
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	:	Dict     					=    ["image_processor", "tokenizer"]
		__lowercase	:	List[Any]     					=    "BlipImageProcessor"
		__lowercase	:	List[str]     					=    ("BertTokenizer", "BertTokenizerFast")



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



		@property
		def __UpperCamelCase			(			self	)  -> Optional[Any]:
				"""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 os
import sys
import unittest


_UpperCAmelCase :			str              =	os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))

import check_dummies  # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init  # noqa: E402


# Align TRANSFORMERS_PATH in check_dummies with the current path
_UpperCAmelCase :			Tuple              =	os.path.join(git_repo_path, "src", "diffusers")


class     lowercase (		unittest.TestCase       ):



		def __UpperCamelCase			(			self	)  -> Tuple:
				"""simple docstring"""
				UpperCamelCase          =					find_backend('    if not is_torch_available():'	)
				self.assertEqual(A_    ,				'torch'	)

				# backend_with_underscore = find_backend("    if not is_tensorflow_text_available():")
				# self.assertEqual(backend_with_underscore, "tensorflow_text")

				UpperCamelCase          =					find_backend('    if not (is_torch_available() and is_transformers_available()):'	)
				self.assertEqual(A_    ,				'torch_and_transformers'	)

				# double_backend_with_underscore = find_backend(
				#    "    if not (is_sentencepiece_available() and is_tensorflow_text_available()):"
				# )
				# self.assertEqual(double_backend_with_underscore, "sentencepiece_and_tensorflow_text")

				UpperCamelCase          =					find_backend(
				    '    if not (is_torch_available() and is_transformers_available() and is_onnx_available()):'	)
				self.assertEqual(A_    ,				'torch_and_transformers_and_onnx'	)



		def __UpperCamelCase			(			self	)  -> Optional[Any]:
				"""simple docstring"""
				UpperCamelCase          =					read_init()
				# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
				self.assertIn('torch'    ,				A_	)
				self.assertIn('torch_and_transformers'    ,				A_	)
				self.assertIn('flax_and_transformers'    ,				A_	)
				self.assertIn('torch_and_transformers_and_onnx'    ,				A_	)

				# Likewise, we can't assert on the exact content of a key
				self.assertIn('UNet2DModel'    ,				objects['torch']	)
				self.assertIn('FlaxUNet2DConditionModel'    ,				objects['flax']	)
				self.assertIn('StableDiffusionPipeline'    ,				objects['torch_and_transformers']	)
				self.assertIn('FlaxStableDiffusionPipeline'    ,				objects['flax_and_transformers']	)
				self.assertIn('LMSDiscreteScheduler'    ,				objects['torch_and_scipy']	)
				self.assertIn('OnnxStableDiffusionPipeline'    ,				objects['torch_and_transformers_and_onnx']	)



		def __UpperCamelCase			(			self	)  -> Any:
				"""simple docstring"""
				UpperCamelCase          =					create_dummy_object('CONSTANT'    ,				'\'torch\''	)
				self.assertEqual(A_    ,				'\nCONSTANT = None\n'	)

				UpperCamelCase          =					create_dummy_object('function'    ,				'\'torch\''	)
				self.assertEqual(
				    A_    ,				'\ndef function(*args, **kwargs):\n    requires_backends(function, \'torch\')\n'	)

				UpperCamelCase          =					'\nclass FakeClass(metaclass=DummyObject):\n    _backends = \'torch\'\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, \'torch\')\n\n    @classmethod\n    def from_config(cls, *args, **kwargs):\n        requires_backends(cls, \'torch\')\n\n    @classmethod\n    def from_pretrained(cls, *args, **kwargs):\n        requires_backends(cls, \'torch\')\n'
				UpperCamelCase          =					create_dummy_object('FakeClass'    ,				'\'torch\''	)
				self.assertEqual(A_    ,				A_	)



		def __UpperCamelCase			(			self	)  -> List[str]:
				"""simple docstring"""
				UpperCamelCase          =					'# This file is autogenerated by the command `make fix-copies`, do not edit.\nfrom ..utils import DummyObject, requires_backends\n\n\nCONSTANT = None\n\n\ndef function(*args, **kwargs):\n    requires_backends(function, ["torch"])\n\n\nclass FakeClass(metaclass=DummyObject):\n    _backends = ["torch"]\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, ["torch"])\n\n    @classmethod\n    def from_config(cls, *args, **kwargs):\n        requires_backends(cls, ["torch"])\n\n    @classmethod\n    def from_pretrained(cls, *args, **kwargs):\n        requires_backends(cls, ["torch"])\n'
				UpperCamelCase          =					create_dummy_files({'torch': ['CONSTANT', 'function', 'FakeClass']}	)
				self.assertEqual(dummy_files['torch']    ,				A_	)
