'''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_						:		int								=  False


lowerCAmelCase_						:		str								=  logging.get_logger(__name__)
lowerCAmelCase_						:		List[str]								=  """ybelkada/fonts"""
def     __A				(  )  ->      Any:




						'''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     __A				(  UpperCAmelCase  ,UpperCAmelCase  ,UpperCAmelCase       )  ->      str:




						'''simple docstring'''
						requires_backends(UpperCAmelCase  ,["torch"]       )
						_check_torch_version()

						_UpperCamelCase      :      Optional[int]       =    image_tensor.unsqueeze(0       )
						_UpperCamelCase      :      List[str]       =    torch.nn.functional.unfold(UpperCAmelCase  ,(patch_height, patch_width)  ,stride=(patch_height, patch_width)       )
						_UpperCamelCase      :      List[Any]       =    patches.reshape(image_tensor.size(0       )  ,image_tensor.size(1       )  ,UpperCAmelCase  ,UpperCAmelCase  ,-1       )
						_UpperCamelCase      :      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     __A				(  UpperCAmelCase  ,UpperCAmelCase = 3_6  ,UpperCAmelCase = "black"  ,UpperCAmelCase = "white"  ,UpperCAmelCase = 5  ,UpperCAmelCase = 5  ,UpperCAmelCase = 5  ,UpperCAmelCase = 5  ,UpperCAmelCase = None  ,UpperCAmelCase = None  ,)  ->      int:




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

						_UpperCamelCase      :      Optional[Any]       =    textwrap.TextWrapper(width=8_0       )
						_UpperCamelCase      :      str       =    wrapper.wrap(text=UpperCAmelCase       )
						_UpperCamelCase      :      Optional[int]       =    "\n".join(UpperCAmelCase       )

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

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

						# Create the actual image with a bit of padding around the text.
						_UpperCamelCase      :      str       =    text_width + left_padding + right_padding
						_UpperCamelCase      :      Dict       =    text_height + top_padding + bottom_padding
						_UpperCamelCase      :      List[str]       =    Image.new("RGB"  ,(image_width, image_height)  ,UpperCAmelCase       )
						_UpperCamelCase      :      List[Any]       =    ImageDraw.Draw(UpperCAmelCase       )
						draw.text(xy=(left_padding, top_padding)  ,text=UpperCAmelCase  ,fill=UpperCAmelCase  ,font=UpperCAmelCase       )
						return image
def     __A				(  UpperCAmelCase  ,UpperCAmelCase  ,**UpperCAmelCase       )  ->      Any:




						'''simple docstring'''
						requires_backends(UpperCAmelCase  ,"vision"       )

						# Convert to PIL image if necessary
						_UpperCamelCase      :      Any       =    to_pil_image(UpperCAmelCase       )

						_UpperCamelCase      :      int       =    render_text(UpperCAmelCase  ,**UpperCAmelCase       )
						_UpperCamelCase      :      Tuple       =    max(header_image.width  ,image.width       )

						_UpperCamelCase      :      Any       =    int(image.height * (new_width / image.width)       )
						_UpperCamelCase      :      List[Any]       =    int(header_image.height * (new_width / header_image.width)       )

						_UpperCamelCase      :      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
						_UpperCamelCase      :      Union[str, Any]       =    to_numpy_array(UpperCAmelCase       )

						if infer_channel_dimension_format(UpperCAmelCase       ) == ChannelDimension.LAST:
												_UpperCamelCase      :      int       =    to_channel_dimension_format(UpperCAmelCase  ,ChannelDimension.LAST       )

						return new_image






class 		SCREAMING_SNAKE_CASE  (       SCREAMING_SNAKE_CASE						):







				'''simple docstring'''
				UpperCAmelCase__					      =				['''flattened_patches''']





				def __init__(    self       :    Tuple							,		lowercase__       :    Any = True							,		lowercase__       :    str = True							,		lowercase__       :    Any = None							,		lowercase__       :    Dict = 2_048							,		lowercase__       :    Union[str, Any] = False							,		**lowercase__       :    int							,		)				->Optional[Any]:


										'''simple docstring'''




										super().__init__(**lowercase__       )
										_UpperCamelCase      :      str       =    patch_size if patch_size is not None else {"height": 16, "width": 16}
										_UpperCamelCase      :      Optional[int]       =    do_normalize
										_UpperCamelCase      :      str       =    do_convert_rgb
										_UpperCamelCase      :      Dict       =    max_patches
										_UpperCamelCase      :      List[str]       =    is_vqa





				def 			snake_case__					(    self       :    List[str]							,		lowercase__       :    List[Any]							,		lowercase__       :    int							,		lowercase__       :    Dict							,		**lowercase__       :    str       )				->Any:


