





import inspect
import tempfile
import unittest

from huggingface_hub import hf_hub_download

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

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


_a						=   1e-4

if is_torch_available():
			import torch

			from transformers import AutoformerConfig, AutoformerForPrediction, AutoformerModel
			from transformers.models.autoformer.modeling_autoformer import AutoformerDecoder, AutoformerEncoder






@require_torch
class       __A					:

			'''simple docstring'''







			def __init__(					self ,						__lowerCAmelCase ,						__lowerCAmelCase=1_6 ,						__lowerCAmelCase=1_3 ,						__lowerCAmelCase=7 ,						__lowerCAmelCase=1_4 ,						__lowerCAmelCase=1_0 ,						__lowerCAmelCase=1_9 ,						__lowerCAmelCase=5 ,						__lowerCAmelCase=4 ,						__lowerCAmelCase=True ,						__lowerCAmelCase=1_6 ,						__lowerCAmelCase=2 ,						__lowerCAmelCase=4 ,						__lowerCAmelCase=4 ,						__lowerCAmelCase="gelu" ,						__lowerCAmelCase=0.1 ,						__lowerCAmelCase=0.1 ,						__lowerCAmelCase=[1, 2, 3, 4, 5] ,						__lowerCAmelCase=2_5 ,						__lowerCAmelCase=5 ,						):



				'''simple docstring'''







				lowerCamelCase__              =	d_model
				lowerCamelCase__              =	parent
				lowerCamelCase__              =	batch_size
				lowerCamelCase__              =	prediction_length
				lowerCamelCase__              =	context_length
				lowerCamelCase__              =	cardinality
				lowerCamelCase__              =	num_time_features
				lowerCamelCase__              =	lags_sequence
				lowerCamelCase__              =	embedding_dimension
				lowerCamelCase__              =	is_training
				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__              =	context_length
				lowerCamelCase__              =	prediction_length + label_length
				lowerCamelCase__              =	label_length

				lowerCamelCase__              =	moving_average
				lowerCamelCase__              =	autocorrelation_factor





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				return AutoformerConfig(
				    d_model=self.d_model ,						encoder_layers=self.num_hidden_layers ,						decoder_layers=self.num_hidden_layers ,						encoder_attention_heads=self.num_attention_heads ,						decoder_attention_heads=self.num_attention_heads ,						encoder_ffn_dim=self.intermediate_size ,						decoder_ffn_dim=self.intermediate_size ,						dropout=self.hidden_dropout_prob ,						attention_dropout=self.attention_probs_dropout_prob ,						prediction_length=self.prediction_length ,						context_length=self.context_length ,						label_length=self.label_length ,						lags_sequence=self.lags_sequence ,						num_time_features=self.num_time_features ,						num_static_categorical_features=1 ,						cardinality=[self.cardinality] ,						embedding_dimension=[self.embedding_dimension] ,						moving_average=self.moving_average ,						)





			def 	__lowerCamelCase       (					self ,						__lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__              =	config.context_length + max(config.lags_sequence							)

				lowerCamelCase__              =	ids_tensor([self.batch_size, 1] ,						config.cardinality[0]							)
				lowerCamelCase__              =	floats_tensor([self.batch_size, _past_length, config.num_time_features]							)
				lowerCamelCase__              =	floats_tensor([self.batch_size, _past_length]							)
				lowerCamelCase__              =	floats_tensor([self.batch_size, _past_length]							) > 0.5

				# decoder inputs
				lowerCamelCase__              =	floats_tensor([self.batch_size, config.prediction_length, config.num_time_features]							)
				lowerCamelCase__              =	floats_tensor([self.batch_size, config.prediction_length]							)

				lowerCamelCase__              =	{
				    '''past_values''': past_values,
				    '''static_categorical_features''': static_categorical_features,
				    '''past_time_features''': past_time_features,
				    '''past_observed_mask''': past_observed_mask,
				    '''future_time_features''': future_time_features,
				    '''future_values''': future_values,
				}
				return inputs_dict





