





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:
      UpperCAmelCase_					:   Tuple 							=						False


UpperCAmelCase_					:   int 							=						logging.get_logger(__name__)
UpperCAmelCase_					:   Optional[int] 							=						"ybelkada/fonts"






def 							_lowerCAmelCase (  )	->			Optional[Any]:


     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   :					Optional[Any]					,   _a   :					int					,   _a   :					Any			)	->			Dict:
     requires_backends(__lowerCAmelCase					,   ["""torch"""]			)
     _check_torch_version()

     lowerCAmelCase_					:       List[Any]    =		image_tensor.unsqueeze(0			)
     lowerCAmelCase_					:       str    =		torch.nn.functional.unfold(__lowerCAmelCase					,   (patch_height, patch_width)					,   stride=(patch_height, patch_width)			)
     lowerCAmelCase_					:       Tuple    =		patches.reshape(image_tensor.size(0			)					,   image_tensor.size(1			)					,   __lowerCAmelCase					,   __lowerCAmelCase					,   -1			)
     lowerCAmelCase_					:       Union[str, 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 (  _a   :					Optional[int]					,   _a   :					List[str] = 36					,   _a   :					int = "black"					,   _a   :					List[str] = "white"					,   _a   :					Optional[int] = 5					,   _a   :					Optional[Any] = 5					,   _a   :					List[Any] = 5					,   _a   :					Optional[Any] = 5					,   _a   :					Dict = None					,   _a   :					Dict = None					,   )	->			Image.Image:
     requires_backends(__lowerCAmelCase					,   """vision"""			)
     # Add new lines so that each line is no more than 80 characters.

     lowerCAmelCase_					:       Tuple    =		textwrap.TextWrapper(width=80			)
     lowerCAmelCase_					:       Any    =		wrapper.wrap(text=__lowerCAmelCase			)
     lowerCAmelCase_					:       List[str]    =		"""\n""".join(__lowerCAmelCase			)

     if font_bytes is not None and font_path is None:
          lowerCAmelCase_					:       Any    =		io.BytesIO(__lowerCAmelCase			)
     elif font_path is not None:
          lowerCAmelCase_					:       str    =		font_path
     else:
          lowerCAmelCase_					:       Optional[Any]    =		hf_hub_download(__lowerCAmelCase					,   """Arial.TTF"""			)
     lowerCAmelCase_					:       Optional[int]    =		ImageFont.truetype(__lowerCAmelCase					,   encoding="""UTF-8"""					,   size=__lowerCAmelCase			)

     # Use a temporary canvas to determine the width and height in pixels when
     # rendering the text.
     lowerCAmelCase_					:       Optional[int]    =		ImageDraw.Draw(Image.new("""RGB"""					,   (1, 1)					,   __lowerCAmelCase			)			)
     lowerCAmelCase_   ,  lowerCAmelCase_   ,  lowerCAmelCase_   ,  lowerCAmelCase_					:       Tuple    =		temp_draw.textbbox((0, 0)					,   __lowerCAmelCase					,   __lowerCAmelCase			)

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






def 							_lowerCAmelCase (  _a   :					int					,   _a   :					List[str]					,   **_a   :					List[Any]			)	->			Tuple:
     requires_backends(__lowerCAmelCase					,   """vision"""			)

     # Convert to PIL image if necessary
     lowerCAmelCase_					:       str    =		to_pil_image(__lowerCAmelCase			)

     lowerCAmelCase_					:       List[str]    =		render_text(__lowerCAmelCase					,   **__lowerCAmelCase			)
     lowerCAmelCase_					:       int    =		max(header_image.width					,   image.width			)

     lowerCAmelCase_					:       Union[str, Any]    =		int(image.height * (new_width / image.width)			)
     lowerCAmelCase_					:       List[Any]    =		int(header_image.height * (new_width / header_image.width)			)

