




import argparse
import os
import re

import packaging.version


__A        =    "examples/"
__A        =    {
    "examples": (re.compile(R"^check_min_version\(\"[^\"]+\"\)\s*$", re.MULTILINE), "check_min_version(\"VERSION\")\n"),
    "init": (re.compile(R"^__version__\s+=\s+\"([^\"]+)\"\s*$", re.MULTILINE), "__version__ = \"VERSION\"\n"),
    "setup": (re.compile(R"^(\s*)version\s*=\s*\"[^\"]+\",", re.MULTILINE), R"\1version=\"VERSION\","),
    "doc": (re.compile(R"^(\s*)release\s*=\s*\"[^\"]+\"$", re.MULTILINE), "release = \"VERSION\"\n"),
}
__A        =    {
    "init": "src/diffusers/__init__.py",
    "setup": "setup.py",
}
__A        =    "README.md"







def 			lowerCamelCase_		(       UpperCamelCase__				:      List[Any] ,     UpperCamelCase__				:      Dict ,     UpperCamelCase__				:      Optional[Any]      )     ->       Optional[Any]:





				"""simple docstring"""


				with open(UpperCamelCase__ ,     'r' ,     encoding='utf-8' ,     newline='\n'      ) as f:
								__lowerCamelCase		       =    f.read()
				__lowerCamelCase       ,						__lowerCamelCase		       =    REPLACE_PATTERNS[pattern]
				__lowerCamelCase		       =    replace.replace('VERSION' ,     UpperCamelCase__      )
				__lowerCamelCase		       =    re_pattern.sub(UpperCamelCase__ ,     UpperCamelCase__      )
				with open(UpperCamelCase__ ,     'w' ,     encoding='utf-8' ,     newline='\n'      ) as f:
								f.write(UpperCamelCase__      )







def 			lowerCamelCase_		(       UpperCamelCase__				:      str      )     ->       Optional[Any]:





				"""simple docstring"""


				for folder, directories, fnames in os.walk(UpperCamelCase__      ):
								# Removing some of the folders with non-actively maintained examples from the walk
								if "research_projects" in directories:
												directories.remove('research_projects'      )
								if "legacy" in directories:
												directories.remove('legacy'      )
								for fname in fnames:
												if fname.endswith('.py'      ):
																update_version_in_file(os.path.join(UpperCamelCase__ ,     UpperCamelCase__      ) ,     UpperCamelCase__ ,     pattern='examples'      )







def 			lowerCamelCase_		(       UpperCamelCase__				:      int ,     UpperCamelCase__				:      List[str]=False      )     ->       List[Any]:





				"""simple docstring"""


				for pattern, fname in REPLACE_FILES.items():
								update_version_in_file(UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__      )
				if not patch:
								update_version_in_examples(UpperCamelCase__      )







def 			lowerCamelCase_		(       )     ->       Dict:





				"""simple docstring"""


				__lowerCamelCase		       =    '🤗 Transformers currently provides the following architectures'
				__lowerCamelCase		       =    '1. Want to contribute a new model?'
				with open(UpperCamelCase__ ,     'r' ,     encoding='utf-8' ,     newline='\n'      ) as f:
								__lowerCamelCase		       =    f.readlines()

				# Find the start of the list.
				__lowerCamelCase		       =    0
				while not lines[start_index].startswith(_start_prompt      ):
								start_index += 1
				start_index += 1

				__lowerCamelCase		       =    start_index
				# Update the lines in the model list.
				while not lines[index].startswith(_end_prompt      ):
								if lines[index].startswith('1.'      ):
												__lowerCamelCase		       =    lines[index].replace(
												    'https://huggingface.co/docs/diffusers/main/model_doc' ,     'https://huggingface.co/docs/diffusers/model_doc' ,     )
								index += 1

				with open(UpperCamelCase__ ,     'w' ,     encoding='utf-8' ,     newline='\n'      ) as f:
								f.writelines(UpperCamelCase__      )







def 			lowerCamelCase_		(       )     ->       List[str]:





