



"""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 	snake_case_  (				):


       '''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 	snake_case_  (				A_ :		Union[str, Any],    A_ :		Optional[int],    A_ :		List[Any]						):


       '''simple docstring'''





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

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


       '''simple docstring'''





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

       _lowerCamelCase						:    Optional[Any]         =			textwrap.TextWrapper(width=80						)
       _lowerCamelCase						:    Any         =			wrapper.wrap(text=A_						)
       _lowerCamelCase						:    List[Any]         =			'''\n'''.join(A_						)

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

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

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






def 	snake_case_  (				A_ :		np.ndarray,    A_ :		str,    **A_ :		Dict						):


       '''simple docstring'''





       requires_backends(A_,    '''vision'''						)

       # Convert to PIL image if necessary
       _lowerCamelCase						:    int         =			to_pil_image(A_						)

       _lowerCamelCase						:    Optional[int]         =			render_text(A_,    **A_						)
       _lowerCamelCase						:    Optional[int]         =			max(header_image.width,    image.width						)

       _lowerCamelCase						:    int         =			int(image.height * (new_width / image.width)						)
       _lowerCamelCase						:    Dict         =			int(header_image.height * (new_width / header_image.width)						)

       _lowerCamelCase						:    Optional[Any]         =			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						:    List[Any]         =			to_numpy_array(A_						)

       if infer_channel_dimension_format(A_						) == ChannelDimension.LAST:
              _lowerCamelCase						:    List[Any]         =			to_channel_dimension_format(A_,    ChannelDimension.LAST						)

       return new_image







class 	__snake_case	(     _lowercase):
  snake_case__    :       Union[str, Any]        =						["flattened_patches"]




  def __init__(			self					:						Any						, __lowerCAmelCase					:						bool = True						, __lowerCAmelCase					:						bool = True						, __lowerCAmelCase					:						Dict[str, int] = None						, __lowerCAmelCase					:						int = 2_0_4_8						, __lowerCAmelCase					:						bool = False						, **__lowerCAmelCase					:						str						, ):






         """simple docstring"""






         super().__init__(**__lowerCAmelCase		)
         _lowerCamelCase						:    List[Any]         =			patch_size if patch_size is not None else {'''height''': 1_6, '''width''': 1_6}
         _lowerCamelCase						:    str         =			do_normalize
         _lowerCamelCase						:    Optional[int]         =			do_convert_rgb
         _lowerCamelCase						:    Optional[Any]         =			max_patches
         _lowerCamelCase						:    List[str]         =			is_vqa




  def 						SCREAMING_SNAKE_CASE    (			self					:						int						, __lowerCAmelCase					:						np.ndarray						, __lowerCAmelCase					:						int						, __lowerCAmelCase					:						dict						, **__lowerCAmelCase					:						Dict		):






         """simple docstring"""






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

         # convert to torch
         _lowerCamelCase						:    Dict         =			to_channel_dimension_format(__lowerCAmelCase						, ChannelDimension.FIRST		)
         _lowerCamelCase						:    Optional[Any]         =			torch.from_numpy(__lowerCAmelCase		)

         _lowerCamelCase						:    Tuple         =			patch_size['''height'''], patch_size['''width''']
         _lowerCamelCase						:    int         =			get_image_size(__lowerCAmelCase		)

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

         _lowerCamelCase						:    Dict         =			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[Any]         =			torch_extract_patches(__lowerCAmelCase						, __lowerCAmelCase						, __lowerCAmelCase		)

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

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

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

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

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

         _lowerCamelCase						:    List[Any]         =			to_numpy_array(__lowerCAmelCase		)

         return result




  def 						SCREAMING_SNAKE_CASE    (			self					:						Tuple						, __lowerCAmelCase					:						np.ndarray						, __lowerCAmelCase					:						Optional[Union[str, ChannelDimension]] = None						, **__lowerCAmelCase					:						int		):






         """simple docstring"""






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

         # take mean across the whole `image`
         _lowerCamelCase						:    int         =			np.mean(__lowerCAmelCase		)
         _lowerCamelCase						:    Any         =			np.std(__lowerCAmelCase		)
         _lowerCamelCase						:    Optional[int]         =			max(__lowerCAmelCase						, 1.0 / math.sqrt(np.prod(image.shape		)		)		)

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




  def 						SCREAMING_SNAKE_CASE    (			self					:						Union[str, Any]						, __lowerCAmelCase					:						ImageInput						, __lowerCAmelCase					:						Optional[str] = None						, __lowerCAmelCase					:						bool = None						, __lowerCAmelCase					:						Optional[bool] = None						, __lowerCAmelCase					:						Optional[int] = None						, __lowerCAmelCase					:						Optional[Dict[str, int]] = None						, __lowerCAmelCase					:						Optional[Union[str, TensorType]] = None						, __lowerCAmelCase					:						ChannelDimension = ChannelDimension.FIRST						, **__lowerCAmelCase					:						Optional[Any]						, ):






         """simple docstring"""






         _lowerCamelCase						:    Union[str, Any]         =			do_normalize if do_normalize is not None else self.do_normalize
         _lowerCamelCase						:    str         =			do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
         _lowerCamelCase						:    Any         =			patch_size if patch_size is not None else self.patch_size
         _lowerCamelCase						:    Dict         =			max_patches if max_patches is not None else self.max_patches
         _lowerCamelCase						:    Union[str, Any]         =			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						:    Optional[Any]         =			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						:    Any         =			[convert_to_rgb(__lowerCAmelCase		) for image in images]

