




import functools
import logging
import os
import sys
import threading
from logging import (
    CRITICAL,  # NOQA
    DEBUG,  # NOQA
    ERROR,  # NOQA
    FATAL,  # NOQA
    INFO,  # NOQA
    NOTSET,  # NOQA
    WARN,  # NOQA
    WARNING,  # NOQA
)
from typing import Optional

import huggingface_hub.utils as hf_hub_utils
from tqdm import auto as tqdm_lib


UpperCamelCase								=					threading.Lock()
UpperCamelCase								=					None

UpperCamelCase								=					{
    'debug': logging.DEBUG,
    'info': logging.INFO,
    'warning': logging.WARNING,
    'error': logging.ERROR,
    'critical': logging.CRITICAL,
}

UpperCamelCase								=					logging.WARNING

UpperCamelCase								=					True



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

					lowerCAmelCase__          =			os.getenv("TRANSFORMERS_VERBOSITY"       ,    lowerCAmelCase_      )
					if env_level_str:
										if env_level_str in log_levels:
															return log_levels[env_level_str]
										else:
															logging.getLogger().warning(
															    F'Unknown option TRANSFORMERS_VERBOSITY={env_level_str}, '
															    F'has to be one of: { ", ".join(log_levels.keys()      ) }'      )
					return _default_log_level



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

					return __name__.split("."      )[0]



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

					return logging.getLogger(_get_library_name()      )



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

					global _default_handler

					with _lock:
										if _default_handler:
															# This library has already configured the library root logger.
															return
										lowerCAmelCase__          =			logging.StreamHandler()  # Set sys.stderr as stream.
										lowerCAmelCase__          =			sys.stderr.flush

										# Apply our default configuration to the library root logger.
										lowerCAmelCase__          =			_get_library_root_logger()
										library_root_logger.addHandler(_default_handler      )
										library_root_logger.setLevel(_get_default_logging_level()      )
										lowerCAmelCase__          =			False



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

					global _default_handler

					with _lock:
										if not _default_handler:
															return

										lowerCAmelCase__          =			_get_library_root_logger()
										library_root_logger.removeHandler(_default_handler      )
										library_root_logger.setLevel(logging.NOTSET      )
										lowerCAmelCase__          =			None



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

					return log_levels



def 							_A       (						lowerCAmelCase_					: Optional[str] = None      ):
					"""simple docstring"""

					if name is None:
										lowerCAmelCase__          =			_get_library_name()

					_configure_library_root_logger()
					return logging.getLogger(lowerCAmelCase_      )



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

					_configure_library_root_logger()
					return _get_library_root_logger().getEffectiveLevel()



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

					_configure_library_root_logger()
					_get_library_root_logger().setLevel(lowerCAmelCase_      )



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

					return set_verbosity(lowerCAmelCase_      )



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

					return set_verbosity(lowerCAmelCase_      )



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

					return set_verbosity(lowerCAmelCase_      )



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

					return set_verbosity(lowerCAmelCase_      )



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

					_configure_library_root_logger()

					assert _default_handler is not None
					_get_library_root_logger().removeHandler(_default_handler      )



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

					_configure_library_root_logger()

					assert _default_handler is not None
					_get_library_root_logger().addHandler(_default_handler      )



def 							_A       (						lowerCAmelCase_					: logging.Handler      ):
					"""simple docstring"""

					_configure_library_root_logger()

					assert handler is not None
					_get_library_root_logger().addHandler(lowerCAmelCase_      )



def 							_A       (						lowerCAmelCase_					: logging.Handler      ):
					"""simple docstring"""

					_configure_library_root_logger()

					assert handler is not None and handler not in _get_library_root_logger().handlers
					_get_library_root_logger().removeHandler(lowerCAmelCase_      )



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

					_configure_library_root_logger()
					lowerCAmelCase__          =			False



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

					_configure_library_root_logger()
					lowerCAmelCase__          =			True



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

					lowerCAmelCase__          =			_get_library_root_logger().handlers

					for handler in handlers:
										lowerCAmelCase__          =			logging.Formatter("[%(levelname)s|%(filename)s:%(lineno)s] %(asctime)s >> %(message)s"      )
										handler.setFormatter(lowerCAmelCase_      )



