
"""simple docstring"""






import itertools
import random
import unittest

import numpy as np

from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_speech_available():
					from transformers import SpeechaTextFeatureExtractor

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





def __A						(   a_ :							Dict		,		a_ :							Optional[int]=1.0		,		a_ :							Any=None		,		a_ :							Union[str, Any]=None     )->							List[Any]:




		'''simple docstring'''






		if rng is None:
				SCREAMING_SNAKE_CASE       :     Union[str, Any]				= global_rng

		SCREAMING_SNAKE_CASE       :     Dict				= []
		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 				lowercase__(				unittest.TestCase   ):
	'''simple docstring'''

	def __init__(						self    :List[Any]		, lowerCamelCase_    :Tuple		, lowerCamelCase_    :int=7		, lowerCamelCase_    :List[Any]=4_00		, lowerCamelCase_    :str=20_00		, lowerCamelCase_    :List[Any]=24		, lowerCamelCase_    :Tuple=24		, lowerCamelCase_    :Union[str, Any]=0.0		, lowerCamelCase_    :int=1_60_00		, lowerCamelCase_    :List[str]=True		, lowerCamelCase_    :Union[str, Any]=True		, )							->			List[Any]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= parent
			SCREAMING_SNAKE_CASE       :     List[Any]				= batch_size
			SCREAMING_SNAKE_CASE       :     Dict				= min_seq_length
			SCREAMING_SNAKE_CASE       :     str				= max_seq_length
			SCREAMING_SNAKE_CASE       :     int				= (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
			SCREAMING_SNAKE_CASE       :     Tuple				= feature_size
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= num_mel_bins
			SCREAMING_SNAKE_CASE       :     List[str]				= padding_value
			SCREAMING_SNAKE_CASE       :     List[Any]				= sampling_rate
			SCREAMING_SNAKE_CASE       :     Any				= return_attention_mask
			SCREAMING_SNAKE_CASE       :     int				= do_normalize





	def   __lowerCAmelCase (						self    :Union[str, Any]					)							->			Optional[int]:







			'''simple docstring'''




			return {
			    "feature_size": self.feature_size,
			    "num_mel_bins": self.num_mel_bins,
			    "padding_value": self.padding_value,
			    "sampling_rate": self.sampling_rate,
			    "return_attention_mask": self.return_attention_mask,
			    "do_normalize": self.do_normalize,
			}







	def   __lowerCAmelCase (						self    :List[Any]		, lowerCamelCase_    :Dict=False		, lowerCamelCase_    :List[str]=False					)							->			Optional[Any]:







			'''simple docstring'''




			def _flatten(lowerCamelCase_    :Dict					):
					return list(itertools.chain(*lowerCamelCase_					)					)

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





@require_torch
@require_torchaudio
class 				lowercase__(				_UpperCAmelCase		,						unittest.TestCase   ):
	'''simple docstring'''

	UpperCamelCase				=  SpeechaTextFeatureExtractor if is_speech_available() else None





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







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Any				= SpeechaTextFeatureExtractionTester(self					)





	def   __lowerCAmelCase (						self    :List[str]		, lowerCamelCase_    :Tuple					)							->			int:







			'''simple docstring'''




			self.assertTrue(np.all(np.mean(lowerCamelCase_		, axis=0					) < 1E-3					)					)
			self.assertTrue(np.all(np.abs(np.var(lowerCamelCase_		, axis=0					) - 1					) < 1E-3					)					)





	def   __lowerCAmelCase (						self    :Any					)							->			Optional[Any]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     List[str]				= self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
			# create three inputs of length 800, 1000, and 1200
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= [floats_list((1, x)					)[0] for x in range(8_00		, 14_00		, 2_00					)]
			SCREAMING_SNAKE_CASE       :     List[str]				= [np.asarray(lowerCamelCase_					) for speech_input in speech_inputs]

			# Test feature size
			SCREAMING_SNAKE_CASE       :     str				= feature_extractor(lowerCamelCase_		, padding=lowerCamelCase_		, return_tensors='''np'''					).input_features
			self.assertTrue(input_features.ndim == 3					)
			self.assertTrue(input_features.shape[-1] == feature_extractor.feature_size					)

