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


_lowerCamelCase   :				Tuple     			=						logging.get_logger(__name__)


class   lowercase      (       a       ):
							lowercase__  :    Any									=							["""input_features""", """attention_mask"""]




							def __init__(  self				:   List[str]		,							_UpperCamelCase				:   List[Any]=80		,							_UpperCamelCase				:   Optional[Any]=16_000		,							_UpperCamelCase				:   int=0.0		,							_UpperCamelCase				:   str=10		,							_UpperCamelCase				:   List[str]=25		,							_UpperCamelCase				:   Any="hamming_window"		,							_UpperCamelCase				:   Any=32_768.0		,							_UpperCamelCase				:   str=0.9_7		,							_UpperCamelCase				:   Optional[Any]=1.0		,							_UpperCamelCase				:   Optional[Any]=True		,							_UpperCamelCase				:   Optional[int]=True		,							_UpperCamelCase				:   List[Any]=False		,							**_UpperCamelCase				:   Dict		,							)   ->			Any:



												'''simple docstring'''

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

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

												SCREAMING_SNAKE_CASE           =    win_length * sampling_rate // 1_000
												SCREAMING_SNAKE_CASE           =    hop_length * sampling_rate // 1_000

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




							def 		__snake_case(  self				:   List[Any]		,							_UpperCamelCase				:   np.array )   ->			np.ndarray:



												'''simple docstring'''

												if self.win_function == "hamming_window":
																	SCREAMING_SNAKE_CASE           =    window_function(window_length=self.sample_size		,							name=self.win_function		,							periodic=_UpperCamelCase )
												else:
																	SCREAMING_SNAKE_CASE           =    window_function(window_length=self.sample_size		,							name=self.win_function )

												SCREAMING_SNAKE_CASE           =    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		,							)

												SCREAMING_SNAKE_CASE           =    spectrogram(
												    one_waveform * self.frame_signal_scale		,							window=_UpperCamelCase		,							frame_length=self.sample_size		,							hop_length=self.sample_stride		,							fft_length=self.n_fft		,							center=_UpperCamelCase		,							preemphasis=self.preemphasis_coeff		,							mel_filters=_UpperCamelCase		,							mel_floor=self.mel_floor		,							log_mel="log"		,							)
												return msfc_features.T




							def 		__snake_case(  self				:   List[str]		,							_UpperCamelCase				:   Tuple		,							_UpperCamelCase				:   Optional[int]		,							_UpperCamelCase				:   List[str] )   ->			Tuple:



												'''simple docstring'''

												if self.normalize_means:
																	SCREAMING_SNAKE_CASE           =    x[:input_length].mean(axis=0 )
																	SCREAMING_SNAKE_CASE           =    np.subtract(_UpperCamelCase		,							_UpperCamelCase )
												if self.normalize_vars:
																	SCREAMING_SNAKE_CASE           =    x[:input_length].std(axis=0 )
																	SCREAMING_SNAKE_CASE           =    np.divide(_UpperCamelCase		,							_UpperCamelCase )

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

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

												return x




							def 		__snake_case(  self				:   int		,							_UpperCamelCase				:   List[np.ndarray]		,							_UpperCamelCase				:   Optional[np.ndarray] = None )   ->			List[np.ndarray]:



												'''simple docstring'''

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





							def __call__(  self				:   List[str]		,							_UpperCamelCase				:   Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]		,							_UpperCamelCase				:   Union[bool, str, PaddingStrategy] = False		,							_UpperCamelCase				:   Optional[int] = None		,							_UpperCamelCase				:   bool = False		,							_UpperCamelCase				:   Optional[int] = None		,							_UpperCamelCase				:   Optional[bool] = None		,							_UpperCamelCase				:   Optional[Union[str, TensorType]] = None		,							_UpperCamelCase				:   Optional[int] = None		,							**_UpperCamelCase				:   Dict		,							)   ->			BatchFeature:



												'''simple docstring'''

												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." )

												SCREAMING_SNAKE_CASE           =    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           =    is_batched_numpy or (
												    isinstance(_UpperCamelCase		,							(list, tuple) ) and (isinstance(raw_speech[0]		,							(np.ndarray, tuple, list) ))
												)

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

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

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

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

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

												SCREAMING_SNAKE_CASE           =    padded_inputs.get("attention_mask" )
												if attention_mask is not None:
																	SCREAMING_SNAKE_CASE           =    [np.asarray(_UpperCamelCase		,							dtype=np.intaa ) for array in attention_mask]

