"""simple docstring"""




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

import numpy as np

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


if is_torch_available():
			import torch

if is_vision_available():
			import PIL


__lowerCAmelCase    :							str													=						logging.get_logger(__name__)







def 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__     ):





		"""simple docstring"""




		def constraint_to_multiple_of(lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__=0			,  lowerCamelCase__=None     ):
				A        =   round(val / multiple     ) * multiple

				if max_val is not None and x > max_val:
						A        =   math.floor(val / multiple     ) * multiple

				if x < min_val:
						A        =   math.ceil(val / multiple     ) * multiple

				return x

		A        =   (output_size, output_size) if isinstance(lowerCamelCase__			,  lowerCamelCase__     ) else output_size

		A   ,					A        =   get_image_size(lowerCamelCase__     )
		A   ,					A        =   output_size

		# determine new height and width
		A        =   output_height / input_height
		A        =   output_width / input_width

		if keep_aspect_ratio:
				# scale as little as possible
				if abs(1 - scale_width     ) < abs(1 - scale_height     ):
						# fit width
						A        =   scale_width
				else:
						# fit height
						A        =   scale_height

		A        =   constraint_to_multiple_of(scale_height * input_height			,  multiple=lowerCamelCase__     )
		A        =   constraint_to_multiple_of(scale_width * input_width			,  multiple=lowerCamelCase__     )

		return (new_height, new_width)






class       a_      (     __UpperCamelCase			):
					UpperCamelCase_ :	Dict			=    ["pixel_values"]




					def __init__(	self						:     Tuple  ,						snake_case__						:     bool = True  ,						snake_case__						:     Dict[str, int] = None  ,						snake_case__						:     PILImageResampling = PILImageResampling.BILINEAR  ,						snake_case__						:     bool = False  ,						snake_case__						:     int = 1  ,						snake_case__						:     bool = True  ,						snake_case__						:     Union[int, float] = 1 / 255  ,						snake_case__						:     bool = True  ,						snake_case__						:     Optional[Union[float, List[float]]] = None  ,						snake_case__						:     Optional[Union[float, List[float]]] = None  ,						**snake_case__						:     Dict  ,						):
							super().__init__(**snake_case__     )
							A        =   size if size is not None else {"""height""": 384, """width""": 384}
							A        =   get_size_dict(snake_case__     )
							A        =   do_resize
							A        =   size
							A        =   keep_aspect_ratio
							A        =   ensure_multiple_of
							A        =   resample
							A        =   do_rescale
							A        =   rescale_factor
							A        =   do_normalize
							A        =   image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
							A        =   image_std if image_std is not None else IMAGENET_STANDARD_STD




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[str]  ,						snake_case__						:     np.ndarray  ,						snake_case__						:     Dict[str, int]  ,						snake_case__						:     bool = False  ,						snake_case__						:     int = 1  ,						snake_case__						:     PILImageResampling = PILImageResampling.BICUBIC  ,						snake_case__						:     Optional[Union[str, ChannelDimension]] = None  ,						**snake_case__						:     Optional[int]  ,						):
							A        =   get_size_dict(snake_case__     )
							if "height" not in size or "width" not in size:
									raise ValueError(F"""The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}"""     )
							A        =   get_resize_output_image_size(
							    snake_case__  ,						output_size=(size["""height"""], size["""width"""])  ,						keep_aspect_ratio=snake_case__  ,						multiple=snake_case__  ,						)
							return resize(snake_case__  ,						size=snake_case__  ,						resample=snake_case__  ,						data_format=snake_case__  ,						**snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Tuple  ,						snake_case__						:     np.ndarray  ,						snake_case__						:     Union[int, float]  ,						snake_case__						:     Optional[Union[str, ChannelDimension]] = None  ,						**snake_case__						:     List[str]  ,						):
							return rescale(snake_case__  ,						scale=snake_case__  ,						data_format=snake_case__  ,						**snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     str  ,						snake_case__						:     np.ndarray  ,						snake_case__						:     Union[float, List[float]]  ,						snake_case__						:     Union[float, List[float]]  ,						snake_case__						:     Optional[Union[str, ChannelDimension]] = None  ,						**snake_case__						:     str  ,						):
							return normalize(snake_case__  ,						mean=snake_case__  ,						std=snake_case__  ,						data_format=snake_case__  ,						**snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[Any]  ,						snake_case__						:     ImageInput  ,						snake_case__						:     bool = None  ,						snake_case__						:     int = None  ,						snake_case__						:     bool = None  ,						snake_case__						:     int = None  ,						snake_case__						:     PILImageResampling = None  ,						snake_case__						:     bool = None  ,						snake_case__						:     float = None  ,						snake_case__						:     bool = None  ,						snake_case__						:     Optional[Union[float, List[float]]] = None  ,						snake_case__						:     Optional[Union[float, List[float]]] = None  ,						snake_case__						:     Optional[Union[str, TensorType]] = None  ,						snake_case__						:     ChannelDimension = ChannelDimension.FIRST  ,						**snake_case__						:     str  ,						):
							A        =   do_resize if do_resize is not None else self.do_resize
							A        =   size if size is not None else self.size
							A        =   get_size_dict(snake_case__     )
							A        =   keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
							A        =   ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
							A        =   resample if resample is not None else self.resample
							A        =   do_rescale if do_rescale is not None else self.do_rescale
							A        =   rescale_factor if rescale_factor is not None else self.rescale_factor
							A        =   do_normalize if do_normalize is not None else self.do_normalize
							A        =   image_mean if image_mean is not None else self.image_mean
							A        =   image_std if image_std is not None else self.image_std

							A        =   make_list_of_images(snake_case__     )

							if not valid_images(snake_case__     ):
									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 or resample is None:
									raise ValueError("""Size and resample must be specified if do_resize 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.
							A        =   [to_numpy_array(snake_case__     ) for image in images]

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

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

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

							A        =   [to_channel_dimension_format(snake_case__  ,						snake_case__     ) for image in images]

							A        =   {"""pixel_values""": images}
							return BatchFeature(data=snake_case__  ,						tensor_type=snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[str]  ,						snake_case__						:     int  ,						snake_case__						:     List[Tuple] = None     ):
							A        =   outputs.logits

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

									if is_torch_tensor(snake_case__     ):
											A        =   target_sizes.numpy()

									A        =   []

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

							return semantic_segmentation





"""simple docstring"""




import argparse

import torch

from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging


logging.set_verbosity_info()







def 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__     ):





		"""simple docstring"""




		lowerCAmelCase__        =   MobileBertConfig.from_json_file(lowerCamelCase__     )
		print(f"""Building PyTorch model from configuration: {config}"""     )
		lowerCAmelCase__        =   MobileBertForPreTraining(lowerCamelCase__     )
		# Load weights from tf checkpoint
		lowerCAmelCase__        =   load_tf_weights_in_mobilebert(lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__     )
		# Save pytorch-model
		print(f"""Save PyTorch model to {pytorch_dump_path}"""     )
		torch.save(model.state_dict()			,  lowerCamelCase__     )


if __name__ == "__main__":
			__lowerCAmelCase    :							List[str]													=						argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
			)
			parser.add_argument(
			    "--mobilebert_config_file",
			    default=None,
			    type=str,
			    required=True,
			    help=(
			        "The config json file corresponding to the pre-trained MobileBERT model. \n"
			        "This specifies the model architecture."
			    ),
			)
			parser.add_argument(
			    "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
			)
			__lowerCAmelCase    :							Optional[int]													=						parser.parse_args()
			convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)




