



"""simple docstring"""

import itertools
import random
import unittest

import numpy as np

from transformers import WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, WavaVecaConfig, WavaVecaFeatureExtractor
from transformers.testing_utils import require_torch, slow

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


lowercase__			   =  random.Random()





def       __magic_name__    (   _lowerCamelCase				: Dict  ,       _lowerCamelCase				: List[str]=1.0  ,       _lowerCamelCase				: List[str]=None  ,       _lowerCamelCase				: List[Any]=None					):
							if rng is None:
														__a		:		Dict									=       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




class SCREAMING_SNAKE_CASE__					(		unittest.TestCase      ):



						def __init__(self						,     _lowercase						,     _lowercase=7						,     _lowercase=400						,     _lowercase=2000						,     _lowercase=1						,     _lowercase=0.0						,     _lowercase=16000						,     _lowercase=True						,     _lowercase=True						,     ):





													'''simple docstring'''

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



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													return {
													    "feature_size": self.feature_size,
													    "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						,     _lowercase=False						,     _lowercase=False							):





													'''simple docstring'''

													def _flatten(_lowercase							):
																				return list(itertools.chain(*_lowercase							)							)

													if equal_length:
																				__a		:		List[str]									=       floats_list((self.batch_size, self.max_seq_length)							)
													else:
																				# make sure that inputs increase in size
																				__a		:		str									=       [
																				    _flatten(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		:		Dict									=       [np.asarray(_lowercase							) for x in speech_inputs]

													return speech_inputs







class SCREAMING_SNAKE_CASE__					(		__snake_case     ,					unittest.TestCase      ):
						_lowerCAmelCase							=					WavaVecaFeatureExtractor



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		Optional[int]									=       WavaVecaFeatureExtractionTester(self							)



						def     lowerCAmelCase__(self						,     _lowercase							):





													'''simple docstring'''

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



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		Dict									=       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
													# create three inputs of length 800, 1000, and 1200
													__a		:		str									=       [floats_list((1, x)							)[0] for x in range(800						,     1400						,     200							)]
													__a		:		str									=       [np.asarray(_lowercase							) for speech_input in speech_inputs]

													# Test not batched input
													__a		:		int									=       feat_extract(speech_inputs[0]						,     return_tensors="""np"""							).input_values
													__a		:		List[Any]									=       feat_extract(np_speech_inputs[0]						,     return_tensors="""np"""							).input_values
													self.assertTrue(np.allclose(_lowercase						,     _lowercase						,     atol=1e-3							)							)

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

													# Test 2-D numpy arrays are batched.
													__a		:		Optional[int]									=       [floats_list((1, x)							)[0] for x in (800, 800, 800)]
													__a		:		Dict									=       np.asarray(_lowercase							)
													__a		:		List[str]									=       feat_extract(_lowercase						,     return_tensors="""np"""							).input_values
													__a		:		Union[str, Any]									=       feat_extract(_lowercase						,     return_tensors="""np"""							).input_values
													for enc_seq_a, enc_seq_a in zip(_lowercase						,     _lowercase							):
																				self.assertTrue(np.allclose(_lowercase						,     _lowercase						,     atol=1e-3							)							)



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		str									=       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
													__a		:		Dict									=       [floats_list((1, x)							)[0] for x in range(800						,     1400						,     200							)]

													__a		:		Optional[int]									=       ["""longest""", """max_length""", """do_not_pad"""]
													__a		:		int									=       [None, 1600, None]
													for max_length, padding in zip(_lowercase						,     _lowercase							):
																				__a		:		Optional[int]									=       feat_extract(_lowercase						,     padding=_lowercase						,     max_length=_lowercase						,     return_tensors="""np"""							)
																				__a		:		List[Any]									=       processed.input_values

																				self._check_zero_mean_unit_variance(input_values[0][:800]							)
																				self.assertTrue(input_values[0][800:].sum() < 1e-6							)
																				self._check_zero_mean_unit_variance(input_values[1][:1000]							)
																				self.assertTrue(input_values[0][1000:].sum() < 1e-6							)
																				self._check_zero_mean_unit_variance(input_values[2][:1200]							)



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		Dict									=       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
													__a		:		Optional[int]									=       range(800						,     1400						,     200							)
													__a		:		Optional[Any]									=       [floats_list((1, x)							)[0] for x in lengths]

													__a		:		Any									=       ["""longest""", """max_length""", """do_not_pad"""]
													__a		:		Any									=       [None, 1600, None]

													for max_length, padding in zip(_lowercase						,     _lowercase							):
																				__a		:		Dict									=       feat_extract(_lowercase						,     max_length=_lowercase						,     padding=_lowercase							)
																				__a		:		str									=       processed.input_values

																				self._check_zero_mean_unit_variance(input_values[0][:800]							)
																				self._check_zero_mean_unit_variance(input_values[1][:1000]							)
																				self._check_zero_mean_unit_variance(input_values[2][:1200]							)



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		List[Any]									=       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
													__a		:		List[Any]									=       [floats_list((1, x)							)[0] for x in range(800						,     1400						,     200							)]
													__a		:		Dict									=       feat_extract(
													    _lowercase						,     truncation=_lowercase						,     max_length=1000						,     padding="""max_length"""						,     return_tensors="""np"""							)
													__a		:		Any									=       processed.input_values

													self._check_zero_mean_unit_variance(input_values[0, :800]							)
													self._check_zero_mean_unit_variance(input_values[1]							)
													self._check_zero_mean_unit_variance(input_values[2]							)