     lowerCAmelCase_					:       int    =		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
     lowerCAmelCase_					:       Dict    =		to_numpy_array(__lowerCAmelCase			)

     if infer_channel_dimension_format(__lowerCAmelCase			) == ChannelDimension.LAST:
          lowerCAmelCase_					:       int    =		to_channel_dimension_format(__lowerCAmelCase					,   ChannelDimension.LAST			)

     return new_image







class      lowercase__    (							__UpperCAmelCase  ):
  __UpperCamelCase	     =  ["""flattened_patches"""]







  def __init__(	self    ,						_lowercase = True    ,						_lowercase = True    ,						_lowercase = None    ,						_lowercase = 2_048    ,						_lowercase = False    ,						**_lowercase    ,						):
       super().__init__(**lowerCAmelCase_						)
       lowerCAmelCase_					:       Any    =		patch_size if patch_size is not None else {"""height""": 16, """width""": 16}
       lowerCAmelCase_					:       Union[str, Any]    =		do_normalize
       lowerCAmelCase_					:       List[str]    =		do_convert_rgb
       lowerCAmelCase_					:       Any    =		max_patches
       lowerCAmelCase_					:       Union[str, Any]    =		is_vqa







  def 	UpperCAmelCase__		(	self    ,						_lowercase    ,						_lowercase    ,						_lowercase    ,						**_lowercase						):
       requires_backends(self.extract_flattened_patches    ,						"""torch"""						)
       _check_torch_version()

       # convert to torch
       lowerCAmelCase_					:       Dict    =		to_channel_dimension_format(lowerCAmelCase_    ,						ChannelDimension.FIRST						)
       lowerCAmelCase_					:       Any    =		torch.from_numpy(lowerCAmelCase_						)

       lowerCAmelCase_   ,  lowerCAmelCase_					:       str    =		patch_size["""height"""], patch_size["""width"""]
       lowerCAmelCase_   ,  lowerCAmelCase_					:       Optional[Any]    =		get_image_size(lowerCAmelCase_						)

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

       lowerCAmelCase_					:       Any    =		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]
       lowerCAmelCase_					:       Optional[int]    =		torch_extract_patches(lowerCAmelCase_    ,						lowerCAmelCase_    ,						lowerCAmelCase_						)

       lowerCAmelCase_					:       Dict    =		patches.shape
       lowerCAmelCase_					:       Optional[Any]    =		patches_shape[1]
       lowerCAmelCase_					:       Any    =		patches_shape[2]
       lowerCAmelCase_					:       Any    =		patches_shape[3]

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

       # [rows * columns, 1]
       lowerCAmelCase_					:       List[str]    =		torch.arange(lowerCAmelCase_						).reshape([rows, 1]						).repeat(1    ,						lowerCAmelCase_						).reshape([rows * columns, 1]						)
       lowerCAmelCase_					:       Union[str, Any]    =		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]
       lowerCAmelCase_					:       Dict    =		row_ids.to(torch.floataa						)
       lowerCAmelCase_					:       List[str]    =		col_ids.to(torch.floataa						)

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

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

       lowerCAmelCase_					:       List[str]    =		to_numpy_array(lowerCAmelCase_						)

       return result







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







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

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

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







  def 	UpperCAmelCase__		(	self    ,						_lowercase    ,						_lowercase = None    ,						_lowercase = None    ,						_lowercase = None    ,						_lowercase = None    ,						_lowercase = None    ,						_lowercase = None    ,						_lowercase = ChannelDimension.FIRST    ,						**_lowercase    ,						):
       lowerCAmelCase_					:       int    =		do_normalize if do_normalize is not None else self.do_normalize
       lowerCAmelCase_					:       Any    =		do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
       lowerCAmelCase_					:       Optional[Any]    =		patch_size if patch_size is not None else self.patch_size
       lowerCAmelCase_					:       int    =		max_patches if max_patches is not None else self.max_patches
       lowerCAmelCase_					:       str    =		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 """						)

       lowerCAmelCase_					:       Dict    =		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:
            lowerCAmelCase_					:       Dict    =		[convert_to_rgb(lowerCAmelCase_						) for image in images]

