

import itertools
import os
import random
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_speech_available():
	from transformers import WhisperFeatureExtractor

if is_torch_available():
	import torch

A_ :				Union[str, Any]						 =    random.Random()

def 		UpperCamelCase      (lowercase_:				str					,   lowercase_:				Tuple=1.0					,   lowercase_:				Optional[Any]=None					,   lowercase_:				List[Any]=None      )  ->   Optional[Any]:
	if rng is None:
		A__							:					Union[str, Any]       =	global_rng

	A__							:					Any       =	[]
	for batch_idx in range(shape[0]      ):
		values.append([]      )
		for _ in range(shape[1]      ):
			values[-1].append(rng.random() * scale      )

	return values



@require_torch
@require_torchaudio
class 					_a		(unittest.TestCase       ):


					'''simple docstring'''






					def __init__(				self					, A__					, A__=7					, A__=400					, A__=2000					, A__=10					, A__=160					, A__=8					, A__=0.0					, A__=4000					, A__=False					, A__=True					, ):
						A__							:					Union[str, Any]       =	parent
						A__							:					Dict       =	batch_size
						A__							:					int       =	min_seq_length
						A__							:					Optional[Any]       =	max_seq_length
						A__							:					Union[str, Any]       =	(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
						A__							:					List[str]       =	padding_value
						A__							:					List[str]       =	sampling_rate
						A__							:					Optional[int]       =	return_attention_mask
						A__							:					Any       =	do_normalize
						A__							:					Dict       =	feature_size
						A__							:					Optional[Any]       =	chunk_length
						A__							:					Any       =	hop_length






					def 						__A    (				self    ):
						return {
						    "feature_size": self.feature_size,
						    "hop_length": self.hop_length,
						    "chunk_length": self.chunk_length,
						    "padding_value": self.padding_value,
						    "sampling_rate": self.sampling_rate,
						    "return_attention_mask": self.return_attention_mask,
						    "do_normalize": self.do_normalize,
						}






					def 						__A    (				self					, A__=False					, A__=False    ):
						def _flatten(A__    ):
							return list(itertools.chain(*A__    )    )

						if equal_length:
							A__							:					Dict       =	[floats_list((self.max_seq_length, self.feature_size)    ) for _ in range(self.batch_size    )]
						else:
							# make sure that inputs increase in size
							A__							:					Optional[Any]       =	[
							    floats_list((x, self.feature_size)    )
							    for x in range(self.min_seq_length					, self.max_seq_length					, self.seq_length_diff    )
							]
						if numpify:
							A__							:					Tuple       =	[np.asarray(A__    ) for x in speech_inputs]
						return speech_inputs



@require_torch
@require_torchaudio
class 					_a		(__magic_name__	,     unittest.TestCase       ):


					'''simple docstring'''






					UpperCAmelCase__:     List[str]													= WhisperFeatureExtractor if is_speech_available() else None






					def 						__A    (				self    ):
						A__							:					Dict       =	WhisperFeatureExtractionTester(self    )






					def 						__A    (				self    ):
						A__							:					Dict       =	self.feature_extraction_class(**self.feat_extract_dict    )

						with tempfile.TemporaryDirectory() as tmpdirname:
							A__							:					Union[str, Any]       =	feat_extract_first.save_pretrained(A__    )[0]
							check_json_file_has_correct_format(A__    )
							A__							:					Union[str, Any]       =	self.feature_extraction_class.from_pretrained(A__    )

						A__							:					str       =	feat_extract_first.to_dict()
						A__							:					Union[str, Any]       =	feat_extract_second.to_dict()
						A__							:					Optional[int]       =	feat_extract_first.mel_filters
						A__							:					List[Any]       =	feat_extract_second.mel_filters
						self.assertTrue(np.allclose(A__					, A__    )    )
						self.assertEqual(A__					, A__    )






					def 						__A    (				self    ):
						A__							:					int       =	self.feature_extraction_class(**self.feat_extract_dict    )

						with tempfile.TemporaryDirectory() as tmpdirname:
							A__							:					Union[str, Any]       =	os.path.join(A__					, """feat_extract.json"""    )
							feat_extract_first.to_json_file(A__    )
							A__							:					str       =	self.feature_extraction_class.from_json_file(A__    )

