from math import ceil
from typing import List, Optional, Union

import numpy as np

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import BatchFeature, SequenceFeatureExtractor
from ...utils import TensorType, logging


__UpperCamelCase	: str    		=  logging.get_logger(__name__)





class       lowercase__		(     UpperCamelCase_):
						UpperCamelCase_  =	["""audio_values""", """audio_mask"""]


						def __init__( self			:       Optional[Any]					,	UpperCamelCase__			:       List[str]=2048					,	UpperCamelCase__			:       List[Any]=1					,	UpperCamelCase__			:       str=[16, 16]					,	UpperCamelCase__			:       List[str]=128					,	UpperCamelCase__			:       Union[str, Any]=4_4100					,	UpperCamelCase__			:       Optional[int]=86					,	UpperCamelCase__			:       Dict=2048					,	UpperCamelCase__			:       Any=0.0					,	**UpperCamelCase__			:       List[Any]					,	):





												'''simple docstring'''





												super().__init__(
												    feature_size=UpperCamelCase__					,	sampling_rate=UpperCamelCase__					,	padding_value=UpperCamelCase__					,	**UpperCamelCase__					,	)

												SCREAMING_SNAKE_CASE							:					str           =							spectrogram_length
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							num_channels
												SCREAMING_SNAKE_CASE							:					List[Any]           =							patch_size
												SCREAMING_SNAKE_CASE							:					str           =							feature_size // self.patch_size[1]
												SCREAMING_SNAKE_CASE							:					Any           =							n_fft
												SCREAMING_SNAKE_CASE							:					List[str]           =							sampling_rate // hop_length_to_sampling_rate
												SCREAMING_SNAKE_CASE							:					List[str]           =							sampling_rate
												SCREAMING_SNAKE_CASE							:					Dict           =							padding_value
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							mel_filter_bank(
												    num_frequency_bins=1 + n_fft // 2					,	num_mel_filters=UpperCamelCase__					,	min_frequency=0.0					,	max_frequency=2_2050.0					,	sampling_rate=UpperCamelCase__					,	norm='''slaney'''					,	mel_scale='''slaney'''					,	).T


						def  __A ( self			:       str					,	UpperCamelCase__			:       np.array		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							spectrogram(
												    UpperCamelCase__					,	window_function(self.n_fft					,	'''hann'''		)					,	frame_length=self.n_fft					,	hop_length=self.hop_length					,	power=2.0					,	mel_filters=self.mel_filters.T					,	log_mel='''dB'''					,	db_range=80.0					,	)
												SCREAMING_SNAKE_CASE							:					Any           =							log_spec[:, :-1]
												SCREAMING_SNAKE_CASE							:					List[Any]           =							log_spec - 20.0
												SCREAMING_SNAKE_CASE							:					int           =							np.clip(log_spec / 40.0					,	-2.0					,	0.0		) + 1.0
												return log_spec



						def __call__( self			:       Optional[int]					,	UpperCamelCase__			:       Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]					,	UpperCamelCase__			:       Optional[Union[str, TensorType]] = None					,	UpperCamelCase__			:       Optional[bool] = True					,	UpperCamelCase__			:       Optional[int] = None					,	UpperCamelCase__			:       bool = False					,	UpperCamelCase__			:       bool = False					,	**UpperCamelCase__			:       List[str]					,	):





												'''simple docstring'''





												if sampling_rate is not None:
																		if sampling_rate != self.sampling_rate:
																								raise ValueError(
																								    '''This feature extractor is set to support sampling rate'''
																								    f""" of {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled"""
																								    f""" with {self.sampling_rate} and not {sampling_rate}."""		)
												else:
																		logger.warning(
																		    '''It is strongly recommended to pass the `sampling_rate` argument to this function. '''
																		    '''Failing to do so can result in silent errors that might be hard to debug.'''		)

												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							isinstance(UpperCamelCase__					,	np.ndarray		) and len(raw_speech.shape		) > 1
												if is_batched_numpy and len(raw_speech.shape		) > 2:
																		raise ValueError(f"""Only mono-channel audio is supported for input to {self}"""		)
												SCREAMING_SNAKE_CASE							:					List[str]           =							is_batched_numpy or (
												    isinstance(UpperCamelCase__					,	(list, tuple)		) and (isinstance(raw_speech[0]					,	(np.ndarray, tuple, list)		))
												)
												if is_batched:
																		SCREAMING_SNAKE_CASE							:					List[Any]           =							[np.asarray([speech]					,	dtype=np.floataa		).T for speech in raw_speech]
												elif not is_batched and not isinstance(UpperCamelCase__					,	np.ndarray		):
																		SCREAMING_SNAKE_CASE							:					Any           =							np.asarray(UpperCamelCase__					,	dtype=np.floataa		)
												elif isinstance(UpperCamelCase__					,	np.ndarray		) and raw_speech.dtype is np.dtype(np.floataa		):
																		SCREAMING_SNAKE_CASE							:					Any           =							raw_speech.astype(np.floataa		)
												# always return batch
												if not is_batched:
																		SCREAMING_SNAKE_CASE							:					Dict           =							[np.asarray([raw_speech]		).T]

												# Convert audio signals to log mel spectrograms, truncate by time axis
												SCREAMING_SNAKE_CASE							:					List[str]           =							[
												    self._np_extract_fbank_features(waveform.squeeze()		).T[: self.spectrogram_length] for waveform in raw_speech
												]
												if isinstance(audio_features[0]					,	UpperCamelCase__		):
																		SCREAMING_SNAKE_CASE							:					Tuple           =							[np.asarray(UpperCamelCase__					,	dtype=np.floataa		) for feature in audio_features]

