
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


__magic_name__  :  List[Any]     			=   get_tests_dir('fixtures/test_sentencepiece_bpe_char.model')


@require_torch
class 							lowerCamelCase       (		unittest.TestCase					):



  """simple docstring"""





  def 					lowercase_						(					self					):
      A_								=					tempfile.mkdtemp()

      A_								=					SpeechTaTokenizer(__UpperCamelCase					)
      tokenizer.save_pretrained(self.tmpdirname					)

      A_								=					{
          "feature_size": 1,
          "padding_value": 0.0,
          "sampling_rate": 16000,
          "do_normalize": False,
          "num_mel_bins": 80,
          "hop_length": 16,
          "win_length": 64,
          "win_function": "hann_window",
          "fmin": 80,
          "fmax": 7600,
          "mel_floor": 1E-10,
          "reduction_factor": 2,
          "return_attention_mask": True,
      }

      A_								=					os.path.join(self.tmpdirname						,    __UpperCamelCase					)
      with open(self.feature_extraction_file						,    "w"						,    encoding="utf-8"					) as fp:
          fp.write(json.dumps(__UpperCamelCase					) + "\n"					)

  def 					lowercase_						(					self						,    **__UpperCamelCase					):
      return SpeechTaTokenizer.from_pretrained(self.tmpdirname						,    **__UpperCamelCase					)

  def 					lowercase_						(					self						,    **__UpperCamelCase					):
      return SpeechTaFeatureExtractor.from_pretrained(self.tmpdirname						,    **__UpperCamelCase					)

  def 					lowercase_						(					self					):
      shutil.rmtree(self.tmpdirname					)

  def 					lowercase_						(					self					):
      A_								=					self.get_tokenizer()
      A_								=					self.get_feature_extractor()

      A_								=					SpeechTaProcessor(tokenizer=__UpperCamelCase						,    feature_extractor=__UpperCamelCase					)

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

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

      self.assertEqual(processor.feature_extractor.to_json_string()						,    feature_extractor.to_json_string()					)
      self.assertIsInstance(processor.feature_extractor						,    __UpperCamelCase					)

  def 					lowercase_						(					self					):
      A_								=					SpeechTaProcessor(tokenizer=self.get_tokenizer()						,    feature_extractor=self.get_feature_extractor()					)
      processor.save_pretrained(self.tmpdirname					)

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

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

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

      self.assertEqual(processor.feature_extractor.to_json_string()						,    feature_extractor_add_kwargs.to_json_string()					)
      self.assertIsInstance(processor.feature_extractor						,    __UpperCamelCase					)

  def 					lowercase_						(					self					):
      A_								=					self.get_feature_extractor()
      A_								=					self.get_tokenizer()

      A_								=					SpeechTaProcessor(tokenizer=__UpperCamelCase						,    feature_extractor=__UpperCamelCase					)

      A_								=					floats_list((3, 1000)					)

      A_								=					feature_extractor(audio=__UpperCamelCase						,    return_tensors="np"					)
      A_								=					processor(audio=__UpperCamelCase						,    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 					lowercase_						(					self					):
      A_								=					self.get_feature_extractor()
      A_								=					self.get_tokenizer()

      A_								=					SpeechTaProcessor(tokenizer=__UpperCamelCase						,    feature_extractor=__UpperCamelCase					)

      A_								=					floats_list((3, 1000)					)

      A_								=					feature_extractor(audio_target=__UpperCamelCase						,    return_tensors="np"					)
      A_								=					processor(audio_target=__UpperCamelCase						,    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 					lowercase_						(					self					):
      A_								=					self.get_feature_extractor()
      A_								=					self.get_tokenizer()

      A_								=					SpeechTaProcessor(tokenizer=__UpperCamelCase						,    feature_extractor=__UpperCamelCase					)

      A_								=					"This is a test string"

