
from typing import List, Optional, Union

import numpy as np

from ....audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ....feature_extraction_sequence_utils import SequenceFeatureExtractor
from ....feature_extraction_utils import BatchFeature
from ....file_utils import PaddingStrategy, TensorType
from ....utils import logging


UpperCAmelCase       =					logging.get_logger(__name__)







class 			A_  (     __lowerCamelCase  ):




	'''simple docstring'''


	_UpperCamelCase	:	List[str]									=			["""input_features""", """attention_mask"""]





	def __init__(			self						,       snake_case=80						,       snake_case=1_6000						,       snake_case=0.0						,       snake_case=10						,       snake_case=25						,       snake_case="hamming_window"						,       snake_case=32_768.0						,       snake_case=0.97						,       snake_case=1.0						,       snake_case=True						,       snake_case=True						,       snake_case=False						,       **snake_case						,       ):
		super().__init__(feature_size=snake_case						,       sampling_rate=snake_case						,       padding_value=snake_case						,       **snake_case			)

		lowercase	    =       feature_size
		lowercase	    =       sampling_rate
		lowercase	    =       padding_value
		lowercase	    =       hop_length
		lowercase	    =       win_length
		lowercase	    =       frame_signal_scale
		lowercase	    =       preemphasis_coeff
		lowercase	    =       mel_floor
		lowercase	    =       normalize_means
		lowercase	    =       normalize_vars
		lowercase	    =       win_function
		lowercase	    =       return_attention_mask

		lowercase	    =       win_length * sampling_rate // 1000
		lowercase	    =       hop_length * sampling_rate // 1000

		lowercase	    =       optimal_fft_length(self.sample_size			)
		lowercase	    =       (self.n_fft // 2) + 1





	def       SCREAMING_SNAKE_CASE__		(			self						,       snake_case			):
		if self.win_function == "hamming_window":
			lowercase	    =       window_function(window_length=self.sample_size						,       name=self.win_function						,       periodic=snake_case			)
		else:
			lowercase	    =       window_function(window_length=self.sample_size						,       name=self.win_function			)

		lowercase	    =       mel_filter_bank(
		    num_frequency_bins=self.n_freqs						,       num_mel_filters=self.feature_size						,       min_frequency=0.0						,       max_frequency=self.sampling_rate / 2.0						,       sampling_rate=self.sampling_rate						,       )

		lowercase	    =       spectrogram(
		    one_waveform * self.frame_signal_scale						,       window=snake_case						,       frame_length=self.sample_size						,       hop_length=self.sample_stride						,       fft_length=self.n_fft						,       center=snake_case						,       preemphasis=self.preemphasis_coeff						,       mel_filters=snake_case						,       mel_floor=self.mel_floor						,       log_mel='log'						,       )
		return msfc_features.T





	def       SCREAMING_SNAKE_CASE__		(			self						,       snake_case						,       snake_case						,       snake_case			):
		# make sure we normalize float32 arrays
		if self.normalize_means:
			lowercase	    =       x[:input_length].mean(axis=0			)
			lowercase	    =       np.subtract(snake_case						,       snake_case			)
		if self.normalize_vars:
			lowercase	    =       x[:input_length].std(axis=0			)
			lowercase	    =       np.divide(snake_case						,       snake_case			)

		if input_length < x.shape[0]:
			lowercase	    =       padding_value

		# make sure array is in float32
		lowercase	    =       x.astype(np.floataa			)

		return x





	def       SCREAMING_SNAKE_CASE__		(			self						,       snake_case						,       snake_case = None			):
		lowercase	    =       attention_mask.sum(-1			) if attention_mask is not None else [x.shape[0] for x in input_features]
		return [self._normalize_one(snake_case						,       snake_case						,       self.padding_value			) for x, n in zip(snake_case						,       snake_case			)]





	def __call__(			self						,       snake_case						,       snake_case = False						,       snake_case = None						,       snake_case = False						,       snake_case = None						,       snake_case = None						,       snake_case = None						,       snake_case = None						,       **snake_case						,       ):

		if sampling_rate is not None:
			if sampling_rate != self.sampling_rate:
				raise ValueError(
				    F'''The model corresponding to this feature extractor: {self} was trained using a sampling rate of'''
				    F''' {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with'''
				    F''' {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.'			)

		lowercase	    =       isinstance(snake_case						,       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}'''			)
		lowercase	    =       is_batched_numpy or (
		    isinstance(snake_case						,       (list, tuple)			) and (isinstance(raw_speech[0]						,       (np.ndarray, tuple, list)			))
		)

		if is_batched:
			lowercase	    =       [np.asarray(snake_case						,       dtype=np.floataa			) for speech in raw_speech]
		elif not is_batched and not isinstance(snake_case						,       np.ndarray			):
			lowercase	    =       np.asarray(snake_case						,       dtype=np.floataa			)
		elif isinstance(snake_case						,       np.ndarray			) and raw_speech.dtype is np.dtype(np.floataa			):
			lowercase	    =       raw_speech.astype(np.floataa			)

