

"""simple docstring"""




import io
import math
from typing import Dict, Optional, Union

import numpy as np
from huggingface_hub import hf_hub_download

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import convert_to_rgb, normalize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    get_image_size,
    infer_channel_dimension_format,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_vision_available, logging
from ...utils.import_utils import requires_backends


if is_vision_available():
						import textwrap

						from PIL import Image, ImageDraw, ImageFont

if is_torch_available():
						import torch

						from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
						lowerCAmelCase__							=				False


lowerCAmelCase__							=				logging.get_logger(__name__)
lowerCAmelCase__							=				'ybelkada/fonts'



def        _lowerCamelCase ( ):







		if is_torch_available() and not is_torch_greater_or_equal_than_1_11:
				raise ImportError(
				    F'You are using torch=={torch.__version__}, but torch>=1.11.0 is required to use '
				    '''Pix2StructImageProcessor. Please upgrade torch.'''    )



def        _lowerCamelCase ( __a,							__a,							__a    ):
		requires_backends(lowercase__,							['''torch''']    )
		_check_torch_version()

		SCREAMING_SNAKE_CASE_						 = image_tensor.unsqueeze(0    )
		SCREAMING_SNAKE_CASE_						 = torch.nn.functional.unfold(lowercase__,							(patch_height, patch_width),							stride=(patch_height, patch_width)    )
		SCREAMING_SNAKE_CASE_						 = patches.reshape(image_tensor.size(0    ),							image_tensor.size(1    ),							lowercase__,							lowercase__,							-1    )
		SCREAMING_SNAKE_CASE_						 = patches.permute(0,							4,							2,							3,							1    ).reshape(
		    image_tensor.size(2    ) // patch_height,							image_tensor.size(3    ) // patch_width,							image_tensor.size(1    ) * patch_height * patch_width,							)
		return patches.unsqueeze(0    )



def        _lowerCamelCase ( __a,							__a = 36,							__a = "black",							__a = "white",							__a = 5,							__a = 5,							__a = 5,							__a = 5,							__a = None,							__a = None,							):
		requires_backends(lowercase__,							'''vision'''    )
		# Add new lines so that each line is no more than 80 characters.

		SCREAMING_SNAKE_CASE_						 = textwrap.TextWrapper(width=80    )
		SCREAMING_SNAKE_CASE_						 = wrapper.wrap(text=lowercase__    )
		SCREAMING_SNAKE_CASE_						 = '''\n'''.join(lowercase__    )

		if font_bytes is not None and font_path is None:
				SCREAMING_SNAKE_CASE_						 = io.BytesIO(lowercase__    )
		elif font_path is not None:
				SCREAMING_SNAKE_CASE_						 = font_path
		else:
				SCREAMING_SNAKE_CASE_						 = hf_hub_download(lowercase__,							'''Arial.TTF'''    )
		SCREAMING_SNAKE_CASE_						 = ImageFont.truetype(lowercase__,							encoding='''UTF-8''',							size=lowercase__    )

		# Use a temporary canvas to determine the width and height in pixels when
		# rendering the text.
		SCREAMING_SNAKE_CASE_						 = ImageDraw.Draw(Image.new('''RGB''',							(1, 1),							lowercase__    )    )
		SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = temp_draw.textbbox((0, 0),							lowercase__,							lowercase__    )

		# Create the actual image with a bit of padding around the text.
		SCREAMING_SNAKE_CASE_						 = text_width + left_padding + right_padding
		SCREAMING_SNAKE_CASE_						 = text_height + top_padding + bottom_padding
		SCREAMING_SNAKE_CASE_						 = Image.new('''RGB''',							(image_width, image_height),							lowercase__    )
		SCREAMING_SNAKE_CASE_						 = ImageDraw.Draw(lowercase__    )
		draw.text(xy=(left_padding, top_padding),							text=lowercase__,							fill=lowercase__,							font=lowercase__    )
		return image



def        _lowerCamelCase ( __a,							__a,							**__a    ):
		requires_backends(lowercase__,							'''vision'''    )

		# Convert to PIL image if necessary
		SCREAMING_SNAKE_CASE_						 = to_pil_image(lowercase__    )

		SCREAMING_SNAKE_CASE_						 = render_text(lowercase__,							**lowercase__    )
		SCREAMING_SNAKE_CASE_						 = max(header_image.width,							image.width    )

		SCREAMING_SNAKE_CASE_						 = int(image.height * (new_width / image.width)    )
		SCREAMING_SNAKE_CASE_						 = int(header_image.height * (new_width / header_image.width)    )

		SCREAMING_SNAKE_CASE_						 = Image.new('''RGB''',							(new_width, new_height + new_header_height),							'''white'''    )
		new_image.paste(header_image.resize((new_width, new_header_height)    ),							(0, 0)    )
		new_image.paste(image.resize((new_width, new_height)    ),							(0, new_header_height)    )

