



from manim import *

class       _SCREAMING_SNAKE_CASE	(UpperCamelCase ):





     def 	__snake_case    (       self :							Optional[int]				)->List[str]:
      __SCREAMING_SNAKE_CASE       :    Tuple       =	Rectangle(height=0.5     ,  width=0.5				)
      __SCREAMING_SNAKE_CASE       :    Any       =	Rectangle(height=0.4_6     ,  width=0.4_6				).set_stroke(width=0				)

      __SCREAMING_SNAKE_CASE       :    Any       =	[mem.copy() for i in range(6				)]
      __SCREAMING_SNAKE_CASE       :    Any       =	[mem.copy() for i in range(6				)]
      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	VGroup(*UpperCamelCase				).arrange(UpperCamelCase     ,  buff=0				)
      __SCREAMING_SNAKE_CASE       :    Any       =	VGroup(*UpperCamelCase				).arrange(UpperCamelCase     ,  buff=0				)
      __SCREAMING_SNAKE_CASE       :    List[str]       =	VGroup(UpperCamelCase     ,  UpperCamelCase				).arrange(UpperCamelCase     ,  buff=0				)
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	Text("CPU"     ,  font_size=2_4				)
      __SCREAMING_SNAKE_CASE       :    Any       =	Group(UpperCamelCase     ,  UpperCamelCase				).arrange(UpperCamelCase     ,  buff=0.5     ,  aligned_edge=UpperCamelCase				)
      cpu.move_to([-2.5, -0.5, 0]				)
      self.add(UpperCamelCase				)

      __SCREAMING_SNAKE_CASE       :    int       =	[mem.copy() for i in range(4				)]
      __SCREAMING_SNAKE_CASE       :    Any       =	VGroup(*UpperCamelCase				).arrange(UpperCamelCase     ,  buff=0				)
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	Text("GPU"     ,  font_size=2_4				)
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	Group(UpperCamelCase     ,  UpperCamelCase				).arrange(UpperCamelCase     ,  buff=0.5     ,  aligned_edge=UpperCamelCase				)
      gpu.move_to([-1, -1, 0]				)
      self.add(UpperCamelCase				)

      __SCREAMING_SNAKE_CASE       :    Tuple       =	[mem.copy() for i in range(6				)]
      __SCREAMING_SNAKE_CASE       :    int       =	VGroup(*UpperCamelCase				).arrange(UpperCamelCase     ,  buff=0				)

      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	Text("Model"     ,  font_size=2_4				)
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	Group(UpperCamelCase     ,  UpperCamelCase				).arrange(UpperCamelCase     ,  buff=0.5     ,  aligned_edge=UpperCamelCase				)
      model.move_to([3, -1.0, 0]				)
      self.add(UpperCamelCase				)

      __SCREAMING_SNAKE_CASE       :    Any       =	[]
      for i, rect in enumerate(UpperCamelCase				):
       rect.set_stroke(UpperCamelCase				)
       # target = fill.copy().set_fill(YELLOW, opacity=0.7)
       # target.move_to(rect)
       # self.add(target)

       __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	Rectangle(height=0.4_6 / 4     ,  width=0.4_6 / 3				).set_stroke(width=0.0				).set_fill(UpperCamelCase     ,  opacity=0.7				)

       if i == 0:
        cpu_target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT				)     ,  buff=0.0_2     ,  direction=UpperCamelCase				)
        cpu_target.set_x(cpu_target.get_x() + 0.1				)
       elif i == 3:
        cpu_target.next_to(cpu_targs[0]     ,  direction=UpperCamelCase     ,  buff=0.0				)
       else:
        cpu_target.next_to(cpu_targs[i - 1]     ,  direction=UpperCamelCase     ,  buff=0.0				)
       self.add(UpperCamelCase				)
       cpu_targs.append(UpperCamelCase				)

      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	[mem.copy() for i in range(6				)]
      __SCREAMING_SNAKE_CASE       :    Tuple       =	VGroup(*UpperCamelCase				).arrange(UpperCamelCase     ,  buff=0				)

      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	Text("Loaded Checkpoint"     ,  font_size=2_4				)
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	Group(UpperCamelCase     ,  UpperCamelCase				).arrange(UpperCamelCase     ,  aligned_edge=UpperCamelCase     ,  buff=0.4				)
      checkpoint.move_to([3, 0.5, 0]				)

      __SCREAMING_SNAKE_CASE       :    Dict       =	Square(side_length=2.2				)
      key.move_to([-5, 2, 0]				)

      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	MarkupText(
          F"""<b>Key:</b>\n\n<span fgcolor='{YELLOW}'>●</span> Empty Model"""     ,  font_size=1_8     ,  )

      key_text.move_to([-5, 2.4, 0]				)

      self.add(UpperCamelCase     ,  UpperCamelCase				)

