         lowerCAmelCase__          =			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        a				(	self					:					Optional[int]       )				->	Tuple:
         lowerCAmelCase__          =			self.prepare_config_and_inputs()
         (
             lowerCAmelCase__     
         )          =			config_and_inputs
         lowerCAmelCase__          =			{"input_ids": input_ids}
         return config, inputs_dict






@require_tf
class 			__lowerCamelCase	(   _a     ,    _a     ,    unittest.TestCase   ):




    """simple docstring"""

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

    snake_case__ =				False
    snake_case__ =				False
    snake_case__ =				False
    snake_case__ =				False
    snake_case__ =				False


    def        a				(	self					:					List[Any]       )				->	Union[str, Any]:
         lowerCAmelCase__          =			TFDPRModelTester(self       )
         lowerCAmelCase__          =			ConfigTester(self       ,     config_class=_a       ,     hidden_size=37       )


    def        a				(	self					:					int       )				->	Union[str, Any]:
         self.config_tester.run_common_tests()


    def        a				(	self					:					List[Any]       )				->	List[str]:
         lowerCAmelCase__          =			self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_dpr_context_encoder(*_a       )


    def        a				(	self					:					int       )				->	Dict:
         lowerCAmelCase__          =			self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_dpr_question_encoder(*_a       )


    def        a				(	self					:					List[Any]       )				->	Optional[Any]:
         lowerCAmelCase__          =			self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_dpr_reader(*_a       )


    @slow
    def        a				(	self					:					Any       )				->	Tuple:
         for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              lowerCAmelCase__          =			TFDPRContextEncoder.from_pretrained(_a       )
              self.assertIsNotNone(_a       )

         for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              lowerCAmelCase__          =			TFDPRContextEncoder.from_pretrained(_a       )
              self.assertIsNotNone(_a       )

         for model_name in TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              lowerCAmelCase__          =			TFDPRQuestionEncoder.from_pretrained(_a       )
              self.assertIsNotNone(_a       )

         for model_name in TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              lowerCAmelCase__          =			TFDPRReader.from_pretrained(_a       )
              self.assertIsNotNone(_a       )






@require_tf
class 			__lowerCamelCase	(   unittest.TestCase   ):




    """simple docstring"""

    @slow
    def        a				(	self					:					str       )				->	str:
         lowerCAmelCase__          =			TFDPRQuestionEncoder.from_pretrained("facebook/dpr-question_encoder-single-nq-base"       )

         lowerCAmelCase__          =			tf.constant(
             [[101, 7_592, 1_010, 2_003, 2_026, 3_899, 10_140, 1_029, 102]]       )  # [CLS] hello, is my dog cute? [SEP]
         lowerCAmelCase__          =			model(_a       )[0]  # embedding shape = (1, 768)
         # compare the actual values for a slice.
         lowerCAmelCase__          =			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 math import pi, sqrt



def 							_A       (						lowerCAmelCase_					: float      ):
     """simple docstring"""

     if num <= 0:
          raise ValueError("math domain error"      )
     if num > 171.5:
          raise OverflowError("math range error"      )
     elif num - int(lowerCAmelCase_      ) not in (0, 0.5):
          raise NotImplementedError("num must be an integer or a half-integer"      )
     elif num == 0.5:
          return sqrt(lowerCAmelCase_      )
     else:
          return 1.0 if num == 1 else (num - 1) * gamma(num - 1      )



def 							_A       (						):
     """simple docstring"""

     assert gamma(0.5      ) == sqrt(lowerCAmelCase_      )
     assert gamma(1      ) == 1.0
     assert gamma(2      ) == 1.0


if __name__ == "__main__":
     from doctest import testmod

     testmod()
     UpperCamelCase								=					1.0
     while num:
          UpperCamelCase								=					float(input('Gamma of: '))
          print(F"""gamma({num}) = {gamma(num)}""")
          print('\nEnter 0 to exit...')