				"""simple docstring"""


				with open(REPLACE_FILES['init'] ,     'r'      ) as f:
								__lowerCamelCase		       =    f.read()
				__lowerCamelCase		       =    REPLACE_PATTERNS['init'][0].search(UpperCamelCase__      ).groups()[0]
				return packaging.version.parse(UpperCamelCase__      )







def 			lowerCamelCase_		(       UpperCamelCase__				:      str=False      )     ->       Any:





				"""simple docstring"""


				__lowerCamelCase		       =    get_version()
				if patch and default_version.is_devrelease:
								raise ValueError('Can\'t create a patch version from the dev branch, checkout a released version!'      )
				if default_version.is_devrelease:
								__lowerCamelCase		       =    default_version.base_version
				elif patch:
								__lowerCamelCase		       =    F"""{default_version.major}.{default_version.minor}.{default_version.micro + 1}"""
				else:
								__lowerCamelCase		       =    F"""{default_version.major}.{default_version.minor + 1}.0"""

				# Now let's ask nicely if that's the right one.
				__lowerCamelCase		       =    input(F"""Which version are you releasing? [{default_version}]"""      )
				if len(UpperCamelCase__      ) == 0:
								__lowerCamelCase		       =    default_version

				print(F"""Updating version to {version}."""      )
				global_version_update(UpperCamelCase__ ,     patch=UpperCamelCase__      )







def 			lowerCamelCase_		(       )     ->       Tuple:





				"""simple docstring"""


				__lowerCamelCase		       =    get_version()
				__lowerCamelCase		       =    F"""{current_version.major}.{current_version.minor + 1}.0.dev0"""
				__lowerCamelCase		       =    current_version.base_version

				# Check with the user we got that right.
				__lowerCamelCase		       =    input(F"""Which version are we developing now? [{dev_version}]"""      )
				if len(UpperCamelCase__      ) == 0:
								__lowerCamelCase		       =    dev_version

				print(F"""Updating version to {version}."""      )
				global_version_update(UpperCamelCase__      )


#    print("Cleaning main README, don't forget to run `make fix-copies`.")
#    clean_main_ref_in_model_list()


if __name__ == "__main__":
					__A        =    argparse.ArgumentParser()
					parser.add_argument("--post_release", action="store_true", help="Whether this is pre or post release.")
					parser.add_argument("--patch", action="store_true", help="Whether or not this is a patch release.")
					__A        =    parser.parse_args()
					if not args.post_release:
										pre_release_work(patch=args.patch)
					elif args.patch:
										print("Nothing to do after a patch :-)")
					else:
										post_release_work()








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        =    random.Random()







def 			lowerCamelCase_		(       UpperCamelCase__				:      Optional[int] ,     UpperCamelCase__				:      List[str]=1.0 ,     UpperCamelCase__				:      Union[str, Any]=None ,     UpperCamelCase__				:      Optional[int]=None      )     ->       Optional[Any]:





				"""simple docstring"""


				if rng is None:
								__lowerCamelCase		       =    global_rng

				__lowerCamelCase		       =    []
				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     __lowerCAmelCase (       unittest.TestCase ):

			"""simple docstring"""







			def __init__(      self			,						lowerCamelCase__			,						lowerCamelCase__=7			,						lowerCamelCase__=400			,						lowerCamelCase__=2_000			,						lowerCamelCase__=10			,						lowerCamelCase__=160			,						lowerCamelCase__=8			,						lowerCamelCase__=0.0			,						lowerCamelCase__=4_000			,						lowerCamelCase__=False			,						lowerCamelCase__=True			,						)    ->      List[str]:



