





import random
import unittest

import torch

from diffusers import IFSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin





@skip_mps
class 	lowerCAmelCase__(       __lowercase     ,   __lowercase     ,   unittest.TestCase			):






       '''simple docstring'''


       __snake_case       	=			IFSuperResolutionPipeline
       __snake_case       	=			TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'width', 'height'}
       __snake_case       	=			TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
       __snake_case       	=			PipelineTesterMixin.required_optional_params - {'latents'}




       def 			UpperCamelCase_	(					self							)	->   str:
            return self._get_superresolution_dummy_components()




       def 			UpperCamelCase_	(					self			,  __lowerCamelCase			,  __lowerCamelCase=0							)	->   Dict:
            if str(__lowerCamelCase							).startswith("mps"							):
                 _SCREAMING_SNAKE_CASE				:		Optional[Any]  			=							torch.manual_seed(__lowerCamelCase							)
            else:
                 _SCREAMING_SNAKE_CASE				:		int  			=							torch.Generator(device=__lowerCamelCase							).manual_seed(__lowerCamelCase							)

            _SCREAMING_SNAKE_CASE				:		int  			=							floats_tensor((1, 3, 3_2, 3_2)			,  rng=random.Random(__lowerCamelCase							)							).to(__lowerCamelCase							)

            _SCREAMING_SNAKE_CASE				:		Tuple  			=							{
                "prompt": "A painting of a squirrel eating a burger",
                "image": image,
                "generator": generator,
                "num_inference_steps": 2,
                "output_type": "numpy",
            }

            return inputs




       @unittest.skipIf(
           torch_device != "cuda" or not is_xformers_available()			,  reason="XFormers attention is only available with CUDA and `xformers` installed"			,  )
       def 			UpperCamelCase_	(					self							)	->   List[str]:
            self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3							)




       def 			UpperCamelCase_	(					self							)	->   Optional[int]:
            self._test_save_load_optional_components()




       @unittest.skipIf(torch_device != "cuda"			,  reason="float16 requires CUDA"							)
       def 			UpperCamelCase_	(					self							)	->   List[Any]:
            # Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
            super().test_save_load_floataa(expected_max_diff=1E-1							)




       def 			UpperCamelCase_	(					self							)	->   List[Any]:
            self._test_attention_slicing_forward_pass(expected_max_diff=1E-2							)




       def 			UpperCamelCase_	(					self							)	->   Optional[Any]:
            self._test_save_load_local()







       def 			UpperCamelCase_	(					self							)	->   Optional[int]:
            self._test_inference_batch_single_identical(
                expected_max_diff=1E-2			,  )

'''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


lowercase__		    =	get_tests_dir("fixtures/test_sentencepiece_bpe_char.model")






@require_torch
class      snake_case__    (	unittest.TestCase ):







		"""simple docstring"""

		def 	lowerCAmelCase							(	self							:       Optional[Any]	)		->       Optional[int]:






				"""simple docstring"""







				snake_case   :    List[str]			       =		tempfile.mkdtemp()

				snake_case   :    Optional[int]			       =		SpeechTaTokenizer(UpperCamelCase__	)
				tokenizer.save_pretrained(self.tmpdirname	)

				snake_case   :    Any			       =		{
				    '''feature_size''': 1,
				    '''padding_value''': 0.0,
				    '''sampling_rate''': 1_6000,
				    '''do_normalize''': False,
				    '''num_mel_bins''': 80,
				    '''hop_length''': 16,
				    '''win_length''': 64,
				    '''win_function''': '''hann_window''',
				    '''fmin''': 80,
				    '''fmax''': 7600,
				    '''mel_floor''': 1e-1_0,
				    '''reduction_factor''': 2,
				    '''return_attention_mask''': True,
				}

				snake_case   :    Tuple			       =		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 	lowerCAmelCase							(	self							:       Dict   ,     **UpperCamelCase__							:       Any	)		->       str:






				"""simple docstring"""







				return SpeechTaTokenizer.from_pretrained(self.tmpdirname   ,     **UpperCamelCase__	)



		def 	lowerCAmelCase							(	self							:       Union[str, Any]   ,     **UpperCamelCase__							:       int	)		->       Optional[Any]:






				"""simple docstring"""







				return SpeechTaFeatureExtractor.from_pretrained(self.tmpdirname   ,     **UpperCamelCase__	)



		def 	lowerCAmelCase							(	self							:       Any	)		->       Any:






				"""simple docstring"""







				shutil.rmtree(self.tmpdirname	)



		def 	lowerCAmelCase							(	self							:       Tuple	)		->       Dict:






				"""simple docstring"""







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

				snake_case   :    Dict			       =		SpeechTaProcessor(tokenizer=UpperCamelCase__   ,     feature_extractor=UpperCamelCase__	)

				processor.save_pretrained(self.tmpdirname	)
				snake_case   :    Dict			       =		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 	lowerCAmelCase							(	self							:       str	)		->       Any:






				"""simple docstring"""







				snake_case   :    int			       =		SpeechTaProcessor(tokenizer=self.get_tokenizer()   ,     feature_extractor=self.get_feature_extractor()	)
				processor.save_pretrained(self.tmpdirname	)

				snake_case   :    int			       =		self.get_tokenizer(bos_token='''(BOS)'''   ,     eos_token='''(EOS)'''	)
				snake_case   :    str			       =		self.get_feature_extractor(do_normalize=UpperCamelCase__   ,     padding_value=1.0	)

				snake_case   :    Tuple			       =		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 	lowerCAmelCase							(	self							:       Optional[Any]	)		->       Dict:






				"""simple docstring"""







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

				snake_case   :    Union[str, Any]			       =		SpeechTaProcessor(tokenizer=UpperCamelCase__   ,     feature_extractor=UpperCamelCase__	)

				snake_case   :    Union[str, Any]			       =		floats_list((3, 1000)	)

				snake_case   :    List[str]			       =		feature_extractor(audio=UpperCamelCase__   ,     return_tensors='''np'''	)
				snake_case   :    Any			       =		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 	lowerCAmelCase							(	self							:       Dict	)		->       str:






				"""simple docstring"""







				snake_case   :    str			       =		self.get_feature_extractor()
				snake_case   :    Optional[int]			       =		self.get_tokenizer()

				snake_case   :    Union[str, Any]			       =		SpeechTaProcessor(tokenizer=UpperCamelCase__   ,     feature_extractor=UpperCamelCase__	)

				snake_case   :    Dict			       =		floats_list((3, 1000)	)

				snake_case   :    str			       =		feature_extractor(audio_target=UpperCamelCase__   ,     return_tensors='''np'''	)
				snake_case   :    List[Any]			       =		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 	lowerCAmelCase							(	self							:       List[Any]	)		->       int:






				"""simple docstring"""







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

				snake_case   :    List[str]			       =		SpeechTaProcessor(tokenizer=UpperCamelCase__   ,     feature_extractor=UpperCamelCase__	)

				snake_case   :    Optional[Any]			       =		'''This is a test string'''

				snake_case   :    Dict			       =		processor(text=UpperCamelCase__	)
				snake_case   :    List[str]			       =		tokenizer(UpperCamelCase__	)

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



		def 	lowerCAmelCase							(	self							:       Optional[Any]	)		->       Dict:






				"""simple docstring"""







				snake_case   :    Optional[int]			       =		self.get_feature_extractor()
				snake_case   :    str			       =		self.get_tokenizer()

				snake_case   :    str			       =		SpeechTaProcessor(tokenizer=UpperCamelCase__   ,     feature_extractor=UpperCamelCase__	)

				snake_case   :    Dict			       =		'''This is a test string'''

				snake_case   :    Any			       =		processor(text_target=UpperCamelCase__	)
				snake_case   :    List[str]			       =		tokenizer(UpperCamelCase__	)

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



		def 	lowerCAmelCase							(	self							:       str	)		->       Optional[int]:






				"""simple docstring"""







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

				snake_case   :    Optional[Any]			       =		SpeechTaProcessor(tokenizer=UpperCamelCase__   ,     feature_extractor=UpperCamelCase__	)

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

				snake_case   :    List[str]			       =		processor.batch_decode(UpperCamelCase__	)
				snake_case   :    List[str]			       =		tokenizer.batch_decode(UpperCamelCase__	)

				self.assertListEqual(UpperCamelCase__   ,     UpperCamelCase__	)



		def 	lowerCAmelCase							(	self							:       Optional[Any]	)		->       Union[str, Any]:






				"""simple docstring"""







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

				snake_case   :    List[Any]			       =		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'''   ,     )