		# always return batch
		if not is_batched:
			lowercase	    =       [raw_speech]

		# extract fbank features
		lowercase	    =       [self._extract_mfsc_features(snake_case			) for one_waveform in raw_speech]

		# convert into correct format for padding
		lowercase	    =       BatchFeature({'input_features': features}			)

		lowercase	    =       self.pad(
		    snake_case						,       padding=snake_case						,       max_length=snake_case						,       truncation=snake_case						,       pad_to_multiple_of=snake_case						,       return_attention_mask=snake_case						,       **snake_case						,       )
		# make sure list is in array format
		lowercase	    =       padded_inputs.get('input_features'			)
		if isinstance(input_features[0]						,       snake_case			):
			lowercase	    =       [np.asarray(snake_case						,       dtype=np.floataa			) for feature in input_features]

		lowercase	    =       padded_inputs.get('attention_mask'			)
		if attention_mask is not None:
			lowercase	    =       [np.asarray(snake_case						,       dtype=np.intaa			) for array in attention_mask]

		if self.normalize_means or self.normalize_vars:
			lowercase	    =       (
			    np.array(snake_case						,       dtype=np.intaa			)
			    if self._get_padding_strategies(snake_case						,       max_length=snake_case			) is not PaddingStrategy.DO_NOT_PAD
			    and padding
			    else None
			)
			lowercase	    =       self.normalize(
			    padded_inputs['input_features']						,       attention_mask=snake_case			)

		if return_tensors is not None:
			lowercase	    =       padded_inputs.convert_to_tensors(snake_case			)

		return padded_inputs





'''simple docstring'''





import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_torch_available, is_vision_available


if is_torch_available():
    import torch


if is_vision_available():
    from PIL import Image

    from transformers import MgpstrProcessor, ViTImageProcessor




@require_torch
@require_vision
class 				UpperCAmelCase			(			unittest.TestCase						):
 UpperCAmelCase				=					ViTImageProcessor if is_vision_available() else None


 @property
 def        __SCREAMING_SNAKE_CASE					(      self							:	List[Any] ):
    return self.image_processor_tester.prepare_image_processor_dict()


 def        __SCREAMING_SNAKE_CASE					(      self							:	List[str] ):
    UpperCAmelCase__	:Any      =      (3, 3_2, 1_2_8)
    UpperCAmelCase__	:Optional[int]      =      tempfile.mkdtemp()

    # fmt: off
    UpperCAmelCase__	:Union[str, Any]      =      ['''[GO]''', '''[s]''', '''0''', '''1''', '''2''', '''3''', '''4''', '''5''', '''6''', '''7''', '''8''', '''9''', '''a''', '''b''', '''c''', '''d''', '''e''', '''f''', '''g''', '''h''', '''i''', '''j''', '''k''', '''l''', '''m''', '''n''', '''o''', '''p''', '''q''', '''r''', '''s''', '''t''', '''u''', '''v''', '''w''', '''x''', '''y''', '''z''']
    # fmt: on
    UpperCAmelCase__	:str      =      dict(zip(__lowerCamelCase			,       range(len(__lowerCamelCase ) ) ) )

    UpperCAmelCase__	:str      =      os.path.join(self.tmpdirname			,       VOCAB_FILES_NAMES['''vocab_file'''] )
    with open(self.vocab_file			,       '''w'''			,       encoding='''utf-8''' ) as fp:
       fp.write(json.dumps(__lowerCamelCase ) + '''\n''' )

