




from __future__ import annotations

import unittest

import numpy as np

from transformers import OPTConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
	import tensorflow as tf

	from transformers import GPTaTokenizer, TFOPTForCausalLM, TFOPTModel

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	Union[str, Any]     ,							lowercase     :	str     ,							lowercase     :	Tuple=None     ,							lowercase     :	Optional[int]=None					):


						'''simple docstring'''



						if attention_mask is None:
												lowerCamelCase_							=   tf.cast(tf.math.not_equal(lowercase     ,							config.pad_token_id					)     ,							tf.inta					)
						return {"input_ids": input_ids, "attention_mask": attention_mask}







@require_tf
class   A:



	'''simple docstring'''


	UpperCamelCase						       =						OPTConfig
	UpperCamelCase						       =						{}
	UpperCamelCase						       =						'''gelu'''

	def __init__(    self :  str						,							A_ :  Optional[int]						,							A_ :  Union[str, Any]=13						,							A_ :  Any=7						,							A_ :  Any=True						,							A_ :  Optional[int]=False						,							A_ :  int=99						,							A_ :  Tuple=16						,							A_ :  Dict=2						,							A_ :  Dict=4						,							A_ :  Optional[Any]=4						,							A_ :  Union[str, Any]="gelu"						,							A_ :  List[Any]=0.1						,							A_ :  List[str]=0.1						,							A_ :  str=20						,							A_ :  Union[str, Any]=2						,							A_ :  Optional[int]=1						,							A_ :  List[Any]=0						,							A_ :  Dict=16						,							A_ :  Union[str, Any]=16						,							)       ->     Optional[int]:

							"""simple docstring"""






							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_act
							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
							lowerCamelCase_							=   embed_dim
							lowerCamelCase_							=   word_embed_proj_dim
							lowerCamelCase_							=   False

	def     a__			(    self :  int  )       ->     Optional[int]:

							"""simple docstring"""






							lowerCamelCase_							=   ids_tensor([self.batch_size, self.seq_length - 1]						,							self.vocab_size  )
							lowerCamelCase_							=   tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size  )						,							1  )
							lowerCamelCase_							=   tf.concat([input_ids, eos_tensor]						,							axis=1  )

							lowerCamelCase_							=   self.config_cls(
							    vocab_size=self.vocab_size						,							hidden_size=self.hidden_size						,							num_hidden_layers=self.num_hidden_layers						,							num_attention_heads=self.num_attention_heads						,							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_id=self.eos_token_id						,							bos_token_id=self.bos_token_id						,							pad_token_id=self.pad_token_id						,							embed_dim=self.embed_dim						,							word_embed_proj_dim=self.word_embed_proj_dim						,							is_encoder_decoder=A_						,							**self.config_updates						,							)
							lowerCamelCase_							=   prepare_opt_inputs_dict(A_						,							A_  )
							return config, inputs_dict





	def     a__			(    self :  Optional[int]						,							A_ :  Optional[int]						,							A_ :  List[Any]  )       ->     Union[str, Any]:

							"""simple docstring"""






							lowerCamelCase_							=   TFOPTModel(config=A_  )
							lowerCamelCase_							=   inputs_dict['input_ids']

							lowerCamelCase_							=   input_ids[:1, :]
							lowerCamelCase_							=   inputs_dict['attention_mask'][:1, :]
							lowerCamelCase_							=   1

							# first forward pass
							lowerCamelCase_							=   model(A_						,							attention_mask=A_						,							use_cache=A_  )

							lowerCamelCase_					,    lowerCamelCase_							=   outputs.to_tuple()

							# create hypothetical next token and extent to next_input_ids
							lowerCamelCase_							=   ids_tensor((self.batch_size, 3)						,							config.vocab_size  )
							lowerCamelCase_							=   tf.cast(ids_tensor((self.batch_size, 3)						,							2  )						,							tf.inta  )

