




from typing import Dict, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, logging


if is_torch_available():
					import torch


UpperCamelCase__         =      logging.get_logger(__name__)
class       a__   ( snake_case__				):
						_a							:   List[str] =   ["""pixel_values"""]






						def __init__(       self		,      _A = True		,      _A = None		,      _A = PILImageResampling.BILINEAR		,      _A = True		,      _A = None		,      _A = True		,      _A = 1 / 2_5_5		,      _A = True		,      _A = None		,      _A = None		,      **_A		,      ):




											"""simple docstring"""



											super().__init__(**_A      )
											__lowerCAmelCase           =	size if size is not None else {"shortest_edge": 2_5_6}
											__lowerCAmelCase           =	get_size_dict(_A		,      default_to_square=_A      )
											__lowerCAmelCase           =	crop_size if crop_size is not None else {"height": 2_2_4, "width": 2_2_4}
											__lowerCAmelCase           =	get_size_dict(_A		,      param_name="crop_size"      )
											__lowerCAmelCase           =	do_resize
											__lowerCAmelCase           =	size
											__lowerCAmelCase           =	resample
											__lowerCAmelCase           =	do_center_crop
											__lowerCAmelCase           =	crop_size
											__lowerCAmelCase           =	do_rescale
											__lowerCAmelCase           =	rescale_factor
											__lowerCAmelCase           =	do_normalize
											__lowerCAmelCase           =	image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
											__lowerCAmelCase           =	image_std if image_std is not None else IMAGENET_STANDARD_STD






						def     __SCREAMING_SNAKE_CASE(       self		,      _A		,      _A		,      _A = PILImageResampling.BICUBIC		,      _A = None		,      **_A		,      ):




											"""simple docstring"""



											__lowerCAmelCase           =	get_size_dict(_A		,      default_to_square=_A      )
											if "shortest_edge" not in size:
																raise ValueError(f"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""      )
											__lowerCAmelCase           =	get_resize_output_image_size(_A		,      size=size["shortest_edge"]		,      default_to_square=_A      )
											return resize(_A		,      size=_A		,      resample=_A		,      data_format=_A		,      **_A      )






						def     __SCREAMING_SNAKE_CASE(       self		,      _A		,      _A		,      _A = None		,      **_A		,      ):




											"""simple docstring"""



											__lowerCAmelCase           =	get_size_dict(_A      )
											if "height" not in size or "width" not in size:
																raise ValueError(f"""The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}"""      )
											return center_crop(_A		,      size=(size["height"], size["width"])		,      data_format=_A		,      **_A      )






						def     __SCREAMING_SNAKE_CASE(       self		,      _A		,      _A		,      _A = None		,      **_A      ):




											"""simple docstring"""



											return rescale(_A		,      scale=_A		,      data_format=_A		,      **_A      )






						def     __SCREAMING_SNAKE_CASE(       self		,      _A		,      _A		,      _A		,      _A = None		,      **_A		,      ):




											"""simple docstring"""



											return normalize(_A		,      mean=_A		,      std=_A		,      data_format=_A		,      **_A      )






						def     __SCREAMING_SNAKE_CASE(       self		,      _A		,      _A = None		,      _A = None		,      _A = None		,      _A = None		,      _A = None		,      _A = None		,      _A = None		,      _A = None		,      _A = None		,      _A = None		,      _A = None		,      _A = ChannelDimension.FIRST		,      **_A		,      ):




											"""simple docstring"""



											__lowerCAmelCase           =	do_resize if do_resize is not None else self.do_resize
											__lowerCAmelCase           =	size if size is not None else self.size
											__lowerCAmelCase           =	get_size_dict(_A		,      default_to_square=_A      )
											__lowerCAmelCase           =	resample if resample is not None else self.resample
											__lowerCAmelCase           =	do_center_crop if do_center_crop is not None else self.do_center_crop
											__lowerCAmelCase           =	crop_size if crop_size is not None else self.crop_size
											__lowerCAmelCase           =	get_size_dict(_A		,      param_name="crop_size"      )
											__lowerCAmelCase           =	do_rescale if do_rescale is not None else self.do_rescale
											__lowerCAmelCase           =	rescale_factor if rescale_factor is not None else self.rescale_factor
											__lowerCAmelCase           =	do_normalize if do_normalize is not None else self.do_normalize
											__lowerCAmelCase           =	image_mean if image_mean is not None else self.image_mean
											__lowerCAmelCase           =	image_std if image_std is not None else self.image_std

