

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__			(       _a				,      _a=1.0				,      _a=None				,      _a=None):
	if rng is None:
		SCREAMING_SNAKE_CASE :   Optional[Any]					  =							global_rng

	SCREAMING_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






@require_torch
@require_torchaudio
class _UpperCamelCase				(				unittest.TestCase ):
			'''simple docstring'''







			def __init__(      self				:			Dict   ,   a				:			Optional[Any]   ,   a				:			int=7   ,   a				:			int=400   ,   a				:			Optional[Any]=2000   ,   a				:			List[str]=10   ,   a				:			List[str]=160   ,   a				:			str=8   ,   a				:			Optional[int]=0.0   ,   a				:			Optional[Any]=4000   ,   a				:			Union[str, Any]=False   ,   a				:			int=True   ,   )				->							Any:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   List[str]					  =							parent
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							batch_size
				SCREAMING_SNAKE_CASE :   Dict					  =							min_seq_length
				SCREAMING_SNAKE_CASE :   int					  =							max_seq_length
				SCREAMING_SNAKE_CASE :   Dict					  =							(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
				SCREAMING_SNAKE_CASE :   List[Any]					  =							padding_value
				SCREAMING_SNAKE_CASE :   Dict					  =							sampling_rate
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							return_attention_mask
				SCREAMING_SNAKE_CASE :   Tuple					  =							do_normalize
				SCREAMING_SNAKE_CASE :   Tuple					  =							feature_size
				SCREAMING_SNAKE_CASE :   List[str]					  =							chunk_length
				SCREAMING_SNAKE_CASE :   List[str]					  =							hop_length
			def 				__UpperCamelCase			(      self				:			Optional[Any]						)				->							Union[str, 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 				__UpperCamelCase			(      self				:			Union[str, Any]   ,   a				:			List[Any]=False   ,   a				:			int=False						)				->							Dict:

				"""simple docstring"""

				def _flatten(a				:			Optional[Any]						):
					return list(itertools.chain(*a						)						)

				if equal_length:
					SCREAMING_SNAKE_CASE :   int					  =							[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 :   Tuple					  =							[
					    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 :   Dict					  =							[np.asarray(a						) for x in speech_inputs]
				return speech_inputs






@require_torch
@require_torchaudio
class _UpperCamelCase				(				__A				,						unittest.TestCase ):
			'''simple docstring'''







			lowerCamelCase__							     =WhisperFeatureExtractor if is_speech_available() else None
			def 				__UpperCamelCase			(      self				:			List[str]						)				->							Dict:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Optional[int]					  =							WhisperFeatureExtractionTester(self						)
			def 				__UpperCamelCase			(      self				:			Dict						)				->							Dict:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   str					  =							self.feature_extraction_class(**self.feat_extract_dict						)

				with tempfile.TemporaryDirectory() as tmpdirname:
					SCREAMING_SNAKE_CASE :   Any					  =							feat_extract_first.save_pretrained(a						)[0]
					check_json_file_has_correct_format(a						)
					SCREAMING_SNAKE_CASE :   str					  =							self.feature_extraction_class.from_pretrained(a						)

				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							feat_extract_first.to_dict()
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							feat_extract_second.to_dict()
				SCREAMING_SNAKE_CASE :   List[Any]					  =							feat_extract_first.mel_filters
				SCREAMING_SNAKE_CASE :   int					  =							feat_extract_second.mel_filters
				self.assertTrue(np.allclose(a   ,   a						)						)
				self.assertEqual(a   ,   a						)
			def 				__UpperCamelCase			(      self				:			Optional[int]						)				->							Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Tuple					  =							self.feature_extraction_class(**self.feat_extract_dict						)

				with tempfile.TemporaryDirectory() as tmpdirname:
					SCREAMING_SNAKE_CASE :   List[Any]					  =							os.path.join(a   ,   "feat_extract.json"						)
					feat_extract_first.to_json_file(a						)
					SCREAMING_SNAKE_CASE :   Tuple					  =							self.feature_extraction_class.from_json_file(a						)

				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							feat_extract_first.to_dict()
				SCREAMING_SNAKE_CASE :   str					  =							feat_extract_second.to_dict()
				SCREAMING_SNAKE_CASE :   Tuple					  =							feat_extract_first.mel_filters
				SCREAMING_SNAKE_CASE :   int					  =							feat_extract_second.mel_filters
				self.assertTrue(np.allclose(a   ,   a						)						)
				self.assertEqual(a   ,   a						)
			def 				__UpperCamelCase			(      self				:			Optional[Any]						)				->							str:

				"""simple docstring"""

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

				# Test feature size
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							feature_extractor(a   ,   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
				SCREAMING_SNAKE_CASE :   List[str]					  =							feature_extractor(speech_inputs[0]   ,   return_tensors="np"						).input_features
				SCREAMING_SNAKE_CASE :   int					  =							feature_extractor(np_speech_inputs[0]   ,   return_tensors="np"						).input_features
				self.assertTrue(np.allclose(a   ,   a   ,   atol=1e-3						)						)

