
"""simple docstring"""
import unittest

from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
			import os

			# The slow tests are often failing with OOM error on GPU
			# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
			# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
			SCREAMING_SNAKE_CASE_					     =	'''platform'''
			import jax
			import jax.numpy as jnp
			import numpy as np

			from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel
@require_flax
class       lowerCAmelCase_		:



							'''simple docstring'''





							_snake_case						      =		PegasusConfig
							_snake_case						      =		{}
							_snake_case						      =		'''gelu'''




							def __init__( self    ,				snake_case_    ,				snake_case_=13    ,				snake_case_=7    ,				snake_case_=True    ,				snake_case_=False    ,				snake_case_=99    ,				snake_case_=32    ,				snake_case_=5    ,				snake_case_=4    ,				snake_case_=37    ,				snake_case_=0.1    ,				snake_case_=0.1    ,				snake_case_=20    ,				snake_case_=2    ,				snake_case_=1    ,				snake_case_=0    ,				)       ->  str:
													__lowerCAmelCase     =  parent
													__lowerCAmelCase     =  batch_size
													__lowerCAmelCase     =  seq_length
													__lowerCAmelCase     =  is_training
													__lowerCAmelCase     =  use_labels
													__lowerCAmelCase     =  vocab_size
													__lowerCAmelCase     =  hidden_size
													__lowerCAmelCase     =  num_hidden_layers
													__lowerCAmelCase     =  num_attention_heads
													__lowerCAmelCase     =  intermediate_size

													__lowerCAmelCase     =  hidden_dropout_prob
													__lowerCAmelCase     =  attention_probs_dropout_prob
													__lowerCAmelCase     =  max_position_embeddings
													__lowerCAmelCase     =  eos_token_id
													__lowerCAmelCase     =  pad_token_id
													__lowerCAmelCase     =  bos_token_id




							def    A__						( self				)       ->  Optional[int]:
													__lowerCAmelCase     =  ids_tensor([self.batch_size, self.seq_length - 1]    ,				self.vocab_size				).clip(3    ,				self.vocab_size				)
													__lowerCAmelCase     =  np.expand_dims(np.array([self.eos_token_id] * self.batch_size				)    ,				1				)
													__lowerCAmelCase     =  np.concatenate([input_ids, eos_tensor]    ,				axis=1				)

													__lowerCAmelCase     =  ids_tensor([self.batch_size, self.seq_length]    ,				self.vocab_size				)

													__lowerCAmelCase     =  self.config_cls(
													    vocab_size=self.vocab_size    ,				d_model=self.hidden_size    ,				encoder_layers=self.num_hidden_layers    ,				decoder_layers=self.num_hidden_layers    ,				encoder_attention_heads=self.num_attention_heads    ,				decoder_attention_heads=self.num_attention_heads    ,				encoder_ffn_dim=self.intermediate_size    ,				decoder_ffn_dim=self.intermediate_size    ,				dropout=self.hidden_dropout_prob    ,				attention_dropout=self.attention_probs_dropout_prob    ,				max_position_embeddings=self.max_position_embeddings    ,				eos_token_ids=[2]    ,				bos_token_id=self.bos_token_id    ,				pad_token_id=self.pad_token_id    ,				decoder_start_token_id=self.pad_token_id    ,				**self.config_updates    ,				)
													__lowerCAmelCase     =  prepare_pegasus_inputs_dict(snake_case_    ,				snake_case_    ,				snake_case_				)
													return config, inputs_dict




							def    A__						( self    ,				snake_case_    ,				snake_case_    ,				snake_case_				)       ->  Dict:
													__lowerCAmelCase     =  20
													__lowerCAmelCase     =  model_class_name(snake_case_				)

													__lowerCAmelCase     =  model.encode(inputs_dict["""input_ids"""]				)

													__lowerCAmelCase   ,     __lowerCAmelCase     =  (
													    inputs_dict["""decoder_input_ids"""],
													    inputs_dict["""decoder_attention_mask"""],
													)

													__lowerCAmelCase     =  model.init_cache(decoder_input_ids.shape[0]    ,				snake_case_    ,				snake_case_				)
													__lowerCAmelCase     =  jnp.ones((decoder_input_ids.shape[0], max_decoder_length)    ,				dtype="""i4"""				)

													__lowerCAmelCase     =  jnp.broadcast_to(
													    jnp.arange(decoder_input_ids.shape[-1] - 1				)[None, :]    ,				(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)    ,				)
													__lowerCAmelCase     =  model.decode(
													    decoder_input_ids[:, :-1]    ,				snake_case_    ,				decoder_attention_mask=snake_case_    ,				past_key_values=snake_case_    ,				decoder_position_ids=snake_case_    ,				)

