




'''simple docstring'''





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      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE=13      ,_SCREAMING_SNAKE_CASE=7      ,_SCREAMING_SNAKE_CASE=False      ,_SCREAMING_SNAKE_CASE=True      ,_SCREAMING_SNAKE_CASE=True      ,_SCREAMING_SNAKE_CASE=True      ,_SCREAMING_SNAKE_CASE=99      ,_SCREAMING_SNAKE_CASE=32      ,_SCREAMING_SNAKE_CASE=5      ,_SCREAMING_SNAKE_CASE=4      ,_SCREAMING_SNAKE_CASE=37      ,_SCREAMING_SNAKE_CASE="gelu"      ,_SCREAMING_SNAKE_CASE=0.1      ,_SCREAMING_SNAKE_CASE=0.1      ,_SCREAMING_SNAKE_CASE=512      ,_SCREAMING_SNAKE_CASE=16      ,_SCREAMING_SNAKE_CASE=2      ,_SCREAMING_SNAKE_CASE=0.0_2      ,_SCREAMING_SNAKE_CASE=3      ,_SCREAMING_SNAKE_CASE=4      ,_SCREAMING_SNAKE_CASE=None      ,_SCREAMING_SNAKE_CASE=0      ,)							->    Optional[Any]:
          _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        _lowercase			(				self					)							->    int:
          _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        _lowercase			(				self					)							->    Any:
          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        _lowercase			(				self      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE					)							->    int:
          _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        _lowercase			(				self      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE					)							->    Any:
          _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        _lowercase			(				self      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE					)							->    List[str]:
          _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        _lowercase			(				self					)							->    List[Any]:
          _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      _a       (		__lowerCAmelCase      ,      __lowerCAmelCase      ,      unittest.TestCase							):
     SCREAMING_SNAKE_CASE_							:					List[Any]					       =		(
         (
             DPRContextEncoder,
             DPRQuestionEncoder,
             DPRReader,
         )
         if is_torch_available()
         else ()
     )
     SCREAMING_SNAKE_CASE_							:					List[Any]					       =		{"""feature-extraction""": DPRQuestionEncoder} if is_torch_available() else {}

     SCREAMING_SNAKE_CASE_							:					Dict					       =		False
     SCREAMING_SNAKE_CASE_							:					Tuple					       =		False  # why?
     SCREAMING_SNAKE_CASE_							:					List[Any]					       =		False
     SCREAMING_SNAKE_CASE_							:					Tuple					       =		False






     def        _lowercase			(				self					)							->    Optional[int]:
          _snake_case							=   DPRModelTester(self					)
          _snake_case							=   ConfigTester(self      ,config_class=_SCREAMING_SNAKE_CASE      ,hidden_size=37					)






     def        _lowercase			(				self					)							->    Optional[int]:
          self.config_tester.run_common_tests()






     def        _lowercase			(				self					)							->    Tuple:
          _snake_case							=   self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_context_encoder(*_SCREAMING_SNAKE_CASE					)






     def        _lowercase			(				self					)							->    List[Any]:
          _snake_case							=   self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_question_encoder(*_SCREAMING_SNAKE_CASE					)






     def        _lowercase			(				self					)							->    Any:
          _snake_case							=   self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_reader(*_SCREAMING_SNAKE_CASE					)






     def        _lowercase			(				self					)							->    List[str]:
          _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        _lowercase			(				self					)							->    Optional[int]:
          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      _a       (		unittest.TestCase							):






     @slow
     def        _lowercase			(				self					)							->    List[str]:
          _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        _lowercase			(				self					)							->    Dict:
          _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					)					)








'''simple docstring'''





import io
import json
import subprocess


UpperCamelCase_		:    Any									=		[
    ['''en''', '''ru'''],
    ['''ru''', '''en'''],
    ['''en''', '''de'''],
    ['''de''', '''en'''],
]

UpperCamelCase_		:    Union[str, Any]									=		8





def 			__a			(     _UpperCamelCase:					int      ,		_UpperCamelCase:					Optional[Any]					)		->				Optional[int]:

     """simple docstring"""




     _snake_case							=   {}
     for src, tgt in pairs:
          _snake_case							=   F"""{src}-{tgt}"""
          _snake_case							=   F"""sacrebleu -t wmt19 -l {pair} --echo src""".split()
          _snake_case							=   subprocess.run(_UpperCamelCase      ,		stdout=subprocess.PIPE					).stdout.decode("utf-8"					).splitlines()
          _snake_case							=   F"""sacrebleu -t wmt19 -l {pair} --echo ref""".split()
          _snake_case							=   subprocess.run(_UpperCamelCase      ,		stdout=subprocess.PIPE					).stdout.decode("utf-8"					).splitlines()
          _snake_case							=   {"src": src_lines[:n_objs], "tgt": tgt_lines[:n_objs]}
     return text


UpperCamelCase_		:    Tuple									=		get_all_data(pairs, n_objs)
UpperCamelCase_		:    Optional[Any]									=		'''./fsmt_val_data.json'''
with io.open(filename, '''w''', encoding='''utf-8''') as f:
   UpperCamelCase_		:    List[str]									=		json.dump(text, f, indent=2, ensure_ascii=False)


