'''simple docstring'''

import json
import os
import shutil
import tempfile
import unittest

from transformers import is_speech_available, is_torch_available
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_torch
from transformers.utils import FEATURE_EXTRACTOR_NAME


if is_speech_available() and is_torch_available():
 from transformers import SpeechTaFeatureExtractor, SpeechTaProcessor

 from .test_feature_extraction_speechta import floats_list


_A      :				int 				=    get_tests_dir('''fixtures/test_sentencepiece_bpe_char.model''')







@require_torch
class     _lowercase					(    unittest.TestCase      ):


 '''simple docstring'''






 def a (				self       :     Union[str, Any]							)  ->   List[str]:
  __lowerCAmelCase    							=						tempfile.mkdtemp()

  __lowerCAmelCase    							=						SpeechTaTokenizer(SCREAMING_SNAKE_CASE__							)
  tokenizer.save_pretrained(self.tmpdirname							)

  __lowerCAmelCase    							=						{
      """feature_size""": 1,
      """padding_value""": 0.0,
      """sampling_rate""": 1_60_00,
      """do_normalize""": False,
      """num_mel_bins""": 80,
      """hop_length""": 16,
      """win_length""": 64,
      """win_function""": """hann_window""",
      """fmin""": 80,
      """fmax""": 76_00,
      """mel_floor""": 1e-1_0,
      """reduction_factor""": 2,
      """return_attention_mask""": True,
  }

  __lowerCAmelCase    							=						os.path.join(self.tmpdirname				,	SCREAMING_SNAKE_CASE__							)
  with open(self.feature_extraction_file				,	"""w"""				,	encoding="""utf-8"""							) as fp:
   fp.write(json.dumps(SCREAMING_SNAKE_CASE__							) + """\n"""							)
 def a (				self       :     Union[str, Any]				,	**SCREAMING_SNAKE_CASE__       :     Optional[int]							)  ->   Optional[Any]:
  return SpeechTaTokenizer.from_pretrained(self.tmpdirname				,	**SCREAMING_SNAKE_CASE__							)
 def a (				self       :     Tuple				,	**SCREAMING_SNAKE_CASE__       :     Optional[int]							)  ->   Optional[Any]:
  return SpeechTaFeatureExtractor.from_pretrained(self.tmpdirname				,	**SCREAMING_SNAKE_CASE__							)
 def a (				self       :     Optional[int]							)  ->   List[str]:
  shutil.rmtree(self.tmpdirname							)
 def a (				self       :     int							)  ->   List[Any]:
  __lowerCAmelCase    							=						self.get_tokenizer()
  __lowerCAmelCase    							=						self.get_feature_extractor()

  __lowerCAmelCase    							=						SpeechTaProcessor(tokenizer=SCREAMING_SNAKE_CASE__				,	feature_extractor=SCREAMING_SNAKE_CASE__							)

  processor.save_pretrained(self.tmpdirname							)
  __lowerCAmelCase    							=						SpeechTaProcessor.from_pretrained(self.tmpdirname							)

  self.assertEqual(processor.tokenizer.get_vocab()				,	tokenizer.get_vocab()							)
  self.assertIsInstance(processor.tokenizer				,	SCREAMING_SNAKE_CASE__							)

  self.assertEqual(processor.feature_extractor.to_json_string()				,	feature_extractor.to_json_string()							)
  self.assertIsInstance(processor.feature_extractor				,	SCREAMING_SNAKE_CASE__							)
 def a (				self       :     Any							)  ->   str:
  __lowerCAmelCase    							=						SpeechTaProcessor(tokenizer=self.get_tokenizer()				,	feature_extractor=self.get_feature_extractor()							)
  processor.save_pretrained(self.tmpdirname							)

  __lowerCAmelCase    							=						self.get_tokenizer(bos_token="""(BOS)"""				,	eos_token="""(EOS)"""							)
  __lowerCAmelCase    							=						self.get_feature_extractor(do_normalize=SCREAMING_SNAKE_CASE__				,	padding_value=1.0							)

