



"""simple docstring"""





import argparse
import json

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation
def      a__			(   lowerCAmelCase__   ):
						UpperCAmelCase_    				=							384
						UpperCAmelCase_    				=							7
						if "tiny" in model_name:
												UpperCAmelCase_    				=							96
												UpperCAmelCase_    				=							(2, 2, 6, 2)
												UpperCAmelCase_    				=							(3, 6, 12, 24)
						elif "small" in model_name:
												UpperCAmelCase_    				=							96
												UpperCAmelCase_    				=							(2, 2, 18, 2)
												UpperCAmelCase_    				=							(3, 6, 12, 24)
						elif "base" in model_name:
												UpperCAmelCase_    				=							128
												UpperCAmelCase_    				=							(2, 2, 18, 2)
												UpperCAmelCase_    				=							(4, 8, 16, 32)
												UpperCAmelCase_    				=							12
												UpperCAmelCase_    				=							512
						elif "large" in model_name:
												UpperCAmelCase_    				=							192
												UpperCAmelCase_    				=							(2, 2, 18, 2)
												UpperCAmelCase_    				=							(6, 12, 24, 48)
												UpperCAmelCase_    				=							12
												UpperCAmelCase_    				=							768

						# set label information
						UpperCAmelCase_    				=							150
						UpperCAmelCase_    				=							"huggingface/label-files"
						UpperCAmelCase_    				=							"ade20k-id2label.json"
						UpperCAmelCase_    				=							json.load(open(hf_hub_download(lowerCAmelCase__			,    lowerCAmelCase__			,    repo_type="dataset"   )			,    "r"   )   )
						UpperCAmelCase_    				=							{int(lowerCAmelCase__   ): v for k, v in idalabel.items()}
						UpperCAmelCase_    				=							{v: k for k, v in idalabel.items()}

						UpperCAmelCase_    				=							SwinConfig(
						    embed_dim=lowerCAmelCase__			,    depths=lowerCAmelCase__			,    num_heads=lowerCAmelCase__			,    window_size=lowerCAmelCase__			,    out_features=["stage1", "stage2", "stage3", "stage4"]			,    )
						UpperCAmelCase_    				=							UperNetConfig(
						    backbone_config=lowerCAmelCase__			,    auxiliary_in_channels=lowerCAmelCase__			,    num_labels=lowerCAmelCase__			,    idalabel=lowerCAmelCase__			,    labelaid=lowerCAmelCase__			,    )

						return config
def      a__			(   lowerCAmelCase__   ):
						UpperCAmelCase_    				=							[]

						# fmt: off
						# stem
						rename_keys.append(("backbone.patch_embed.projection.weight", "backbone.embeddings.patch_embeddings.projection.weight")   )
						rename_keys.append(("backbone.patch_embed.projection.bias", "backbone.embeddings.patch_embeddings.projection.bias")   )
						rename_keys.append(("backbone.patch_embed.norm.weight", "backbone.embeddings.norm.weight")   )
						rename_keys.append(("backbone.patch_embed.norm.bias", "backbone.embeddings.norm.bias")   )
						# stages
						for i in range(len(config.backbone_config.depths   )   ):
												for j in range(config.backbone_config.depths[i]   ):
																		rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.norm1.weight""", f"""backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.weight""")   )
																		rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.norm1.bias""", f"""backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.bias""")   )
																		rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_bias_table""", f"""backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table""")   )
																		rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_index""", f"""backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index""")   )
																		rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.weight""", f"""backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight""")   )
																		rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.bias""", f"""backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias""")   )
																		rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.norm2.weight""", f"""backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.weight""")   )
																		rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.norm2.bias""", f"""backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.bias""")   )
																		rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.weight""", f"""backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight""")   )
																		rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.bias""", f"""backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias""")   )
																		rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.ffn.layers.1.weight""", f"""backbone.encoder.layers.{i}.blocks.{j}.output.dense.weight""")   )
																		rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.ffn.layers.1.bias""", f"""backbone.encoder.layers.{i}.blocks.{j}.output.dense.bias""")   )

												if i < 3:
																		rename_keys.append((f"""backbone.stages.{i}.downsample.reduction.weight""", f"""backbone.encoder.layers.{i}.downsample.reduction.weight""")   )
																		rename_keys.append((f"""backbone.stages.{i}.downsample.norm.weight""", f"""backbone.encoder.layers.{i}.downsample.norm.weight""")   )
																		rename_keys.append((f"""backbone.stages.{i}.downsample.norm.bias""", f"""backbone.encoder.layers.{i}.downsample.norm.bias""")   )
												rename_keys.append((f"""backbone.norm{i}.weight""", f"""backbone.hidden_states_norms.stage{i+1}.weight""")   )
												rename_keys.append((f"""backbone.norm{i}.bias""", f"""backbone.hidden_states_norms.stage{i+1}.bias""")   )

