
'''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:
      lowercase     :							Optional[int]       						=     False


lowercase     :							Optional[int]       						=     logging.get_logger(__name__)
lowercase     :							str       						=     '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_							(  snake_case__						,							snake_case__						,							snake_case__		):




    '''simple docstring'''



    requires_backends(snake_case__						,							['''torch''']		)
    _check_torch_version()

    A			:  Tuple   =		image_tensor.unsqueeze(0		)
    A			:  List[Any]   =		torch.nn.functional.unfold(snake_case__						,							(patch_height, patch_width)						,							stride=(patch_height, patch_width)		)
    A			:  List[str]   =		patches.reshape(image_tensor.size(0		)						,							image_tensor.size(1		)						,							snake_case__						,							snake_case__						,							-1		)
    A			:  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_							(  snake_case__						,							snake_case__ = 36						,							snake_case__ = "black"						,							snake_case__ = "white"						,							snake_case__ = 5						,							snake_case__ = 5						,							snake_case__ = 5						,							snake_case__ = 5						,							snake_case__ = None						,							snake_case__ = None						,							):




    '''simple docstring'''



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

    A			:  Dict   =		textwrap.TextWrapper(width=80		)
    A			:  Dict   =		wrapper.wrap(text=snake_case__		)
    A			:  List[str]   =		'''\n'''.join(snake_case__		)

    if font_bytes is not None and font_path is None:
        A			:  Dict   =		io.BytesIO(snake_case__		)
    elif font_path is not None:
        A			:  Optional[int]   =		font_path
    else:
        A			:  int   =		hf_hub_download(snake_case__						,							'''Arial.TTF'''		)
    A			:  List[Any]   =		ImageFont.truetype(snake_case__						,							encoding='''UTF-8'''						,							size=snake_case__		)

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

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







def 						lowerCAmelCase_							(  snake_case__						,							snake_case__						,							**snake_case__		):




    '''simple docstring'''



    requires_backends(snake_case__						,							'''vision'''		)

    # Convert to PIL image if necessary
    A			:  List[Any]   =		to_pil_image(snake_case__		)

    A			:  Any   =		render_text(snake_case__						,							**snake_case__		)
    A			:  List[Any]   =		max(header_image.width						,							image.width		)

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

    A			:  str   =		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
    A			:  Optional[int]   =		to_numpy_array(snake_case__		)

    if infer_channel_dimension_format(snake_case__		) == ChannelDimension.LAST:
        A			:  Tuple   =		to_channel_dimension_format(snake_case__						,							ChannelDimension.LAST		)

    return new_image







class 					A							(				__snake_case		):
     __magic_name__          =						['''flattened_patches''']





     def __init__(				self		,     SCREAMING_SNAKE_CASE = True		,     SCREAMING_SNAKE_CASE = True		,     SCREAMING_SNAKE_CASE = None		,     SCREAMING_SNAKE_CASE = 2048		,     SCREAMING_SNAKE_CASE = False		,     **SCREAMING_SNAKE_CASE		,     )					->							None:






         """simple docstring"""






         super().__init__(**SCREAMING_SNAKE_CASE      )
         A			:  List[Any]   =		patch_size if patch_size is not None else {'''height''': 16, '''width''': 16}
         A			:  int   =		do_normalize
         A			:  List[str]   =		do_convert_rgb
         A			:  Optional[Any]   =		max_patches
         A			:  Union[str, Any]   =		is_vqa





     def 							__lowerCAmelCase				(				self		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     **SCREAMING_SNAKE_CASE      )					->							np.ndarray:






         """simple docstring"""






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

         # convert to torch
         A			:  List[str]   =		to_channel_dimension_format(SCREAMING_SNAKE_CASE		,     ChannelDimension.FIRST      )
         A			:  Tuple   =		torch.from_numpy(SCREAMING_SNAKE_CASE      )

         A,    A			:  List[str]   =		patch_size['''height'''], patch_size['''width''']
         A,    A			:  Any   =		get_image_size(SCREAMING_SNAKE_CASE      )

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

         A			:  Optional[Any]   =		torch.nn.functional.interpolate(
             image.unsqueeze(0      )		,     size=(resized_height, resized_width)		,     mode='''bilinear'''		,     align_corners=SCREAMING_SNAKE_CASE		,     antialias=SCREAMING_SNAKE_CASE		,     ).squeeze(0      )

