

"""simple docstring"""





from argparse import ArgumentParser

from .add_new_model import AddNewModelCommand
from .add_new_model_like import AddNewModelLikeCommand
from .convert import ConvertCommand
from .download import DownloadCommand
from .env import EnvironmentCommand
from .lfs import LfsCommands
from .pt_to_tf import PTtoTFCommand
from .run import RunCommand
from .serving import ServeCommand
from .user import UserCommands

def   __SCREAMING_SNAKE_CASE  (      ):
	lowerCAmelCase__	:						Optional[Any]  				=	ArgumentParser('''Transformers CLI tool'''						,     usage='''transformers-cli <command> [<args>]''' )
	lowerCAmelCase__	:						str  				=	parser.add_subparsers(help='''transformers-cli command helpers''' )

	# Register commands
	ConvertCommand.register_subcommand(A_ )
	DownloadCommand.register_subcommand(A_ )
	EnvironmentCommand.register_subcommand(A_ )
	RunCommand.register_subcommand(A_ )
	ServeCommand.register_subcommand(A_ )
	UserCommands.register_subcommand(A_ )
	AddNewModelCommand.register_subcommand(A_ )
	AddNewModelLikeCommand.register_subcommand(A_ )
	LfsCommands.register_subcommand(A_ )
	PTtoTFCommand.register_subcommand(A_ )

	# Let's go
	lowerCAmelCase__	:						Optional[int]  				=	parser.parse_args()

	if not hasattr(A_						,     '''func''' ):
		parser.print_help()
		exit(1 )

	# Run
	lowerCAmelCase__	:						List[Any]  				=	args.func(A_ )
	service.run()


if __name__ == "__main__":
							main()








'''simple docstring'''
import json
import os
import unittest

from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin




@require_tokenizers
class 			lowerCamelCase_   (	__a     ,       unittest.TestCase      ):
      lowerCAmelCase__		     =     LEDTokenizer
      lowerCAmelCase__		     =     LEDTokenizerFast
      lowerCAmelCase__		     =     True






      def lowercase_							(					self				:    int ):


          '''simple docstring'''


          super().setUp()
          UpperCAmelCase__			:		List[Any]													=				[
              '''l''',
              '''o''',
              '''w''',
              '''e''',
              '''r''',
              '''s''',
              '''t''',
              '''i''',
              '''d''',
              '''n''',
              '''\u0120''',
              '''\u0120l''',
              '''\u0120n''',
              '''\u0120lo''',
              '''\u0120low''',
              '''er''',
              '''\u0120lowest''',
              '''\u0120newer''',
              '''\u0120wider''',
              '''<unk>''',
          ]
          UpperCAmelCase__			:		Optional[Any]													=				dict(zip(_A						,						range(len(_A ) ) ) )
          UpperCAmelCase__			:		Union[str, Any]													=				['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
          UpperCAmelCase__			:		Any													=				{'''unk_token''': '''<unk>'''}

          UpperCAmelCase__			:		Dict													=				os.path.join(self.tmpdirname						,						VOCAB_FILES_NAMES['''vocab_file'''] )
          UpperCAmelCase__			:		Tuple													=				os.path.join(self.tmpdirname						,						VOCAB_FILES_NAMES['''merges_file'''] )
          with open(self.vocab_file						,						'''w'''						,						encoding='''utf-8''' ) as fp:
              fp.write(json.dumps(_A ) + '''\n''' )
          with open(self.merges_file						,						'''w'''						,						encoding='''utf-8''' ) as fp:
              fp.write('''\n'''.join(_A ) )






      def lowercase_							(					self				:    Optional[int]						,						**_A				:    Any ):


          '''simple docstring'''


          kwargs.update(self.special_tokens_map )
          return self.tokenizer_class.from_pretrained(self.tmpdirname						,						**_A )






      def lowercase_							(					self				:    Union[str, Any]						,						**_A				:    Optional[Any] ):


          '''simple docstring'''


          kwargs.update(self.special_tokens_map )
          return self.rust_tokenizer_class.from_pretrained(self.tmpdirname						,						**_A )






      def lowercase_							(					self				:    Tuple						,						_A				:    List[str] ):


          '''simple docstring'''


          return "lower newer", "lower newer"






      @cached_property
      def lowercase_							(					self				:    List[Any] ):


          '''simple docstring'''


          return LEDTokenizer.from_pretrained('''allenai/led-base-16384''' )






      @cached_property
      def lowercase_							(					self				:    Any ):


          '''simple docstring'''


          return LEDTokenizerFast.from_pretrained('''allenai/led-base-16384''' )






      @require_torch
      def lowercase_							(					self				:    Optional[Any] ):


          '''simple docstring'''


          UpperCAmelCase__			:		Union[str, Any]													=				['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
          UpperCAmelCase__			:		Dict													=				[0, 250, 251, 17_818, 13, 39_186, 1_938, 4, 2]

          for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              UpperCAmelCase__			:		Union[str, Any]													=				tokenizer(_A						,						max_length=len(_A )						,						padding=_A						,						return_tensors='''pt''' )
              self.assertIsInstance(_A						,						_A )

              self.assertEqual((2, 9)						,						batch.input_ids.shape )
              self.assertEqual((2, 9)						,						batch.attention_mask.shape )
              UpperCAmelCase__			:		int													=				batch.input_ids.tolist()[0]
              self.assertListEqual(_A						,						_A )






