
import collections
import json
import math
import os
import re
import time
from fnmatch import fnmatch
from typing import Dict

import requests
from slack_sdk import WebClient


lowerCamelCase_  :	List[str] 				=					WebClient(token=os.environ["""CI_SLACK_BOT_TOKEN"""])







def     A__   (  lowerCamelCase  )					->		List[Any]:
				UpperCamelCase_:							Optional[Any]		=  test_results.split(""" """  )

				UpperCamelCase_:							int		=  0
				UpperCamelCase_:							str		=  0

				# When the output is short enough, the output is surrounded by = signs: "== OUTPUT =="
				# When it is too long, those signs are not present.
				UpperCamelCase_:							Union[str, Any]		=  expressions[-2] if """=""" in expressions[-1] else expressions[-1]

				for i, expression in enumerate(lowerCamelCase  ):
								if "failed" in expression:
												failed += int(expressions[i - 1]  )
								if "passed" in expression:
												success += int(expressions[i - 1]  )

				return failed, success, time_spent







def     A__   (  lowerCamelCase  )					->		Optional[Any]:
				UpperCamelCase_:							Union[str, Any]		=  {}
				UpperCamelCase_:							List[str]		=  None
				UpperCamelCase_:							Dict		=  False
				for line in failures_short_lines.split("""\n"""  ):
								if re.search(r"""_ \[doctest\]"""       ,      lowerCamelCase  ):
												UpperCamelCase_:							Optional[Any]		=  True
												UpperCamelCase_:							Optional[int]		=  line.split(""" """  )[2]
								elif in_error and not line.split(""" """  )[0].isdigit():
												UpperCamelCase_:							Dict		=  line
												UpperCamelCase_:							int		=  False

				return failures

class     _UpperCamelCase			:






			'''simple docstring'''

			def __init__(    self	:    List[str]  ,		snake_case_	:    str  ,		snake_case_	:    Dict ):
							UpperCamelCase_:							Any		=  title

							UpperCamelCase_:							Optional[int]		=  doc_test_results["""time_spent"""].split(""",""" )[0]
							UpperCamelCase_:							Tuple		=  doc_test_results["""success"""]
							UpperCamelCase_:							Tuple		=  doc_test_results["""failures"""]
							UpperCamelCase_:							str		=  self.n_success + self.n_failures

							# Failures and success of the modeling tests
							UpperCamelCase_:							List[Any]		=  doc_test_results

			@property
			def  lowerCAmelCase__		(    self	:    Optional[Any] ):
							UpperCamelCase_:							Optional[int]		=  [self._time_spent]
							UpperCamelCase_:							Optional[Any]		=  0

							for time in time_spent:
											UpperCamelCase_:							List[Any]		=  time.split(""":""" )

											# Time can be formatted as xx:xx:xx, as .xx, or as x.xx if the time spent was less than a minute.
											if len(snake_case_ ) == 1:
															UpperCamelCase_:							str		=  [0, 0, time_parts[0]]

											UpperCamelCase_,				UpperCamelCase_,				UpperCamelCase_:							Optional[int]		=  int(time_parts[0] ), int(time_parts[1] ), float(time_parts[2] )
											total_secs += hours * 3600 + minutes * 60 + seconds

							UpperCamelCase_,				UpperCamelCase_,				UpperCamelCase_:							List[str]		=  total_secs // 3600, (total_secs % 3600) // 60, total_secs % 60
							return f'''{int(snake_case_ )}h{int(snake_case_ )}m{int(snake_case_ )}s'''

			@property
			def  lowerCAmelCase__		(    self	:    Union[str, Any] ):
							return {"type": "header", "text": {"type": "plain_text", "text": self.title}}

			@property
			def  lowerCAmelCase__		(    self	:    List[Any] ):
							return {
							    "type": "section",
							    "text": {
							        "type": "plain_text",
							        "text": f'''🌞 There were no failures: all {self.n_tests} tests passed. The suite ran in {self.time}.''',
							        "emoji": True,
							    },
							    "accessory": {
							        "type": "button",
							        "text": {"type": "plain_text", "text": "Check Action results", "emoji": True},
							        "url": f'''https://github.com/huggingface/transformers/actions/runs/{os.environ["GITHUB_RUN_ID"]}''',
							    },
							}

