



"""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,
							    BertTokenizerFast,
							    BlipImageProcessor,
							    GPTaTokenizer,
							    InstructBlipProcessor,
							    PreTrainedTokenizerFast,
							)






@require_vision
class      UpperCAmelCase__		(      unittest.TestCase     ):



		def 					A_		( self  :    Dict				)			->						Dict:






									'''simple docstring'''

									A         =					tempfile.mkdtemp()

									A         =					BlipImageProcessor()
									A         =					GPTaTokenizer.from_pretrained('hf-internal-testing/tiny-random-GPT2Model'				)
									A         =					BertTokenizerFast.from_pretrained('hf-internal-testing/tiny-random-bert'				)

									A         =					InstructBlipProcessor(snake_case      ,							snake_case      ,							snake_case				)

									processor.save_pretrained(self.tmpdirname				)



		def 					A_		( self  :    List[str]      ,							**snake_case  :    str				)			->						Dict:






									'''simple docstring'''

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



		def 					A_		( self  :    int      ,							**snake_case  :    Optional[Any]				)			->						Any:






									'''simple docstring'''

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



		def 					A_		( self  :    Any      ,							**snake_case  :    Union[str, Any]				)			->						Any:






									'''simple docstring'''

									return AutoProcessor.from_pretrained(self.tmpdirname      ,							**snake_case				).qformer_tokenizer



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






									'''simple docstring'''

									shutil.rmtree(self.tmpdirname				)



		def 					A_		( self  :    List[Any]				)			->						Tuple:






									'''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 					A_		( self  :    List[Any]				)			->						Optional[int]:






									'''simple docstring'''

									A         =					InstructBlipProcessor(
									    tokenizer=self.get_tokenizer()      ,							image_processor=self.get_image_processor()      ,							qformer_tokenizer=self.get_qformer_tokenizer()      ,							)
									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         =					InstructBlipProcessor.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      ,							snake_case				)

									self.assertEqual(processor.image_processor.to_json_string()      ,							image_processor_add_kwargs.to_json_string()				)
									self.assertIsInstance(processor.image_processor      ,							snake_case				)
									self.assertIsInstance(processor.qformer_tokenizer      ,							snake_case				)



		def 					A_		( self  :    Optional[Any]				)			->						Dict:






									'''simple docstring'''

									A         =					self.get_image_processor()
									A         =					self.get_tokenizer()
									A         =					self.get_qformer_tokenizer()

									A         =					InstructBlipProcessor(
									    tokenizer=snake_case      ,							image_processor=snake_case      ,							qformer_tokenizer=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 					A_		( self  :    Tuple				)			->						List[str]:






									'''simple docstring'''

									A         =					self.get_image_processor()
									A         =					self.get_tokenizer()
									A         =					self.get_qformer_tokenizer()

									A         =					InstructBlipProcessor(
									    tokenizer=snake_case      ,							image_processor=snake_case      ,							qformer_tokenizer=snake_case				)

									A         =					'lower newer'

									A         =					processor(text=snake_case				)

									A         =					tokenizer(snake_case      ,							return_token_type_ids=snake_case				)
									A         =					qformer_tokenizer(snake_case      ,							return_token_type_ids=snake_case				)

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

									for key in encoded_tokens_qformer.keys():
																self.assertListEqual(encoded_tokens_qformer[key]      ,							encoded_processor['qformer_' + key]				)



		def 					A_		( self  :    List[Any]				)			->						Optional[int]:






									'''simple docstring'''

									A         =					self.get_image_processor()
									A         =					self.get_tokenizer()
									A         =					self.get_qformer_tokenizer()

									A         =					InstructBlipProcessor(
									    tokenizer=snake_case      ,							image_processor=snake_case      ,							qformer_tokenizer=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', 'attention_mask', 'qformer_input_ids', 'qformer_attention_mask', 'pixel_values']      ,							)

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



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






									'''simple docstring'''

									A         =					self.get_image_processor()
									A         =					self.get_tokenizer()
									A         =					self.get_qformer_tokenizer()

									A         =					InstructBlipProcessor(
									    tokenizer=snake_case      ,							image_processor=snake_case      ,							qformer_tokenizer=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 					A_		( self  :    Tuple				)			->						List[Any]:






									'''simple docstring'''

									A         =					self.get_image_processor()
									A         =					self.get_tokenizer()
									A         =					self.get_qformer_tokenizer()

									A         =					InstructBlipProcessor(
									    tokenizer=snake_case      ,							image_processor=snake_case      ,							qformer_tokenizer=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', 'attention_mask', 'qformer_input_ids', 'qformer_attention_mask', 'pixel_values']      ,							)








"""simple docstring"""







from ...configuration_utils import PretrainedConfig
from ...utils import logging


A    =			logging.get_logger(__name__)

A    =			{
    'google/fnet-base': 'https://huggingface.co/google/fnet-base/resolve/main/config.json',
    'google/fnet-large': 'https://huggingface.co/google/fnet-large/resolve/main/config.json'
    # See all FNet models at https://huggingface.co/models?filter=fnet
}






class      UpperCAmelCase__		(      UpperCamelCase     ):
		lowerCAmelCase_       :	List[Any] 							=     """fnet"""



		def __init__( self  :    Tuple      ,							snake_case  :    Optional[int]=32_000      ,							snake_case  :    Optional[Any]=768      ,							snake_case  :    Union[str, Any]=12      ,							snake_case  :    List[str]=3_072      ,							snake_case  :    str="gelu_new"      ,							snake_case  :    Any=0.1      ,							snake_case  :    List[str]=512      ,							snake_case  :    Any=4      ,							snake_case  :    str=0.02      ,							snake_case  :    Union[str, Any]=1E-1_2      ,							snake_case  :    Optional[Any]=False      ,							snake_case  :    List[Any]=512      ,							snake_case  :    int=3      ,							snake_case  :    Any=1      ,							snake_case  :    Union[str, Any]=2      ,							**snake_case  :    Union[str, Any]      ,							)			->						Dict:






									'''simple docstring'''

									super().__init__(pad_token_id=snake_case      ,							bos_token_id=snake_case      ,							eos_token_id=snake_case      ,							**snake_case				)

									A         =					vocab_size
									A         =					max_position_embeddings
									A         =					hidden_size
									A         =					num_hidden_layers
									A         =					intermediate_size
									A         =					hidden_act
									A         =					hidden_dropout_prob
									A         =					initializer_range
									A         =					type_vocab_size
									A         =					layer_norm_eps
									A         =					use_tpu_fourier_optimizations
									A         =					tpu_short_seq_length



