



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:
  a_								=    False


a_								=    logging.get_logger(__name__)
a_								=    """ybelkada/fonts"""





def 						a__	(						):
   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 						a__	(						_UpperCamelCase	:       int       ,_UpperCamelCase	:       Tuple       ,_UpperCamelCase	:       Optional[int]							):
   requires_backends(lowerCAmelCase_       ,['''torch''']							)
   _check_torch_version()

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

   __lowerCamelCase  							=			textwrap.TextWrapper(width=80							)
   __lowerCamelCase  							=			wrapper.wrap(text=lowerCAmelCase_							)
   __lowerCamelCase  							=			'''\n'''.join(lowerCAmelCase_							)

   if font_bytes is not None and font_path is None:
      __lowerCamelCase  							=			io.BytesIO(lowerCAmelCase_							)
   elif font_path is not None:
      __lowerCamelCase  							=			font_path
   else:
      __lowerCamelCase  							=			hf_hub_download(lowerCAmelCase_       ,'''Arial.TTF'''							)
   __lowerCamelCase  							=			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  							=			ImageDraw.Draw(Image.new('''RGB'''       ,(1, 1)       ,lowerCAmelCase_							)							)
   __lowerCamelCase  							=			temp_draw.textbbox((0, 0)       ,lowerCAmelCase_       ,lowerCAmelCase_							)

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





def 						a__	(						_UpperCamelCase	:       List[Any]       ,_UpperCamelCase	:       Union[str, Any]       ,**_UpperCamelCase	:       str							):
   requires_backends(lowerCAmelCase_       ,'''vision'''							)

   # Convert to PIL image if necessary
   __lowerCamelCase  							=			to_pil_image(lowerCAmelCase_							)

   __lowerCamelCase  							=			render_text(lowerCAmelCase_       ,**lowerCAmelCase_							)
   __lowerCamelCase  							=			max(header_image.width       ,image.width							)

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

   __lowerCamelCase  							=			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  							=			to_numpy_array(lowerCAmelCase_							)

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

   return new_image





class        __lowerCAmelCase   (		UpperCAmelCase__      ):
 lowerCAmelCase__				    =     ["""flattened_patches"""]




 def __init__(  self		, __UpperCAmelCase = True		, __UpperCAmelCase = True		, __UpperCAmelCase = None		, __UpperCAmelCase = 2048		, __UpperCAmelCase = False		, **__UpperCAmelCase		, ):



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




 def 				lowerCamelCase					(  self		, __UpperCAmelCase		, __UpperCAmelCase		, __UpperCAmelCase		, **__UpperCAmelCase ):



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

    # convert to torch
    __lowerCamelCase  							=			to_channel_dimension_format(__lowercase		, ChannelDimension.FIRST )
    __lowerCamelCase  							=			torch.from_numpy(__lowercase )

    __lowerCamelCase  							=			patch_size['''height'''], patch_size['''width''']
    __lowerCamelCase  							=			get_image_size(__lowercase )

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

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

    # [1, rows, columns, patch_height * patch_width * image_channels]
    __lowerCamelCase  							=			torch_extract_patches(__lowercase		, __lowercase		, __lowercase )

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

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

    # [rows * columns, 1]
    __lowerCamelCase  							=			torch.arange(__lowercase ).reshape([rows, 1] ).repeat(1		, __lowercase ).reshape([rows * columns, 1] )
    __lowerCamelCase  							=			torch.arange(__lowercase ).reshape([1, columns] ).repeat(__lowercase		, 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  							=			row_ids.to(torch.floataa )
    __lowerCamelCase  							=			col_ids.to(torch.floataa )

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

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

    __lowerCamelCase  							=			to_numpy_array(__lowercase )

    return result




 def 				lowerCamelCase					(  self		, __UpperCAmelCase		, __UpperCAmelCase = None		, **__UpperCAmelCase ):



    '''simple docstring'''
    if image.dtype == np.uinta:
       __lowerCamelCase  							=			image.astype(np.floataa )

