



"""simple docstring"""







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


lowercase__		:    Dict				   =     WebClient(token=os.environ["""CI_SLACK_BOT_TOKEN"""])

def     UpperCamelCase_   (   lowerCAmelCase__				:    Dict						)						->       Optional[Any]:

  """simple docstring"""




  lowerCAmelCase_       :    Dict								=			test_results.split(' '						)

  lowerCAmelCase_       :    List[Any]								=			0
  lowerCAmelCase_       :    List[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.
  lowerCAmelCase_       :    List[str]								=			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     UpperCamelCase_   (   lowerCAmelCase__				:    Optional[int]						)						->       List[str]:

  """simple docstring"""




  lowerCAmelCase_       :    int								=			{}
  lowerCAmelCase_       :    Optional[Any]								=			None
  lowerCAmelCase_       :    Any								=			False
  for line in failures_short_lines.split('\n'						):
    if re.search(R'_ \[doctest\]'   ,       lowerCAmelCase__						):
      lowerCAmelCase_       :    Any								=			True
      lowerCAmelCase_       :    Optional[int]								=			line.split(' '						)[2]
    elif in_error and not line.split(' '						)[0].isdigit():
      lowerCAmelCase_       :    Dict								=			line
      lowerCAmelCase_       :    str								=			False

  return failures







class 				UpperCamelCase__       :




      """simple docstring"""




      def __init__(				self    :				Tuple   ,      SCREAMING_SNAKE_CASE_    :				str   ,      SCREAMING_SNAKE_CASE_    :				Dict	):
        lowerCAmelCase_       :    str								=			title

        lowerCAmelCase_       :    str								=			doc_test_results['time_spent'].split(','	)[0]
        lowerCAmelCase_       :    List[str]								=			doc_test_results['success']
        lowerCAmelCase_       :    Optional[int]								=			doc_test_results['failures']
        lowerCAmelCase_       :    Any								=			self.n_success + self.n_failures

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



      @property
      def 					SCREAMING_SNAKE_CASE__			(				self    :				Dict	):
        lowerCAmelCase_       :    Dict								=			[self._time_spent]
        lowerCAmelCase_       :    Any								=			0

        for time in time_spent:
          lowerCAmelCase_       :    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(__UpperCAmelCase	) == 1:
            lowerCAmelCase_       :    int								=			[0, 0, time_parts[0]]

          lowerCAmelCase_						,lowerCAmelCase_						,lowerCAmelCase_       :    Union[str, Any]								=			int(time_parts[0]	), int(time_parts[1]	), float(time_parts[2]	)
          total_secs += hours * 3_6_0_0 + minutes * 6_0 + seconds

        lowerCAmelCase_						,lowerCAmelCase_						,lowerCAmelCase_       :    List[Any]								=			total_secs // 3_6_0_0, (total_secs % 3_6_0_0) // 6_0, total_secs % 6_0
        return F"{int(__UpperCAmelCase	)}h{int(__UpperCAmelCase	)}m{int(__UpperCAmelCase	)}s"



      @property
      def 					SCREAMING_SNAKE_CASE__			(				self    :				str	):
        return {"type": "header", "text": {"type": "plain_text", "text": self.title}}



      @property
      def 					SCREAMING_SNAKE_CASE__			(				self    :				Optional[int]	):
        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 					SCREAMING_SNAKE_CASE__			(				self    :				Dict	):
        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 					SCREAMING_SNAKE_CASE__			(				self    :				Union[str, Any]	):
        lowerCAmelCase_       :    str								=			4_0
        lowerCAmelCase_       :    List[str]								=			{k: v['failed'] for k, v in doc_test_results.items() if isinstance(__UpperCAmelCase   ,      __UpperCAmelCase	)}

        lowerCAmelCase_       :    Tuple								=			''
        for category, failures in category_failures.items():
          if len(__UpperCAmelCase	) == 0:
            continue

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

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

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



      @property
      def 					SCREAMING_SNAKE_CASE__			(				self    :				List[Any]	):
        lowerCAmelCase_       :    Union[str, Any]								=			[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(__UpperCAmelCase	)



      @staticmethod
      def 					SCREAMING_SNAKE_CASE__			(				):
        lowerCAmelCase_       :    Tuple								=			[
            {
                '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(__UpperCAmelCase	)}	)	)

