






'''simple docstring'''


import json
import os
import shutil
import tempfile
import unittest

from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor, WavaVecaProcessor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_wavaveca import floats_list




class 							lowerCAmelCase					(							unittest.TestCase   ):







   """simple docstring"""







   def    __magic_name__    ( self			)    ->   Optional[int]:
    __a    :	Optional[Any]									=						'<pad> <s> </s> <unk> | E T A O N I H S R D L U M W C F G Y P B V K \' X J Q Z'.split(' '			)
    __a    :	Union[str, Any]									=						dict(zip(_A   ,   range(len(_A			)			)			)			)

    __a    :	Dict									=						{
        'pad_token': '<pad>',
        'unk_token': '<unk>',
        'bos_token': '<s>',
        'eos_token': '</s>',
    }
    __a    :	str									=						{
        'feature_size': 1,
        'padding_value': 0.0,
        'sampling_rate': 16000,
        'return_attention_mask': False,
        'do_normalize': True,
    }

    __a    :	str									=						tempfile.mkdtemp()
    __a    :	List[str]									=						os.path.join(self.tmpdirname   ,   VOCAB_FILES_NAMES['vocab_file']			)
    __a    :	str									=						os.path.join(self.tmpdirname   ,   _A			)
    with open(self.vocab_file   ,   'w'   ,   encoding='utf-8'			) as fp:
     fp.write(json.dumps(_A			) + '\n'			)

    with open(self.feature_extraction_file   ,   'w'   ,   encoding='utf-8'			) as fp:
     fp.write(json.dumps(_A			) + '\n'			)



   def    __magic_name__    ( self   ,   **_A			)    ->   Union[str, Any]:
    __a    :	Dict									=						self.add_kwargs_tokens_map.copy()
    kwargs.update(_A			)
    return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname   ,   **_A			)



   def    __magic_name__    ( self   ,   **_A			)    ->   Tuple:
    return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname   ,   **_A			)



   def    __magic_name__    ( self			)    ->   List[str]:
    shutil.rmtree(self.tmpdirname			)



   def    __magic_name__    ( self			)    ->   Any:
    __a    :	Dict									=						self.get_tokenizer()
    __a    :	Optional[Any]									=						self.get_feature_extractor()

    __a    :	Dict									=						WavaVecaProcessor(tokenizer=_A   ,   feature_extractor=_A			)

    processor.save_pretrained(self.tmpdirname			)
    __a    :	Union[str, Any]									=						WavaVecaProcessor.from_pretrained(self.tmpdirname			)

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

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



   def    __magic_name__    ( self			)    ->   List[Any]:
    __a    :	Dict									=						WavaVecaProcessor(tokenizer=self.get_tokenizer()   ,   feature_extractor=self.get_feature_extractor()			)
    processor.save_pretrained(self.tmpdirname			)

    __a    :	str									=						self.get_tokenizer(bos_token='(BOS)'   ,   eos_token='(EOS)'			)
    __a    :	str									=						self.get_feature_extractor(do_normalize=_A   ,   padding_value=1.0			)

    __a    :	Any									=						WavaVecaProcessor.from_pretrained(
        self.tmpdirname   ,   bos_token='(BOS)'   ,   eos_token='(EOS)'   ,   do_normalize=_A   ,   padding_value=1.0			)

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

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



   def    __magic_name__    ( self			)    ->   List[str]:
    __a    :	Union[str, Any]									=						self.get_feature_extractor()
    __a    :	Optional[Any]									=						self.get_tokenizer()

    __a    :	List[Any]									=						WavaVecaProcessor(tokenizer=_A   ,   feature_extractor=_A			)

    __a    :	Optional[Any]									=						floats_list((3, 1000)			)

    __a    :	Optional[Any]									=						feature_extractor(_A   ,   return_tensors='np'			)
    __a    :	Optional[Any]									=						processor(_A   ,   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    __magic_name__    ( self			)    ->   Optional[Any]:
    __a    :	Any									=						self.get_feature_extractor()
    __a    :	Tuple									=						self.get_tokenizer()

    __a    :	List[str]									=						WavaVecaProcessor(tokenizer=_A   ,   feature_extractor=_A			)

    __a    :	List[Any]									=						'This is a test string'

    __a    :	Union[str, Any]									=						processor(text=_A			)

    __a    :	str									=						tokenizer(_A			)

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



   def    __magic_name__    ( self			)    ->   Any:
    __a    :	Dict									=						self.get_feature_extractor()
    __a    :	Union[str, Any]									=						self.get_tokenizer()

    __a    :	Dict									=						WavaVecaProcessor(tokenizer=_A   ,   feature_extractor=_A			)

    __a    :	Any									=						[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

    __a    :	int									=						processor.batch_decode(_A			)
    __a    :	Union[str, Any]									=						tokenizer.batch_decode(_A			)

    self.assertListEqual(_A   ,   _A			)



   def    __magic_name__    ( self			)    ->   Optional[int]:
    __a    :	str									=						self.get_feature_extractor()
    __a    :	Tuple									=						self.get_tokenizer()