							# append to next input_ids and
							lowerCamelCase_							=   tf.concat([input_ids, next_tokens]						,							axis=-1  )
							lowerCamelCase_							=   tf.concat([attention_mask, next_attn_mask]						,							axis=-1  )

							lowerCamelCase_							=   model(A_						,							attention_mask=A_  )[0]
							lowerCamelCase_							=   model(A_						,							attention_mask=A_						,							past_key_values=A_  )[0]

							self.parent.assertEqual(next_tokens.shape[1]						,							output_from_past.shape[1]  )

							# select random slice
							lowerCamelCase_							=   int(ids_tensor((1,)						,							output_from_past.shape[-1]  )  )
							lowerCamelCase_							=   output_from_no_past[:, -3:, random_slice_idx]
							lowerCamelCase_							=   output_from_past[:, :, random_slice_idx]

							# test that outputs are equal for slice
							tf.debugging.assert_near(A_						,							A_						,							rtol=1E-3  )







@require_tf
class   A(						UpperCamelCase							,				UpperCamelCase							,				unittest.TestCase       ):



	'''simple docstring'''


	UpperCamelCase						       =						(TFOPTModel, TFOPTForCausalLM) if is_tf_available() else ()
	UpperCamelCase						       =						(TFOPTForCausalLM,) if is_tf_available() else ()
	UpperCamelCase						       =						(
	    {'''feature-extraction''': TFOPTModel, '''text-generation''': TFOPTForCausalLM} if is_tf_available() else {}
	)
	UpperCamelCase						       =						False
	UpperCamelCase						       =						False
	UpperCamelCase						       =						False
	UpperCamelCase						       =						10

	def     a__			(    self :  str  )       ->     Optional[int]:

							"""simple docstring"""






							lowerCamelCase_							=   TFOPTModelTester(self  )
							lowerCamelCase_							=   ConfigTester(self						,							config_class=A_  )

	def     a__			(    self :  Union[str, Any]  )       ->     Union[str, Any]:

							"""simple docstring"""






							self.config_tester.run_common_tests()

	def     a__			(    self :  List[str]  )       ->     List[Any]:

							"""simple docstring"""






							lowerCamelCase_							=   self.model_tester.prepare_config_and_inputs_for_common()
							self.model_tester.check_decoder_model_past_large_inputs(*A_  )





	def     a__			(    self :  Dict  )       ->     Optional[int]:

							"""simple docstring"""






							lowerCamelCase_					,    lowerCamelCase_							=   self.model_tester.prepare_config_and_inputs_for_common()

							def _get_word_embedding_weight(A_ :  Optional[int]						,							A_ :  List[Any]  ):
													if hasattr(A_						,							'weight'  ):
																			return embedding_layer.weight
													else:
																			# Here we build the word embeddings weights if not exists.
																			# And then we retry to get the attribute once built.
																			model.build()
																			if hasattr(A_						,							'weight'  ):
																									return embedding_layer.weight
																			else:
																									return None

							for model_class in self.all_model_classes:
													for size in [config.vocab_size - 10, config.vocab_size + 10]:
																			# build the embeddings
																			lowerCamelCase_							=   model_class(config=A_  )
																			lowerCamelCase_							=   _get_word_embedding_weight(A_						,							model.get_input_embeddings()  )
																			lowerCamelCase_							=   _get_word_embedding_weight(A_						,							model.get_output_embeddings()  )

																			# reshape the embeddings
																			model.resize_token_embeddings(A_  )
																			lowerCamelCase_							=   _get_word_embedding_weight(A_						,							model.get_input_embeddings()  )
																			lowerCamelCase_							=   _get_word_embedding_weight(A_						,							model.get_output_embeddings()  )

																			# check that the resized embeddings size matches the desired size.
																			lowerCamelCase_							=   size if size is not None else config.vocab_size

																			self.assertEqual(new_input_embeddings.shape[0]						,							A_  )