      @require_torch
      def lowercase_							(					self				:    Tuple ):


          '''simple docstring'''


          UpperCAmelCase__			:		Optional[int]													=				['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
          for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              UpperCAmelCase__			:		List[str]													=				tokenizer(_A						,						padding=_A						,						return_tensors='''pt''' )
              self.assertIn('''input_ids'''						,						_A )
              self.assertIn('''attention_mask'''						,						_A )
              self.assertNotIn('''labels'''						,						_A )
              self.assertNotIn('''decoder_attention_mask'''						,						_A )






      @require_torch
      def lowercase_							(					self				:    str ):


          '''simple docstring'''


          UpperCAmelCase__			:		Tuple													=				[
              '''Summary of the text.''',
              '''Another summary.''',
          ]
          for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              UpperCAmelCase__			:		Optional[Any]													=				tokenizer(text_target=_A						,						max_length=32						,						padding='''max_length'''						,						return_tensors='''pt''' )
              self.assertEqual(32						,						targets['''input_ids'''].shape[1] )






      @require_torch
      def lowercase_							(					self				:    Tuple ):


          '''simple docstring'''


          for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              UpperCAmelCase__			:		Any													=				tokenizer(
                  ['''I am a small frog''' * 1_024, '''I am a small frog''']						,						padding=_A						,						truncation=_A						,						return_tensors='''pt''' )
              self.assertIsInstance(_A						,						_A )
              self.assertEqual(batch.input_ids.shape						,						(2, 5_122) )






      @require_torch
      def lowercase_							(					self				:    List[str] ):


          '''simple docstring'''


          UpperCAmelCase__			:		Any													=				['''A long paragraph for summarization.''']
          UpperCAmelCase__			:		List[Any]													=				[
              '''Summary of the text.''',
          ]
          for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              UpperCAmelCase__			:		Optional[Any]													=				tokenizer(_A						,						return_tensors='''pt''' )
              UpperCAmelCase__			:		int													=				tokenizer(text_target=_A						,						return_tensors='''pt''' )
              UpperCAmelCase__			:		str													=				inputs['''input_ids''']
              UpperCAmelCase__			:		Tuple													=				targets['''input_ids''']
              self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item() )
              self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item() )
              self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item() )
              self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item() )






      @require_torch
      def lowercase_							(					self				:    List[str] ):


          '''simple docstring'''


          for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              UpperCAmelCase__			:		Tuple													=				['''Summary of the text.''', '''Another summary.''']
              UpperCAmelCase__			:		List[str]													=				[[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

              UpperCAmelCase__			:		List[str]													=				tokenizer(_A						,						padding=_A )
              UpperCAmelCase__			:		str													=				[[0] * len(_A ) for x in encoded_output['''input_ids''']]
              UpperCAmelCase__			:		Any													=				tokenizer.pad(_A )
              self.assertSequenceEqual(outputs['''global_attention_mask''']						,						_A )






      def lowercase_							(					self				:    Tuple ):


          '''simple docstring'''


          pass






      def lowercase_							(					self				:    Dict ):


          '''simple docstring'''


          for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
              with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
                  UpperCAmelCase__			:		Optional[int]													=				self.rust_tokenizer_class.from_pretrained(_A						,						**_A )
                  UpperCAmelCase__			:		int													=				self.tokenizer_class.from_pretrained(_A						,						**_A )
                  UpperCAmelCase__			:		Any													=				'''A, <mask> AllenNLP sentence.'''
                  UpperCAmelCase__			:		Dict													=				tokenizer_r.encode_plus(_A						,						add_special_tokens=_A						,						return_token_type_ids=_A )
                  UpperCAmelCase__			:		Optional[int]													=				tokenizer_p.encode_plus(_A						,						add_special_tokens=_A						,						return_token_type_ids=_A )
                  self.assertEqual(sum(tokens_r['''token_type_ids'''] )						,						sum(tokens_p['''token_type_ids'''] ) )
                  self.assertEqual(
                      sum(tokens_r['''attention_mask'''] ) / len(tokens_r['''attention_mask'''] )						,						sum(tokens_p['''attention_mask'''] ) / len(tokens_p['''attention_mask'''] )						,						)

                  UpperCAmelCase__			:		str													=				tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids'''] )
                  UpperCAmelCase__			:		Tuple													=				tokenizer_p.convert_ids_to_tokens(tokens_p['''input_ids'''] )
                  self.assertSequenceEqual(tokens_p['''input_ids''']						,						[0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2] )
                  self.assertSequenceEqual(tokens_r['''input_ids''']						,						[0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2] )

                  self.assertSequenceEqual(
                      _A						,						['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>'''] )
                  self.assertSequenceEqual(
                      _A						,						['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>'''] )
