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 						a__							:



			def __init__(						self      ,						UpperCAmelCase      ,						UpperCAmelCase=1_3      ,						UpperCAmelCase=7      ,						UpperCAmelCase=False      ,						UpperCAmelCase=True      ,						UpperCAmelCase=True      ,						UpperCAmelCase=True      ,						UpperCAmelCase=9_9      ,						UpperCAmelCase=3_2      ,						UpperCAmelCase=5      ,						UpperCAmelCase=4      ,						UpperCAmelCase=3_7      ,						UpperCAmelCase="gelu"      ,						UpperCAmelCase=0.1      ,						UpperCAmelCase=0.1      ,						UpperCAmelCase=5_1_2      ,						UpperCAmelCase=1_6      ,						UpperCAmelCase=2      ,						UpperCAmelCase=0.02      ,						UpperCAmelCase=3      ,						UpperCAmelCase=4      ,						UpperCAmelCase=None      ,						UpperCAmelCase=0      ,						) ->    Tuple:
								__a							=   parent
								__a							=   batch_size
								__a							=   seq_length
								__a							=   is_training
								__a							=   use_input_mask
								__a							=   use_token_type_ids
								__a							=   use_labels
								__a							=   vocab_size
								__a							=   hidden_size
								__a							=   num_hidden_layers
								__a							=   num_attention_heads
								__a							=   intermediate_size
								__a							=   hidden_act
								__a							=   hidden_dropout_prob
								__a							=   attention_probs_dropout_prob
								__a							=   max_position_embeddings
								__a							=   type_vocab_size
								__a							=   type_sequence_label_size
								__a							=   initializer_range
								__a							=   num_labels
								__a							=   num_choices
								__a							=   scope
								__a							=   projection_dim



			def __SCREAMING_SNAKE_CASE							(						self    ) ->    Optional[int]:
								__a							=   ids_tensor([self.batch_size, self.seq_length]      ,						self.vocab_size    )

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

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

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

								__a							=   self.get_config()

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



			def __SCREAMING_SNAKE_CASE							(						self    ) ->    str:
								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 __SCREAMING_SNAKE_CASE							(						self      ,						UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase    ) ->    List[str]:
								__a							=   DPRContextEncoder(config=UpperCAmelCase    )
								model.to(UpperCAmelCase    )
								model.eval()
								__a							=   model(UpperCAmelCase      ,						attention_mask=UpperCAmelCase      ,						token_type_ids=UpperCAmelCase    )
								__a							=   model(UpperCAmelCase      ,						token_type_ids=UpperCAmelCase    )
								__a							=   model(UpperCAmelCase    )
								self.parent.assertEqual(result.pooler_output.shape      ,						(self.batch_size, self.projection_dim or self.hidden_size)    )



			def __SCREAMING_SNAKE_CASE							(						self      ,						UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase    ) ->    Dict:
								__a							=   DPRQuestionEncoder(config=UpperCAmelCase    )
								model.to(UpperCAmelCase    )
								model.eval()
								__a							=   model(UpperCAmelCase      ,						attention_mask=UpperCAmelCase      ,						token_type_ids=UpperCAmelCase    )
								__a							=   model(UpperCAmelCase      ,						token_type_ids=UpperCAmelCase    )
								__a							=   model(UpperCAmelCase    )
								self.parent.assertEqual(result.pooler_output.shape      ,						(self.batch_size, self.projection_dim or self.hidden_size)    )



			def __SCREAMING_SNAKE_CASE							(						self      ,						UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase    ) ->    str:
								__a							=   DPRReader(config=UpperCAmelCase    )
								model.to(UpperCAmelCase    )
								model.eval()
								__a							=   model(
								    UpperCAmelCase      ,						attention_mask=UpperCAmelCase      ,						)

								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 __SCREAMING_SNAKE_CASE							(						self    ) ->    Optional[int]:
								__a							=   self.prepare_config_and_inputs()
								(
								    (
								    __a
								)					,      (
								    __a
								)					,      (
								    __a
								)					,      (
								    __a
								)					,      (
								    __a
								)					,      (
								    __a
								)					,      (
								    __a
								)					,      
								)							=   config_and_inputs
								__a							=   {'input_ids': input_ids}
								return config, inputs_dict






@require_torch
class 						a__							(   __snake_case      ,						__snake_case      ,						unittest.TestCase		):
			A__				:   int								=			(
			    (
			        DPRContextEncoder,
			        DPRQuestionEncoder,
			        DPRReader,
			    )
			    if is_torch_available()
			    else ()
			)
			A__				:   List[str]								=			{'feature-extraction': DPRQuestionEncoder} if is_torch_available() else {}

