



import os
import re
import shutil
from argparse import ArgumentParser, Namespace

from datasets.commands import BaseDatasetsCLICommand
from datasets.utils.logging import get_logger


__snake_case				:		List[Any]										='<<<<<<< This should probably be modified because it mentions: '

__snake_case				:		Union[str, Any]										='=======\n>>>>>>>\n'

__snake_case				:		Tuple										=[
    'TextEncoderConfig',
    'ByteTextEncoder',
    'SubwordTextEncoder',
    'encoder_config',
    'maybe_build_from_corpus',
    'manual_dir',
]

__snake_case				:		List[Any]										=[
    # (pattern, replacement)
    # Order is important here for some replacements
    (R'tfds\.core', R'datasets'),
    (R'tf\.io\.gfile\.GFile', R'open'),
    (R'tf\.([\w\d]+)', R'datasets.Value(\'\1\')'),
    (R'tfds\.features\.Text\(\)', R'datasets.Value(\'string\')'),
    (R'tfds\.features\.Text\(', R'datasets.Value(\'string\'),'),
    (R'features\s*=\s*tfds.features.FeaturesDict\(', R'features=datasets.Features('),
    (R'tfds\.features\.FeaturesDict\(', R'dict('),
    (R'The TensorFlow Datasets Authors', R'The TensorFlow Datasets Authors and the HuggingFace Datasets Authors'),
    (R'tfds\.', R'datasets.'),
    (R'dl_manager\.manual_dir', R'self.config.data_dir'),
    (R'self\.builder_config', R'self.config'),
]






def 	lowerCAmelCase__		(    lowerCamelCase_		:		List[Any]):





				'''simple docstring'''

				return ConvertCommand(args.tfds_path       ,args.datasets_directory)



class        lowerCamelCase__			(							lowercase_):




		'''simple docstring'''



		@staticmethod
		def 		lowerCAmelCase__      (__lowerCamelCase )     ->			Tuple:

						"""simple docstring"""






						lowerCAmelCase__     :				Tuple												=     parser.add_parser(
						    '''convert'''		,help='''Convert a TensorFlow Datasets dataset to a HuggingFace Datasets dataset.'''		,)
						train_parser.add_argument(
						    '''--tfds_path'''		,type=__lowerCamelCase		,required=__lowerCamelCase		,help='''Path to a TensorFlow Datasets folder to convert or a single tfds file to convert.'''		,)
						train_parser.add_argument(
						    '''--datasets_directory'''		,type=__lowerCamelCase		,required=__lowerCamelCase		,help='''Path to the HuggingFace Datasets folder.''' )
						train_parser.set_defaults(func=__lowerCamelCase )



		def __init__(self		,__lowerCamelCase		,__lowerCamelCase		,*__lowerCamelCase )     ->			Tuple:

						"""simple docstring"""






						lowerCAmelCase__     :				Union[str, Any]												=     get_logger('''datasets-cli/converting''' )

						lowerCAmelCase__     :				Optional[int]												=     tfds_path
						lowerCAmelCase__     :				str												=     datasets_directory



		def 		lowerCAmelCase__      (self )     ->			Any:

						"""simple docstring"""






						if os.path.isdir(self._tfds_path ):
										lowerCAmelCase__     :				Tuple												=     os.path.abspath(self._tfds_path )
						elif os.path.isfile(self._tfds_path ):
										lowerCAmelCase__     :				Optional[Any]												=     os.path.dirname(self._tfds_path )
						else:
										raise ValueError('''--tfds_path is neither a directory nor a file. Please check path.''' )

						lowerCAmelCase__     :				List[Any]												=     os.path.abspath(self._datasets_directory )

						self._logger.info(f"""Converting datasets from {abs_tfds_path} to {abs_datasets_path}""" )

						lowerCAmelCase__     :				str												=     []
						lowerCAmelCase__     :				Union[str, Any]												=     []
						lowerCAmelCase__     :				List[Any]												=     {}

						if os.path.isdir(self._tfds_path ):
										lowerCAmelCase__     :				Tuple												=     os.listdir(__lowerCamelCase )
						else:
										lowerCAmelCase__     :				Dict												=     [os.path.basename(self._tfds_path )]

