
import copy
import os
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Dict, Mapping, Optional, Union


if TYPE_CHECKING:
					from ...processing_utils import ProcessorMixin
					from ...utils import TensorType

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


SCREAMING_SNAKE_CASE							:Any										=		logging.get_logger(__name__)

SCREAMING_SNAKE_CASE							:List[str]										=		{
    'google/owlvit-base-patch32': 'https://huggingface.co/google/owlvit-base-patch32/resolve/main/config.json',
    'google/owlvit-base-patch16': 'https://huggingface.co/google/owlvit-base-patch16/resolve/main/config.json',
    'google/owlvit-large-patch14': 'https://huggingface.co/google/owlvit-large-patch14/resolve/main/config.json',
}





class 							UpperCAmelCase				(  __snake_case  ):




	'''simple docstring'''





	snake_case_    =					'owlvit_text_model'






	def __init__(	self :			Dict							,A :			List[str]=4_94_08							,A :			List[str]=5_12							,A :			Any=20_48							,A :			int=12							,A :			Optional[int]=8							,A :			str=16							,A :			List[str]="quick_gelu"							,A :			Optional[Any]=1E-5							,A :			Optional[int]=0.0							,A :			str=0.02							,A :			Optional[Any]=1.0							,A :			Optional[int]=0							,A :			Tuple=4_94_06							,A :			Tuple=4_94_07							,**A :			Optional[Any]							,):
					super().__init__(pad_token_id=A							,bos_token_id=A							,eos_token_id=A							,**A			)

					__A             =			vocab_size
					__A             =			hidden_size
					__A             =			intermediate_size
					__A             =			num_hidden_layers
					__A             =			num_attention_heads
					__A             =			max_position_embeddings
					__A             =			hidden_act
					__A             =			layer_norm_eps
					__A             =			attention_dropout
					__A             =			initializer_range
					__A             =			initializer_factor







	@classmethod
	def     UpperCamelCase_  (	cls :			Optional[Any]							,A :			str							,**A :			List[str]			):
					cls._set_token_in_kwargs(A			)

					__A      ,							__A             =			cls.get_config_dict(A							,**A			)

					# get the text config dict if we are loading from OwlViTConfig
					if config_dict.get("model_type"			) == "owlvit":
									__A             =			config_dict["text_config"]

					if "model_type" in config_dict and hasattr(cls							,"model_type"			) and config_dict["model_type"] != cls.model_type:
									logger.warning(
									    f'''You are using a model of type {config_dict['model_type']} to instantiate a model of type '''
									    f'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.'''			)

					return cls.from_dict(A							,**A			)





class 							UpperCAmelCase				(  __snake_case  ):




	'''simple docstring'''





	snake_case_    =					'owlvit_vision_model'






	def __init__(	self :			Optional[Any]							,A :			Optional[Any]=7_68							,A :			Optional[int]=30_72							,A :			int=12							,A :			int=12							,A :			Dict=3							,A :			List[Any]=7_68							,A :			Any=32							,A :			Optional[int]="quick_gelu"							,A :			Optional[Any]=1E-5							,A :			Optional[Any]=0.0							,A :			List[Any]=0.02							,A :			Dict=1.0							,**A :			Dict							,):
					super().__init__(**A			)

					__A             =			hidden_size
					__A             =			intermediate_size
					__A             =			num_hidden_layers
					__A             =			num_attention_heads
					__A             =			num_channels
					__A             =			image_size
					__A             =			patch_size
					__A             =			hidden_act
					__A             =			layer_norm_eps
					__A             =			attention_dropout
					__A             =			initializer_range
					__A             =			initializer_factor







	@classmethod
	def     UpperCamelCase_  (	cls :			List[Any]							,A :			List[Any]							,**A :			Tuple			):
					cls._set_token_in_kwargs(A			)

					__A      ,							__A             =			cls.get_config_dict(A							,**A			)

					# get the vision config dict if we are loading from OwlViTConfig
					if config_dict.get("model_type"			) == "owlvit":
									__A             =			config_dict["vision_config"]

					if "model_type" in config_dict and hasattr(cls							,"model_type"			) and config_dict["model_type"] != cls.model_type:
									logger.warning(
									    f'''You are using a model of type {config_dict['model_type']} to instantiate a model of type '''
									    f'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.'''			)

					return cls.from_dict(A							,**A			)





class 							UpperCAmelCase				(  __snake_case  ):




	'''simple docstring'''





	snake_case_    =					'owlvit'
	snake_case_    =					True






	def __init__(	self :			Tuple							,A :			Optional[int]=None							,A :			str=None							,A :			str=5_12							,A :			str=2.65_92							,A :			Tuple=True							,**A :			Union[str, Any]							,):
					super().__init__(**A			)

					if text_config is None:
									__A             =			{}
									logger.info("text_config is None. Initializing the OwlViTTextConfig with default values."			)

					if vision_config is None:
									__A             =			{}
									logger.info("vision_config is None. initializing the OwlViTVisionConfig with default values."			)