			# Test not batched input
			SCREAMING_SNAKE_CASE       :     Optional[int]				= feature_extractor(speech_inputs[0]		, return_tensors='''np'''					).input_features
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= feature_extractor(np_speech_inputs[0]		, return_tensors='''np'''					).input_features
			self.assertTrue(np.allclose(lowerCamelCase_		, lowerCamelCase_		, atol=1E-3					)					)

			# Test batched
			SCREAMING_SNAKE_CASE       :     Tuple				= feature_extractor(lowerCamelCase_		, return_tensors='''np'''					).input_features
			SCREAMING_SNAKE_CASE       :     str				= 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.
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= [floats_list((1, x)					)[0] for x in (8_00, 8_00, 8_00)]
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= np.asarray(lowerCamelCase_					)
			SCREAMING_SNAKE_CASE       :     Dict				= feature_extractor(lowerCamelCase_		, return_tensors='''np'''					).input_features
			SCREAMING_SNAKE_CASE       :     Tuple				= 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   __lowerCAmelCase (						self    :Union[str, Any]					)							->			Optional[Any]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Tuple				= self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= [floats_list((1, x)					)[0] for x in range(8_00		, 14_00		, 2_00					)]

			SCREAMING_SNAKE_CASE       :     Optional[Any]				= ['''longest''', '''max_length''', '''do_not_pad''']
			SCREAMING_SNAKE_CASE       :     str				= [None, 16, None]
			for max_length, padding in zip(lowerCamelCase_		, lowerCamelCase_					):
					SCREAMING_SNAKE_CASE       :     Union[str, Any]				= feature_extractor(
					    lowerCamelCase_		, padding=lowerCamelCase_		, max_length=lowerCamelCase_		, return_attention_mask=lowerCamelCase_					)
					SCREAMING_SNAKE_CASE       :     Optional[int]				= inputs.input_features
					SCREAMING_SNAKE_CASE       :     Tuple				= inputs.attention_mask
					SCREAMING_SNAKE_CASE       :     List[str]				= [np.sum(lowerCamelCase_					) for x in attention_mask]

					self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]]					)
					self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]]					)
					self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]]					)





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







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     List[Any]				= self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
			SCREAMING_SNAKE_CASE       :     Optional[int]				= [floats_list((1, x)					)[0] for x in range(8_00		, 14_00		, 2_00					)]

			SCREAMING_SNAKE_CASE       :     Dict				= ['''longest''', '''max_length''', '''do_not_pad''']
			SCREAMING_SNAKE_CASE       :     Optional[int]				= [None, 16, None]
			for max_length, padding in zip(lowerCamelCase_		, lowerCamelCase_					):
					SCREAMING_SNAKE_CASE       :     Dict				= feature_extractor(
					    lowerCamelCase_		, max_length=lowerCamelCase_		, padding=lowerCamelCase_		, return_tensors='''np'''		, return_attention_mask=lowerCamelCase_					)
					SCREAMING_SNAKE_CASE       :     List[str]				= inputs.input_features
					SCREAMING_SNAKE_CASE       :     Any				= inputs.attention_mask
					SCREAMING_SNAKE_CASE       :     List[Any]				= [np.sum(lowerCamelCase_					) for x in attention_mask]

					self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]]					)
					self.assertTrue(input_features[0][fbank_feat_lengths[0] :].sum() < 1E-6					)
					self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]]					)
					self.assertTrue(input_features[0][fbank_feat_lengths[1] :].sum() < 1E-6					)
					self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]]					)





	def   __lowerCAmelCase (						self    :str					)							->			str:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Any				= self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
			SCREAMING_SNAKE_CASE       :     List[Any]				= [floats_list((1, x)					)[0] for x in range(8_00		, 14_00		, 2_00					)]
			SCREAMING_SNAKE_CASE       :     List[Any]				= feature_extractor(
			    lowerCamelCase_		, padding='''max_length'''		, max_length=4		, truncation=lowerCamelCase_		, return_tensors='''np'''		, return_attention_mask=lowerCamelCase_		, )
			SCREAMING_SNAKE_CASE       :     int				= inputs.input_features
			SCREAMING_SNAKE_CASE       :     Tuple				= inputs.attention_mask
			SCREAMING_SNAKE_CASE       :     Tuple				= np.sum(attention_mask == 1		, axis=1					)

			self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]					)
			self._check_zero_mean_unit_variance(input_features[1]					)
			self._check_zero_mean_unit_variance(input_features[2]					)