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				lowerCamelCase__              =	self.get_config()
				lowerCamelCase__              =	self.prepare_autoformer_inputs_dict(__lowerCAmelCase							)
				return config, inputs_dict





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				lowerCamelCase__       ,			lowerCamelCase__              =	self.prepare_config_and_inputs()
				return config, inputs_dict





			def 	__lowerCamelCase       (					self ,						__lowerCAmelCase ,						__lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__              =	AutoformerModel(config=__lowerCAmelCase							).to(__lowerCAmelCase							).eval()
				lowerCamelCase__              =	model(**__lowerCAmelCase							)

				lowerCamelCase__              =	outputs.encoder_last_hidden_state
				lowerCamelCase__              =	outputs.last_hidden_state

				with tempfile.TemporaryDirectory() as tmpdirname:
					lowerCamelCase__              =	model.get_encoder()
					encoder.save_pretrained(__lowerCAmelCase							)
					lowerCamelCase__              =	AutoformerEncoder.from_pretrained(__lowerCAmelCase							).to(__lowerCAmelCase							)

				lowerCamelCase__       ,			lowerCamelCase__       ,			lowerCamelCase__       ,			lowerCamelCase__       ,			lowerCamelCase__              =	model.create_network_inputs(**__lowerCAmelCase							)
				lowerCamelCase__       ,			lowerCamelCase__              =	model.decomposition_layer(transformer_inputs[:, : config.context_length, ...]							)

				lowerCamelCase__              =	torch.cat(
				    (transformer_inputs[:, : config.context_length, ...], feature[:, : config.context_length, ...]) ,						dim=-1 ,						)
				lowerCamelCase__              =	encoder(inputs_embeds=__lowerCAmelCase							)[0]
				self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1E-3							)

				lowerCamelCase__              =	(
				    torch.mean(transformer_inputs[:, : config.context_length, ...] ,						dim=1							)
				    .unsqueeze(1							)
				    .repeat(1 ,						config.prediction_length ,						1							)
				)
				lowerCamelCase__              =	torch.zeros(
				    [transformer_inputs.shape[0], config.prediction_length, transformer_inputs.shape[2]] ,						device=enc_input.device ,						)

				lowerCamelCase__              =	torch.cat(
				    (
				        torch.cat((seasonal_input[:, -config.label_length :, ...], zeros) ,						dim=1							),
				        feature[:, config.context_length - config.label_length :, ...],
				    ) ,						dim=-1 ,						)
				lowerCamelCase__              =	torch.cat(
				    (
				        torch.cat((trend_input[:, -config.label_length :, ...], mean) ,						dim=1							),
				        feature[:, config.context_length - config.label_length :, ...],
				    ) ,						dim=-1 ,						)

				with tempfile.TemporaryDirectory() as tmpdirname:
					lowerCamelCase__              =	model.get_decoder()
					decoder.save_pretrained(__lowerCAmelCase							)
					lowerCamelCase__              =	AutoformerDecoder.from_pretrained(__lowerCAmelCase							).to(__lowerCAmelCase							)

				lowerCamelCase__              =	decoder(
				    trend=__lowerCAmelCase ,						inputs_embeds=__lowerCAmelCase ,						encoder_hidden_states=__lowerCAmelCase ,						)[0]

				self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1E-3							)






@require_torch
class       __A					(					lowerCAmelCase    ,       lowerCAmelCase    ,       unittest.TestCase				):

			'''simple docstring'''







			lowerCAmelCase_							  =		(AutoformerModel, AutoformerForPrediction) if is_torch_available() else ()
			lowerCAmelCase_							  =		(AutoformerForPrediction,) if is_torch_available() else ()
			lowerCAmelCase_							  =		{"""feature-extraction""": AutoformerModel} if is_torch_available() else {}
			lowerCAmelCase_							  =		False
			lowerCAmelCase_							  =		False
			lowerCAmelCase_							  =		False
			lowerCAmelCase_							  =		False
			lowerCAmelCase_							  =		False
			lowerCAmelCase_							  =		False





