code: import os
  from argparse import ArgumentParser
  from pathlib import Path
  from shutil import copyfile
  from typing import List
  
  from datasets import config
  from datasets.builder import DatasetBuilder
  from datasets.commands import BaseDatasetsCLICommand
  from datasets.download.download_config import DownloadConfig
  from datasets.download.download_manager import DownloadMode
  from datasets.load import dataset_module_factory, import_main_class
  from datasets.utils.info_utils import VerificationMode
  
  
  
  def 							_lowerCamelCase(				__snake_case						,						**__snake_case	)  ->   str:
  		return RunBeamCommand(
  		    args.dataset						,						args.name						,						args.cache_dir						,						args.beam_pipeline_options						,						args.data_dir						,						args.all_configs						,						args.save_info or args.save_infos						,						args.ignore_verifications						,						args.force_redownload						,						**__snake_case						,						)
  
  
  
  
  
  class 					UpperCamelCase	(     _a      ):
  
  
  
  
  					@staticmethod
  					def 							UpperCamelCase_						( _lowerCAmelCase :					int						):
  
  
  							"""simple docstring"""
  
  
  
  
  							__snake_case		       =     parser.add_parser("run_beam"      ,help="Run a Beam dataset processing pipeline"						)
  							run_beam_parser.add_argument("dataset"      ,type=lowerCamelCase__      ,help="Name of the dataset to download"						)
  							run_beam_parser.add_argument("--name"      ,type=lowerCamelCase__      ,default=lowerCamelCase__      ,help="Dataset config name"						)
  							run_beam_parser.add_argument(
  							    "--cache_dir"      ,type=lowerCamelCase__      ,default=lowerCamelCase__      ,help="Cache directory where the datasets are stored"      ,)
  							run_beam_parser.add_argument(
  							    "--beam_pipeline_options"      ,type=lowerCamelCase__      ,default=""      ,help="Beam pipeline options, separated by commas. Example:: `--beam_pipeline_options=job_name=my-job,project=my-project`"      ,)
  							run_beam_parser.add_argument(
  							    "--data_dir"      ,type=lowerCamelCase__      ,default=lowerCamelCase__      ,help="Can be used to specify a manual directory to get the files from"      ,)
  							run_beam_parser.add_argument("--all_configs"      ,action="store_true"      ,help="Test all dataset configurations"						)
  							run_beam_parser.add_argument("--save_info"      ,action="store_true"      ,help="Save the dataset infos file"						)
  							run_beam_parser.add_argument(
  							    "--ignore_verifications"      ,action="store_true"      ,help="Run the test without checksums and splits checks"						)
  							run_beam_parser.add_argument("--force_redownload"      ,action="store_true"      ,help="Force dataset redownload"						)
  							# aliases
  							run_beam_parser.add_argument("--save_infos"      ,action="store_true"      ,help="alias for save_info"						)
  							run_beam_parser.set_defaults(func=lowerCamelCase__						)
  
  
  
  
  					def __init__( self :					Optional[Any]      ,_lowerCAmelCase :					Optional[Any]      ,_lowerCAmelCase :					Optional[Any]      ,_lowerCAmelCase :					Optional[int]      ,_lowerCAmelCase :					Any      ,_lowerCAmelCase :					str      ,_lowerCAmelCase :					str      ,_lowerCAmelCase :					Union[str, Any]      ,_lowerCAmelCase :					Optional[int]      ,_lowerCAmelCase :					Dict      ,**_lowerCAmelCase :					int      ,):
  
  
  							"""simple docstring"""
  
  
  
  
  							__snake_case		       =     dataset
  							__snake_case		       =     name
  							__snake_case		       =     cache_dir
  							__snake_case		       =     beam_pipeline_options
  							__snake_case		       =     data_dir
  							__snake_case		       =     all_configs
  							__snake_case		       =     save_infos
  							__snake_case		       =     ignore_verifications
  							__snake_case		       =     force_redownload
  							__snake_case		       =     config_kwargs
  
  
  
  
  
  
  
  					def 							UpperCamelCase_						( self :					int						):
  
  
  							"""simple docstring"""
  
