

from argparse import ArgumentParser

from . import BaseTransformersCLICommand





def       __lowerCamelCase			(				UpperCamelCase__   ):







							'''simple docstring'''






							return DownloadCommand(args.model     ,							args.cache_dir     ,							args.force     ,							args.trust_remote_code   )







class 				lowercase       ( lowercase_    ):




				@staticmethod
				def 					a							(      snake_case ):
											snake_case_           =					parser.add_parser('download' )
											download_parser.add_argument(
											    '--cache-dir'   ,     type=snake_case   ,     default=snake_case   ,     help='Path to location to store the models' )
											download_parser.add_argument(
											    '--force'   ,     action='store_true'   ,     help='Force the model to be download even if already in cache-dir' )
											download_parser.add_argument(
											    '--trust-remote-code'   ,     action='store_true'   ,     help='Whether or not to allow for custom models defined on the Hub in their own modeling files. Use only if you\'ve reviewed the code as it will execute on your local machine'   ,     )
											download_parser.add_argument('model'   ,     type=snake_case   ,     help='Name of the model to download' )
											download_parser.set_defaults(func=snake_case )




				def __init__(      self   ,     snake_case   ,     snake_case   ,     snake_case   ,     snake_case ):
											snake_case_           =					model
											snake_case_           =					cache
											snake_case_           =					force
											snake_case_           =					trust_remote_code




				def 					a							(      self ):
											from ..models.auto import AutoModel, AutoTokenizer

											AutoModel.from_pretrained(
											    self._model   ,     cache_dir=self._cache   ,     force_download=self._force   ,     trust_remote_code=self._trust_remote_code )
											AutoTokenizer.from_pretrained(
											    self._model   ,     cache_dir=self._cache   ,     force_download=self._force   ,     trust_remote_code=self._trust_remote_code )









import json
import os
import unittest

from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta 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, filter_roberta_detectors







@require_tokenizers
class 				lowercase       ( lowercase_			,						unittest.TestCase    ):
				__SCREAMING_SNAKE_CASE      :				Optional[int]     =    MvpTokenizer
				__SCREAMING_SNAKE_CASE      :				Optional[int]     =    MvpTokenizerFast
				__SCREAMING_SNAKE_CASE      :				Optional[int]     =    True
				__SCREAMING_SNAKE_CASE      :				Tuple     =    filter_roberta_detectors




				def 					a							(      self ):
											super().setUp()
											snake_case_           =					[
											    'l',
											    'o',
											    'w',
											    'e',
											    'r',
											    's',
											    't',
											    'i',
											    'd',
											    'n',
											    '\u0120',
											    '\u0120l',
											    '\u0120n',
											    '\u0120lo',
											    '\u0120low',
											    'er',
											    '\u0120lowest',
											    '\u0120newer',
											    '\u0120wider',
											    '<unk>',
											]
											snake_case_           =					dict(zip(snake_case   ,     range(len(snake_case ) ) ) )
											snake_case_           =					['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
											snake_case_           =					{'unk_token': '<unk>'}

											snake_case_           =					os.path.join(self.tmpdirname   ,     VOCAB_FILES_NAMES['vocab_file'] )
											snake_case_           =					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(snake_case ) + '\n' )
											with open(self.merges_file   ,     'w'   ,     encoding='utf-8' ) as fp:
																		fp.write('\n'.join(snake_case ) )




				def 					a							(      self   ,     **snake_case ):
											kwargs.update(self.special_tokens_map )
											return self.tokenizer_class.from_pretrained(self.tmpdirname   ,     **snake_case )




				def 					a							(      self   ,     **snake_case ):
											kwargs.update(self.special_tokens_map )
											return self.rust_tokenizer_class.from_pretrained(self.tmpdirname   ,     **snake_case )




				def 					a							(      self   ,     snake_case ):
											return "lower newer", "lower newer"




				@cached_property
				def 					a							(      self ):
											return MvpTokenizer.from_pretrained('RUCAIBox/mvp' )




				@cached_property
				def 					a							(      self ):
											return MvpTokenizerFast.from_pretrained('RUCAIBox/mvp' )




