



import inspect
import unittest

from transformers import YolosConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
      import torch
      from torch import nn

      from transformers import YolosForObjectDetection, YolosModel
      from transformers.models.yolos.modeling_yolos import YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
      from PIL import Image

      from transformers import AutoImageProcessor

class       _SCREAMING_SNAKE_CASE	:
     def __init__(       self :							Tuple     ,  UpperCamelCase :							Optional[Any]     ,  UpperCamelCase :							Optional[int]=1_3     ,  UpperCamelCase :							Optional[Any]=[3_0, 3_0]     ,  UpperCamelCase :							Dict=2     ,  UpperCamelCase :							List[Any]=3     ,  UpperCamelCase :							Dict=True     ,  UpperCamelCase :							Optional[Any]=True     ,  UpperCamelCase :							Tuple=3_2     ,  UpperCamelCase :							Tuple=5     ,  UpperCamelCase :							List[str]=4     ,  UpperCamelCase :							int=3_7     ,  UpperCamelCase :							Any="gelu"     ,  UpperCamelCase :							int=0.1     ,  UpperCamelCase :							str=0.1     ,  UpperCamelCase :							Any=1_0     ,  UpperCamelCase :							int=0.0_2     ,  UpperCamelCase :							Union[str, Any]=3     ,  UpperCamelCase :							Dict=None     ,  UpperCamelCase :							List[str]=8     ,  UpperCamelCase :							str=1_0     ,  )->str:
      __SCREAMING_SNAKE_CASE       :    Dict       =	parent
      __SCREAMING_SNAKE_CASE       :    Dict       =	batch_size
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	image_size
      __SCREAMING_SNAKE_CASE       :    int       =	patch_size
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	num_channels
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	is_training
      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	use_labels
      __SCREAMING_SNAKE_CASE       :    Any       =	hidden_size
      __SCREAMING_SNAKE_CASE       :    Tuple       =	num_hidden_layers
      __SCREAMING_SNAKE_CASE       :    str       =	num_attention_heads
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	intermediate_size
      __SCREAMING_SNAKE_CASE       :    Tuple       =	hidden_act
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	hidden_dropout_prob
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	attention_probs_dropout_prob
      __SCREAMING_SNAKE_CASE       :    List[str]       =	type_sequence_label_size
      __SCREAMING_SNAKE_CASE       :    Dict       =	initializer_range
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	num_labels
      __SCREAMING_SNAKE_CASE       :    str       =	scope
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	n_targets
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	num_detection_tokens
      # we set the expected sequence length (which is used in several tests)
      # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token) + num_detection_tokens
      __SCREAMING_SNAKE_CASE       :    Any       =	(image_size[1] // patch_size) * (image_size[0] // patch_size)
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	num_patches + 1 + self.num_detection_tokens
     def 	__snake_case    (       self :							Union[str, Any]				)->List[Any]:
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]]				)

      __SCREAMING_SNAKE_CASE       :    Dict       =	None
      if self.use_labels:
       # labels is a list of Dict (each Dict being the labels for a given example in the batch)
       __SCREAMING_SNAKE_CASE       :    Any       =	[]
       for i in range(self.batch_size				):
        __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	{}
        __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	torch.randint(
            high=self.num_labels     ,  size=(self.n_targets,)     ,  device=UpperCamelCase				)
        __SCREAMING_SNAKE_CASE       :    str       =	torch.rand(self.n_targets     ,  4     ,  device=UpperCamelCase				)
        labels.append(UpperCamelCase				)

