





from ...processing_utils import ProcessorMixin
class 			_lowercase						(       __a					):
     _UpperCAmelCase										=					'''WhisperFeatureExtractor'''
     _UpperCAmelCase										=					'''WhisperTokenizer'''







     def __init__( self    ,   A__    ,   A__    ) ->	Optional[Any]:
      super().__init__(A__    ,   A__    )
      snake_case				=			self.feature_extractor
      snake_case				=			False







     def     UpperCamelCase			( self    ,   A__=None    ,   A__=None    ,   A__=True    ) ->	Union[str, Any]:
      return self.tokenizer.get_decoder_prompt_ids(task=A__    ,   language=A__    ,   no_timestamps=A__    )







     def __call__( self    ,   *A__    ,   **A__    ) ->	Dict:
      # For backward compatibility
      if self._in_target_context_manager:
       return self.current_processor(*A__    ,   **A__    )

      snake_case				=			kwargs.pop('''audio'''    ,   A__    )
      snake_case				=			kwargs.pop('''sampling_rate'''    ,   A__    )
      snake_case				=			kwargs.pop('''text'''    ,   A__    )
      if len(A__    ) > 0:
       snake_case				=			args[0]
       snake_case				=			args[1:]

      if audio is None and text is None:
       raise ValueError('''You need to specify either an `audio` or `text` input to process.'''    )

      if audio is not None:
       snake_case				=			self.feature_extractor(A__    ,   *A__    ,   sampling_rate=A__    ,   **A__    )
      if text is not None:
       snake_case				=			self.tokenizer(A__    ,   **A__    )

      if text is None:
       return inputs

      elif audio is None:
       return encodings
      else:
       snake_case				=			encodings['''input_ids''']
       return inputs







     def     UpperCamelCase			( self    ,   *A__    ,   **A__    ) ->	Optional[Any]:
      return self.tokenizer.batch_decode(*A__    ,   **A__    )







     def     UpperCamelCase			( self    ,   *A__    ,   **A__    ) ->	str:
      return self.tokenizer.decode(*A__    ,   **A__    )







     def     UpperCamelCase			( self    ,   A__    ,   A__="np"    ) ->	Optional[Any]:
      return self.tokenizer.get_prompt_ids(A__    ,   return_tensors=A__    )








'''simple docstring'''







import argparse
import json
import logging
import os
import sys
from unittest.mock import patch

from transformers.testing_utils import TestCasePlus, get_gpu_count, slow


_lowercase       						=			[
    os.path.join(os.path.dirname(__file__), dirname)
    for dirname in [
        'text-classification',
        'language-modeling',
        'summarization',
        'token-classification',
        'question-answering',
    ]
]
sys.path.extend(SRC_DIRS)


if SRC_DIRS is not None:
   import run_clm_flax
   import run_flax_glue
   import run_flax_ner
   import run_mlm_flax
   import run_qa
   import run_summarization_flax
   import run_ta_mlm_flax


logging.basicConfig(level=logging.DEBUG)

_lowercase       						=			logging.getLogger()

def    __UpperCamelCase	(	) ->Tuple:
 snake_case				=			argparse.ArgumentParser()
 parser.add_argument('''-f'''     )
 snake_case				=			parser.parse_args()
 return args.f

def    __UpperCamelCase	(	a    : Dict					,   a    : Tuple="eval"     ) ->List[Any]:
 snake_case				=			os.path.join(a					,   f"""{split}_results.json"""     )
 if os.path.exists(a     ):
  with open(a					,   '''r'''     ) as f:
   return json.load(a     )
 raise ValueError(f"""can't find {path}"""     )


_lowercase       						=			logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)






class 			_lowercase						(       __a					):







     def     UpperCamelCase			( self    ) ->	List[str]:
      snake_case				=			self.get_auto_remove_tmp_dir()
      snake_case				=			F"""
            run_glue.py
            --model_name_or_path distilbert-base-uncased
            --output_dir {tmp_dir}
            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv
            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --learning_rate=1e-4
            --eval_steps=2
            --warmup_steps=2
            --seed=42
            --max_seq_length=128
            """.split()

      with patch.object(A__    ,   '''argv'''    ,   A__    ):
       run_flax_glue.main()
       snake_case				=			get_results(A__    )
       self.assertGreaterEqual(result['''eval_accuracy''']    ,   0.7_5    )