      __SCREAMING_SNAKE_CASE       :    str       =	MarkupText(
          F"""<span fgcolor='{BLUE}'>●</span> Checkpoint"""     ,  font_size=1_8     ,  )

      blue_text.next_to(UpperCamelCase     ,  DOWN * 2.4     ,  aligned_edge=key_text.get_left()				)

      __SCREAMING_SNAKE_CASE       :    str       =	MarkupText(
          F"""Next, a <i><span fgcolor=\"{BLUE}\">second</span></i> model is loaded into memory,\nwith the weights of a <span fgcolor=\"{BLUE}\">single shard</span>."""     ,  font_size=2_4     ,  )
      step_a.move_to([2, 2, 0]				)
      self.play(Write(UpperCamelCase				)     ,  Write(UpperCamelCase				)				)

      self.play(Write(UpperCamelCase     ,  run_time=1				)     ,  Create(UpperCamelCase     ,  run_time=1				)				)

      __SCREAMING_SNAKE_CASE       :    Any       =	[]
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	[]
      for i, rect in enumerate(UpperCamelCase				):
       __SCREAMING_SNAKE_CASE       :    List[str]       =	fill.copy().set_fill(UpperCamelCase     ,  opacity=0.7				)
       target.move_to(UpperCamelCase				)
       first_animations.append(GrowFromCenter(UpperCamelCase     ,  run_time=1				)				)

       __SCREAMING_SNAKE_CASE       :    Optional[int]       =	target.copy()
       cpu_target.generate_target()
       if i < 5:
        cpu_target.target.move_to(cpu_left_col_base[i + 1]				)
       else:
        cpu_target.target.move_to(cpu_right_col_base[i - 5]				)
       second_animations.append(MoveToTarget(UpperCamelCase     ,  run_time=1.5				)				)

      self.play(*UpperCamelCase				)
      self.play(*UpperCamelCase				)
      self.wait()











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						(				):



 """simple docstring"""





 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						(				__lowerCamelCase   :     List[str]		,	__lowerCamelCase   :     int		,	__lowerCamelCase   :     Dict     ):



 """simple docstring"""





 requires_backends(__lowerCamelCase		,	["torch"]     )
 _check_torch_version()

 __SCREAMING_SNAKE_CASE       :    List[Any]       =	image_tensor.unsqueeze(0     )
 __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	torch.nn.functional.unfold(__lowerCamelCase		,	(patch_height, patch_width)		,	stride=(patch_height, patch_width)     )
 __SCREAMING_SNAKE_CASE       :    List[Any]       =	patches.reshape(image_tensor.size(0     )		,	image_tensor.size(1     )		,	__lowerCamelCase		,	__lowerCamelCase		,	-1     )
 __SCREAMING_SNAKE_CASE       :    Any       =	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						(				__lowerCamelCase   :     str		,	__lowerCamelCase   :     int = 36		,	__lowerCamelCase   :     str = "black"		,	__lowerCamelCase   :     str = "white"		,	__lowerCamelCase   :     int = 5		,	__lowerCamelCase   :     int = 5		,	__lowerCamelCase   :     int = 5		,	__lowerCamelCase   :     int = 5		,	__lowerCamelCase   :     Optional[bytes] = None		,	__lowerCamelCase   :     Optional[str] = None		,	):



 """simple docstring"""





 requires_backends(__lowerCamelCase		,	"vision"     )
 # Add new lines so that each line is no more than 80 characters.

 __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	textwrap.TextWrapper(width=80     )
 __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	wrapper.wrap(text=__lowerCamelCase     )
 __SCREAMING_SNAKE_CASE       :    str       =	"\n".join(__lowerCamelCase     )

 if font_bytes is not None and font_path is None:
  __SCREAMING_SNAKE_CASE       :    List[Any]       =	io.BytesIO(__lowerCamelCase     )
 elif font_path is not None:
  __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	font_path
 else:
  __SCREAMING_SNAKE_CASE       :    Tuple       =	hf_hub_download(__lowerCamelCase		,	"Arial.TTF"     )
 __SCREAMING_SNAKE_CASE       :    List[Any]       =	ImageFont.truetype(__lowerCamelCase		,	encoding="UTF-8"		,	size=__lowerCamelCase     )

 # Use a temporary canvas to determine the width and height in pixels when
 # rendering the text.
 __SCREAMING_SNAKE_CASE       :    str       =	ImageDraw.Draw(Image.new("RGB"		,	(1, 1)		,	__lowerCamelCase     )     )
 __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    int       =	temp_draw.textbbox((0, 0)		,	__lowerCamelCase		,	__lowerCamelCase     )

