


'''simple docstring'''





import itertools
import os
import random
import tempfile
import unittest

import numpy as np

from transformers import TvltFeatureExtractor, is_datasets_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_torch_available():
			import torch

if is_datasets_available():
			from datasets import load_dataset

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







def        lowerCAmelCase_  (      __lowerCamelCase							,			__lowerCamelCase=1.0							,			__lowerCamelCase=None							,			__lowerCamelCase=None			):
							if rng is None:
														__snake_case	:    str											=     global_rng

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

							return values

class        a	(unittest.TestCase   ):




							"""simple docstring"""


							def __init__(							self :						List[Any]					,			lowerCamelCase :						Dict					,			lowerCamelCase :						List[Any]=7					,			lowerCamelCase :						Union[str, Any]=400					,			lowerCamelCase :						List[Any]=2000					,			lowerCamelCase :						Optional[int]=2048					,			lowerCamelCase :						Any=128					,			lowerCamelCase :						int=1					,			lowerCamelCase :						Optional[Any]=512					,			lowerCamelCase :						Tuple=30					,			lowerCamelCase :						Optional[int]=44100					,			)			->					str:
														__snake_case	:    List[str]											=     parent
														__snake_case	:    int											=     batch_size
														__snake_case	:    int											=     min_seq_length
														__snake_case	:    Dict											=     max_seq_length
														__snake_case	:    Optional[int]											=     (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
														__snake_case	:    Any											=     spectrogram_length
														__snake_case	:    Dict											=     feature_size
														__snake_case	:    Dict											=     num_audio_channels
														__snake_case	:    int											=     hop_length
														__snake_case	:    int											=     chunk_length
														__snake_case	:    List[str]											=     sampling_rate

							def 					__snake_case      (							self :						Any       )			->					str:
														return {
														    "spectrogram_length": self.spectrogram_length,
														    "feature_size": self.feature_size,
														    "num_audio_channels": self.num_audio_channels,
														    "hop_length": self.hop_length,
														    "chunk_length": self.chunk_length,
														    "sampling_rate": self.sampling_rate,
														}






							def 					__snake_case      (							self :						Optional[int]					,			lowerCamelCase :						int=False					,			lowerCamelCase :						List[str]=False       )			->					Optional[int]:
														def _flatten(lowerCamelCase :						List[Any]       ):
																					return list(itertools.chain(*lowerCamelCase       )       )

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

@require_torch
@require_torchaudio
class        a	(_lowerCAmelCase  ,       unittest.TestCase   ):




							"""simple docstring"""


							__UpperCAmelCase					:							Optional[int]  						=						TvltFeatureExtractor

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

							def 					__snake_case      (							self :						Optional[int]       )			->					int:
														__snake_case	:    str											=     self.feature_extraction_class(**self.feat_extract_dict       )
														self.assertTrue(hasattr(lowerCamelCase					,			"spectrogram_length"       )       )
														self.assertTrue(hasattr(lowerCamelCase					,			"feature_size"       )       )
														self.assertTrue(hasattr(lowerCamelCase					,			"num_audio_channels"       )       )
														self.assertTrue(hasattr(lowerCamelCase					,			"hop_length"       )       )
														self.assertTrue(hasattr(lowerCamelCase					,			"chunk_length"       )       )
														self.assertTrue(hasattr(lowerCamelCase					,			"sampling_rate"       )       )

							def 					__snake_case      (							self :						Any       )			->					Any:
														__snake_case	:    Union[str, Any]											=     self.feature_extraction_class(**self.feat_extract_dict       )

														with tempfile.TemporaryDirectory() as tmpdirname:
																					__snake_case	:    Optional[int]											=     feat_extract_first.save_pretrained(lowerCamelCase       )[0]
																					check_json_file_has_correct_format(lowerCamelCase       )
																					__snake_case	:    Any											=     self.feature_extraction_class.from_pretrained(lowerCamelCase       )

														__snake_case	:    str											=     feat_extract_first.to_dict()
														__snake_case	:    Optional[Any]											=     feat_extract_second.to_dict()
														__snake_case	:    Optional[Any]											=     dict_first.pop("mel_filters"       )
														__snake_case	:    int											=     dict_second.pop("mel_filters"       )
														self.assertTrue(np.allclose(lowerCamelCase					,			lowerCamelCase       )       )
														self.assertEqual(lowerCamelCase					,			lowerCamelCase       )

							def 					__snake_case      (							self :						Dict       )			->					List[Any]:
														__snake_case	:    int											=     self.feature_extraction_class(**self.feat_extract_dict       )

														with tempfile.TemporaryDirectory() as tmpdirname:
																					__snake_case	:    Dict											=     os.path.join(lowerCamelCase					,			"feat_extract.json"       )
																					feat_extract_first.to_json_file(lowerCamelCase       )
																					__snake_case	:    Optional[Any]											=     self.feature_extraction_class.from_json_file(lowerCamelCase       )

