




"""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


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

@require_torch
class     lowercase (      unittest.TestCase):







				def    a_					(							self				:   Any    ):



					"""simple docstring"""

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

					A_					:			List[Any]						=						SpeechTaTokenizer(SCREAMING_SNAKE_CASE_    )
					tokenizer.save_pretrained(self.tmpdirname    )

					A_					:			Optional[Any]						=						{
					    '''feature_size''': 1,
					    '''padding_value''': 0.0,
					    '''sampling_rate''': 1_60_00,
					    '''do_normalize''': False,
					    '''num_mel_bins''': 80,
					    '''hop_length''': 16,
					    '''win_length''': 64,
					    '''win_function''': '''hann_window''',
					    '''fmin''': 80,
					    '''fmax''': 76_00,
					    '''mel_floor''': 1E-10,
					    '''reduction_factor''': 2,
					    '''return_attention_mask''': True,
					}

					A_					:			Optional[Any]						=						os.path.join(self.tmpdirname ,       SCREAMING_SNAKE_CASE_    )
					with open(self.feature_extraction_file ,       '''w''' ,       encoding='''utf-8'''    ) as fp:
						fp.write(json.dumps(SCREAMING_SNAKE_CASE_    ) + '''\n'''    )







				def    a_					(							self				:   Union[str, Any] ,       **_lowerCamelCase				:   List[Any]    ):



					"""simple docstring"""

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







				def    a_					(							self				:   List[Any] ,       **_lowerCamelCase				:   Dict    ):



					"""simple docstring"""

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







				def    a_					(							self				:   Dict    ):



					"""simple docstring"""

					shutil.rmtree(self.tmpdirname    )







				def    a_					(							self				:   int    ):



					"""simple docstring"""

					A_					:			int						=						self.get_tokenizer()
					A_					:			Dict						=						self.get_feature_extractor()

					A_					:			Optional[Any]						=						SpeechTaProcessor(tokenizer=SCREAMING_SNAKE_CASE_ ,       feature_extractor=SCREAMING_SNAKE_CASE_    )

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

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

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







				def    a_					(							self				:   Tuple    ):



					"""simple docstring"""

					A_					:			Optional[int]						=						SpeechTaProcessor(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_					:			Optional[int]						=						self.get_feature_extractor(do_normalize=SCREAMING_SNAKE_CASE_ ,       padding_value=1.0    )

					A_					:			str						=						SpeechTaProcessor.from_pretrained(
					    self.tmpdirname ,       bos_token='''(BOS)''' ,       eos_token='''(EOS)''' ,       do_normalize=SCREAMING_SNAKE_CASE_ ,       padding_value=1.0    )

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

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







				def    a_					(							self				:   List[Any]    ):



					"""simple docstring"""

					A_					:			Any						=						self.get_feature_extractor()
					A_					:			Dict						=						self.get_tokenizer()

					A_					:			Tuple						=						SpeechTaProcessor(tokenizer=SCREAMING_SNAKE_CASE_ ,       feature_extractor=SCREAMING_SNAKE_CASE_    )

					A_					:			List[str]						=						floats_list((3, 10_00)    )

					A_					:			Any						=						feature_extractor(audio=SCREAMING_SNAKE_CASE_ ,       return_tensors='''np'''    )
					A_					:			Dict						=						processor(audio=SCREAMING_SNAKE_CASE_ ,       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    a_					(							self				:   Union[str, Any]    ):



					"""simple docstring"""

					A_					:			Any						=						self.get_feature_extractor()
					A_					:			str						=						self.get_tokenizer()

					A_					:			int						=						SpeechTaProcessor(tokenizer=SCREAMING_SNAKE_CASE_ ,       feature_extractor=SCREAMING_SNAKE_CASE_    )

					A_					:			int						=						floats_list((3, 10_00)    )

					A_					:			List[Any]						=						feature_extractor(audio_target=SCREAMING_SNAKE_CASE_ ,       return_tensors='''np'''    )
					A_					:			Optional[int]						=						processor(audio_target=SCREAMING_SNAKE_CASE_ ,       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    a_					(							self				:   List[Any]    ):



