



import io
import math
from typing import Dict, Optional, Union

import numpy as np
from huggingface_hub import hf_hub_download

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import convert_to_rgb, normalize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    get_image_size,
    infer_channel_dimension_format,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_vision_available, logging
from ...utils.import_utils import requires_backends


if is_vision_available():
	import textwrap

	from PIL import Image, ImageDraw, ImageFont

if is_torch_available():
	import torch

	from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
	a	:   Optional[Any]       			=       False


a	:   Tuple       			=       logging.get_logger(__name__)
a	:   int       			=       '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__:						Optional[Any]						,		lowerCAmelCase__:						Optional[Any]						,		lowerCAmelCase__:						Dict   ):





			"""simple docstring"""


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

			UpperCAmelCase_:						Any					=			image_tensor.unsqueeze(0   )
			UpperCAmelCase_:						Union[str, Any]					=			torch.nn.functional.unfold(_lowerCAmelCase						,		(patch_height, patch_width)						,		stride=(patch_height, patch_width)   )
			UpperCAmelCase_:						Dict					=			patches.reshape(image_tensor.size(0   )						,		image_tensor.size(1   )						,		_lowerCAmelCase						,		_lowerCAmelCase						,		-1   )
			UpperCAmelCase_:						str					=			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__:						List[str] = 3_6						,		lowerCAmelCase__:						str = "black"						,		lowerCAmelCase__:						str = "white"						,		lowerCAmelCase__:						Tuple = 5						,		lowerCAmelCase__:						str = 5						,		lowerCAmelCase__:						Any = 5						,		lowerCAmelCase__:						Dict = 5						,		lowerCAmelCase__:						Union[str, Any] = None						,		lowerCAmelCase__:						Optional[Any] = None						,		):





			"""simple docstring"""


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

			UpperCAmelCase_:						List[Any]					=			textwrap.TextWrapper(width=8_0   )
			UpperCAmelCase_:						Optional[int]					=			wrapper.wrap(text=_lowerCAmelCase   )
			UpperCAmelCase_:						List[Any]					=			"""\n""".join(_lowerCAmelCase   )

			if font_bytes is not None and font_path is None:
						UpperCAmelCase_:						Union[str, Any]					=			io.BytesIO(_lowerCAmelCase   )
			elif font_path is not None:
						UpperCAmelCase_:						List[str]					=			font_path
			else:
						UpperCAmelCase_:						Tuple					=			hf_hub_download(_lowerCAmelCase						,		"""Arial.TTF"""   )
			UpperCAmelCase_:						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.
			UpperCAmelCase_:						str					=			ImageDraw.Draw(Image.new("""RGB"""						,		(1, 1)						,		_lowerCAmelCase   )   )
			UpperCAmelCase_:						Any					=			temp_draw.textbbox((0, 0)						,		_lowerCAmelCase						,		_lowerCAmelCase   )

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







def       lowerCAmelCase_     (lowerCAmelCase__:						str						,		lowerCAmelCase__:						Optional[int]						,		**lowerCAmelCase__:						List[str]   ):





			"""simple docstring"""


			requires_backends(_lowerCAmelCase						,		"""vision"""   )

			# Convert to PIL image if necessary
			UpperCAmelCase_:						Tuple					=			to_pil_image(_lowerCAmelCase   )

			UpperCAmelCase_:						Any					=			render_text(_lowerCAmelCase						,		**_lowerCAmelCase   )
			UpperCAmelCase_:						Optional[Any]					=			max(header_image.width						,		image.width   )

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

			UpperCAmelCase_:						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
			UpperCAmelCase_:						Union[str, Any]					=			to_numpy_array(_lowerCAmelCase   )

			if infer_channel_dimension_format(_lowerCAmelCase   ) == ChannelDimension.LAST:
						UpperCAmelCase_:						Optional[Any]					=			to_channel_dimension_format(_lowerCAmelCase						,		ChannelDimension.LAST   )

			return new_image







class 							_a							(					_a ):
			A							=     ['''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_,					)    ->					Optional[Any]:
						super().__init__(**snake_case_ )
						UpperCAmelCase_:						List[str]					=			patch_size if patch_size is not None else {"""height""": 16, """width""": 16}
						UpperCAmelCase_:						List[str]					=			do_normalize
						UpperCAmelCase_:						Tuple					=			do_convert_rgb
						UpperCAmelCase_:						str					=			max_patches
						UpperCAmelCase_:						Dict					=			is_vqa

			def 			__snake_case    (self,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_,					**SCREAMING_SNAKE_CASE_ )    ->					int:
						requires_backends(self.extract_flattened_patches,					"""torch""" )
						_check_torch_version()

