





'''simple docstring'''

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


__lowerCamelCase : str											=			logging.get_logger(__name__)

class       UpperCAmelCase (						_lowercase  ):
		UpperCAmelCase					:	Any 			=		['''audio_values''', '''audio_mask''']




		def __init__(self      :	Union[str, Any]							,			A__      :	Any=2_0_4_8							,			A__      :	Any=1							,			A__      :	List[Any]=[1_6, 1_6]							,			A__      :	Union[str, Any]=1_2_8							,			A__      :	Optional[Any]=4_4_1_0_0							,			A__      :	int=8_6							,			A__      :	str=2_0_4_8							,			A__      :	str=0.0							,			**A__      :	Optional[int]							,			)					-> Optional[Any]:
						super().__init__(
						    feature_size=A__							,			sampling_rate=A__							,			padding_value=A__							,			**A__							,			)

						lowercase         = spectrogram_length
						lowercase         = num_channels
						lowercase         = patch_size
						lowercase         = feature_size // self.patch_size[1]
						lowercase         = n_fft
						lowercase         = sampling_rate // hop_length_to_sampling_rate
						lowercase         = sampling_rate
						lowercase         = padding_value
						lowercase         = mel_filter_bank(
						    num_frequency_bins=1 + n_fft // 2							,			num_mel_filters=A__							,			min_frequency=0.0							,			max_frequency=2_2_0_5_0.0							,			sampling_rate=A__							,			norm="slaney"							,			mel_scale="slaney"							,			).T




		def 				UpperCAmelCase__   (self      :	Tuple							,			A__      :	np.array				)					-> np.ndarray:
						lowercase         = spectrogram(
						    A__							,			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=8_0.0							,			)
						lowercase         = log_spec[:, :-1]
						lowercase         = log_spec - 2_0.0
						lowercase         = np.clip(log_spec / 4_0.0							,			-2.0							,			0.0				) + 1.0
						return log_spec





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

						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."				)

						lowercase         = isinstance(A__							,			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(A__							,			(list, tuple)				) and (isinstance(raw_speech[0]							,			(np.ndarray, tuple, list)				))
						)
						if is_batched:
										lowercase         = [np.asarray([speech]							,			dtype=np.floataa				).T for speech in raw_speech]
						elif not is_batched and not isinstance(A__							,			np.ndarray				):
										lowercase         = np.asarray(A__							,			dtype=np.floataa				)
						elif isinstance(A__							,			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         = [np.asarray([raw_speech]				).T]

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

						# Create audio attention mask
						lowercase         = 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:
										lowercase         = [
										    (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
										]
										lowercase         = np.array(A__				).astype(np.floataa				)

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

						# return as BatchFeature
						if return_attention_mask:
										lowercase         = {"audio_values": padded_audio_features, "audio_mask": audio_mask}
						else:
										lowercase         = {"audio_values": padded_audio_features}

						lowercase         = BatchFeature(data=A__							,			tensor_type=A__				)
						return encoded_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					:	Optional[int] 			=		ViTImageProcessor if is_vision_available() else None




		@property
		def 				UpperCAmelCase__   (self      :	Optional[int]				)					-> Optional[Any]:
						return self.image_processor_tester.prepare_image_processor_dict()




		def 				UpperCAmelCase__   (self      :	str				)					-> int:
						lowercase         = (3, 3_2, 1_2_8)
						lowercase         = tempfile.mkdtemp()

						# fmt: off
						lowercase         = ["[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
						lowercase         = dict(zip(A__							,			range(len(A__				)				)				)				)

						lowercase         = 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(A__				) + "\n"				)

						lowercase         = {
						    "do_normalize": False,
						    "do_resize": True,
						    "image_processor_type": "ViTImageProcessor",
						    "resample": 3,
						    "size": {"height": 3_2, "width": 1_2_8},
						}
						lowercase         = os.path.join(self.tmpdirname							,			A__				)
						with open(self.image_processor_file							,			"w"							,			encoding="utf-8"				) as fp:
										json.dump(A__							,			A__				)




		def 				UpperCAmelCase__   (self      :	Dict							,			**A__      :	List[Any]				)					-> Union[str, Any]:
						return MgpstrTokenizer.from_pretrained(self.tmpdirname							,			**A__				)




		def 				UpperCAmelCase__   (self      :	Any							,			**A__      :	Tuple				)					-> Tuple:
						return ViTImageProcessor.from_pretrained(self.tmpdirname							,			**A__				)




		def 				UpperCAmelCase__   (self      :	Optional[int]				)					-> int:
						shutil.rmtree(self.tmpdirname				)