																			# check that weights remain the same after resizing
																			lowerCamelCase_							=   True
																			for pa, pa in zip(old_input_embeddings.value()						,							new_input_embeddings.value()  ):
																									if tf.math.reduce_sum(tf.math.abs(pa - pa  )  ) > 0:
																															lowerCamelCase_							=   False
																			self.assertTrue(A_  )

																			if old_output_embeddings is not None and new_output_embeddings is not None:
																									self.assertEqual(new_output_embeddings.shape[0]						,							A_  )

																									lowerCamelCase_							=   True
																									for pa, pa in zip(old_output_embeddings.value()						,							new_output_embeddings.value()  ):
																															if tf.math.reduce_sum(tf.math.abs(pa - pa  )  ) > 0:
																																					lowerCamelCase_							=   False
																									self.assertTrue(A_  )

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	Optional[int]					):


						'''simple docstring'''



						return tf.constant(lowercase     ,							dtype=tf.intaa					)







@require_tf
class   A(						unittest.TestCase       ):



	'''simple docstring'''


	UpperCamelCase						       =						99

	def     a__			(    self :  Union[str, Any]  )       ->     Optional[int]:

							"""simple docstring"""






							lowerCamelCase_							=   tf.ones((4, 1)						,							dtype=tf.intaa  ) * 2
							lowerCamelCase_							=   tf.concat([ids_tensor((4, 6)						,							self.vocab_size - 3  ) + 3, eos_column_vector]						,							axis=1  )
							lowerCamelCase_							=   input_ids.shape[0]
							lowerCamelCase_							=   OPTConfig(
							    vocab_size=self.vocab_size						,							hidden_size=24						,							num_hidden_layers=2						,							num_attention_heads=2						,							ffn_dim=32						,							max_position_embeddings=48						,							eos_token_id=2						,							pad_token_id=1						,							bos_token_id=0						,							)
							return config, input_ids, batch_size







@require_sentencepiece
@require_tf
class   A(						unittest.TestCase       ):



	'''simple docstring'''


	@slow
	def     a__			(    self :  Tuple  )       ->     Optional[int]:

							"""simple docstring"""






							lowerCamelCase_							=   TFOPTModel.from_pretrained('facebook/opt-350m'  )
							lowerCamelCase_							=   _long_tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]  )
							lowerCamelCase_							=   tf.not_equal(A_						,							model.config.pad_token_id  )
							with tf.GradientTape():
													lowerCamelCase_							=   model(input_ids=A_						,							attention_mask=A_  ).last_hidden_state
							lowerCamelCase_							=   (1, 11, 512)
							self.assertEqual(output.shape						,							A_  )
							lowerCamelCase_							=   tf.constant(
							    [[-0.2873, -1.9218, -0.3033], [-1.2710, -0.1338, -0.1902], [0.4095, 0.1214, -1.3121]]  )
							self.assertTrue(np.allclose(output[:, :3, :3]						,							A_						,							atol=4E-3  )  )

							lowerCamelCase_							=   tf.function(A_						,							jit_compile=A_  )
							lowerCamelCase_							=   xla_generate(A_						,							A_  )[0]
							self.assertTrue(np.allclose(output[:, :3, :3]						,							A_						,							atol=4E-2  )  )







@require_tf
@slow
class   A(						unittest.TestCase       ):



	'''simple docstring'''


	def     a__			(    self :  Any  )       ->     Dict:

							"""simple docstring"""






							super().setUp()
							lowerCamelCase_							=   'facebook/opt-350m'





	def     a__			(    self :  List[Any]  )       ->     str:

							"""simple docstring"""






							lowerCamelCase_							=   TFOPTForCausalLM.from_pretrained(self.path_model  )
							lowerCamelCase_							=   GPTaTokenizer.from_pretrained(self.path_model  )

							lowerCamelCase_							=   [
							    'Today is a beautiful day and I want to',
							    'In the city of',
							    'Paris is the capital of France and',
							    'Computers and mobile phones have taken',
							]
							# verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False
							lowerCamelCase_							=   tokenizer(A_						,							return_tensors='tf'						,							padding=A_						,							add_special_tokens=A_  )
							lowerCamelCase_							=   tf.math.reduce_mean(model(inputs.input_ids						,							attention_mask=inputs.attention_mask  )[0]						,							axis=-1  )
							lowerCamelCase_							=   tf.constant(
							    [
							        [1.3851, -13.8923, -10.5229, -10.7533, -0.2309, -10.2384, -0.5365, -9.0947, -5.1670],
							        [-4.7073, -10.6276, -3.9415, -21.5242, -0.2822, -0.2822, -0.2822, -0.2822, -0.2822],
							        [0.6247, -3.4229, -8.9179, -1.4297, -14.1650, 1.4146, -9.0218, -0.2703, -0.2703],
							        [6.4783, -1.9913, -10.7926, -2.3336, 1.5092, -0.9974, -6.8213, 1.3477, 1.3477],
							    ]  )
							self.assertTrue(np.allclose(A_						,							A_						,							atol=1E-4  )  )

							lowerCamelCase_							=   tf.function(A_						,							jit_compile=A_  )
							lowerCamelCase_							=   tf.math.reduce_mean(xla_generate(inputs.input_ids						,							attention_mask=inputs.attention_mask  )[0]						,							axis=-1  )
							self.assertTrue(np.allclose(A_						,							A_						,							atol=1E-4  )  )







@require_tf
@slow
class   A(						unittest.TestCase       ):



	'''simple docstring'''


	@property
	def     a__			(    self :  List[Any]  )       ->     Dict:

							"""simple docstring"""






							return [
							    "Today is a beautiful day and I want",
							    "In the city of",
							    "Paris is the capital of France and",
							    "Computers and mobile phones have taken",
							]

	def     a__			(    self :  List[Any]  )       ->     int:

							"""simple docstring"""






							lowerCamelCase_							=   'facebook/opt-125m'

							lowerCamelCase_							=   [
							    'Today is a beautiful day and I want to',
							    'In the city of New York, the city',
							    'Paris is the capital of France and the capital',
							    'Computers and mobile phones have taken over the',
							]

							lowerCamelCase_							=   []
							lowerCamelCase_							=   GPTaTokenizer.from_pretrained(A_  )
							lowerCamelCase_							=   TFOPTForCausalLM.from_pretrained(A_  )

							for prompt in self.prompts:
													lowerCamelCase_							=   tokenizer(A_						,							return_tensors='tf'  ).input_ids

													lowerCamelCase_							=   model.generate(A_						,							max_length=10  )

													lowerCamelCase_							=   tokenizer.batch_decode(A_						,							skip_special_tokens=A_  )
													predicted_outputs += generated_string

							self.assertListEqual(A_						,							A_  )

	def     a__			(    self :  Optional[int]  )       ->     List[str]:

							"""simple docstring"""






							lowerCamelCase_							=   'facebook/opt-350m'

							lowerCamelCase_							=   GPTaTokenizer.from_pretrained(A_  )
							lowerCamelCase_							=   TFOPTForCausalLM.from_pretrained(A_  )

							lowerCamelCase_							=   'left'

