
'''simple docstring'''





import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch

from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search

from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from utils import ROUGE_KEYS


logging.basicConfig(level=logging.DEBUG)
SCREAMING_SNAKE_CASE     =       logging.getLogger()







def snake_case_     (					lowercase__   ,       lowercase__		):
  UpperCAmelCase__      :					Any				       =							"\n".join(lowercase__		)
  Path(lowercase__		).open("w"		).writelines(lowercase__		)


SCREAMING_SNAKE_CASE     =       """patrickvonplaten/t5-tiny-random"""
SCREAMING_SNAKE_CASE     =       """sshleifer/bart-tiny-random"""
SCREAMING_SNAKE_CASE     =       """sshleifer/tiny-mbart"""

SCREAMING_SNAKE_CASE     =       logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks






class 							UpperCAmelCase_							(    A				):

 '''simple docstring'''







 def 							UpperCamelCase  (					self		:  Optional[int]			,				snake_case__		:  Optional[Any] ):



   '''simple docstring'''




   UpperCAmelCase__      :					Tuple				       =							Path(self.get_auto_remove_tmp_dir() ) / "utest_input.source"
   UpperCAmelCase__      :					Optional[int]				       =							input_file_name.parent / "utest_output.txt"
   assert not output_file_name.exists()
   UpperCAmelCase__      :					Optional[Any]				       =							[" New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County."]
   _dump_articles(snake_case__			,				snake_case__ )

   UpperCAmelCase__      :					List[Any]				       =							str(Path(self.get_auto_remove_tmp_dir() ) / "scores.json" )
   UpperCAmelCase__      :					int				       =							"translation_en_to_de" if model == T5_TINY else "summarization"
   UpperCAmelCase__      :					Optional[Any]				       =							F"""
            run_eval_search.py
            {model}
            {input_file_name}
            {output_file_name}
            --score_path {score_path}
            --task {task}
            --num_beams 2
            --length_penalty 2.0
            """.split()

   with patch.object(snake_case__			,				"argv"			,				snake_case__ ):
     run_generate()
     assert Path(snake_case__ ).exists()
     # os.remove(Path(output_file_name))







 def 							UpperCamelCase  (					self		:  int ):



   '''simple docstring'''




   self.run_eval_tester(snake_case__ )







 @parameterized.expand([BART_TINY, MBART_TINY] )
 @slow
 def 							UpperCamelCase  (					self		:  Dict			,				snake_case__		:  Optional[int] ):



   '''simple docstring'''




   self.run_eval_tester(snake_case__ )







 @parameterized.expand([T5_TINY, MBART_TINY] )
 @slow
 def 							UpperCamelCase  (					self		:  Union[str, Any]			,				snake_case__		:  Optional[Any] ):



   '''simple docstring'''




   UpperCAmelCase__      :					Optional[Any]				       =							Path(self.get_auto_remove_tmp_dir() ) / "utest_input.source"
   UpperCAmelCase__      :					Dict				       =							input_file_name.parent / "utest_output.txt"
   assert not output_file_name.exists()

   UpperCAmelCase__      :					str				       =							{
       "en": ["Machine learning is great, isn't it?", "I like to eat bananas", "Tomorrow is another great day!"],
       "de": [
           "Maschinelles Lernen ist großartig, oder?",
           "Ich esse gerne Bananen",
           "Morgen ist wieder ein toller Tag!",
       ],
   }

   UpperCAmelCase__      :					Optional[int]				       =							Path(self.get_auto_remove_tmp_dir() )
   UpperCAmelCase__      :					str				       =							str(tmp_dir / "scores.json" )
   UpperCAmelCase__      :					List[Any]				       =							str(tmp_dir / "val.target" )
   _dump_articles(snake_case__			,				text["en"] )
   _dump_articles(snake_case__			,				text["de"] )
   UpperCAmelCase__      :					Optional[Any]				       =							"translation_en_to_de" if model == T5_TINY else "summarization"
   UpperCAmelCase__      :					Dict				       =							F"""
            run_eval_search.py
            {model}
            {str(snake_case__ )}
            {str(snake_case__ )}
            --score_path {score_path}
            --reference_path {reference_path}
            --task {task}
            """.split()
   testargs.extend(["--search", "num_beams=1:2 length_penalty=0.9:1.0"] )

   with patch.object(snake_case__			,				"argv"			,				snake_case__ ):
     with CaptureStdout() as cs:
       run_search()
     UpperCAmelCase__      :					Optional[Any]				       =							[" num_beams | length_penalty", model, "Best score args"]
     UpperCAmelCase__      :					List[str]				       =							["Info"]
     if "translation" in task:
       expected_strings.append("bleu" )
     else:
       expected_strings.extend(snake_case__ )
     for w in expected_strings:
       assert w in cs.out
     for w in un_expected_strings:
       assert w not in cs.out
     assert Path(snake_case__ ).exists()
     os.remove(Path(snake_case__ ) )