						for f_name in file_names:
										self._logger.info(f"""Looking at file {f_name}""" )
										lowerCAmelCase__     :				str												=     os.path.join(__lowerCamelCase		,__lowerCamelCase )
										lowerCAmelCase__     :				List[Any]												=     os.path.join(__lowerCamelCase		,__lowerCamelCase )

										if not os.path.isfile(__lowerCamelCase ) or "__init__" in f_name or "_test" in f_name or ".py" not in f_name:
														self._logger.info('''Skipping file''' )
														continue

										with open(__lowerCamelCase		,encoding='''utf-8''' ) as f:
														lowerCAmelCase__     :				List[str]												=     f.readlines()

										lowerCAmelCase__     :				Tuple												=     []
										lowerCAmelCase__     :				Optional[int]												=     False
										lowerCAmelCase__     :				List[Any]												=     False
										lowerCAmelCase__     :				Optional[int]												=     []
										for line in lines:
														lowerCAmelCase__     :				List[Any]												=     line

														# Convert imports
														if "import tensorflow.compat.v2 as tf" in out_line:
																		continue
														elif "@tfds.core" in out_line:
																		continue
														elif "builder=self" in out_line:
																		continue
														elif "import tensorflow_datasets.public_api as tfds" in out_line:
																		lowerCAmelCase__     :				Optional[Any]												=     '''import datasets\n'''
														elif "import tensorflow" in out_line:
																		# order is important here
																		lowerCAmelCase__     :				int												=     ''''''
																		continue
														elif "from absl import logging" in out_line:
																		lowerCAmelCase__     :				Any												=     '''from datasets import logging\n'''
														elif "getLogger" in out_line:
																		lowerCAmelCase__     :				str												=     out_line.replace('''getLogger'''		,'''get_logger''' )
														elif any(expression in out_line for expression in TO_HIGHLIGHT ):
																		lowerCAmelCase__     :				int												=     True
																		lowerCAmelCase__     :				str												=     list(filter(lambda __lowerCamelCase : e in out_line		,__lowerCamelCase ) )
																		out_lines.append(HIGHLIGHT_MESSAGE_PRE + str(__lowerCamelCase ) + '''\n''' )
																		out_lines.append(__lowerCamelCase )
																		out_lines.append(__lowerCamelCase )
																		continue
														else:
																		for pattern, replacement in TO_CONVERT:
																						lowerCAmelCase__     :				Union[str, Any]												=     re.sub(__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase )

                # Take care of saving utilities (to later move them together with main script)
														if "tensorflow_datasets" in out_line:
																		lowerCAmelCase__     :				Optional[Any]												=     re.match(R'''from\stensorflow_datasets.*import\s([^\.\r\n]+)'''		,__lowerCamelCase )
																		tfds_imports.extend(imp.strip() for imp in match.group(1 ).split(''',''' ) )
																		lowerCAmelCase__     :				Tuple												=     '''from . import ''' + match.group(1 )

														# Check we have not forget anything
														if "tf." in out_line or "tfds." in out_line or "tensorflow_datasets" in out_line:
																		raise ValueError(f"""Error converting {out_line.strip()}""" )

														if "GeneratorBasedBuilder" in out_line or "BeamBasedBuilder" in out_line:
																		lowerCAmelCase__     :				Union[str, Any]												=     True
														out_lines.append(__lowerCamelCase )

										if is_builder or "wmt" in f_name:
														# We create a new directory for each dataset
														lowerCAmelCase__     :				List[str]												=     f_name.replace('''.py'''		,'''''' )
														lowerCAmelCase__     :				Optional[int]												=     os.path.join(__lowerCamelCase		,__lowerCamelCase )
														lowerCAmelCase__     :				Optional[int]												=     os.path.join(__lowerCamelCase		,__lowerCamelCase )
														os.makedirs(__lowerCamelCase		,exist_ok=__lowerCamelCase )
														self._logger.info(f"""Adding directory {output_dir}""" )
														imports_to_builder_map.update({imp: output_dir for imp in tfds_imports} )
										else:
														# Utilities will be moved at the end
														utils_files.append(__lowerCamelCase )

										if needs_manual_update:
														with_manual_update.append(__lowerCamelCase )

										with open(__lowerCamelCase		,'''w'''		,encoding='''utf-8''' ) as f:
														f.writelines(__lowerCamelCase )
										self._logger.info(f"""Converted in {output_file}""" )

