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, BlipImageProcessor, BlipProcessor, PreTrainedTokenizerFast






@require_vision
class   __lowercase       (     unittest.TestCase     ):

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


									'''simple docstring'''

									lowercase       		=		tempfile.mkdtemp()

									lowercase       		=		BlipImageProcessor()
									lowercase       		=		BertTokenizer.from_pretrained('''hf-internal-testing/tiny-random-BertModel''' )

									lowercase       		=		BlipProcessor(__lowerCamelCase				,				__lowerCamelCase )

									processor.save_pretrained(self.tmpdirname )

						def 							__a		(						self					:   int				,				**__lowerCamelCase					:   Optional[int] )				->		Optional[Any]:


									'''simple docstring'''

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

						def 							__a		(						self					:   Tuple				,				**__lowerCamelCase					:   Any )				->		List[str]:


									'''simple docstring'''

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

						def 							__a		(						self					:   str )				->		List[Any]:


									'''simple docstring'''

									shutil.rmtree(self.tmpdirname )

						def 							__a		(						self					:   List[str] )				->		int:


									'''simple docstring'''

									lowercase       		=		[np.random.randint(2_55				,				size=(3, 30, 4_00)				,				dtype=np.uinta )]

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

									return image_inputs

						def 							__a		(						self					:   str )				->		str:


									'''simple docstring'''

									lowercase       		=		BlipProcessor(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       		=		BlipProcessor.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					:   Tuple )				->		Union[str, Any]:


									'''simple docstring'''

									lowercase       		=		self.get_image_processor()
									lowercase       		=		self.get_tokenizer()

									lowercase       		=		BlipProcessor(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_feat_extract.keys():
												self.assertAlmostEqual(input_feat_extract[key].sum()				,				input_processor[key].sum()				,				delta=1E-2 )

						def 							__a		(						self					:   Tuple )				->		Tuple:


									'''simple docstring'''

									lowercase       		=		self.get_image_processor()
									lowercase       		=		self.get_tokenizer()

									lowercase       		=		BlipProcessor(tokenizer=__lowerCamelCase				,				image_processor=__lowerCamelCase )

									lowercase       		=		'''lower newer'''

									lowercase       		=		processor(text=__lowerCamelCase )

									lowercase       		=		tokenizer(__lowerCamelCase				,				return_token_type_ids=__lowerCamelCase )

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

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


									'''simple docstring'''

									lowercase       		=		self.get_image_processor()
									lowercase       		=		self.get_tokenizer()

									lowercase       		=		BlipProcessor(tokenizer=__lowerCamelCase				,				image_processor=__lowerCamelCase )

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

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

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

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

						def 							__a		(						self					:   Any )				->		Dict:


									'''simple docstring'''

									lowercase       		=		self.get_image_processor()
									lowercase       		=		self.get_tokenizer()

									lowercase       		=		BlipProcessor(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 							__a		(						self					:   List[Any] )				->		Optional[int]:


									'''simple docstring'''

									lowercase       		=		self.get_image_processor()
									lowercase       		=		self.get_tokenizer()

									lowercase       		=		BlipProcessor(tokenizer=__lowerCamelCase				,				image_processor=__lowerCamelCase )

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

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

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








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_UpperCAmelCase						:  Tuple									=			{
    """configuration_m2m_100""": ["""M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP""", """M2M100Config""", """M2M100OnnxConfig"""],
    """tokenization_m2m_100""": ["""M2M100Tokenizer"""],
}


try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_UpperCAmelCase						:  Tuple									=			[
					    """M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """M2M100ForConditionalGeneration""",
					    """M2M100Model""",
					    """M2M100PreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_mam_aaa import M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP, MaMaaaConfig, MaMaaaOnnxConfig
					from .tokenization_mam_aaa import MaMaaaTokenizer

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_mam_aaa import (
										    M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST,
										    MaMaaaForConditionalGeneration,
										    MaMaaaModel,
										    MaMaaaPreTrainedModel,
										)


else:
					import sys

					_UpperCAmelCase						:  Any									=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