						# decode head
						rename_keys.extend(
						    [
						        ("decode_head.conv_seg.weight", "decode_head.classifier.weight"),
						        ("decode_head.conv_seg.bias", "decode_head.classifier.bias"),
						        ("auxiliary_head.conv_seg.weight", "auxiliary_head.classifier.weight"),
						        ("auxiliary_head.conv_seg.bias", "auxiliary_head.classifier.bias"),
						    ]   )
						# fmt: on

						return rename_keys
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__   ):
						UpperCAmelCase_    				=							dct.pop(lowerCAmelCase__   )
						UpperCAmelCase_    				=							val
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__   ):
						UpperCAmelCase_    				=							[int(backbone_config.embed_dim * 2**i   ) for i in range(len(backbone_config.depths   )   )]
						for i in range(len(backbone_config.depths   )   ):
												UpperCAmelCase_    				=							num_features[i]
												for j in range(backbone_config.depths[i]   ):
																		# fmt: off
																		# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
																		UpperCAmelCase_    				=							state_dict.pop(f"""backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.weight"""   )
																		UpperCAmelCase_    				=							state_dict.pop(f"""backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.bias"""   )
																		# next, add query, keys and values (in that order) to the state dict
																		UpperCAmelCase_    				=							in_proj_weight[:dim, :]
																		UpperCAmelCase_    				=							in_proj_bias[: dim]
																		UpperCAmelCase_    				=							in_proj_weight[
																		    dim : dim * 2, :
																		]
																		UpperCAmelCase_    				=							in_proj_bias[
																		    dim : dim * 2
																		]
																		UpperCAmelCase_    				=							in_proj_weight[
																		    -dim :, :
																		]
																		UpperCAmelCase_    				=							in_proj_bias[-dim :]
																		# fmt: on
def      a__			(   lowerCAmelCase__   ):
						UpperCAmelCase_				,					UpperCAmelCase_    				=							x.shape
						UpperCAmelCase_    				=							x.reshape(lowerCAmelCase__			,    4			,    in_channel // 4   )
						UpperCAmelCase_    				=							x[:, [0, 2, 1, 3], :].transpose(1			,    2   ).reshape(lowerCAmelCase__			,    lowerCAmelCase__   )
						return x
def      a__			(   lowerCAmelCase__   ):
						UpperCAmelCase_				,					UpperCAmelCase_    				=							x.shape
						UpperCAmelCase_    				=							x.reshape(lowerCAmelCase__			,    in_channel // 4			,    4   )
						UpperCAmelCase_    				=							x[:, :, [0, 2, 1, 3]].transpose(1			,    2   ).reshape(lowerCAmelCase__			,    lowerCAmelCase__   )

						return x
def      a__			(   lowerCAmelCase__   ):
						UpperCAmelCase_    				=							x.shape[0]
						UpperCAmelCase_    				=							x.reshape(4			,    in_channel // 4   )
						UpperCAmelCase_    				=							x[[0, 2, 1, 3], :].transpose(0			,    1   ).reshape(lowerCAmelCase__   )
						return x
def      a__			(   lowerCAmelCase__   ):
						UpperCAmelCase_    				=							x.shape[0]
						UpperCAmelCase_    				=							x.reshape(in_channel // 4			,    4   )
						UpperCAmelCase_    				=							x[:, [0, 2, 1, 3]].transpose(0			,    1   ).reshape(lowerCAmelCase__   )
						return x
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__   ):
						UpperCAmelCase_    				=							{
						    "upernet-swin-tiny": "https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210531_112542-e380ad3e.pth",
						    "upernet-swin-small": "https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210526_192015-ee2fff1c.pth",
						    "upernet-swin-base": "https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K_20210531_125459-429057bf.pth",
						    "upernet-swin-large": "https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k_20220318_091743-9ba68901.pth",
						}
						UpperCAmelCase_    				=							model_name_to_url[model_name]
						UpperCAmelCase_    				=							torch.hub.load_state_dict_from_url(lowerCAmelCase__			,    map_location="cpu"			,    file_name=lowerCAmelCase__   )[
						    "state_dict"
						]

						for name, param in state_dict.items():
												print(lowerCAmelCase__			,    param.shape   )