  
  
  
  							import apache_beam as beam
  
  							if self._name is not None and self._all_configs:
  									print("Both parameters `name` and `all_configs` can't be used at once."						)
  									exit(1						)
  							__snake_case		       =     self._dataset, self._name
  							__snake_case		       =     dataset_module_factory(lowerCamelCase__						)
  							__snake_case		       =     import_main_class(dataset_module.module_path						)
  							__snake_case		       =     []
  							if self._beam_pipeline_options:
  									__snake_case		       =     beam.options.pipeline_options.PipelineOptions(
  									    flags=[F"""--{opt.strip()}""" for opt in self._beam_pipeline_options.split(","						) if opt]						)
  							else:
  									__snake_case		       =     None
  							if self._all_configs and len(builder_cls.BUILDER_CONFIGS						) > 0:
  									for builder_config in builder_cls.BUILDER_CONFIGS:
  											builders.append(
  											    builder_cls(
  											        config_name=builder_config.name      ,data_dir=self._data_dir      ,hash=dataset_module.hash      ,beam_options=lowerCamelCase__      ,cache_dir=self._cache_dir      ,base_path=dataset_module.builder_kwargs.get("base_path"						)      ,)						)
  							else:
  									builders.append(
  									    builder_cls(
  									        config_name=lowerCamelCase__      ,data_dir=self._data_dir      ,beam_options=lowerCamelCase__      ,cache_dir=self._cache_dir      ,base_path=dataset_module.builder_kwargs.get("base_path"						)      ,**self._config_kwargs      ,)						)
  
  							for builder in builders:
  									builder.download_and_prepare(
  									    download_mode=DownloadMode.REUSE_CACHE_IF_EXISTS
  									    if not self._force_redownload
  									    else DownloadMode.FORCE_REDOWNLOAD      ,download_config=DownloadConfig(cache_dir=config.DOWNLOADED_DATASETS_PATH						)      ,verification_mode=VerificationMode.NO_CHECKS
  									    if self._ignore_verifications
  									    else VerificationMode.ALL_CHECKS      ,try_from_hf_gcs=lowerCamelCase__      ,)
  									if self._save_infos:
  											builder._save_infos()
  
  							print("Apache beam run successful."						)
  
  							# If save_infos=True, the dataset infos file is created next to the loaded module file.
  							# Let's move it to the original directory of the dataset script, to allow the user to
  							# upload them on S3 at the same time afterwards.
  							if self._save_infos:
  									__snake_case		       =     os.path.join(builder_cls.get_imported_module_dir()      ,config.DATASETDICT_INFOS_FILENAME						)
  
  									__snake_case		       =     Path(lowerCamelCase__						).name + ".py"
  
  									__snake_case		       =     os.path.join(lowerCamelCase__      ,lowerCamelCase__						)
  									if os.path.isfile(lowerCamelCase__						):
  											__snake_case		       =     os.path.dirname(lowerCamelCase__						)
  									elif os.path.isfile(lowerCamelCase__						):
  											__snake_case		       =     path
  									else:  # in case of a remote dataset
  											print(F"""Dataset Infos file saved at {dataset_infos_path}"""						)
  											exit(1						)
  
  									# Move datasetinfo back to the user
  									__snake_case		       =     os.path.join(lowerCamelCase__      ,config.DATASETDICT_INFOS_FILENAME						)
  									copyfile(lowerCamelCase__      ,lowerCamelCase__						)
  									print(F"""Dataset Infos file saved at {user_dataset_infos_path}"""						)
  
  
  
  
  
  
style_context: import shutil
  import tempfile
  import unittest
  
  import numpy as np
  import pytest
  
  from transformers.testing_utils import require_torch, require_vision
  from transformers.utils import is_torch_available, is_vision_available
  
  
  if is_torch_available():
  		from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
  else:
  		lowerCamelCase__           =				False
  
  if is_vision_available():
  		from PIL import Image
  
  		from transformers import (
  		    AutoProcessor,
  		    PixaStructImageProcessor,
  		    PixaStructProcessor,
  		    PreTrainedTokenizerFast,
  		    TaTokenizer,
  		)
  
  
  
  
  @unittest.skipIf(
      not is_torch_greater_or_equal_than_1_11			,      reason="""`Pix2StructImageProcessor` requires `torch>=1.11.0`."""			,      )
  @require_vision
  @require_torch
  class 					UpperCamelCase	(     unittest.TestCase      ):
  
  
  
  
  					def 							UpperCamelCase_						( self :					Optional[int]						):
  
  
  							"""simple docstring"""
  
  
  
  
  							__snake_case		       =     tempfile.mkdtemp()
  
  							__snake_case		       =     PixaStructImageProcessor()
  							__snake_case		       =     TaTokenizer.from_pretrained("t5-small"						)
  