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

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




 def 				lowerCamelCase					(  self		, __UpperCAmelCase		, __UpperCAmelCase = None		, __UpperCAmelCase = None		, __UpperCAmelCase = None		, __UpperCAmelCase = None		, __UpperCAmelCase = None		, __UpperCAmelCase = None		, __UpperCAmelCase = ChannelDimension.FIRST		, **__UpperCAmelCase		, ):



    '''simple docstring'''
    __lowerCamelCase  							=			do_normalize if do_normalize is not None else self.do_normalize
    __lowerCamelCase  							=			do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
    __lowerCamelCase  							=			patch_size if patch_size is not None else self.patch_size
    __lowerCamelCase  							=			max_patches if max_patches is not None else self.max_patches
    __lowerCamelCase  							=			self.is_vqa

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

    __lowerCamelCase  							=			make_list_of_images(__lowercase )

    if not valid_images(__lowercase ):
       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  							=			[convert_to_rgb(__lowercase ) for image in images]

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

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

       if isinstance(__lowercase		, __lowercase ):
          __lowerCamelCase  							=			[header_text] * len(__lowercase )

       __lowerCamelCase  							=			[
           render_header(__lowercase		, header_text[i]		, font_bytes=__lowercase		, font_path=__lowercase )
           for i, image in enumerate(__lowercase )
       ]

    if do_normalize:
       __lowerCamelCase  							=			[self.normalize(image=__lowercase ) for image in images]

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

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

    __lowerCamelCase  							=			BatchFeature(
        data={'''flattened_patches''': images, '''attention_mask''': attention_masks}		, tensor_type=__lowercase )

    return encoded_outputs










import platform
from argparse import ArgumentParser

import huggingface_hub

from .. import __version__ as version
from ..utils import is_accelerate_available, is_torch_available, is_transformers_available, is_xformers_available
from . import BaseDiffusersCLICommand



def 	lowerCAmelCase       (						lowerCAmelCase_       )->       Union[str, Any]:
				return EnvironmentCommand()







class  snake_case__(							UpperCAmelCase__							):







						'''simple docstring'''




						@staticmethod
						def   lowercase_       ( __lowercase							)    ->			List[Any]:
										lowerCAmelCase_       :					List[str]			 = parser.add_parser('''env'''							)
										download_parser.set_defaults(func=__lowercase							)




						def   lowercase_       ( self							)    ->			int:
										lowerCAmelCase_       :					Optional[Any]			 = huggingface_hub.__version__

										lowerCAmelCase_       :					str			 = '''not installed'''
										lowerCAmelCase_       :					str			 = '''NA'''
										if is_torch_available():
														import torch

														lowerCAmelCase_       :					Any			 = torch.__version__
														lowerCAmelCase_       :					str			 = torch.cuda.is_available()

										lowerCAmelCase_       :					List[str]			 = '''not installed'''
										if is_transformers_available():
														import transformers

														lowerCAmelCase_       :					Any			 = transformers.__version__

										lowerCAmelCase_       :					Optional[Any]			 = '''not installed'''
										if is_accelerate_available():
														import accelerate

														lowerCAmelCase_       :					List[Any]			 = accelerate.__version__

										lowerCAmelCase_       :					List[str]			 = '''not installed'''
										if is_xformers_available():
														import xformers

														lowerCAmelCase_       :					Optional[Any]			 = xformers.__version__

										lowerCAmelCase_       :					int			 = {
										    '''`diffusers` version''': version,
										    '''Platform''': platform.platform(),
										    '''Python version''': platform.python_version(),
										    '''PyTorch version (GPU?)''': f"""{pt_version} ({pt_cuda_available})""",
										    '''Huggingface_hub version''': hub_version,
										    '''Transformers version''': transformers_version,
										    '''Accelerate version''': accelerate_version,
										    '''xFormers version''': xformers_version,
										    '''Using GPU in script?''': '''<fill in>''',
										    '''Using distributed or parallel set-up in script?''': '''<fill in>''',
										}

										print('''\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n'''							)
										print(self.format_dict(__lowercase							)							)

										return info




						@staticmethod
						def   lowercase_       ( __lowercase							)    ->			str:
										return "\n".join([f"""- {prop}: {val}""" for prop, val in d.items()]							) + "\n"