




"""simple docstring"""
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
from typing import List






def 				__lowerCAmelCase					(					__lowerCAmelCase			:		Optional[int]  ,	__lowerCAmelCase			:		Dict						)    ->       Dict:





 if name != "training_function" and name != "main":
  raise ValueError(f"Incorrect function name passed: {name}, choose either \'main\' or \'training_function\'"						)
 _UpperCamelCase      ,	_UpperCamelCase      :	Dict					      =  [], False
 for line in lines:
  if not found_start and f"def {name}" in line:
   _UpperCamelCase      :	Optional[int]					      =  True
   good_lines.append(lowerCamelCase_						)
   continue
  if found_start:
   if name == "training_function" and "def main" in line:
    return good_lines
   if name == "main" and "if __name__" in line:
    return good_lines
   good_lines.append(lowerCamelCase_						)






def 				__lowerCAmelCase					(					__lowerCAmelCase			:		int						)    ->       Optional[Any]:
 return [line for line in lines if not line.lstrip().startswith("#"						) and line != "\n"]






def 				__lowerCAmelCase					(					__lowerCAmelCase			:		Optional[int]  ,	__lowerCAmelCase			:		Dict  ,	__lowerCAmelCase			:		Optional[int]  ,	__lowerCAmelCase			:		Tuple = None						)    ->       Tuple:





 with open(lowerCamelCase_  ,	"r"						) as f:
  _UpperCamelCase      :	Tuple					      =  f.readlines()
 with open(os.path.abspath(os.path.join("examples"  ,	"nlp_example.py"						)						)  ,	"r"						) as f:
  _UpperCamelCase      :	List[str]					      =  f.readlines()
 with open(lowerCamelCase_  ,	"r"						) as f:
  _UpperCamelCase      :	str					      =  f.readlines()
 if secondary_filename is not None:
  with open(lowerCamelCase_  ,	"r"						) as f:
   _UpperCamelCase      :	Tuple					      =  f.readlines()

    # This is our base, we remove all the code from here in our `full_filename` and `feature_filename` to find the new content
 if parser_only:
  _UpperCamelCase      :	List[str]					      =  clean_lines(get_function_contents_by_name(lowerCamelCase_  ,	"main"						)						)
  _UpperCamelCase      :	int					      =  clean_lines(get_function_contents_by_name(lowerCamelCase_  ,	"main"						)						)
  _UpperCamelCase      :	List[Any]					      =  clean_lines(get_function_contents_by_name(lowerCamelCase_  ,	"main"						)						)
  if secondary_filename is not None:
   _UpperCamelCase      :	Union[str, Any]					      =  clean_lines(get_function_contents_by_name(lowerCamelCase_  ,	"main"						)						)
 else:
  _UpperCamelCase      :	Dict					      =  clean_lines(get_function_contents_by_name(lowerCamelCase_  ,	"training_function"						)						)
  _UpperCamelCase      :	List[Any]					      =  clean_lines(get_function_contents_by_name(lowerCamelCase_  ,	"training_function"						)						)
  _UpperCamelCase      :	int					      =  clean_lines(get_function_contents_by_name(lowerCamelCase_  ,	"training_function"						)						)
  if secondary_filename is not None:
   _UpperCamelCase      :	Optional[Any]					      =  clean_lines(
       get_function_contents_by_name(lowerCamelCase_  ,	"training_function"						)						)

 _UpperCamelCase      :	int					      =  "train_dataloader, eval_dataloader = get_dataloaders(accelerator, batch_size)\n"

 # Specific code in our script that differs from the full version, aka what is new
 _UpperCamelCase      :	Union[str, Any]					      =  []
 _UpperCamelCase      :	Any					      =  []  # We keep track of the idxs just in case it's a repeated statement
 _UpperCamelCase      :	Optional[int]					      =  iter(lowerCamelCase_						)
 for i in range(len(lowerCamelCase_						) - 1						):
  if i not in passed_idxs:
   _UpperCamelCase      :	int					      =  next(lowerCamelCase_						)
   if (line not in full_file_func) and (line.lstrip() != _dl_line):
    if "TESTING_MOCKED_DATALOADERS" not in line:
     new_feature_code.append(lowerCamelCase_						)
     passed_idxs.append(lowerCamelCase_						)
    else:
     # Skip over the `config['num_epochs'] = 2` statement
     _UpperCamelCase      :	Tuple					      =  next(lowerCamelCase_						)