														__snake_case	:    int											=     feat_extract_first.to_dict()
														__snake_case	:    Optional[Any]											=     feat_extract_second.to_dict()
														__snake_case	:    Optional[Any]											=     dict_first.pop("mel_filters"       )
														__snake_case	:    Optional[int]											=     dict_second.pop("mel_filters"       )
														self.assertTrue(np.allclose(lowerCamelCase					,			lowerCamelCase       )       )
														self.assertEqual(lowerCamelCase					,			lowerCamelCase       )

							def 					__snake_case      (							self :						List[Any]       )			->					Optional[int]:
														# Initialize feature_extractor
														__snake_case	:    Dict											=     self.feature_extraction_class(**self.feat_extract_dict       )

														# create three inputs of length 800, 1000, and 1200
														__snake_case	:    Union[str, Any]											=     [floats_list((1, x)       )[0] for x in range(800					,			1400					,			200       )]
														__snake_case	:    Optional[int]											=     [np.asarray(lowerCamelCase       ) for speech_input in speech_inputs]

														# Test not batched input
														__snake_case	:    Union[str, Any]											=     feature_extractor(np_speech_inputs[0]					,			return_tensors="np"					,			sampling_rate=44100       ).audio_values

														self.assertTrue(encoded_audios.ndim == 4       )
														self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size       )
														self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length       )
														self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels       )

														# Test batched
														__snake_case	:    Tuple											=     feature_extractor(lowerCamelCase					,			return_tensors="np"					,			sampling_rate=44100       ).audio_values

														self.assertTrue(encoded_audios.ndim == 4       )
														self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size       )
														self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length       )
														self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels       )

														# Test audio masking
														__snake_case	:    List[Any]											=     feature_extractor(
														    lowerCamelCase					,			return_tensors="np"					,			sampling_rate=44100					,			mask_audio=lowerCamelCase       ).audio_values

														self.assertTrue(encoded_audios.ndim == 4       )
														self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size       )
														self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length       )
														self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels       )

														# Test 2-D numpy arrays are batched.
														__snake_case	:    List[str]											=     [floats_list((1, x)       )[0] for x in (800, 800, 800)]
														__snake_case	:    List[str]											=     np.asarray(lowerCamelCase       )
														__snake_case	:    Tuple											=     feature_extractor(lowerCamelCase					,			return_tensors="np"					,			sampling_rate=44100       ).audio_values
														self.assertTrue(encoded_audios.ndim == 4       )
														self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size       )
														self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length       )
														self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels       )

							def 					__snake_case      (							self :						Any					,			lowerCamelCase :						Union[str, Any]       )			->					List[Any]:
														__snake_case	:    List[Any]											=     load_dataset("hf-internal-testing/librispeech_asr_dummy"					,			"clean"					,			split="validation"       )
														# automatic decoding with librispeech
														__snake_case	:    Union[str, Any]											=     ds.sort("id"       ).select(range(lowerCamelCase       )       )[:num_samples]["audio"]

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






							def 					__snake_case      (							self :						Tuple       )			->					Tuple:
														__snake_case	:    Any											=     self._load_datasamples(1       )
														__snake_case	:    Any											=     TvltFeatureExtractor()
														__snake_case	:    str											=     feature_extractor(lowerCamelCase					,			return_tensors="pt"       ).audio_values

														self.assertEquals(audio_values.shape					,			(1, 1, 192, 128)       )

														__snake_case	:    Dict											=     torch.tensor([[-0.30_32, -0.27_08], [-0.44_34, -0.40_07]]       )
														self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]					,			lowerCamelCase					,			atol=1E-4       )       )





import argparse
import glob
import importlib.util
import os
import re

import black
from doc_builder.style_doc import style_docstrings_in_code


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
_snake_case  :	List[Any]			      =       "src/diffusers"
_snake_case  :	str			      =       "."


