"""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        lowercase_   :



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



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					Dict    =   ids_tensor([self.batch_size, self.seq_length]				,    self.vocab_size    )

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

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

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

													_snake_case						:					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=lowercase_				,    initializer_range=self.initializer_range				,    )
													_snake_case						:					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 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_    ):
													_snake_case						:					Any    =   TFDPRContextEncoder(config=lowercase_    )
													_snake_case						:					Tuple    =   model(lowercase_				,    attention_mask=lowercase_				,    token_type_ids=lowercase_    )
													_snake_case						:					int    =   model(lowercase_				,    token_type_ids=lowercase_    )
													_snake_case						:					Optional[Any]    =   model(lowercase_    )
													self.parent.assertEqual(result.pooler_output.shape				,    (self.batch_size, self.projection_dim or self.hidden_size)    )



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



							def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_    ):
													_snake_case						:					str    =   TFDPRReader(config=lowercase_    )
													_snake_case						:					Dict    =   model(lowercase_				,    attention_mask=lowercase_    )

													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 	UpperCamelCase	(						self    ):
													_snake_case						:					str    =   self.prepare_config_and_inputs()
													(
													    _snake_case
													)						:					Dict    =   config_and_inputs
													_snake_case						:					List[str]    =   {"input_ids": input_ids}
													return config, inputs_dict




@require_tf
class        lowercase_   (					__snake_case						,			__snake_case						,			unittest.TestCase		):
							_lowerCamelCase						=		(
							    (
							        TFDPRContextEncoder,
							        TFDPRQuestionEncoder,
							        TFDPRReader,
							    )
							    if is_tf_available()
							    else ()
							)
							_lowerCamelCase						=		{'feature-extraction': TFDPRQuestionEncoder} if is_tf_available() else {}

							_lowerCamelCase						=		False
							_lowerCamelCase						=		False
							_lowerCamelCase						=		False
							_lowerCamelCase						=		False
							_lowerCamelCase						=		False



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					Dict    =   TFDPRModelTester(self    )
													_snake_case						:					List[str]    =   ConfigTester(self				,    config_class=lowercase_				,    hidden_size=37    )



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



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					List[Any]    =   self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_dpr_context_encoder(*lowercase_    )



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					List[Any]    =   self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_dpr_question_encoder(*lowercase_    )



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					Any    =   self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_dpr_reader(*lowercase_    )



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

													for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																			_snake_case						:					Optional[Any]    =   TFDPRContextEncoder.from_pretrained(lowercase_    )
																			self.assertIsNotNone(lowercase_    )

													for model_name in TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																			_snake_case						:					Tuple    =   TFDPRQuestionEncoder.from_pretrained(lowercase_    )
																			self.assertIsNotNone(lowercase_    )

													for model_name in TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																			_snake_case						:					str    =   TFDPRReader.from_pretrained(lowercase_    )
																			self.assertIsNotNone(lowercase_    )




@require_tf
class        lowercase_   (					unittest.TestCase		):



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

													_snake_case						:					List[str]    =   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]
													_snake_case						:					Any    =   model(lowercase_    )[0]  # embedding shape = (1, 768)
													# compare the actual values for a slice.
													_snake_case						:					Optional[int]    =   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    )    )
import os

import pytest
from attr import dataclass


__SCREAMING_SNAKE_CASE						:   int         =			'us-east-1'  # defaults region



@dataclass
class        lowercase_   :
							_lowerCamelCase						=		42
							_lowerCamelCase						=		'arn:aws:iam::558105141721:role/sagemaker_execution_role'
							_lowerCamelCase						=		{
							    'task_name': 'mnli',
							    'per_device_train_batch_size': 16,
							    'per_device_eval_batch_size': 16,
							    'do_train': True,
							    'do_eval': True,
							    'do_predict': True,
							    'output_dir': '/opt/ml/model',
							    'overwrite_output_dir': True,
							    'max_steps': 500,
							    'save_steps': 5_500,
							}
							_lowerCamelCase						=		{**hyperparameters, 'max_steps': 1_000}



							@property
							def 	UpperCamelCase	(						self    ):
													if self.framework == "pytorch":
																			return [
																			    {"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
																			    {"Name": "eval_accuracy", "Regex": r"eval_accuracy.*=\D*(.*?)$"},
																			    {"Name": "eval_loss", "Regex": r"eval_loss.*=\D*(.*?)$"},
																			]
													else:
																			return [
																			    {"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
																			    {"Name": "eval_accuracy", "Regex": r"loss.*=\D*(.*?)]?$"},
																			    {"Name": "eval_loss", "Regex": r"sparse_categorical_accuracy.*=\D*(.*?)]?$"},
																			]



							@property
							def 	UpperCamelCase	(						self    ):
													return f"""{self.framework}-transfromers-test"""



							@property
							def 	UpperCamelCase	(						self    ):
													return f"""./tests/sagemaker/scripts/{self.framework}"""



							@property
							def 	UpperCamelCase	(						self    ):
													if self.framework == "pytorch":
																			return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-pytorch-training:1.7.1-transformers4.6.1-gpu-py36-cu110-ubuntu18.04"
													else:
																			return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-tensorflow-training:2.4.1-transformers4.6.1-gpu-py37-cu110-ubuntu18.04"




@pytest.fixture(scope="class"						)
def  snake_case						(__lowercase						)	->	str:

						'''simple docstring'''



						_snake_case						:					List[str]    =   SageMakerTestEnvironment(framework=request.cls.framework						)