    # Extract out just the new parts from the full_file_training_func
 _UpperCamelCase      :	Tuple					      =  []
 _UpperCamelCase      :	str					      =  []  # We keep track of the idxs just in case it's a repeated statement
 for i, line in enumerate(lowerCamelCase_						):
  if i not in passed_idxs:
   if (line not in full_file_func) and (line.lstrip() != _dl_line):
    if "TESTING_MOCKED_DATALOADERS" not in line:
     new_full_example_parts.append(lowerCamelCase_						)
     passed_idxs.append(lowerCamelCase_						)

    # Finally, get the overall diff
 _UpperCamelCase      :	Any					      =  [line for line in new_feature_code if line not in new_full_example_parts]
 if secondary_filename is not None:
  _UpperCamelCase      :	Optional[Any]					      =  [line for line in full_file_contents if line not in secondary_file_func]
  _UpperCamelCase      :	Optional[int]					      =  [line for line in diff_from_example if line not in diff_from_two]

 return diff_from_example




import tempfile
import unittest

from transformers import DPRConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
			import torch

			from transformers import DPRContextEncoder, DPRQuestionEncoder, DPRReader, DPRReaderTokenizer
			from transformers.models.dpr.modeling_dpr import (
			    DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
			    DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
			    DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
			)

class 						_SCREAMING_SNAKE_CASE				:






		"""simple docstring"""







		def __init__(      self     :					List[Any]	,       __SCREAMING_SNAKE_CASE     :					List[Any]	,       __SCREAMING_SNAKE_CASE     :					List[Any]=13	,       __SCREAMING_SNAKE_CASE     :					str=7	,       __SCREAMING_SNAKE_CASE     :					Any=False	,       __SCREAMING_SNAKE_CASE     :					Dict=True	,       __SCREAMING_SNAKE_CASE     :					Any=True	,       __SCREAMING_SNAKE_CASE     :					List[str]=True	,       __SCREAMING_SNAKE_CASE     :					Union[str, Any]=99	,       __SCREAMING_SNAKE_CASE     :					Dict=32	,       __SCREAMING_SNAKE_CASE     :					Union[str, Any]=5	,       __SCREAMING_SNAKE_CASE     :					Dict=4	,       __SCREAMING_SNAKE_CASE     :					Tuple=37	,       __SCREAMING_SNAKE_CASE     :					List[str]="gelu"	,       __SCREAMING_SNAKE_CASE     :					List[str]=0.1	,       __SCREAMING_SNAKE_CASE     :					Optional[Any]=0.1	,       __SCREAMING_SNAKE_CASE     :					List[Any]=512	,       __SCREAMING_SNAKE_CASE     :					Optional[int]=16	,       __SCREAMING_SNAKE_CASE     :					Any=2	,       __SCREAMING_SNAKE_CASE     :					Dict=0.0_2	,       __SCREAMING_SNAKE_CASE     :					Union[str, Any]=3	,       __SCREAMING_SNAKE_CASE     :					int=4	,       __SCREAMING_SNAKE_CASE     :					Optional[Any]=None	,       __SCREAMING_SNAKE_CASE     :					List[Any]=0	,       ):