													__lowerCAmelCase     =  jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]    ,				dtype="""i4"""				)
													__lowerCAmelCase     =  model.decode(
													    decoder_input_ids[:, -1:]    ,				snake_case_    ,				decoder_attention_mask=snake_case_    ,				past_key_values=outputs_cache.past_key_values    ,				decoder_position_ids=snake_case_    ,				)

													__lowerCAmelCase     =  model.decode(snake_case_    ,				snake_case_				)

													__lowerCAmelCase     =  np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])				)				)
													self.parent.assertTrue(diff < 1e-3    ,				msg=f"""Max diff is {diff}"""				)




							def    A__						( self    ,				snake_case_    ,				snake_case_    ,				snake_case_				)       ->  List[str]:
													__lowerCAmelCase     =  20
													__lowerCAmelCase     =  model_class_name(snake_case_				)

													__lowerCAmelCase     =  model.encode(inputs_dict["""input_ids"""]				)

													__lowerCAmelCase   ,     __lowerCAmelCase     =  (
													    inputs_dict["""decoder_input_ids"""],
													    inputs_dict["""decoder_attention_mask"""],
													)

													__lowerCAmelCase     =  jnp.concatenate(
													    [
													        decoder_attention_mask,
													        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])				),
													    ]    ,				axis=-1    ,				)

													__lowerCAmelCase     =  model.init_cache(decoder_input_ids.shape[0]    ,				snake_case_    ,				snake_case_				)
													__lowerCAmelCase     =  jnp.broadcast_to(
													    jnp.arange(decoder_input_ids.shape[-1] - 1				)[None, :]    ,				(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)    ,				)

													__lowerCAmelCase     =  model.decode(
													    decoder_input_ids[:, :-1]    ,				snake_case_    ,				decoder_attention_mask=snake_case_    ,				past_key_values=snake_case_    ,				decoder_position_ids=snake_case_    ,				)
													__lowerCAmelCase     =  jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]    ,				dtype="""i4"""				)
													__lowerCAmelCase     =  model.decode(
													    decoder_input_ids[:, -1:]    ,				snake_case_    ,				past_key_values=outputs_cache.past_key_values    ,				decoder_attention_mask=snake_case_    ,				decoder_position_ids=snake_case_    ,				)

													__lowerCAmelCase     =  model.decode(snake_case_    ,				snake_case_    ,				decoder_attention_mask=snake_case_				)

													__lowerCAmelCase     =  np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])				)				)
													self.parent.assertTrue(diff < 1e-3    ,				msg=f"""Max diff is {diff}"""				)

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase=None ,    _lowerCAmelCase=None ,    ):
						if attention_mask is None:
												__lowerCAmelCase     =  np.not_equal(_lowerCAmelCase ,    config.pad_token_id						).astype(np.inta						)
						if decoder_attention_mask is None:
												__lowerCAmelCase     =  np.concatenate(
												    [
												        np.ones(decoder_input_ids[:, :1].shape ,    dtype=np.inta						),
												        np.not_equal(decoder_input_ids[:, 1:] ,    config.pad_token_id						).astype(np.inta						),
												    ] ,    axis=-1 ,    )
						return {
						    "input_ids": input_ids,
						    "decoder_input_ids": decoder_input_ids,
						    "attention_mask": attention_mask,
						    "decoder_attention_mask": decoder_attention_mask,
						}



@require_flax
class       lowerCAmelCase_		(				A__				,				unittest.TestCase ):



							'''simple docstring'''





							_snake_case						      =		(
							    (
							        FlaxPegasusForConditionalGeneration,
							        FlaxPegasusModel,
							    )
							    if is_flax_available()
							    else ()
							)
							_snake_case						      =		(FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
							_snake_case						      =		True
							_snake_case						      =		False
							_snake_case						      =		False
							_snake_case						      =		False




							def    A__						( self				)       ->  List[str]:
													__lowerCAmelCase     =  FlaxPegasusModelTester(self				)
													__lowerCAmelCase     =  ConfigTester(self    ,				config_class=snake_case_				)




							def    A__						( self				)       ->  Dict:
													self.config_tester.run_common_tests()




