

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, BlipaProcessor, BlipImageProcessor, GPTaTokenizer, PreTrainedTokenizerFast







@require_vision
class 				lowercase       ( unittest.TestCase    ):




				def 					a							(      self ):
											snake_case_           =					tempfile.mkdtemp()

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

											snake_case_           =					BlipaProcessor(snake_case   ,     snake_case )

											processor.save_pretrained(self.tmpdirname )




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




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




				def 					a							(      self ):
											shutil.rmtree(self.tmpdirname )




				def 					a							(      self ):
											snake_case_           =					[np.random.randint(255   ,     size=(3, 30, 400)   ,     dtype=np.uinta )]

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

											return image_inputs




				def 					a							(      self ):
											snake_case_           =					BlipaProcessor(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=snake_case   ,     padding_value=1.0 )

											snake_case_           =					BlipaProcessor.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 )




				def 					a							(      self ):
											snake_case_           =					self.get_image_processor()
											snake_case_           =					self.get_tokenizer()

											snake_case_           =					BlipaProcessor(tokenizer=snake_case   ,     image_processor=snake_case )

											snake_case_           =					self.prepare_image_inputs()

											snake_case_           =					image_processor(snake_case   ,     return_tensors='np' )
											snake_case_           =					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 ):
											snake_case_           =					self.get_image_processor()
											snake_case_           =					self.get_tokenizer()

											snake_case_           =					BlipaProcessor(tokenizer=snake_case   ,     image_processor=snake_case )

											snake_case_           =					'lower newer'

											snake_case_           =					processor(text=snake_case )

											snake_case_           =					tokenizer(snake_case   ,     return_token_type_ids=snake_case )

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




				def 					a							(      self ):
											snake_case_           =					self.get_image_processor()
											snake_case_           =					self.get_tokenizer()

											snake_case_           =					BlipaProcessor(tokenizer=snake_case   ,     image_processor=snake_case )

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

											snake_case_           =					processor(text=snake_case   ,     images=snake_case )

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

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




				def 					a							(      self ):
											snake_case_           =					self.get_image_processor()
											snake_case_           =					self.get_tokenizer()

											snake_case_           =					BlipaProcessor(tokenizer=snake_case   ,     image_processor=snake_case )

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

											snake_case_           =					processor.batch_decode(snake_case )
											snake_case_           =					tokenizer.batch_decode(snake_case )

											self.assertListEqual(snake_case   ,     snake_case )




				def 					a							(      self ):
											snake_case_           =					self.get_image_processor()
											snake_case_           =					self.get_tokenizer()

											snake_case_           =					BlipaProcessor(tokenizer=snake_case   ,     image_processor=snake_case )

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

											snake_case_           =					processor(text=snake_case   ,     images=snake_case )

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







from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_UpperCAmelCase	:     int		       = logging.get_logger(__name__)

_UpperCAmelCase	:     Any		       = {
    """facebook/levit-128S""": """https://huggingface.co/facebook/levit-128S/resolve/main/config.json""",
    # See all LeViT models at https://huggingface.co/models?filter=levit
}







class 				lowercase       ( lowercase_    ):
				__SCREAMING_SNAKE_CASE      :				int     =    '''levit'''




				def __init__(      self   ,     snake_case=224   ,     snake_case=3   ,     snake_case=3   ,     snake_case=2   ,     snake_case=1   ,     snake_case=16   ,     snake_case=[128, 256, 384]   ,     snake_case=[4, 8, 12]   ,     snake_case=[4, 4, 4]   ,     snake_case=[16, 16, 16]   ,     snake_case=0   ,     snake_case=[2, 2, 2]   ,     snake_case=[2, 2, 2]   ,     snake_case=0.02   ,     **snake_case   ,     ):
											super().__init__(**snake_case )
											snake_case_           =					image_size
											snake_case_           =					num_channels
											snake_case_           =					kernel_size
											snake_case_           =					stride
											snake_case_           =					padding
											snake_case_           =					hidden_sizes
											snake_case_           =					num_attention_heads
											snake_case_           =					depths
											snake_case_           =					key_dim
											snake_case_           =					drop_path_rate
											snake_case_           =					patch_size
											snake_case_           =					attention_ratio
											snake_case_           =					mlp_ratio
											snake_case_           =					initializer_range
											snake_case_           =					[
											    ['Subsample', key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
											    ['Subsample', key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
											]







class 				lowercase       ( lowercase_    ):
				__SCREAMING_SNAKE_CASE      :				Dict     =    version.parse('''1.11'''    )




				@property
				def 					a							(      self ):
											return OrderedDict(
											    [
											        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
											    ] )




				@property
				def 					a							(      self ):
											return 1e-4




