
from typing import Dict, List, Optional, 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_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


UpperCAmelCase	      = logging.get_logger(__name__)







class        A_   (			a_   ):




	'''simple docstring'''


	_UpperCamelCase     :     Union[str, Any]							 =       ["""pixel_values"""]





	def __init__(       self		,    snake_case = True		,    snake_case = None		,    snake_case = PILImageResampling.BICUBIC		,    snake_case = True		,    snake_case = True		,    snake_case = 1 / 255		,    snake_case = None		,    snake_case = True		,    snake_case = None		,    snake_case = None		,    **snake_case		,    ):
		super().__init__(**_A       )
		lowercase       =    size if size is not None else {'height': 224, 'width': 224}
		lowercase       =    get_size_dict(_A       )
		lowercase       =    crop_size if crop_size is not None else {'height': 224, 'width': 224}
		lowercase       =    get_size_dict(_A		,    default_to_square=_A		,    param_name='crop_size'       )

		lowercase       =    do_resize
		lowercase       =    do_rescale
		lowercase       =    do_normalize
		lowercase       =    do_center_crop
		lowercase       =    crop_size
		lowercase       =    size
		lowercase       =    resample
		lowercase       =    rescale_factor
		lowercase       =    image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
		lowercase       =    image_std if image_std is not None else IMAGENET_DEFAULT_STD





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case		,    snake_case = PILImageResampling.BILINEAR		,    snake_case = None		,    **snake_case		,    ):
		lowercase       =    get_size_dict(_A       )

		if "shortest_edge" in size:
			lowercase       =    get_resize_output_image_size(_A		,    size=size['shortest_edge']		,    default_to_square=_A       )
			# size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
		elif "height" in size and "width" in size:
			lowercase       =    (size['height'], size['width'])
		else:
			raise ValueError(F'''Size must contain \'height\' and \'width\' keys or \'shortest_edge\' key. Got {size.keys()}'''       )
		return resize(_A		,    size=_A		,    resample=_A		,    data_format=_A		,    **_A       )





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case		,    snake_case = None		,    **snake_case		,    ):
		lowercase       =    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, width). Got {size.keys()}'''       )
		return center_crop(_A		,    size=(size['height'], size['width'])		,    data_format=_A		,    **_A       )





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case		,    snake_case = None		,    **snake_case       ):
		return rescale(_A		,    scale=_A		,    data_format=_A		,    **_A       )





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case		,    snake_case		,    snake_case = None		,    **snake_case		,    ):
		return normalize(_A		,    mean=_A		,    std=_A		,    data_format=_A		,    **_A       )





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case = None		,    snake_case = None		,    snake_case = None		,    snake_case = None		,    snake_case = None		,    snake_case = None		,    snake_case = None		,    snake_case = None		,    snake_case = None		,    snake_case = None		,    snake_case = None		,    snake_case = ChannelDimension.FIRST		,    **snake_case		,    ):
		lowercase       =    do_resize if do_resize is not None else self.do_resize
		lowercase       =    do_rescale if do_rescale is not None else self.do_rescale
		lowercase       =    do_normalize if do_normalize is not None else self.do_normalize
		lowercase       =    do_center_crop if do_center_crop is not None else self.do_center_crop
		lowercase       =    crop_size if crop_size is not None else self.crop_size
		lowercase       =    get_size_dict(_A		,    param_name='crop_size'		,    default_to_square=_A       )
		lowercase       =    resample if resample is not None else self.resample
		lowercase       =    rescale_factor if rescale_factor is not None else self.rescale_factor
		lowercase       =    image_mean if image_mean is not None else self.image_mean
		lowercase       =    image_std if image_std is not None else self.image_std

		lowercase       =    size if size is not None else self.size
		lowercase       =    get_size_dict(_A       )

		if not is_batched(_A       ):
			lowercase       =    [images]

		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.'       )

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

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

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

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

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

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

		lowercase       =    {'pixel_values': images}
		return BatchFeature(data=_A		,    tensor_type=_A       )








import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM





def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__ )  ->				Union[str, Any]:







			'''simple docstring'''

			if "model" in orig_key:
						UpperCAmelCase   		=				orig_key.replace('''model.'''			,			'''''' )
			if "norm1" in orig_key:
						UpperCAmelCase   		=				orig_key.replace('''norm1'''			,			'''attention.output.LayerNorm''' )
			if "norm2" in orig_key:
						UpperCAmelCase   		=				orig_key.replace('''norm2'''			,			'''output.LayerNorm''' )
			if "norm" in orig_key:
						UpperCAmelCase   		=				orig_key.replace('''norm'''			,			'''LayerNorm''' )
			if "transformer" in orig_key:
						UpperCAmelCase   		=				orig_key.split('''.''' )[0].split('''_''' )[-1]
						UpperCAmelCase   		=				orig_key.replace(F"""transformer_{layer_num}"""			,			F"""encoder.layer.{layer_num}""" )
			if "mha.attn" in orig_key:
						UpperCAmelCase   		=				orig_key.replace('''mha.attn'''			,			'''attention.self''' )
			if "mha" in orig_key:
						UpperCAmelCase   		=				orig_key.replace('''mha'''			,			'''attention''' )
			if "W_q" in orig_key:
						UpperCAmelCase   		=				orig_key.replace('''W_q'''			,			'''self.query''' )
			if "W_k" in orig_key:
						UpperCAmelCase   		=				orig_key.replace('''W_k'''			,			'''self.key''' )
			if "W_v" in orig_key:
						UpperCAmelCase   		=				orig_key.replace('''W_v'''			,			'''self.value''' )
			if "ff1" in orig_key:
						UpperCAmelCase   		=				orig_key.replace('''ff1'''			,			'''intermediate.dense''' )
			if "ff2" in orig_key:
						UpperCAmelCase   		=				orig_key.replace('''ff2'''			,			'''output.dense''' )
			if "ff" in orig_key:
						UpperCAmelCase   		=				orig_key.replace('''ff'''			,			'''output.dense''' )
			if "mlm_class" in orig_key:
						UpperCAmelCase   		=				orig_key.replace('''mlm.mlm_class'''			,			'''cls.predictions.decoder''' )
			if "mlm" in orig_key:
						UpperCAmelCase   		=				orig_key.replace('''mlm'''			,			'''cls.predictions.transform''' )
			if "cls" not in orig_key:
						UpperCAmelCase   		=				'''yoso.''' + orig_key

			return orig_key





def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__			,			UpperCamelCase__ )  ->				Dict:







			'''simple docstring'''

			for key in orig_state_dict.copy().keys():
						UpperCAmelCase   		=				orig_state_dict.pop(UpperCamelCase__ )

						if ("pooler" in key) or ("sen_class" in key):
									continue
						else:
									UpperCAmelCase   		=				val

			UpperCAmelCase   		=				orig_state_dict['''cls.predictions.decoder.bias''']
			UpperCAmelCase   		=				torch.arange(UpperCamelCase__ ).expand((1, -1) ) + 2

			return orig_state_dict





def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__			,			UpperCamelCase__			,			UpperCamelCase__ )  ->				int:







			'''simple docstring'''

			UpperCAmelCase   		=				torch.load(UpperCamelCase__			,			map_location='''cpu''' )['''model_state_dict''']
			UpperCAmelCase   		=				YosoConfig.from_json_file(UpperCamelCase__ )
			UpperCAmelCase   		=				YosoForMaskedLM(UpperCamelCase__ )

			UpperCAmelCase   		=				convert_checkpoint_helper(config.max_position_embeddings			,			UpperCamelCase__ )

			print(model.load_state_dict(UpperCamelCase__ ) )
			model.eval()
			model.save_pretrained(UpperCamelCase__ )

			print(F"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}""" )


if __name__ == "__main__":
				__A				:    Any       =   argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    "--pytorch_model_path", default=None, type=str, required=True, help="Path to YOSO pytorch checkpoint."
				)
				parser.add_argument(
				    "--config_file",
				    default=None,
				    type=str,
				    required=True,
				    help="The json file for YOSO model config.",
				)
				parser.add_argument(
				    "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
				)
				__A				:    List[str]       =   parser.parse_args()
				convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)