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



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Optional[Any]	):
        print('Sending the following payload'	)
        print(json.dumps({'blocks': json.loads(self.payload	)}	)	)

        lowerCAmelCase_       :    Any								=			F"{self.n_failures} failures out of {self.n_tests} tests," if self.n_failures else 'All tests passed.'

        lowerCAmelCase_       :    Dict								=			client.chat_postMessage(
            channel=os.environ['CI_SLACK_CHANNEL_ID_DAILY']   ,      blocks=self.payload   ,      text=__UpperCAmelCase   ,      )



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Optional[Any]   ,      SCREAMING_SNAKE_CASE_    :				str   ,      SCREAMING_SNAKE_CASE_    :				Tuple   ,      SCREAMING_SNAKE_CASE_    :				Any   ,      SCREAMING_SNAKE_CASE_    :				List[str]	):
        lowerCAmelCase_       :    Optional[int]								=			''
        for key, value in failures.items():
          lowerCAmelCase_       :    str								=			value[:2_0_0] + ' [Truncated]' if len(__UpperCAmelCase	) > 2_5_0 else value
          failures_text += F"*{key}*\n_{value}_\n\n"

        lowerCAmelCase_       :    str								=			job_name
        lowerCAmelCase_       :    Union[str, Any]								=			{'type': 'section', 'text': {'type': 'mrkdwn', 'text': text}}

        if job_link is not None:
          lowerCAmelCase_       :    Tuple								=			{
              '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 					SCREAMING_SNAKE_CASE__			(				self    :				int	):


        if self.thread_ts is None:
          raise ValueError('Can only post reply if a post has been made.'	)

        lowerCAmelCase_       :    str								=			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'	)

        lowerCAmelCase_       :    Dict								=			sorted(self.doc_test_results.items()   ,      key=lambda SCREAMING_SNAKE_CASE_	: t[0]	)
        for job, job_result in sorted_dict:
          if len(job_result['failures']	):
            lowerCAmelCase_       :    List[str]								=			F"*Num failures* :{len(job_result['failed']	)} \n"
            lowerCAmelCase_       :    List[str]								=			job_result['failures']
            lowerCAmelCase_       :    Any								=			self.get_reply_blocks(__UpperCAmelCase   ,      __UpperCAmelCase   ,      __UpperCAmelCase   ,      text=__UpperCAmelCase	)

            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=__UpperCAmelCase   ,      thread_ts=self.thread_ts['ts']   ,      )

            time.sleep(1	)




def     UpperCamelCase_   (   )						->       Tuple:

  """simple docstring"""




  lowerCAmelCase_       :    Optional[int]								=			os.environ['GITHUB_RUN_ID']
  lowerCAmelCase_       :    int								=			f"https://api.github.com/repos/huggingface/transformers/actions/runs/{run_id}/jobs?per_page=100"
  lowerCAmelCase_       :    List[Any]								=			requests.get(lowerCAmelCase__						).json()
  lowerCAmelCase_       :    Optional[int]								=			{}

  try:
    jobs.update({job['name']: job['html_url'] for job in result['jobs']}						)
    lowerCAmelCase_       :    str								=			math.ceil((result['total_count'] - 100) / 100						)

    for i in range(lowerCAmelCase__						):
      lowerCAmelCase_       :    List[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     UpperCamelCase_   (   lowerCAmelCase__				:    str						)						->       Dict:

  """simple docstring"""




  lowerCAmelCase_       :    Any								=			{}

  if os.path.exists(lowerCAmelCase__						):
    lowerCAmelCase_       :    int								=			os.listdir(lowerCAmelCase__						)
    for file in files:
      try:
        with open(os.path.join(lowerCAmelCase__   ,       lowerCAmelCase__						)   ,       encoding='utf-8'						) as f:
          lowerCAmelCase_       :    Dict								=			f.read()
      except UnicodeDecodeError as e:
        raise ValueError(f"Could not open {os.path.join(lowerCAmelCase__   ,       lowerCAmelCase__						)}."						) from e

  return _artifact

def     UpperCamelCase_   (   )						->       int:

  """simple docstring"""




  class 				UpperCamelCase__       :