							'''simple docstring'''
							__lowerCamelCase		       =    parent
							__lowerCamelCase		       =    batch_size
							__lowerCamelCase		       =    min_seq_length
							__lowerCamelCase		       =    max_seq_length
							__lowerCamelCase		       =    (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
							__lowerCamelCase		       =    padding_value
							__lowerCamelCase		       =    sampling_rate
							__lowerCamelCase		       =    return_attention_mask
							__lowerCamelCase		       =    do_normalize
							__lowerCamelCase		       =    feature_size
							__lowerCamelCase		       =    chunk_length
							__lowerCamelCase		       =    hop_length


			def       lowercase_    (      self							)    ->      Any:



							'''simple docstring'''
							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       lowercase_    (      self			,						lowerCamelCase__=False			,						lowerCamelCase__=False							)    ->      Optional[int]:



							'''simple docstring'''
							def _flatten(lowerCamelCase__							):
											return list(itertools.chain(*lowerCamelCase__							)							)

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







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

			"""simple docstring"""







			snake_case_   		=       WhisperFeatureExtractor if is_speech_available() else None


			def       lowercase_    (      self							)    ->      Any:



							'''simple docstring'''
							__lowerCamelCase		       =    WhisperFeatureExtractionTester(self							)


			def       lowercase_    (      self							)    ->      List[str]:



							'''simple docstring'''
							__lowerCamelCase		       =    self.feature_extraction_class(**self.feat_extract_dict							)

							with tempfile.TemporaryDirectory() as tmpdirname:
											__lowerCamelCase		       =    feat_extract_first.save_pretrained(lowerCamelCase__							)[0]
											check_json_file_has_correct_format(lowerCamelCase__							)
											__lowerCamelCase		       =    self.feature_extraction_class.from_pretrained(lowerCamelCase__							)

							__lowerCamelCase		       =    feat_extract_first.to_dict()
							__lowerCamelCase		       =    feat_extract_second.to_dict()
							__lowerCamelCase		       =    feat_extract_first.mel_filters
							__lowerCamelCase		       =    feat_extract_second.mel_filters
							self.assertTrue(np.allclose(lowerCamelCase__			,						lowerCamelCase__							)							)
							self.assertEqual(lowerCamelCase__			,						lowerCamelCase__							)


			def       lowercase_    (      self							)    ->      Dict:



							'''simple docstring'''
							__lowerCamelCase		       =    self.feature_extraction_class(**self.feat_extract_dict							)

							with tempfile.TemporaryDirectory() as tmpdirname:
											__lowerCamelCase		       =    os.path.join(lowerCamelCase__			,						'feat_extract.json'							)
											feat_extract_first.to_json_file(lowerCamelCase__							)
											__lowerCamelCase		       =    self.feature_extraction_class.from_json_file(lowerCamelCase__							)

							__lowerCamelCase		       =    feat_extract_first.to_dict()
							__lowerCamelCase		       =    feat_extract_second.to_dict()
							__lowerCamelCase		       =    feat_extract_first.mel_filters
							__lowerCamelCase		       =    feat_extract_second.mel_filters
							self.assertTrue(np.allclose(lowerCamelCase__			,						lowerCamelCase__							)							)
							self.assertEqual(lowerCamelCase__			,						lowerCamelCase__							)


			def       lowercase_    (      self							)    ->      Dict:



							'''simple docstring'''
							# Tests that all call wrap to encode_plus and batch_encode_plus
							__lowerCamelCase		       =    self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
							# create three inputs of length 800, 1000, and 1200
							__lowerCamelCase		       =    [floats_list((1, x)							)[0] for x in range(800			,						1_400			,						200							)]
							__lowerCamelCase		       =    [np.asarray(lowerCamelCase__							) for speech_input in speech_inputs]

							# Test feature size
							__lowerCamelCase		       =    feature_extractor(lowerCamelCase__			,						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
							__lowerCamelCase		       =    feature_extractor(speech_inputs[0]			,						return_tensors='np'							).input_features
							__lowerCamelCase		       =    feature_extractor(np_speech_inputs[0]			,						return_tensors='np'							).input_features
							self.assertTrue(np.allclose(lowerCamelCase__			,						lowerCamelCase__			,						atol=1e-3							)							)

