
import json
import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
					from PIL import Image

					from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor







@require_tokenizers
@require_vision
class 			__A( unittest.TestCase			):




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Optional[Any]:


								'''simple docstring'''




								__a										=    tempfile.mkdtemp()

								# fmt: off
								__a										=    ['''[UNK]''', '''[CLS]''', '''[SEP]''', '''[PAD]''', '''[MASK]''', '''want''', '''##want''', '''##ed''', '''wa''', '''un''', '''runn''', '''##ing''', ''',''', '''low''', '''lowest''']
								# fmt: on
								__a										=    os.path.join(self.tmpdirname		,     VOCAB_FILES_NAMES['''vocab_file''']				)
								with open(self.vocab_file		,     '''w'''		,     encoding='''utf-8'''				) as vocab_writer:
												vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]				)				)

								__a										=    {
								    '''do_resize''': True,
								    '''size''': {'''height''': 18, '''width''': 18},
								    '''do_normalize''': True,
								    '''image_mean''': [0.5, 0.5, 0.5],
								    '''image_std''': [0.5, 0.5, 0.5],
								}
								__a										=    os.path.join(self.tmpdirname		,     _snake_case				)
								with open(self.image_processor_file		,     '''w'''		,     encoding='''utf-8'''				) as fp:
												json.dump(_snake_case		,     _snake_case				)




				def 				SCREAMING_SNAKE_CASE_		(     self		,     **_snake_case				)    ->      Any:


								'''simple docstring'''




								return BertTokenizer.from_pretrained(self.tmpdirname		,     **_snake_case				)




				def 				SCREAMING_SNAKE_CASE_		(     self		,     **_snake_case				)    ->      Optional[int]:


								'''simple docstring'''




								return ViTImageProcessor.from_pretrained(self.tmpdirname		,     **_snake_case				)




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Tuple:


								'''simple docstring'''




								shutil.rmtree(self.tmpdirname				)




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      int:


								'''simple docstring'''




								__a										=    [np.random.randint(255		,     size=(3, 30, 400)		,     dtype=np.uinta				)]

								__a										=    [Image.fromarray(np.moveaxis(_snake_case		,     0		,     -1				)				) for x in image_inputs]

								return image_inputs




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      List[Any]:


								'''simple docstring'''




								__a										=    self.get_tokenizer()
								__a										=    self.get_image_processor()

								__a										=    VisionTextDualEncoderProcessor(tokenizer=_snake_case		,     image_processor=_snake_case				)

								processor.save_pretrained(self.tmpdirname				)
								__a										=    VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname				)

								self.assertEqual(processor.tokenizer.get_vocab()		,     tokenizer.get_vocab()				)
								self.assertIsInstance(processor.tokenizer		,     (BertTokenizer, BertTokenizerFast)				)

								self.assertEqual(processor.image_processor.to_json_string()		,     image_processor.to_json_string()				)
								self.assertIsInstance(processor.image_processor		,     _snake_case				)




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


								'''simple docstring'''




								__a										=    VisionTextDualEncoderProcessor(
								    tokenizer=self.get_tokenizer()		,     image_processor=self.get_image_processor()				)
								processor.save_pretrained(self.tmpdirname				)

								__a										=    self.get_tokenizer(bos_token='''(BOS)'''		,     eos_token='''(EOS)'''				)
								__a										=    self.get_image_processor(do_normalize=_snake_case		,     padding_value=1.0				)

								__a										=    VisionTextDualEncoderProcessor.from_pretrained(
								    self.tmpdirname		,     bos_token='''(BOS)'''		,     eos_token='''(EOS)'''		,     do_normalize=_snake_case		,     padding_value=1.0				)

								self.assertEqual(processor.tokenizer.get_vocab()		,     tokenizer_add_kwargs.get_vocab()				)
								self.assertIsInstance(processor.tokenizer		,     (BertTokenizer, BertTokenizerFast)				)

								self.assertEqual(processor.image_processor.to_json_string()		,     image_processor_add_kwargs.to_json_string()				)
								self.assertIsInstance(processor.image_processor		,     _snake_case				)




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Optional[int]:


								'''simple docstring'''




								__a										=    self.get_image_processor()
								__a										=    self.get_tokenizer()

								__a										=    VisionTextDualEncoderProcessor(tokenizer=_snake_case		,     image_processor=_snake_case				)

								__a										=    self.prepare_image_inputs()

								__a										=    image_processor(_snake_case		,     return_tensors='''np'''				)
								__a										=    processor(images=_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 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Optional[Any]:


								'''simple docstring'''




								__a										=    self.get_image_processor()
								__a										=    self.get_tokenizer()

								__a										=    VisionTextDualEncoderProcessor(tokenizer=_snake_case		,     image_processor=_snake_case				)

								__a										=    '''lower newer'''

								__a										=    processor(text=_snake_case				)

								__a										=    tokenizer(_snake_case				)

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




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      str:


								'''simple docstring'''




								__a										=    self.get_image_processor()
								__a										=    self.get_tokenizer()

								__a										=    VisionTextDualEncoderProcessor(tokenizer=_snake_case		,     image_processor=_snake_case				)

								__a										=    '''lower newer'''
								__a										=    self.prepare_image_inputs()

								__a										=    processor(text=_snake_case		,     images=_snake_case				)

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

								# test if it raises when no input is passed
								with self.assertRaises(_snake_case				):
												processor()




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      int:


								'''simple docstring'''




								__a										=    self.get_image_processor()
								__a										=    self.get_tokenizer()

								__a										=    VisionTextDualEncoderProcessor(tokenizer=_snake_case		,     image_processor=_snake_case				)

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

								__a										=    processor.batch_decode(_snake_case				)
								__a										=    tokenizer.batch_decode(_snake_case				)

								self.assertListEqual(_snake_case		,     _snake_case				)




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      List[Any]:


								'''simple docstring'''




								__a										=    self.get_image_processor()
								__a										=    self.get_tokenizer()

								__a										=    VisionTextDualEncoderProcessor(tokenizer=_snake_case		,     image_processor=_snake_case				)

								__a										=    '''lower newer'''
								__a										=    self.prepare_image_inputs()

								__a										=    processor(text=_snake_case		,     images=_snake_case				)

								self.assertListEqual(list(inputs.keys()				)		,     processor.model_input_names				)

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


A     :	Optional[int]    = {
    'configuration_whisper': ['WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'WhisperConfig', 'WhisperOnnxConfig'],
    'feature_extraction_whisper': ['WhisperFeatureExtractor'],
    'processing_whisper': ['WhisperProcessor'],
    'tokenization_whisper': ['WhisperTokenizer'],
}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A     :	int    = ['WhisperTokenizerFast']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A     :	str    = [
					    'WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'WhisperForConditionalGeneration',
					    'WhisperModel',
					    'WhisperPreTrainedModel',
					    'WhisperForAudioClassification',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A     :	Tuple    = [
					    'TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'TFWhisperForConditionalGeneration',
					    'TFWhisperModel',
					    'TFWhisperPreTrainedModel',
					]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A     :	int    = [
					    'FlaxWhisperForConditionalGeneration',
					    'FlaxWhisperModel',
					    'FlaxWhisperPreTrainedModel',
					    'FlaxWhisperForAudioClassification',
					]


if TYPE_CHECKING:
					from .configuration_whisper import WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP, WhisperConfig, WhisperOnnxConfig
					from .feature_extraction_whisper import WhisperFeatureExtractor
					from .processing_whisper import WhisperProcessor
					from .tokenization_whisper import WhisperTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_whisper_fast import WhisperTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_whisper import (
										    WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    WhisperForAudioClassification,
										    WhisperForConditionalGeneration,
										    WhisperModel,
										    WhisperPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_whisper import (
										    TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFWhisperForConditionalGeneration,
										    TFWhisperModel,
										    TFWhisperPreTrainedModel,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_whisper import (
										    FlaxWhisperForAudioClassification,
										    FlaxWhisperForConditionalGeneration,
										    FlaxWhisperModel,
										    FlaxWhisperPreTrainedModel,
										)

else:
					import sys

					A     :	Tuple    = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)