					"""simple docstring"""
					_snake_case														=   parent
					_snake_case														=   batch_size
					_snake_case														=   seq_length
					_snake_case														=   is_training
					_snake_case														=   use_input_mask
					_snake_case														=   use_token_type_ids
					_snake_case														=   use_labels
					_snake_case														=   vocab_size
					_snake_case														=   hidden_size
					_snake_case														=   num_hidden_layers
					_snake_case														=   num_attention_heads
					_snake_case														=   intermediate_size
					_snake_case														=   hidden_act
					_snake_case														=   hidden_dropout_prob
					_snake_case														=   attention_probs_dropout_prob
					_snake_case														=   max_position_embeddings
					_snake_case														=   type_vocab_size
					_snake_case														=   type_sequence_label_size
					_snake_case														=   initializer_range
					_snake_case														=   num_labels
					_snake_case														=   num_choices
					_snake_case														=   scope
					_snake_case														=   projection_dim



		def 					_a					(      self     :					Tuple ):






					"""simple docstring"""
					_snake_case														=   ids_tensor([self.batch_size, self.seq_length]	,       self.vocab_size )

					_snake_case														=   None
					if self.use_input_mask:
								_snake_case														=   random_attention_mask([self.batch_size, self.seq_length] )

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

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

					_snake_case														=   self.get_config()

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



		def 					_a					(      self     :					List[Any] ):






					"""simple docstring"""
					return DPRConfig(
					    projection_dim=self.projection_dim	,       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	,       initializer_range=self.initializer_range	,       )



		def 					_a					(      self     :					List[Any]	,       __SCREAMING_SNAKE_CASE     :					Union[str, Any]	,       __SCREAMING_SNAKE_CASE     :					List[str]	,       __SCREAMING_SNAKE_CASE     :					List[str]	,       __SCREAMING_SNAKE_CASE     :					List[Any]	,       __SCREAMING_SNAKE_CASE     :					Any	,       __SCREAMING_SNAKE_CASE     :					List[Any]	,       __SCREAMING_SNAKE_CASE     :					List[str] ):






					"""simple docstring"""
					_snake_case														=   DPRContextEncoder(config=__SCREAMING_SNAKE_CASE )
					model.to(__SCREAMING_SNAKE_CASE )
					model.eval()
					_snake_case														=   model(__SCREAMING_SNAKE_CASE	,       attention_mask=__SCREAMING_SNAKE_CASE	,       token_type_ids=__SCREAMING_SNAKE_CASE )
					_snake_case														=   model(__SCREAMING_SNAKE_CASE	,       token_type_ids=__SCREAMING_SNAKE_CASE )
					_snake_case														=   model(__SCREAMING_SNAKE_CASE )
					self.parent.assertEqual(result.pooler_output.shape	,       (self.batch_size, self.projection_dim or self.hidden_size) )



		def 					_a					(      self     :					str	,       __SCREAMING_SNAKE_CASE     :					Optional[Any]	,       __SCREAMING_SNAKE_CASE     :					List[str]	,       __SCREAMING_SNAKE_CASE     :					List[str]	,       __SCREAMING_SNAKE_CASE     :					List[str]	,       __SCREAMING_SNAKE_CASE     :					Dict	,       __SCREAMING_SNAKE_CASE     :					Optional[Any]	,       __SCREAMING_SNAKE_CASE     :					str ):






					"""simple docstring"""
					_snake_case														=   DPRQuestionEncoder(config=__SCREAMING_SNAKE_CASE )
					model.to(__SCREAMING_SNAKE_CASE )
					model.eval()
					_snake_case														=   model(__SCREAMING_SNAKE_CASE	,       attention_mask=__SCREAMING_SNAKE_CASE	,       token_type_ids=__SCREAMING_SNAKE_CASE )
					_snake_case														=   model(__SCREAMING_SNAKE_CASE	,       token_type_ids=__SCREAMING_SNAKE_CASE )
					_snake_case														=   model(__SCREAMING_SNAKE_CASE )
					self.parent.assertEqual(result.pooler_output.shape	,       (self.batch_size, self.projection_dim or self.hidden_size) )



		def 					_a					(      self     :					List[str]	,       __SCREAMING_SNAKE_CASE     :					int	,       __SCREAMING_SNAKE_CASE     :					List[Any]	,       __SCREAMING_SNAKE_CASE     :					int	,       __SCREAMING_SNAKE_CASE     :					Union[str, Any]	,       __SCREAMING_SNAKE_CASE     :					Any	,       __SCREAMING_SNAKE_CASE     :					Optional[int]	,       __SCREAMING_SNAKE_CASE     :					Any ):






