


from argparse import ArgumentParser

from . import BaseTransformersCLICommand







def        lowerCAmelCase_  (      __lowerCamelCase			):
							return DownloadCommand(args.model							,			args.cache_dir							,			args.force							,			args.trust_remote_code			)

class        a	(_A   ):




							"""simple docstring"""


							@staticmethod
							def 					__snake_case      (							lowerCamelCase :						Any       )			->					List[Any]:
														__snake_case	:    Any											=     parser.add_parser("download"       )
														download_parser.add_argument(
														    "--cache-dir"					,			type=lowerCamelCase					,			default=lowerCamelCase					,			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=lowerCamelCase					,			help="Name of the model to download"       )
														download_parser.set_defaults(func=lowerCamelCase       )

							def __init__(							self :						Optional[Any]					,			lowerCamelCase :						Union[str, Any]					,			lowerCamelCase :						Any					,			lowerCamelCase :						List[Any]					,			lowerCamelCase :						List[str]       )			->					Union[str, Any]:
														__snake_case	:    List[Any]											=     model
														__snake_case	:    int											=     cache
														__snake_case	:    Union[str, Any]											=     force
														__snake_case	:    Dict											=     trust_remote_code






							def 					__snake_case      (							self :						List[str]       )			->					Tuple:
														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 unittest

from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


_snake_case  :	Optional[int]			      =       "▁"

_snake_case  :	Tuple			      =       get_tests_dir("fixtures/test_sentencepiece.model")

@require_sentencepiece
class        a	(_lowerCAmelCase  ,       unittest.TestCase   ):




							"""simple docstring"""


							__UpperCAmelCase					:							Optional[Any]  						=						BertGenerationTokenizer
							__UpperCAmelCase					:							List[Any]  						=						False
							__UpperCAmelCase					:							List[Any]  						=						True

							def 					__snake_case      (							self :						Optional[int]       )			->					Optional[int]:
														super().setUp()

														__snake_case	:    Tuple											=     BertGenerationTokenizer(lowerCamelCase					,			keep_accents=lowerCamelCase       )
														tokenizer.save_pretrained(self.tmpdirname       )

							def 					__snake_case      (							self :						Dict       )			->					int:
														__snake_case	:    str											=     "<s>"
														__snake_case	:    Any											=     1

														self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCamelCase       )					,			lowerCamelCase       )
														self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCamelCase       )					,			lowerCamelCase       )

							def 					__snake_case      (							self :						str       )			->					Optional[Any]:
														__snake_case	:    int											=     list(self.get_tokenizer().get_vocab().keys()       )

														self.assertEqual(vocab_keys[0]					,			"<unk>"       )
														self.assertEqual(vocab_keys[1]					,			"<s>"       )
														self.assertEqual(vocab_keys[-1]					,			"<pad>"       )
														self.assertEqual(len(lowerCamelCase       )					,			1002       )

							def 					__snake_case      (							self :						List[str]       )			->					Optional[int]:
														self.assertEqual(self.get_tokenizer().vocab_size					,			1000       )

							def 					__snake_case      (							self :						List[str]       )			->					Union[str, Any]:
														__snake_case	:    List[Any]											=     BertGenerationTokenizer(lowerCamelCase					,			keep_accents=lowerCamelCase       )

														__snake_case	:    int											=     tokenizer.tokenize("This is a test"       )
														self.assertListEqual(lowerCamelCase					,			["▁This", "▁is", "▁a", "▁t", "est"]       )

														self.assertListEqual(
														    tokenizer.convert_tokens_to_ids(lowerCamelCase       )					,			[285, 46, 10, 170, 382]					,			)

														__snake_case	:    str											=     tokenizer.tokenize("I was born in 92000, and this is falsé."       )
														self.assertListEqual(
														    lowerCamelCase					,			[
														        SPIECE_UNDERLINE + "I",
														        SPIECE_UNDERLINE + "was",
														        SPIECE_UNDERLINE + "b",
														        "or",
														        "n",
														        SPIECE_UNDERLINE + "in",
														        SPIECE_UNDERLINE + "",
														        "9",
														        "2",
														        "0",
														        "0",
														        "0",
														        ",",
														        SPIECE_UNDERLINE + "and",
														        SPIECE_UNDERLINE + "this",
														        SPIECE_UNDERLINE + "is",
														        SPIECE_UNDERLINE + "f",
														        "al",
														        "s",
														        "é",
														        ".",
														    ]					,			)
														__snake_case	:    Any											=     tokenizer.convert_tokens_to_ids(lowerCamelCase       )
														self.assertListEqual(
														    lowerCamelCase					,			[8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4]					,			)

