






import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

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


if is_vision_available():
							from PIL import Image

							from transformers import AlignProcessor, EfficientNetImageProcessor
@require_vision
class    _UpperCAmelCase      ( unittest.TestCase			):

				"""simple docstring"""




				def        lowercase__     (  self		:					Dict						):

								'''simple docstring'''
								lowercase__     				=    tempfile.mkdtemp()

								lowercase__     				=    [
								    '''[UNK]''',
								    '''[CLS]''',
								    '''[SEP]''',
								    '''[PAD]''',
								    '''[MASK]''',
								    '''want''',
								    '''##want''',
								    '''##ed''',
								    '''wa''',
								    '''un''',
								    '''runn''',
								    '''##ing''',
								    ''',''',
								    '''low''',
								    '''lowest''',
								]
								lowercase__     				=    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]						)						)

								lowercase__     				=    {
								    '''do_resize''': True,
								    '''size''': 20,
								    '''do_center_crop''': True,
								    '''crop_size''': 18,
								    '''do_normalize''': True,
								    '''image_mean''': [0.48145466, 0.4578275, 0.40821073],
								    '''image_std''': [0.26862954, 0.26130258, 0.27577711],
								}
								lowercase__     				=    os.path.join(self.tmpdirname,						lowerCamelCase						)
								with open(self.image_processor_file,						'''w''',						encoding='''utf-8'''						) as fp:
												json.dump(lowerCamelCase,						lowerCamelCase						)




				def        lowercase__     (  self		:					List[str],						**lowerCamelCase		:					List[str]						):

								'''simple docstring'''
								return BertTokenizer.from_pretrained(self.tmpdirname,						**lowerCamelCase						)




				def        lowercase__     (  self		:					Any,						**lowerCamelCase		:					int						):

								'''simple docstring'''
								return BertTokenizerFast.from_pretrained(self.tmpdirname,						**lowerCamelCase						)




				def        lowercase__     (  self		:					Dict,						**lowerCamelCase		:					Optional[Any]						):

								'''simple docstring'''
								return EfficientNetImageProcessor.from_pretrained(self.tmpdirname,						**lowerCamelCase						)




				def        lowercase__     (  self		:					Dict						):

								'''simple docstring'''
								shutil.rmtree(self.tmpdirname						)




				def        lowercase__     (  self		:					str						):

								'''simple docstring'''
								lowercase__     				=    [np.random.randint(255,						size=(3, 30, 400),						dtype=np.uinta						)]
								lowercase__     				=    [Image.fromarray(np.moveaxis(lowerCamelCase,						0,						-1						)						) for x in image_inputs]
								return image_inputs




				def        lowercase__     (  self		:					Tuple						):

								'''simple docstring'''
								lowercase__     				=    self.get_tokenizer()
								lowercase__     				=    self.get_rust_tokenizer()
								lowercase__     				=    self.get_image_processor()

								lowercase__     				=    AlignProcessor(tokenizer=lowerCamelCase,						image_processor=lowerCamelCase						)
								processor_slow.save_pretrained(self.tmpdirname						)
								lowercase__     				=    AlignProcessor.from_pretrained(self.tmpdirname,						use_fast=lowerCamelCase						)

								lowercase__     				=    AlignProcessor(tokenizer=lowerCamelCase,						image_processor=lowerCamelCase						)
								processor_fast.save_pretrained(self.tmpdirname						)
								lowercase__     				=    AlignProcessor.from_pretrained(self.tmpdirname						)

								self.assertEqual(processor_slow.tokenizer.get_vocab(),						tokenizer_slow.get_vocab()						)
								self.assertEqual(processor_fast.tokenizer.get_vocab(),						tokenizer_fast.get_vocab()						)
								self.assertEqual(tokenizer_slow.get_vocab(),						tokenizer_fast.get_vocab()						)
								self.assertIsInstance(processor_slow.tokenizer,						lowerCamelCase						)
								self.assertIsInstance(processor_fast.tokenizer,						lowerCamelCase						)

								self.assertEqual(processor_slow.image_processor.to_json_string(),						image_processor.to_json_string()						)
								self.assertEqual(processor_fast.image_processor.to_json_string(),						image_processor.to_json_string()						)
								self.assertIsInstance(processor_slow.image_processor,						lowerCamelCase						)
								self.assertIsInstance(processor_fast.image_processor,						lowerCamelCase						)




				def        lowercase__     (  self		:					Union[str, Any]						):