     @slow
     def     UpperCamelCase			( self    ) ->	List[Any]:
      snake_case				=			self.get_auto_remove_tmp_dir()
      snake_case				=			F"""
            run_clm_flax.py
            --model_name_or_path distilgpt2
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --do_train
            --do_eval
            --block_size 128
            --per_device_train_batch_size 4
            --per_device_eval_batch_size 4
            --num_train_epochs 2
            --logging_steps 2 --eval_steps 2
            --output_dir {tmp_dir}
            --overwrite_output_dir
            """.split()

      with patch.object(A__    ,   '''argv'''    ,   A__    ):
       run_clm_flax.main()
       snake_case				=			get_results(A__    )
       self.assertLess(result['''eval_perplexity''']    ,   1_00    )







     @slow
     def     UpperCamelCase			( self    ) ->	int:
      snake_case				=			self.get_auto_remove_tmp_dir()
      snake_case				=			F"""
            run_summarization.py
            --model_name_or_path t5-small
            --train_file tests/fixtures/tests_samples/xsum/sample.json
            --validation_file tests/fixtures/tests_samples/xsum/sample.json
            --test_file tests/fixtures/tests_samples/xsum/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --num_train_epochs=3
            --warmup_steps=8
            --do_train
            --do_eval
            --do_predict
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --predict_with_generate
        """.split()

      with patch.object(A__    ,   '''argv'''    ,   A__    ):
       run_summarization_flax.main()
       snake_case				=			get_results(A__    ,   split='''test'''    )
       self.assertGreaterEqual(result['''test_rouge1''']    ,   10    )
       self.assertGreaterEqual(result['''test_rouge2''']    ,   2    )
       self.assertGreaterEqual(result['''test_rougeL''']    ,   7    )
       self.assertGreaterEqual(result['''test_rougeLsum''']    ,   7    )







     @slow
     def     UpperCamelCase			( self    ) ->	Union[str, Any]:
      snake_case				=			self.get_auto_remove_tmp_dir()
      snake_case				=			F"""
            run_mlm.py
            --model_name_or_path distilroberta-base
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --max_seq_length 128
            --per_device_train_batch_size 4
            --per_device_eval_batch_size 4
            --logging_steps 2 --eval_steps 2
            --do_train
            --do_eval
            --num_train_epochs=1
        """.split()

      with patch.object(A__    ,   '''argv'''    ,   A__    ):
       run_mlm_flax.main()
       snake_case				=			get_results(A__    )
       self.assertLess(result['''eval_perplexity''']    ,   42    )







     @slow
     def     UpperCamelCase			( self    ) ->	Dict:
      snake_case				=			self.get_auto_remove_tmp_dir()
      snake_case				=			F"""
            run_t5_mlm_flax.py
            --model_name_or_path t5-small
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --do_train
            --do_eval
            --max_seq_length 128
            --per_device_train_batch_size 4
            --per_device_eval_batch_size 4
            --num_train_epochs 2
            --logging_steps 2 --eval_steps 2
            --output_dir {tmp_dir}
            --overwrite_output_dir
            """.split()

      with patch.object(A__    ,   '''argv'''    ,   A__    ):
       run_ta_mlm_flax.main()
       snake_case				=			get_results(A__    )
       self.assertGreaterEqual(result['''eval_accuracy''']    ,   0.4_2    )







     @slow
     def     UpperCamelCase			( self    ) ->	int:
      # with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
      snake_case				=			7 if get_gpu_count() > 1 else 2

      snake_case				=			self.get_auto_remove_tmp_dir()
      snake_case				=			F"""
            run_flax_ner.py
            --model_name_or_path bert-base-uncased
            --train_file tests/fixtures/tests_samples/conll/sample.json
            --validation_file tests/fixtures/tests_samples/conll/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --do_train
            --do_eval
            --warmup_steps=2
            --learning_rate=2e-4
            --logging_steps 2 --eval_steps 2
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=2
            --num_train_epochs={epochs}
            --seed 7
        """.split()

      with patch.object(A__    ,   '''argv'''    ,   A__    ):
       run_flax_ner.main()
       snake_case				=			get_results(A__    )
       self.assertGreaterEqual(result['''eval_accuracy''']    ,   0.7_5    )
       self.assertGreaterEqual(result['''eval_f1''']    ,   0.3    )







     @slow
     def     UpperCamelCase			( self    ) ->	Any:
      snake_case				=			self.get_auto_remove_tmp_dir()
      snake_case				=			F"""
            run_qa.py
            --model_name_or_path bert-base-uncased
            --version_2_with_negative
            --train_file tests/fixtures/tests_samples/SQUAD/sample.json
            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --num_train_epochs=3
            --warmup_steps=2
            --do_train
            --do_eval
            --logging_steps 2 --eval_steps 2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
        """.split()

      with patch.object(A__    ,   '''argv'''    ,   A__    ):
       run_qa.main()
       snake_case				=			get_results(A__    )
       self.assertGreaterEqual(result['''eval_f1''']    ,   30    )
       self.assertGreaterEqual(result['''eval_exact''']    ,   30    )