											__lowerCAmelCase           =	make_list_of_images(_A      )

											if not valid_images(_A      ):
																raise ValueError(
																    "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
																    "torch.Tensor, tf.Tensor or jax.ndarray."      )

											if do_resize and size is None:
																raise ValueError("Size must be specified if do_resize is True."      )

											if do_center_crop and crop_size is None:
																raise ValueError("Crop size must be specified if do_center_crop is True."      )

											if do_rescale and rescale_factor is None:
																raise ValueError("Rescale factor must be specified if do_rescale is True."      )

											if do_normalize and (image_mean is None or image_std is None):
																raise ValueError("Image mean and std must be specified if do_normalize is True."      )

											# All transformations expect numpy arrays.
											__lowerCAmelCase           =	[to_numpy_array(_A      ) for image in images]

											if do_resize:
																__lowerCAmelCase           =	[self.resize(image=_A		,      size=_A		,      resample=_A      ) for image in images]

											if do_center_crop:
																__lowerCAmelCase           =	[self.center_crop(image=_A		,      size=_A      ) for image in images]

											if do_rescale:
																__lowerCAmelCase           =	[self.rescale(image=_A		,      scale=_A      ) for image in images]

											if do_normalize:
																__lowerCAmelCase           =	[self.normalize(image=_A		,      mean=_A		,      std=_A      ) for image in images]

											__lowerCAmelCase           =	[to_channel_dimension_format(_A		,      _A      ) for image in images]

											__lowerCAmelCase           =	{"pixel_values": images}
											return BatchFeature(data=_A		,      tensor_type=_A      )






						def     __SCREAMING_SNAKE_CASE(       self		,      _A		,      _A = None      ):




											"""simple docstring"""



											__lowerCAmelCase           =	outputs.logits

											# Resize logits and compute semantic segmentation maps
											if target_sizes is not None:
																if len(_A      ) != len(_A      ):
																					raise ValueError(
																					    "Make sure that you pass in as many target sizes as the batch dimension of the logits"      )

																if is_torch_tensor(_A      ):
																					__lowerCAmelCase           =	target_sizes.numpy()

																__lowerCAmelCase           =	[]

																for idx in range(len(_A      )      ):
																					__lowerCAmelCase           =	torch.nn.functional.interpolate(
																					    logits[idx].unsqueeze(dim=0      )		,      size=target_sizes[idx]		,      mode="bilinear"		,      align_corners=_A      )
																					__lowerCAmelCase           =	resized_logits[0].argmax(dim=0      )
																					semantic_segmentation.append(_A      )
											else:
																__lowerCAmelCase           =	logits.argmax(dim=1      )
																__lowerCAmelCase           =	[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]      )]

											return semantic_segmentation










import argparse
import os

import torch

from transformers import FlavaConfig, FlavaForPreTraining
from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint







def      _a				(						SCREAMING_SNAKE_CASE_							:    Any					):
					# encoder.embeddings are double copied in original FLAVA
					return sum(param.float().sum() if "encoder.embeddings" not in key else 0 for key, param in state_dict.items()					)







def      _a				(						SCREAMING_SNAKE_CASE_							:    Dict				,				SCREAMING_SNAKE_CASE_							:    Dict					):
					__lowerCAmelCase           =	{}

					for key, value in state_dict.items():
										if "text_encoder.embeddings" in key or "image_encoder.embeddings" in key:
															continue