	def   __lowerCAmelCase (						self    :Optional[int]					)							->			int:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
			SCREAMING_SNAKE_CASE       :     Optional[int]				= [floats_list((1, x)					)[0] for x in range(8_00		, 14_00		, 2_00					)]
			SCREAMING_SNAKE_CASE       :     Optional[int]				= feature_extractor(
			    lowerCamelCase_		, padding='''longest'''		, max_length=4		, truncation=lowerCamelCase_		, return_tensors='''np'''		, return_attention_mask=lowerCamelCase_		, )
			SCREAMING_SNAKE_CASE       :     Tuple				= inputs.input_features
			SCREAMING_SNAKE_CASE       :     Any				= inputs.attention_mask
			SCREAMING_SNAKE_CASE       :     Dict				= np.sum(attention_mask == 1		, axis=1					)

			self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]					)
			self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]]					)
			self._check_zero_mean_unit_variance(input_features[2]					)

			# make sure that if max_length < longest -> then pad to max_length
			self.assertEqual(input_features.shape		, (3, 4, 24)					)

			SCREAMING_SNAKE_CASE       :     int				= [floats_list((1, x)					)[0] for x in range(8_00		, 14_00		, 2_00					)]
			SCREAMING_SNAKE_CASE       :     Dict				= feature_extractor(
			    lowerCamelCase_		, padding='''longest'''		, max_length=16		, truncation=lowerCamelCase_		, return_tensors='''np'''		, return_attention_mask=lowerCamelCase_		, )
			SCREAMING_SNAKE_CASE       :     Tuple				= inputs.input_features
			SCREAMING_SNAKE_CASE       :     Dict				= inputs.attention_mask
			SCREAMING_SNAKE_CASE       :     List[Any]				= np.sum(attention_mask == 1		, axis=1					)

			self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]					)
			self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]]					)
			self._check_zero_mean_unit_variance(input_features[2]					)

			# make sure that if max_length < longest -> then pad to max_length
			self.assertEqual(input_features.shape		, (3, 6, 24)					)





	def   __lowerCAmelCase (						self    :List[str]					)							->			Optional[int]:







			'''simple docstring'''




			import torch

			SCREAMING_SNAKE_CASE       :     Optional[int]				= self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= np.random.rand(1_00		, 32					).astype(np.floataa					)
			SCREAMING_SNAKE_CASE       :     int				= np_speech_inputs.tolist()

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





	def   __lowerCAmelCase (						self    :Tuple		, lowerCamelCase_    :List[Any]					)							->			Optional[Any]:







			'''simple docstring'''




			from datasets import load_dataset

			SCREAMING_SNAKE_CASE       :     Optional[Any]				= load_dataset('''hf-internal-testing/librispeech_asr_dummy'''		, '''clean'''		, split='''validation'''					)
			# automatic decoding with librispeech
			SCREAMING_SNAKE_CASE       :     List[str]				= ds.sort('''id'''					).select(range(lowerCamelCase_					)					)[:num_samples]['''audio''']

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







	def   __lowerCAmelCase (						self    :Union[str, Any]					)							->			Optional[int]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     str				= np.array([
			    -1.5_7_4_5, -1.7_7_1_3, -1.7_0_2_0, -1.6_0_6_9, -1.2_2_5_0, -1.1_1_0_5, -0.9_0_7_2, -0.8_2_4_1,
			    -1.2_3_1_0, -0.8_0_9_8, -0.3_3_2_0, -0.4_1_0_1, -0.7_9_8_5, -0.4_9_9_6, -0.8_2_1_3, -0.9_1_2_8,
			    -1.0_4_2_0, -1.1_2_8_6, -1.0_4_4_0, -0.7_9_9_9, -0.8_4_0_5, -1.2_2_7_5, -1.5_4_4_3, -1.4_6_2_5,
			]					)
			# fmt: on

			SCREAMING_SNAKE_CASE       :     Dict				= self._load_datasamples(1					)
			SCREAMING_SNAKE_CASE       :     Tuple				= self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
			SCREAMING_SNAKE_CASE       :     List[str]				= feature_extractor(lowerCamelCase_		, return_tensors='''pt'''					).input_features
			self.assertEquals(input_features.shape		, (1, 5_84, 24)					)
			self.assertTrue(np.allclose(input_features[0, 0, :30]		, lowerCamelCase_		, atol=1E-4					)					)