			@property
			def  lowerCAmelCase__		(    self	:    Any ):
							return {
							    "type": "section",
							    "text": {
							        "type": "plain_text",
							        "text": (
							            f'''There were {self.n_failures} failures, out of {self.n_tests} tests.\nThe suite ran in'''
							            f''' {self.time}.'''
							        ),
							        "emoji": True,
							    },
							    "accessory": {
							        "type": "button",
							        "text": {"type": "plain_text", "text": "Check Action results", "emoji": True},
							        "url": f'''https://github.com/huggingface/transformers/actions/runs/{os.environ["GITHUB_RUN_ID"]}''',
							    },
							}

			@property
			def  lowerCAmelCase__		(    self	:    Tuple ):
							UpperCamelCase_:							Dict		=  40
							UpperCamelCase_:							Optional[int]		=  {k: v["""failed"""] for k, v in doc_test_results.items() if isinstance(snake_case_  ,		snake_case_ )}

							UpperCamelCase_:							Any		=  """"""
							for category, failures in category_failures.items():
											if len(snake_case_ ) == 0:
															continue

											if report != "":
															report += "\n\n"

											report += f'''*{category} failures*:'''.ljust(line_length // 2 ).rjust(line_length // 2 ) + "\n"
											report += "`"
											report += "`\n`".join(snake_case_ )
											report += "`"

							return {
							    "type": "section",
							    "text": {
							        "type": "mrkdwn",
							        "text": f'''The following examples had failures:\n\n\n{report}\n''',
							    },
							}

			@property
			def  lowerCAmelCase__		(    self	:    List[str] ):
							UpperCamelCase_:							int		=  [self.header]

							if self.n_failures > 0:
											blocks.append(self.failures )

							if self.n_failures > 0:
											blocks.extend([self.category_failures] )

							if self.n_failures == 0:
											blocks.append(self.no_failures )

							return json.dumps(snake_case_ )

			@staticmethod
			def  lowerCAmelCase__		(    ):
							UpperCamelCase_:							Union[str, Any]		=  [
							    {
							        """type""": """section""",
							        """text""": {
							            """type""": """plain_text""",
							            """text""": """There was an issue running the tests.""",
							        },
							        """accessory""": {
							            """type""": """button""",
							            """text""": {"""type""": """plain_text""", """text""": """Check Action results""", """emoji""": True},
							            """url""": f'''https://github.com/huggingface/transformers/actions/runs/{os.environ["GITHUB_RUN_ID"]}''',
							        },
							    }
							]

							print("""Sending the following payload""" )
							print(json.dumps({"""blocks""": json.loads(snake_case_ )} ) )

							client.chat_postMessage(
							    channel=os.environ["""CI_SLACK_CHANNEL_ID_DAILY"""]  ,		text="""There was an issue running the tests."""  ,		blocks=snake_case_  ,		)

			def  lowerCAmelCase__		(    self	:    Tuple ):
							print("""Sending the following payload""" )
							print(json.dumps({"""blocks""": json.loads(self.payload )} ) )

							UpperCamelCase_:							str		=  f'''{self.n_failures} failures out of {self.n_tests} tests,''' if self.n_failures else """All tests passed."""

