






'''simple docstring'''





import inspect
import json
import os
import tempfile
import unittest

from transformers.models.speech_to_text_a import SpeechaTextaTokenizer
from transformers.models.speech_to_text_a.tokenization_speech_to_text_a import VOCAB_FILES_NAMES

from ...test_tokenization_common import TokenizerTesterMixin


class 				snake_case__     (			UpperCamelCase      ,							unittest.TestCase):
					a_       =			SpeechaTextaTokenizer
					a_       =			False




					def 						A     (       self       :						Any     )  ->    Optional[Any]:
								super().setUp()

								UpperCAmelCase_     :   str       =   '''<s> <pad> </s> <unk> here@@ a couple of@@ words for the he@@ re@@ vocab'''.split(''' '''     )
								UpperCAmelCase_     :   Optional[int]       =   ['''he re</w> 123''', '''here a 1456''']
								UpperCAmelCase_     :   Dict       =   dict(zip(_A							,				range(len(_A     )     )     )     )

								UpperCAmelCase_     :   Union[str, Any]       =   {'''pad_token''': '''<pad>''', '''unk_token''': '''<unk>''', '''bos_token''': '''<s>''', '''eos_token''': '''</s>'''}

								UpperCAmelCase_     :   Optional[int]       =   tempfile.mkdtemp()
								UpperCAmelCase_     :   List[str]       =   os.path.join(self.tmpdirname							,				VOCAB_FILES_NAMES['''vocab_file''']     )
								UpperCAmelCase_     :   Dict       =   os.path.join(self.tmpdirname							,				VOCAB_FILES_NAMES['''merges_file''']     )

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

								with open(self.merges_file							,				'''w'''     ) as fp:
											fp.write('''\n'''.join(_A     )     )




					def 						A     (       self       :						Any     )  ->    Union[str, Any]:
								UpperCAmelCase_     :   str       =   list(self.get_tokenizer().get_vocab().keys()     )

								self.assertEqual(vocab_keys[0]							,				'''<s>'''     )
								self.assertEqual(vocab_keys[1]							,				'''<pad>'''     )
								self.assertEqual(vocab_keys[-1]							,				'''vocab'''     )
								self.assertEqual(len(_A     )							,				14     )




					def 						A     (       self       :						List[Any]     )  ->    List[str]:
								self.assertEqual(self.get_tokenizer().vocab_size							,				14     )




					def 						A     (       self       :						str     )  ->    List[Any]:
								UpperCAmelCase_     :   str       =   SpeechaTextaTokenizer.from_pretrained(self.tmpdirname     )

								# make sure @@ is correctly concatenated
								UpperCAmelCase_     :   Union[str, Any]       =   [4, 6, 8, 7, 10]  # ["here@@", "couple", "words", "of@@", "the"]
								UpperCAmelCase_     :   List[Any]       =   tokenizer.decode(_A     )

								self.assertTrue(output_string == '''herecouple words ofthe'''     )




					def 						A     (       self       :						Optional[Any]     )  ->    int:
								UpperCAmelCase_     :   Union[str, Any]       =   SpeechaTextaTokenizer.from_pretrained(self.tmpdirname     )

								with tempfile.TemporaryDirectory() as tmp_dirname:
											tokenizer.save_pretrained(_A     )
											os.remove(os.path.join(_A							,				'''merges.txt'''     )     )

											# load tokenizer without merges file should not throw an error
											UpperCAmelCase_     :   Tuple       =   SpeechaTextaTokenizer.from_pretrained(_A     )

								with tempfile.TemporaryDirectory() as tmp_dirname:
											# save tokenizer and load again
											tokenizer.save_pretrained(_A     )
											UpperCAmelCase_     :   Dict       =   SpeechaTextaTokenizer.from_pretrained(_A     )

								self.assertIsNotNone(_A     )





					def 						A     (       self       :						int     )  ->    List[str]:
								if not self.test_slow_tokenizer:
											return

								UpperCAmelCase_     :   str       =   inspect.signature(self.tokenizer_class.__init__     )
								UpperCAmelCase_     :   Any       =   self.get_tokenizer()

								for parameter_name, parameter in signature.parameters.items():
											if parameter.default != inspect.Parameter.empty and parameter_name != "merges_file":
														self.assertIn(_A							,				tokenizer.init_kwargs     )







'''simple docstring'''





import unittest

import numpy as np
import PIL
import torch

from diffusers.image_processor import VaeImageProcessor


class 				snake_case__     (			unittest.TestCase):




					@property
					def 						A     (       self       :						Optional[Any]     )  ->    int:
								UpperCAmelCase_     :   Optional[int]       =   1
								UpperCAmelCase_     :   List[str]       =   3
								UpperCAmelCase_     :   Optional[int]       =   8
								UpperCAmelCase_     :   Optional[Any]       =   8

								UpperCAmelCase_     :   Any       =   torch.rand((batch_size, num_channels, height, width)     )

								return sample




					def 						A     (       self       :						Dict							,				_A       :						str     )  ->    List[Any]:
								if isinstance(image[0]							,				PIL.Image.Image     ):
											return np.stack([np.array(_A     ) for i in image]							,				axis=0     )
								elif isinstance(_A							,				torch.Tensor     ):
											return image.cpu().numpy().transpose(0							,				2							,				3							,				1     )
								return image




					def 						A     (       self       :						Tuple     )  ->    Optional[Any]:
								UpperCAmelCase_     :   int       =   VaeImageProcessor(do_resize=_A							,				do_normalize=_A     )