							# use different length sentences to test batching
							lowerCamelCase_							=   [
							    'Hello, my dog is a little',
							    'Today, I',
							]

							lowerCamelCase_							=   tokenizer(A_						,							return_tensors='tf'						,							padding=A_  )
							lowerCamelCase_							=   inputs['input_ids']

							lowerCamelCase_							=   model.generate(input_ids=A_						,							attention_mask=inputs['attention_mask']  )

							lowerCamelCase_							=   tokenizer(sentences[0]						,							return_tensors='tf'  ).input_ids
							lowerCamelCase_							=   model.generate(input_ids=A_  )

							lowerCamelCase_							=   inputs_non_padded.shape[-1] - tf.math.reduce_sum(
							    tf.cast(inputs['attention_mask'][-1]						,							tf.intaa  )  )
							lowerCamelCase_							=   tokenizer(sentences[1]						,							return_tensors='tf'  ).input_ids
							lowerCamelCase_							=   model.generate(input_ids=A_						,							max_length=model.config.max_length - num_paddings  )

							lowerCamelCase_							=   tokenizer.batch_decode(A_						,							skip_special_tokens=A_  )
							lowerCamelCase_							=   tokenizer.decode(output_non_padded[0]						,							skip_special_tokens=A_  )
							lowerCamelCase_							=   tokenizer.decode(output_padded[0]						,							skip_special_tokens=A_  )

							lowerCamelCase_							=   [
							    'Hello, my dog is a little bit of a dork.\nI\'m a little bit',
							    'Today, I was in the middle of a conversation with a friend about the',
							]
							self.assertListEqual(A_						,							A_  )
							self.assertListEqual(A_						,							[non_padded_sentence, padded_sentence]  )





	def     a__			(    self :  Dict  )       ->     Union[str, Any]:

							"""simple docstring"""






							lowerCamelCase_							=   'facebook/opt-350m'

							lowerCamelCase_							=   [
							    'Today is a beautiful day and I want to',
							    'In the city of San Francisco, the city',
							    'Paris is the capital of France and the capital',
							    'Computers and mobile phones have taken over the',
							]

							lowerCamelCase_							=   []
							lowerCamelCase_							=   GPTaTokenizer.from_pretrained(A_  )
							lowerCamelCase_							=   TFOPTForCausalLM.from_pretrained(A_  )

							for prompt in self.prompts:
													lowerCamelCase_							=   tokenizer(A_						,							return_tensors='tf'  ).input_ids

													lowerCamelCase_							=   model.generate(A_						,							max_length=10  )

													lowerCamelCase_							=   tokenizer.batch_decode(A_						,							skip_special_tokens=A_  )
													predicted_outputs += generated_string

							self.assertListEqual(A_						,							A_  )









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    :		Optional[Any]			   =       False


lowerCamelCase    :		Union[str, Any]			   =       logging.get_logger(__name__)
lowerCamelCase    :		Any			   =       "ybelkada/fonts"

def       _SCREAMING_SNAKE_CASE      (							):


      '''simple docstring'''



      if is_torch_available() and not is_torch_greater_or_equal_than_1_11:
            raise ImportError(
                f"""You are using torch=={torch.__version__}, but torch>=1.11.0 is required to use """
                'Pix2StructImageProcessor. Please upgrade torch.'					)

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	Dict     ,							lowercase     :	List[str]     ,							lowercase     :	List[Any]					):


      '''simple docstring'''



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

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


      '''simple docstring'''



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

      lowerCamelCase_							=   textwrap.TextWrapper(width=80					)
      lowerCamelCase_							=   wrapper.wrap(text=lowercase					)
      lowerCamelCase_							=   '\n'.join(lowercase					)

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

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

      # 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)     ,							lowercase					)
      lowerCamelCase_							=   ImageDraw.Draw(lowercase					)
      draw.text(xy=(left_padding, top_padding)     ,							text=lowercase     ,							fill=lowercase     ,							font=lowercase					)
      return image

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	np.ndarray     ,							lowercase     :	str     ,							**lowercase     :	List[Any]					):


      '''simple docstring'''



      requires_backends(lowercase     ,							'vision'					)

      # Convert to PIL image if necessary
      lowerCamelCase_							=   to_pil_image(lowercase					)

      lowerCamelCase_							=   render_text(lowercase     ,							**lowercase					)
      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(lowercase					)

      if infer_channel_dimension_format(lowercase					) == ChannelDimension.LAST:
            lowerCamelCase_							=   to_channel_dimension_format(lowercase     ,							ChannelDimension.LAST					)

      return new_image







class   A(						UpperCamelCase       ):



