





"""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


A			      =	[
    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)

A			      =	logging.getLogger()

def      __A   (      )		->		str:
				__a			:    Tuple      							=	argparse.ArgumentParser()
				parser.add_argument('''-f''')
				__a			:    Dict      							=	parser.parse_args()
				return args.f

def      __A   (      a_    :Tuple ,							a_    :int="eval")		->		Optional[int]:
				__a			:    str      							=	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}""")


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






class 					__lowercase      (      _UpperCamelCase				):







					'''simple docstring'''




					def 			_lowerCamelCase		(    self  ):
									__a			:    List[Any]      							=	self.get_auto_remove_tmp_dir()
									__a			:    List[str]      							=	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(_UpperCAmelCase						,      '''argv'''						,      _UpperCAmelCase  ):
													run_flax_glue.main()
													__a			:    Optional[Any]      							=	get_results(_UpperCAmelCase  )
													self.assertGreaterEqual(result['''eval_accuracy''']						,      0.7_5  )







					@slow
					def 			_lowerCamelCase		(    self  ):
									__a			:    Union[str, Any]      							=	self.get_auto_remove_tmp_dir()
									__a			:    List[str]      							=	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(_UpperCAmelCase						,      '''argv'''						,      _UpperCAmelCase  ):
													run_clm_flax.main()
													__a			:    str      							=	get_results(_UpperCAmelCase  )
													self.assertLess(result['''eval_perplexity''']						,      100  )







					@slow
					def 			_lowerCamelCase		(    self  ):
									__a			:    List[Any]      							=	self.get_auto_remove_tmp_dir()
									__a			:    str      							=	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(_UpperCAmelCase						,      '''argv'''						,      _UpperCAmelCase  ):
													run_summarization_flax.main()
													__a			:    Dict      							=	get_results(_UpperCAmelCase						,      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 			_lowerCamelCase		(    self  ):
									__a			:    int      							=	self.get_auto_remove_tmp_dir()
									__a			:    int      							=	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(_UpperCAmelCase						,      '''argv'''						,      _UpperCAmelCase  ):
													run_mlm_flax.main()
													__a			:    Optional[int]      							=	get_results(_UpperCAmelCase  )
													self.assertLess(result['''eval_perplexity''']						,      42  )







					@slow
					def 			_lowerCamelCase		(    self  ):
									__a			:    Dict      							=	self.get_auto_remove_tmp_dir()
									__a			:    Union[str, Any]      							=	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(_UpperCAmelCase						,      '''argv'''						,      _UpperCAmelCase  ):
													run_ta_mlm_flax.main()
													__a			:    Any      							=	get_results(_UpperCAmelCase  )
													self.assertGreaterEqual(result['''eval_accuracy''']						,      0.4_2  )







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

									__a			:    Tuple      							=	self.get_auto_remove_tmp_dir()
									__a			:    str      							=	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(_UpperCAmelCase						,      '''argv'''						,      _UpperCAmelCase  ):
													run_flax_ner.main()
													__a			:    int      							=	get_results(_UpperCAmelCase  )
													self.assertGreaterEqual(result['''eval_accuracy''']						,      0.7_5  )
													self.assertGreaterEqual(result['''eval_f1''']						,      0.3  )







					@slow
					def 			_lowerCamelCase		(    self  ):
									__a			:    Tuple      							=	self.get_auto_remove_tmp_dir()
									__a			:    Union[str, Any]      							=	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(_UpperCAmelCase						,      '''argv'''						,      _UpperCAmelCase  ):
													run_qa.main()
													__a			:    List[Any]      							=	get_results(_UpperCAmelCase  )
													self.assertGreaterEqual(result['''eval_f1''']						,      30  )
													self.assertGreaterEqual(result['''eval_exact''']						,      30  )






from typing import List, Optional

import numpy as np

from ...processing_utils import ProcessorMixin
from ...utils import to_numpy

class     snake_case_		(   a							):






 '''simple docstring'''





 __UpperCamelCase       =      'EncodecFeatureExtractor'
 __UpperCamelCase       =      ('T5Tokenizer', 'T5TokenizerFast')

 def __init__(  self,		A_,		A_		)	->       Optional[int]:
    super().__init__(A_,		A_		)
    UpperCAmelCase__						=self.feature_extractor
    UpperCAmelCase__						=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_		)	->       Any:
    # For backward compatibility
    if self._in_target_context_manager:
       return self.current_processor(*A_,		**A_		)

    UpperCAmelCase__						=kwargs.pop("audio",		A_		)
    UpperCAmelCase__						=kwargs.pop("sampling_rate",		A_		)
    UpperCAmelCase__						=kwargs.pop("text",		A_		)
    if len(A_		) > 0:
       UpperCAmelCase__						=args[0]
       UpperCAmelCase__						=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 text is not None:
       UpperCAmelCase__						=self.tokenizer(A_,		**A_		)

    if audio is not None:
       UpperCAmelCase__						=self.feature_extractor(A_,		*A_,		sampling_rate=A_,		**A_		)

    if audio is None:
       return inputs

    elif text is None:
       return audio_inputs

    else:
       UpperCAmelCase__						=audio_inputs["input_values"]
       if "padding_mask" in audio_inputs:
          UpperCAmelCase__						=audio_inputs["padding_mask"]
       return inputs

 def  __UpperCAmelCase						(  self,		*A_,		**A_		)	->       Dict:
    UpperCAmelCase__						=kwargs.pop("audio",		A_		)
    UpperCAmelCase__						=kwargs.pop("padding_mask",		A_		)

    if len(A_		) > 0:
       UpperCAmelCase__						=args[0]
       UpperCAmelCase__						=args[1:]

    if audio_values is not None:
       return self._decode_audio(A_,		padding_mask=A_		)
    else:
       return self.tokenizer.batch_decode(*A_,		**A_		)

 def  __UpperCAmelCase						(  self,		*A_,		**A_		)	->       int:
    return self.tokenizer.decode(*A_,		**A_		)






 def  __UpperCAmelCase						(  self,		A_,		A_ = None		)	->       List[np.ndarray]:
    UpperCAmelCase__						=to_numpy(A_		)
    UpperCAmelCase__			,							UpperCAmelCase__			,							UpperCAmelCase__						=audio_values.shape

    if padding_mask is None:
       return list(A_		)

    UpperCAmelCase__						=to_numpy(A_		)

    # match the sequence length of the padding mask to the generated audio arrays by padding with the **non-padding**
    # token (so that the generated audio values are **not** treated as padded tokens)
    UpperCAmelCase__						=seq_len - padding_mask.shape[-1]
    UpperCAmelCase__						=1 - self.feature_extractor.padding_value
    UpperCAmelCase__						=np.pad(A_,		((0, 0), (0, difference)),		"constant",		constant_values=A_		)

    UpperCAmelCase__						=audio_values.tolist()
    for i in range(A_		):
       UpperCAmelCase__						=np.asarray(audio_values[i]		)[
           padding_mask[i][None, :] != self.feature_extractor.padding_value
       ]
       UpperCAmelCase__						=sliced_audio.reshape(A_,		-1		)

    return audio_values