					"""simple docstring"""
					_snake_case														=   DPRReader(config=__SCREAMING_SNAKE_CASE )
					model.to(__SCREAMING_SNAKE_CASE )
					model.eval()
					_snake_case														=   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     :					Any ):






					"""simple docstring"""
					_snake_case														=   self.prepare_config_and_inputs()
					(
					    (
					    _snake_case
					)							,			(
					    _snake_case
					)							,			(
					    _snake_case
					)							,			(
					    _snake_case
					)							,			(
					    _snake_case
					)							,			(
					    _snake_case
					)							,			(
					    _snake_case
					)							,			
					)														=   config_and_inputs
					_snake_case														=   {"input_ids": input_ids}
					return config, inputs_dict

@require_torch
class 						_SCREAMING_SNAKE_CASE				(  _lowercase   ,  _lowercase   ,  unittest.TestCase				):






		"""simple docstring"""







		A_    			=   (
		    (
		        DPRContextEncoder,
		        DPRQuestionEncoder,
		        DPRReader,
		    )
		    if is_torch_available()
		    else ()
		)
		A_    			=   {'''feature-extraction''': DPRQuestionEncoder} if is_torch_available() else {}

		A_    			=   False
		A_    			=   False  # why?
		A_    			=   False
		A_    			=   False



		def 					_a					(      self     :					Union[str, Any] ):






					"""simple docstring"""
					_snake_case														=   DPRModelTester(self )
					_snake_case														=   ConfigTester(self	,       config_class=__SCREAMING_SNAKE_CASE	,       hidden_size=37 )



		def 					_a					(      self     :					int ):






					"""simple docstring"""
					self.config_tester.run_common_tests()



		def 					_a					(      self     :					List[str] ):






					"""simple docstring"""
					_snake_case														=   self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_context_encoder(*__SCREAMING_SNAKE_CASE )



		def 					_a					(      self     :					List[str] ):






					"""simple docstring"""
					_snake_case														=   self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_question_encoder(*__SCREAMING_SNAKE_CASE )



		def 					_a					(      self     :					Optional[Any] ):






					"""simple docstring"""
					_snake_case														=   self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_reader(*__SCREAMING_SNAKE_CASE )



		def 					_a					(      self     :					Optional[Any] ):






					"""simple docstring"""
					_snake_case														=   self.model_tester.prepare_config_and_inputs()[0]

					_snake_case														=   DPRQuestionEncoder(config=__SCREAMING_SNAKE_CASE )
					model.to(__SCREAMING_SNAKE_CASE )
					model.eval()

					with tempfile.TemporaryDirectory() as tmp_dirname:
								model.save_pretrained(__SCREAMING_SNAKE_CASE )
								_snake_case														=   DPRQuestionEncoder.from_pretrained(__SCREAMING_SNAKE_CASE	,       projection_dim=512 )

					self.assertIsNotNone(__SCREAMING_SNAKE_CASE )







		@slow
		def 					_a					(      self     :					List[str] ):






					"""simple docstring"""
					for model_name in DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
								_snake_case														=   DPRContextEncoder.from_pretrained(__SCREAMING_SNAKE_CASE )
								self.assertIsNotNone(__SCREAMING_SNAKE_CASE )

					for model_name in DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
								_snake_case														=   DPRContextEncoder.from_pretrained(__SCREAMING_SNAKE_CASE )
								self.assertIsNotNone(__SCREAMING_SNAKE_CASE )

					for model_name in DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
								_snake_case														=   DPRQuestionEncoder.from_pretrained(__SCREAMING_SNAKE_CASE )
								self.assertIsNotNone(__SCREAMING_SNAKE_CASE )

					for model_name in DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
								_snake_case														=   DPRReader.from_pretrained(__SCREAMING_SNAKE_CASE )
								self.assertIsNotNone(__SCREAMING_SNAKE_CASE )