					"""simple docstring"""

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

					A_					:			int						=						SpeechTaProcessor(tokenizer=SCREAMING_SNAKE_CASE_ ,       feature_extractor=SCREAMING_SNAKE_CASE_    )

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

					A_					:			int						=						processor(text=SCREAMING_SNAKE_CASE_    )
					A_					:			List[Any]						=						tokenizer(SCREAMING_SNAKE_CASE_    )

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







				def    a_					(							self				:   List[str]    ):



					"""simple docstring"""

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

					A_					:			Optional[int]						=						SpeechTaProcessor(tokenizer=SCREAMING_SNAKE_CASE_ ,       feature_extractor=SCREAMING_SNAKE_CASE_    )

					A_					:			Any						=						'''This is a test string'''

					A_					:			Dict						=						processor(text_target=SCREAMING_SNAKE_CASE_    )
					A_					:			Optional[int]						=						tokenizer(SCREAMING_SNAKE_CASE_    )

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







				def    a_					(							self				:   List[str]    ):



					"""simple docstring"""

					A_					:			str						=						self.get_feature_extractor()
					A_					:			Optional[Any]						=						self.get_tokenizer()

					A_					:			int						=						SpeechTaProcessor(tokenizer=SCREAMING_SNAKE_CASE_ ,       feature_extractor=SCREAMING_SNAKE_CASE_    )

					A_					:			Union[str, Any]						=						[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

					A_					:			Union[str, Any]						=						processor.batch_decode(SCREAMING_SNAKE_CASE_    )
					A_					:			List[str]						=						tokenizer.batch_decode(SCREAMING_SNAKE_CASE_    )

					self.assertListEqual(SCREAMING_SNAKE_CASE_ ,       SCREAMING_SNAKE_CASE_    )







				def    a_					(							self				:   List[Any]    ):



					"""simple docstring"""

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

					A_					:			Tuple						=						SpeechTaProcessor(tokenizer=SCREAMING_SNAKE_CASE_ ,       feature_extractor=SCREAMING_SNAKE_CASE_    )

					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 shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
		from PIL import Image

		from transformers import AutoProcessor, BertTokenizer, CLIPImageProcessor, GitProcessor, PreTrainedTokenizerFast

@require_vision
class     lowercase (      unittest.TestCase):







				def    a_					(							self				:   Any    ):



					"""simple docstring"""

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

					A_					:			List[str]						=						CLIPImageProcessor()
					A_					:			Any						=						BertTokenizer.from_pretrained(
					    '''hf-internal-testing/tiny-random-BertModel''' ,       model_input_names=['''input_ids''', '''attention_mask''']    )

					A_					:			str						=						GitProcessor(_lowerCamelCase ,       _lowerCamelCase    )

					processor.save_pretrained(self.tmpdirname    )







				def    a_					(							self				:   Tuple ,       **_lowerCamelCase				:   List[str]    ):



					"""simple docstring"""

					return AutoProcessor.from_pretrained(self.tmpdirname ,       **_lowerCamelCase    ).tokenizer







				def    a_					(							self				:   List[Any] ,       **_lowerCamelCase				:   int    ):



					"""simple docstring"""

					return AutoProcessor.from_pretrained(self.tmpdirname ,       **_lowerCamelCase    ).image_processor







				def    a_					(							self				:   Optional[Any]    ):



					"""simple docstring"""

					shutil.rmtree(self.tmpdirname    )







				def    a_					(							self				:   Optional[Any]    ):



					"""simple docstring"""

					A_					:			List[str]						=						[np.random.randint(2_55 ,       size=(3, 30, 4_00) ,       dtype=np.uinta    )]

					A_					:			Any						=						[Image.fromarray(np.moveaxis(_lowerCamelCase ,       0 ,       -1    )    ) for x in image_inputs]

					return image_inputs







				def    a_					(							self				:   str    ):



					"""simple docstring"""