						A__							:					Dict       =	feat_extract_first.to_dict()
						A__							:					Tuple       =	feat_extract_second.to_dict()
						A__							:					Union[str, Any]       =	feat_extract_first.mel_filters
						A__							:					int       =	feat_extract_second.mel_filters
						self.assertTrue(np.allclose(A__					, A__    )    )
						self.assertEqual(A__					, A__    )






					def 						__A    (				self    ):
						# Tests that all call wrap to encode_plus and batch_encode_plus
						A__							:					List[str]       =	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
						# create three inputs of length 800, 1000, and 1200
						A__							:					List[Any]       =	[floats_list((1, x)    )[0] for x in range(800					, 1400					, 200    )]
						A__							:					int       =	[np.asarray(A__    ) for speech_input in speech_inputs]

						# Test feature size
						A__							:					Union[str, Any]       =	feature_extractor(A__					, padding="""max_length"""					, return_tensors="""np"""    ).input_features
						self.assertTrue(input_features.ndim == 3    )
						self.assertTrue(input_features.shape[-1] == feature_extractor.nb_max_frames    )
						self.assertTrue(input_features.shape[-2] == feature_extractor.feature_size    )

						# Test not batched input
						A__							:					Optional[Any]       =	feature_extractor(speech_inputs[0]					, return_tensors="""np"""    ).input_features
						A__							:					Optional[int]       =	feature_extractor(np_speech_inputs[0]					, return_tensors="""np"""    ).input_features
						self.assertTrue(np.allclose(A__					, A__					, atol=1e-3    )    )

						# Test batched
						A__							:					Any       =	feature_extractor(A__					, return_tensors="""np"""    ).input_features
						A__							:					Optional[Any]       =	feature_extractor(A__					, return_tensors="""np"""    ).input_features
						for enc_seq_a, enc_seq_a in zip(A__					, A__    ):
							self.assertTrue(np.allclose(A__					, A__					, atol=1e-3    )    )

						# Test 2-D numpy arrays are batched.
						A__							:					List[str]       =	[floats_list((1, x)    )[0] for x in (800, 800, 800)]
						A__							:					List[Any]       =	np.asarray(A__    )
						A__							:					List[str]       =	feature_extractor(A__					, return_tensors="""np"""    ).input_features
						A__							:					str       =	feature_extractor(A__					, return_tensors="""np"""    ).input_features
						for enc_seq_a, enc_seq_a in zip(A__					, A__    ):
							self.assertTrue(np.allclose(A__					, A__					, atol=1e-3    )    )

						# Test truncation required
						A__							:					List[Any]       =	[floats_list((1, x)    )[0] for x in range(200					, (feature_extractor.n_samples + 500)					, 200    )]
						A__							:					Tuple       =	[np.asarray(A__    ) for speech_input in speech_inputs]

						A__							:					List[str]       =	[x[: feature_extractor.n_samples] for x in speech_inputs]
						A__							:					List[str]       =	[np.asarray(A__    ) for speech_input in speech_inputs_truncated]

						A__							:					Any       =	feature_extractor(A__					, return_tensors="""np"""    ).input_features
						A__							:					str       =	feature_extractor(A__					, return_tensors="""np"""    ).input_features
						for enc_seq_a, enc_seq_a in zip(A__					, A__    ):
							self.assertTrue(np.allclose(A__					, A__					, atol=1e-3    )    )






					def 						__A    (				self    ):
						import torch

						A__							:					str       =	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
						A__							:					Optional[int]       =	np.random.rand(100					, 32    ).astype(np.floataa    )
						A__							:					List[Any]       =	np_speech_inputs.tolist()

						for inputs in [py_speech_inputs, np_speech_inputs]:
							A__							:					str       =	feature_extractor.pad([{"""input_features""": inputs}]					, return_tensors="""np"""    )
							self.assertTrue(np_processed.input_features.dtype == np.floataa    )
							A__							:					Optional[Any]       =	feature_extractor.pad([{"""input_features""": inputs}]					, return_tensors="""pt"""    )
							self.assertTrue(pt_processed.input_features.dtype == torch.floataa    )






					def 						__A    (				self					, A__    ):
						A__							:					Tuple       =	load_dataset("""hf-internal-testing/librispeech_asr_dummy"""					, """clean"""					, split="""validation"""    )
						# automatic decoding with librispeech
						A__							:					Optional[int]       =	ds.sort("""id"""    ).select(range(A__    )    )[:num_samples]["""audio"""]