				@require_torch
				def 					a							(      self ):
											snake_case_           =					['A long paragraph for summarization.', 'Another paragraph for summarization.']
											snake_case_           =					[0, 250, 251, 1_7818, 13, 3_9186, 1938, 4, 2]

											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																		snake_case_           =					tokenizer(snake_case   ,     max_length=len(snake_case )   ,     padding=snake_case   ,     return_tensors='pt' )
																		self.assertIsInstance(snake_case   ,     snake_case )

																		self.assertEqual((2, 9)   ,     batch.input_ids.shape )
																		self.assertEqual((2, 9)   ,     batch.attention_mask.shape )
																		snake_case_           =					batch.input_ids.tolist()[0]
																		self.assertListEqual(snake_case   ,     snake_case )
																		# Test that special tokens are reset




				@require_torch
				def 					a							(      self ):
											snake_case_           =					['A long paragraph for summarization.', 'Another paragraph for summarization.']
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																		snake_case_           =					tokenizer(snake_case   ,     padding=snake_case   ,     return_tensors='pt' )
																		# check if input_ids are returned and no labels
																		self.assertIn('input_ids'   ,     snake_case )
																		self.assertIn('attention_mask'   ,     snake_case )
																		self.assertNotIn('labels'   ,     snake_case )
																		self.assertNotIn('decoder_attention_mask'   ,     snake_case )




				@require_torch
				def 					a							(      self ):
											snake_case_           =					[
											    'Summary of the text.',
											    'Another summary.',
											]
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																		snake_case_           =					tokenizer(text_target=snake_case   ,     max_length=32   ,     padding='max_length'   ,     return_tensors='pt' )
																		self.assertEqual(32   ,     targets['input_ids'].shape[1] )




				@require_torch
				def 					a							(      self ):
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																		snake_case_           =					tokenizer(
																		    ['I am a small frog' * 1024, 'I am a small frog']   ,     padding=snake_case   ,     truncation=snake_case   ,     return_tensors='pt' )
																		self.assertIsInstance(snake_case   ,     snake_case )
																		self.assertEqual(batch.input_ids.shape   ,     (2, 1024) )




				@require_torch
				def 					a							(      self ):
											snake_case_           =					['A long paragraph for summarization.']
											snake_case_           =					[
											    'Summary of the text.',
											]
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																		snake_case_           =					tokenizer(snake_case   ,     text_target=snake_case   ,     return_tensors='pt' )
																		snake_case_           =					inputs['input_ids']
																		snake_case_           =					inputs['labels']
																		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() )




				def 					a							(      self ):
											pass




				def 					a							(      self ):
											for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																		with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
																									snake_case_           =					self.rust_tokenizer_class.from_pretrained(snake_case   ,     **snake_case )
																									snake_case_           =					self.tokenizer_class.from_pretrained(snake_case   ,     **snake_case )
																									snake_case_           =					'A, <mask> AllenNLP sentence.'
																									snake_case_           =					tokenizer_r.encode_plus(snake_case   ,     add_special_tokens=snake_case   ,     return_token_type_ids=snake_case )
																									snake_case_           =					tokenizer_p.encode_plus(snake_case   ,     add_special_tokens=snake_case   ,     return_token_type_ids=snake_case )

																									# token_type_ids should put 0 everywhere
																									self.assertEqual(sum(tokens_r['token_type_ids'] )   ,     sum(tokens_p['token_type_ids'] ) )

																									# attention_mask should put 1 everywhere, so sum over length should be 1
																									self.assertEqual(
																									    sum(tokens_r['attention_mask'] ) / len(tokens_r['attention_mask'] )   ,     sum(tokens_p['attention_mask'] ) / len(tokens_p['attention_mask'] )   ,     )

																									snake_case_           =					tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids'] )
																									snake_case_           =					tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids'] )

																									# Rust correctly handles the space before the mask while python doesnt
																									self.assertSequenceEqual(tokens_p['input_ids']   ,     [0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2] )
																									self.assertSequenceEqual(tokens_r['input_ids']   ,     [0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2] )

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