					A_					:			Union[str, Any]						=						GitProcessor(tokenizer=self.get_tokenizer() ,       image_processor=self.get_image_processor()    )
					processor.save_pretrained(self.tmpdirname    )

					A_					:			Union[str, Any]						=						self.get_tokenizer(bos_token='''(BOS)''' ,       eos_token='''(EOS)'''    )
					A_					:			List[Any]						=						self.get_image_processor(do_normalize=_lowerCamelCase ,       padding_value=1.0    )

					A_					:			List[str]						=						GitProcessor.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.image_processor.to_json_string() ,       image_processor_add_kwargs.to_json_string()    )
					self.assertIsInstance(processor.image_processor ,       _lowerCamelCase    )







				def    a_					(							self				:   Optional[Any]    ):



					"""simple docstring"""

					A_					:			Tuple						=						self.get_image_processor()
					A_					:			Optional[Any]						=						self.get_tokenizer()

					A_					:			int						=						GitProcessor(tokenizer=_lowerCamelCase ,       image_processor=_lowerCamelCase    )

					A_					:			Tuple						=						self.prepare_image_inputs()

					A_					:			Any						=						image_processor(_lowerCamelCase ,       return_tensors='''np'''    )
					A_					:			Optional[Any]						=						processor(images=_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    a_					(							self				:   Optional[int]    ):



					"""simple docstring"""

					A_					:			Union[str, Any]						=						self.get_image_processor()
					A_					:			Union[str, Any]						=						self.get_tokenizer()

					A_					:			int						=						GitProcessor(tokenizer=_lowerCamelCase ,       image_processor=_lowerCamelCase    )

					A_					:			List[str]						=						'''lower newer'''

					A_					:			Tuple						=						processor(text=_lowerCamelCase    )

					A_					:			List[str]						=						tokenizer(_lowerCamelCase ,       return_token_type_ids=_lowerCamelCase    )

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







				def    a_					(							self				:   Tuple    ):



					"""simple docstring"""

					A_					:			Optional[int]						=						self.get_image_processor()
					A_					:			Optional[Any]						=						self.get_tokenizer()

					A_					:			Optional[Any]						=						GitProcessor(tokenizer=_lowerCamelCase ,       image_processor=_lowerCamelCase    )

					A_					:			Tuple						=						'''lower newer'''
					A_					:			str						=						self.prepare_image_inputs()

					A_					:			Optional[int]						=						processor(text=_lowerCamelCase ,       images=_lowerCamelCase    )

					self.assertListEqual(list(inputs.keys()    ) ,       ['''input_ids''', '''attention_mask''', '''pixel_values''']    )

					# test if it raises when no input is passed
					with pytest.raises(_lowerCamelCase    ):
						processor()







				def    a_					(							self				:   Optional[int]    ):



					"""simple docstring"""

					A_					:			Optional[int]						=						self.get_image_processor()
					A_					:			List[Any]						=						self.get_tokenizer()

					A_					:			Optional[Any]						=						GitProcessor(tokenizer=_lowerCamelCase ,       image_processor=_lowerCamelCase    )

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

					A_					:			str						=						processor.batch_decode(_lowerCamelCase    )
					A_					:			int						=						tokenizer.batch_decode(_lowerCamelCase    )

					self.assertListEqual(_lowerCamelCase ,       _lowerCamelCase    )







				def    a_					(							self				:   Any    ):



					"""simple docstring"""

					A_					:			Dict						=						self.get_image_processor()
					A_					:			List[str]						=						self.get_tokenizer()

					A_					:			Any						=						GitProcessor(tokenizer=_lowerCamelCase ,       image_processor=_lowerCamelCase    )

					A_					:			int						=						'''lower newer'''
					A_					:			Union[str, Any]						=						self.prepare_image_inputs()

					A_					:			List[str]						=						processor(text=_lowerCamelCase ,       images=_lowerCamelCase    )

					# For now the processor supports only ['input_ids', 'attention_mask', 'pixel_values']
					self.assertListEqual(list(inputs.keys()    ) ,       ['''input_ids''', '''attention_mask''', '''pixel_values''']    )