							UpperCamelCase_:							Optional[Any]		=  client.chat_postMessage(
							    channel=os.environ["""CI_SLACK_CHANNEL_ID_DAILY"""]  ,		blocks=self.payload  ,		text=snake_case_  ,		)

			def  lowerCAmelCase__		(    self	:    int  ,		snake_case_	:    List[Any]  ,		snake_case_	:    Dict  ,		snake_case_	:    List[Any]  ,		snake_case_	:    Tuple ):
							UpperCamelCase_:							Tuple		=  """"""
							for key, value in failures.items():
											UpperCamelCase_:							Dict		=  value[:200] + """ [Truncated]""" if len(snake_case_ ) > 250 else value
											failures_text += f'''*{key}*\n_{value}_\n\n'''

							UpperCamelCase_:							Tuple		=  job_name
							UpperCamelCase_:							Any		=  {"""type""": """section""", """text""": {"""type""": """mrkdwn""", """text""": text}}

							if job_link is not None:
											UpperCamelCase_:							Union[str, Any]		=  {
											    """type""": """button""",
											    """text""": {"""type""": """plain_text""", """text""": """GitHub Action job""", """emoji""": True},
											    """url""": job_link,
											}

							return [
							    {"type": "header", "text": {"type": "plain_text", "text": title.upper(), "emoji": True}},
							    content,
							    {"type": "section", "text": {"type": "mrkdwn", "text": failures_text}},
							]




			def  lowerCAmelCase__		(    self	:    int ):
							if self.thread_ts is None:
											raise ValueError("""Can only post reply if a post has been made.""" )

							UpperCamelCase_:							Tuple		=  self.doc_test_results.pop("""job_link""" )
							self.doc_test_results.pop("""failures""" )
							self.doc_test_results.pop("""success""" )
							self.doc_test_results.pop("""time_spent""" )

							UpperCamelCase_:							Optional[int]		=  sorted(self.doc_test_results.items()  ,		key=lambda snake_case_ : t[0] )
							for job, job_result in sorted_dict:
											if len(job_result["""failures"""] ):
															UpperCamelCase_:							Optional[Any]		=  f'''*Num failures* :{len(job_result["failed"] )} \n'''
															UpperCamelCase_:							Optional[int]		=  job_result["""failures"""]
															UpperCamelCase_:							Union[str, Any]		=  self.get_reply_blocks(snake_case_  ,		snake_case_  ,		snake_case_  ,		text=snake_case_ )

															print("""Sending the following reply""" )
															print(json.dumps({"""blocks""": blocks} ) )

															client.chat_postMessage(
															    channel=os.environ["""CI_SLACK_CHANNEL_ID_DAILY"""]  ,		text=f'''Results for {job}'''  ,		blocks=snake_case_  ,		thread_ts=self.thread_ts["""ts"""]  ,		)

															time.sleep(1 )







def     A__   (  )					->		str:
				UpperCamelCase_:							Optional[Any]		=  os.environ["""GITHUB_RUN_ID"""]
				UpperCamelCase_:							Dict		=  F'''https://api.github.com/repos/huggingface/transformers/actions/runs/{run_id}/jobs?per_page=100'''
				UpperCamelCase_:							Optional[int]		=  requests.get(lowerCamelCase  ).json()
				UpperCamelCase_:							Optional[int]		=  {}

				try:
								jobs.update({job["""name"""]: job["""html_url"""] for job in result["""jobs"""]}  )
								UpperCamelCase_:							List[Any]		=  math.ceil((result["""total_count"""] - 1_00) / 1_00  )

								for i in range(lowerCamelCase  ):
												UpperCamelCase_:							Any		=  requests.get(url + F'''&page={i + 2}'''  ).json()
												jobs.update({job["""name"""]: job["""html_url"""] for job in result["""jobs"""]}  )

								return jobs
				except Exception as e:
								print("""Unknown error, could not fetch links."""       ,      lowerCamelCase  )

				return {}







def     A__   (  lowerCamelCase  )					->		Tuple:
				UpperCamelCase_:							List[Any]		=  {}

				if os.path.exists(lowerCamelCase  ):
								UpperCamelCase_:							Optional[int]		=  os.listdir(lowerCamelCase  )
								for file in files:
												try:
																with open(os.path.join(lowerCamelCase       ,      lowerCamelCase  )       ,      encoding="""utf-8"""  ) as f:
																				UpperCamelCase_:							Tuple		=  f.read()
												except UnicodeDecodeError as e:
																raise ValueError(F'''Could not open {os.path.join(lowerCamelCase       ,      lowerCamelCase  )}.'''  ) from e

				return _artifact







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

				class     _UpperCamelCase			:






							'''simple docstring'''

							def __init__(    self	:    Tuple  ,		snake_case_	:    str ):
											UpperCamelCase_:							Union[str, Any]		=  name
											UpperCamelCase_:							Optional[Any]		=  []

							def __str__(    self	:    Optional[int] ):
											return self.name




							def  lowerCAmelCase__		(    self	:    List[Any]  ,		snake_case_	:    str ):
											self.paths.append({"""name""": self.name, """path""": path} )




				UpperCamelCase_:							Dict[str, Artifact]		=  {}

				UpperCamelCase_:							Dict		=  filter(os.path.isdir       ,      os.listdir()  )
				for directory in directories:
								UpperCamelCase_:							Union[str, Any]		=  directory
								if artifact_name not in _available_artifacts:
												UpperCamelCase_:							Optional[Any]		=  Artifact(lowerCamelCase  )

												_available_artifacts[artifact_name].add_path(lowerCamelCase  )

				return _available_artifacts


if __name__ == "__main__":
							lowerCamelCase_  :	int 				=					get_job_links()
							lowerCamelCase_  :	int 				=					retrieve_available_artifacts()

							lowerCamelCase_  :	Optional[Any] 				=					collections.OrderedDict(
							    [
							        ("""*.py""", """API Examples"""),
							        ("""*.md""", """MD Examples"""),
							    ]
							)

							# This dict will contain all the information relative to each doc test category:
							# - failed: list of failed tests
							# - failures: dict in the format 'test': 'error_message'
							lowerCamelCase_  :	Optional[Any] 				=					{
							    v: {
							        """failed""": [],
							        """failures""": {},
							    }
							    for v in docs.values()
							}

							# Link to the GitHub Action job
							lowerCamelCase_  :	str 				=					github_actions_job_links.get("""run_doctests""")

							lowerCamelCase_  :	Any 				=					available_artifacts["""doc_tests_gpu_test_reports"""].paths[0]
							lowerCamelCase_  :	List[str] 				=					retrieve_artifact(artifact_path["""name"""])
							if "stats" in artifact:
														lowerCamelCase_				,    lowerCamelCase_				,    lowerCamelCase_  :	Dict 				=					handle_test_results(artifact["""stats"""])
														lowerCamelCase_  :	int 				=					failed
														lowerCamelCase_  :	int 				=					success
														lowerCamelCase_  :	Tuple 				=					time_spent[1:-1] + """, """

														lowerCamelCase_  :	Union[str, Any] 				=					extract_first_line_failure(artifact["""failures_short"""])
														for line in artifact["summary_short"].split("""\n"""):
																					if re.search("""FAILED""", line):
																												lowerCamelCase_  :	Optional[Any] 				=					line.replace("""FAILED """, """""")
																												lowerCamelCase_  :	int 				=					line.split()[0].replace("""\n""", """""")

																												if "::" in line:
																																			lowerCamelCase_				,    lowerCamelCase_  :	Tuple 				=					line.split("""::""")
																												else:
																																			lowerCamelCase_				,    lowerCamelCase_  :	List[Any] 				=					line, line

																												for file_regex in docs.keys():
																																			if fnmatch(file_path, file_regex):
																																										lowerCamelCase_  :	Dict 				=					docs[file_regex]
																																										doc_test_results[category]["failed"].append(test)

																																										lowerCamelCase_  :	Dict 				=					all_failures[test] if test in all_failures else """N/A"""
																																										lowerCamelCase_  :	List[str] 				=					failure
																																										break

							lowerCamelCase_  :	List[str] 				=					Message("""🤗 Results of the doc tests.""", doc_test_results)
							message.post()
							message.post_reply()








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_  :	List[str] 				=					False


lowerCamelCase_  :	int 				=					logging.get_logger(__name__)
lowerCamelCase_  :	Optional[int] 				=					"""ybelkada/fonts"""