        """simple docstring"""




        def __init__(				self    :				Optional[int]   ,      SCREAMING_SNAKE_CASE_    :				str	):
          lowerCAmelCase_       :    int								=			name
          lowerCAmelCase_       :    List[str]								=			[]



        def __str__(				self    :				int	):
          return self.name





        def 					SCREAMING_SNAKE_CASE__			(				self    :				Any   ,      SCREAMING_SNAKE_CASE_    :				str	):
          self.paths.append({'name': self.name, 'path': path}	)




  lowerCAmelCase_       :    int								=			{}

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

      _available_artifacts[artifact_name].add_path(lowerCAmelCase__						)

  return _available_artifacts


if __name__ == "__main__":
     lowercase__		:    List[Any]				   =     get_job_links()
     lowercase__		:    List[Any]				   =     retrieve_available_artifacts()

     lowercase__		:    Dict				   =     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'
     lowercase__		:    Tuple				   =     {
         v: {
             """failed""": [],
             """failures""": {},
         }
         for v in docs.values()
     }

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

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

          lowercase__		:    Tuple				   =     extract_first_line_failure(artifact["""failures_short"""])
          for line in artifact["summary_short"].split("""\n"""):
               if re.search("""FAILED""", line):
                    lowercase__		:    Dict				   =     line.replace("""FAILED """, """""")
                    lowercase__		:    int				   =     line.split()[0].replace("""\n""", """""")

                    if "::" in line:
                         lowercase__   ,					lowercase__		:    Union[str, Any]				   =     line.split("""::""")
                    else:
                         lowercase__   ,					lowercase__		:    Union[str, Any]				   =     line, line

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

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

     lowercase__		:    int				   =     Message("""🤗 Results of the doc tests.""", doc_test_results)
     message.post()
     message.post_reply()





"""simple docstring"""







from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


lowercase__		:    Tuple				   =     logging.get_logger(__name__)







class 				UpperCamelCase__       (     lowercase_  ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE      =					["""pixel_values"""]



      def __init__(				self    :				Any   ,      SCREAMING_SNAKE_CASE_    :				bool = True   ,      SCREAMING_SNAKE_CASE_    :				Optional[Dict[str, int]] = None   ,      SCREAMING_SNAKE_CASE_    :				PILImageResampling = PILImageResampling.BICUBIC   ,      SCREAMING_SNAKE_CASE_    :				bool = True   ,      SCREAMING_SNAKE_CASE_    :				bool = True   ,      SCREAMING_SNAKE_CASE_    :				Union[int, float] = 1 / 2_5_5   ,      SCREAMING_SNAKE_CASE_    :				Dict[str, int] = None   ,      SCREAMING_SNAKE_CASE_    :				bool = True   ,      SCREAMING_SNAKE_CASE_    :				Optional[Union[float, List[float]]] = None   ,      SCREAMING_SNAKE_CASE_    :				Optional[Union[float, List[float]]] = None   ,      **SCREAMING_SNAKE_CASE_    :				int   ,      ):
        super().__init__(**SCREAMING_SNAKE_CASE_	)
        lowerCAmelCase_       :    Dict								=			size if size is not None else {'height': 2_2_4, 'width': 2_2_4}
        lowerCAmelCase_       :    List[Any]								=			get_size_dict(SCREAMING_SNAKE_CASE_	)
        lowerCAmelCase_       :    Tuple								=			crop_size if crop_size is not None else {'height': 2_2_4, 'width': 2_2_4}
        lowerCAmelCase_       :    List[str]								=			get_size_dict(SCREAMING_SNAKE_CASE_   ,      default_to_square=SCREAMING_SNAKE_CASE_   ,      param_name='crop_size'	)

        lowerCAmelCase_       :    List[Any]								=			do_resize
        lowerCAmelCase_       :    Any								=			do_rescale
        lowerCAmelCase_       :    int								=			do_normalize
        lowerCAmelCase_       :    List[str]								=			do_center_crop
        lowerCAmelCase_       :    Dict								=			crop_size
        lowerCAmelCase_       :    Optional[Any]								=			size
        lowerCAmelCase_       :    Tuple								=			resample
        lowerCAmelCase_       :    Optional[int]								=			rescale_factor
        lowerCAmelCase_       :    Optional[Any]								=			image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
        lowerCAmelCase_       :    List[str]								=			image_std if image_std is not None else IMAGENET_DEFAULT_STD