    UpperCAmelCase__	:Dict      =      {
        '''do_normalize''': False,
        '''do_resize''': True,
        '''image_processor_type''': '''ViTImageProcessor''',
        '''resample''': 3,
        '''size''': {'''height''': 3_2, '''width''': 1_2_8},
    }
    UpperCAmelCase__	:Optional[Any]      =      os.path.join(self.tmpdirname			,       __lowerCamelCase )
    with open(self.image_processor_file			,       '''w'''			,       encoding='''utf-8''' ) as fp:
       json.dump(__lowerCamelCase			,       __lowerCamelCase )


 def        __SCREAMING_SNAKE_CASE					(      self							:	Tuple			,       **__lowerCamelCase							:	Any ):
    return MgpstrTokenizer.from_pretrained(self.tmpdirname			,       **__lowerCamelCase )


 def        __SCREAMING_SNAKE_CASE					(      self							:	int			,       **__lowerCamelCase							:	str ):
    return ViTImageProcessor.from_pretrained(self.tmpdirname			,       **__lowerCamelCase )


 def        __SCREAMING_SNAKE_CASE					(      self							:	int ):
    shutil.rmtree(self.tmpdirname )


 def        __SCREAMING_SNAKE_CASE					(      self							:	Union[str, Any] ):
    UpperCAmelCase__	:Optional[int]      =      np.random.randint(2_5_5			,       size=(3, 3_0, 4_0_0)			,       dtype=np.uinta )

    UpperCAmelCase__	:Union[str, Any]      =      Image.fromarray(np.moveaxis(__lowerCamelCase			,       0			,       -1 ) )

    return image_input


 def        __SCREAMING_SNAKE_CASE					(      self							:	int ):
    UpperCAmelCase__	:Optional[Any]      =      self.get_tokenizer()
    UpperCAmelCase__	:str      =      self.get_image_processor()

    UpperCAmelCase__	:List[str]      =      MgpstrProcessor(tokenizer=__lowerCamelCase			,       image_processor=__lowerCamelCase )
    processor.save_pretrained(self.tmpdirname )
    UpperCAmelCase__	:Optional[int]      =      MgpstrProcessor.from_pretrained(self.tmpdirname			,       use_fast=__lowerCamelCase )

    self.assertEqual(processor.char_tokenizer.get_vocab()			,       tokenizer.get_vocab() )
    self.assertIsInstance(processor.char_tokenizer			,       __lowerCamelCase )

    self.assertEqual(processor.image_processor.to_json_string()			,       image_processor.to_json_string() )
    self.assertIsInstance(processor.image_processor			,       __lowerCamelCase )


 def        __SCREAMING_SNAKE_CASE					(      self							:	Union[str, Any] ):
    UpperCAmelCase__	:Any      =      self.get_tokenizer()
    UpperCAmelCase__	:int      =      self.get_image_processor()

    UpperCAmelCase__	:Union[str, Any]      =      MgpstrProcessor(tokenizer=__lowerCamelCase			,       image_processor=__lowerCamelCase )
    processor.save_pretrained(self.tmpdirname )

    UpperCAmelCase__	:List[Any]      =      self.get_tokenizer(bos_token='''(BOS)'''			,       eos_token='''(EOS)''' )
    UpperCAmelCase__	:List[Any]      =      self.get_image_processor(do_normalize=__lowerCamelCase			,       padding_value=1.0 )

    UpperCAmelCase__	:Any      =      MgpstrProcessor.from_pretrained(
        self.tmpdirname			,       bos_token='''(BOS)'''			,       eos_token='''(EOS)'''			,       do_normalize=__lowerCamelCase			,       padding_value=1.0 )

    self.assertEqual(processor.char_tokenizer.get_vocab()			,       tokenizer_add_kwargs.get_vocab() )
    self.assertIsInstance(processor.char_tokenizer			,       __lowerCamelCase )

    self.assertEqual(processor.image_processor.to_json_string()			,       image_processor_add_kwargs.to_json_string() )
    self.assertIsInstance(processor.image_processor			,       __lowerCamelCase )


 def        __SCREAMING_SNAKE_CASE					(      self							:	List[Any] ):
    UpperCAmelCase__	:Tuple      =      self.get_image_processor()
    UpperCAmelCase__	:Any      =      self.get_tokenizer()

    UpperCAmelCase__	:Any      =      MgpstrProcessor(tokenizer=__lowerCamelCase			,       image_processor=__lowerCamelCase )

    UpperCAmelCase__	:Optional[Any]      =      self.prepare_image_inputs()