      __SCREAMING_SNAKE_CASE       :    Dict       =	self.get_config()

      return config, pixel_values, labels
     def 	__snake_case    (       self :							Optional[Any]				)->Dict:
      return YolosConfig(
          image_size=self.image_size     ,  patch_size=self.patch_size     ,  num_channels=self.num_channels     ,  hidden_size=self.hidden_size     ,  num_hidden_layers=self.num_hidden_layers     ,  num_attention_heads=self.num_attention_heads     ,  intermediate_size=self.intermediate_size     ,  hidden_act=self.hidden_act     ,  hidden_dropout_prob=self.hidden_dropout_prob     ,  attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,  is_decoder=UpperCamelCase     ,  initializer_range=self.initializer_range     ,  num_detection_tokens=self.num_detection_tokens     ,  num_labels=self.num_labels     ,  )
     def 	__snake_case    (       self :							List[str]     ,  UpperCamelCase :							Dict     ,  UpperCamelCase :							Tuple     ,  UpperCamelCase :							Tuple				)->str:
      __SCREAMING_SNAKE_CASE       :    Tuple       =	YolosModel(config=UpperCamelCase				)
      model.to(UpperCamelCase				)
      model.eval()
      __SCREAMING_SNAKE_CASE       :    str       =	model(UpperCamelCase				)
      self.parent.assertEqual(
          result.last_hidden_state.shape     ,  (self.batch_size, self.expected_seq_len, self.hidden_size)				)
     def 	__snake_case    (       self :							Any     ,  UpperCamelCase :							Tuple     ,  UpperCamelCase :							str     ,  UpperCamelCase :							str				)->Any:
      __SCREAMING_SNAKE_CASE       :    List[str]       =	YolosForObjectDetection(UpperCamelCase				)
      model.to(UpperCamelCase				)
      model.eval()

      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	model(pixel_values=UpperCamelCase				)
      __SCREAMING_SNAKE_CASE       :    str       =	model(UpperCamelCase				)

      self.parent.assertEqual(result.logits.shape     ,  (self.batch_size, self.num_detection_tokens, self.num_labels + 1)				)
      self.parent.assertEqual(result.pred_boxes.shape     ,  (self.batch_size, self.num_detection_tokens, 4)				)

      __SCREAMING_SNAKE_CASE       :    List[Any]       =	model(pixel_values=UpperCamelCase     ,  labels=UpperCamelCase				)

      self.parent.assertEqual(result.loss.shape     ,  ()				)
      self.parent.assertEqual(result.logits.shape     ,  (self.batch_size, self.num_detection_tokens, self.num_labels + 1)				)
      self.parent.assertEqual(result.pred_boxes.shape     ,  (self.batch_size, self.num_detection_tokens, 4)				)






     def 	__snake_case    (       self :							Union[str, Any]				)->Any:
      __SCREAMING_SNAKE_CASE       :    Any       =	self.prepare_config_and_inputs()
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	config_and_inputs
      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	{'''pixel_values''': pixel_values}
      return config, inputs_dict






@require_torch
class       _SCREAMING_SNAKE_CASE	(UpperCamelCase_			,    UpperCamelCase_			,    unittest.TestCase ):
     lowerCAmelCase					=			(YolosModel, YolosForObjectDetection) if is_torch_available() else ()
     lowerCAmelCase					=			(
         {"""feature-extraction""": YolosModel, """object-detection""": YolosForObjectDetection} if is_torch_available() else {}
     )

     lowerCAmelCase					=			False
     lowerCAmelCase					=			False
     lowerCAmelCase					=			False
     lowerCAmelCase					=			False
     def 	__snake_case    (       self :							List[Any]     ,  UpperCamelCase :							List[Any]     ,  UpperCamelCase :							Tuple     ,  UpperCamelCase :							Tuple=False				)->str:
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	super()._prepare_for_class(UpperCamelCase     ,  UpperCamelCase     ,  return_labels=UpperCamelCase				)

      if return_labels:
       if model_class.__name__ == "YolosForObjectDetection":
        __SCREAMING_SNAKE_CASE       :    Any       =	[]
        for i in range(self.model_tester.batch_size				):
         __SCREAMING_SNAKE_CASE       :    List[Any]       =	{}
         __SCREAMING_SNAKE_CASE       :    int       =	torch.ones(
             size=(self.model_tester.n_targets,)     ,  device=UpperCamelCase     ,  dtype=torch.long				)
         __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	torch.ones(
             self.model_tester.n_targets     ,  4     ,  device=UpperCamelCase     ,  dtype=torch.float				)
         labels.append(UpperCamelCase				)
        __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	labels