										'''simple docstring'''




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

										# convert to torch
										_UpperCamelCase      :      Optional[int]       =    to_channel_dimension_format(lowercase__							,		ChannelDimension.FIRST       )
										_UpperCamelCase      :      Tuple       =    torch.from_numpy(lowercase__       )

										_UpperCamelCase      :      Tuple       =    patch_size["height"], patch_size["width"]
										_UpperCamelCase      :      List[Any]       =    get_image_size(lowercase__       )

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

										_UpperCamelCase      :      str       =    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]
										_UpperCamelCase      :      Any       =    torch_extract_patches(lowercase__							,		lowercase__							,		lowercase__       )

										_UpperCamelCase      :      int       =    patches.shape
										_UpperCamelCase      :      Any       =    patches_shape[1]
										_UpperCamelCase      :      Optional[Any]       =    patches_shape[2]
										_UpperCamelCase      :      int       =    patches_shape[3]

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

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

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

										# [max_patches, 2 + patch_height * patch_width * image_channels]
										_UpperCamelCase      :      List[str]       =    torch.nn.functional.pad(lowercase__							,		[0, 0, 0, max_patches - (rows * columns)]       ).float()

										_UpperCamelCase      :      Optional[Any]       =    to_numpy_array(lowercase__       )

										return result





				def 			snake_case__					(    self       :    str							,		lowercase__       :    List[str]							,		lowercase__       :    Optional[int] = None							,		**lowercase__       :    Union[str, Any]       )				->Optional[int]:


										'''simple docstring'''




										if image.dtype == np.uinta:
																_UpperCamelCase      :      Optional[int]       =    image.astype(np.floataa       )

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

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







				def 			snake_case__					(    self       :    Any							,		lowercase__       :    Dict							,		lowercase__       :    int = None							,		lowercase__       :    int = None							,		lowercase__       :    Dict = None							,		lowercase__       :    Optional[int] = None							,		lowercase__       :    Dict = None							,		lowercase__       :    Optional[Any] = None							,		lowercase__       :    Any = ChannelDimension.FIRST							,		**lowercase__       :    List[str]							,		)				->str:


										'''simple docstring'''




										_UpperCamelCase      :      str       =    do_normalize if do_normalize is not None else self.do_normalize
										_UpperCamelCase      :      Optional[Any]       =    do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
										_UpperCamelCase      :      Any       =    patch_size if patch_size is not None else self.patch_size
										_UpperCamelCase      :      Optional[Any]       =    max_patches if max_patches is not None else self.max_patches
										_UpperCamelCase      :      Any       =    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 "       )

										_UpperCamelCase      :      Any       =    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:
																_UpperCamelCase      :      Union[str, Any]       =    [convert_to_rgb(lowercase__       ) for image in images]

										# All transformations expect numpy arrays.
										_UpperCamelCase      :      Optional[int]       =    [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."       )
																_UpperCamelCase      :      Optional[Any]       =    kwargs.pop("font_bytes"							,		lowercase__       )
																_UpperCamelCase      :      Union[str, Any]       =    kwargs.pop("font_path"							,		lowercase__       )

																if isinstance(lowercase__							,		lowercase__       ):
																						_UpperCamelCase      :      Dict       =    [header_text] * len(lowercase__       )

																_UpperCamelCase      :      Union[str, Any]       =    [
																    render_header(lowercase__							,		header_text[i]							,		font_bytes=lowercase__							,		font_path=lowercase__       )
																    for i, image in enumerate(lowercase__       )
																]

										if do_normalize:
																_UpperCamelCase      :      Tuple       =    [self.normalize(image=lowercase__       ) for image in images]

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

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

										_UpperCamelCase      :      Union[str, Any]       =    BatchFeature(
										    data={"flattened_patches": images, "attention_mask": attention_masks}							,		tensor_type=lowercase__       )

										return encoded_outputs






'''simple docstring'''


import unittest

from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


_UpperCamelCase 							=  get_tests_dir("""fixtures/test_sentencepiece.model""")
_UpperCamelCase 							=  get_tests_dir("""fixtures/test_sentencepiece_bpe.model""")

_UpperCamelCase 							=  """pt""" if is_torch_available() else """tf"""







@require_sentencepiece
@require_tokenizers
class 							__magic_name__	(       lowerCAmelCase    ,		unittest.TestCase ):

     """simple docstring"""






     lowerCamelCase__       						=				CamembertTokenizer
     lowerCamelCase__       						=				CamembertTokenizerFast
     lowerCamelCase__       						=				True
     lowerCamelCase__       						=				True


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          super().setUp()

          # We have a SentencePiece fixture for testing
          __A	:       List[Any]      =							CamembertTokenizer(lowerCamelCase		)
          tokenizer.save_pretrained(self.tmpdirname		)