						for utils_file in utils_files:
										try:
														lowerCAmelCase__     :				List[Any]												=     os.path.basename(__lowerCamelCase )
														lowerCAmelCase__     :				Union[str, Any]												=     imports_to_builder_map[f_name.replace('''.py'''		,'''''' )]
														self._logger.info(f"""Moving {dest_folder} to {utils_file}""" )
														shutil.copy(__lowerCamelCase		,__lowerCamelCase )
										except KeyError:
														self._logger.error(f"""Cannot find destination folder for {utils_file}. Please copy manually.""" )

						if with_manual_update:
										for file_path in with_manual_update:
														self._logger.warning(
														    f"""You need to manually update file {file_path} to remove configurations using 'TextEncoderConfig'.""" )





'''simple docstring'''

import unittest

from transformers import TrOCRConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers.models.trocr.modeling_trocr import TrOCRDecoder, TrOCRForCausalLM
@require_torch
class   __lowerCAmelCase						:





      '''simple docstring'''






      def __init__(self    :    str   ,						UpperCamelCase    :    Tuple   ,						UpperCamelCase    :    Optional[int]=99   ,						UpperCamelCase    :    Optional[int]=13   ,						UpperCamelCase    :    Tuple=16   ,						UpperCamelCase    :    Union[str, Any]=7   ,						UpperCamelCase    :    List[Any]=True   ,						UpperCamelCase    :    List[str]=True   ,						UpperCamelCase    :    str=True   ,						UpperCamelCase    :    Tuple=False   ,						UpperCamelCase    :    str=True   ,						UpperCamelCase    :    Tuple=2   ,						UpperCamelCase    :    Optional[int]=32   ,						UpperCamelCase    :    Any=4   ,						UpperCamelCase    :    Optional[int]=4   ,						UpperCamelCase    :    Tuple=30   ,						UpperCamelCase    :    str=0   ,						UpperCamelCase    :    Tuple=1   ,						UpperCamelCase    :    List[Any]=2   ,						UpperCamelCase    :    str=None   ,						):

             '''simple docstring'''







             lowercase__      			= parent
             lowercase__      			= batch_size
             lowercase__      			= decoder_seq_length
             # For common tests
             lowercase__      			= self.decoder_seq_length
             lowercase__      			= is_training
             lowercase__      			= use_attention_mask
             lowercase__      			= use_labels

             lowercase__      			= vocab_size
             lowercase__      			= d_model
             lowercase__      			= d_model
             lowercase__      			= decoder_layers
             lowercase__      			= decoder_layers
             lowercase__      			= decoder_ffn_dim
             lowercase__      			= decoder_attention_heads
             lowercase__      			= decoder_attention_heads
             lowercase__      			= eos_token_id
             lowercase__      			= bos_token_id
             lowercase__      			= pad_token_id
             lowercase__      			= decoder_start_token_id
             lowercase__      			= use_cache
             lowercase__      			= max_position_embeddings

             lowercase__      			= None
             lowercase__      			= decoder_seq_length
             lowercase__      			= 2
             lowercase__      			= 1



      def       UpperCamelCase__		(self    :    str ):

             '''simple docstring'''







             lowercase__      			= ids_tensor([self.batch_size, self.decoder_seq_length]   ,						self.vocab_size )

             lowercase__      			= None
             if self.use_attention_mask:
                    lowercase__      			= ids_tensor([self.batch_size, self.decoder_seq_length]   ,						vocab_size=2 )

             lowercase__      			= None
             if self.use_labels:
                    lowercase__      			= ids_tensor([self.batch_size, self.decoder_seq_length]   ,						self.vocab_size )

             lowercase__      			= TrOCRConfig(
                 vocab_size=self.vocab_size   ,						d_model=self.d_model   ,						decoder_layers=self.decoder_layers   ,						decoder_ffn_dim=self.decoder_ffn_dim   ,						decoder_attention_heads=self.decoder_attention_heads   ,						eos_token_id=self.eos_token_id   ,						bos_token_id=self.bos_token_id   ,						use_cache=self.use_cache   ,						pad_token_id=self.pad_token_id   ,						decoder_start_token_id=self.decoder_start_token_id   ,						max_position_embeddings=self.max_position_embeddings   ,						)

             return (config, input_ids, attention_mask, lm_labels)



      def       UpperCamelCase__		(self    :    Tuple   ,						UpperCamelCase    :    List[Any]   ,						UpperCamelCase    :    Optional[Any]   ,						UpperCamelCase    :    Tuple   ,						UpperCamelCase    :    List[str]   ,						):

