
from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType


class     lowercase (		_SCREAMING_SNAKE_CASE       ):
		__lowercase	:	Tuple     					=    ["image_processor", "tokenizer"]
		__lowercase	:	Tuple     					=    "BlipImageProcessor"
		__lowercase	:	Optional[Any]     					=    "AutoTokenizer"



		def __init__(			self    ,				A_    ,				A_	)  -> List[str]:
				"""simple docstring"""
				UpperCamelCase          =					False
				super().__init__(A_    ,				A_	)
				UpperCamelCase          =					self.image_processor



		def __call__(			self    ,				A_ = None    ,				A_ = None    ,				A_ = True    ,				A_ = False    ,				A_ = None    ,				A_ = None    ,				A_ = 0    ,				A_ = None    ,				A_ = None    ,				A_ = False    ,				A_ = False    ,				A_ = False    ,				A_ = False    ,				A_ = False    ,				A_ = True    ,				A_ = None    ,				**A_    ,				)  -> BatchEncoding:
				"""simple docstring"""
				if images is None and text is None:
						raise ValueError('You have to specify either images or text.'	)

				# Get only text
				if images is None:
						UpperCamelCase          =					self.tokenizer
						UpperCamelCase          =					self.tokenizer(
						    text=A_    ,				add_special_tokens=A_    ,				padding=A_    ,				truncation=A_    ,				max_length=A_    ,				stride=A_    ,				pad_to_multiple_of=A_    ,				return_attention_mask=A_    ,				return_overflowing_tokens=A_    ,				return_special_tokens_mask=A_    ,				return_offsets_mapping=A_    ,				return_token_type_ids=A_    ,				return_length=A_    ,				verbose=A_    ,				return_tensors=A_    ,				**A_    ,				)
						return text_encoding

				# add pixel_values
				UpperCamelCase          =					self.image_processor(A_    ,				return_tensors=A_	)

				if text is not None:
						UpperCamelCase          =					self.tokenizer(
						    text=A_    ,				add_special_tokens=A_    ,				padding=A_    ,				truncation=A_    ,				max_length=A_    ,				stride=A_    ,				pad_to_multiple_of=A_    ,				return_attention_mask=A_    ,				return_overflowing_tokens=A_    ,				return_special_tokens_mask=A_    ,				return_offsets_mapping=A_    ,				return_token_type_ids=A_    ,				return_length=A_    ,				verbose=A_    ,				return_tensors=A_    ,				**A_    ,				)
				else:
						UpperCamelCase          =					None

				if text_encoding is not None:
						encoding_image_processor.update(A_	)

				return encoding_image_processor



		def __UpperCamelCase			(			self    ,				*A_    ,				**A_	)  -> Optional[int]:
				"""simple docstring"""
				return self.tokenizer.batch_decode(*A_    ,				**A_	)



		def __UpperCamelCase			(			self    ,				*A_    ,				**A_	)  -> List[Any]:
				"""simple docstring"""
				return self.tokenizer.decode(*A_    ,				**A_	)



		@property
		# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
		def __UpperCamelCase			(			self	)  -> Optional[int]:
				"""simple docstring"""
				UpperCamelCase          =					self.tokenizer.model_input_names
				UpperCamelCase          =					self.image_processor.model_input_names
				return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names	)	)


import unittest

import torch
from torch import nn

from diffusers.models.activations import get_activation


class     lowercase (		unittest.TestCase       ):



		def __UpperCamelCase			(			self	)  -> List[Any]:
				"""simple docstring"""
				UpperCamelCase          =					get_activation('swish'	)

				self.assertIsInstance(A_    ,				nn.SiLU	)

				self.assertEqual(act(torch.tensor(-100    ,				dtype=torch.floataa	)	).item()    ,				0	)
				self.assertNotEqual(act(torch.tensor(-1    ,				dtype=torch.floataa	)	).item()    ,				0	)
				self.assertEqual(act(torch.tensor(0    ,				dtype=torch.floataa	)	).item()    ,				0	)
				self.assertEqual(act(torch.tensor(20    ,				dtype=torch.floataa	)	).item()    ,				20	)



		def __UpperCamelCase			(			self	)  -> Optional[int]:
				"""simple docstring"""
				UpperCamelCase          =					get_activation('silu'	)

				self.assertIsInstance(A_    ,				nn.SiLU	)

				self.assertEqual(act(torch.tensor(-100    ,				dtype=torch.floataa	)	).item()    ,				0	)
				self.assertNotEqual(act(torch.tensor(-1    ,				dtype=torch.floataa	)	).item()    ,				0	)
				self.assertEqual(act(torch.tensor(0    ,				dtype=torch.floataa	)	).item()    ,				0	)
				self.assertEqual(act(torch.tensor(20    ,				dtype=torch.floataa	)	).item()    ,				20	)



		def __UpperCamelCase			(			self	)  -> str:
				"""simple docstring"""
				UpperCamelCase          =					get_activation('mish'	)

				self.assertIsInstance(A_    ,				nn.Mish	)

				self.assertEqual(act(torch.tensor(-200    ,				dtype=torch.floataa	)	).item()    ,				0	)
				self.assertNotEqual(act(torch.tensor(-1    ,				dtype=torch.floataa	)	).item()    ,				0	)
				self.assertEqual(act(torch.tensor(0    ,				dtype=torch.floataa	)	).item()    ,				0	)
				self.assertEqual(act(torch.tensor(20    ,				dtype=torch.floataa	)	).item()    ,				20	)



		def __UpperCamelCase			(			self	)  -> Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase          =					get_activation('gelu'	)

				self.assertIsInstance(A_    ,				nn.GELU	)

				self.assertEqual(act(torch.tensor(-100    ,				dtype=torch.floataa	)	).item()    ,				0	)
				self.assertNotEqual(act(torch.tensor(-1    ,				dtype=torch.floataa	)	).item()    ,				0	)
				self.assertEqual(act(torch.tensor(0    ,				dtype=torch.floataa	)	).item()    ,				0	)
				self.assertEqual(act(torch.tensor(20    ,				dtype=torch.floataa	)	).item()    ,				20	)