 # Create the actual image with a bit of padding around the text.
 __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	text_width + left_padding + right_padding
 __SCREAMING_SNAKE_CASE       :    Tuple       =	text_height + top_padding + bottom_padding
 __SCREAMING_SNAKE_CASE       :    Tuple       =	Image.new("RGB"		,	(image_width, image_height)		,	__lowerCamelCase     )
 __SCREAMING_SNAKE_CASE       :    List[Any]       =	ImageDraw.Draw(__lowerCamelCase     )
 draw.text(xy=(left_padding, top_padding)		,	text=__lowerCamelCase		,	fill=__lowerCamelCase		,	font=__lowerCamelCase     )
 return image






def 	_lowerCAmelCase						(				__lowerCamelCase   :     np.ndarray		,	__lowerCamelCase   :     str		,	**__lowerCamelCase   :     Union[str, Any]     ):



 """simple docstring"""





 requires_backends(__lowerCamelCase		,	"vision"     )

 # Convert to PIL image if necessary
 __SCREAMING_SNAKE_CASE       :    int       =	to_pil_image(__lowerCamelCase     )

 __SCREAMING_SNAKE_CASE       :    int       =	render_text(__lowerCamelCase		,	**__lowerCamelCase     )
 __SCREAMING_SNAKE_CASE       :    Tuple       =	max(header_image.width		,	image.width     )

 __SCREAMING_SNAKE_CASE       :    Tuple       =	int(image.height * (new_width / image.width)     )
 __SCREAMING_SNAKE_CASE       :    Any       =	int(header_image.height * (new_width / header_image.width)     )

 __SCREAMING_SNAKE_CASE       :    Tuple       =	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       :    Dict       =	to_numpy_array(__lowerCamelCase     )

 if infer_channel_dimension_format(__lowerCamelCase     ) == ChannelDimension.LAST:
  __SCREAMING_SNAKE_CASE       :    Any       =	to_channel_dimension_format(__lowerCamelCase		,	ChannelDimension.LAST     )

 return new_image






class       _SCREAMING_SNAKE_CASE	(UpperCamelCase ):
     lowerCAmelCase					=			["""flattened_patches"""]





     def __init__(       self :							Tuple     ,  UpperCamelCase :							bool = True     ,  UpperCamelCase :							bool = True     ,  UpperCamelCase :							Dict[str, int] = None     ,  UpperCamelCase :							int = 2_0_4_8     ,  UpperCamelCase :							bool = False     ,  **UpperCamelCase :							Optional[int]     ,  )->None:
      super().__init__(**UpperCamelCase				)
      __SCREAMING_SNAKE_CASE       :    Tuple       =	patch_size if patch_size is not None else {"height": 1_6, "width": 1_6}
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	do_normalize
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	do_convert_rgb
      __SCREAMING_SNAKE_CASE       :    List[str]       =	max_patches
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	is_vqa





     def 	__snake_case    (       self :							Tuple     ,  UpperCamelCase :							np.ndarray     ,  UpperCamelCase :							int     ,  UpperCamelCase :							dict     ,  **UpperCamelCase :							Tuple				)->np.ndarray:
      requires_backends(self.extract_flattened_patches     ,  "torch"				)
      _check_torch_version()

      # convert to torch
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	to_channel_dimension_format(UpperCamelCase     ,  ChannelDimension.FIRST				)
      __SCREAMING_SNAKE_CASE       :    int       =	torch.from_numpy(UpperCamelCase				)

      __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    List[str]       =	patch_size["height"], patch_size["width"]
      __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    Optional[Any]       =	get_image_size(UpperCamelCase				)

      # maximize scale s.t.
      __SCREAMING_SNAKE_CASE       :    List[str]       =	math.sqrt(max_patches * (patch_height / image_height) * (patch_width / image_width)				)
      __SCREAMING_SNAKE_CASE       :    List[str]       =	max(min(math.floor(scale * image_height / patch_height				)     ,  UpperCamelCase				)     ,  1				)
      __SCREAMING_SNAKE_CASE       :    Tuple       =	max(min(math.floor(scale * image_width / patch_width				)     ,  UpperCamelCase				)     ,  1				)
      __SCREAMING_SNAKE_CASE       :    List[str]       =	max(num_feasible_rows * patch_height     ,  1				)
      __SCREAMING_SNAKE_CASE       :    int       =	max(num_feasible_cols * patch_width     ,  1				)

      __SCREAMING_SNAKE_CASE       :    Any       =	torch.nn.functional.interpolate(
          image.unsqueeze(0				)     ,  size=(resized_height, resized_width)     ,  mode="bilinear"     ,  align_corners=UpperCamelCase     ,  antialias=UpperCamelCase     ,  ).squeeze(0				)