      return inputs_dict
     def 	__snake_case    (       self :							Optional[Any]				)->Dict:
      __SCREAMING_SNAKE_CASE       :    str       =	YolosModelTester(self				)
      __SCREAMING_SNAKE_CASE       :    str       =	ConfigTester(self     ,  config_class=UpperCamelCase     ,  has_text_modality=UpperCamelCase     ,  hidden_size=3_7				)
     def 	__snake_case    (       self :							str				)->Dict:
      self.config_tester.run_common_tests()
     def 	__snake_case    (       self :							List[Any]				)->int:
      # YOLOS does not use inputs_embeds
      pass
     def 	__snake_case    (       self :							Any				)->List[Any]:
      __SCREAMING_SNAKE_CASE       :    Any       =	self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
       __SCREAMING_SNAKE_CASE       :    Dict       =	model_class(UpperCamelCase				)
       self.assertIsInstance(model.get_input_embeddings()     ,  (nn.Module)				)
       __SCREAMING_SNAKE_CASE       :    List[str]       =	model.get_output_embeddings()
       self.assertTrue(x is None or isinstance(UpperCamelCase     ,  nn.Linear				)				)
     def 	__snake_case    (       self :							Union[str, Any]				)->Union[str, Any]:
      __SCREAMING_SNAKE_CASE       :    Tuple       =	self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
       __SCREAMING_SNAKE_CASE       :    Any       =	model_class(UpperCamelCase				)
       __SCREAMING_SNAKE_CASE       :    Any       =	inspect.signature(model.forward				)
       # signature.parameters is an OrderedDict => so arg_names order is deterministic
       __SCREAMING_SNAKE_CASE       :    Optional[int]       =	[*signature.parameters.keys()]

       __SCREAMING_SNAKE_CASE       :    List[str]       =	['''pixel_values''']
       self.assertListEqual(arg_names[:1]     ,  UpperCamelCase				)
     def 	__snake_case    (       self :							Dict				)->Dict:
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*UpperCamelCase				)
     def 	__snake_case    (       self :							List[Any]				)->Tuple:
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	self.model_tester.prepare_config_and_inputs_for_common()
      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	True

      # in YOLOS, the seq_len is different
      __SCREAMING_SNAKE_CASE       :    List[str]       =	self.model_tester.expected_seq_len
      for model_class in self.all_model_classes:
       __SCREAMING_SNAKE_CASE       :    List[Any]       =	True
       __SCREAMING_SNAKE_CASE       :    Optional[int]       =	False
       __SCREAMING_SNAKE_CASE       :    Any       =	True
       __SCREAMING_SNAKE_CASE       :    int       =	model_class(UpperCamelCase				)
       model.to(UpperCamelCase				)
       model.eval()
       with torch.no_grad():
        __SCREAMING_SNAKE_CASE       :    int       =	model(**self._prepare_for_class(UpperCamelCase     ,  UpperCamelCase				)				)
       __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	outputs.attentions
       self.assertEqual(len(UpperCamelCase				)     ,  self.model_tester.num_hidden_layers				)

       # check that output_attentions also work using config
       del inputs_dict["output_attentions"]
       __SCREAMING_SNAKE_CASE       :    Optional[int]       =	True
       __SCREAMING_SNAKE_CASE       :    str       =	model_class(UpperCamelCase				)
       model.to(UpperCamelCase				)
       model.eval()
       with torch.no_grad():
        __SCREAMING_SNAKE_CASE       :    List[Any]       =	model(**self._prepare_for_class(UpperCamelCase     ,  UpperCamelCase				)				)
       __SCREAMING_SNAKE_CASE       :    Any       =	outputs.attentions
       self.assertEqual(len(UpperCamelCase				)     ,  self.model_tester.num_hidden_layers				)

       self.assertListEqual(
           list(attentions[0].shape[-3:]				)     ,  [self.model_tester.num_attention_heads, seq_len, seq_len]     ,  )
       __SCREAMING_SNAKE_CASE       :    Optional[int]       =	len(UpperCamelCase				)

       # Check attention is always last and order is fine
       __SCREAMING_SNAKE_CASE       :    Tuple       =	True
       __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	True
       __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	model_class(UpperCamelCase				)
       model.to(UpperCamelCase				)
       model.eval()
       with torch.no_grad():
        __SCREAMING_SNAKE_CASE       :    Any       =	model(**self._prepare_for_class(UpperCamelCase     ,  UpperCamelCase				)				)

       __SCREAMING_SNAKE_CASE       :    Dict       =	1
       self.assertEqual(out_len + added_hidden_states     ,  len(UpperCamelCase				)				)

       __SCREAMING_SNAKE_CASE       :    int       =	outputs.attentions

       self.assertEqual(len(UpperCamelCase				)     ,  self.model_tester.num_hidden_layers				)
       self.assertListEqual(
           list(self_attentions[0].shape[-3:]				)     ,  [self.model_tester.num_attention_heads, seq_len, seq_len]     ,  )
     def 	__snake_case    (       self :							str				)->Tuple:
      def check_hidden_states_output(UpperCamelCase :							List[Any]     ,  UpperCamelCase :							Any     ,  UpperCamelCase :							Tuple				):
       __SCREAMING_SNAKE_CASE       :    List[Any]       =	model_class(UpperCamelCase				)
       model.to(UpperCamelCase				)
       model.eval()

       with torch.no_grad():
        __SCREAMING_SNAKE_CASE       :    str       =	model(**self._prepare_for_class(UpperCamelCase     ,  UpperCamelCase				)				)