"""simple docstring"""






import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union

import torch
from torch import nn

from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging


lowerCamelCase__ :       List[Any]						     =			logging.get_logger(__name__)




class 				lowercase__(				_UpperCAmelCase   ):
	'''simple docstring'''

	def __init__(						self    :List[str]		, lowerCamelCase_    :Union[List[ControlNetModel], Tuple[ControlNetModel]]					)							->			Union[str, Any]:







			'''simple docstring'''




			super().__init__()
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= nn.ModuleList(lowerCamelCase_					)





	def   __lowerCAmelCase (						self    :Any		, lowerCamelCase_    :torch.FloatTensor		, lowerCamelCase_    :Union[torch.Tensor, float, int]		, lowerCamelCase_    :torch.Tensor		, lowerCamelCase_    :List[torch.tensor]		, lowerCamelCase_    :List[float]		, lowerCamelCase_    :Optional[torch.Tensor] = None		, lowerCamelCase_    :Optional[torch.Tensor] = None		, lowerCamelCase_    :Optional[torch.Tensor] = None		, lowerCamelCase_    :Optional[Dict[str, Any]] = None		, lowerCamelCase_    :bool = False		, lowerCamelCase_    :bool = True		, )							->			Union[ControlNetOutput, Tuple]:







			'''simple docstring'''




			for i, (image, scale, controlnet) in enumerate(zip(lowerCamelCase_		, lowerCamelCase_		, self.nets					)					):
					SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE       :     List[Any]				= controlnet(
					    lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, )

					# merge samples
					if i == 0:
							SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE       :     int				= down_samples, mid_sample
					else:
							SCREAMING_SNAKE_CASE       :     Optional[int]				= [
							    samples_prev + samples_curr
							    for samples_prev, samples_curr in zip(lowerCamelCase_		, lowerCamelCase_					)
							]
							mid_block_res_sample += mid_sample

			return down_block_res_samples, mid_block_res_sample





	def   __lowerCAmelCase (						self    :Any		, lowerCamelCase_    :Union[str, os.PathLike]		, lowerCamelCase_    :bool = True		, lowerCamelCase_    :Callable = None		, lowerCamelCase_    :bool = False		, lowerCamelCase_    :Optional[str] = None		, )							->			Optional[int]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Optional[Any]				= 0
			SCREAMING_SNAKE_CASE       :     Any				= save_directory
			for controlnet in self.nets:
					controlnet.save_pretrained(
					    lowerCamelCase_		, is_main_process=lowerCamelCase_		, save_function=lowerCamelCase_		, safe_serialization=lowerCamelCase_		, variant=lowerCamelCase_		, )

					idx += 1
					SCREAMING_SNAKE_CASE       :     Union[str, Any]				= model_path_to_save + f"_{idx}"







	@classmethod
	def   __lowerCAmelCase (						cls    :Dict		, lowerCamelCase_    :Optional[Union[str, os.PathLike]]		, **lowerCamelCase_    :Tuple					)							->			List[Any]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     str				= 0
			SCREAMING_SNAKE_CASE       :     Optional[int]				= []

			# load controlnet and append to list until no controlnet directory exists anymore
			# first controlnet has to be saved under `./mydirectory/controlnet` to be compliant with `DiffusionPipeline.from_prertained`
			# second, third, ... controlnets have to be saved under `./mydirectory/controlnet_1`, `./mydirectory/controlnet_2`, ...
			SCREAMING_SNAKE_CASE       :     Dict				= pretrained_model_path
			while os.path.isdir(lowerCamelCase_					):
					SCREAMING_SNAKE_CASE       :     Tuple				= ControlNetModel.from_pretrained(lowerCamelCase_		, **lowerCamelCase_					)
					controlnets.append(lowerCamelCase_					)

					idx += 1
					SCREAMING_SNAKE_CASE       :     Union[str, Any]				= pretrained_model_path + f"_{idx}"

			logger.info(f"{len(lowerCamelCase_					)} controlnets loaded from {pretrained_model_path}."					)

			if len(lowerCamelCase_					) == 0:
					raise ValueError(
					    f"No ControlNets found under {os.path.dirname(lowerCamelCase_					)}. Expected at least {pretrained_model_path + '_0'}."					)

			return cls(lowerCamelCase_					)





