


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__				:	List[str]					       =					random.Random()


def    __lowerCamelCase   (      A__						: Tuple						,						A__						: Optional[int]=1.0						,						A__						: Dict=None						,						A__						: Optional[int]=None					)     ->    str:
   if rng is None:
      lowerCamelCase_						:   Dict		= global_rng

   lowerCamelCase_						:   int		= []
   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						):




       '''simple docstring'''



       def __init__(						self				:				str		,				__a				:				List[Any]		,				__a				:				List[Any]=7		,				__a				:				List[Any]=400		,				__a				:				Union[str, Any]=2_000		,				__a				:				List[str]=2_048		,				__a				:				List[Any]=128		,				__a				:				int=1		,				__a				:				Any=512		,				__a				:				List[Any]=30		,				__a				:				Any=44_100		,				)					->Optional[int]:
          lowerCamelCase_						:   int		= parent
          lowerCamelCase_						:   List[str]		= batch_size
          lowerCamelCase_						:   str		= min_seq_length
          lowerCamelCase_						:   int		= max_seq_length
          lowerCamelCase_						:   Union[str, Any]		= (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
          lowerCamelCase_						:   List[str]		= spectrogram_length
          lowerCamelCase_						:   Tuple		= feature_size
          lowerCamelCase_						:   Tuple		= num_audio_channels
          lowerCamelCase_						:   Tuple		= hop_length
          lowerCamelCase_						:   str		= chunk_length
          lowerCamelCase_						:   Tuple		= sampling_rate



       def 				_lowerCAmelCase							(						self				:				Union[str, Any]							)					->Union[str, Any]:
          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 				_lowerCAmelCase							(						self				:				Optional[Any]		,				__a				:				Tuple=False		,				__a				:				Optional[int]=False							)					->Tuple:
          def _flatten(__a				:				int							):
             return list(itertools.chain(*__a							)							)

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







@require_torch
@require_torchaudio
class   SCREAMING_SNAKE_CASE_			(a__						,       unittest.TestCase						):




       '''simple docstring'''



       _a   							=							TvltFeatureExtractor



       def 				_lowerCAmelCase							(						self				:				Optional[Any]							)					->Optional[Any]:
          lowerCamelCase_						:   Dict		= TvltFeatureExtractionTester(self							)



       def 				_lowerCAmelCase							(						self				:				Tuple							)					->List[str]:
          lowerCamelCase_						:   Optional[Any]		= self.feature_extraction_class(**self.feat_extract_dict							)
          self.assertTrue(hasattr(__a		,				"""spectrogram_length"""							)							)
          self.assertTrue(hasattr(__a		,				"""feature_size"""							)							)
          self.assertTrue(hasattr(__a		,				"""num_audio_channels"""							)							)
          self.assertTrue(hasattr(__a		,				"""hop_length"""							)							)
          self.assertTrue(hasattr(__a		,				"""chunk_length"""							)							)
          self.assertTrue(hasattr(__a		,				"""sampling_rate"""							)							)



       def 				_lowerCAmelCase							(						self				:				str							)					->List[Any]:
          lowerCamelCase_						:   List[str]		= self.feature_extraction_class(**self.feat_extract_dict							)

          with tempfile.TemporaryDirectory() as tmpdirname:
             lowerCamelCase_						:   str		= feat_extract_first.save_pretrained(__a							)[0]
             check_json_file_has_correct_format(__a							)
             lowerCamelCase_						:   Union[str, Any]		= self.feature_extraction_class.from_pretrained(__a							)

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



       def 				_lowerCAmelCase							(						self				:				Union[str, Any]							)					->List[Any]:
          lowerCamelCase_						:   Optional[Any]		= self.feature_extraction_class(**self.feat_extract_dict							)

          with tempfile.TemporaryDirectory() as tmpdirname:
             lowerCamelCase_						:   str		= os.path.join(__a		,				"""feat_extract.json"""							)
             feat_extract_first.to_json_file(__a							)
             lowerCamelCase_						:   str		= self.feature_extraction_class.from_json_file(__a							)

          lowerCamelCase_						:   List[str]		= feat_extract_first.to_dict()
          lowerCamelCase_						:   Union[str, Any]		= feat_extract_second.to_dict()
          lowerCamelCase_						:   str		= dict_first.pop("""mel_filters"""							)
          lowerCamelCase_						:   Tuple		= dict_second.pop("""mel_filters"""							)
          self.assertTrue(np.allclose(__a		,				__a							)							)
          self.assertEqual(__a		,				__a							)



       def 				_lowerCAmelCase							(						self				:				Tuple							)					->Optional[Any]:
          # Initialize feature_extractor
          lowerCamelCase_						:   Tuple		= self.feature_extraction_class(**self.feat_extract_dict							)

          # create three inputs of length 800, 1000, and 1200
          lowerCamelCase_						:   List[str]		= [floats_list((1, x)							)[0] for x in range(800		,				1_400		,				200							)]
          lowerCamelCase_						:   Optional[Any]		= [np.asarray(__a							) for speech_input in speech_inputs]

          # Test not batched input
          lowerCamelCase_						:   int		= feature_extractor(np_speech_inputs[0]		,				return_tensors="""np"""		,				sampling_rate=44_100							).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
          lowerCamelCase_						:   Optional[int]		= feature_extractor(__a		,				return_tensors="""np"""		,				sampling_rate=44_100							).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
          lowerCamelCase_						:   List[str]		= feature_extractor(
              __a		,				return_tensors="""np"""		,				sampling_rate=44_100		,				mask_audio=__a							).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.
          lowerCamelCase_						:   Optional[int]		= [floats_list((1, x)							)[0] for x in (800, 800, 800)]
          lowerCamelCase_						:   List[str]		= np.asarray(__a							)
          lowerCamelCase_						:   Optional[int]		= feature_extractor(__a		,				return_tensors="""np"""		,				sampling_rate=44_100							).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 				_lowerCAmelCase							(						self				:				str		,				__a				:				Optional[int]							)					->List[Any]:
          lowerCamelCase_						:   str		= load_dataset("""hf-internal-testing/librispeech_asr_dummy"""		,				"""clean"""		,				split="""validation"""							)
          # automatic decoding with librispeech
          lowerCamelCase_						:   List[str]		= ds.sort("""id"""							).select(range(__a							)							)[:num_samples]["""audio"""]

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




       def 				_lowerCAmelCase							(						self				:				Optional[Any]							)					->Tuple:
          lowerCamelCase_						:   List[Any]		= self._load_datasamples(1							)
          lowerCamelCase_						:   Dict		= TvltFeatureExtractor()
          lowerCamelCase_						:   Tuple		= feature_extractor(__a		,				return_tensors="""pt"""							).audio_values

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

          lowerCamelCase_						:   Union[str, Any]		= torch.tensor([[-0.3_032, -0.2_708], [-0.4_434, -0.4_007]]							)
          self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]		,				__a		,				atol=1e-4							)							)









import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
      snake_case__				:	Optional[int]					       =					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.',
      )

      snake_case__				:	Tuple					       =					parser.parse_args()

      snake_case__				:	Dict					       =					UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

      snake_case__				:	Optional[int]					       =					CLIPImageProcessor()
      snake_case__				:	Optional[Any]					       =					CLIPVisionModelWithProjection.from_pretrained('openai/clip-vit-large-patch14')

      snake_case__				:	Dict					       =					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)