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Any   ,      SCREAMING_SNAKE_CASE_    :				np.ndarray   ,      SCREAMING_SNAKE_CASE_    :				Dict[str, int]   ,      SCREAMING_SNAKE_CASE_    :				PILImageResampling = PILImageResampling.BILINEAR   ,      SCREAMING_SNAKE_CASE_    :				Optional[Union[str, ChannelDimension]] = None   ,      **SCREAMING_SNAKE_CASE_    :				str   ,      ):
        lowerCAmelCase_       :    str								=			get_size_dict(SCREAMING_SNAKE_CASE_	)

        if "shortest_edge" in size:
          lowerCAmelCase_       :    Any								=			get_resize_output_image_size(SCREAMING_SNAKE_CASE_   ,      size=size['shortest_edge']   ,      default_to_square=SCREAMING_SNAKE_CASE_	)
          # size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
        elif "height" in size and "width" in size:
          lowerCAmelCase_       :    Union[str, Any]								=			(size['height'], size['width'])
        else:
          raise ValueError(F"Size must contain 'height' and 'width' keys or 'shortest_edge' key. Got {size.keys()}"	)
        return resize(SCREAMING_SNAKE_CASE_   ,      size=SCREAMING_SNAKE_CASE_   ,      resample=SCREAMING_SNAKE_CASE_   ,      data_format=SCREAMING_SNAKE_CASE_   ,      **SCREAMING_SNAKE_CASE_	)



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Optional[int]   ,      SCREAMING_SNAKE_CASE_    :				np.ndarray   ,      SCREAMING_SNAKE_CASE_    :				Dict[str, int]   ,      SCREAMING_SNAKE_CASE_    :				Optional[Union[str, ChannelDimension]] = None   ,      **SCREAMING_SNAKE_CASE_    :				int   ,      ):
        lowerCAmelCase_       :    Any								=			get_size_dict(SCREAMING_SNAKE_CASE_	)
        if "height" not in size or "width" not in size:
          raise ValueError(F"The `size` parameter must contain the keys (height, width). Got {size.keys()}"	)
        return center_crop(SCREAMING_SNAKE_CASE_   ,      size=(size['height'], size['width'])   ,      data_format=SCREAMING_SNAKE_CASE_   ,      **SCREAMING_SNAKE_CASE_	)



      def 					SCREAMING_SNAKE_CASE__			(				self    :				int   ,      SCREAMING_SNAKE_CASE_    :				np.ndarray   ,      SCREAMING_SNAKE_CASE_    :				float   ,      SCREAMING_SNAKE_CASE_    :				Optional[Union[str, ChannelDimension]] = None   ,      **SCREAMING_SNAKE_CASE_    :				Optional[int]	):
        return rescale(SCREAMING_SNAKE_CASE_   ,      scale=SCREAMING_SNAKE_CASE_   ,      data_format=SCREAMING_SNAKE_CASE_   ,      **SCREAMING_SNAKE_CASE_	)



      def 					SCREAMING_SNAKE_CASE__			(				self    :				List[Any]   ,      SCREAMING_SNAKE_CASE_    :				np.ndarray   ,      SCREAMING_SNAKE_CASE_    :				Union[float, List[float]]   ,      SCREAMING_SNAKE_CASE_    :				Union[float, List[float]]   ,      SCREAMING_SNAKE_CASE_    :				Optional[Union[str, ChannelDimension]] = None   ,      **SCREAMING_SNAKE_CASE_    :				str   ,      ):
        return normalize(SCREAMING_SNAKE_CASE_   ,      mean=SCREAMING_SNAKE_CASE_   ,      std=SCREAMING_SNAKE_CASE_   ,      data_format=SCREAMING_SNAKE_CASE_   ,      **SCREAMING_SNAKE_CASE_	)