             '''simple docstring'''







             lowercase__      			= True
             lowercase__      			= TrOCRDecoder(config=UpperCamelCase ).to(UpperCamelCase ).eval()
             lowercase__      			= input_ids[:2]

             input_ids[input_ids == 0] += 1
             # first forward pass
             lowercase__      			= model(UpperCamelCase   ,						use_cache=UpperCamelCase )
             lowercase__      			= model(UpperCamelCase )
             lowercase__      			= model(UpperCamelCase   ,						use_cache=UpperCamelCase )

             self.parent.assertTrue(len(UpperCamelCase ) == len(UpperCamelCase ) )
             self.parent.assertTrue(len(UpperCamelCase ) == len(UpperCamelCase ) + 1 )

             lowercase__      			= outputs['''past_key_values''']

             # create hypothetical next token and extent to next_input_ids
             lowercase__      			= ids_tensor((2, 1)   ,						config.vocab_size - 1 ) + 1

             # append to next input_ids and
             lowercase__      			= torch.cat([input_ids, next_tokens]   ,						dim=-1 )

             lowercase__      			= model(UpperCamelCase )['''last_hidden_state''']
             lowercase__      			= model(UpperCamelCase   ,						past_key_values=UpperCamelCase )['''last_hidden_state''']

             # select random slice
             lowercase__      			= ids_tensor((1,)   ,						output_from_past.shape[-1] ).item()
             lowercase__      			= output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
             lowercase__      			= output_from_past[:, 0, random_slice_idx].detach()

             # test that outputs are equal for slice
             assert torch.allclose(UpperCamelCase   ,						UpperCamelCase   ,						atol=1E-3 )






      def       UpperCamelCase__		(self    :    Optional[Any] ):

             '''simple docstring'''







             lowercase__      			= self.prepare_config_and_inputs()
             lowercase__			,lowercase__			,lowercase__			,lowercase__      			= config_and_inputs

             lowercase__      			= {'''input_ids''': input_ids, '''attention_mask''': attention_mask}
             return config, inputs_dict






@require_torch
class   __lowerCAmelCase						(lowercase_  ,     lowercase_  ,     lowercase_  ,     unittest.TestCase     ):





      '''simple docstring'''






      lowerCAmelCase__					:     List[str]  					=							(TrOCRDecoder, TrOCRForCausalLM) if is_torch_available() else ()
      lowerCAmelCase__					:     List[Any]  					=							(TrOCRForCausalLM,) if is_torch_available() else ()
      lowerCAmelCase__					:     Optional[Any]  					=							{"""text-generation""": TrOCRForCausalLM} if is_torch_available() else {}
      lowerCAmelCase__					:     Optional[Any]  					=							True
      lowerCAmelCase__					:     List[str]  					=							False



      def       UpperCamelCase__		(self    :    Any ):

             '''simple docstring'''







             lowercase__      			= TrOCRStandaloneDecoderModelTester(self   ,						is_training=UpperCamelCase )
             lowercase__      			= ConfigTester(self   ,						config_class=UpperCamelCase )



      def       UpperCamelCase__		(self    :    List[str] ):

             '''simple docstring'''







             pass



      def       UpperCamelCase__		(self    :    Optional[int] ):

             '''simple docstring'''







             pass



      def       UpperCamelCase__		(self    :    Any ):

             '''simple docstring'''







             pass



      def       UpperCamelCase__		(self    :    Any ):

             '''simple docstring'''







             self.config_tester.run_common_tests()



      def       UpperCamelCase__		(self    :    Union[str, Any] ):

             '''simple docstring'''







             lowercase__      			= self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_decoder_model_past(*UpperCamelCase )



      def       UpperCamelCase__		(self    :    Optional[int] ):

             '''simple docstring'''







             return






      @unittest.skip('''The model doesn\'t support left padding''' )  # and it's not used enough to be worth fixing :)
      def       UpperCamelCase__		(self    :    List[str] ):

             '''simple docstring'''







             pass