		def 				UpperCAmelCase__   (self      :	str				)					-> List[str]:
						lowercase         = np.random.randint(2_5_5							,			size=(3, 3_0, 4_0_0)							,			dtype=np.uinta				)

						lowercase         = Image.fromarray(np.moveaxis(A__							,			0							,			-1				)				)

						return image_input




		def 				UpperCAmelCase__   (self      :	str				)					-> List[Any]:
						lowercase         = self.get_tokenizer()
						lowercase         = self.get_image_processor()

						lowercase         = MgpstrProcessor(tokenizer=A__							,			image_processor=A__				)
						processor.save_pretrained(self.tmpdirname				)
						lowercase         = MgpstrProcessor.from_pretrained(self.tmpdirname							,			use_fast=A__				)

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

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




		def 				UpperCAmelCase__   (self      :	Any				)					-> Optional[int]:
						lowercase         = self.get_tokenizer()
						lowercase         = self.get_image_processor()

						lowercase         = MgpstrProcessor(tokenizer=A__							,			image_processor=A__				)
						processor.save_pretrained(self.tmpdirname				)

						lowercase         = self.get_tokenizer(bos_token="(BOS)"							,			eos_token="(EOS)"				)
						lowercase         = self.get_image_processor(do_normalize=A__							,			padding_value=1.0				)

						lowercase         = MgpstrProcessor.from_pretrained(
						    self.tmpdirname							,			bos_token="(BOS)"							,			eos_token="(EOS)"							,			do_normalize=A__							,			padding_value=1.0				)

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

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




		def 				UpperCAmelCase__   (self      :	List[Any]				)					-> List[Any]:
						lowercase         = self.get_image_processor()
						lowercase         = self.get_tokenizer()

						lowercase         = MgpstrProcessor(tokenizer=A__							,			image_processor=A__				)

						lowercase         = self.prepare_image_inputs()

						lowercase         = image_processor(A__							,			return_tensors="np"				)
						lowercase         = processor(images=A__							,			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 				UpperCAmelCase__   (self      :	List[Any]				)					-> Union[str, Any]:
						lowercase         = self.get_image_processor()
						lowercase         = self.get_tokenizer()

						lowercase         = MgpstrProcessor(tokenizer=A__							,			image_processor=A__				)

						lowercase         = "test"

						lowercase         = processor(text=A__				)

						lowercase         = tokenizer(A__				)
						for key in encoded_tok.keys():
										self.assertListEqual(encoded_tok[key]							,			encoded_processor[key]				)




		def 				UpperCAmelCase__   (self      :	int				)					-> Optional[int]:
						lowercase         = self.get_image_processor()
						lowercase         = self.get_tokenizer()

						lowercase         = MgpstrProcessor(tokenizer=A__							,			image_processor=A__				)

						lowercase         = "test"
						lowercase         = self.prepare_image_inputs()

						lowercase         = processor(text=A__							,			images=A__				)

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

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




		def 				UpperCAmelCase__   (self      :	List[Any]				)					-> Union[str, Any]:
						lowercase         = self.get_image_processor()
						lowercase         = self.get_tokenizer()

						lowercase         = MgpstrProcessor(tokenizer=A__							,			image_processor=A__				)

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

						lowercase         = processor.char_decode(A__				)
						lowercase         = tokenizer.batch_decode(A__				)
						lowercase         = [seq.replace(" "							,			""				) for seq in decoded_tok]

						self.assertListEqual(A__							,			A__				)




		def 				UpperCAmelCase__   (self      :	Any				)					-> Optional[int]:
						lowercase         = self.get_image_processor()
						lowercase         = self.get_tokenizer()

						lowercase         = MgpstrProcessor(tokenizer=A__							,			image_processor=A__				)

						lowercase         = None
						lowercase         = self.prepare_image_inputs()

						lowercase         = processor(text=A__							,			images=A__				)

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





		def 				UpperCAmelCase__   (self      :	int				)					-> Optional[Any]:
						lowercase         = self.get_image_processor()
						lowercase         = self.get_tokenizer()

						lowercase         = MgpstrProcessor(tokenizer=A__							,			image_processor=A__				)

						lowercase         = torch.randn(1							,			2_7							,			3_8				)
						lowercase         = torch.randn(1							,			2_7							,			5_0_2_5_7				)
						lowercase         = torch.randn(1							,			2_7							,			3_0_5_2_2				)

						lowercase         = processor.batch_decode([char_input, bpe_input, wp_input]				)

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