# This is to make sure the diffusers module imported is the one in the repo.
_snake_case  :	List[str]			      =       importlib.util.spec_from_file_location(
    "diffusers",
    os.path.join(DIFFUSERS_PATH, "__init__.py"),
    submodule_search_locations=[DIFFUSERS_PATH],
)
_snake_case  :	Dict			      =       spec.loader.load_module()







def        lowerCAmelCase_  (      __lowerCamelCase							,			__lowerCamelCase			):
							return line.startswith(__lowerCamelCase			) or len(__lowerCamelCase			) <= 1 or re.search(R"^\s*\)(\s*->.*:|:)\s*$"							,			__lowerCamelCase			) is not None







def        lowerCAmelCase_  (      __lowerCamelCase			):
							__snake_case	:    str											=     object_name.split("."			)
							__snake_case	:    Any											=     0

							# First let's find the module where our object lives.
							__snake_case	:    Optional[int]											=     parts[i]
							while i < len(__lowerCamelCase			) and not os.path.isfile(os.path.join(__lowerCamelCase							,			F'{module}.py'			)			):
														i += 1
														if i < len(__lowerCamelCase			):
																					__snake_case	:    Dict											=     os.path.join(__lowerCamelCase							,			parts[i]			)
							if i >= len(__lowerCamelCase			):
														raise ValueError(F'`object_name` should begin with the name of a module of diffusers but got {object_name}.'			)

							with open(os.path.join(__lowerCamelCase							,			F'{module}.py'			)							,			"r"							,			encoding="utf-8"							,			newline="\n"			) as f:
														__snake_case	:    Optional[Any]											=     f.readlines()

							# Now let's find the class / func in the code!
							__snake_case	:    Any											=     ""
							__snake_case	:    Any											=     0
							for name in parts[i + 1 :]:
														while (
														    line_index < len(__lowerCamelCase			) and re.search(RF'^{indent}(class|def)\s+{name}(\(|\:)'							,			lines[line_index]			) is None
														):
																					line_index += 1
														indent += "    "
														line_index += 1

							if line_index >= len(__lowerCamelCase			):
														raise ValueError(F' {object_name} does not match any function or class in {module}.'			)

							# We found the beginning of the class / func, now let's find the end (when the indent diminishes).
							__snake_case	:    Optional[Any]											=     line_index
							while line_index < len(__lowerCamelCase			) and _should_continue(lines[line_index]							,			__lowerCamelCase			):
														line_index += 1
							# Clean up empty lines at the end (if any).
							while len(lines[line_index - 1]			) <= 1:
														line_index -= 1

							__snake_case	:    Dict											=     lines[start_index:line_index]
							return "".join(__lowerCamelCase			)


_snake_case  :	Any			      =       re.compile(R"^(\s*)#\s*Copied from\s+diffusers\.(\S+\.\S+)\s*($|\S.*$)")
_snake_case  :	Union[str, Any]			      =       re.compile(R"^\s*(\S+)->(\S+)(\s+.*|$)")
_snake_case  :	Optional[int]			      =       re.compile(R"<FILL\s+[^>]*>")







def        lowerCAmelCase_  (      __lowerCamelCase			):
							__snake_case	:    Dict											=     code.split("\n"			)
							__snake_case	:    List[str]											=     0
							while idx < len(__lowerCamelCase			) and len(lines[idx]			) == 0:
														idx += 1
							if idx < len(__lowerCamelCase			):
														return re.search(R"^(\s*)\S"							,			lines[idx]			).groups()[0]
							return ""







def        lowerCAmelCase_  (      __lowerCamelCase			):
							__snake_case	:    List[str]											=     len(get_indent(__lowerCamelCase			)			) > 0
							if has_indent:
														__snake_case	:    str											=     F'class Bla:\n{code}'
							__snake_case	:    List[Any]											=     black.Mode(target_versions={black.TargetVersion.PYaa}							,			line_length=1_1_9							,			preview=__lowerCamelCase			)
							__snake_case	:    int											=     black.format_str(__lowerCamelCase							,			mode=__lowerCamelCase			)
							__snake_case				,    __snake_case	:    List[str]											=     style_docstrings_in_code(__lowerCamelCase			)
							return result[len("class Bla:\n"			) :] if has_indent else result







def        lowerCAmelCase_  (      __lowerCamelCase							,			__lowerCamelCase=False			):
							with open(__lowerCamelCase							,			"r"							,			encoding="utf-8"							,			newline="\n"			) as f:
														__snake_case	:    List[Any]											=     f.readlines()
							__snake_case	:    int											=     []
							__snake_case	:    Optional[Any]											=     0
							# Not a for loop cause `lines` is going to change (if `overwrite=True`).
							while line_index < len(__lowerCamelCase			):
														__snake_case	:    List[Any]											=     _re_copy_warning.search(lines[line_index]			)
														if search is None:
																					line_index += 1
																					continue