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

						UpperCAmelCase_:						List[Any]					=			patch_size["""height"""], patch_size["""width"""]
						UpperCAmelCase_:						Tuple					=			get_image_size(snake_case_ )

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

						UpperCAmelCase_:						List[str]					=			torch.nn.functional.interpolate(
						    image.unsqueeze(0 ),					size=(resized_height, resized_width),					mode="""bilinear""",					align_corners=snake_case_,					antialias=snake_case_,					).squeeze(0 )

						# [1, rows, columns, patch_height * patch_width * image_channels]
						UpperCAmelCase_:						str					=			torch_extract_patches(snake_case_,					snake_case_,					snake_case_ )

						UpperCAmelCase_:						Tuple					=			patches.shape
						UpperCAmelCase_:						List[Any]					=			patches_shape[1]
						UpperCAmelCase_:						Dict					=			patches_shape[2]
						UpperCAmelCase_:						List[str]					=			patches_shape[3]

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

						# [rows * columns, 1]
						UpperCAmelCase_:						Optional[int]					=			torch.arange(snake_case_ ).reshape([rows, 1] ).repeat(1,					snake_case_ ).reshape([rows * columns, 1] )
						UpperCAmelCase_:						Optional[int]					=			torch.arange(snake_case_ ).reshape([1, columns] ).repeat(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]
						UpperCAmelCase_:						Optional[int]					=			row_ids.to(torch.floataa )
						UpperCAmelCase_:						Tuple					=			col_ids.to(torch.floataa )

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

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

						UpperCAmelCase_:						int					=			to_numpy_array(snake_case_ )

						return result

			def 			__snake_case    (self,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_ = None,					**SCREAMING_SNAKE_CASE_ )    ->					str:
						if image.dtype == np.uinta:
									UpperCAmelCase_:						int					=			image.astype(np.floataa )

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

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

			def 			__snake_case    (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_,					)    ->					Optional[Any]:
						UpperCAmelCase_:						Any					=			do_normalize if do_normalize is not None else self.do_normalize
						UpperCAmelCase_:						Dict					=			do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
						UpperCAmelCase_:						str					=			patch_size if patch_size is not None else self.patch_size
						UpperCAmelCase_:						List[Any]					=			max_patches if max_patches is not None else self.max_patches
						UpperCAmelCase_:						int					=			self.is_vqa

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

						UpperCAmelCase_:						Tuple					=			make_list_of_images(snake_case_ )

						if not valid_images(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:
									UpperCAmelCase_:						Union[str, Any]					=			[convert_to_rgb(snake_case_ ) for image in images]

						# All transformations expect numpy arrays.
						UpperCAmelCase_:						Tuple					=			[to_numpy_array(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.""" )
									UpperCAmelCase_:						Any					=			kwargs.pop("""font_bytes""",					snake_case_ )
									UpperCAmelCase_:						str					=			kwargs.pop("""font_path""",					snake_case_ )

									if isinstance(snake_case_,					snake_case_ ):
												UpperCAmelCase_:						List[str]					=			[header_text] * len(snake_case_ )

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

						if do_normalize:
									UpperCAmelCase_:						Union[str, Any]					=			[self.normalize(image=snake_case_ ) for image in images]

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

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

						UpperCAmelCase_:						List[Any]					=			BatchFeature(
						    data={"""flattened_patches""": images, """attention_mask""": attention_masks},					tensor_type=snake_case_ )

						return encoded_outputs











'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__a          =		{
    "configuration_bloom": ["BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP", "BloomConfig", "BloomOnnxConfig"],
}
try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __a          =		["BloomTokenizerFast"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __a          =		[
           "BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST",
           "BloomForCausalLM",
           "BloomModel",
           "BloomPreTrainedModel",
           "BloomForSequenceClassification",
           "BloomForTokenClassification",
           "BloomForQuestionAnswering",
       ]

if TYPE_CHECKING:
       from .configuration_bloom import BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP, BloomConfig, BloomOnnxConfig

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_bloom_fast import BloomTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_bloom import (
                  BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST,
                  BloomForCausalLM,
                  BloomForQuestionAnswering,
                  BloomForSequenceClassification,
                  BloomForTokenClassification,
                  BloomModel,
                  BloomPreTrainedModel,
              )

else:
       import sys

       __a          =		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