		# Convert back to the original framework if necessary
		SCREAMING_SNAKE_CASE_						 = to_numpy_array(lowercase__    )

		if infer_channel_dimension_format(lowercase__    ) == ChannelDimension.LAST:
				SCREAMING_SNAKE_CASE_						 = to_channel_dimension_format(lowercase__,							ChannelDimension.LAST    )

		return new_image





class  snake_case     (							__a   ):
							UpperCAmelCase__						     =      ["flattened_patches"]

							def __init__(self       ,       SCREAMING_SNAKE_CASE_ = True       ,       SCREAMING_SNAKE_CASE_ = True       ,       SCREAMING_SNAKE_CASE_ = None       ,       SCREAMING_SNAKE_CASE_ = 20_48       ,       SCREAMING_SNAKE_CASE_ = False       ,       **SCREAMING_SNAKE_CASE_       ,       ):

									"""simple docstring"""







									super().__init__(**lowerCAmelCase_    )
									SCREAMING_SNAKE_CASE_						 = patch_size if patch_size is not None else {'''height''': 16, '''width''': 16}
									SCREAMING_SNAKE_CASE_						 = do_normalize
									SCREAMING_SNAKE_CASE_						 = do_convert_rgb
									SCREAMING_SNAKE_CASE_						 = max_patches
									SCREAMING_SNAKE_CASE_						 = is_vqa

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       **SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									requires_backends(self.extract_flattened_patches       ,       '''torch'''    )
									_check_torch_version()

									# convert to torch
									SCREAMING_SNAKE_CASE_						 = to_channel_dimension_format(lowerCAmelCase_       ,       ChannelDimension.FIRST    )
									SCREAMING_SNAKE_CASE_						 = torch.from_numpy(lowerCAmelCase_    )

									SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = patch_size['''height'''], patch_size['''width''']
									SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = get_image_size(lowerCAmelCase_    )

									# maximize scale s.t.
									SCREAMING_SNAKE_CASE_						 = math.sqrt(max_patches * (patch_height / image_height) * (patch_width / image_width)    )
									SCREAMING_SNAKE_CASE_						 = max(min(math.floor(scale * image_height / patch_height    )       ,       lowerCAmelCase_    )       ,       1    )
									SCREAMING_SNAKE_CASE_						 = max(min(math.floor(scale * image_width / patch_width    )       ,       lowerCAmelCase_    )       ,       1    )
									SCREAMING_SNAKE_CASE_						 = max(num_feasible_rows * patch_height       ,       1    )
									SCREAMING_SNAKE_CASE_						 = max(num_feasible_cols * patch_width       ,       1    )

									SCREAMING_SNAKE_CASE_						 = torch.nn.functional.interpolate(
									    image.unsqueeze(0    )       ,       size=(resized_height, resized_width)       ,       mode='''bilinear'''       ,       align_corners=lowerCAmelCase_       ,       antialias=lowerCAmelCase_       ,       ).squeeze(0    )

									# [1, rows, columns, patch_height * patch_width * image_channels]
									SCREAMING_SNAKE_CASE_						 = torch_extract_patches(lowerCAmelCase_       ,       lowerCAmelCase_       ,       lowerCAmelCase_    )

									SCREAMING_SNAKE_CASE_						 = patches.shape
									SCREAMING_SNAKE_CASE_						 = patches_shape[1]
									SCREAMING_SNAKE_CASE_						 = patches_shape[2]
									SCREAMING_SNAKE_CASE_						 = patches_shape[3]

									# [rows * columns, patch_height * patch_width * image_channels]
									SCREAMING_SNAKE_CASE_						 = patches.reshape([rows * columns, depth]    )

									# [rows * columns, 1]
									SCREAMING_SNAKE_CASE_						 = torch.arange(lowerCAmelCase_    ).reshape([rows, 1]    ).repeat(1       ,       lowerCAmelCase_    ).reshape([rows * columns, 1]    )
									SCREAMING_SNAKE_CASE_						 = torch.arange(lowerCAmelCase_    ).reshape([1, columns]    ).repeat(lowerCAmelCase_       ,       1    ).reshape([rows * columns, 1]    )

									# Offset by 1 so the ids do not contain zeros, which represent padding.
									row_ids += 1
									col_ids += 1

									# Prepare additional patch features.
									# [rows * columns, 1]
									SCREAMING_SNAKE_CASE_						 = row_ids.to(torch.floataa    )
									SCREAMING_SNAKE_CASE_						 = col_ids.to(torch.floataa    )

									# [rows * columns, 2 + patch_height * patch_width * image_channels]
									SCREAMING_SNAKE_CASE_						 = torch.cat([row_ids, col_ids, patches]       ,       -1    )

									# [max_patches, 2 + patch_height * patch_width * image_channels]
									SCREAMING_SNAKE_CASE_						 = torch.nn.functional.pad(lowerCAmelCase_       ,       [0, 0, 0, max_patches - (rows * columns)]    ).float()

									SCREAMING_SNAKE_CASE_						 = to_numpy_array(lowerCAmelCase_    )

									return result

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_ = None       ,       **SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									if image.dtype == np.uinta:
											SCREAMING_SNAKE_CASE_						 = image.astype(np.floataa    )