         # All transformations expect numpy arrays.
         _lowerCamelCase						:    List[str]         =			[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						:    str         =			kwargs.pop('''font_bytes'''						, __lowerCAmelCase		)
                _lowerCamelCase						:    Optional[Any]         =			kwargs.pop('''font_path'''						, __lowerCAmelCase		)

                if isinstance(__lowerCAmelCase						, __lowerCAmelCase		):
                       _lowerCamelCase						:    int         =			[header_text] * len(__lowerCAmelCase		)

                _lowerCamelCase						:    int         =			[
                    render_header(__lowerCAmelCase						, header_text[i]						, font_bytes=__lowerCAmelCase						, font_path=__lowerCAmelCase		)
                    for i, image in enumerate(__lowerCAmelCase		)
                ]

         if do_normalize:
                _lowerCamelCase						:    Optional[int]         =			[self.normalize(image=__lowerCAmelCase		) for image in images]

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

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

         _lowerCamelCase						:    Dict         =			BatchFeature(
             data={'''flattened_patches''': images, '''attention_mask''': attention_masks}						, tensor_type=__lowerCAmelCase		)

         return encoded_outputs







"""simple docstring"""






import json
import os
import unittest

from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
    VOCAB_FILES_NAMES,
    BlenderbotSmallTokenizer,
)

from ...test_tokenization_common import TokenizerTesterMixin






class 	__snake_case	(     _lowercase  ,					unittest.TestCase):
  snake_case__    :       Optional[int]        =						BlenderbotSmallTokenizer
  snake_case__    :       List[str]        =						False




  def 						SCREAMING_SNAKE_CASE    (			self					:						Dict		):






         """simple docstring"""






         super().setUp()

         _lowerCamelCase						:    str         =			['''__start__''', '''adapt''', '''act''', '''ap@@''', '''te''', '''__end__''', '''__unk__''']
         _lowerCamelCase						:    Any         =			dict(zip(__lowerCAmelCase						, range(len(__lowerCAmelCase		)		)		)		)

         _lowerCamelCase						:    Any         =			['''#version: 0.2''', '''a p''', '''t e</w>''', '''ap t</w>''', '''a d''', '''ad apt</w>''', '''a c''', '''ac t</w>''', '''''']
         _lowerCamelCase						:    List[str]         =			{'''unk_token''': '''__unk__''', '''bos_token''': '''__start__''', '''eos_token''': '''__end__'''}

         _lowerCamelCase						:    Dict         =			os.path.join(self.tmpdirname						, VOCAB_FILES_NAMES['''vocab_file''']		)
         _lowerCamelCase						:    List[str]         =			os.path.join(self.tmpdirname						, VOCAB_FILES_NAMES['''merges_file''']		)
         with open(self.vocab_file						, '''w'''						, encoding='''utf-8'''		) as fp:
                fp.write(json.dumps(__lowerCAmelCase		) + '''\n'''		)
         with open(self.merges_file						, '''w'''						, encoding='''utf-8'''		) as fp:
                fp.write('''\n'''.join(__lowerCAmelCase		)		)




  def 						SCREAMING_SNAKE_CASE    (			self					:						Tuple						, **__lowerCAmelCase					:						List[str]		):






         """simple docstring"""






         kwargs.update(self.special_tokens_map		)
         return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname						, **__lowerCAmelCase		)




  def 						SCREAMING_SNAKE_CASE    (			self					:						List[Any]						, __lowerCAmelCase					:						List[str]		):






         """simple docstring"""






         _lowerCamelCase						:    int         =			'''adapt act apte'''
         _lowerCamelCase						:    Tuple         =			'''adapt act apte'''
         return input_text, output_text




  def 						SCREAMING_SNAKE_CASE    (			self					:						int		):






         """simple docstring"""






         _lowerCamelCase						:    Tuple         =			BlenderbotSmallTokenizer(self.vocab_file						, self.merges_file						, **self.special_tokens_map		)
         _lowerCamelCase						:    int         =			'''adapt act apte'''
         _lowerCamelCase						:    Optional[Any]         =			['''adapt''', '''act''', '''ap@@''', '''te''']
         _lowerCamelCase						:    Optional[Any]         =			tokenizer.tokenize(__lowerCAmelCase		)
         self.assertListEqual(__lowerCAmelCase						, __lowerCAmelCase		)

         _lowerCamelCase						:    List[Any]         =			[tokenizer.bos_token] + tokens + [tokenizer.eos_token]

         _lowerCamelCase						:    Dict         =			[0, 1, 2, 3, 4, 5]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowerCAmelCase		)						, __lowerCAmelCase		)




  def 						SCREAMING_SNAKE_CASE    (			self					:						List[str]		):






         """simple docstring"""






         _lowerCamelCase						:    str         =			BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M'''		)
         assert tok('''sam'''		).input_ids == [1_3_8_4]
         _lowerCamelCase						:    List[str]         =			'''I am a small frog.'''
         _lowerCamelCase						:    str         =			tok([src_text]						, padding=__lowerCAmelCase						, truncation=__lowerCAmelCase		)['''input_ids''']
         _lowerCamelCase						:    Any         =			tok.batch_decode(__lowerCAmelCase						, skip_special_tokens=__lowerCAmelCase						, clean_up_tokenization_spaces=__lowerCAmelCase		)[0]
         assert src_text != decoded  # I wish it did!
         assert decoded == "i am a small frog ."




  def 						SCREAMING_SNAKE_CASE    (			self					:						Any		):






         """simple docstring"""






         _lowerCamelCase						:    Union[str, Any]         =			BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M'''		)
         _lowerCamelCase						:    Optional[Any]         =			'''I am a small frog .'''
         _lowerCamelCase						:    str         =			'''.'''
         _lowerCamelCase						:    str         =			tok(__lowerCAmelCase		)['''input_ids''']
         _lowerCamelCase						:    Dict         =			tok(__lowerCAmelCase		)['''input_ids''']

         assert encoded[-1] == encoded_dot[0]