'''simple docstring'''





import unittest

from transformers import BigBirdConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
      import jax

      from transformers.models.big_bird.modeling_flax_big_bird import (
          FlaxBigBirdForCausalLM,
          FlaxBigBirdForMaskedLM,
          FlaxBigBirdForMultipleChoice,
          FlaxBigBirdForPreTraining,
          FlaxBigBirdForQuestionAnswering,
          FlaxBigBirdForSequenceClassification,
          FlaxBigBirdForTokenClassification,
          FlaxBigBirdModel,
      )






class 							UpperCAmelCase_							(    unittest.TestCase				):

 '''simple docstring'''







 def __init__(					self		:  Tuple			,				snake_case__		:  List[str]			,				snake_case__		:  Union[str, Any]=2			,				snake_case__		:  int=56			,				snake_case__		:  Any=True			,				snake_case__		:  Union[str, Any]=True			,				snake_case__		:  Optional[int]=True			,				snake_case__		:  Any=True			,				snake_case__		:  Tuple=99			,				snake_case__		:  Any=32			,				snake_case__		:  List[Any]=2			,				snake_case__		:  Optional[Any]=2			,				snake_case__		:  Any=7			,				snake_case__		:  Union[str, Any]="gelu_new"			,				snake_case__		:  List[Any]=0.1			,				snake_case__		:  Optional[Any]=0.1			,				snake_case__		:  Optional[int]=5_12			,				snake_case__		:  Optional[int]=16			,				snake_case__		:  int=2			,				snake_case__		:  List[Any]=0.02			,				snake_case__		:  int=4			,				snake_case__		:  Optional[Any]="block_sparse"			,				snake_case__		:  Union[str, Any]=True			,				snake_case__		:  List[Any]=False			,				snake_case__		:  Optional[Any]=2			,				snake_case__		:  Optional[Any]=3			,				):



   '''simple docstring'''




   UpperCAmelCase__      :					List[Any]				       =							parent
   UpperCAmelCase__      :					Dict				       =							batch_size
   UpperCAmelCase__      :					Tuple				       =							seq_length
   UpperCAmelCase__      :					Optional[int]				       =							is_training
   UpperCAmelCase__      :					Optional[int]				       =							use_attention_mask
   UpperCAmelCase__      :					List[Any]				       =							use_token_type_ids
   UpperCAmelCase__      :					Dict				       =							use_labels
   UpperCAmelCase__      :					Tuple				       =							vocab_size
   UpperCAmelCase__      :					str				       =							hidden_size
   UpperCAmelCase__      :					List[str]				       =							num_hidden_layers
   UpperCAmelCase__      :					Union[str, Any]				       =							num_attention_heads
   UpperCAmelCase__      :					Optional[int]				       =							intermediate_size
   UpperCAmelCase__      :					Optional[int]				       =							hidden_act
   UpperCAmelCase__      :					Union[str, Any]				       =							hidden_dropout_prob
   UpperCAmelCase__      :					str				       =							attention_probs_dropout_prob
   UpperCAmelCase__      :					str				       =							max_position_embeddings
   UpperCAmelCase__      :					Any				       =							type_vocab_size
   UpperCAmelCase__      :					Dict				       =							type_sequence_label_size
   UpperCAmelCase__      :					Dict				       =							initializer_range
   UpperCAmelCase__      :					Any				       =							num_choices

   UpperCAmelCase__      :					List[str]				       =							rescale_embeddings
   UpperCAmelCase__      :					Union[str, Any]				       =							attention_type
   UpperCAmelCase__      :					Union[str, Any]				       =							use_bias
   UpperCAmelCase__      :					Union[str, Any]				       =							block_size
   UpperCAmelCase__      :					Any				       =							num_random_blocks







 def 							UpperCamelCase  (					self		:  Optional[Any] ):



   '''simple docstring'''




   UpperCAmelCase__      :					Optional[int]				       =							ids_tensor([self.batch_size, self.seq_length]			,				self.vocab_size )

   UpperCAmelCase__      :					Optional[Any]				       =							None
   if self.use_attention_mask:
     UpperCAmelCase__      :					List[Any]				       =							random_attention_mask([self.batch_size, self.seq_length] )

   UpperCAmelCase__      :					Union[str, Any]				       =							None
   if self.use_token_type_ids:
     UpperCAmelCase__      :					Any				       =							ids_tensor([self.batch_size, self.seq_length]			,				self.type_vocab_size )

   UpperCAmelCase__      :					Union[str, Any]				       =							BigBirdConfig(
       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=snake_case__			,				initializer_range=self.initializer_range			,				attention_type=self.attention_type			,				block_size=self.block_size			,				num_random_blocks=self.num_random_blocks			,				use_bias=self.use_bias			,				rescale_embeddings=self.rescale_embeddings			,				)

   return config, input_ids, token_type_ids, attention_mask







 def 							UpperCamelCase  (					self		:  Tuple ):