									# take mean across the whole `image`
									SCREAMING_SNAKE_CASE_						 = np.mean(lowerCAmelCase_    )
									SCREAMING_SNAKE_CASE_						 = np.std(lowerCAmelCase_    )
									SCREAMING_SNAKE_CASE_						 = max(lowerCAmelCase_       ,       1.0 / math.sqrt(np.prod(image.shape    )    )    )

									return normalize(lowerCAmelCase_       ,       mean=lowerCAmelCase_       ,       std=lowerCAmelCase_       ,       **lowerCAmelCase_    )







							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_ = None       ,       SCREAMING_SNAKE_CASE_ = None       ,       SCREAMING_SNAKE_CASE_ = None       ,       SCREAMING_SNAKE_CASE_ = None       ,       SCREAMING_SNAKE_CASE_ = None       ,       SCREAMING_SNAKE_CASE_ = None       ,       SCREAMING_SNAKE_CASE_ = ChannelDimension.FIRST       ,       **SCREAMING_SNAKE_CASE_       ,       ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = do_normalize if do_normalize is not None else self.do_normalize
									SCREAMING_SNAKE_CASE_						 = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
									SCREAMING_SNAKE_CASE_						 = patch_size if patch_size is not None else self.patch_size
									SCREAMING_SNAKE_CASE_						 = max_patches if max_patches is not None else self.max_patches
									SCREAMING_SNAKE_CASE_						 = self.is_vqa

									if kwargs.get('''data_format'''       ,       lowerCAmelCase_    ) is not None:
											raise ValueError('''data_format is not an accepted input as the outputs are '''    )

									SCREAMING_SNAKE_CASE_						 = make_list_of_images(lowerCAmelCase_    )

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

									# PIL RGBA images are converted to RGB
									if do_convert_rgb:
											SCREAMING_SNAKE_CASE_						 = [convert_to_rgb(lowerCAmelCase_    ) for image in images]

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

									if is_vqa:
											if header_text is None:
													raise ValueError('''A header text must be provided for VQA models.'''    )
											SCREAMING_SNAKE_CASE_						 = kwargs.pop('''font_bytes'''       ,       lowerCAmelCase_    )
											SCREAMING_SNAKE_CASE_						 = kwargs.pop('''font_path'''       ,       lowerCAmelCase_    )

											if isinstance(lowerCAmelCase_       ,       lowerCAmelCase_    ):
													SCREAMING_SNAKE_CASE_						 = [header_text] * len(lowerCAmelCase_    )

											SCREAMING_SNAKE_CASE_						 = [
											    render_header(lowerCAmelCase_       ,       header_text[i]       ,       font_bytes=lowerCAmelCase_       ,       font_path=lowerCAmelCase_    )
											    for i, image in enumerate(lowerCAmelCase_    )
											]

									if do_normalize:
											SCREAMING_SNAKE_CASE_						 = [self.normalize(image=lowerCAmelCase_    ) for image in images]

									# convert to torch tensor and permute
									SCREAMING_SNAKE_CASE_						 = [
									    self.extract_flattened_patches(image=lowerCAmelCase_       ,       max_patches=lowerCAmelCase_       ,       patch_size=lowerCAmelCase_    )
									    for image in images
									]

									# create attention mask in numpy
									SCREAMING_SNAKE_CASE_						 = [(image.sum(axis=-1    ) != 0).astype(np.floataa    ) for image in images]

									SCREAMING_SNAKE_CASE_						 = BatchFeature(
									    data={'''flattened_patches''': images, '''attention_mask''': attention_masks}       ,       tensor_type=lowerCAmelCase_    )

									return encoded_outputs

"""simple docstring"""




import argparse

from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging


logging.set_verbosity_info()



def        _lowerCamelCase ( __a,							__a,							__a,							__a    ):
		# Initialise PyTorch model
		SCREAMING_SNAKE_CASE_						 = BigBirdConfig.from_json_file(__a    )
		print(F'Building PyTorch model from configuration: {config}'    )

		if is_trivia_qa:
				SCREAMING_SNAKE_CASE_						 = BigBirdForQuestionAnswering(__a    )
		else:
				SCREAMING_SNAKE_CASE_						 = BigBirdForPreTraining(__a    )

		# Load weights from tf checkpoint
		load_tf_weights_in_big_bird(__a,							__a,							is_trivia_qa=__a    )

		# Save pytorch-model
		print(F'Save PyTorch model to {pytorch_dump_path}'    )
		model.save_pretrained(__a    )


if __name__ == "__main__":
						lowerCAmelCase__							=				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(
						    '--big_bird_config_file',
						    default=None,
						    type=str,
						    required=True,
						    help=(
						        'The config json file corresponding to the pre-trained BERT 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.'
						)
						parser.add_argument(
						    '--is_trivia_qa', action='store_true', help='Whether to convert a model with a trivia_qa head.'
						)
						lowerCAmelCase__							=				parser.parse_args()
						convert_tf_checkpoint_to_pytorch(
						    args.tf_checkpoint_path, args.big_bird_config_file, args.pytorch_dump_path, args.is_trivia_qa
						)