




'''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 tensorflow as tf

   from transformers import (
       TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
       TFXLMForMultipleChoice,
       TFXLMForQuestionAnsweringSimple,
       TFXLMForSequenceClassification,
       TFXLMForTokenClassification,
       TFXLMModel,
       TFXLMWithLMHeadModel,
       XLMConfig,
   )

class      _a       :






     def __init__(				self      ,_SCREAMING_SNAKE_CASE      ,)							->    str:
          _snake_case							=   parent
          _snake_case							=   13
          _snake_case							=   7
          _snake_case							=   True
          _snake_case							=   True
          _snake_case							=   True
          _snake_case							=   True
          _snake_case							=   True
          _snake_case							=   False
          _snake_case							=   False
          _snake_case							=   False
          _snake_case							=   2
          _snake_case							=   99
          _snake_case							=   0
          _snake_case							=   32
          _snake_case							=   2
          _snake_case							=   4
          _snake_case							=   0.1
          _snake_case							=   0.1
          _snake_case							=   512
          _snake_case							=   16
          _snake_case							=   2
          _snake_case							=   0.0_2
          _snake_case							=   3
          _snake_case							=   4
          _snake_case							=   "last"
          _snake_case							=   True
          _snake_case							=   None
          _snake_case							=   0






     def        _lowercase			(				self					)							->    Union[str, Any]:
          _snake_case							=   ids_tensor([self.batch_size, self.seq_length]      ,self.vocab_size					)
          _snake_case							=   random_attention_mask([self.batch_size, self.seq_length]      ,dtype=tf.floataa					)

          _snake_case							=   None
          if self.use_input_lengths:
               _snake_case							=   (
                   ids_tensor([self.batch_size]      ,vocab_size=2					) + self.seq_length - 2
               )  # small variation of seq_length

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

          _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]      ,2      ,dtype=tf.floataa					)
               _snake_case							=   ids_tensor([self.batch_size]      ,self.num_choices					)

          _snake_case							=   XLMConfig(
              vocab_size=self.vocab_size      ,n_special=self.n_special      ,emb_dim=self.hidden_size      ,n_layers=self.num_hidden_layers      ,n_heads=self.num_attention_heads      ,dropout=self.hidden_dropout_prob      ,attention_dropout=self.attention_probs_dropout_prob      ,gelu_activation=self.gelu_activation      ,sinusoidal_embeddings=self.sinusoidal_embeddings      ,asm=self.asm      ,causal=self.causal      ,n_langs=self.n_langs      ,max_position_embeddings=self.max_position_embeddings      ,initializer_range=self.initializer_range      ,summary_type=self.summary_type      ,use_proj=self.use_proj      ,bos_token_id=self.bos_token_id      ,)

          return (
              config,
              input_ids,
              token_type_ids,
              input_lengths,
              sequence_labels,
              token_labels,
              is_impossible_labels,
              choice_labels,
              input_mask,
          )






     def        _lowercase			(				self      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,)							->    Optional[int]:
          _snake_case							=   TFXLMModel(config=_SCREAMING_SNAKE_CASE					)
          _snake_case							=   {"input_ids": input_ids, "lengths": input_lengths, "langs": token_type_ids}
          _snake_case							=   model(_SCREAMING_SNAKE_CASE					)

          _snake_case							=   [input_ids, input_mask]
          _snake_case							=   model(_SCREAMING_SNAKE_CASE					)
          self.parent.assertEqual(result.last_hidden_state.shape      ,(self.batch_size, self.seq_length, 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      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,)							->    int:
          _snake_case							=   TFXLMWithLMHeadModel(_SCREAMING_SNAKE_CASE					)

          _snake_case							=   {"input_ids": input_ids, "lengths": input_lengths, "langs": token_type_ids}
          _snake_case							=   model(_SCREAMING_SNAKE_CASE					)

          _snake_case							=   outputs

          self.parent.assertEqual(result.logits.shape      ,(self.batch_size, self.seq_length, self.vocab_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      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,)							->    int:
          _snake_case							=   TFXLMForQuestionAnsweringSimple(_SCREAMING_SNAKE_CASE					)

          _snake_case							=   {"input_ids": input_ids, "lengths": input_lengths}

          _snake_case							=   model(_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)					)






     def        _lowercase			(				self      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,)							->    Tuple:
          _snake_case							=   TFXLMForSequenceClassification(_SCREAMING_SNAKE_CASE					)

          _snake_case							=   {"input_ids": input_ids, "lengths": input_lengths}

          _snake_case							=   model(_SCREAMING_SNAKE_CASE					)

          self.parent.assertEqual(result.logits.shape      ,(self.batch_size, self.type_sequence_label_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      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,)							->    Dict:
          _snake_case							=   self.num_labels
          _snake_case							=   TFXLMForTokenClassification(config=_SCREAMING_SNAKE_CASE					)
          _snake_case							=   {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
          _snake_case							=   model(_SCREAMING_SNAKE_CASE					)
          self.parent.assertEqual(result.logits.shape      ,(self.batch_size, self.seq_length, self.num_labels)					)