														__snake_case	:    Optional[Any]											=     tokenizer.convert_ids_to_tokens(lowerCamelCase       )
														self.assertListEqual(
														    lowerCamelCase					,			[
														        SPIECE_UNDERLINE + "I",
														        SPIECE_UNDERLINE + "was",
														        SPIECE_UNDERLINE + "b",
														        "or",
														        "n",
														        SPIECE_UNDERLINE + "in",
														        SPIECE_UNDERLINE + "",
														        "<unk>",
														        "2",
														        "0",
														        "0",
														        "0",
														        ",",
														        SPIECE_UNDERLINE + "and",
														        SPIECE_UNDERLINE + "this",
														        SPIECE_UNDERLINE + "is",
														        SPIECE_UNDERLINE + "f",
														        "al",
														        "s",
														        "<unk>",
														        ".",
														    ]					,			)

							@cached_property
							def 					__snake_case      (							self :						str       )			->					List[Any]:
														return BertGenerationTokenizer.from_pretrained("google/bert_for_seq_generation_L-24_bbc_encoder"       )

							@slow
							def 					__snake_case      (							self :						Tuple       )			->					Union[str, Any]:
														__snake_case	:    Union[str, Any]											=     "Hello World!"
														__snake_case	:    List[str]											=     [18536, 2260, 101]

														self.assertListEqual(lowerCamelCase					,			self.big_tokenizer.encode(lowerCamelCase       )       )

							@slow
							def 					__snake_case      (							self :						str       )			->					Optional[int]:
														__snake_case	:    List[Any]											=     (
														    "This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"
														    " add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"
														)
														__snake_case	:    Any											=     [
														    871,
														    419,
														    358,
														    946,
														    991,
														    2521,
														    452,
														    358,
														    1357,
														    387,
														    7751,
														    3536,
														    112,
														    985,
														    456,
														    126,
														    865,
														    938,
														    5400,
														    5734,
														    458,
														    1368,
														    467,
														    786,
														    2462,
														    5246,
														    1159,
														    633,
														    865,
														    4519,
														    457,
														    582,
														    852,
														    2557,
														    427,
														    916,
														    508,
														    405,
														    34324,
														    497,
														    391,
														    408,
														    11342,
														    1244,
														    385,
														    100,
														    938,
														    985,
														    456,
														    574,
														    362,
														    12597,
														    3200,
														    3129,
														    1172,
														]

														self.assertListEqual(lowerCamelCase					,			self.big_tokenizer.encode(lowerCamelCase       )       )

							@require_torch
							@slow
							def 					__snake_case      (							self :						Optional[Any]       )			->					str:
														import torch

														from transformers import BertGenerationConfig, BertGenerationEncoder

														# Build sequence
														__snake_case	:    List[Any]											=     list(self.big_tokenizer.get_vocab().keys()       )[:10]
														__snake_case	:    Union[str, Any]											=     " ".join(lowerCamelCase       )
														__snake_case	:    Optional[int]											=     self.big_tokenizer.encode_plus(lowerCamelCase					,			return_tensors="pt"					,			return_token_type_ids=lowerCamelCase       )
														__snake_case	:    Optional[Any]											=     self.big_tokenizer.batch_encode_plus(
														    [sequence + " " + sequence]					,			return_tensors="pt"					,			return_token_type_ids=lowerCamelCase       )

														__snake_case	:    List[Any]											=     BertGenerationConfig()
														__snake_case	:    Dict											=     BertGenerationEncoder(lowerCamelCase       )

														assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

														with torch.no_grad():
																					model(**lowerCamelCase       )
																					model(**lowerCamelCase       )






							@slow
							def 					__snake_case      (							self :						List[Any]       )			->					List[Any]:
														# fmt: off
														__snake_case	:    Optional[Any]											=     {"input_ids": [[39286, 458, 36335, 2001, 456, 13073, 13266, 455, 113, 7746, 1741, 11157, 391, 13073, 13266, 455, 113, 3967, 35412, 113, 4936, 109, 3870, 2377, 113, 30084, 45720, 458, 134, 17496, 112, 503, 11672, 113, 118, 112, 5665, 13347, 38687, 112, 1496, 31389, 112, 3268, 47264, 134, 962, 112, 16377, 8035, 23130, 430, 12169, 15518, 28592, 458, 146, 41697, 109, 391, 12169, 15518, 16689, 458, 146, 41358, 109, 452, 726, 4034, 111, 763, 35412, 5082, 388, 1903, 111, 9051, 391, 2870, 48918, 1900, 1123, 550, 998, 112, 9586, 15985, 455, 391, 410, 22955, 37636, 114], [448, 17496, 419, 3663, 385, 763, 113, 27533, 2870, 3283, 13043, 1639, 24713, 523, 656, 24013, 18550, 2521, 517, 27014, 21244, 420, 1212, 1465, 391, 927, 4833, 388, 578, 11786, 114, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [484, 2169, 7687, 21932, 18146, 726, 363, 17032, 3391, 114, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
														# fmt: on

														self.tokenizer_integration_test_util(
														    expected_encoding=lowerCamelCase					,			model_name="google/bert_for_seq_generation_L-24_bbc_encoder"					,			revision="c817d1fd1be2ffa69431227a1fe320544943d4db"					,			)