       __SCREAMING_SNAKE_CASE       :    Tuple       =	outputs.hidden_states

       __SCREAMING_SNAKE_CASE       :    Optional[int]       =	getattr(
           self.model_tester     ,  "expected_num_hidden_layers"     ,  self.model_tester.num_hidden_layers + 1				)
       self.assertEqual(len(UpperCamelCase				)     ,  UpperCamelCase				)

       # YOLOS has a different seq_length
       __SCREAMING_SNAKE_CASE       :    List[str]       =	self.model_tester.expected_seq_len

       self.assertListEqual(
           list(hidden_states[0].shape[-2:]				)     ,  [seq_length, self.model_tester.hidden_size]     ,  )

      __SCREAMING_SNAKE_CASE       :    Tuple       =	self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
       __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	True
       check_hidden_states_output(UpperCamelCase     ,  UpperCamelCase     ,  UpperCamelCase				)

       # check that output_hidden_states also work using config
       del inputs_dict["output_hidden_states"]
       __SCREAMING_SNAKE_CASE       :    str       =	True

       check_hidden_states_output(UpperCamelCase     ,  UpperCamelCase     ,  UpperCamelCase				)
     def 	__snake_case    (       self :							Union[str, Any]				)->Union[str, Any]:
      __SCREAMING_SNAKE_CASE       :    Any       =	self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_object_detection(*UpperCamelCase				)






     @slow
     def 	__snake_case    (       self :							int				)->int:
      for model_name in YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       __SCREAMING_SNAKE_CASE       :    Tuple       =	YolosModel.from_pretrained(UpperCamelCase				)
       self.assertIsNotNone(UpperCamelCase				)






def 	_lowerCAmelCase						(				):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE       :    List[Any]       =	Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"     )
 return image






@require_torch
@require_vision
class       _SCREAMING_SNAKE_CASE	(unittest.TestCase ):
     @cached_property
     def 	__snake_case    (       self :							Dict				)->Any:
      return AutoImageProcessor.from_pretrained("hustvl/yolos-small"				) if is_vision_available() else None






     @slow
     def 	__snake_case    (       self :							Any				)->List[Any]:
      __SCREAMING_SNAKE_CASE       :    int       =	YolosForObjectDetection.from_pretrained("hustvl/yolos-small"				).to(UpperCamelCase				)

      __SCREAMING_SNAKE_CASE       :    Dict       =	self.default_image_processor
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	prepare_img()
      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	image_processor(images=UpperCamelCase     ,  return_tensors="pt"				).to(UpperCamelCase				)

      # forward pass
      with torch.no_grad():
       __SCREAMING_SNAKE_CASE       :    List[str]       =	model(inputs.pixel_values				)

      # verify outputs
      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	torch.Size((1, 1_0_0, 9_2)				)
      self.assertEqual(outputs.logits.shape     ,  UpperCamelCase				)

      __SCREAMING_SNAKE_CASE       :    Dict       =	torch.tensor(
          [[-2_4.0_2_4_8, -1_0.3_0_2_4, -1_4.8_2_9_0], [-4_2.0_3_9_2, -1_6.8_2_0_0, -2_7.4_3_3_4], [-2_7.2_7_4_3, -1_1.8_1_5_4, -1_8.7_1_4_8]]     ,  device=UpperCamelCase     ,  )
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	torch.tensor(
          [[0.2_5_5_9, 0.5_4_5_5, 0.4_7_0_6], [0.2_9_8_9, 0.7_2_7_9, 0.1_8_7_5], [0.7_7_3_2, 0.4_0_1_7, 0.4_4_6_2]]     ,  device=UpperCamelCase				)
      self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]     ,  UpperCamelCase     ,  atol=1E-4				)				)
      self.assertTrue(torch.allclose(outputs.pred_boxes[0, :3, :3]     ,  UpperCamelCase     ,  atol=1E-4				)				)