												# Create audio attention mask
												SCREAMING_SNAKE_CASE							:					Tuple           =							max(
												    [ceil(feature.shape[0] / self.patch_size[0]		) * self.freq_len for feature in audio_features]		)  # The maximum number of audio patches in a batch
												if return_attention_mask:
																		SCREAMING_SNAKE_CASE							:					int           =							[
																		    (ceil(feature.shape[0] / self.patch_size[0]		) * self.freq_len) * [1]
																		    + (max_patch_len - ceil(feature.shape[0] / self.patch_size[0]		) * self.freq_len) * [0]
																		    for feature in audio_features
																		]
																		SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							np.array(UpperCamelCase__		).astype(np.floataa		)

												# convert into correct format for padding
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							max_patch_len // self.freq_len * self.patch_size[0]  # The maximum audio size in a batch
												SCREAMING_SNAKE_CASE							:					List[str]           =							np.ones([len(UpperCamelCase__		), 1, max_time_len, self.feature_size]		).astype(np.floataa		)
												SCREAMING_SNAKE_CASE							:					Any           =							padded_audio_features * self.padding_value
												for i in range(len(UpperCamelCase__		)		):
																		SCREAMING_SNAKE_CASE							:					Optional[Any]           =							audio_features[i]
																		SCREAMING_SNAKE_CASE							:					Optional[int]           =							feature

												# return as BatchFeature
												if return_attention_mask:
																		SCREAMING_SNAKE_CASE							:					Optional[Any]           =							{'''audio_values''': padded_audio_features, '''audio_mask''': audio_mask}
												else:
																		SCREAMING_SNAKE_CASE							:					Dict           =							{'''audio_values''': padded_audio_features}

												SCREAMING_SNAKE_CASE							:					Optional[int]           =							BatchFeature(data=UpperCamelCase__					,	tensor_type=UpperCamelCase__		)
												return encoded_inputs






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


__UpperCamelCase	: str    		=  [
    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)

__UpperCamelCase	: int    		=  logging.getLogger()
def    A			(					):
						SCREAMING_SNAKE_CASE							:					str           =							argparse.ArgumentParser()
						parser.add_argument('''-f'''		)
						SCREAMING_SNAKE_CASE							:					List[str]           =							parser.parse_args()
						return args.f
def    A			(					_lowercase	,						_lowercase="eval"		):
						SCREAMING_SNAKE_CASE							:					Dict           =							os.path.join(_lowercase	,						f"""{split}_results.json"""		)
						if os.path.exists(_lowercase		):
												with open(_lowercase	,						'''r'''		) as f:
																		return json.load(_lowercase		)
						raise ValueError(f"""can't find {path}"""		)


__UpperCamelCase	: Optional[Any]    		=  logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)





class       lowercase__		(     UpperCamelCase_):


						def  __A ( self			:       Optional[int]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					int           =							self.get_auto_remove_tmp_dir()
												SCREAMING_SNAKE_CASE							:					Tuple           =							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()
																		SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							get_results(UpperCamelCase__		)
																		self.assertGreaterEqual(result['''eval_accuracy''']					,	0.75		)


						@slow
						def  __A ( self			:       Optional[int]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Dict           =							self.get_auto_remove_tmp_dir()
												SCREAMING_SNAKE_CASE							:					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()
																		SCREAMING_SNAKE_CASE							:					Dict           =							get_results(UpperCamelCase__		)
																		self.assertLess(result['''eval_perplexity''']					,	100		)


						@slow
						def  __A ( self			:       Any		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					List[str]           =							self.get_auto_remove_tmp_dir()
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							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()
																		SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							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  __A ( self			:       Optional[int]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					List[Any]           =							self.get_auto_remove_tmp_dir()
												SCREAMING_SNAKE_CASE							:					Dict           =							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()
																		SCREAMING_SNAKE_CASE							:					List[Any]           =							get_results(UpperCamelCase__		)
																		self.assertLess(result['''eval_perplexity''']					,	42		)


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





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Dict           =							self.get_auto_remove_tmp_dir()
												SCREAMING_SNAKE_CASE							:					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()
																		SCREAMING_SNAKE_CASE							:					Optional[int]           =							get_results(UpperCamelCase__		)
																		self.assertGreaterEqual(result['''eval_accuracy''']					,	0.42		)


						@slow
						def  __A ( self			:       Union[str, Any]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Tuple           =							7 if get_gpu_count() > 1 else 2

												SCREAMING_SNAKE_CASE							:					Dict           =							self.get_auto_remove_tmp_dir()
												SCREAMING_SNAKE_CASE							:					Any           =							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()
																		SCREAMING_SNAKE_CASE							:					List[str]           =							get_results(UpperCamelCase__		)
																		self.assertGreaterEqual(result['''eval_accuracy''']					,	0.75		)
																		self.assertGreaterEqual(result['''eval_f1''']					,	0.3		)



						@slow
						def  __A ( self			:       List[str]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					List[str]           =							self.get_auto_remove_tmp_dir()
												SCREAMING_SNAKE_CASE							:					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()
																		SCREAMING_SNAKE_CASE							:					str           =							get_results(UpperCamelCase__		)
																		self.assertGreaterEqual(result['''eval_f1''']					,	30		)
																		self.assertGreaterEqual(result['''eval_exact''']					,	30		)