						UpperCAmelCase_    				=							get_upernet_config(lowerCAmelCase__   )
						UpperCAmelCase_    				=							UperNetForSemanticSegmentation(lowerCAmelCase__   )
						model.eval()

						# replace "bn" => "batch_norm"
						for key in state_dict.copy().keys():
												UpperCAmelCase_    				=							state_dict.pop(lowerCAmelCase__   )
												if "bn" in key:
																		UpperCAmelCase_    				=							key.replace("bn"			,    "batch_norm"   )
												UpperCAmelCase_    				=							val

						# rename keys
						UpperCAmelCase_    				=							create_rename_keys(lowerCAmelCase__   )
						for src, dest in rename_keys:
												rename_key(lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__   )
						read_in_q_k_v(lowerCAmelCase__			,    config.backbone_config   )

						# fix downsample parameters
						for key, value in state_dict.items():
												if "downsample" in key:
																		if "reduction" in key:
																								UpperCAmelCase_    				=							reverse_correct_unfold_reduction_order(lowerCAmelCase__   )
																		if "norm" in key:
																								UpperCAmelCase_    				=							reverse_correct_unfold_norm_order(lowerCAmelCase__   )

						model.load_state_dict(lowerCAmelCase__   )

						# verify on image
						UpperCAmelCase_    				=							"https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg"
						UpperCAmelCase_    				=							Image.open(requests.get(lowerCAmelCase__			,    stream=lowerCAmelCase__   ).raw   ).convert("RGB"   )

						UpperCAmelCase_    				=							SegformerImageProcessor()
						UpperCAmelCase_    				=							processor(lowerCAmelCase__			,    return_tensors="pt"   ).pixel_values

						with torch.no_grad():
												UpperCAmelCase_    				=							model(lowerCAmelCase__   )
												UpperCAmelCase_    				=							outputs.logits

						print(logits.shape   )
						print("First values of logits:"			,    logits[0, 0, :3, :3]   )
						# assert values
						if model_name == "upernet-swin-tiny":
												UpperCAmelCase_    				=							torch.tensor(
												    [[-7.5958, -7.5958, -7.4302], [-7.5958, -7.5958, -7.4302], [-7.4797, -7.4797, -7.3068]]   )
						elif model_name == "upernet-swin-small":
												UpperCAmelCase_    				=							torch.tensor(
												    [[-7.1921, -7.1921, -6.9532], [-7.1921, -7.1921, -6.9532], [-7.0908, -7.0908, -6.8534]]   )
						elif model_name == "upernet-swin-base":
												UpperCAmelCase_    				=							torch.tensor(
												    [[-6.5851, -6.5851, -6.4330], [-6.5851, -6.5851, -6.4330], [-6.4763, -6.4763, -6.3254]]   )
						elif model_name == "upernet-swin-large":
												UpperCAmelCase_    				=							torch.tensor(
												    [[-7.5297, -7.5297, -7.3802], [-7.5297, -7.5297, -7.3802], [-7.4044, -7.4044, -7.2586]]   )
						print("Logits:"			,    outputs.logits[0, 0, :3, :3]   )
						assert torch.allclose(outputs.logits[0, 0, :3, :3]			,    lowerCAmelCase__			,    atol=1e-4   )
						print("Looks ok!"   )

						if pytorch_dump_folder_path is not None:
												print(f"""Saving model {model_name} to {pytorch_dump_folder_path}"""   )
												model.save_pretrained(lowerCAmelCase__   )
												print(f"""Saving processor to {pytorch_dump_folder_path}"""   )
												processor.save_pretrained(lowerCAmelCase__   )

						if push_to_hub:
												print(f"""Pushing model and processor for {model_name} to hub"""   )
												model.push_to_hub(f"""openmmlab/{model_name}"""   )
												processor.push_to_hub(f"""openmmlab/{model_name}"""   )


if __name__ == "__main__":
		lowerCamelCase      =		argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    """--model_name""",
		    default="""upernet-swin-tiny""",
		    type=str,
		    choices=[F"upernet-swin-{size}" for size in ["""tiny""", """small""", """base""", """large"""]],
		    help="""Name of the Swin + UperNet model you'd like to convert.""",
		)
		parser.add_argument(
		    """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
		)
		parser.add_argument(
		    """--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
		)

		lowerCamelCase      =		parser.parse_args()
		convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)