 '''simple docstring'''


 UpperCamelCase						       =						['''flattened_patches''']

 def __init__(    self :  Dict						,							A_ :  bool = True						,							A_ :  bool = True						,							A_ :  Dict[str, int] = None						,							A_ :  int = 2048						,							A_ :  bool = False						,							**A_ :  str						,							)       ->     None:

       """simple docstring"""






       super().__init__(**A_  )
       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 :  Union[str, Any]						,							A_ :  np.ndarray						,							A_ :  int						,							A_ :  dict						,							**A_ :  Any  )       ->     np.ndarray:

       """simple docstring"""






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

       # convert to torch
       lowerCamelCase_							=   to_channel_dimension_format(A_						,							ChannelDimension.FIRST  )
       lowerCamelCase_							=   torch.from_numpy(A_  )

       lowerCamelCase_					,    lowerCamelCase_							=   patch_size['height'], patch_size['width']
       lowerCamelCase_					,    lowerCamelCase_							=   get_image_size(A_  )

       # 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  )						,							A_  )						,							1  )
       lowerCamelCase_							=   max(min(math.floor(scale * image_width / patch_width  )						,							A_  )						,							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=A_						,							antialias=A_						,							).squeeze(0  )

       # [1, rows, columns, patch_height * patch_width * image_channels]
       lowerCamelCase_							=   torch_extract_patches(A_						,							A_						,							A_  )

       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(A_  ).reshape([rows, 1]  ).repeat(1						,							A_  ).reshape([rows * columns, 1]  )
       lowerCamelCase_							=   torch.arange(A_  ).reshape([1, columns]  ).repeat(A_						,							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(A_						,							[0, 0, 0, max_patches - (rows * columns)]  ).float()

       lowerCamelCase_							=   to_numpy_array(A_  )

       return result

 def     a__			(    self :  Optional[Any]						,							A_ :  np.ndarray						,							A_ :  Optional[Union[str, ChannelDimension]] = None						,							**A_ :  str  )       ->     np.ndarray:

       """simple docstring"""






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

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

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





 def     a__			(    self :  Optional[Any]						,							A_ :  ImageInput						,							A_ :  Optional[str] = None						,							A_ :  bool = None						,							A_ :  Optional[bool] = None						,							A_ :  Optional[int] = None						,							A_ :  Optional[Dict[str, int]] = None						,							A_ :  Optional[Union[str, TensorType]] = None						,							A_ :  ChannelDimension = ChannelDimension.FIRST						,							**A_ :  Optional[int]						,							)       ->     ImageInput:

       """simple docstring"""






       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'						,							A_  ) is not None:
             raise ValueError('data_format is not an accepted input as the outputs are '  )

       lowerCamelCase_							=   make_list_of_images(A_  )

       if not valid_images(A_  ):
             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(A_  ) for image in images]

       # All transformations expect numpy arrays.
       lowerCamelCase_							=   [to_numpy_array(A_  ) 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'						,							A_  )
             lowerCamelCase_							=   kwargs.pop('font_path'						,							A_  )

             if isinstance(A_						,							A_  ):
                   lowerCamelCase_							=   [header_text] * len(A_  )

             lowerCamelCase_							=   [
                 render_header(A_						,							header_text[i]						,							font_bytes=A_						,							font_path=A_  )
                 for i, image in enumerate(A_  )
             ]

       if do_normalize:
             lowerCamelCase_							=   [self.normalize(image=A_  ) for image in images]

       # convert to torch tensor and permute
       lowerCamelCase_							=   [
           self.extract_flattened_patches(image=A_						,							max_patches=A_						,							patch_size=A_  )
           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=A_  )

       return encoded_outputs