    __a    :	Any									=						WavaVecaProcessor(tokenizer=_A   ,   feature_extractor=_A			)

    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 unittest

import numpy as np
import torch

from diffusers import DDPMPipeline, DDPMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device


enable_full_determinism()




class 							lowerCAmelCase					(							unittest.TestCase   ):







   """simple docstring"""







   @property
   def    __magic_name__    ( self			)    ->   Any:
    torch.manual_seed(0			)
    __a    :	Optional[Any]									=						UNetaDModel(
        block_out_channels=(32, 64)   ,   layers_per_block=2   ,   sample_size=32   ,   in_channels=3   ,   out_channels=3   ,   down_block_types=('DownBlock2D', 'AttnDownBlock2D')   ,   up_block_types=('AttnUpBlock2D', 'UpBlock2D')   ,   )
    return model



   def    __magic_name__    ( self			)    ->   Tuple:
    __a    :	Union[str, Any]									=						'cpu'
    __a    :	Tuple									=						self.dummy_uncond_unet
    __a    :	str									=						DDPMScheduler()

    __a    :	int									=						DDPMPipeline(unet=_A   ,   scheduler=_A			)
    ddpm.to(_A			)
    ddpm.set_progress_bar_config(disable=_A			)

    __a    :	str									=						torch.Generator(device=_A			).manual_seed(0			)
    __a    :	int									=						ddpm(generator=_A   ,   num_inference_steps=2   ,   output_type='numpy'			).images

    __a    :	Union[str, Any]									=						torch.Generator(device=_A			).manual_seed(0			)
    __a    :	Optional[int]									=						ddpm(generator=_A   ,   num_inference_steps=2   ,   output_type='numpy'   ,   return_dict=_A			)[0]

    __a    :	List[str]									=						image[0, -3:, -3:, -1]
    __a    :	List[str]									=						image_from_tuple[0, -3:, -3:, -1]

    assert image.shape == (1, 32, 32, 3)
    __a    :	List[Any]									=						np.array(
        [9.9_5_6E-0_1, 5.7_8_5E-0_1, 4.6_7_5E-0_1, 9.9_3_0E-0_1, 0.0, 1.000, 1.1_9_9E-0_3, 2.6_4_8E-0_4, 5.1_0_1E-0_4]			)

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



   def    __magic_name__    ( self			)    ->   Tuple:
    __a    :	Optional[Any]									=						self.dummy_uncond_unet
    __a    :	List[str]									=						DDPMScheduler(prediction_type='sample'			)

    __a    :	List[str]									=						DDPMPipeline(unet=_A   ,   scheduler=_A			)
    ddpm.to(_A			)
    ddpm.set_progress_bar_config(disable=_A			)

    __a    :	Optional[Any]									=						torch.manual_seed(0			)
    __a    :	int									=						ddpm(generator=_A   ,   num_inference_steps=2   ,   output_type='numpy'			).images

    __a    :	Union[str, Any]									=						torch.manual_seed(0			)
    __a    :	int									=						ddpm(generator=_A   ,   num_inference_steps=2   ,   output_type='numpy'			)[0]

    __a    :	Dict									=						image[0, -3:, -3:, -1]
    __a    :	int									=						image_eps[0, -3:, -3:, -1]

    assert image.shape == (1, 32, 32, 3)
    __a    :	int									=						1E-2 if torch_device != 'mps' else 3E-2
    assert np.abs(image_slice.flatten() - image_eps_slice.flatten()			).max() < tolerance




@slow
@require_torch_gpu
class 							lowerCAmelCase					(							unittest.TestCase   ):







   """simple docstring"""







   def    __magic_name__    ( self			)    ->   List[str]:
    __a    :	Union[str, Any]									=						'google/ddpm-cifar10-32'

    __a    :	str									=						UNetaDModel.from_pretrained(_A			)
    __a    :	List[str]									=						DDPMScheduler.from_pretrained(_A			)

    __a    :	List[str]									=						DDPMPipeline(unet=_A   ,   scheduler=_A			)
    ddpm.to(_A			)
    ddpm.set_progress_bar_config(disable=_A			)

    __a    :	int									=						torch.manual_seed(0			)
    __a    :	Optional[Any]									=						ddpm(generator=_A   ,   output_type='numpy'			).images

    __a    :	int									=						image[0, -3:, -3:, -1]

    assert image.shape == (1, 32, 32, 3)
    __a    :	Optional[Any]									=						np.array([0.4_200, 0.3_588, 0.1_939, 0.3_847, 0.3_382, 0.2_647, 0.4_155, 0.3_582, 0.3_385]			)
    assert np.abs(image_slice.flatten() - expected_slice			).max() < 1E-2




