






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


a_							=    random.Random()


def        _a  (     UpperCamelCase_					:				List[str]    ,   UpperCamelCase_					:				Dict=1.0    ,   UpperCamelCase_					:				str=None    ,   UpperCamelCase_					:				int=None   ) ->   Tuple:
						"""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


class      lowercase__     (			unittest.TestCase	):

	def __init__(							self		,    __UpperCAmelCase		,    __UpperCAmelCase=7		,    __UpperCAmelCase=400		,    __UpperCAmelCase=2000		,    __UpperCAmelCase=1		,    __UpperCAmelCase=0.0		,    __UpperCAmelCase=16000		,    __UpperCAmelCase=True		,    __UpperCAmelCase=True		,    )->					Optional[Any]:







							'''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__       =						feature_size
							lowerCAmelCase__       =						padding_value
							lowerCAmelCase__       =						sampling_rate
							lowerCAmelCase__       =						return_attention_mask
							lowerCAmelCase__       =						do_normalize

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







							'''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 		UpperCAmelCase  (							self		,    __UpperCAmelCase=False		,    __UpperCAmelCase=False       )->					Any:







							'''simple docstring'''





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

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

							return speech_inputs





class      lowercase__     (			UpperCAmelCase_,					unittest.TestCase	):
	a_         =WavaVecaFeatureExtractor

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







							'''simple docstring'''





							lowerCAmelCase__       =						WavaVecaFeatureExtractionTester(self       )

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase       )->					Union[str, Any]:







							'''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 		UpperCAmelCase  (							self       )->					Any:







							'''simple docstring'''





							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		,    1400		,    200       )]
							lowerCAmelCase__       =						[np.asarray(_lowercase       ) for speech_input in speech_inputs]

							# Test not batched input
							lowerCAmelCase__       =						feat_extract(speech_inputs[0]		,    return_tensors="np"       ).input_values
							lowerCAmelCase__       =						feat_extract(np_speech_inputs[0]		,    return_tensors="np"       ).input_values
							self.assertTrue(np.allclose(_lowercase		,    _lowercase		,    atol=1E-3       )       )

							# Test batched
							lowerCAmelCase__       =						feat_extract(_lowercase		,    return_tensors="np"       ).input_values
							lowerCAmelCase__       =						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.
							lowerCAmelCase__       =						[floats_list((1, x)       )[0] for x in (800, 800, 800)]
							lowerCAmelCase__       =						np.asarray(_lowercase       )
							lowerCAmelCase__       =						feat_extract(_lowercase		,    return_tensors="np"       ).input_values
							lowerCAmelCase__       =						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 		UpperCAmelCase  (							self       )->					Union[str, Any]:







							'''simple docstring'''





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

							lowerCAmelCase__       =						['longest', 'max_length', 'do_not_pad']
							lowerCAmelCase__       =						[None, 1600, None]
							for max_length, padding in zip(_lowercase		,    _lowercase       ):
													lowerCAmelCase__       =						feat_extract(_lowercase		,    padding=_lowercase		,    max_length=_lowercase		,    return_tensors="np"       )
													lowerCAmelCase__       =						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 		UpperCAmelCase  (							self       )->					Optional[int]:







							'''simple docstring'''





							lowerCAmelCase__       =						self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
							lowerCAmelCase__       =						range(800		,    1400		,    200       )
							lowerCAmelCase__       =						[floats_list((1, x)       )[0] for x in lengths]

							lowerCAmelCase__       =						['longest', 'max_length', 'do_not_pad']
							lowerCAmelCase__       =						[None, 1600, None]

							for max_length, padding in zip(_lowercase		,    _lowercase       ):
													lowerCAmelCase__       =						feat_extract(_lowercase		,    max_length=_lowercase		,    padding=_lowercase       )
													lowerCAmelCase__       =						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 		UpperCAmelCase  (							self       )->					List[Any]:







							'''simple docstring'''





							lowerCAmelCase__       =						self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
							lowerCAmelCase__       =						[floats_list((1, x)       )[0] for x in range(800		,    1400		,    200       )]
							lowerCAmelCase__       =						feat_extract(
							    _lowercase		,    truncation=_lowercase		,    max_length=1000		,    padding="max_length"		,    return_tensors="np"       )
							lowerCAmelCase__       =						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 		UpperCAmelCase  (							self       )->					Optional[int]:







							'''simple docstring'''





							lowerCAmelCase__       =						self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
							lowerCAmelCase__       =						[floats_list((1, x)       )[0] for x in range(800		,    1400		,    200       )]
							lowerCAmelCase__       =						feat_extract(
							    _lowercase		,    truncation=_lowercase		,    max_length=1000		,    padding="longest"		,    return_tensors="np"       )
							lowerCAmelCase__       =						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)       )

							lowerCAmelCase__       =						[floats_list((1, x)       )[0] for x in range(800		,    1400		,    200       )]
							lowerCAmelCase__       =						feat_extract(
							    _lowercase		,    truncation=_lowercase		,    max_length=2000		,    padding="longest"		,    return_tensors="np"       )
							lowerCAmelCase__       =						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 		UpperCAmelCase  (							self       )->					Dict:







							'''simple docstring'''





							import torch

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

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


	@slow
	@require_torch
	def 		UpperCAmelCase  (							self       )->					str:







							'''simple docstring'''





							for model_id in WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST:
													lowerCAmelCase__       =						WavaVecaConfig.from_pretrained(_lowercase       )
													lowerCAmelCase__       =						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"       )














import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


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

	parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')

	parser.add_argument(
	    '''--txt2img_unclip''',
	    default='''kakaobrain/karlo-v1-alpha''',
	    type=str,
	    required=False,
	    help='''The pretrained txt2img unclip.''',
	)

	a_							=    parser.parse_args()

	a_							=    UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

	a_							=    CLIPImageProcessor()
	a_							=    CLIPVisionModelWithProjection.from_pretrained('''openai/clip-vit-large-patch14''')

	a_							=    UnCLIPImageVariationPipeline(
	    decoder=txtaimg.decoder,
	    text_encoder=txtaimg.text_encoder,
	    tokenizer=txtaimg.tokenizer,
	    text_proj=txtaimg.text_proj,
	    feature_extractor=feature_extractor,
	    image_encoder=image_encoder,
	    super_res_first=txtaimg.super_res_first,
	    super_res_last=txtaimg.super_res_last,
	    decoder_scheduler=txtaimg.decoder_scheduler,
	    super_res_scheduler=txtaimg.super_res_scheduler,
	)

	imgaimg.save_pretrained(args.dump_path)