						return [x["array"] for x in speech_samples]






					def 						__A    (				self    ):
						# fmt: off
						A__							:					Optional[int]       =	torch.tensor(
						    [
						        0.1_1_9_3, -0.0_9_4_6, -0.1_0_9_8, -0.0_1_9_6, 0.0_2_2_5, -0.0_6_9_0, -0.1_7_3_6, 0.0_9_5_1,
						        0.0_9_7_1, -0.0_8_1_7, -0.0_7_0_2, 0.0_1_6_2, 0.0_2_6_0, 0.0_0_1_7, -0.0_1_9_2, -0.1_6_7_8,
						        0.0_7_0_9, -0.1_8_6_7, -0.0_6_5_5, -0.0_2_7_4, -0.0_2_3_4, -0.1_8_8_4, -0.0_5_1_6, -0.0_5_5_4,
						        -0.0_2_7_4, -0.1_4_2_5, -0.1_4_2_3, 0.0_8_3_7, 0.0_3_7_7, -0.0_8_5_4
						    ]    )
						# fmt: on

						A__							:					Any       =	self._load_datasamples(1    )
						A__							:					Optional[Any]       =	WhisperFeatureExtractor()
						A__							:					Union[str, Any]       =	feature_extractor(A__					, return_tensors="""pt"""    ).input_features
						self.assertEqual(input_features.shape					, (1, 80, 3000)    )
						self.assertTrue(torch.allclose(input_features[0, 0, :30]					, A__					, atol=1e-4    )    )






					def 						__A    (				self    ):
						A__							:					Dict       =	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
						A__							:					Optional[Any]       =	self._load_datasamples(1    )[0]
						A__							:					int       =	((audio - audio.min()) / (audio.max() - audio.min())) * 6_5535  # Rescale to [0, 65535] to show issue
						A__							:					Optional[Any]       =	feat_extract.zero_mean_unit_var_norm([audio]					, attention_mask=A__    )[0]

						self.assertTrue(np.all(np.mean(A__    ) < 1e-3    )    )
						self.assertTrue(np.all(np.abs(np.var(A__    ) - 1    ) < 1e-3    )    )






from pathlib import Path

import cva
import numpy as np
from matplotlib import pyplot as plt

def 		UpperCamelCase      (lowercase_:				np.ndarray					,   lowercase_:				np.ndarray					,   lowercase_:				np.ndarray					,   lowercase_:				int					,   lowercase_:				int      )  ->   np.ndarray:
	A__							:					Any       =	cva.getAffineTransform(lowercase_					,   lowercase_      )
	return cva.warpAffine(lowercase_					,   lowercase_					,   (rows, cols)      )


if __name__ == "__main__":
	# read original image
	A_ :				List[Any]						 =    cva.imread(
	    str(Path(__file__).resolve().parent.parent / 'image_data' / 'lena.jpg')
	)
	# turn image in gray scale value
	A_ :				List[Any]						 =    cva.cvtColor(image, cva.COLOR_BGR2GRAY)
	# get image shape
	A_ ,    A_ :				Optional[Any]						 =    gray_img.shape

	# set different points to rotate image
	A_ :				str						 =    np.array([[50, 50], [200, 50], [50, 200]], np.floataa)
	A_ :				Dict						 =    np.array([[10, 100], [200, 50], [100, 250]], np.floataa)
	A_ :				Optional[int]						 =    np.array([[50, 50], [150, 50], [120, 200]], np.floataa)
	A_ :				Optional[int]						 =    np.array([[10, 100], [80, 50], [180, 250]], np.floataa)

	# add all rotated images in a list
	A_ :				Dict						 =    [
	    gray_img,
	    get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
	    get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
	    get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
	]

	# plot different image rotations
	A_ :				Union[str, Any]						 =    plt.figure(1)
	A_ :				Union[str, Any]						 =    ['Original', 'Rotation 1', 'Rotation 2', 'Rotation 3']
	for i, image in enumerate(images):
		plt.subplot(2, 2, i + 1), plt.imshow(image, 'gray')
		plt.title(titles[i])
		plt.axis('off')
		plt.subplots_adjust(left=0.0, bottom=0.05, right=1.0, top=0.95)
	plt.show()