      # verify postprocessing
      __SCREAMING_SNAKE_CASE       :    List[str]       =	image_processor.post_process_object_detection(
          UpperCamelCase     ,  threshold=0.3     ,  target_sizes=[image.size[::-1]]				)[0]
      __SCREAMING_SNAKE_CASE       :    List[str]       =	torch.tensor([0.9_9_9_4, 0.9_7_9_0, 0.9_9_6_4, 0.9_9_7_2, 0.9_8_6_1]				).to(UpperCamelCase				)
      __SCREAMING_SNAKE_CASE       :    str       =	[7_5, 7_5, 1_7, 6_3, 1_7]
      __SCREAMING_SNAKE_CASE       :    str       =	torch.tensor([3_3_5.0_6_0_9, 7_9.3_8_4_8, 3_7_5.4_2_1_6, 1_8_7.2_4_9_5]				).to(UpperCamelCase				)

      self.assertEqual(len(results["scores"]				)     ,  5				)
      self.assertTrue(torch.allclose(results["scores"]     ,  UpperCamelCase     ,  atol=1E-4				)				)
      self.assertSequenceEqual(results["labels"].tolist()     ,  UpperCamelCase				)
      self.assertTrue(torch.allclose(results["boxes"][0, :]     ,  UpperCamelCase				)				)











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 	_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						(				__lowerCamelCase   :     List[str]		,	__lowerCamelCase   :     int		,	__lowerCamelCase   :     Dict     ):



 """simple docstring"""





 requires_backends(__lowerCamelCase		,	["torch"]     )
 _check_torch_version()

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



 """simple docstring"""





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

 __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	textwrap.TextWrapper(width=80     )
 __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	wrapper.wrap(text=__lowerCamelCase     )
 __SCREAMING_SNAKE_CASE       :    str       =	"\n".join(__lowerCamelCase     )

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

 # Use a temporary canvas to determine the width and height in pixels when
 # rendering the text.
 __SCREAMING_SNAKE_CASE       :    str       =	ImageDraw.Draw(Image.new("RGB"		,	(1, 1)		,	__lowerCamelCase     )     )
 __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    int       =	temp_draw.textbbox((0, 0)		,	__lowerCamelCase		,	__lowerCamelCase     )

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






def 	_lowerCAmelCase						(				__lowerCamelCase   :     np.ndarray		,	__lowerCamelCase   :     str		,	**__lowerCamelCase   :     Union[str, Any]     ):



 """simple docstring"""





 requires_backends(__lowerCamelCase		,	"vision"     )

 # Convert to PIL image if necessary
 __SCREAMING_SNAKE_CASE       :    int       =	to_pil_image(__lowerCamelCase     )

 __SCREAMING_SNAKE_CASE       :    int       =	render_text(__lowerCamelCase		,	**__lowerCamelCase     )
 __SCREAMING_SNAKE_CASE       :    Tuple       =	max(header_image.width		,	image.width     )

 __SCREAMING_SNAKE_CASE       :    Tuple       =	int(image.height * (new_width / image.width)     )
 __SCREAMING_SNAKE_CASE       :    Any       =	int(header_image.height * (new_width / header_image.width)     )

 __SCREAMING_SNAKE_CASE       :    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
 __SCREAMING_SNAKE_CASE       :    Dict       =	to_numpy_array(__lowerCamelCase     )

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

 return new_image






class       _SCREAMING_SNAKE_CASE	(UpperCamelCase ):
     lowerCAmelCase					=			["""flattened_patches"""]





     def __init__(       self :							Tuple     ,  UpperCamelCase :							bool = True     ,  UpperCamelCase :							bool = True     ,  UpperCamelCase :							Dict[str, int] = None     ,  UpperCamelCase :							int = 2_0_4_8     ,  UpperCamelCase :							bool = False     ,  **UpperCamelCase :							Optional[int]     ,  )->None:
      super().__init__(**UpperCamelCase				)
      __SCREAMING_SNAKE_CASE       :    Tuple       =	patch_size if patch_size is not None else {"height": 1_6, "width": 1_6}
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	do_normalize
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	do_convert_rgb
      __SCREAMING_SNAKE_CASE       :    List[str]       =	max_patches
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	is_vqa





     def 	__snake_case    (       self :							Tuple     ,  UpperCamelCase :							np.ndarray     ,  UpperCamelCase :							int     ,  UpperCamelCase :							dict     ,  **UpperCamelCase :							Tuple				)->np.ndarray:
      requires_backends(self.extract_flattened_patches     ,  "torch"				)
      _check_torch_version()