       # All transformations expect numpy arrays.
       lowerCAmelCase_					:       Union[str, Any]    =		[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."""						)
            lowerCAmelCase_					:       Union[str, Any]    =		kwargs.pop("""font_bytes"""    ,						lowerCAmelCase_						)
            lowerCAmelCase_					:       List[str]    =		kwargs.pop("""font_path"""    ,						lowerCAmelCase_						)

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

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

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

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

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

       lowerCAmelCase_					:       int    =		BatchFeature(
           data={"""flattened_patches""": images, """attention_mask""": attention_masks}    ,						tensor_type=lowerCAmelCase_						)

       return encoded_outputs







import inspect
import os
import unittest
from pathlib import Path

import torch

import accelerate
from accelerate.test_utils import execute_subprocess_async
from accelerate.test_utils.testing import run_command





class      lowercase__    (							unittest.TestCase  ):
  __UpperCamelCase	     =  inspect.getfile(accelerate.test_utils  )
  __UpperCamelCase	     =  os.path.sep.join(mod_file.split(os.path.sep  )[:-1] + ["""scripts""", """test_cli.py"""]  )

  __UpperCamelCase	     =  ["""accelerate""", """launch"""]
  __UpperCamelCase	     =  Path.home() / """.cache/huggingface/accelerate"""
  __UpperCamelCase	     =  """default_config.yaml"""
  __UpperCamelCase	     =  config_folder / config_file
  __UpperCamelCase	     =  config_folder / """_default_config.yaml"""

  __UpperCamelCase	     =  Path("""tests/test_configs"""  )







  @classmethod
  def 	UpperCAmelCase__		(	cls						):
       if cls.config_path.is_file():
            cls.config_path.rename(cls.changed_path						)







  @classmethod
  def 	UpperCAmelCase__		(	cls						):
       if cls.changed_path.is_file():
            cls.changed_path.rename(cls.config_path						)







  def 	UpperCAmelCase__		(	self						):
       lowerCAmelCase_					:       int    =		self.base_cmd
       if torch.cuda.is_available() and (torch.cuda.device_count() > 1):
            cmd += ["--multi_gpu"]
       execute_subprocess_async(cmd + [self.test_file_path]    ,						env=os.environ.copy()						)







  def 	UpperCAmelCase__		(	self						):
       for config in sorted(self.test_config_path.glob("""**/*.yaml"""						)						):
            with self.subTest(config_file=_lowercase						):
                 execute_subprocess_async(
                     self.base_cmd + ["""--config_file""", str(_lowercase						), self.test_file_path]    ,						env=os.environ.copy()						)







  def 	UpperCAmelCase__		(	self						):
       execute_subprocess_async(["""accelerate""", """test"""]    ,						env=os.environ.copy()						)





class      lowercase__    (							unittest.TestCase  ):
  __UpperCamelCase	     =  """test-tpu"""
  __UpperCamelCase	     =  """us-central1-a"""
  __UpperCamelCase	     =  """ls"""
  __UpperCamelCase	     =  ["""accelerate""", """tpu-config"""]
  __UpperCamelCase	     =  """cd /usr/share"""
  __UpperCamelCase	     =  """tests/test_samples/test_command_file.sh"""
  __UpperCamelCase	     =  """Running gcloud compute tpus tpu-vm ssh"""







  def 	UpperCAmelCase__		(	self						):
       lowerCAmelCase_					:       str    =		run_command(
           self.cmd
           + ["""--command""", self.command, """--tpu_zone""", self.tpu_zone, """--tpu_name""", self.tpu_name, """--debug"""]    ,						return_stdout=_lowercase    ,						)
       self.assertIn(
           F'{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all'    ,						_lowercase    ,						)