      # [1, rows, columns, patch_height * patch_width * image_channels]
      __SCREAMING_SNAKE_CASE       :    List[str]       =	torch_extract_patches(UpperCamelCase     ,  UpperCamelCase     ,  UpperCamelCase				)

      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	patches.shape
      __SCREAMING_SNAKE_CASE       :    int       =	patches_shape[1]
      __SCREAMING_SNAKE_CASE       :    List[str]       =	patches_shape[2]
      __SCREAMING_SNAKE_CASE       :    List[str]       =	patches_shape[3]

      # [rows * columns, patch_height * patch_width * image_channels]
      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	patches.reshape([rows * columns, depth]				)

      # [rows * columns, 1]
      __SCREAMING_SNAKE_CASE       :    Any       =	torch.arange(UpperCamelCase				).reshape([rows, 1]				).repeat(1     ,  UpperCamelCase				).reshape([rows * columns, 1]				)
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	torch.arange(UpperCamelCase				).reshape([1, columns]				).repeat(UpperCamelCase     ,  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       :    str       =	row_ids.to(torch.floataa				)
      __SCREAMING_SNAKE_CASE       :    int       =	col_ids.to(torch.floataa				)

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

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

      __SCREAMING_SNAKE_CASE       :    str       =	to_numpy_array(UpperCamelCase				)

      return result





     def 	__snake_case    (       self :							Optional[Any]     ,  UpperCamelCase :							np.ndarray     ,  UpperCamelCase :							Optional[Union[str, ChannelDimension]] = None     ,  **UpperCamelCase :							Optional[int]				)->np.ndarray:
      if image.dtype == np.uinta:
       __SCREAMING_SNAKE_CASE       :    Optional[int]       =	image.astype(np.floataa				)

      # take mean across the whole `image`
      __SCREAMING_SNAKE_CASE       :    int       =	np.mean(UpperCamelCase				)
      __SCREAMING_SNAKE_CASE       :    List[str]       =	np.std(UpperCamelCase				)
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	max(UpperCamelCase     ,  1.0 / math.sqrt(np.prod(image.shape				)				)				)

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






     def 	__snake_case    (       self :							Union[str, Any]     ,  UpperCamelCase :							ImageInput     ,  UpperCamelCase :							Optional[str] = None     ,  UpperCamelCase :							bool = None     ,  UpperCamelCase :							Optional[bool] = None     ,  UpperCamelCase :							Optional[int] = None     ,  UpperCamelCase :							Optional[Dict[str, int]] = None     ,  UpperCamelCase :							Optional[Union[str, TensorType]] = None     ,  UpperCamelCase :							ChannelDimension = ChannelDimension.FIRST     ,  **UpperCamelCase :							Dict     ,  )->ImageInput:
      __SCREAMING_SNAKE_CASE       :    int       =	do_normalize if do_normalize is not None else self.do_normalize
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	patch_size if patch_size is not None else self.patch_size
      __SCREAMING_SNAKE_CASE       :    List[str]       =	max_patches if max_patches is not None else self.max_patches
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	self.is_vqa

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

      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	make_list_of_images(UpperCamelCase				)

      if not valid_images(UpperCamelCase				):
       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       :    Optional[int]       =	[convert_to_rgb(UpperCamelCase				) for image in images]

      # All transformations expect numpy arrays.
      __SCREAMING_SNAKE_CASE       :    str       =	[to_numpy_array(UpperCamelCase				) 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       :    Any       =	kwargs.pop("font_bytes"     ,  UpperCamelCase				)
       __SCREAMING_SNAKE_CASE       :    Any       =	kwargs.pop("font_path"     ,  UpperCamelCase				)

       if isinstance(UpperCamelCase     ,  UpperCamelCase				):
        __SCREAMING_SNAKE_CASE       :    Dict       =	[header_text] * len(UpperCamelCase				)

       __SCREAMING_SNAKE_CASE       :    Optional[int]       =	[
           render_header(UpperCamelCase     ,  header_text[i]     ,  font_bytes=UpperCamelCase     ,  font_path=UpperCamelCase				)
           for i, image in enumerate(UpperCamelCase				)
       ]

      if do_normalize:
       __SCREAMING_SNAKE_CASE       :    List[str]       =	[self.normalize(image=UpperCamelCase				) for image in images]

      # convert to torch tensor and permute
      __SCREAMING_SNAKE_CASE       :    str       =	[
          self.extract_flattened_patches(image=UpperCamelCase     ,  max_patches=UpperCamelCase     ,  patch_size=UpperCamelCase				)
          for image in images
      ]

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

      __SCREAMING_SNAKE_CASE       :    Dict       =	BatchFeature(
          data={"flattened_patches": images, "attention_mask": attention_masks}     ,  tensor_type=UpperCamelCase				)

      return encoded_outputs