				# Test batched
				SCREAMING_SNAKE_CASE :   List[str]					  =							feature_extractor(a   ,   return_tensors="np"						).input_features
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							feature_extractor(a   ,   return_tensors="np"						).input_features
				for enc_seq_a, enc_seq_a in zip(a   ,   a						):
					self.assertTrue(np.allclose(a   ,   a   ,   atol=1e-3						)						)

				# Test 2-D numpy arrays are batched.
				SCREAMING_SNAKE_CASE :   Tuple					  =							[floats_list((1, x)						)[0] for x in (800, 800, 800)]
				SCREAMING_SNAKE_CASE :   Tuple					  =							np.asarray(a						)
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							feature_extractor(a   ,   return_tensors="np"						).input_features
				SCREAMING_SNAKE_CASE :   List[str]					  =							feature_extractor(a   ,   return_tensors="np"						).input_features
				for enc_seq_a, enc_seq_a in zip(a   ,   a						):
					self.assertTrue(np.allclose(a   ,   a   ,   atol=1e-3						)						)

				# Test truncation required
				SCREAMING_SNAKE_CASE :   str					  =							[floats_list((1, x)						)[0] for x in range(200   ,   (feature_extractor.n_samples + 500)   ,   200						)]
				SCREAMING_SNAKE_CASE :   List[Any]					  =							[np.asarray(a						) for speech_input in speech_inputs]

				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							[x[: feature_extractor.n_samples] for x in speech_inputs]
				SCREAMING_SNAKE_CASE :   int					  =							[np.asarray(a						) for speech_input in speech_inputs_truncated]

				SCREAMING_SNAKE_CASE :   Tuple					  =							feature_extractor(a   ,   return_tensors="np"						).input_features
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							feature_extractor(a   ,   return_tensors="np"						).input_features
				for enc_seq_a, enc_seq_a in zip(a   ,   a						):
					self.assertTrue(np.allclose(a   ,   a   ,   atol=1e-3						)						)
			def 				__UpperCamelCase			(      self				:			Optional[Any]						)				->							Tuple:

				"""simple docstring"""

				import torch

				SCREAMING_SNAKE_CASE :   List[str]					  =							self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()						)
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							np.random.rand(100   ,   32						).astype(np.floataa						)
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							np_speech_inputs.tolist()

				for inputs in [py_speech_inputs, np_speech_inputs]:
					SCREAMING_SNAKE_CASE :   str					  =							feature_extractor.pad([{"input_features": inputs}]   ,   return_tensors="np"						)
					self.assertTrue(np_processed.input_features.dtype == np.floataa						)
					SCREAMING_SNAKE_CASE :   Optional[Any]					  =							feature_extractor.pad([{"input_features": inputs}]   ,   return_tensors="pt"						)
					self.assertTrue(pt_processed.input_features.dtype == torch.floataa						)
			def 				__UpperCamelCase			(      self				:			Any   ,   a				:			Optional[int]						)				->							int:

				"""simple docstring"""

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

				return [x["array"] for x in speech_samples]
			def 				__UpperCamelCase			(      self				:			Optional[int]						)				->							Optional[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Optional[int]					  =							torch.tensor(
				    [
				        0.1193, -0.0946, -0.1098, -0.0196, 0.0225, -0.0690, -0.1736, 0.0951,
				        0.0971, -0.0817, -0.0702, 0.0162, 0.0260, 0.0017, -0.0192, -0.1678,
				        0.0709, -0.1867, -0.0655, -0.0274, -0.0234, -0.1884, -0.0516, -0.0554,
				        -0.0274, -0.1425, -0.1423, 0.0837, 0.0377, -0.0854
				    ]						)
				# fmt: on

				SCREAMING_SNAKE_CASE :   Optional[int]					  =							self._load_datasamples(1						)
				SCREAMING_SNAKE_CASE :   List[Any]					  =							WhisperFeatureExtractor()
				SCREAMING_SNAKE_CASE :   List[str]					  =							feature_extractor(a   ,   return_tensors="pt"						).input_features
				self.assertEqual(input_features.shape   ,   (1, 80, 3000)						)
				self.assertTrue(torch.allclose(input_features[0, 0, :30]   ,   a   ,   atol=1e-4						)						)






			def 				__UpperCamelCase			(      self				:			List[str]						)				->							Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   int					  =							self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()						)
				SCREAMING_SNAKE_CASE :   int					  =							self._load_datasamples(1						)[0]
				SCREAMING_SNAKE_CASE :   Tuple					  =							((audio - audio.min()) / (audio.max() - audio.min())) * 6_5535  # Rescale to [0, 65535] to show issue
				SCREAMING_SNAKE_CASE :   int					  =							feat_extract.zero_mean_unit_var_norm([audio]   ,   attention_mask=a						)[0]

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


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)