  __lowerCAmelCase    							=						SpeechTaProcessor.from_pretrained(
      self.tmpdirname				,	bos_token="""(BOS)"""				,	eos_token="""(EOS)"""				,	do_normalize=SCREAMING_SNAKE_CASE__				,	padding_value=1.0							)

  self.assertEqual(processor.tokenizer.get_vocab()				,	tokenizer_add_kwargs.get_vocab()							)
  self.assertIsInstance(processor.tokenizer				,	SCREAMING_SNAKE_CASE__							)

  self.assertEqual(processor.feature_extractor.to_json_string()				,	feature_extractor_add_kwargs.to_json_string()							)
  self.assertIsInstance(processor.feature_extractor				,	SCREAMING_SNAKE_CASE__							)
 def a (				self       :     Optional[int]							)  ->   Dict:
  __lowerCAmelCase    							=						self.get_feature_extractor()
  __lowerCAmelCase    							=						self.get_tokenizer()

  __lowerCAmelCase    							=						SpeechTaProcessor(tokenizer=SCREAMING_SNAKE_CASE__				,	feature_extractor=SCREAMING_SNAKE_CASE__							)

  __lowerCAmelCase    							=						floats_list((3, 10_00)							)

  __lowerCAmelCase    							=						feature_extractor(audio=SCREAMING_SNAKE_CASE__				,	return_tensors="""np"""							)
  __lowerCAmelCase    							=						processor(audio=SCREAMING_SNAKE_CASE__				,	return_tensors="""np"""							)

  for key in input_feat_extract.keys():
   self.assertAlmostEqual(input_feat_extract[key].sum()				,	input_processor[key].sum()				,	delta=1e-2							)
 def a (				self       :     Optional[int]							)  ->   str:
  __lowerCAmelCase    							=						self.get_feature_extractor()
  __lowerCAmelCase    							=						self.get_tokenizer()

  __lowerCAmelCase    							=						SpeechTaProcessor(tokenizer=SCREAMING_SNAKE_CASE__				,	feature_extractor=SCREAMING_SNAKE_CASE__							)

  __lowerCAmelCase    							=						floats_list((3, 10_00)							)

  __lowerCAmelCase    							=						feature_extractor(audio_target=SCREAMING_SNAKE_CASE__				,	return_tensors="""np"""							)
  __lowerCAmelCase    							=						processor(audio_target=SCREAMING_SNAKE_CASE__				,	return_tensors="""np"""							)

  for key in input_feat_extract.keys():
   self.assertAlmostEqual(input_feat_extract[key].sum()				,	input_processor[key].sum()				,	delta=1e-2							)
 def a (				self       :     Union[str, Any]							)  ->   Optional[int]:
  __lowerCAmelCase    							=						self.get_feature_extractor()
  __lowerCAmelCase    							=						self.get_tokenizer()

  __lowerCAmelCase    							=						SpeechTaProcessor(tokenizer=SCREAMING_SNAKE_CASE__				,	feature_extractor=SCREAMING_SNAKE_CASE__							)

  __lowerCAmelCase    							=						"""This is a test string"""

  __lowerCAmelCase    							=						processor(text=SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase    							=						tokenizer(SCREAMING_SNAKE_CASE__							)

  for key in encoded_tok.keys():
   self.assertListEqual(encoded_tok[key]				,	encoded_processor[key]							)
 def a (				self       :     Union[str, Any]							)  ->   List[Any]:
  __lowerCAmelCase    							=						self.get_feature_extractor()
  __lowerCAmelCase    							=						self.get_tokenizer()

  __lowerCAmelCase    							=						SpeechTaProcessor(tokenizer=SCREAMING_SNAKE_CASE__				,	feature_extractor=SCREAMING_SNAKE_CASE__							)

  __lowerCAmelCase    							=						"""This is a test string"""

  __lowerCAmelCase    							=						processor(text_target=SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase    							=						tokenizer(SCREAMING_SNAKE_CASE__							)

  for key in encoded_tok.keys():
   self.assertListEqual(encoded_tok[key]				,	encoded_processor[key]							)
 def a (				self       :     Optional[int]							)  ->   str:
  __lowerCAmelCase    							=						self.get_feature_extractor()
  __lowerCAmelCase    							=						self.get_tokenizer()

  __lowerCAmelCase    							=						SpeechTaProcessor(tokenizer=SCREAMING_SNAKE_CASE__				,	feature_extractor=SCREAMING_SNAKE_CASE__							)