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







def     A__   (  lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase  )					->		Optional[int]:
				requires_backends(lowerCamelCase       ,      ["""torch"""]  )
				_check_torch_version()

				UpperCamelCase_:							Tuple		=  image_tensor.unsqueeze(0  )
				UpperCamelCase_:							Any		=  torch.nn.functional.unfold(lowerCamelCase       ,      (patch_height, patch_width)       ,      stride=(patch_height, patch_width)  )
				UpperCamelCase_:							int		=  patches.reshape(image_tensor.size(0  )       ,      image_tensor.size(1  )       ,      lowerCamelCase       ,      lowerCamelCase       ,      -1  )
				UpperCamelCase_:							Any		=  patches.permute(0       ,      4       ,      2       ,      3       ,      1  ).reshape(
				    image_tensor.size(2  ) // patch_height       ,      image_tensor.size(3  ) // patch_width       ,      image_tensor.size(1  ) * patch_height * patch_width       ,      )
				return patches.unsqueeze(0  )







def     A__   (  lowerCamelCase       ,      lowerCamelCase = 36       ,      lowerCamelCase = "black"       ,      lowerCamelCase = "white"       ,      lowerCamelCase = 5       ,      lowerCamelCase = 5       ,      lowerCamelCase = 5       ,      lowerCamelCase = 5       ,      lowerCamelCase = None       ,      lowerCamelCase = None       ,      )					->		Image.Image:
				requires_backends(lowerCamelCase       ,      """vision"""  )
				# Add new lines so that each line is no more than 80 characters.

				UpperCamelCase_:							List[str]		=  textwrap.TextWrapper(width=80  )
				UpperCamelCase_:							Optional[int]		=  wrapper.wrap(text=lowerCamelCase  )
				UpperCamelCase_:							List[str]		=  """\n""".join(lowerCamelCase  )

				if font_bytes is not None and font_path is None:
								UpperCamelCase_:							List[Any]		=  io.BytesIO(lowerCamelCase  )
				elif font_path is not None:
								UpperCamelCase_:							List[Any]		=  font_path
				else:
								UpperCamelCase_:							Tuple		=  hf_hub_download(lowerCamelCase       ,      """Arial.TTF"""  )
				UpperCamelCase_:							Optional[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_,				UpperCamelCase_,				UpperCamelCase_,				UpperCamelCase_:							Optional[int]		=  temp_draw.textbbox((0, 0)       ,      lowerCamelCase       ,      lowerCamelCase  )

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







def     A__   (  lowerCamelCase       ,      lowerCamelCase       ,      **lowerCamelCase  )					->		List[str]:
				requires_backends(lowerCamelCase       ,      """vision"""  )

				# Convert to PIL image if necessary
				UpperCamelCase_:							List[str]		=  to_pil_image(lowerCamelCase  )

				UpperCamelCase_:							Union[str, Any]		=  render_text(lowerCamelCase       ,      **lowerCamelCase  )
				UpperCamelCase_:							Tuple		=  max(header_image.width       ,      image.width  )

				UpperCamelCase_:							Tuple		=  int(image.height * (new_width / image.width)  )
				UpperCamelCase_:							Dict		=  int(header_image.height * (new_width / header_image.width)  )

				UpperCamelCase_:							str		=  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_:							Optional[Any]		=  to_numpy_array(lowerCamelCase  )

				if infer_channel_dimension_format(lowerCamelCase  ) == ChannelDimension.LAST:
								UpperCamelCase_:							Tuple		=  to_channel_dimension_format(lowerCamelCase       ,      ChannelDimension.LAST  )

				return new_image





class     _UpperCamelCase			(   _A					):






			'''simple docstring'''