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		Optional[Any]									=       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
													__a		:		Union[str, Any]									=       [floats_list((1, x)							)[0] for x in range(800						,     1400						,     200							)]
													__a		:		Union[str, Any]									=       feat_extract(
													    _lowercase						,     truncation=_lowercase						,     max_length=1000						,     padding="""longest"""						,     return_tensors="""np"""							)
													__a		:		Optional[int]									=       processed.input_values

													self._check_zero_mean_unit_variance(input_values[0, :800]							)
													self._check_zero_mean_unit_variance(input_values[1, :1000]							)
													self._check_zero_mean_unit_variance(input_values[2]							)

													# make sure that if max_length < longest -> then pad to max_length
													self.assertTrue(input_values.shape == (3, 1000)							)

													__a		:		List[str]									=       [floats_list((1, x)							)[0] for x in range(800						,     1400						,     200							)]
													__a		:		Union[str, Any]									=       feat_extract(
													    _lowercase						,     truncation=_lowercase						,     max_length=2000						,     padding="""longest"""						,     return_tensors="""np"""							)
													__a		:		str									=       processed.input_values

													self._check_zero_mean_unit_variance(input_values[0, :800]							)
													self._check_zero_mean_unit_variance(input_values[1, :1000]							)
													self._check_zero_mean_unit_variance(input_values[2]							)

													# make sure that if max_length > longest -> then pad to longest
													self.assertTrue(input_values.shape == (3, 1200)							)



						@require_torch
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													import torch

													__a		:		Dict									=       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
													__a		:		List[str]									=       np.random.rand(100							).astype(np.floataa							)
													__a		:		Tuple									=       np_speech_inputs.tolist()

													for inputs in [py_speech_inputs, np_speech_inputs]:
																				__a		:		List[Any]									=       feature_extractor.pad([{"""input_values""": inputs}]						,     return_tensors="""np"""							)
																				self.assertTrue(np_processed.input_values.dtype == np.floataa							)
																				__a		:		Dict									=       feature_extractor.pad([{"""input_values""": inputs}]						,     return_tensors="""pt"""							)
																				self.assertTrue(pt_processed.input_values.dtype == torch.floataa							)





						@slow
						@require_torch
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													for model_id in WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST:
																				__a		:		Dict									=       WavaVecaConfig.from_pretrained(_lowercase							)
																				__a		:		Any									=       WavaVecaFeatureExtractor.from_pretrained(_lowercase							)

																				# only "layer" feature extraction norm should make use of
																				# attention_mask
																				self.assertEqual(feat_extract.return_attention_mask						,     config.feat_extract_norm == """layer"""							)






"""simple docstring"""

import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
	lowercase__			   =  argparse.ArgumentParser()

	parser.add_argument(
	    "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert."
	)
	parser.add_argument(
	    "--original_config_file",
	    type=str,
	    required=True,
	    help="The YAML config file corresponding to the original architecture.",
	)
	parser.add_argument(
	    "--num_in_channels",
	    default=None,
	    type=int,
	    help="The number of input channels. If `None` number of input channels will be automatically inferred.",
	)
	parser.add_argument(
	    "--image_size",
	    default=512,
	    type=int,
	    help=(
	        "The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2"
	        " Base. Use 768 for Stable Diffusion v2."
	    ),
	)
	parser.add_argument(
	    "--extract_ema",
	    action="store_true",
	    help=(
	        "Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights"
	        " or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield"
	        " higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning."
	    ),
	)
	parser.add_argument(
	    "--upcast_attention",
	    action="store_true",
	    help=(
	        "Whether the attention computation should always be upcasted. This is necessary when running stable"
	        " diffusion 2.1."
	    ),
	)
	parser.add_argument(
	    "--from_safetensors",
	    action="store_true",
	    help="If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.",
	)
	parser.add_argument(
	    "--to_safetensors",
	    action="store_true",
	    help="Whether to store pipeline in safetensors format or not.",
	)
	parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")
	parser.add_argument("--device", type=str, help="Device to use (e.g. cpu, cuda:0, cuda:1, etc.)")





	def       __magic_name__    (   _lowerCamelCase				: Optional[Any]					):
								if string == "True":
															return True
								elif string == "False":
															return False
								else:
															raise ValueError(F'''could not parse string as bool {string}'''					)

	parser.add_argument(
	    "--use_linear_projection", help="Override for use linear projection", required=False, type=parse_bool
	)

	parser.add_argument("--cross_attention_dim", help="Override for cross attention_dim", required=False, type=int)

	lowercase__			   =  parser.parse_args()

	lowercase__			   =  download_controlnet_from_original_ckpt(
	    checkpoint_path=args.checkpoint_path,
	    original_config_file=args.original_config_file,
	    image_size=args.image_size,
	    extract_ema=args.extract_ema,
	    num_in_channels=args.num_in_channels,
	    upcast_attention=args.upcast_attention,
	    from_safetensors=args.from_safetensors,
	    device=args.device,
	    use_linear_projection=args.use_linear_projection,
	    cross_attention_dim=args.cross_attention_dim,
	)







	controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)

