
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      SCREAMING_SNAKE_CASE__  (			unittest.TestCase      ):





	'''simple docstring'''



	def 				A							(						self			:					List[Any]			):


				'''simple docstring'''


				_snake_case						=    tempfile.mkdtemp()

				_snake_case						=    CLIPImageProcessor()
				_snake_case						=    BertTokenizer.from_pretrained(
				    'hf-internal-testing/tiny-random-BertModel'    ,						model_input_names=['input_ids', 'attention_mask']			)

				_snake_case						=    GitProcessor(lowercase    ,						lowercase			)

				processor.save_pretrained(self.tmpdirname			)



	def 				A							(						self			:					Optional[Any]    ,						**lowercase			:					Union[str, Any]			):


				'''simple docstring'''


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



	def 				A							(						self			:					List[Any]    ,						**lowercase			:					Dict			):


				'''simple docstring'''


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



	def 				A							(						self			:					str			):


				'''simple docstring'''


				shutil.rmtree(self.tmpdirname			)



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


				'''simple docstring'''


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

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

				return image_inputs



	def 				A							(						self			:					Optional[Any]			):


				'''simple docstring'''


				_snake_case						=    GitProcessor(tokenizer=self.get_tokenizer()    ,						image_processor=self.get_image_processor()			)
				processor.save_pretrained(self.tmpdirname			)

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

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

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

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



	def 				A							(						self			:					str			):


				'''simple docstring'''


				_snake_case						=    self.get_image_processor()
				_snake_case						=    self.get_tokenizer()

				_snake_case						=    GitProcessor(tokenizer=lowercase    ,						image_processor=lowercase			)

				_snake_case						=    self.prepare_image_inputs()

				_snake_case						=    image_processor(lowercase    ,						return_tensors='np'			)
				_snake_case						=    processor(images=lowercase    ,						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			:					Dict			):


				'''simple docstring'''


				_snake_case						=    self.get_image_processor()
				_snake_case						=    self.get_tokenizer()

				_snake_case						=    GitProcessor(tokenizer=lowercase    ,						image_processor=lowercase			)

				_snake_case						=    'lower newer'

				_snake_case						=    processor(text=lowercase			)

				_snake_case						=    tokenizer(lowercase    ,						return_token_type_ids=lowercase			)

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



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


				'''simple docstring'''


				_snake_case						=    self.get_image_processor()
				_snake_case						=    self.get_tokenizer()

				_snake_case						=    GitProcessor(tokenizer=lowercase    ,						image_processor=lowercase			)

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

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

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

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



	def 				A							(						self			:					Any			):


				'''simple docstring'''


				_snake_case						=    self.get_image_processor()
				_snake_case						=    self.get_tokenizer()

				_snake_case						=    GitProcessor(tokenizer=lowercase    ,						image_processor=lowercase			)

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

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

				self.assertListEqual(lowercase    ,						lowercase			)





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


				'''simple docstring'''


				_snake_case						=    self.get_image_processor()
				_snake_case						=    self.get_tokenizer()

				_snake_case						=    GitProcessor(tokenizer=lowercase    ,						image_processor=lowercase			)

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

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

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

# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
    is_vision_available,
)


_lowerCamelCase :       int     					=    {
    '''configuration_tvlt''': ['''TVLT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TvltConfig'''],
    '''feature_extraction_tvlt''': ['''TvltFeatureExtractor'''],
    '''processing_tvlt''': ['''TvltProcessor'''],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_lowerCamelCase :       str     					=    [
			    '''TVLT_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''TvltModel''',
			    '''TvltForPreTraining''',
			    '''TvltForAudioVisualClassification''',
			    '''TvltPreTrainedModel''',
			]

try:
			if not is_vision_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_lowerCamelCase :       str     					=    ['''TvltImageProcessor''']


if TYPE_CHECKING:
			from .configuration_tvlt import TVLT_PRETRAINED_CONFIG_ARCHIVE_MAP, TvltConfig
			from .processing_tvlt import TvltProcessor
			from .feature_extraction_tvlt import TvltFeatureExtractor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tvlt import (
						    TVLT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TvltForAudioVisualClassification,
						    TvltForPreTraining,
						    TvltModel,
						    TvltPreTrainedModel,
						)

			try:
						if not is_vision_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .image_processing_tvlt import TvltImageProcessor


else:
			import sys

			_lowerCamelCase :       Dict     					=    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)