    UpperCAmelCase__	:str      =      image_processor(__lowerCamelCase			,       return_tensors='''np''' )
    UpperCAmelCase__	:Tuple      =      processor(images=__lowerCamelCase			,       return_tensors='''np''' )

    for key in input_image_proc.keys():
       self.assertAlmostEqual(input_image_proc[key].sum()			,       input_processor[key].sum()			,       delta=1e-2 )


 def        __SCREAMING_SNAKE_CASE					(      self							:	Optional[int] ):
    UpperCAmelCase__	:Optional[Any]      =      self.get_image_processor()
    UpperCAmelCase__	:List[Any]      =      self.get_tokenizer()

    UpperCAmelCase__	:List[str]      =      MgpstrProcessor(tokenizer=__lowerCamelCase			,       image_processor=__lowerCamelCase )

    UpperCAmelCase__	:Union[str, Any]      =      '''test'''

    UpperCAmelCase__	:Tuple      =      processor(text=__lowerCamelCase )

    UpperCAmelCase__	:Tuple      =      tokenizer(__lowerCamelCase )
    for key in encoded_tok.keys():
       self.assertListEqual(encoded_tok[key]			,       encoded_processor[key] )


 def        __SCREAMING_SNAKE_CASE					(      self							:	str ):
    UpperCAmelCase__	:int      =      self.get_image_processor()
    UpperCAmelCase__	:Dict      =      self.get_tokenizer()

    UpperCAmelCase__	:int      =      MgpstrProcessor(tokenizer=__lowerCamelCase			,       image_processor=__lowerCamelCase )

    UpperCAmelCase__	:List[Any]      =      '''test'''
    UpperCAmelCase__	:List[Any]      =      self.prepare_image_inputs()

    UpperCAmelCase__	:Any      =      processor(text=__lowerCamelCase			,       images=__lowerCamelCase )

    self.assertListEqual(list(inputs.keys() )			,       ['''pixel_values''', '''labels'''] )

    # test if it raises when no input is passed
    with pytest.raises(__lowerCamelCase ):
       processor()


 def        __SCREAMING_SNAKE_CASE					(      self							:	List[Any] ):
    UpperCAmelCase__	:int      =      self.get_image_processor()
    UpperCAmelCase__	:Optional[int]      =      self.get_tokenizer()

    UpperCAmelCase__	:Union[str, Any]      =      MgpstrProcessor(tokenizer=__lowerCamelCase			,       image_processor=__lowerCamelCase )

    UpperCAmelCase__	:Any      =      [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9], [3, 4, 3, 1, 1, 8, 9]]

    UpperCAmelCase__	:Optional[Any]      =      processor.char_decode(__lowerCamelCase )
    UpperCAmelCase__	:int      =      tokenizer.batch_decode(__lowerCamelCase )
    UpperCAmelCase__	:int      =      [seq.replace(''' '''			,       '''''' ) for seq in decoded_tok]

    self.assertListEqual(__lowerCamelCase			,       __lowerCamelCase )


 def        __SCREAMING_SNAKE_CASE					(      self							:	Union[str, Any] ):
    UpperCAmelCase__	:int      =      self.get_image_processor()
    UpperCAmelCase__	:Optional[int]      =      self.get_tokenizer()

    UpperCAmelCase__	:Dict      =      MgpstrProcessor(tokenizer=__lowerCamelCase			,       image_processor=__lowerCamelCase )

    UpperCAmelCase__	:List[str]      =      None
    UpperCAmelCase__	:Optional[int]      =      self.prepare_image_inputs()

    UpperCAmelCase__	:Tuple      =      processor(text=__lowerCamelCase			,       images=__lowerCamelCase )

    self.assertListEqual(list(inputs.keys() )			,       processor.model_input_names )





 def        __SCREAMING_SNAKE_CASE					(      self							:	Tuple ):
    UpperCAmelCase__	:Dict      =      self.get_image_processor()
    UpperCAmelCase__	:int      =      self.get_tokenizer()

    UpperCAmelCase__	:Any      =      MgpstrProcessor(tokenizer=__lowerCamelCase			,       image_processor=__lowerCamelCase )

    UpperCAmelCase__	:Union[str, Any]      =      torch.randn(1			,       2_7			,       3_8 )
    UpperCAmelCase__	:Any      =      torch.randn(1			,       2_7			,       5_0_2_5_7 )
    UpperCAmelCase__	:Union[str, Any]      =      torch.randn(1			,       2_7			,       3_0_5_2_2 )

    UpperCAmelCase__	:Optional[Any]      =      processor.batch_decode([char_input, bpe_input, wp_input] )

    self.assertListEqual(list(results.keys() )			,       ['''generated_text''', '''scores''', '''char_preds''', '''bpe_preds''', '''wp_preds'''] )