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

												if return_tensors is not None:
																	SCREAMING_SNAKE_CASE           =    padded_inputs.convert_to_tensors(_UpperCamelCase )

												return padded_inputs





import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_vision_available


if is_vision_available():
							from PIL import Image

							from transformers import ChineseCLIPImageProcessor, ChineseCLIPProcessor


@require_vision
class   lowercase      (       unittest.TestCase       ):




							def 		__snake_case(  self				:   Tuple )   ->			Tuple:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    tempfile.mkdtemp()

												SCREAMING_SNAKE_CASE           =    [
												    "[UNK]",
												    "[CLS]",
												    "[SEP]",
												    "[PAD]",
												    "[MASK]",
												    "的",
												    "价",
												    "格",
												    "是",
												    "15",
												    "便",
												    "alex",
												    "##andra",
												    "，",
												    "。",
												    "-",
												    "t",
												    "shirt",
												]
												SCREAMING_SNAKE_CASE           =    os.path.join(self.tmpdirname		,							VOCAB_FILES_NAMES["vocab_file"] )
												with open(self.vocab_file		,							"w"		,							encoding="utf-8" ) as vocab_writer:
																	vocab_writer.write("".join([x + "\n" for x in vocab_tokens] ) )

												SCREAMING_SNAKE_CASE           =    {
												    "do_resize": True,
												    "size": {"height": 224, "width": 224},
												    "do_center_crop": True,
												    "crop_size": {"height": 18, "width": 18},
												    "do_normalize": True,
												    "image_mean": [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3],
												    "image_std": [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1],
												    "do_convert_rgb": True,
												}
												SCREAMING_SNAKE_CASE           =    os.path.join(self.tmpdirname		,							_UpperCamelCase )
												with open(self.image_processor_file		,							"w"		,							encoding="utf-8" ) as fp:
																	json.dump(_UpperCamelCase		,							_UpperCamelCase )




							def 		__snake_case(  self				:   Union[str, Any]		,							**_UpperCamelCase				:   str )   ->			int:



												'''simple docstring'''

												return BertTokenizer.from_pretrained(self.tmpdirname		,							**_UpperCamelCase )




							def 		__snake_case(  self				:   List[str]		,							**_UpperCamelCase				:   Tuple )   ->			str:



												'''simple docstring'''

												return BertTokenizerFast.from_pretrained(self.tmpdirname		,							**_UpperCamelCase )




							def 		__snake_case(  self				:   List[Any]		,							**_UpperCamelCase				:   Optional[Any] )   ->			Any:



												'''simple docstring'''

												return ChineseCLIPImageProcessor.from_pretrained(self.tmpdirname		,							**_UpperCamelCase )




							def 		__snake_case(  self				:   Optional[Any] )   ->			int:



												'''simple docstring'''

												shutil.rmtree(self.tmpdirname )




							def 		__snake_case(  self				:   Dict )   ->			Optional[int]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    [np.random.randint(255		,							size=(3, 30, 400)		,							dtype=np.uinta )]

												SCREAMING_SNAKE_CASE           =    [Image.fromarray(np.moveaxis(_UpperCamelCase		,							0		,							-1 ) ) for x in image_inputs]

												return image_inputs




							def 		__snake_case(  self				:   List[str] )   ->			Any:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self.get_tokenizer()
												SCREAMING_SNAKE_CASE           =    self.get_rust_tokenizer()
												SCREAMING_SNAKE_CASE           =    self.get_image_processor()

												SCREAMING_SNAKE_CASE           =    ChineseCLIPProcessor(tokenizer=_UpperCamelCase		,							image_processor=_UpperCamelCase )
												processor_slow.save_pretrained(self.tmpdirname )
												SCREAMING_SNAKE_CASE           =    ChineseCLIPProcessor.from_pretrained(self.tmpdirname		,							use_fast=_UpperCamelCase )

												SCREAMING_SNAKE_CASE           =    ChineseCLIPProcessor(tokenizer=_UpperCamelCase		,							image_processor=_UpperCamelCase )
												processor_fast.save_pretrained(self.tmpdirname )
												SCREAMING_SNAKE_CASE           =    ChineseCLIPProcessor.from_pretrained(self.tmpdirname )

												self.assertEqual(processor_slow.tokenizer.get_vocab()		,							tokenizer_slow.get_vocab() )
												self.assertEqual(processor_fast.tokenizer.get_vocab()		,							tokenizer_fast.get_vocab() )
												self.assertEqual(tokenizer_slow.get_vocab()		,							tokenizer_fast.get_vocab() )
												self.assertIsInstance(processor_slow.tokenizer		,							_UpperCamelCase )
												self.assertIsInstance(processor_fast.tokenizer		,							_UpperCamelCase )

												self.assertEqual(processor_slow.image_processor.to_json_string()		,							image_processor.to_json_string() )
												self.assertEqual(processor_fast.image_processor.to_json_string()		,							image_processor.to_json_string() )
												self.assertIsInstance(processor_slow.image_processor		,							_UpperCamelCase )
												self.assertIsInstance(processor_fast.image_processor		,							_UpperCamelCase )




							def 		__snake_case(  self				:   str )   ->			Tuple:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    ChineseCLIPProcessor(tokenizer=self.get_tokenizer()		,							image_processor=self.get_image_processor() )
												processor.save_pretrained(self.tmpdirname )