										__lowerCAmelCase           =	key.replace("heads.cmd.mim_head.cls.predictions"				,				"mmm_image_head"					)
										__lowerCAmelCase           =	key.replace("heads.cmd.mlm_head.cls.predictions"				,				"mmm_text_head"					)
										__lowerCAmelCase           =	key.replace("heads.cmd.itm_head.cls"				,				"itm_head"					)
										__lowerCAmelCase           =	key.replace("heads.cmd.itm_head.pooler"				,				"itm_head.pooler"					)
										__lowerCAmelCase           =	key.replace("heads.cmd.clip_head.logit_scale"				,				"flava.logit_scale"					)
										__lowerCAmelCase           =	key.replace("heads.fairseq_mlm.cls.predictions"				,				"mlm_head"					)
										__lowerCAmelCase           =	key.replace("heads.imagenet.mim_head.cls.predictions"				,				"mim_head"					)
										__lowerCAmelCase           =	key.replace("mm_text_projection"				,				"flava.text_to_mm_projection"					)
										__lowerCAmelCase           =	key.replace("mm_image_projection"				,				"flava.image_to_mm_projection"					)
										__lowerCAmelCase           =	key.replace("image_encoder.module"				,				"flava.image_model"					)
										__lowerCAmelCase           =	key.replace("text_encoder.module"				,				"flava.text_model"					)
										__lowerCAmelCase           =	key.replace("mm_encoder.module.encoder.cls_token"				,				"flava.multimodal_model.cls_token"					)
										__lowerCAmelCase           =	key.replace("mm_encoder.module"				,				"flava.multimodal_model"					)
										__lowerCAmelCase           =	key.replace("text_projection"				,				"flava.text_projection"					)
										__lowerCAmelCase           =	key.replace("image_projection"				,				"flava.image_projection"					)

										__lowerCAmelCase           =	value.float()

					for key, value in codebook_state_dict.items():
										__lowerCAmelCase           =	value

					return upgrade







@torch.no_grad()
def      _a				(						SCREAMING_SNAKE_CASE_							:    List[Any]				,				SCREAMING_SNAKE_CASE_							:    Optional[int]				,				SCREAMING_SNAKE_CASE_							:    List[str]				,				SCREAMING_SNAKE_CASE_							:    int=None					):
					if config_path is not None:
										__lowerCAmelCase           =	FlavaConfig.from_pretrained(SCREAMING_SNAKE_CASE_					)
					else:
										__lowerCAmelCase           =	FlavaConfig()

					__lowerCAmelCase           =	FlavaForPreTraining(SCREAMING_SNAKE_CASE_					).eval()

					__lowerCAmelCase           =	convert_dalle_checkpoint(SCREAMING_SNAKE_CASE_				,				SCREAMING_SNAKE_CASE_				,				save_checkpoint=SCREAMING_SNAKE_CASE_					)

					if os.path.exists(SCREAMING_SNAKE_CASE_					):
										__lowerCAmelCase           =	torch.load(SCREAMING_SNAKE_CASE_				,				map_location="cpu"					)
					else:
										__lowerCAmelCase           =	torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE_				,				map_location="cpu"					)

					__lowerCAmelCase           =	upgrade_state_dict(SCREAMING_SNAKE_CASE_				,				SCREAMING_SNAKE_CASE_					)
					hf_model.load_state_dict(SCREAMING_SNAKE_CASE_					)
					__lowerCAmelCase           =	hf_model.state_dict()
					__lowerCAmelCase           =	count_parameters(SCREAMING_SNAKE_CASE_					)
					__lowerCAmelCase           =	count_parameters(SCREAMING_SNAKE_CASE_					) + count_parameters(SCREAMING_SNAKE_CASE_					)

					assert torch.allclose(SCREAMING_SNAKE_CASE_				,				SCREAMING_SNAKE_CASE_				,				atol=1E-3					)

					hf_model.save_pretrained(SCREAMING_SNAKE_CASE_					)


if __name__ == "__main__":
					UpperCamelCase__         =      argparse.ArgumentParser()
					parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
					parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to flava checkpoint""")
					parser.add_argument("""--codebook_path""", default=None, type=str, help="""Path to flava codebook checkpoint""")
					parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
					UpperCamelCase__         =      parser.parse_args()

					convert_flava_checkpoint(args.checkpoint_path, args.codebook_path, args.pytorch_dump_folder_path, args.config_path)




