'''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     ):



   @property
   def  _UpperCamelCase		(    self     )				->     Optional[int]:
        """simple docstring"""





        torch.manual_seed(0     )
        _UpperCamelCase  :str       =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  _UpperCamelCase		(    self     )				->     Any:
        """simple docstring"""





        _UpperCamelCase  :List[str]       ="""cpu"""
        _UpperCamelCase  :Optional[Any]       =self.dummy_uncond_unet
        _UpperCamelCase  :Optional[Any]       =DDPMScheduler()

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

        _UpperCamelCase  :str       =torch.Generator(device=lowerCAmelCase__     ).manual_seed(0     )
        _UpperCamelCase  :str       =ddpm(generator=lowerCAmelCase__					,      num_inference_steps=2					,      output_type="""numpy"""     ).images

        _UpperCamelCase  :Optional[int]       =torch.Generator(device=lowerCAmelCase__     ).manual_seed(0     )
        _UpperCamelCase  :Dict       =ddpm(generator=lowerCAmelCase__					,      num_inference_steps=2					,      output_type="""numpy"""					,      return_dict=lowerCAmelCase__     )[0]

        _UpperCamelCase  :List[str]       =image[0, -3:, -3:, -1]
        _UpperCamelCase  :Optional[int]       =image_from_tuple[0, -3:, -3:, -1]

        assert image.shape == (1, 32, 32, 3)
        _UpperCamelCase  :Dict       =np.array(
            [9.9_56e-01, 5.7_85e-01, 4.6_75e-01, 9.9_30e-01, 0.0, 1.000, 1.1_99e-03, 2.6_48e-04, 5.1_01e-04]     )

        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  _UpperCamelCase		(    self     )				->     Optional[int]:
        """simple docstring"""





        _UpperCamelCase  :Dict       =self.dummy_uncond_unet
        _UpperCamelCase  :str       =DDPMScheduler(prediction_type="""sample"""     )

        _UpperCamelCase  :Union[str, Any]       =DDPMPipeline(unet=lowerCAmelCase__					,      scheduler=lowerCAmelCase__     )
        ddpm.to(lowerCAmelCase__     )
        ddpm.set_progress_bar_config(disable=lowerCAmelCase__     )

        _UpperCamelCase  :str       =torch.manual_seed(0     )
        _UpperCamelCase  :List[str]       =ddpm(generator=lowerCAmelCase__					,      num_inference_steps=2					,      output_type="""numpy"""     ).images

        _UpperCamelCase  :str       =torch.manual_seed(0     )
        _UpperCamelCase  :int       =ddpm(generator=lowerCAmelCase__					,      num_inference_steps=2					,      output_type="""numpy"""     )[0]

        _UpperCamelCase  :Any       =image[0, -3:, -3:, -1]
        _UpperCamelCase  :List[str]       =image_eps[0, -3:, -3:, -1]

        assert image.shape == (1, 32, 32, 3)
        _UpperCamelCase  :Optional[Any]       =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     ):



   def  _UpperCamelCase		(    self     )				->     int:
        """simple docstring"""





        _UpperCamelCase  :Optional[int]       ="""google/ddpm-cifar10-32"""

        _UpperCamelCase  :Optional[int]       =UNetaDModel.from_pretrained(lowerCAmelCase__     )
        _UpperCamelCase  :Any       =DDPMScheduler.from_pretrained(lowerCAmelCase__     )

        _UpperCamelCase  :Tuple       =DDPMPipeline(unet=lowerCAmelCase__					,      scheduler=lowerCAmelCase__     )
        ddpm.to(lowerCAmelCase__     )
        ddpm.set_progress_bar_config(disable=lowerCAmelCase__     )

        _UpperCamelCase  :Optional[int]       =torch.manual_seed(0     )
        _UpperCamelCase  :Optional[int]       =ddpm(generator=lowerCAmelCase__					,      output_type="""numpy"""     ).images

        _UpperCamelCase  :Any       =image[0, -3:, -3:, -1]

        assert image.shape == (1, 32, 32, 3)
        _UpperCamelCase  :Any       =np.array([0.4200, 0.3588, 0.1939, 0.3847, 0.3382, 0.2647, 0.4155, 0.3582, 0.3385]     )
        assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1e-2
'''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     ):



   def  _UpperCamelCase		(    self     )				->     str:
        """simple docstring"""





        _UpperCamelCase  :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(""" """     )
        _UpperCamelCase  :Optional[Any]       =dict(zip(lowerCAmelCase__					,      range(len(lowerCAmelCase__     )     )     )     )

        _UpperCamelCase  :int       ={
            """pad_token""": """<pad>""",
            """unk_token""": """<unk>""",
            """bos_token""": """<s>""",
            """eos_token""": """</s>""",
        }
        _UpperCamelCase  :Union[str, Any]       ={
            """feature_size""": 1,
            """padding_value""": 0.0,
            """sampling_rate""": 16_000,
            """return_attention_mask""": False,
            """do_normalize""": True,
        }