														# There is some copied code here, let's retrieve the original.
														__snake_case				,    __snake_case				,    __snake_case	:    List[str]											=     search.groups()
														__snake_case	:    List[str]											=     find_code_in_diffusers(__lowerCamelCase			)
														__snake_case	:    str											=     get_indent(__lowerCamelCase			)

														__snake_case	:    Optional[Any]											=     line_index + 1 if indent == theoretical_indent else line_index + 2
														__snake_case	:    Tuple											=     theoretical_indent
														__snake_case	:    str											=     start_index

														# Loop to check the observed code, stop when indentation diminishes or if we see a End copy comment.
														__snake_case	:    str											=     True
														while line_index < len(__lowerCamelCase			) and should_continue:
																					line_index += 1
																					if line_index >= len(__lowerCamelCase			):
																												break
																					__snake_case	:    Union[str, Any]											=     lines[line_index]
																					__snake_case	:    Any											=     _should_continue(__lowerCamelCase							,			__lowerCamelCase			) and re.search(F'^{indent}# End copy'							,			__lowerCamelCase			) is None
														# Clean up empty lines at the end (if any).
														while len(lines[line_index - 1]			) <= 1:
																					line_index -= 1

														__snake_case	:    int											=     lines[start_index:line_index]
														__snake_case	:    int											=     "".join(__lowerCamelCase			)

														# Remove any nested `Copied from` comments to avoid circular copies
														__snake_case	:    Union[str, Any]											=     [line for line in theoretical_code.split("\n"			) if _re_copy_warning.search(__lowerCamelCase			) is None]
														__snake_case	:    Optional[Any]											=     "\n".join(__lowerCamelCase			)

														# Before comparing, use the `replace_pattern` on the original code.
														if len(__lowerCamelCase			) > 0:
																					__snake_case	:    Optional[int]											=     replace_pattern.replace("with"							,			""			).split(","			)
																					__snake_case	:    Tuple											=     [_re_replace_pattern.search(__lowerCamelCase			) for p in patterns]
																					for pattern in patterns:
																												if pattern is None:
																																			continue
																												__snake_case				,    __snake_case				,    __snake_case	:    Optional[Any]											=     pattern.groups()
																												__snake_case	:    Tuple											=     re.sub(__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase			)
																												if option.strip() == "all-casing":
																																			__snake_case	:    List[Any]											=     re.sub(obja.lower()							,			obja.lower()							,			__lowerCamelCase			)
																																			__snake_case	:    Union[str, Any]											=     re.sub(obja.upper()							,			obja.upper()							,			__lowerCamelCase			)

            # Blackify after replacement. To be able to do that, we need the header (class or function definition)
            # from the previous line
																					__snake_case	:    str											=     blackify(lines[start_index - 1] + theoretical_code			)
																					__snake_case	:    Tuple											=     theoretical_code[len(lines[start_index - 1]			) :]

														# Test for a diff and act accordingly.
														if observed_code != theoretical_code:
																					diffs.append([object_name, start_index]			)
																					if overwrite:
																												__snake_case	:    str											=     lines[:start_index] + [theoretical_code] + lines[line_index:]
																												__snake_case	:    List[Any]											=     start_index + 1

							if overwrite and len(__lowerCamelCase			) > 0:
														# Warn the user a file has been modified.
														print(F'Detected changes, rewriting {filename}.'			)
														with open(__lowerCamelCase							,			"w"							,			encoding="utf-8"							,			newline="\n"			) as f:
																					f.writelines(__lowerCamelCase			)
							return diffs







def        lowerCAmelCase_  (      __lowerCamelCase = False			):
							__snake_case	:    Optional[Any]											=     glob.glob(os.path.join(__lowerCamelCase							,			"**/*.py"			)							,			recursive=__lowerCamelCase			)
							__snake_case	:    Dict											=     []
							for filename in all_files:
														__snake_case	:    Union[str, Any]											=     is_copy_consistent(__lowerCamelCase							,			__lowerCamelCase			)
														diffs += [F'- {filename}: copy does not match {d[0]} at line {d[1]}' for d in new_diffs]
							if not overwrite and len(__lowerCamelCase			) > 0:
														__snake_case	:    Optional[Any]											=     "\n".join(__lowerCamelCase			)
														raise Exception(
														    "Found the following copy inconsistencies:\n"
														    + diff
														    + "\nRun `make fix-copies` or `python utils/check_copies.py --fix_and_overwrite` to fix them."			)


if __name__ == "__main__":
			_snake_case  :	Optional[int]			      =       argparse.ArgumentParser()
			parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
			_snake_case  :	Union[str, Any]			      =       parser.parse_args()

			check_copies(args.fix_and_overwrite)