							def    A__						( self				)       ->  List[Any]:
													__lowerCAmelCase   ,     __lowerCAmelCase     =  self.model_tester.prepare_config_and_inputs_for_common()
													for model_class in self.all_model_classes:
																			self.model_tester.check_use_cache_forward(snake_case_    ,				snake_case_    ,				snake_case_				)




							def    A__						( self				)       ->  str:
													__lowerCAmelCase   ,     __lowerCAmelCase     =  self.model_tester.prepare_config_and_inputs_for_common()
													for model_class in self.all_model_classes:
																			self.model_tester.check_use_cache_forward_with_attn_mask(snake_case_    ,				snake_case_    ,				snake_case_				)




							def    A__						( self				)       ->  Union[str, Any]:
													__lowerCAmelCase   ,     __lowerCAmelCase     =  self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																			with self.subTest(model_class.__name__				):
																									__lowerCAmelCase     =  self._prepare_for_class(snake_case_    ,				snake_case_				)
																									__lowerCAmelCase     =  model_class(snake_case_				)

																									@jax.jit
																									def encode_jitted(snake_case_    ,				snake_case_=None    ,				**snake_case_				):
																															return model.encode(input_ids=snake_case_    ,				attention_mask=snake_case_				)

																									with self.subTest("""JIT Enabled"""				):
																															__lowerCAmelCase     =  encode_jitted(**snake_case_				).to_tuple()

																									with self.subTest("""JIT Disabled"""				):
																															with jax.disable_jit():
																																					__lowerCAmelCase     =  encode_jitted(**snake_case_				).to_tuple()

																									self.assertEqual(len(snake_case_				)    ,				len(snake_case_				)				)
																									for jitted_output, output in zip(snake_case_    ,				snake_case_				):
																															self.assertEqual(jitted_output.shape    ,				output.shape				)




							def    A__						( self				)       ->  Optional[int]:
													__lowerCAmelCase   ,     __lowerCAmelCase     =  self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																			with self.subTest(model_class.__name__				):
																									__lowerCAmelCase     =  model_class(snake_case_				)
																									__lowerCAmelCase     =  model.encode(inputs_dict["""input_ids"""]    ,				inputs_dict["""attention_mask"""]				)

																									__lowerCAmelCase     =  {
																									    """decoder_input_ids""": inputs_dict["""decoder_input_ids"""],
																									    """decoder_attention_mask""": inputs_dict["""decoder_attention_mask"""],
																									    """encoder_outputs""": encoder_outputs,
																									}

																									@jax.jit
																									def decode_jitted(snake_case_    ,				snake_case_    ,				snake_case_				):
																															return model.decode(
																															    decoder_input_ids=snake_case_    ,				decoder_attention_mask=snake_case_    ,				encoder_outputs=snake_case_    ,				)

																									with self.subTest("""JIT Enabled"""				):
																															__lowerCAmelCase     =  decode_jitted(**snake_case_				).to_tuple()

																									with self.subTest("""JIT Disabled"""				):
																															with jax.disable_jit():
																																					__lowerCAmelCase     =  decode_jitted(**snake_case_				).to_tuple()

																									self.assertEqual(len(snake_case_				)    ,				len(snake_case_				)				)
																									for jitted_output, output in zip(snake_case_    ,				snake_case_				):
																															self.assertEqual(jitted_output.shape    ,				output.shape				)




							@slow
							def    A__						( self				)       ->  Tuple:
													for model_class_name in self.all_model_classes:
																			__lowerCAmelCase     =  model_class_name.from_pretrained("""google/pegasus-large"""    ,				from_pt=snake_case_				)
																			__lowerCAmelCase     =  np.ones((1, 1)				)
																			__lowerCAmelCase     =  model(snake_case_				)
																			self.assertIsNotNone(snake_case_				)




							@slow
							def    A__						( self				)       ->  Any:
													__lowerCAmelCase     =  FlaxPegasusForConditionalGeneration.from_pretrained("""google/pegasus-xsum"""				)
													__lowerCAmelCase     =  PegasusTokenizer.from_pretrained("""google/pegasus-xsum"""				)

													__lowerCAmelCase     =  [
													    """ PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.""",
													    """ The London trio are up for best UK act and best album, as well as getting two nominations in the best song category.\"We got told like this morning 'Oh I think you're nominated'\", said Dappy.\"And I was like 'Oh yeah, which one?' And now we've got nominated for four awards. I mean, wow!\"Bandmate Fazer added: \"We thought it's best of us to come down and mingle with everyone and say hello to the cameras. And now we find we've got four nominations.\"The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn't be too disappointed if they didn't win this time around.\"At the end of the day we're grateful to be where we are in our careers.\"If it don't happen then it don't happen - live to fight another day and keep on making albums and hits for the fans.\"Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a  re-working of The Killers' All These Things That I've Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year's Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border.\"We just done Edinburgh the other day,\" said Dappy.\"We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!\" """,
													]