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				lowerCamelCase__              =	AutoformerModelTester(self							)
				lowerCamelCase__              =	ConfigTester(self ,						config_class=__lowerCAmelCase ,						has_text_modality=__lowerCAmelCase							)





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				self.config_tester.run_common_tests()





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				lowerCamelCase__       ,			lowerCamelCase__              =	self.model_tester.prepare_config_and_inputs()
				for model_class in self.all_model_classes:
					lowerCamelCase__              =	model_class(__lowerCAmelCase							)

					with tempfile.TemporaryDirectory() as tmpdirname:
						model.save_pretrained(__lowerCAmelCase							)
						lowerCamelCase__       ,			lowerCamelCase__              =	model_class.from_pretrained(__lowerCAmelCase ,						output_loading_info=__lowerCAmelCase							)
					self.assertEqual(info['''missing_keys'''] ,						[]							)





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				lowerCamelCase__              =	self.model_tester.prepare_config_and_inputs_for_common()
				self.model_tester.check_encoder_decoder_model_standalone(*__lowerCAmelCase							)





			@unittest.skip(reason='''Model has no tokens embeddings'''							)
			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				pass





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				lowerCamelCase__              =	inspect.signature(getattr(__lowerCAmelCase ,						'''forward'''							)							)
				# The main input is the name of the argument after `self`
				lowerCamelCase__              =	list(model_signature.parameters.keys()							)[1]
				self.assertEqual(AutoformerModel.main_input_name ,						__lowerCAmelCase							)





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				lowerCamelCase__       ,			lowerCamelCase__              =	self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
					lowerCamelCase__              =	model_class(__lowerCAmelCase							)
					lowerCamelCase__              =	inspect.signature(model.forward							)
					# signature.parameters is an OrderedDict => so arg_names order is deterministic
					lowerCamelCase__              =	[*signature.parameters.keys()]

					lowerCamelCase__              =	[
					    '''past_values''',
					    '''past_time_features''',
					    '''past_observed_mask''',
					    '''static_categorical_features''',
					    '''static_real_features''',
					    '''future_values''',
					    '''future_time_features''',
					]

					if model.__class__.__name__ in ["AutoformerForPrediction"]:
						expected_arg_names.append('''future_observed_mask'''							)

					expected_arg_names.extend(
					    [
					        '''decoder_attention_mask''',
					        '''head_mask''',
					        '''decoder_head_mask''',
					        '''cross_attn_head_mask''',
					        '''encoder_outputs''',
					        '''past_key_values''',
					        '''output_hidden_states''',
					        '''output_attentions''',
					        '''use_cache''',
					        '''return_dict''',
					    ]							)

					self.assertListEqual(arg_names[: len(__lowerCAmelCase							)] ,						__lowerCAmelCase							)





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				lowerCamelCase__       ,			lowerCamelCase__              =	self.model_tester.prepare_config_and_inputs_for_common()
				lowerCamelCase__              =	True

				lowerCamelCase__              =	getattr(self.model_tester ,						'''seq_length''' ,						__lowerCAmelCase							)
				lowerCamelCase__              =	getattr(self.model_tester ,						'''decoder_seq_length''' ,						__lowerCAmelCase							)
				lowerCamelCase__              =	getattr(self.model_tester ,						'''encoder_seq_length''' ,						__lowerCAmelCase							)
				lowerCamelCase__              =	getattr(self.model_tester ,						'''d_model''' ,						__lowerCAmelCase							)
				lowerCamelCase__              =	getattr(self.model_tester ,						'''num_attention_heads''' ,						__lowerCAmelCase							)
				lowerCamelCase__              =	d_model // num_attention_heads

				for model_class in self.all_model_classes:
					lowerCamelCase__              =	True
					lowerCamelCase__              =	False
					lowerCamelCase__              =	True
					lowerCamelCase__              =	model_class(__lowerCAmelCase							)
					model.to(__lowerCAmelCase							)
					model.eval()
					with torch.no_grad():
						lowerCamelCase__              =	model(**self._prepare_for_class(__lowerCAmelCase ,						__lowerCAmelCase							)							)
					lowerCamelCase__              =	outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
					self.assertEqual(len(__lowerCAmelCase							) ,						self.model_tester.num_hidden_layers							)