"""simple docstring"""





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


lowerCamelCase      =		logging.get_logger(__name__)

lowerCamelCase      =		{
    """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    lowercase__     (					SCREAMING_SNAKE_CASE   ):







       '''simple docstring'''


       UpperCamelCase		=  '''owlvit_text_model'''



       def __init__(							self    :						List[Any]	,       _UpperCAmelCase    :						str=49408	,       _UpperCAmelCase    :						str=512	,       _UpperCAmelCase    :						Optional[Any]=2048	,       _UpperCAmelCase    :						Optional[int]=12	,       _UpperCAmelCase    :						Tuple=8	,       _UpperCAmelCase    :						List[str]=16	,       _UpperCAmelCase    :						List[str]="quick_gelu"	,       _UpperCAmelCase    :						Dict=1e-5	,       _UpperCAmelCase    :						Union[str, Any]=0.0	,       _UpperCAmelCase    :						List[Any]=0.02	,       _UpperCAmelCase    :						Optional[int]=1.0	,       _UpperCAmelCase    :						Dict=0	,       _UpperCAmelCase    :						Dict=49406	,       _UpperCAmelCase    :						Union[str, Any]=49407	,       **_UpperCAmelCase    :						List[str]	,       )				->		List[str]:


             '''simple docstring'''




             super().__init__(pad_token_id=_UpperCAmelCase	,       bos_token_id=_UpperCAmelCase	,       eos_token_id=_UpperCAmelCase	,       **_UpperCAmelCase					)

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







       @classmethod
       def   lowercase__       (							cls    :						int	,       _UpperCAmelCase    :						Union[str, os.PathLike]	,       **_UpperCAmelCase    :						List[str]					)				->		"PretrainedConfig":


             '''simple docstring'''




             cls._set_token_in_kwargs(_UpperCAmelCase					)

             UpperCAmelCase_				,					UpperCAmelCase_    				=							cls.get_config_dict(_UpperCAmelCase	,       **_UpperCAmelCase					)

             # get the text config dict if we are loading from OwlViTConfig
             if config_dict.get("model_type"					) == "owlvit":
                   UpperCAmelCase_    				=							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(_UpperCAmelCase	,       **_UpperCAmelCase					)



class    lowercase__     (					SCREAMING_SNAKE_CASE   ):







       '''simple docstring'''


       UpperCamelCase		=  '''owlvit_vision_model'''



       def __init__(							self    :						str	,       _UpperCAmelCase    :						List[str]=768	,       _UpperCAmelCase    :						Optional[Any]=3072	,       _UpperCAmelCase    :						Tuple=12	,       _UpperCAmelCase    :						Optional[Any]=12	,       _UpperCAmelCase    :						List[Any]=3	,       _UpperCAmelCase    :						List[str]=768	,       _UpperCAmelCase    :						int=32	,       _UpperCAmelCase    :						Dict="quick_gelu"	,       _UpperCAmelCase    :						Optional[Any]=1e-5	,       _UpperCAmelCase    :						Optional[int]=0.0	,       _UpperCAmelCase    :						List[Any]=0.02	,       _UpperCAmelCase    :						List[str]=1.0	,       **_UpperCAmelCase    :						List[str]	,       )				->		Dict:


             '''simple docstring'''




             super().__init__(**_UpperCAmelCase					)

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







       @classmethod
       def   lowercase__       (							cls    :						Any	,       _UpperCAmelCase    :						Union[str, os.PathLike]	,       **_UpperCAmelCase    :						Union[str, Any]					)				->		"PretrainedConfig":


             '''simple docstring'''




             cls._set_token_in_kwargs(_UpperCAmelCase					)

             UpperCAmelCase_				,					UpperCAmelCase_    				=							cls.get_config_dict(_UpperCAmelCase	,       **_UpperCAmelCase					)

             # get the vision config dict if we are loading from OwlViTConfig
             if config_dict.get("model_type"					) == "owlvit":
                   UpperCAmelCase_    				=							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(_UpperCAmelCase	,       **_UpperCAmelCase					)



class    lowercase__     (					SCREAMING_SNAKE_CASE   ):