								UpperCAmelCase_     :   List[Any]       =   self.dummy_sample
								UpperCAmelCase_     :   Optional[int]       =   self.to_np(_A     )

								for output_type in ["pt", "np", "pil"]:
											UpperCAmelCase_     :   Any       =   image_processor.postprocess(
											    image_processor.preprocess(_A     )							,				output_type=_A							,				)
											UpperCAmelCase_     :   Union[str, Any]       =   self.to_np(_A     )
											UpperCAmelCase_     :   Union[str, Any]       =   (input_np * 2_55).round() if output_type == '''pil''' else input_np
											assert (
											    np.abs(in_np - out_np     ).max() < 1e-6
											), F"decoded output does not match input for output_type {output_type}"




					def 						A     (       self       :						Optional[Any]     )  ->    int:
								UpperCAmelCase_     :   Union[str, Any]       =   VaeImageProcessor(do_resize=_A							,				do_normalize=_A     )
								UpperCAmelCase_     :   List[Any]       =   self.dummy_sample.cpu().numpy().transpose(0							,				2							,				3							,				1     )

								for output_type in ["pt", "np", "pil"]:
											UpperCAmelCase_     :   Union[str, Any]       =   image_processor.postprocess(image_processor.preprocess(_A     )							,				output_type=_A     )

											UpperCAmelCase_     :   List[Any]       =   self.to_np(_A     )
											UpperCAmelCase_     :   Any       =   (input_np * 2_55).round() if output_type == '''pil''' else input_np
											assert (
											    np.abs(in_np - out_np     ).max() < 1e-6
											), F"decoded output does not match input for output_type {output_type}"




					def 						A     (       self       :						Any     )  ->    Optional[Any]:
								UpperCAmelCase_     :   Tuple       =   VaeImageProcessor(do_resize=_A							,				do_normalize=_A     )

								UpperCAmelCase_     :   Optional[Any]       =   self.dummy_sample.cpu().numpy().transpose(0							,				2							,				3							,				1     )
								UpperCAmelCase_     :   List[Any]       =   image_processor.numpy_to_pil(_A     )

								for output_type in ["pt", "np", "pil"]:
											UpperCAmelCase_     :   str       =   image_processor.postprocess(image_processor.preprocess(_A     )							,				output_type=_A     )
											for i, o in zip(_A							,				_A     ):
														UpperCAmelCase_     :   Optional[int]       =   np.array(_A     )
														UpperCAmelCase_     :   Dict       =   self.to_np(_A     ) if output_type == '''pil''' else (self.to_np(_A     ) * 2_55).round()
														assert (
														    np.abs(in_np - out_np     ).max() < 1e-6
														), F"decoded output does not match input for output_type {output_type}"




					def 						A     (       self       :						Optional[Any]     )  ->    Optional[Any]:
								UpperCAmelCase_     :   int       =   VaeImageProcessor(do_resize=_A							,				do_normalize=_A     )

								UpperCAmelCase_     :   Optional[Any]       =   self.dummy_sample
								UpperCAmelCase_     :   Optional[Any]       =   input_pt_ad.squeeze(0     )

								UpperCAmelCase_     :   Tuple       =   image_processor.postprocess(
								    image_processor.preprocess(_A     )							,				output_type='''np'''							,				)
								UpperCAmelCase_     :   Any       =   image_processor.postprocess(
								    image_processor.preprocess(_A     )							,				output_type='''np'''							,				)

								UpperCAmelCase_     :   str       =   self.to_np(self.dummy_sample     )
								UpperCAmelCase_     :   Tuple       =   input_np_ad.squeeze(0     )

								UpperCAmelCase_     :   Optional[int]       =   image_processor.postprocess(
								    image_processor.preprocess(_A     )							,				output_type='''np'''							,				)
								UpperCAmelCase_     :   Optional[Any]       =   image_processor.postprocess(
								    image_processor.preprocess(_A     )							,				output_type='''np'''							,				)

								assert np.abs(out_pt_ad - out_pt_ad     ).max() < 1e-6
								assert np.abs(out_np_ad - out_np_ad     ).max() < 1e-6





					def 						A     (       self       :						Dict     )  ->    Dict:
								UpperCAmelCase_     :   List[Any]       =   VaeImageProcessor(do_resize=_A							,				do_normalize=_A     )

								UpperCAmelCase_     :   Optional[int]       =   self.dummy_sample
								UpperCAmelCase_     :   Dict       =   list(_A     )

								UpperCAmelCase_     :   str       =   image_processor.postprocess(
								    image_processor.preprocess(_A     )							,				output_type='''np'''							,				)

								UpperCAmelCase_     :   Optional[Any]       =   image_processor.postprocess(
								    image_processor.preprocess(_A     )							,				output_type='''np'''							,				)

								UpperCAmelCase_     :   Dict       =   self.to_np(self.dummy_sample     )
								list(_A     )

								UpperCAmelCase_     :   str       =   image_processor.postprocess(
								    image_processor.preprocess(_A     )							,				output_type='''np'''							,				)

								UpperCAmelCase_     :   Optional[int]       =   image_processor.postprocess(
								    image_processor.preprocess(_A     )							,				output_type='''np'''							,				)

								assert np.abs(out_pt_ad - out_pt_list     ).max() < 1e-6
								assert np.abs(out_np_ad - out_np_list     ).max() < 1e-6