					# check that output_attentions also work using config
					del inputs_dict["output_attentions"]
					lowerCamelCase__              =	True
					lowerCamelCase__              =	model_class(__lowerCAmelCase							)
					model.to(__lowerCAmelCase							)
					model.eval()
					with torch.no_grad():
						lowerCamelCase__              =	model(**self._prepare_for_class(__lowerCAmelCase ,						__lowerCAmelCase							)							)
					lowerCamelCase__              =	outputs.encoder_attentions
					self.assertEqual(len(__lowerCAmelCase							) ,						self.model_tester.num_hidden_layers							)

					self.assertListEqual(
					    list(attentions[0].shape[-3:]							) ,						[self.model_tester.num_attention_heads, encoder_seq_length, dim] ,						)
					lowerCamelCase__              =	len(__lowerCAmelCase							)

					lowerCamelCase__              =	7

					if "last_hidden_state" in outputs:
						correct_outlen += 1

					if "trend" in outputs:
						correct_outlen += 1

					if "past_key_values" in outputs:
						correct_outlen += 1  # past_key_values have been returned

					if "loss" in outputs:
						correct_outlen += 1

					if "params" in outputs:
						correct_outlen += 1

					self.assertEqual(__lowerCAmelCase ,						__lowerCAmelCase							)

					# decoder attentions
					lowerCamelCase__              =	outputs.decoder_attentions
					self.assertIsInstance(__lowerCAmelCase ,						(list, tuple)							)
					self.assertEqual(len(__lowerCAmelCase							) ,						self.model_tester.num_hidden_layers							)
					self.assertListEqual(
					    list(decoder_attentions[0].shape[-3:]							) ,						[self.model_tester.num_attention_heads, decoder_seq_length, dim] ,						)

					# cross attentions
					lowerCamelCase__              =	outputs.cross_attentions
					self.assertIsInstance(__lowerCAmelCase ,						(list, tuple)							)
					self.assertEqual(len(__lowerCAmelCase							) ,						self.model_tester.num_hidden_layers							)
					self.assertListEqual(
					    list(cross_attentions[0].shape[-3:]							) ,						[self.model_tester.num_attention_heads, decoder_seq_length, dim] ,						)

				# Check attention is always last and order is fine
				lowerCamelCase__              =	True
				lowerCamelCase__              =	True
				lowerCamelCase__              =	model_class(__lowerCAmelCase							)
				model.to(__lowerCAmelCase							)
				model.eval()
				with torch.no_grad():
					lowerCamelCase__              =	model(**self._prepare_for_class(__lowerCAmelCase ,						__lowerCAmelCase							)							)

				self.assertEqual(out_len + 2 ,						len(__lowerCAmelCase							)							)

				lowerCamelCase__              =	outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions

				self.assertEqual(len(__lowerCAmelCase							) ,						self.model_tester.num_hidden_layers							)
				self.assertListEqual(
				    list(self_attentions[0].shape[-3:]							) ,						[self.model_tester.num_attention_heads, encoder_seq_length, dim] ,						)





			@is_flaky()
			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				super().test_retain_grad_hidden_states_attentions()





def lowerCAmelCase__(__snake_case="train-batch.pt"							)       ->   Optional[int]:







	'''simple docstring'''







	lowerCamelCase__              =	hf_hub_download(repo_id='''hf-internal-testing/tourism-monthly-batch''' ,filename=__snake_case ,repo_type='''dataset'''							)
	lowerCamelCase__              =	torch.load(__snake_case ,map_location=__snake_case							)
	return batch






@require_torch
@slow
class       __A					(					unittest.TestCase				):