       '''simple docstring'''


       UpperCamelCase		=  '''owlvit'''
       UpperCamelCase		=  True



       def __init__(							self    :						Tuple	,       _UpperCAmelCase    :						Any=None	,       _UpperCAmelCase    :						Dict=None	,       _UpperCAmelCase    :						Tuple=512	,       _UpperCAmelCase    :						Any=2.6592	,       _UpperCAmelCase    :						Union[str, Any]=True	,       **_UpperCAmelCase    :						Union[str, Any]	,       )				->		Tuple:


             '''simple docstring'''




             super().__init__(**_UpperCAmelCase					)

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

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

             UpperCAmelCase_    				=							OwlViTTextConfig(**_UpperCAmelCase					)
             UpperCAmelCase_    				=							OwlViTVisionConfig(**_UpperCAmelCase					)

             UpperCAmelCase_    				=							projection_dim
             UpperCAmelCase_    				=							logit_scale_init_value
             UpperCAmelCase_    				=							return_dict
             UpperCAmelCase_    				=							1.0



       @classmethod
       def   lowercase__       (							cls    :						Dict	,       _UpperCAmelCase    :						Union[str, os.PathLike]	,       **_UpperCAmelCase    :						Tuple					)				->		"PretrainedConfig":


             '''simple docstring'''




             cls._set_token_in_kwargs(_UpperCAmelCase					)

             UpperCAmelCase_				,					UpperCAmelCase_    				=							cls.get_config_dict(_UpperCAmelCase	,       **_UpperCAmelCase					)

             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(_UpperCAmelCase	,       **_UpperCAmelCase					)



       @classmethod
       def   lowercase__       (							cls    :						List[str]	,       _UpperCAmelCase    :						Dict	,       _UpperCAmelCase    :						Dict	,       **_UpperCAmelCase    :						Any					)				->		Union[str, Any]:


             '''simple docstring'''




             UpperCAmelCase_    				=							{}
             UpperCAmelCase_    				=							text_config
             UpperCAmelCase_    				=							vision_config

             return cls.from_dict(_UpperCAmelCase	,       **_UpperCAmelCase					)







       def   lowercase__       (							self    :						str					)				->		List[str]:


             '''simple docstring'''




             UpperCAmelCase_    				=							copy.deepcopy(self.__dict__					)
             UpperCAmelCase_    				=							self.text_config.to_dict()
             UpperCAmelCase_    				=							self.vision_config.to_dict()
             UpperCAmelCase_    				=							self.__class__.model_type
             return output



class    lowercase__     (					SCREAMING_SNAKE_CASE   ):







       '''simple docstring'''


       @property
       def   lowercase__       (							self    :						Union[str, Any]					)				->		Mapping[str, Mapping[int, str]]:


             '''simple docstring'''




             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   lowercase__       (							self    :						Tuple					)				->		Mapping[str, Mapping[int, str]]:


             '''simple docstring'''




             return OrderedDict(
                 [
                     ("logits_per_image", {0: "batch"}),
                     ("logits_per_text", {0: "batch"}),
                     ("text_embeds", {0: "batch"}),
                     ("image_embeds", {0: "batch"}),
                 ]					)



       @property
       def   lowercase__       (							self    :						Any					)				->		float:


             '''simple docstring'''




             return 1e-4



       def   lowercase__       (							self    :						List[str]	,       _UpperCAmelCase    :						"ProcessorMixin"	,       _UpperCAmelCase    :						int = -1	,       _UpperCAmelCase    :						int = -1	,       _UpperCAmelCase    :						Optional["TensorType"] = None	,       )				->		Mapping[str, Any]:


             '''simple docstring'''




             UpperCAmelCase_    				=							super().generate_dummy_inputs(
                 processor.tokenizer	,       batch_size=_UpperCAmelCase	,       seq_length=_UpperCAmelCase	,       framework=_UpperCAmelCase					)
             UpperCAmelCase_    				=							super().generate_dummy_inputs(
                 processor.image_processor	,       batch_size=_UpperCAmelCase	,       framework=_UpperCAmelCase					)
             return {**text_input_dict, **image_input_dict}







       @property
       def   lowercase__       (							self    :						Dict					)				->		int:


             '''simple docstring'''




             return 14




