




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        __lowerCAmelCase					(			unittest.TestCase					):

			"""simple docstring"""







			def  lowercase_       ( self				)       -> str:



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

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

							__lowerCamelCase									=       BlipaProcessor(lowerCamelCase__     ,		lowerCamelCase__				)

							processor.save_pretrained(self.tmpdirname				)


			def  lowercase_       ( self     ,		**lowerCamelCase__				)       -> Dict:



							'''simple docstring'''
							return AutoProcessor.from_pretrained(self.tmpdirname     ,		**lowerCamelCase__				).tokenizer


			def  lowercase_       ( self     ,		**lowerCamelCase__				)       -> Optional[int]:



							'''simple docstring'''
							return AutoProcessor.from_pretrained(self.tmpdirname     ,		**lowerCamelCase__				).image_processor


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



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


			def  lowercase_       ( self				)       -> List[Any]:



							'''simple docstring'''
							__lowerCamelCase									=       [np.random.randint(255     ,		size=(3, 30, 400)     ,		dtype=np.uinta				)]

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

							return image_inputs


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



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

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

							__lowerCamelCase									=       BlipaProcessor.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				)       -> Optional[Any]:



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

							__lowerCamelCase									=       BlipaProcessor(tokenizer=lowerCamelCase__     ,		image_processor=lowerCamelCase__				)

							__lowerCamelCase									=       self.prepare_image_inputs()

							__lowerCamelCase									=       image_processor(lowerCamelCase__     ,		return_tensors='np'				)
							__lowerCamelCase									=       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  lowercase_       ( self				)       -> Tuple:



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

							__lowerCamelCase									=       BlipaProcessor(tokenizer=lowerCamelCase__     ,		image_processor=lowerCamelCase__				)

							__lowerCamelCase									=       'lower newer'

							__lowerCamelCase									=       processor(text=lowerCamelCase__				)

							__lowerCamelCase									=       tokenizer(lowerCamelCase__     ,		return_token_type_ids=lowerCamelCase__				)

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


			def  lowercase_       ( self				)       -> Tuple:



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

							__lowerCamelCase									=       BlipaProcessor(tokenizer=lowerCamelCase__     ,		image_processor=lowerCamelCase__				)

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

							__lowerCamelCase									=       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  lowercase_       ( self				)       -> Tuple:



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

							__lowerCamelCase									=       BlipaProcessor(tokenizer=lowerCamelCase__     ,		image_processor=lowerCamelCase__				)

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

							__lowerCamelCase									=       processor.batch_decode(lowerCamelCase__				)
							__lowerCamelCase									=       tokenizer.batch_decode(lowerCamelCase__				)

							self.assertListEqual(lowerCamelCase__     ,		lowerCamelCase__				)






			def  lowercase_       ( self				)       -> Dict:



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

							__lowerCamelCase									=       BlipaProcessor(tokenizer=lowerCamelCase__     ,		image_processor=lowerCamelCase__				)

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

							__lowerCamelCase									=       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']				)








import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
					import torch

					from transformers.activations import gelu_new, gelu_python, get_activation







@require_torch
class        __lowerCAmelCase					(			unittest.TestCase					):

			"""simple docstring"""







			def  lowercase_       ( self				)       -> int:



							'''simple docstring'''
							__lowerCamelCase									=       torch.tensor([-100, -1, -0.1, 0, 0.1, 1.0, 100]				)
							__lowerCamelCase									=       get_activation('gelu'				)
							self.assertTrue(torch.allclose(gelu_python(lowerCamelCase__				)     ,		torch_builtin(lowerCamelCase__				)				)				)
							self.assertFalse(torch.allclose(gelu_python(lowerCamelCase__				)     ,		gelu_new(lowerCamelCase__				)				)				)


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



							'''simple docstring'''
							__lowerCamelCase									=       torch.tensor([-100, -1, -0.1, 0, 0.1, 1.0, 100]				)
							__lowerCamelCase									=       get_activation('gelu'				)
							__lowerCamelCase									=       get_activation('gelu_10'				)

							__lowerCamelCase									=       torch_builtin(lowerCamelCase__				)
							__lowerCamelCase									=       geluaa(lowerCamelCase__				)

							__lowerCamelCase									=       torch.where(y_gelu_aa < 10.0     ,		1     ,		0				)

							self.assertTrue(torch.max(lowerCamelCase__				).item() == 10.0				)
							self.assertTrue(torch.allclose(y_gelu * clipped_mask     ,		y_gelu_aa * clipped_mask				)				)


			def  lowercase_       ( self				)       -> Any:



							'''simple docstring'''
							get_activation('gelu'				)
							get_activation('gelu_10'				)
							get_activation('gelu_fast'				)
							get_activation('gelu_new'				)
							get_activation('gelu_python'				)
							get_activation('gelu_pytorch_tanh'				)
							get_activation('linear'				)
							get_activation('mish'				)
							get_activation('quick_gelu'				)
							get_activation('relu'				)
							get_activation('sigmoid'				)
							get_activation('silu'				)
							get_activation('swish'				)
							get_activation('tanh'				)
							with self.assertRaises(lowerCamelCase__				):
											get_activation('bogus'				)
							with self.assertRaises(lowerCamelCase__				):
											get_activation(lowerCamelCase__				)






			def  lowercase_       ( self				)       -> Dict:



							'''simple docstring'''
							__lowerCamelCase									=       get_activation('gelu'				)
							__lowerCamelCase									=       1
							__lowerCamelCase									=       get_activation('gelu'				)
							self.assertEqual(acta.a     ,		1				)
							with self.assertRaises(lowerCamelCase__				):
											__lowerCamelCase									=       acta.a