			__UpperCamelCase :  Optional[int]    =	["""flattened_patches"""]

			def __init__(    self	:    int  ,		snake_case_	:    bool = True  ,		snake_case_	:    bool = True  ,		snake_case_	:    Dict[str, int] = None  ,		snake_case_	:    int = 2048  ,		snake_case_	:    bool = False  ,		**snake_case_	:    Any  ,		):
							super().__init__(**snake_case_ )
							UpperCamelCase_:							int		=  patch_size if patch_size is not None else {"""height""": 16, """width""": 16}
							UpperCamelCase_:							Tuple		=  do_normalize
							UpperCamelCase_:							List[Any]		=  do_convert_rgb
							UpperCamelCase_:							Tuple		=  max_patches
							UpperCamelCase_:							Tuple		=  is_vqa

			def  lowerCAmelCase__		(    self	:    int  ,		snake_case_	:    np.ndarray  ,		snake_case_	:    int  ,		snake_case_	:    dict  ,		**snake_case_	:    Tuple ):
							requires_backends(self.extract_flattened_patches  ,		"""torch""" )
							_check_torch_version()

							# convert to torch
							UpperCamelCase_:							int		=  to_channel_dimension_format(snake_case_  ,		ChannelDimension.FIRST )
							UpperCamelCase_:							List[str]		=  torch.from_numpy(snake_case_ )

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

							# maximize scale s.t.
							UpperCamelCase_:							List[Any]		=  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[str]		=  max(min(math.floor(scale * image_width / patch_width )  ,		snake_case_ )  ,		1 )
							UpperCamelCase_:							int		=  max(num_feasible_rows * patch_height  ,		1 )
							UpperCamelCase_:							Optional[Any]		=  max(num_feasible_cols * patch_width  ,		1 )

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

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

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

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

							# [rows * columns, 1]
							UpperCamelCase_:							Optional[Any]		=  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_:							Union[str, Any]		=  row_ids.to(torch.floataa )
							UpperCamelCase_:							str		=  col_ids.to(torch.floataa )

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

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

							UpperCamelCase_:							List[Any]		=  to_numpy_array(snake_case_ )

							return result

			def  lowerCAmelCase__		(    self	:    List[Any]  ,		snake_case_	:    np.ndarray  ,		snake_case_	:    Optional[Union[str, ChannelDimension]] = None  ,		**snake_case_	:    Tuple ):
							if image.dtype == np.uinta:
											UpperCamelCase_:							List[str]		=  image.astype(np.floataa )

							# take mean across the whole `image`
							UpperCamelCase_:							str		=  np.mean(snake_case_ )
							UpperCamelCase_:							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  lowerCAmelCase__		(    self	:    str  ,		snake_case_	:    ImageInput  ,		snake_case_	:    Optional[str] = None  ,		snake_case_	:    bool = None  ,		snake_case_	:    Optional[bool] = None  ,		snake_case_	:    Optional[int] = None  ,		snake_case_	:    Optional[Dict[str, int]] = None  ,		snake_case_	:    Optional[Union[str, TensorType]] = None  ,		snake_case_	:    ChannelDimension = ChannelDimension.FIRST  ,		**snake_case_	:    Union[str, Any]  ,		):
							UpperCamelCase_:							Tuple		=  do_normalize if do_normalize is not None else self.do_normalize
							UpperCamelCase_:							Tuple		=  do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
							UpperCamelCase_:							Optional[Any]		=  patch_size if patch_size is not None else self.patch_size
							UpperCamelCase_:							Optional[int]		=  max_patches if max_patches is not None else self.max_patches
							UpperCamelCase_:							Tuple		=  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_:							Dict		=  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_:							str		=  [convert_to_rgb(snake_case_ ) for image in images]

							# All transformations expect numpy arrays.
							UpperCamelCase_:							Union[str, Any]		=  [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_:							List[Any]		=  kwargs.pop("""font_bytes"""  ,		snake_case_ )
											UpperCamelCase_:							List[Any]		=  kwargs.pop("""font_path"""  ,		snake_case_ )

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

											UpperCamelCase_:							str		=  [
											    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[Any]		=  [(image.sum(axis=-1 ) != 0).astype(np.floataa ) for image in images]

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

							return encoded_outputs