  def 	UpperCAmelCase__		(	self						):
       lowerCAmelCase_					:       str    =		run_command(
           self.cmd
           + [
               """--config_file""",
               """tests/test_configs/0_12_0.yaml""",
               """--command""",
               self.command,
               """--tpu_zone""",
               self.tpu_zone,
               """--tpu_name""",
               self.tpu_name,
               """--debug""",
           ]    ,						return_stdout=_lowercase    ,						)
       self.assertIn(
           F'{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all'    ,						_lowercase    ,						)







  def 	UpperCAmelCase__		(	self						):
       lowerCAmelCase_					:       Optional[Any]    =		run_command(
           self.cmd + ["""--config_file""", """tests/test_configs/latest.yaml""", """--debug"""]    ,						return_stdout=_lowercase						)
       self.assertIn(
           F'{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; echo "hello world"; echo "this is a second command" --worker all'    ,						_lowercase    ,						)







  def 	UpperCAmelCase__		(	self						):
       lowerCAmelCase_					:       Tuple    =		run_command(
           self.cmd + ["""--config_file""", """tests/test_configs/latest.yaml""", """--command""", self.command, """--debug"""]    ,						return_stdout=_lowercase    ,						)
       self.assertIn(
           F'{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all'    ,						_lowercase    ,						)







  def 	UpperCAmelCase__		(	self						):
       lowerCAmelCase_					:       str    =		run_command(
           self.cmd
           + [
               """--config_file""",
               """tests/test_configs/latest.yaml""",
               """--command""",
               self.command,
               """--command""",
               """echo \"Hello World\"""",
               """--debug""",
           ]    ,						return_stdout=_lowercase    ,						)
       self.assertIn(
           F'{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls; echo "Hello World" --worker all'    ,						_lowercase    ,						)







  def 	UpperCAmelCase__		(	self						):
       lowerCAmelCase_					:       str    =		run_command(
           self.cmd
           + ["""--config_file""", """tests/test_configs/latest.yaml""", """--command_file""", self.command_file, """--debug"""]    ,						return_stdout=_lowercase    ,						)
       self.assertIn(
           F'{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; echo "hello world"; echo "this is a second command" --worker all'    ,						_lowercase    ,						)







  def 	UpperCAmelCase__		(	self						):
       lowerCAmelCase_					:       Optional[Any]    =		run_command(
           self.cmd
           + [
               """--config_file""",
               """tests/test_configs/0_12_0.yaml""",
               """--command_file""",
               self.command_file,
               """--tpu_zone""",
               self.tpu_zone,
               """--tpu_name""",
               self.tpu_name,
               """--debug""",
           ]    ,						return_stdout=_lowercase    ,						)
       self.assertIn(
           F'{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; echo "hello world"; echo "this is a second command" --worker all'    ,						_lowercase    ,						)







  def 	UpperCAmelCase__		(	self						):
       lowerCAmelCase_					:       Tuple    =		run_command(
           self.cmd + ["""--config_file""", """tests/test_configs/latest.yaml""", """--install_accelerate""", """--debug"""]    ,						return_stdout=_lowercase    ,						)
       self.assertIn(
           F'{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; pip install accelerate -U; echo "hello world"; echo "this is a second command" --worker all'    ,						_lowercase    ,						)







  def 	UpperCAmelCase__		(	self						):
       lowerCAmelCase_					:       Any    =		run_command(
           self.cmd
           + [
               """--config_file""",
               """tests/test_configs/latest.yaml""",
               """--install_accelerate""",
               """--accelerate_version""",
               """12.0.0""",
               """--debug""",
           ]    ,						return_stdout=_lowercase    ,						)
       self.assertIn(
           F'{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; pip install accelerate==12.0.0; echo "hello world"; echo "this is a second command" --worker all'    ,						_lowercase    ,						)