      # convert to torch
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	to_channel_dimension_format(UpperCamelCase     ,  ChannelDimension.FIRST				)
      __SCREAMING_SNAKE_CASE       :    int       =	torch.from_numpy(UpperCamelCase				)

      __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    List[str]       =	patch_size["height"], patch_size["width"]
      __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    Optional[Any]       =	get_image_size(UpperCamelCase				)

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

      __SCREAMING_SNAKE_CASE       :    Any       =	torch.nn.functional.interpolate(
          image.unsqueeze(0				)     ,  size=(resized_height, resized_width)     ,  mode="bilinear"     ,  align_corners=UpperCamelCase     ,  antialias=UpperCamelCase     ,  ).squeeze(0				)

      # [1, rows, columns, patch_height * patch_width * image_channels]
      __SCREAMING_SNAKE_CASE       :    List[str]       =	torch_extract_patches(UpperCamelCase     ,  UpperCamelCase     ,  UpperCamelCase				)

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

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

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

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

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

      __SCREAMING_SNAKE_CASE       :    str       =	to_numpy_array(UpperCamelCase				)

      return result





     def 	__snake_case    (       self :							Optional[Any]     ,  UpperCamelCase :							np.ndarray     ,  UpperCamelCase :							Optional[Union[str, ChannelDimension]] = None     ,  **UpperCamelCase :							Optional[int]				)->np.ndarray:
      if image.dtype == np.uinta:
       __SCREAMING_SNAKE_CASE       :    Optional[int]       =	image.astype(np.floataa				)

      # take mean across the whole `image`
      __SCREAMING_SNAKE_CASE       :    int       =	np.mean(UpperCamelCase				)
      __SCREAMING_SNAKE_CASE       :    List[str]       =	np.std(UpperCamelCase				)
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	max(UpperCamelCase     ,  1.0 / math.sqrt(np.prod(image.shape				)				)				)

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






     def 	__snake_case    (       self :							Union[str, Any]     ,  UpperCamelCase :							ImageInput     ,  UpperCamelCase :							Optional[str] = None     ,  UpperCamelCase :							bool = None     ,  UpperCamelCase :							Optional[bool] = None     ,  UpperCamelCase :							Optional[int] = None     ,  UpperCamelCase :							Optional[Dict[str, int]] = None     ,  UpperCamelCase :							Optional[Union[str, TensorType]] = None     ,  UpperCamelCase :							ChannelDimension = ChannelDimension.FIRST     ,  **UpperCamelCase :							Dict     ,  )->ImageInput:
      __SCREAMING_SNAKE_CASE       :    int       =	do_normalize if do_normalize is not None else self.do_normalize
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	patch_size if patch_size is not None else self.patch_size
      __SCREAMING_SNAKE_CASE       :    List[str]       =	max_patches if max_patches is not None else self.max_patches
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	self.is_vqa

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

      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	make_list_of_images(UpperCamelCase				)

      if not valid_images(UpperCamelCase				):
       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:
       __SCREAMING_SNAKE_CASE       :    Optional[int]       =	[convert_to_rgb(UpperCamelCase				) for image in images]

      # All transformations expect numpy arrays.
      __SCREAMING_SNAKE_CASE       :    str       =	[to_numpy_array(UpperCamelCase				) for image in images]

      if is_vqa:
       if header_text is None:
        raise ValueError("A header text must be provided for VQA models."				)
       __SCREAMING_SNAKE_CASE       :    Any       =	kwargs.pop("font_bytes"     ,  UpperCamelCase				)
       __SCREAMING_SNAKE_CASE       :    Any       =	kwargs.pop("font_path"     ,  UpperCamelCase				)

       if isinstance(UpperCamelCase     ,  UpperCamelCase				):
        __SCREAMING_SNAKE_CASE       :    Dict       =	[header_text] * len(UpperCamelCase				)

       __SCREAMING_SNAKE_CASE       :    Optional[int]       =	[
           render_header(UpperCamelCase     ,  header_text[i]     ,  font_bytes=UpperCamelCase     ,  font_path=UpperCamelCase				)
           for i, image in enumerate(UpperCamelCase				)
       ]

      if do_normalize:
       __SCREAMING_SNAKE_CASE       :    List[str]       =	[self.normalize(image=UpperCamelCase				) for image in images]

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

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

      __SCREAMING_SNAKE_CASE       :    Dict       =	BatchFeature(
          data={"flattened_patches": images, "attention_mask": attention_masks}     ,  tensor_type=UpperCamelCase				)

      return encoded_outputs