			'''simple docstring'''







			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				lowerCamelCase__              =	AutoformerModel.from_pretrained('''huggingface/autoformer-tourism-monthly'''							).to(__lowerCAmelCase							)
				lowerCamelCase__              =	prepare_batch()

				with torch.no_grad():
					lowerCamelCase__              =	model(
					    past_values=batch['''past_values'''] ,						past_time_features=batch['''past_time_features'''] ,						past_observed_mask=batch['''past_observed_mask'''] ,						static_categorical_features=batch['''static_categorical_features'''] ,						future_values=batch['''future_values'''] ,						future_time_features=batch['''future_time_features'''] ,						)[0]

				lowerCamelCase__              =	torch.Size(
				    (6_4, model.config.prediction_length + model.config.label_length, model.config.feature_size)							)
				self.assertEqual(output.shape ,						__lowerCAmelCase							)

				lowerCamelCase__              =	torch.tensor(
				    [[0.3593, -1.3398, 0.6330], [0.2279, 1.5396, -0.1792], [0.0450, 1.3225, -0.2335]] ,						device=__lowerCAmelCase							)
				self.assertTrue(torch.allclose(output[0, :3, :3] ,						__lowerCAmelCase ,						atol=__lowerCAmelCase							)							)





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				lowerCamelCase__              =	AutoformerForPrediction.from_pretrained('''huggingface/autoformer-tourism-monthly'''							).to(__lowerCAmelCase							)
				lowerCamelCase__              =	prepare_batch('''val-batch.pt'''							)
				with torch.no_grad():
					lowerCamelCase__              =	model(
					    past_values=batch['''past_values'''] ,						past_time_features=batch['''past_time_features'''] ,						past_observed_mask=batch['''past_observed_mask'''] ,						static_categorical_features=batch['''static_categorical_features'''] ,						).encoder_last_hidden_state
				lowerCamelCase__              =	torch.Size((6_4, model.config.context_length, model.config.d_model)							)
				self.assertEqual(output.shape ,						__lowerCAmelCase							)

				lowerCamelCase__              =	torch.tensor(
				    [[-0.0734, -0.9036, 0.8358], [4.7186, 2.4113, 1.9581], [1.7953, 2.3558, 1.2970]] ,						device=__lowerCAmelCase							)
				self.assertTrue(torch.allclose(output[0, :3, :3] ,						__lowerCAmelCase ,						atol=__lowerCAmelCase							)							)





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				lowerCamelCase__              =	AutoformerForPrediction.from_pretrained('''huggingface/autoformer-tourism-monthly'''							).to(__lowerCAmelCase							)
				lowerCamelCase__              =	prepare_batch('''val-batch.pt'''							)
				with torch.no_grad():
					lowerCamelCase__              =	model.generate(
					    static_categorical_features=batch['''static_categorical_features'''] ,						past_time_features=batch['''past_time_features'''] ,						past_values=batch['''past_values'''] ,						future_time_features=batch['''future_time_features'''] ,						past_observed_mask=batch['''past_observed_mask'''] ,						)
				lowerCamelCase__              =	torch.Size((6_4, model.config.num_parallel_samples, model.config.prediction_length)							)
				self.assertEqual(outputs.sequences.shape ,						__lowerCAmelCase							)

				lowerCamelCase__              =	torch.tensor([3130.6763, 4056.5293, 7053.0786] ,						device=__lowerCAmelCase							)
				lowerCamelCase__              =	outputs.sequences.mean(dim=1							)
				self.assertTrue(torch.allclose(mean_prediction[0, -3:] ,						__lowerCAmelCase ,						rtol=1E-1							)							)










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					:

			'''simple docstring'''







			def __init__(					self ,						__lowerCAmelCase ,						__lowerCAmelCase=1_3 ,						__lowerCAmelCase=7 ,						__lowerCAmelCase=True ,						__lowerCAmelCase=True ,						__lowerCAmelCase=True ,						__lowerCAmelCase=True ,						__lowerCAmelCase=9_9 ,						__lowerCAmelCase=3_2 ,						__lowerCAmelCase=2 ,						__lowerCAmelCase=4 ,						__lowerCAmelCase=3_7 ,						__lowerCAmelCase="gelu" ,						__lowerCAmelCase=0.1 ,						__lowerCAmelCase=0.1 ,						__lowerCAmelCase=5_1_2 ,						__lowerCAmelCase=1_6 ,						__lowerCAmelCase=2 ,						__lowerCAmelCase=0.02 ,						__lowerCAmelCase=3 ,						__lowerCAmelCase=4 ,						__lowerCAmelCase=None ,						__lowerCAmelCase=0 ,						):



				'''simple docstring'''







				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 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				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=__lowerCAmelCase ,						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 	__lowerCamelCase       (					self ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__              =	TFDPRContextEncoder(config=__lowerCAmelCase							)
				lowerCamelCase__              =	model(__lowerCAmelCase ,						attention_mask=__lowerCAmelCase ,						token_type_ids=__lowerCAmelCase							)
				lowerCamelCase__              =	model(__lowerCAmelCase ,						token_type_ids=__lowerCAmelCase							)
				lowerCamelCase__              =	model(__lowerCAmelCase							)
				self.parent.assertEqual(result.pooler_output.shape ,						(self.batch_size, self.projection_dim or self.hidden_size)							)





			def 	__lowerCamelCase       (					self ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__              =	TFDPRQuestionEncoder(config=__lowerCAmelCase							)
				lowerCamelCase__              =	model(__lowerCAmelCase ,						attention_mask=__lowerCAmelCase ,						token_type_ids=__lowerCAmelCase							)
				lowerCamelCase__              =	model(__lowerCAmelCase ,						token_type_ids=__lowerCAmelCase							)
				lowerCamelCase__              =	model(__lowerCAmelCase							)
				self.parent.assertEqual(result.pooler_output.shape ,						(self.batch_size, self.projection_dim or self.hidden_size)							)





			def 	__lowerCamelCase       (					self ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__              =	TFDPRReader(config=__lowerCAmelCase							)
				lowerCamelCase__              =	model(__lowerCAmelCase ,						attention_mask=__lowerCAmelCase							)

				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							):



				'''simple docstring'''







				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       __A					(					lowerCAmelCase    ,       lowerCAmelCase    ,       unittest.TestCase				):

			'''simple docstring'''







			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 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				lowerCamelCase__              =	TFDPRModelTester(self							)
				lowerCamelCase__              =	ConfigTester(self ,						config_class=__lowerCAmelCase ,						hidden_size=3_7							)





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				self.config_tester.run_common_tests()





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				lowerCamelCase__              =	self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_dpr_context_encoder(*__lowerCAmelCase							)





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				lowerCamelCase__              =	self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_dpr_question_encoder(*__lowerCAmelCase							)





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				lowerCamelCase__              =	self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_dpr_reader(*__lowerCAmelCase							)





			@slow
			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







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

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

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

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






@require_tf
class       __A					(					unittest.TestCase				):

			'''simple docstring'''







			@slow
			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				lowerCamelCase__              =	TFDPRQuestionEncoder.from_pretrained('''facebook/dpr-question_encoder-single-nq-base'''							)

				lowerCamelCase__              =	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]
				lowerCamelCase__              =	model(__lowerCAmelCase							)[0]  # embedding shape = (1, 768)
				# compare the actual values for a slice.
				lowerCamelCase__              =	tf.constant(
				    [
				        [
				            0.0323_6253,
				            0.1275_3335,
				            0.1681_8509,
				            0.0027_9786,
				            0.389_6933,
				            0.2426_4945,
				            0.217_8971,
				            -0.0233_5227,
				            -0.0848_1959,
				            -0.1432_4117,
				        ]
				    ]							)
				self.assertTrue(numpy.allclose(output[:, :1_0].numpy() ,						expected_slice.numpy() ,						atol=1E-4							)							)