								'''simple docstring'''
								lowercase__     				=    AlignProcessor(tokenizer=self.get_tokenizer(),						image_processor=self.get_image_processor()						)
								processor.save_pretrained(self.tmpdirname						)

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

								lowercase__     				=    AlignProcessor.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        lowercase__     (  self		:					int						):

								'''simple docstring'''
								lowercase__     				=    self.get_image_processor()
								lowercase__     				=    self.get_tokenizer()

								lowercase__     				=    AlignProcessor(tokenizer=lowerCamelCase,						image_processor=lowerCamelCase						)

								lowercase__     				=    self.prepare_image_inputs()

								lowercase__     				=    image_processor(lowerCamelCase,						return_tensors='''np'''						)
								lowercase__     				=    processor(images=lowerCamelCase,						return_tensors='''np'''						)

								for key in input_image_proc.keys():
												self.assertAlmostEqual(input_image_proc[key].sum(),						input_processor[key].sum(),						delta=1E-2						)




				def        lowercase__     (  self		:					Dict						):

								'''simple docstring'''
								lowercase__     				=    self.get_image_processor()
								lowercase__     				=    self.get_tokenizer()

								lowercase__     				=    AlignProcessor(tokenizer=lowerCamelCase,						image_processor=lowerCamelCase						)

								lowercase__     				=    '''lower newer'''

								lowercase__     				=    processor(text=lowerCamelCase						)

								lowercase__     				=    tokenizer(lowerCamelCase,						padding='''max_length''',						max_length=64						)

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




				def        lowercase__     (  self		:					List[str]						):

								'''simple docstring'''
								lowercase__     				=    self.get_image_processor()
								lowercase__     				=    self.get_tokenizer()

								lowercase__     				=    AlignProcessor(tokenizer=lowerCamelCase,						image_processor=lowerCamelCase						)

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

								lowercase__     				=    processor(text=lowerCamelCase,						images=lowerCamelCase						)

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

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




				def        lowercase__     (  self		:					Any						):

								'''simple docstring'''
								lowercase__     				=    self.get_image_processor()
								lowercase__     				=    self.get_tokenizer()

								lowercase__     				=    AlignProcessor(tokenizer=lowerCamelCase,						image_processor=lowerCamelCase						)

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

								lowercase__     				=    processor.batch_decode(lowerCamelCase						)
								lowercase__     				=    tokenizer.batch_decode(lowerCamelCase						)

								self.assertListEqual(lowerCamelCase,						lowerCamelCase						)




				def        lowercase__     (  self		:					Optional[Any]						):

								'''simple docstring'''
								lowercase__     				=    self.get_image_processor()
								lowercase__     				=    self.get_tokenizer()

								lowercase__     				=    AlignProcessor(tokenizer=lowerCamelCase,						image_processor=lowerCamelCase						)

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

								lowercase__     				=    processor(text=lowerCamelCase,						images=lowerCamelCase						)

								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_torch_available,
)


A__			:    Any         =				{'configuration_unispeech': ['UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP', 'UniSpeechConfig']}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A__			:    Union[str, Any]         =				[
							    'UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'UniSpeechForCTC',
							    'UniSpeechForPreTraining',
							    'UniSpeechForSequenceClassification',
							    'UniSpeechModel',
							    'UniSpeechPreTrainedModel',
							]

if TYPE_CHECKING:
							from .configuration_unispeech import UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_unispeech import (
														    UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST,
														    UniSpeechForCTC,
														    UniSpeechForPreTraining,
														    UniSpeechForSequenceClassification,
														    UniSpeechModel,
														    UniSpeechPreTrainedModel,
														)

else:
							import sys

							A__			:    List[Any]         =				_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