  							__snake_case		       =     PixaStructProcessor(_lowerCAmelCase      ,_lowerCAmelCase						)
  
  							processor.save_pretrained(self.tmpdirname						)
  
  
  
  
  					def 							UpperCamelCase_						( self :					Dict      ,**_lowerCAmelCase :					List[str]						):
  
  
  							"""simple docstring"""
  
  
  
  
  							return AutoProcessor.from_pretrained(self.tmpdirname      ,**_lowerCAmelCase						).tokenizer
  
  
  
  
  					def 							UpperCamelCase_						( self :					Any      ,**_lowerCAmelCase :					List[Any]						):
  
  
  							"""simple docstring"""
  
  
  
  
  							return AutoProcessor.from_pretrained(self.tmpdirname      ,**_lowerCAmelCase						).image_processor
  
  
  
  
  					def 							UpperCamelCase_						( self :					Optional[int]						):
  
  
  							"""simple docstring"""
  
  
  
  
  							shutil.rmtree(self.tmpdirname						)
  
  
  
  
  					def 							UpperCamelCase_						( self :					int						):
  
  
  							"""simple docstring"""
  
  
  
  
  							__snake_case		       =     [np.random.randint(255      ,size=(3, 30, 400)      ,dtype=np.uinta						)]
  
  							__snake_case		       =     [Image.fromarray(np.moveaxis(_lowerCAmelCase      ,0      ,-1						)						) for x in image_inputs]
  
  							return image_inputs
  
  
  
  
  					def 							UpperCamelCase_						( self :					Any						):
  
  
  							"""simple docstring"""
  
  
  
  
  							__snake_case		       =     PixaStructProcessor(tokenizer=self.get_tokenizer()      ,image_processor=self.get_image_processor()						)
  							processor.save_pretrained(self.tmpdirname						)
  
  							__snake_case		       =     self.get_tokenizer(bos_token="(BOS)"      ,eos_token="(EOS)"						)
  							__snake_case		       =     self.get_image_processor(do_normalize=_lowerCAmelCase      ,padding_value=1.0						)
  
  							__snake_case		       =     PixaStructProcessor.from_pretrained(
  							    self.tmpdirname      ,bos_token="(BOS)"      ,eos_token="(EOS)"      ,do_normalize=_lowerCAmelCase      ,padding_value=1.0						)
  
  							self.assertEqual(processor.tokenizer.get_vocab()      ,tokenizer_add_kwargs.get_vocab()						)
  							self.assertIsInstance(processor.tokenizer      ,_lowerCAmelCase						)
  
  							self.assertEqual(processor.image_processor.to_json_string()      ,image_processor_add_kwargs.to_json_string()						)
  							self.assertIsInstance(processor.image_processor      ,_lowerCAmelCase						)
  
  
  
  
  					def 							UpperCamelCase_						( self :					Optional[int]						):
  
  
  							"""simple docstring"""
  
  
  
  
  							__snake_case		       =     self.get_image_processor()
  							__snake_case		       =     self.get_tokenizer()
  
  							__snake_case		       =     PixaStructProcessor(tokenizer=_lowerCAmelCase      ,image_processor=_lowerCAmelCase						)
  
  							__snake_case		       =     self.prepare_image_inputs()
  
  							__snake_case		       =     image_processor(_lowerCAmelCase      ,return_tensors="np"						)
  							__snake_case		       =     processor(images=_lowerCAmelCase      ,return_tensors="np"						)
  
  							for key in input_feat_extract.keys():
  									self.assertAlmostEqual(input_feat_extract[key].sum()      ,input_processor[key].sum()      ,delta=1E-2						)
  
  
  
  
  					def 							UpperCamelCase_						( self :					List[str]						):
  
  
  							"""simple docstring"""
  
  
  
  
  							__snake_case		       =     self.get_image_processor()
  							__snake_case		       =     self.get_tokenizer()
  
  							__snake_case		       =     PixaStructProcessor(tokenizer=_lowerCAmelCase      ,image_processor=_lowerCAmelCase						)
  
  							__snake_case		       =     "lower newer"
  
  							__snake_case		       =     processor(text=_lowerCAmelCase						)
  
  							__snake_case		       =     tokenizer(_lowerCAmelCase      ,return_token_type_ids=_lowerCAmelCase      ,add_special_tokens=_lowerCAmelCase						)
  
  							for key in encoded_tok.keys():
  									self.assertListEqual(encoded_tok[key]      ,encoded_processor[key]						)
  
  
  
  
  					def 							UpperCamelCase_						( self :					Any						):
  
  
  							"""simple docstring"""
  