   '''simple docstring'''




   UpperCAmelCase__      :					int				       =							self.prepare_config_and_inputs()
   UpperCAmelCase__       ,			UpperCAmelCase__       ,			UpperCAmelCase__       ,			UpperCAmelCase__      :					Tuple				       =							config_and_inputs
   UpperCAmelCase__      :					str				       =							{
       "input_ids": input_ids,
       "token_type_ids": token_type_ids,
       "attention_mask": attention_mask,
   }
   return config, inputs_dict






@require_flax
class 							UpperCAmelCase_							(    A					,				unittest.TestCase				):

 '''simple docstring'''
 lowercase_					:    Any				=							(
     (
         FlaxBigBirdForCausalLM,
         FlaxBigBirdModel,
         FlaxBigBirdForPreTraining,
         FlaxBigBirdForMaskedLM,
         FlaxBigBirdForMultipleChoice,
         FlaxBigBirdForQuestionAnswering,
         FlaxBigBirdForSequenceClassification,
         FlaxBigBirdForTokenClassification,
     )
     if is_flax_available()
     else ()
 )

 lowercase_					:    Optional[Any]				=							False
 lowercase_					:    str				=							False







 def 							UpperCamelCase  (					self		:  Optional[Any] ):



   '''simple docstring'''




   UpperCAmelCase__      :					int				       =							FlaxBigBirdModelTester(self )







 @slow
 # copied from `test_modeling_flax_common` because it takes much longer than other models
 def 							UpperCamelCase  (					self		:  List[Any] ):



   '''simple docstring'''




   super().test_from_pretrained_save_pretrained()







 @slow
 # copied from `test_modeling_flax_common` because it takes much longer than other models
 def 							UpperCamelCase  (					self		:  Tuple ):



   '''simple docstring'''




   super().test_from_pretrained_with_no_automatic_init()







 @slow
 # copied from `test_modeling_flax_common` because it takes much longer than other models
 def 							UpperCamelCase  (					self		:  Tuple ):



   '''simple docstring'''




   super().test_no_automatic_init()







 @slow
 # copied from `test_modeling_flax_common` because it takes much longer than other models
 def 							UpperCamelCase  (					self		:  Dict ):



   '''simple docstring'''




   super().test_hidden_states_output()







 @slow
 def 							UpperCamelCase  (					self		:  List[Any] ):



   '''simple docstring'''




   for model_class_name in self.all_model_classes:
     UpperCAmelCase__      :					List[Any]				       =							model_class_name.from_pretrained("google/bigbird-roberta-base" )
     self.assertIsNotNone(snake_case__ )







 def 							UpperCamelCase  (					self		:  Union[str, Any] ):



   '''simple docstring'''




   if self.test_attn_probs:
     super().test_attention_outputs()







 @slow
 # copied from `test_modeling_flax_common` because it takes much longer than other models
 def 							UpperCamelCase  (					self		:  int ):



   '''simple docstring'''




   UpperCAmelCase__       ,			UpperCAmelCase__      :					str				       =							self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
     with self.subTest(model_class.__name__ ):
       UpperCAmelCase__      :					Union[str, Any]				       =							self._prepare_for_class(snake_case__			,				snake_case__ )
       UpperCAmelCase__      :					List[Any]				       =							model_class(snake_case__ )

       @jax.jit
       def model_jitted(snake_case__		:  int			,				snake_case__		:  Optional[int]=None			,				**snake_case__		:  Optional[Any] ):
         return model(input_ids=snake_case__			,				attention_mask=snake_case__			,				**snake_case__ )

       with self.subTest("JIT Enabled" ):
         UpperCAmelCase__      :					List[Any]				       =							model_jitted(**snake_case__ ).to_tuple()

       with self.subTest("JIT Disabled" ):
         with jax.disable_jit():
           UpperCAmelCase__      :					Tuple				       =							model_jitted(**snake_case__ ).to_tuple()

       self.assertEqual(len(snake_case__ )			,				len(snake_case__ ) )
       for jitted_output, output in zip(snake_case__			,				snake_case__ ):
         self.assertEqual(jitted_output.shape			,				output.shape )







 def 							UpperCamelCase  (					self		:  List[str]			,				snake_case__		:  int			,				snake_case__		:  Dict			,				snake_case__		:  Any			,				snake_case__		:  Union[str, Any]=1e-5			,				snake_case__		:  Any="outputs"			,				snake_case__		:  Optional[Any]=None ):



   '''simple docstring'''




   if name.startswith("outputs.attentions" ):
     return
   else:
     super().check_pt_flax_outputs(snake_case__			,				snake_case__			,				snake_case__			,				snake_case__			,				snake_case__			,				snake_case__ )