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       Optional[Any]      =							"<pad>"
          __A	:       Union[str, Any]      =							1

          self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCamelCase		)			,     lowerCamelCase		)
          self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCamelCase		)			,     lowerCamelCase		)


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       Union[str, Any]      =							list(self.get_tokenizer().get_vocab().keys()		)

          self.assertEqual(vocab_keys[0]			,     "<s>NOTUSED"		)
          self.assertEqual(vocab_keys[1]			,     "<pad>"		)
          self.assertEqual(vocab_keys[-1]			,     "<mask>"		)
          self.assertEqual(len(lowerCamelCase		)			,     1004		)


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          self.assertEqual(self.get_tokenizer().vocab_size			,     1005		)


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       Optional[Any]      =							CamembertTokenizer(lowerCamelCase		)
          tokenizer.save_pretrained(self.tmpdirname		)
          __A	:       Optional[int]      =							CamembertTokenizerFast.from_pretrained(self.tmpdirname		)

          __A	:       str      =							"I was born in 92000, and this is falsé."

          __A	:       List[Any]      =							tokenizer.encode(lowerCamelCase		)
          __A	:       Tuple      =							rust_tokenizer.encode(lowerCamelCase		)
          self.assertListEqual(lowerCamelCase			,     lowerCamelCase		)

          __A	:       Dict      =							tokenizer.encode(lowerCamelCase			,     add_special_tokens=lowerCamelCase		)
          __A	:       str      =							rust_tokenizer.encode(lowerCamelCase			,     add_special_tokens=lowerCamelCase		)
          self.assertListEqual(lowerCamelCase			,     lowerCamelCase		)

          # <unk> tokens are not the same for `rust` than for `slow`.
          # Because spm gives back raw token instead of `unk` in EncodeAsPieces
          # tokens = tokenizer.tokenize(sequence)
          __A	:       int      =							tokenizer.convert_ids_to_tokens(lowerCamelCase		)
          __A	:       Optional[int]      =							rust_tokenizer.tokenize(lowerCamelCase		)
          self.assertListEqual(lowerCamelCase			,     lowerCamelCase		)


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          if not self.test_rust_tokenizer:
               return

          __A	:       Tuple      =							self.get_tokenizer()
          __A	:       List[Any]      =							self.get_rust_tokenizer()

          __A	:       Any      =							"I was born in 92000, and this is falsé."

          __A	:       int      =							tokenizer.tokenize(lowerCamelCase		)
          __A	:       Any      =							rust_tokenizer.tokenize(lowerCamelCase		)
          self.assertListEqual(lowerCamelCase			,     lowerCamelCase		)

          __A	:       Any      =							tokenizer.encode(lowerCamelCase			,     add_special_tokens=lowerCamelCase		)
          __A	:       Optional[int]      =							rust_tokenizer.encode(lowerCamelCase			,     add_special_tokens=lowerCamelCase		)
          self.assertListEqual(lowerCamelCase			,     lowerCamelCase		)

          __A	:       Union[str, Any]      =							self.get_rust_tokenizer()
          __A	:       Optional[Any]      =							tokenizer.encode(lowerCamelCase		)
          __A	:       Dict      =							rust_tokenizer.encode(lowerCamelCase		)
          self.assertListEqual(lowerCamelCase			,     lowerCamelCase		)


     @slow
     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       Any      =							{"input_ids": [[5, 54, 7196, 297, 30, 23, 776, 18, 11, 3215, 3705, 8252, 22, 3164, 1181, 2116, 29, 16, 813, 25, 791, 3314, 20, 3446, 38, 2_7575, 120, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 468, 17, 11, 9088, 20, 1517, 8, 2_2804, 1_8818, 10, 38, 629, 607, 607, 142, 19, 7196, 867, 56, 1_0326, 24, 2267, 20, 416, 5072, 1_5612, 233, 734, 7, 2399, 27, 16, 3015, 1649, 7, 24, 20, 4338, 2399, 27, 13, 3400, 14, 13, 6189, 8, 930, 9, 6]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
          # fmt: on

          # camembert is a french model. So we also use french texts.
          __A	:       int      =							[
              "Le transformeur est un modèle d'apprentissage profond introduit en 2017, "
              "utilisé principalement dans le domaine du traitement automatique des langues (TAL).",
              "À l'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus "
              "pour gérer des données séquentielles, telles que le langage naturel, pour des tâches "
              "telles que la traduction et la synthèse de texte.",
          ]

          self.tokenizer_integration_test_util(
              expected_encoding=lowerCamelCase			,     model_name="camembert-base"			,     revision="3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf"			,     sequences=lowerCamelCase			,     )