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

					lowerCAmelCase__          =			_get_library_root_logger().handlers

					for handler in handlers:
										handler.setFormatter(lowerCAmelCase_      )



def 							_A       (						self					: Union[str, Any]       ,    *lowerCAmelCase_					: int       ,    **lowerCAmelCase_					: Optional[Any]      ):
					"""simple docstring"""

					lowerCAmelCase__          =			os.getenv("TRANSFORMERS_NO_ADVISORY_WARNINGS"       ,    lowerCAmelCase_      )
					if no_advisory_warnings:
										return
					self.warning(*lowerCAmelCase_       ,    **lowerCAmelCase_      )


UpperCamelCase								=					warning_advice



@functools.lru_cache(lowerCAmelCase_      )
def 							_A       (						self					: List[Any]       ,    *lowerCAmelCase_					: Any       ,    **lowerCAmelCase_					: Tuple      ):
					"""simple docstring"""

					self.warning(*lowerCAmelCase_       ,    **lowerCAmelCase_      )


UpperCamelCase								=					warning_once





class 			__lowerCamelCase	:




				"""simple docstring"""

				def __init__(	self					:					Optional[Any]       ,     *SCREAMING_SNAKE_CASE__					:					int       ,     **SCREAMING_SNAKE_CASE__					:					List[str]       )				->	Optional[Any]:  # pylint: disable=unused-argument
									lowerCAmelCase__          =			args[0] if args else None


				def __iter__(	self					:					Dict       )				->	int:
									return iter(self._iterator       )


				def __getattr__(	self					:					List[str]       ,     SCREAMING_SNAKE_CASE__					:					Optional[int]       )				->	Tuple:

									def empty_fn(*SCREAMING_SNAKE_CASE__					:					int       ,     **SCREAMING_SNAKE_CASE__					:					str       ):  # pylint: disable=unused-argument
														return

									return empty_fn


				def __enter__(	self					:					str       )				->	Union[str, Any]:
									return self


				def __exit__(	self					:					List[Any]       ,     SCREAMING_SNAKE_CASE__					:					Dict       ,     SCREAMING_SNAKE_CASE__					:					Optional[Any]       ,     SCREAMING_SNAKE_CASE__					:					str       )				->	List[str]:
									return






class 			__lowerCamelCase	:




				"""simple docstring"""

				def __call__(	self					:					Any       ,     *SCREAMING_SNAKE_CASE__					:					Optional[Any]       ,     **SCREAMING_SNAKE_CASE__					:					str       )				->	Any:
									if _tqdm_active:
														return tqdm_lib.tqdm(*SCREAMING_SNAKE_CASE__       ,     **SCREAMING_SNAKE_CASE__       )
									else:
														return EmptyTqdm(*SCREAMING_SNAKE_CASE__       ,     **SCREAMING_SNAKE_CASE__       )


				def        a				(	self					:					str       ,     *SCREAMING_SNAKE_CASE__					:					Tuple       ,     **SCREAMING_SNAKE_CASE__					:					Dict       )				->	Any:
									lowerCAmelCase__          =			None
									if _tqdm_active:
														return tqdm_lib.tqdm.set_lock(*SCREAMING_SNAKE_CASE__       ,     **SCREAMING_SNAKE_CASE__       )


				def        a				(	self					:					Union[str, Any]       )				->	Union[str, Any]:
									if _tqdm_active:
														return tqdm_lib.tqdm.get_lock()






UpperCamelCase								=					_tqdm_cls()



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

					global _tqdm_active
					return bool(_tqdm_active      )



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

					global _tqdm_active
					lowerCAmelCase__          =			True
					hf_hub_utils.enable_progress_bars()






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

					global _tqdm_active
					lowerCAmelCase__          =			False
					hf_hub_utils.disable_progress_bars()