													__lowerCAmelCase     =  [
													    """California's largest electricity provider has turned off power to hundreds of thousands of customers.""",
													    """Pop group N-Dubz have revealed they were surprised to get four nominations for this year's Mobo Awards.""",
													]

													__lowerCAmelCase     =  tokenizer(snake_case_    ,				return_tensors="""np"""    ,				truncation=snake_case_    ,				max_length=512    ,				padding=snake_case_				)
													__lowerCAmelCase     =  model.generate(**snake_case_    ,				num_beams=2				).sequences
													__lowerCAmelCase     =  tokenizer.batch_decode(snake_case_    ,				skip_special_tokens=snake_case_				)
													assert tgt_text == decoded


"""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:
			SCREAMING_SNAKE_CASE_					     =	False


SCREAMING_SNAKE_CASE_					     =	logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_					     =	'''ybelkada/fonts'''

def  lowercase     ():
						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  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase						):
						requires_backends(_lowerCAmelCase ,    ["""torch"""]						)
						_check_torch_version()

						__lowerCAmelCase     =  image_tensor.unsqueeze(0						)
						__lowerCAmelCase     =  torch.nn.functional.unfold(_lowerCAmelCase ,    (patch_height, patch_width) ,    stride=(patch_height, patch_width)						)
						__lowerCAmelCase     =  patches.reshape(image_tensor.size(0						) ,    image_tensor.size(1						) ,    _lowerCAmelCase ,    _lowerCAmelCase ,    -1						)
						__lowerCAmelCase     =  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  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase = 36 ,    _lowerCAmelCase = "black" ,    _lowerCAmelCase = "white" ,    _lowerCAmelCase = 5 ,    _lowerCAmelCase = 5 ,    _lowerCAmelCase = 5 ,    _lowerCAmelCase = 5 ,    _lowerCAmelCase = None ,    _lowerCAmelCase = None ,    ):
						requires_backends(_lowerCAmelCase ,    """vision"""						)
						# Add new lines so that each line is no more than 80 characters.

						__lowerCAmelCase     =  textwrap.TextWrapper(width=80						)
						__lowerCAmelCase     =  wrapper.wrap(text=_lowerCAmelCase						)
						__lowerCAmelCase     =  """\n""".join(_lowerCAmelCase						)

						if font_bytes is not None and font_path is None:
												__lowerCAmelCase     =  io.BytesIO(_lowerCAmelCase						)
						elif font_path is not None:
												__lowerCAmelCase     =  font_path
						else:
												__lowerCAmelCase     =  hf_hub_download(_lowerCAmelCase ,    """Arial.TTF"""						)
						__lowerCAmelCase     =  ImageFont.truetype(_lowerCAmelCase ,    encoding="""UTF-8""" ,    size=_lowerCAmelCase						)

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

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

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase ,    **_lowerCAmelCase						):
						requires_backends(_lowerCAmelCase ,    """vision"""						)

						# Convert to PIL image if necessary
						__lowerCAmelCase     =  to_pil_image(_lowerCAmelCase						)

						__lowerCAmelCase     =  render_text(_lowerCAmelCase ,    **_lowerCAmelCase						)
						__lowerCAmelCase     =  max(header_image.width ,    image.width						)

						__lowerCAmelCase     =  int(image.height * (new_width / image.width)						)
						__lowerCAmelCase     =  int(header_image.height * (new_width / header_image.width)						)

						__lowerCAmelCase     =  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     =  to_numpy_array(_lowerCAmelCase						)

						if infer_channel_dimension_format(_lowerCAmelCase						) == ChannelDimension.LAST:
												__lowerCAmelCase     =  to_channel_dimension_format(_lowerCAmelCase ,    ChannelDimension.LAST						)

						return new_image



class       lowerCAmelCase_		(				A__ ):



							'''simple docstring'''