         # [1, rows, columns, patch_height * patch_width * image_channels]
         A			:  Optional[int]   =		torch_extract_patches(SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      )

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

         # [rows * columns, patch_height * patch_width * image_channels]
         A			:  Optional[Any]   =		patches.reshape([rows * columns, depth]      )

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

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

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

         A			:  Any   =		to_numpy_array(SCREAMING_SNAKE_CASE      )

         return result





     def 							__lowerCAmelCase				(				self		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE = None		,     **SCREAMING_SNAKE_CASE      )					->							np.ndarray:






         """simple docstring"""






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

         # take mean across the whole `image`
         A			:  List[Any]   =		np.mean(SCREAMING_SNAKE_CASE      )
         A			:  str   =		np.std(SCREAMING_SNAKE_CASE      )
         A			:  List[str]   =		max(SCREAMING_SNAKE_CASE		,     1.0 / math.sqrt(np.prod(image.shape      )      )      )

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





     def 							__lowerCAmelCase				(				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		,     )					->							ImageInput:






         """simple docstring"""






         A			:  Dict   =		do_normalize if do_normalize is not None else self.do_normalize
         A			:  Tuple   =		do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
         A			:  List[str]   =		patch_size if patch_size is not None else self.patch_size
         A			:  Any   =		max_patches if max_patches is not None else self.max_patches
         A			:  Optional[int]   =		self.is_vqa

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

         A			:  Tuple   =		make_list_of_images(SCREAMING_SNAKE_CASE      )

         if not valid_images(SCREAMING_SNAKE_CASE      ):
             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:
             A			:  List[str]   =		[convert_to_rgb(SCREAMING_SNAKE_CASE      ) for image in images]

         # All transformations expect numpy arrays.
         A			:  List[Any]   =		[to_numpy_array(SCREAMING_SNAKE_CASE      ) for image in images]

         if is_vqa:
             if header_text is None:
                 raise ValueError('''A header text must be provided for VQA models.'''      )
             A			:  str   =		kwargs.pop('''font_bytes'''		,     SCREAMING_SNAKE_CASE      )
             A			:  Union[str, Any]   =		kwargs.pop('''font_path'''		,     SCREAMING_SNAKE_CASE      )

             if isinstance(SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      ):
                 A			:  Optional[Any]   =		[header_text] * len(SCREAMING_SNAKE_CASE      )

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

         if do_normalize:
             A			:  Any   =		[self.normalize(image=SCREAMING_SNAKE_CASE      ) for image in images]

         # convert to torch tensor and permute
         A			:  int   =		[
             self.extract_flattened_patches(image=SCREAMING_SNAKE_CASE		,     max_patches=SCREAMING_SNAKE_CASE		,     patch_size=SCREAMING_SNAKE_CASE      )
             for image in images
         ]

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

         A			:  Optional[Any]   =		BatchFeature(
             data={'''flattened_patches''': images, '''attention_mask''': attention_masks}		,     tensor_type=SCREAMING_SNAKE_CASE      )

         return encoded_outputs






'''simple docstring'''




# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import re

from ..utils import cached_file


# docstyle-ignore
lowercase     :							Tuple       						=     '\nHuman: <<task>>\n\nAssistant: '


lowercase     :							Union[str, Any]       						=     'huggingface-tools/default-prompts'
lowercase     :							int       						=     {'chat': 'chat_prompt_template.txt', 'run': 'run_prompt_template.txt'}







def 						lowerCAmelCase_							(  snake_case__						,							snake_case__						,							snake_case__="run"		):




    '''simple docstring'''



    if prompt_or_repo_id is None:
        A			:  List[Any]   =		DEFAULT_PROMPTS_REPO

    # prompt is considered a repo ID when it does not contain any kind of space
    if re.search('''\\s'''						,							snake_case__		) is not None:
        return prompt_or_repo_id

    A			:  int   =		cached_file(
        snake_case__						,							PROMPT_FILES[mode]						,							repo_type='''dataset'''						,							user_agent={'''agent''': agent_name}		)
    with open(snake_case__						,							'''r'''						,							encoding='''utf-8'''		) as f:
        return f.read()