												SCREAMING_SNAKE_CASE           =    self.get_tokenizer(cls_token="(CLS)"		,							sep_token="(SEP)" )
												SCREAMING_SNAKE_CASE           =    self.get_image_processor(do_normalize=_UpperCamelCase )

												SCREAMING_SNAKE_CASE           =    ChineseCLIPProcessor.from_pretrained(
												    self.tmpdirname		,							cls_token="(CLS)"		,							sep_token="(SEP)"		,							do_normalize=_UpperCamelCase )

												self.assertEqual(processor.tokenizer.get_vocab()		,							tokenizer_add_kwargs.get_vocab() )
												self.assertIsInstance(processor.tokenizer		,							_UpperCamelCase )

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




							def 		__snake_case(  self				:   List[Any] )   ->			Optional[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self.get_image_processor()
												SCREAMING_SNAKE_CASE           =    self.get_tokenizer()

												SCREAMING_SNAKE_CASE           =    ChineseCLIPProcessor(tokenizer=_UpperCamelCase		,							image_processor=_UpperCamelCase )

												SCREAMING_SNAKE_CASE           =    self.prepare_image_inputs()

												SCREAMING_SNAKE_CASE           =    image_processor(_UpperCamelCase		,							return_tensors="np" )
												SCREAMING_SNAKE_CASE           =    processor(images=_UpperCamelCase		,							return_tensors="np" )

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




							def 		__snake_case(  self				:   List[Any] )   ->			str:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self.get_image_processor()
												SCREAMING_SNAKE_CASE           =    self.get_tokenizer()

												SCREAMING_SNAKE_CASE           =    ChineseCLIPProcessor(tokenizer=_UpperCamelCase		,							image_processor=_UpperCamelCase )

												SCREAMING_SNAKE_CASE           =    "Alexandra，T-shirt的价格是15便士。"

												SCREAMING_SNAKE_CASE           =    processor(text=_UpperCamelCase )

												SCREAMING_SNAKE_CASE           =    tokenizer(_UpperCamelCase )

												for key in encoded_tok.keys():
																	self.assertListEqual(encoded_tok[key]		,							encoded_processor[key] )




							def 		__snake_case(  self				:   Optional[Any] )   ->			List[str]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self.get_image_processor()
												SCREAMING_SNAKE_CASE           =    self.get_tokenizer()

												SCREAMING_SNAKE_CASE           =    ChineseCLIPProcessor(tokenizer=_UpperCamelCase		,							image_processor=_UpperCamelCase )

												SCREAMING_SNAKE_CASE           =    "Alexandra，T-shirt的价格是15便士。"
												SCREAMING_SNAKE_CASE           =    self.prepare_image_inputs()

												SCREAMING_SNAKE_CASE           =    processor(text=_UpperCamelCase		,							images=_UpperCamelCase )

												self.assertListEqual(list(inputs.keys() )		,							["input_ids", "token_type_ids", "attention_mask", "pixel_values"] )

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




							def 		__snake_case(  self				:   Optional[Any] )   ->			Dict:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self.get_image_processor()
												SCREAMING_SNAKE_CASE           =    self.get_tokenizer()

												SCREAMING_SNAKE_CASE           =    ChineseCLIPProcessor(tokenizer=_UpperCamelCase		,							image_processor=_UpperCamelCase )

												SCREAMING_SNAKE_CASE           =    [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

												SCREAMING_SNAKE_CASE           =    processor.batch_decode(_UpperCamelCase )
												SCREAMING_SNAKE_CASE           =    tokenizer.batch_decode(_UpperCamelCase )

												self.assertListEqual(_UpperCamelCase		,							_UpperCamelCase )





							def 		__snake_case(  self				:   int )   ->			int:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self.get_image_processor()
												SCREAMING_SNAKE_CASE           =    self.get_tokenizer()

												SCREAMING_SNAKE_CASE           =    ChineseCLIPProcessor(tokenizer=_UpperCamelCase		,							image_processor=_UpperCamelCase )

												SCREAMING_SNAKE_CASE           =    "Alexandra，T-shirt的价格是15便士。"
												SCREAMING_SNAKE_CASE           =    self.prepare_image_inputs()

												SCREAMING_SNAKE_CASE           =    processor(text=_UpperCamelCase		,							images=_UpperCamelCase )

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