  __lowerCAmelCase    							=						[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

  __lowerCAmelCase    							=						processor.batch_decode(SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase    							=						tokenizer.batch_decode(SCREAMING_SNAKE_CASE__							)

  self.assertListEqual(SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__							)





 def a (				self       :     Optional[Any]							)  ->   Optional[int]:
  __lowerCAmelCase    							=						self.get_feature_extractor()
  __lowerCAmelCase    							=						self.get_tokenizer()

  __lowerCAmelCase    							=						SpeechTaProcessor(tokenizer=SCREAMING_SNAKE_CASE__				,	feature_extractor=SCREAMING_SNAKE_CASE__							)

  self.assertListEqual(
      processor.model_input_names				,	feature_extractor.model_input_names				,	msg="""`processor` and `feature_extractor` model input names do not match"""				,	)







'''simple docstring'''

import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionDualGuidedPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


_A      :				Any 				=    False







@nightly
@require_torch_gpu
class     _lowercase					(    unittest.TestCase      ):


 '''simple docstring'''






 def a (				self       :     Tuple							)  ->   Dict:
  # clean up the VRAM after each test
  super().tearDown()
  gc.collect()
  torch.cuda.empty_cache()
 def a (				self       :     Optional[int]							)  ->   Optional[Any]:
  __lowerCAmelCase    							=						VersatileDiffusionDualGuidedPipeline.from_pretrained("""shi-labs/versatile-diffusion"""							)
  # remove text_unet
  pipe.remove_unused_weights()
  pipe.to(SCREAMING_SNAKE_CASE__							)
  pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__							)

  __lowerCAmelCase    							=						load_image(
      """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"""							)

  __lowerCAmelCase    							=						torch.manual_seed(0							)
  __lowerCAmelCase    							=						pipe(
      prompt="""first prompt"""				,	image=SCREAMING_SNAKE_CASE__				,	text_to_image_strength=0.7_5				,	generator=SCREAMING_SNAKE_CASE__				,	guidance_scale=7.5				,	num_inference_steps=2				,	output_type="""numpy"""				,	).images

  with tempfile.TemporaryDirectory() as tmpdirname:
   pipe.save_pretrained(SCREAMING_SNAKE_CASE__							)
   __lowerCAmelCase    							=						VersatileDiffusionDualGuidedPipeline.from_pretrained(SCREAMING_SNAKE_CASE__							)

  pipe.to(SCREAMING_SNAKE_CASE__							)
  pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__							)

  __lowerCAmelCase    							=						generator.manual_seed(0							)
  __lowerCAmelCase    							=						pipe(
      prompt="""first prompt"""				,	image=SCREAMING_SNAKE_CASE__				,	text_to_image_strength=0.7_5				,	generator=SCREAMING_SNAKE_CASE__				,	guidance_scale=7.5				,	num_inference_steps=2				,	output_type="""numpy"""				,	).images

  assert np.abs(image - new_image							).sum() < 1e-5, "Models don't have the same forward pass"





 def a (				self       :     str							)  ->   Tuple:
  __lowerCAmelCase    							=						VersatileDiffusionDualGuidedPipeline.from_pretrained("""shi-labs/versatile-diffusion"""							)
  pipe.remove_unused_weights()
  pipe.to(SCREAMING_SNAKE_CASE__							)
  pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__							)

  __lowerCAmelCase    							=						"""cyberpunk 2077"""
  __lowerCAmelCase    							=						load_image(
      """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"""							)
  __lowerCAmelCase    							=						torch.manual_seed(0							)
  __lowerCAmelCase    							=						pipe(
      prompt=SCREAMING_SNAKE_CASE__				,	image=SCREAMING_SNAKE_CASE__				,	text_to_image_strength=0.7_5				,	generator=SCREAMING_SNAKE_CASE__				,	guidance_scale=7.5				,	num_inference_steps=50				,	output_type="""numpy"""				,	).images

  __lowerCAmelCase    							=						image[0, 2_53:2_56, 2_53:2_56, -1]

  assert image.shape == (1, 5_12, 5_12, 3)
  __lowerCAmelCase    							=						np.array([0.0_7_8_7, 0.0_8_4_9, 0.0_8_2_6, 0.0_8_1_2, 0.0_8_0_7, 0.0_7_9_5, 0.0_8_1_8, 0.0_7_9_8, 0.0_7_7_9]							)

  assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-2