					__A             =			OwlViTTextConfig(**A			)
					__A             =			OwlViTVisionConfig(**A			)

					__A             =			projection_dim
					__A             =			logit_scale_init_value
					__A             =			return_dict
					__A             =			1.0






	@classmethod
	def     UpperCamelCase_  (	cls :			Optional[Any]							,A :			int							,**A :			int			):
					cls._set_token_in_kwargs(A			)

					__A      ,							__A             =			cls.get_config_dict(A							,**A			)

					if "model_type" in config_dict and hasattr(cls							,"model_type"			) and config_dict["model_type"] != cls.model_type:
									logger.warning(
									    f'''You are using a model of type {config_dict['model_type']} to instantiate a model of type '''
									    f'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.'''			)

					return cls.from_dict(A							,**A			)






	@classmethod
	def     UpperCamelCase_  (	cls :			int							,A :			str							,A :			Optional[int]							,**A :			Dict			):
					__A             =			{}
					__A             =			text_config
					__A             =			vision_config

					return cls.from_dict(A							,**A			)







	def     UpperCamelCase_  (	self :			Any			):
					__A             =			copy.deepcopy(self.__dict__			)
					__A             =			self.text_config.to_dict()
					__A             =			self.vision_config.to_dict()
					__A             =			self.__class__.model_type
					return output





class 							UpperCAmelCase				(  __snake_case  ):




	'''simple docstring'''





	@property
	def     UpperCamelCase_  (	self :			Dict			):
					return OrderedDict(
					    [
					        ("input_ids", {0: "batch", 1: "sequence"}),
					        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
					        ("attention_mask", {0: "batch", 1: "sequence"}),
					    ]			)






	@property
	def     UpperCamelCase_  (	self :			Union[str, Any]			):
					return OrderedDict(
					    [
					        ("logits_per_image", {0: "batch"}),
					        ("logits_per_text", {0: "batch"}),
					        ("text_embeds", {0: "batch"}),
					        ("image_embeds", {0: "batch"}),
					    ]			)






	@property
	def     UpperCamelCase_  (	self :			Tuple			):
					return 1E-4






	def     UpperCamelCase_  (	self :			int							,A :			Tuple							,A :			List[str] = -1							,A :			int = -1							,A :			Optional[Any] = None							,):
					__A             =			super().generate_dummy_inputs(
					    processor.tokenizer							,batch_size=A							,seq_length=A							,framework=A			)
					__A             =			super().generate_dummy_inputs(
					    processor.image_processor							,batch_size=A							,framework=A			)
					return {**text_input_dict, **image_input_dict}







	@property
	def     UpperCamelCase_  (	self :			List[Any]			):
					return 14


import argparse
from collections import defaultdict

import yaml


SCREAMING_SNAKE_CASE							:str										=    'docs/source/en/_toctree.yml'


def 						UpperCAmelCase      (     a_						)		->     int:







				"""simple docstring"""



				__A             =			defaultdict(a_						)
				for doc in model_doc:
								counts[doc["local"]] += 1
				__A             =			[key for key, value in counts.items() if value > 1]

				__A             =			[]
				for duplicate_key in duplicates:
								__A             =			list({doc["title"] for doc in model_doc if doc["local"] == duplicate_key}						)
								if len(a_						) > 1:
												raise ValueError(
												    F'''{duplicate_key} is present several times in the documentation table of content at '''
												    "`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the "
												    "others."						)
								# Only add this once
								new_doc.append({"local": duplicate_key, "title": titles[0]}						)

				# Add none duplicate-keys
				new_doc.extend([doc for doc in model_doc if counts[doc["local"]] == 1]						)

				# Sort
				return sorted(a_		,       key=lambda a_						: s["title"].lower()						)


def 						UpperCAmelCase      (     a_=False						)		->     List[Any]:







				"""simple docstring"""



				with open(a_		,       encoding="utf-8"						) as f:
								__A             =			yaml.safe_load(f.read()						)

				# Get to the API doc
				__A             =			0
				while content[api_idx]["title"] != "API":
								api_idx += 1
				__A             =			content[api_idx]["sections"]

				# Then to the model doc
				__A             =			0
				while api_doc[model_idx]["title"] != "Models":
								model_idx += 1

				__A             =			api_doc[model_idx]["sections"]

				__A             =			[(idx, section) for idx, section in enumerate(a_						) if "sections" in section]
				__A             =			False
				for idx, modality_doc in modalities_docs:
								__A             =			modality_doc["sections"]
								__A             =			clean_model_doc_toc(a_						)

								if old_modality_doc != new_modality_doc:
												__A             =			True
												if overwrite:
																__A             =			new_modality_doc

				if diff:
								if overwrite:
												__A             =			model_doc
												__A             =			api_doc
												with open(a_		,       "w"		,       encoding="utf-8"						) as f:
																f.write(yaml.dump(a_		,       allow_unicode=a_						)						)
								else:
												raise ValueError(
												    "The model doc part of the table of content is not properly sorted, run `make style` to fix this."						)


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE							:Tuple										=    argparse.ArgumentParser()
					parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
					SCREAMING_SNAKE_CASE							:List[str]										=    parser.parse_args()

					check_model_doc(args.fix_and_overwrite)