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					:					str       ,     SCREAMING_SNAKE_CASE__					:					List[str]       ,     SCREAMING_SNAKE_CASE__					:					List[Any]=13       ,     SCREAMING_SNAKE_CASE__					:					Optional[Any]=7       ,     SCREAMING_SNAKE_CASE__					:					str=True       ,     SCREAMING_SNAKE_CASE__					:					Optional[Any]=True       ,     SCREAMING_SNAKE_CASE__					:					Dict=True       ,     SCREAMING_SNAKE_CASE__					:					Optional[Any]=True       ,     SCREAMING_SNAKE_CASE__					:					int=99       ,     SCREAMING_SNAKE_CASE__					:					List[str]=32       ,     SCREAMING_SNAKE_CASE__					:					Tuple=2       ,     SCREAMING_SNAKE_CASE__					:					List[str]=4       ,     SCREAMING_SNAKE_CASE__					:					Union[str, Any]=37       ,     SCREAMING_SNAKE_CASE__					:					List[Any]="gelu"       ,     SCREAMING_SNAKE_CASE__					:					int=0.1       ,     SCREAMING_SNAKE_CASE__					:					Dict=0.1       ,     SCREAMING_SNAKE_CASE__					:					List[Any]=512       ,     SCREAMING_SNAKE_CASE__					:					List[Any]=16       ,     SCREAMING_SNAKE_CASE__					:					List[str]=2       ,     SCREAMING_SNAKE_CASE__					:					Optional[int]=0.02       ,     SCREAMING_SNAKE_CASE__					:					Tuple=3       ,     SCREAMING_SNAKE_CASE__					:					int=4       ,     SCREAMING_SNAKE_CASE__					:					Optional[Any]=None       ,     SCREAMING_SNAKE_CASE__					:					str=0       ,     )				->	List[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       )				->	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=SCREAMING_SNAKE_CASE__       ,     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					:					Dict       ,     SCREAMING_SNAKE_CASE__					:					Dict       ,     SCREAMING_SNAKE_CASE__					:					Tuple       ,     SCREAMING_SNAKE_CASE__					:					Tuple       ,     SCREAMING_SNAKE_CASE__					:					Any       ,     SCREAMING_SNAKE_CASE__					:					Any       ,     SCREAMING_SNAKE_CASE__					:					Union[str, Any]       ,     SCREAMING_SNAKE_CASE__					:					List[Any]       )				->	List[str]:
         lowerCAmelCase__          =			TFDPRContextEncoder(config=SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			model(SCREAMING_SNAKE_CASE__       ,     attention_mask=SCREAMING_SNAKE_CASE__       ,     token_type_ids=SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			model(SCREAMING_SNAKE_CASE__       ,     token_type_ids=SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			model(SCREAMING_SNAKE_CASE__       )
         self.parent.assertEqual(result.pooler_output.shape       ,     (self.batch_size, self.projection_dim or self.hidden_size)       )


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


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






@require_tf
class 			__lowerCamelCase	(   UpperCamelCase__     ,    UpperCamelCase__     ,    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					:					Dict       )				->	int:
         lowerCAmelCase__          =			TFDPRModelTester(self       )
         lowerCAmelCase__          =			ConfigTester(self       ,     config_class=SCREAMING_SNAKE_CASE__       ,     hidden_size=37       )


    def        a				(	self					:					List[Any]       )				->	List[Any]:
         self.config_tester.run_common_tests()


    def        a				(	self					:					Tuple       )				->	Dict:
         lowerCAmelCase__          =			self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_dpr_context_encoder(*SCREAMING_SNAKE_CASE__       )


    def        a				(	self					:					Union[str, Any]       )				->	Optional[int]:
         lowerCAmelCase__          =			self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_dpr_question_encoder(*SCREAMING_SNAKE_CASE__       )


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


    @slow
    def        a				(	self					:					List[str]       )				->	Union[str, Any]:
         for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              lowerCAmelCase__          =			TFDPRContextEncoder.from_pretrained(SCREAMING_SNAKE_CASE__       )
              self.assertIsNotNone(SCREAMING_SNAKE_CASE__       )

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

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

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






@require_tf
class 			__lowerCamelCase	(   unittest.TestCase   ):




    """simple docstring"""

    @slow
    def        a				(	self					:					Optional[Any]       )				->	List[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(SCREAMING_SNAKE_CASE__       )[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       )       )