@require_torch
class 						_SCREAMING_SNAKE_CASE				(  unittest.TestCase				):






		"""simple docstring"""







		@slow
		def 					_a					(      self     :					Dict ):






					"""simple docstring"""
					_snake_case														=   DPRQuestionEncoder.from_pretrained("facebook/dpr-question_encoder-single-nq-base"	,       return_dict=__SCREAMING_SNAKE_CASE )
					model.to(__SCREAMING_SNAKE_CASE )

					_snake_case														=   torch.tensor(
					    [[101, 7_592, 1_010, 2_003, 2_026, 3_899, 10_140, 1_029, 102]]	,       dtype=torch.long	,       device=__SCREAMING_SNAKE_CASE )  # [CLS] hello, is my dog cute? [SEP]
					_snake_case														=   model(__SCREAMING_SNAKE_CASE )[0]  # embedding shape = (1, 768)
					# compare the actual values for a slice.
					_snake_case														=   torch.tensor(
					    [
					        [
					            0.0_3_2_3_6_2_5_3,
					            0.1_2_7_5_3_3_3_5,
					            0.1_6_8_1_8_5_0_9,
					            0.0_0_2_7_9_7_8_6,
					            0.3_8_9_6_9_3_3,
					            0.2_4_2_6_4_9_4_5,
					            0.2_1_7_8_9_7_1,
					            -0.0_2_3_3_5_2_2_7,
					            -0.0_8_4_8_1_9_5_9,
					            -0.1_4_3_2_4_1_1_7,
					        ]
					    ]	,       dtype=torch.float	,       device=__SCREAMING_SNAKE_CASE	,       )
					self.assertTrue(torch.allclose(output[:, :10]	,       __SCREAMING_SNAKE_CASE	,       atol=1e-4 ) )







		@slow
		def 					_a					(      self     :					Optional[int] ):






					"""simple docstring"""
					_snake_case														=   DPRReaderTokenizer.from_pretrained("facebook/dpr-reader-single-nq-base" )
					_snake_case														=   DPRReader.from_pretrained("facebook/dpr-reader-single-nq-base" )
					model.to(__SCREAMING_SNAKE_CASE )

					_snake_case														=   tokenizer(
					    questions="What is love ?"	,       titles="Haddaway"	,       texts="What Is Love is a song recorded by the artist Haddaway"	,       padding=__SCREAMING_SNAKE_CASE	,       return_tensors="pt"	,       )
					encoded_inputs.to(__SCREAMING_SNAKE_CASE )

					_snake_case														=   model(**__SCREAMING_SNAKE_CASE )

					# compare the actual values for a slice.
					_snake_case														=   torch.tensor(
					    [[-1_0.3_0_0_5, -1_0.7_7_6_5, -1_1.4_8_7_2, -1_1.6_8_4_1, -1_1.9_3_1_2, -1_0.3_0_0_2, -9.8_5_4_4, -1_1.7_3_7_8, -1_2.0_8_2_1, -1_0.2_9_7_5]]	,       dtype=torch.float	,       device=__SCREAMING_SNAKE_CASE	,       )

					_snake_case														=   torch.tensor(
					    [[-1_1.0_6_8_4, -1_1.7_0_4_1, -1_1.5_3_9_7, -1_0.3_4_6_5, -1_0.8_7_9_1, -6.8_4_4_3, -1_1.9_9_5_9, -1_1.0_3_6_4, -1_0.0_0_9_6, -6.8_4_0_5]]	,       dtype=torch.float	,       device=__SCREAMING_SNAKE_CASE	,       )
					self.assertTrue(torch.allclose(outputs.start_logits[:, :10]	,       __SCREAMING_SNAKE_CASE	,       atol=1e-4 ) )
					self.assertTrue(torch.allclose(outputs.end_logits[:, :10]	,       __SCREAMING_SNAKE_CASE	,       atol=1e-4 ) )