							_snake_case						      =		['''flattened_patches''']




							def __init__( self    ,				snake_case_ = True    ,				snake_case_ = True    ,				snake_case_ = None    ,				snake_case_ = 2_048    ,				snake_case_ = False    ,				**snake_case_    ,				)       ->  None:
													super().__init__(**snake_case_				)
													__lowerCAmelCase     =  patch_size if patch_size is not None else {"""height""": 16, """width""": 16}
													__lowerCAmelCase     =  do_normalize
													__lowerCAmelCase     =  do_convert_rgb
													__lowerCAmelCase     =  max_patches
													__lowerCAmelCase     =  is_vqa




							def    A__						( self    ,				snake_case_    ,				snake_case_    ,				snake_case_    ,				**snake_case_				)       ->  np.ndarray:
													requires_backends(self.extract_flattened_patches    ,				"""torch"""				)
													_check_torch_version()

													# convert to torch
													__lowerCAmelCase     =  to_channel_dimension_format(snake_case_    ,				ChannelDimension.FIRST				)
													__lowerCAmelCase     =  torch.from_numpy(snake_case_				)

													__lowerCAmelCase   ,     __lowerCAmelCase     =  patch_size["""height"""], patch_size["""width"""]
													__lowerCAmelCase   ,     __lowerCAmelCase     =  get_image_size(snake_case_				)

													# maximize scale s.t.
													__lowerCAmelCase     =  math.sqrt(max_patches * (patch_height / image_height) * (patch_width / image_width)				)
													__lowerCAmelCase     =  max(min(math.floor(scale * image_height / patch_height				)    ,				snake_case_				)    ,				1				)
													__lowerCAmelCase     =  max(min(math.floor(scale * image_width / patch_width				)    ,				snake_case_				)    ,				1				)
													__lowerCAmelCase     =  max(num_feasible_rows * patch_height    ,				1				)
													__lowerCAmelCase     =  max(num_feasible_cols * patch_width    ,				1				)

													__lowerCAmelCase     =  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]
													__lowerCAmelCase     =  torch_extract_patches(snake_case_    ,				snake_case_    ,				snake_case_				)

													__lowerCAmelCase     =  patches.shape
													__lowerCAmelCase     =  patches_shape[1]
													__lowerCAmelCase     =  patches_shape[2]
													__lowerCAmelCase     =  patches_shape[3]

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

													# [rows * columns, 1]
													__lowerCAmelCase     =  torch.arange(snake_case_				).reshape([rows, 1]				).repeat(1    ,				snake_case_				).reshape([rows * columns, 1]				)
													__lowerCAmelCase     =  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]
													__lowerCAmelCase     =  row_ids.to(torch.floataa				)
													__lowerCAmelCase     =  col_ids.to(torch.floataa				)

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

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

													__lowerCAmelCase     =  to_numpy_array(snake_case_				)

													return result




							def    A__						( self    ,				snake_case_    ,				snake_case_ = None    ,				**snake_case_				)       ->  np.ndarray:
													if image.dtype == np.uinta:
																			__lowerCAmelCase     =  image.astype(np.floataa				)

													# take mean across the whole `image`
													__lowerCAmelCase     =  np.mean(snake_case_				)
													__lowerCAmelCase     =  np.std(snake_case_				)
													__lowerCAmelCase     =  max(snake_case_    ,				1.0 / math.sqrt(np.prod(image.shape				)				)				)

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




							def    A__						( self    ,				snake_case_    ,				snake_case_ = None    ,				snake_case_ = None    ,				snake_case_ = None    ,				snake_case_ = None    ,				snake_case_ = None    ,				snake_case_ = None    ,				snake_case_ = ChannelDimension.FIRST    ,				**snake_case_    ,				)       ->  ImageInput:
													__lowerCAmelCase     =  do_normalize if do_normalize is not None else self.do_normalize
													__lowerCAmelCase     =  do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
													__lowerCAmelCase     =  patch_size if patch_size is not None else self.patch_size
													__lowerCAmelCase     =  max_patches if max_patches is not None else self.max_patches
													__lowerCAmelCase     =  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 """				)

													__lowerCAmelCase     =  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:
																			__lowerCAmelCase     =  [convert_to_rgb(snake_case_				) for image in images]

													# All transformations expect numpy arrays.
													__lowerCAmelCase     =  [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."""				)
																			__lowerCAmelCase     =  kwargs.pop("""font_bytes"""    ,				snake_case_				)
																			__lowerCAmelCase     =  kwargs.pop("""font_path"""    ,				snake_case_				)

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

																			__lowerCAmelCase     =  [
																			    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:
																			__lowerCAmelCase     =  [self.normalize(image=snake_case_				) for image in images]

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

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

													__lowerCAmelCase     =  BatchFeature(
													    data={"""flattened_patches""": images, """attention_mask""": attention_masks}    ,				tensor_type=snake_case_				)

													return encoded_outputs