      A_								=					processor(text=__UpperCamelCase					)
      A_								=					tokenizer(__UpperCamelCase					)

      for key in encoded_tok.keys():
          self.assertListEqual(encoded_tok[key]						,    encoded_processor[key]					)

  def 					lowercase_						(					self					):
      A_								=					self.get_feature_extractor()
      A_								=					self.get_tokenizer()

      A_								=					SpeechTaProcessor(tokenizer=__UpperCamelCase						,    feature_extractor=__UpperCamelCase					)

      A_								=					"This is a test string"

      A_								=					processor(text_target=__UpperCamelCase					)
      A_								=					tokenizer(__UpperCamelCase					)

      for key in encoded_tok.keys():
          self.assertListEqual(encoded_tok[key]						,    encoded_processor[key]					)

  def 					lowercase_						(					self					):
      A_								=					self.get_feature_extractor()
      A_								=					self.get_tokenizer()

      A_								=					SpeechTaProcessor(tokenizer=__UpperCamelCase						,    feature_extractor=__UpperCamelCase					)

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

      A_								=					processor.batch_decode(__UpperCamelCase					)
      A_								=					tokenizer.batch_decode(__UpperCamelCase					)

      self.assertListEqual(__UpperCamelCase						,    __UpperCamelCase					)

  def 					lowercase_						(					self					):
      A_								=					self.get_feature_extractor()
      A_								=					self.get_tokenizer()

      A_								=					SpeechTaProcessor(tokenizer=__UpperCamelCase						,    feature_extractor=__UpperCamelCase					)

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








'''simple docstring'''







from typing import List, Optional, Tuple, Union

import torch

from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class       lowerCAmelCase			(					__UpperCAmelCase      ):




		def __init__(					self       ,    UpperCamelCase       ,    UpperCamelCase   ):
								super().__init__()
								self.register_modules(unet=UpperCamelCase       ,    scheduler=UpperCamelCase   )




		@torch.no_grad()
		def __call__(					self       ,    UpperCamelCase = 1       ,    UpperCamelCase = None       ,    UpperCamelCase = 1_000       ,    UpperCamelCase = "pil"       ,    UpperCamelCase = True       ,    ):
								# Sample gaussian noise to begin loop
								if isinstance(self.unet.config.sample_size       ,    UpperCamelCase   ):
														_SCREAMING_SNAKE_CASE					=			(
														    batch_size,
														    self.unet.config.in_channels,
														    self.unet.config.sample_size,
														    self.unet.config.sample_size,
														)
								else:
														_SCREAMING_SNAKE_CASE					=			(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

								if self.device.type == "mps":
														# randn does not work reproducibly on mps
														_SCREAMING_SNAKE_CASE					=			randn_tensor(UpperCamelCase       ,    generator=UpperCamelCase   )
														_SCREAMING_SNAKE_CASE					=			image.to(self.device   )
								else:
														_SCREAMING_SNAKE_CASE					=			randn_tensor(UpperCamelCase       ,    generator=UpperCamelCase       ,    device=self.device   )

								# set step values
								self.scheduler.set_timesteps(UpperCamelCase   )

								for t in self.progress_bar(self.scheduler.timesteps   ):
														# 1. predict noise model_output
														_SCREAMING_SNAKE_CASE					=			self.unet(UpperCamelCase       ,    UpperCamelCase   ).sample

														# 2. compute previous image: x_t -> x_t-1
														_SCREAMING_SNAKE_CASE					=			self.scheduler.step(UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    generator=UpperCamelCase   ).prev_sample

								_SCREAMING_SNAKE_CASE					=			(image / 2 + 0.5).clamp(0       ,    1   )
								_SCREAMING_SNAKE_CASE					=			image.cpu().permute(0       ,    2       ,    3       ,    1   ).numpy()
								if output_type == "pil":
														_SCREAMING_SNAKE_CASE					=			self.numpy_to_pil(UpperCamelCase   )

								if not return_dict:
														return (image,)

								return ImagePipelineOutput(images=UpperCamelCase   )