      def 					SCREAMING_SNAKE_CASE__			(				self    :				Tuple   ,      SCREAMING_SNAKE_CASE_    :				ImageInput   ,      SCREAMING_SNAKE_CASE_    :				Optional[bool] = None   ,      SCREAMING_SNAKE_CASE_    :				Dict[str, int] = None   ,      SCREAMING_SNAKE_CASE_    :				PILImageResampling = None   ,      SCREAMING_SNAKE_CASE_    :				bool = None   ,      SCREAMING_SNAKE_CASE_    :				int = None   ,      SCREAMING_SNAKE_CASE_    :				Optional[bool] = None   ,      SCREAMING_SNAKE_CASE_    :				Optional[float] = None   ,      SCREAMING_SNAKE_CASE_    :				Optional[bool] = None   ,      SCREAMING_SNAKE_CASE_    :				Optional[Union[float, List[float]]] = None   ,      SCREAMING_SNAKE_CASE_    :				Optional[Union[float, List[float]]] = None   ,      SCREAMING_SNAKE_CASE_    :				Optional[Union[str, TensorType]] = None   ,      SCREAMING_SNAKE_CASE_    :				Union[str, ChannelDimension] = ChannelDimension.FIRST   ,      **SCREAMING_SNAKE_CASE_    :				Optional[int]   ,      ):
        lowerCAmelCase_       :    Optional[int]								=			do_resize if do_resize is not None else self.do_resize
        lowerCAmelCase_       :    Optional[int]								=			do_rescale if do_rescale is not None else self.do_rescale
        lowerCAmelCase_       :    Optional[Any]								=			do_normalize if do_normalize is not None else self.do_normalize
        lowerCAmelCase_       :    Dict								=			do_center_crop if do_center_crop is not None else self.do_center_crop
        lowerCAmelCase_       :    Dict								=			crop_size if crop_size is not None else self.crop_size
        lowerCAmelCase_       :    Optional[Any]								=			get_size_dict(SCREAMING_SNAKE_CASE_   ,      param_name='crop_size'   ,      default_to_square=SCREAMING_SNAKE_CASE_	)
        lowerCAmelCase_       :    Union[str, Any]								=			resample if resample is not None else self.resample
        lowerCAmelCase_       :    Optional[Any]								=			rescale_factor if rescale_factor is not None else self.rescale_factor
        lowerCAmelCase_       :    str								=			image_mean if image_mean is not None else self.image_mean
        lowerCAmelCase_       :    List[str]								=			image_std if image_std is not None else self.image_std

        lowerCAmelCase_       :    Tuple								=			size if size is not None else self.size
        lowerCAmelCase_       :    str								=			get_size_dict(SCREAMING_SNAKE_CASE_	)

        if not is_batched(SCREAMING_SNAKE_CASE_	):
          lowerCAmelCase_       :    List[Any]								=			[images]

        if not valid_images(SCREAMING_SNAKE_CASE_	):
          raise ValueError(
              'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
              'torch.Tensor, tf.Tensor or jax.ndarray.'	)

        if do_resize and size is None:
          raise ValueError('Size must be specified if do_resize is True.'	)

        if do_center_crop and crop_size is None:
          raise ValueError('Crop size must be specified if do_center_crop is True.'	)

        if do_rescale and rescale_factor is None:
          raise ValueError('Rescale factor must be specified if do_rescale is True.'	)

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

        if do_resize:
          lowerCAmelCase_       :    Any								=			[self.resize(image=SCREAMING_SNAKE_CASE_   ,      size=SCREAMING_SNAKE_CASE_   ,      resample=SCREAMING_SNAKE_CASE_	) for image in images]

        if do_center_crop:
          lowerCAmelCase_       :    Optional[int]								=			[self.center_crop(image=SCREAMING_SNAKE_CASE_   ,      size=SCREAMING_SNAKE_CASE_	) for image in images]

        if do_rescale:
          lowerCAmelCase_       :    Tuple								=			[self.rescale(image=SCREAMING_SNAKE_CASE_   ,      scale=SCREAMING_SNAKE_CASE_	) for image in images]

        if do_normalize:
          lowerCAmelCase_       :    str								=			[self.normalize(image=SCREAMING_SNAKE_CASE_   ,      mean=SCREAMING_SNAKE_CASE_   ,      std=SCREAMING_SNAKE_CASE_	) for image in images]

        lowerCAmelCase_       :    Any								=			[to_channel_dimension_format(SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_	) for image in images]

        lowerCAmelCase_       :    Optional[Any]								=			{'pixel_values': images}
        return BatchFeature(data=SCREAMING_SNAKE_CASE_   ,      tensor_type=SCREAMING_SNAKE_CASE_	)