  
  
  
  							__snake_case		       =     self.get_image_processor()
  							__snake_case		       =     self.get_tokenizer()
  
  							__snake_case		       =     PixaStructProcessor(tokenizer=_lowerCAmelCase      ,image_processor=_lowerCAmelCase						)
  
  							__snake_case		       =     "lower newer"
  							__snake_case		       =     self.prepare_image_inputs()
  
  							__snake_case		       =     processor(text=_lowerCAmelCase      ,images=_lowerCAmelCase						)
  
  							self.assertListEqual(
  							    list(inputs.keys()						)      ,["flattened_patches", "attention_mask", "decoder_attention_mask", "decoder_input_ids"]						)
  
  							# test if it raises when no input is passed
  							with pytest.raises(_lowerCAmelCase						):
  									processor()
  
  
  
  
  					def 							UpperCamelCase_						( self :					str						):
  
  
  							"""simple docstring"""
  
  
  
  
  							__snake_case		       =     self.get_image_processor()
  							__snake_case		       =     self.get_tokenizer()
  
  							__snake_case		       =     PixaStructProcessor(tokenizer=_lowerCAmelCase      ,image_processor=_lowerCAmelCase						)
  
  							__snake_case		       =     "lower newer"
  							__snake_case		       =     self.prepare_image_inputs()
  
  							__snake_case		       =     processor(text=_lowerCAmelCase      ,images=_lowerCAmelCase						)
  
  							__snake_case		       =     [512, 1_024, 2_048, 4_096]
  							__snake_case		       =     [770, 770, 770, 770]
  							# with text
  							for i, max_patch in enumerate(_lowerCAmelCase						):
  									__snake_case		       =     processor(text=_lowerCAmelCase      ,images=_lowerCAmelCase      ,max_patches=_lowerCAmelCase						)
  									self.assertEqual(inputs["flattened_patches"][0].shape[0]      ,_lowerCAmelCase						)
  									self.assertEqual(inputs["flattened_patches"][0].shape[1]      ,expected_hidden_size[i]						)
  
  							# without text input
  							for i, max_patch in enumerate(_lowerCAmelCase						):
  									__snake_case		       =     processor(images=_lowerCAmelCase      ,max_patches=_lowerCAmelCase						)
  									self.assertEqual(inputs["flattened_patches"][0].shape[0]      ,_lowerCAmelCase						)
  									self.assertEqual(inputs["flattened_patches"][0].shape[1]      ,expected_hidden_size[i]						)
  
  
  
  
  					def 							UpperCamelCase_						( self :					Union[str, Any]						):
  
  
  							"""simple docstring"""
  
  
  
  
  							__snake_case		       =     self.get_image_processor()
  							__snake_case		       =     self.get_tokenizer()
  
  							__snake_case		       =     PixaStructProcessor(tokenizer=_lowerCAmelCase      ,image_processor=_lowerCAmelCase						)
  
  							__snake_case		       =     [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
  
  							__snake_case		       =     processor.batch_decode(_lowerCAmelCase						)
  							__snake_case		       =     tokenizer.batch_decode(_lowerCAmelCase						)
  
  							self.assertListEqual(_lowerCAmelCase      ,_lowerCAmelCase						)
  
  
  
  
  
  
  
  					def 							UpperCamelCase_						( self :					Tuple						):
  
  
  							"""simple docstring"""
  
  
  
  
  							__snake_case		       =     self.get_image_processor()
  							__snake_case		       =     self.get_tokenizer()
  
  							__snake_case		       =     PixaStructProcessor(tokenizer=_lowerCAmelCase      ,image_processor=_lowerCAmelCase						)
  
  							__snake_case		       =     "lower newer"
  							__snake_case		       =     self.prepare_image_inputs()
  
  							__snake_case		       =     processor(text=_lowerCAmelCase      ,images=_lowerCAmelCase						)
  
  							# For now the processor supports only ["flattened_patches", "input_ids", "attention_mask", "decoder_attention_mask"]
  							self.assertListEqual(
  							    list(inputs.keys()						)      ,["flattened_patches", "attention_mask", "decoder_attention_mask", "decoder_input_ids"]						)
  
  							__snake_case		       =     processor(text=_lowerCAmelCase						)
  
  							# For now the processor supports only ["flattened_patches", "input_ids", "attention_mask", "decoder_attention_mask"]
  							self.assertListEqual(list(inputs.keys()						)      ,["input_ids", "attention_mask"]						)
  
  
  
  
  
  