     def        _lowercase			(				self      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,)							->    Optional[int]:
          _snake_case							=   self.num_choices
          _snake_case							=   TFXLMForMultipleChoice(config=_SCREAMING_SNAKE_CASE					)
          _snake_case							=   tf.tile(tf.expand_dims(_SCREAMING_SNAKE_CASE      ,1					)      ,(1, self.num_choices, 1)					)
          _snake_case							=   tf.tile(tf.expand_dims(_SCREAMING_SNAKE_CASE      ,1					)      ,(1, self.num_choices, 1)					)
          _snake_case							=   tf.tile(tf.expand_dims(_SCREAMING_SNAKE_CASE      ,1					)      ,(1, self.num_choices, 1)					)
          _snake_case							=   {
              "input_ids": multiple_choice_inputs_ids,
              "attention_mask": multiple_choice_input_mask,
              "token_type_ids": multiple_choice_token_type_ids,
          }
          _snake_case							=   model(_SCREAMING_SNAKE_CASE					)
          self.parent.assertEqual(result.logits.shape      ,(self.batch_size, self.num_choices)					)






     def        _lowercase			(				self					)							->    Optional[Any]:
          _snake_case							=   self.prepare_config_and_inputs()
          (
              (
              _snake_case
          )							,      (
              _snake_case
          )							,      (
              _snake_case
          )							,      (
              _snake_case
          )							,      (
              _snake_case
          )							,      (
              _snake_case
          )							,      (
              _snake_case
          )							,      (
              _snake_case
          )							,      (
              _snake_case
          )							,      
          )							=   config_and_inputs
          _snake_case							=   {
              "input_ids": input_ids,
              "token_type_ids": token_type_ids,
              "langs": token_type_ids,
              "lengths": input_lengths,
          }
          return config, inputs_dict







@require_tf
class      _a       (		__lowerCAmelCase      ,      __lowerCAmelCase      ,      unittest.TestCase							):
     SCREAMING_SNAKE_CASE_							:					List[str]					       =		(
         (
             TFXLMModel,
             TFXLMWithLMHeadModel,
             TFXLMForSequenceClassification,
             TFXLMForQuestionAnsweringSimple,
             TFXLMForTokenClassification,
             TFXLMForMultipleChoice,
         )
         if is_tf_available()
         else ()
     )
     SCREAMING_SNAKE_CASE_							:					Optional[int]					       =		(
         (TFXLMWithLMHeadModel,) if is_tf_available() else ()
     )  # TODO (PVP): Check other models whether language generation is also applicable
     SCREAMING_SNAKE_CASE_							:					Union[str, Any]					       =		(
         {
             """feature-extraction""": TFXLMModel,
             """fill-mask""": TFXLMWithLMHeadModel,
             """question-answering""": TFXLMForQuestionAnsweringSimple,
             """text-classification""": TFXLMForSequenceClassification,
             """text-generation""": TFXLMWithLMHeadModel,
             """token-classification""": TFXLMForTokenClassification,
             """zero-shot""": TFXLMForSequenceClassification,
         }
         if is_tf_available()
         else {}
     )
     SCREAMING_SNAKE_CASE_							:					Union[str, Any]					       =		False
     SCREAMING_SNAKE_CASE_							:					Optional[int]					       =		False






     def        _lowercase			(				self      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE					)							->    Optional[Any]:
          if (
              pipeline_test_casse_name == "QAPipelineTests"
              and tokenizer_name is not None
              and not tokenizer_name.endswith("Fast"					)
          ):
               # `QAPipelineTests` fails for a few models when the slower tokenizer are used.
               # (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
               # TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
               return True

          return False






     def        _lowercase			(				self					)							->    List[str]:
          _snake_case							=   TFXLMModelTester(self					)
          _snake_case							=   ConfigTester(self      ,config_class=_SCREAMING_SNAKE_CASE      ,emb_dim=37					)






     def        _lowercase			(				self					)							->    Tuple:
          self.config_tester.run_common_tests()






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






     def        _lowercase			(				self					)							->    Union[str, Any]:
          _snake_case							=   self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_xlm_lm_head(*_SCREAMING_SNAKE_CASE					)






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






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






     def        _lowercase			(				self					)							->    Optional[int]:
          _snake_case							=   self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_xlm_for_token_classification(*_SCREAMING_SNAKE_CASE					)






     def        _lowercase			(				self					)							->    Optional[int]:
          _snake_case							=   self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_xlm_for_multiple_choice(*_SCREAMING_SNAKE_CASE					)






     @slow
     def        _lowercase			(				self					)							->    Optional[Any]:
          for model_name in TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               _snake_case							=   TFXLMModel.from_pretrained(_SCREAMING_SNAKE_CASE					)
               self.assertIsNotNone(_SCREAMING_SNAKE_CASE					)







@require_tf
class      _a       (		unittest.TestCase							):






     @slow
     def        _lowercase			(				self					)							->    Union[str, Any]:
          _snake_case							=   TFXLMWithLMHeadModel.from_pretrained("xlm-mlm-en-2048"					)
          _snake_case							=   tf.convert_to_tensor([[14, 447]]      ,dtype=tf.intaa					)  # the president
          _snake_case							=   [
              14,
              447,
              14,
              447,
              14,
              447,
              14,
              447,
              14,
              447,
              14,
              447,
              14,
              447,
              14,
              447,
              14,
              447,
              14,
              447,
          ]  # the president the president the president the president the president the president the president the president the president the president
          # TODO(PVP): this and other input_ids I tried for generation give pretty bad results. Not sure why. Model might just not be made for auto-regressive inference
          _snake_case							=   model.generate(_SCREAMING_SNAKE_CASE      ,do_sample=_SCREAMING_SNAKE_CASE					)
          self.assertListEqual(output_ids[0].numpy().tolist()      ,_SCREAMING_SNAKE_CASE					)








'''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)