			A__				:   Optional[int]								=			False
			A__				:   Optional[int]								=			False  # why?
			A__				:   Dict								=			False
			A__				:   List[str]								=			False



			def __SCREAMING_SNAKE_CASE							(						self    ) ->    Optional[Any]:
								__a							=   DPRModelTester(self    )
								__a							=   ConfigTester(self      ,						config_class=UpperCAmelCase      ,						hidden_size=3_7    )



			def __SCREAMING_SNAKE_CASE							(						self    ) ->    Dict:
								self.config_tester.run_common_tests()



			def __SCREAMING_SNAKE_CASE							(						self    ) ->    Dict:
								__a							=   self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_context_encoder(*UpperCAmelCase    )



			def __SCREAMING_SNAKE_CASE							(						self    ) ->    Union[str, Any]:
								__a							=   self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_question_encoder(*UpperCAmelCase    )



			def __SCREAMING_SNAKE_CASE							(						self    ) ->    Any:
								__a							=   self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_reader(*UpperCAmelCase    )



			def __SCREAMING_SNAKE_CASE							(						self    ) ->    Optional[int]:
								__a							=   self.model_tester.prepare_config_and_inputs()[0]

								__a							=   DPRQuestionEncoder(config=UpperCAmelCase    )
								model.to(UpperCAmelCase    )
								model.eval()

								with tempfile.TemporaryDirectory() as tmp_dirname:
													model.save_pretrained(UpperCAmelCase    )
													__a							=   DPRQuestionEncoder.from_pretrained(UpperCAmelCase      ,						projection_dim=5_1_2    )

								self.assertIsNotNone(UpperCAmelCase    )







			@slow
			def __SCREAMING_SNAKE_CASE							(						self    ) ->    Any:
								for model_name in DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													__a							=   DPRContextEncoder.from_pretrained(UpperCAmelCase    )
													self.assertIsNotNone(UpperCAmelCase    )

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

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

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






@require_torch
class 						a__							(   unittest.TestCase		):



			@slow
			def __SCREAMING_SNAKE_CASE							(						self    ) ->    Union[str, Any]:
								__a							=   DPRQuestionEncoder.from_pretrained('facebook/dpr-question_encoder-single-nq-base'      ,						return_dict=UpperCAmelCase    )
								model.to(UpperCAmelCase    )

								__a							=   torch.tensor(
								    [[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]]      ,						dtype=torch.long      ,						device=UpperCAmelCase    )  # [CLS] hello, is my dog cute? [SEP]
								__a							=   model(UpperCAmelCase    )[0]  # embedding shape = (1, 768)
								# compare the actual values for a slice.
								__a							=   torch.tensor(
								    [
								        [
								            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,
								        ]
								    ]      ,						dtype=torch.float      ,						device=UpperCAmelCase      ,						)
								self.assertTrue(torch.allclose(output[:, :1_0]      ,						UpperCAmelCase      ,						atol=1e-4    )    )







			@slow
			def __SCREAMING_SNAKE_CASE							(						self    ) ->    Any:
								__a							=   DPRReaderTokenizer.from_pretrained('facebook/dpr-reader-single-nq-base'    )
								__a							=   DPRReader.from_pretrained('facebook/dpr-reader-single-nq-base'    )
								model.to(UpperCAmelCase    )

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

								__a							=   model(**UpperCAmelCase    )

								# compare the actual values for a slice.
								__a							=   torch.tensor(
								    [[-10.3_005, -10.7_765, -11.4_872, -11.6_841, -11.9_312, -10.3_002, -9.8_544, -11.7_378, -12.0_821, -10.2_975]]      ,						dtype=torch.float      ,						device=UpperCAmelCase      ,						)

								__a							=   torch.tensor(
								    [[-11.0_684, -11.7_041, -11.5_397, -10.3_465, -10.8_791, -6.8_443, -11.9_959, -11.0_364, -10.0_096, -6.8_405]]      ,						dtype=torch.float      ,						device=UpperCAmelCase      ,						)
								self.assertTrue(torch.allclose(outputs.start_logits[:, :1_0]      ,						UpperCAmelCase      ,						atol=1e-4    )    )
								self.assertTrue(torch.allclose(outputs.end_logits[:, :1_0]      ,						UpperCAmelCase      ,						atol=1e-4    )    )





# as due to their complexity multi-gpu tests could impact other tests, and to aid debug we have those in a separate module.

import os
import sys

from transformers.testing_utils import TestCasePlus, execute_subprocess_async, get_gpu_count, require_torch_gpu, slow

from .utils import load_json


class 						a__							(   __snake_case		):



			@classmethod
			def __SCREAMING_SNAKE_CASE							(						cls    ) ->    Optional[int]:
								return cls







			@slow
			@require_torch_gpu
			def __SCREAMING_SNAKE_CASE							(						self    ) ->    Dict:
								__a							=   self.get_auto_remove_tmp_dir()
								__a							=   f'''
            --model_name Helsinki-NLP/opus-mt-en-ro
            --save_dir {output_dir}
            --data_dir {self.test_file_dir_str}/test_data/wmt_en_ro
            --num_beams 2
            --task translation
        '''.split()

								# we want this test to run even if there is only one GPU, but if there are more we use them all
								__a							=   get_gpu_count()
								__a							=   f'''
            -m torch.distributed.launch
            --nproc_per_node={n_gpu}
            {self.test_file_dir}/run_distributed_eval.py
        '''.split()
								__a							=   [sys.executable] + distributed_args + args
								execute_subprocess_async(UpperCAmelCase      ,						env=self.get_env()    )

								__a							=   os.path.join(UpperCAmelCase      ,						'test_bleu.json'    )
								__a							=   load_json(UpperCAmelCase    )
								# print(metrics)
								self.assertGreaterEqual(metrics['bleu']      ,						2_5    )