        _UpperCamelCase  :List[str]       =tempfile.mkdtemp()
        _UpperCamelCase  :Optional[Any]       =os.path.join(self.tmpdirname					,      VOCAB_FILES_NAMES["""vocab_file"""]     )
        _UpperCamelCase  :Tuple       =os.path.join(self.tmpdirname					,      lowerCAmelCase__     )
        with open(self.vocab_file					,      """w"""					,      encoding="""utf-8"""     ) as fp:
             fp.write(json.dumps(lowerCAmelCase__     ) + """\n"""     )

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



   def  _UpperCamelCase		(    self					,      **lowerCAmelCase__     )				->     List[str]:
        """simple docstring"""





        _UpperCamelCase  :int       =self.add_kwargs_tokens_map.copy()
        kwargs.update(lowerCAmelCase__     )
        return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname					,      **lowerCAmelCase__     )



   def  _UpperCamelCase		(    self					,      **lowerCAmelCase__     )				->     str:
        """simple docstring"""





        return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname					,      **lowerCAmelCase__     )



   def  _UpperCamelCase		(    self     )				->     List[str]:
        """simple docstring"""





        shutil.rmtree(self.tmpdirname     )



   def  _UpperCamelCase		(    self     )				->     int:
        """simple docstring"""





        _UpperCamelCase  :Union[str, Any]       =self.get_tokenizer()
        _UpperCamelCase  :int       =self.get_feature_extractor()

        _UpperCamelCase  :Union[str, Any]       =WavaVecaProcessor(tokenizer=lowerCAmelCase__					,      feature_extractor=lowerCAmelCase__     )

        processor.save_pretrained(self.tmpdirname     )
        _UpperCamelCase  :Dict       =WavaVecaProcessor.from_pretrained(self.tmpdirname     )

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

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



   def  _UpperCamelCase		(    self     )				->     Tuple:
        """simple docstring"""





        _UpperCamelCase  :Union[str, Any]       =WavaVecaProcessor(tokenizer=self.get_tokenizer()					,      feature_extractor=self.get_feature_extractor()     )
        processor.save_pretrained(self.tmpdirname     )

        _UpperCamelCase  :Optional[int]       =self.get_tokenizer(bos_token="""(BOS)"""					,      eos_token="""(EOS)"""     )
        _UpperCamelCase  :Optional[int]       =self.get_feature_extractor(do_normalize=lowerCAmelCase__					,      padding_value=1.0     )

        _UpperCamelCase  :Optional[int]       =WavaVecaProcessor.from_pretrained(
            self.tmpdirname					,      bos_token="""(BOS)"""					,      eos_token="""(EOS)"""					,      do_normalize=lowerCAmelCase__					,      padding_value=1.0     )

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

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



   def  _UpperCamelCase		(    self     )				->     int:
        """simple docstring"""





        _UpperCamelCase  :List[Any]       =self.get_feature_extractor()
        _UpperCamelCase  :List[str]       =self.get_tokenizer()

        _UpperCamelCase  :Any       =WavaVecaProcessor(tokenizer=lowerCAmelCase__					,      feature_extractor=lowerCAmelCase__     )

        _UpperCamelCase  :Tuple       =floats_list((3, 1_000)     )

        _UpperCamelCase  :Tuple       =feature_extractor(lowerCAmelCase__					,      return_tensors="""np"""     )
        _UpperCamelCase  :Union[str, Any]       =processor(lowerCAmelCase__					,      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  _UpperCamelCase		(    self     )				->     Tuple:
        """simple docstring"""





        _UpperCamelCase  :Any       =self.get_feature_extractor()
        _UpperCamelCase  :List[Any]       =self.get_tokenizer()

        _UpperCamelCase  :Tuple       =WavaVecaProcessor(tokenizer=lowerCAmelCase__					,      feature_extractor=lowerCAmelCase__     )

        _UpperCamelCase  :Optional[Any]       ="""This is a test string"""

        _UpperCamelCase  :Union[str, Any]       =processor(text=lowerCAmelCase__     )

        _UpperCamelCase  :Union[str, Any]       =tokenizer(lowerCAmelCase__     )

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



   def  _UpperCamelCase		(    self     )				->     Optional[Any]:
        """simple docstring"""





        _UpperCamelCase  :int       =self.get_feature_extractor()
        _UpperCamelCase  :Any       =self.get_tokenizer()

        _UpperCamelCase  :str       =WavaVecaProcessor(tokenizer=lowerCAmelCase__					,      feature_extractor=lowerCAmelCase__     )

        _UpperCamelCase  :Optional[int]       =[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

        _UpperCamelCase  :List[Any]       =processor.batch_decode(lowerCAmelCase__     )
        _UpperCamelCase  :Any       =tokenizer.batch_decode(lowerCAmelCase__     )

        self.assertListEqual(lowerCAmelCase__					,      lowerCAmelCase__     )




   def  _UpperCamelCase		(    self     )				->     Union[str, Any]:
        """simple docstring"""





        _UpperCamelCase  :List[Any]       =self.get_feature_extractor()
        _UpperCamelCase  :Any       =self.get_tokenizer()

        _UpperCamelCase  :List[str]       =WavaVecaProcessor(tokenizer=lowerCAmelCase__					,      feature_extractor=lowerCAmelCase__     )

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