							# Test batched
							__lowerCamelCase		       =    feature_extractor(lowerCamelCase__			,						return_tensors='np'							).input_features
							__lowerCamelCase		       =    feature_extractor(lowerCamelCase__			,						return_tensors='np'							).input_features
							for enc_seq_a, enc_seq_a in zip(lowerCamelCase__			,						lowerCamelCase__							):
											self.assertTrue(np.allclose(lowerCamelCase__			,						lowerCamelCase__			,						atol=1e-3							)							)

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

							# Test truncation required
							__lowerCamelCase		       =    [floats_list((1, x)							)[0] for x in range(200			,						(feature_extractor.n_samples + 500)			,						200							)]
							__lowerCamelCase		       =    [np.asarray(lowerCamelCase__							) for speech_input in speech_inputs]

							__lowerCamelCase		       =    [x[: feature_extractor.n_samples] for x in speech_inputs]
							__lowerCamelCase		       =    [np.asarray(lowerCamelCase__							) for speech_input in speech_inputs_truncated]

							__lowerCamelCase		       =    feature_extractor(lowerCamelCase__			,						return_tensors='np'							).input_features
							__lowerCamelCase		       =    feature_extractor(lowerCamelCase__			,						return_tensors='np'							).input_features
							for enc_seq_a, enc_seq_a in zip(lowerCamelCase__			,						lowerCamelCase__							):
											self.assertTrue(np.allclose(lowerCamelCase__			,						lowerCamelCase__			,						atol=1e-3							)							)


			def       lowercase_    (      self							)    ->      List[str]:



							'''simple docstring'''
							import torch

							__lowerCamelCase		       =    self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
							__lowerCamelCase		       =    np.random.rand(100			,						32							).astype(np.floataa							)
							__lowerCamelCase		       =    np_speech_inputs.tolist()

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


			def       lowercase_    (      self			,						lowerCamelCase__							)    ->      List[str]:



							'''simple docstring'''
							__lowerCamelCase		       =    load_dataset('hf-internal-testing/librispeech_asr_dummy'			,						'clean'			,						split='validation'							)
							# automatic decoding with librispeech
							__lowerCamelCase		       =    ds.sort('id'							).select(range(lowerCamelCase__							)							)[:num_samples]['audio']

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


			def       lowercase_    (      self							)    ->      Tuple:



							'''simple docstring'''
							# fmt: off
							__lowerCamelCase		       =    torch.tensor(
							    [
							        0.11_93, -0.09_46, -0.10_98, -0.01_96, 0.02_25, -0.06_90, -0.17_36, 0.09_51,
							        0.09_71, -0.08_17, -0.07_02, 0.01_62, 0.02_60, 0.00_17, -0.01_92, -0.16_78,
							        0.07_09, -0.18_67, -0.06_55, -0.02_74, -0.02_34, -0.18_84, -0.05_16, -0.05_54,
							        -0.02_74, -0.14_25, -0.14_23, 0.08_37, 0.03_77, -0.08_54
							    ]							)
							# fmt: on

							__lowerCamelCase		       =    self._load_datasamples(1							)
							__lowerCamelCase		       =    WhisperFeatureExtractor()
							__lowerCamelCase		       =    feature_extractor(lowerCamelCase__			,						return_tensors='pt'							).input_features
							self.assertEqual(input_features.shape			,						(1, 80, 3_000)							)
							self.assertTrue(torch.allclose(input_features[0, 0, :30]			,						lowerCamelCase__			,						atol=1e-4							)							)






			def       lowercase_    (      self							)    ->      List[Any]:



							'''simple docstring'''
							__lowerCamelCase		       =    self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
							__lowerCamelCase		       =    self._load_datasamples(1							)[0]
							__lowerCamelCase		       =    ((audio - audio.min()) / (audio.max() - audio.min())) * 65_535  # Rescale to [0, 65535] to show issue
							__lowerCamelCase		       =    feat_extract.zero_mean_unit_var_norm([audio]			,						attention_mask=lowerCamelCase__							)[0]

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


