

import unittest

from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


UpperCamelCase__		 =     get_tests_dir('''fixtures/test_sentencepiece.model''')

@require_sentencepiece
@require_tokenizers
class     __snake_case    (				snake_case__			,	unittest.TestCase  ):
       """simple docstring"""







       __SCREAMING_SNAKE_CASE				    =							ReformerTokenizer
       __SCREAMING_SNAKE_CASE				    =							ReformerTokenizerFast
       __SCREAMING_SNAKE_CASE				    =							True
       __SCREAMING_SNAKE_CASE				    =							False
       __SCREAMING_SNAKE_CASE				    =							True


       def 			a		(		self   )      ->							List[Any]:






              """simple docstring"""


              super().setUp()

              __snake_case       	=							ReformerTokenizer(_UpperCamelCase      , keep_accents=_UpperCamelCase   )
              tokenizer.save_pretrained(self.tmpdirname   )


       def 			a		(		self   )      ->							List[str]:






              """simple docstring"""


              __snake_case       	=							"""<s>"""
              __snake_case       	=							1

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


       def 			a		(		self   )      ->							int:






              """simple docstring"""


              __snake_case       	=							list(self.get_tokenizer().get_vocab().keys()   )

              self.assertEqual(vocab_keys[0]      , """<unk>"""   )
              self.assertEqual(vocab_keys[1]      , """<s>"""   )
              self.assertEqual(vocab_keys[-1]      , """j"""   )
              self.assertEqual(len(_UpperCamelCase   )      , 10_00   )


       def 			a		(		self   )      ->							str:






              """simple docstring"""


              self.assertEqual(self.get_tokenizer().vocab_size      , 10_00   )


       def 			a		(		self   )      ->							Union[str, Any]:






              """simple docstring"""


              if not self.test_rust_tokenizer:
                     return

              __snake_case       	=							self.get_tokenizer()
              __snake_case       	=							self.get_rust_tokenizer()

              __snake_case       	=							"""I was born in 92000, and this is falsé."""

              __snake_case       	=							tokenizer.tokenize(_UpperCamelCase   )
              __snake_case       	=							rust_tokenizer.tokenize(_UpperCamelCase   )
              self.assertListEqual(_UpperCamelCase      , _UpperCamelCase   )

              __snake_case       	=							tokenizer.encode(_UpperCamelCase      , add_special_tokens=_UpperCamelCase   )
              __snake_case       	=							rust_tokenizer.encode(_UpperCamelCase      , add_special_tokens=_UpperCamelCase   )
              self.assertListEqual(_UpperCamelCase      , _UpperCamelCase   )

              __snake_case       	=							self.get_rust_tokenizer()
              __snake_case       	=							tokenizer.encode(_UpperCamelCase   )
              __snake_case       	=							rust_tokenizer.encode(_UpperCamelCase   )
              self.assertListEqual(_UpperCamelCase      , _UpperCamelCase   )


       def 			a		(		self      , _UpperCamelCase=15   )      ->							List[Any]:






              """simple docstring"""


              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(_UpperCamelCase      , **_UpperCamelCase   )

                            # Simple input
                            __snake_case       	=							"""This is a simple input"""
                            __snake_case       	=							["""This is a simple input 1""", """This is a simple input 2"""]
                            __snake_case       	=							("""This is a simple input""", """This is a pair""")
                            __snake_case       	=							[
                                ("""This is a simple input 1""", """This is a simple input 2"""),
                                ("""This is a simple pair 1""", """This is a simple pair 2"""),
                            ]

                            # Simple input tests
                            self.assertRaises(_UpperCamelCase      , tokenizer_r.encode      , _UpperCamelCase      , max_length=_UpperCamelCase      , padding="""max_length"""   )

                            # Simple input
                            self.assertRaises(_UpperCamelCase      , tokenizer_r.encode_plus      , _UpperCamelCase      , max_length=_UpperCamelCase      , padding="""max_length"""   )

                            # Simple input
                            self.assertRaises(
                                _UpperCamelCase      , tokenizer_r.batch_encode_plus      , _UpperCamelCase      , max_length=_UpperCamelCase      , padding="""max_length"""      , )

                            # Pair input
                            self.assertRaises(_UpperCamelCase      , tokenizer_r.encode      , _UpperCamelCase      , max_length=_UpperCamelCase      , padding="""max_length"""   )

                            # Pair input
                            self.assertRaises(_UpperCamelCase      , tokenizer_r.encode_plus      , _UpperCamelCase      , max_length=_UpperCamelCase      , padding="""max_length"""   )

                            # Pair input
                            self.assertRaises(
                                _UpperCamelCase      , tokenizer_r.batch_encode_plus      , _UpperCamelCase      , max_length=_UpperCamelCase      , padding="""max_length"""      , )


       def 			a		(		self   )      ->							str:






              """simple docstring"""


              pass


       def 			a		(		self   )      ->							str:






              """simple docstring"""


              __snake_case       	=							ReformerTokenizer(_UpperCamelCase      , keep_accents=_UpperCamelCase   )

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

              self.assertListEqual(
                  tokenizer.convert_tokens_to_ids(_UpperCamelCase   )      , [2_85, 46, 10, 1_70, 3_82]      , )

              __snake_case       	=							tokenizer.tokenize("""I was born in 92000, and this is falsé."""   )
              self.assertListEqual(
                  _UpperCamelCase      , [
                      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       	=							tokenizer.convert_tokens_to_ids(_UpperCamelCase   )
              self.assertListEqual(
                  _UpperCamelCase      , [8, 21, 84, 55, 24, 19, 7, 0, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 0, 4]      , )

              __snake_case       	=							tokenizer.convert_ids_to_tokens(_UpperCamelCase   )
              self.assertListEqual(
                  _UpperCamelCase      , [
                      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 			a		(		self   )      ->							Dict:






              """simple docstring"""


              return ReformerTokenizer.from_pretrained("""google/reformer-crime-and-punishment"""   )


       @slow
       def 			a		(		self   )      ->							Union[str, Any]:






              """simple docstring"""


              __snake_case       	=							"""Hello World!"""
              __snake_case       	=							[1_26, 32, 2_62, 1_52, 38, 72, 2_87]

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


       @slow
       def 			a		(		self   )      ->							List[Any]:






              """simple docstring"""


              __snake_case       	=							(
                  """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       	=							[
                  1_08,
                  2_65,
                  24,
                  1_11,
                  4,
                  2_58,
                  1_56,
                  35,
                  28,
                  2_75,
                  3,
                  2_59,
                  2_97,
                  2_60,
                  84,
                  4,
                  35,
                  1_10,
                  44,
                  8,
                  2_59,
                  91,
                  2_68,
                  21,
                  11,
                  2_09,
                  2_74,
                  1_09,
                  2_66,
                  2_77,
                  1_17,
                  86,
                  93,
                  3_15,
                  2_58,
                  2_78,
                  2_58,
                  2_77,
                  2_58,
                  0,
                  2_58,
                  2_88,
                  2_58,
                  3_19,
                  2_58,
                  0,
                  2_58,
                  0,
                  2_58,
                  0,
                  2_58,
                  0,
                  2_58,
                  2_87,
                  2_58,
                  3_15,
                  2_58,
                  2_89,
                  2_58,
                  2_78,
                  99,
                  2_69,
                  2_66,
                  2_62,
                  8,
                  2_59,
                  2_41,
                  4,
                  2_17,
                  2_30,
                  2_68,
                  2_66,
                  55,
                  1_68,
                  1_06,
                  75,
                  1_93,
                  2_66,
                  2_23,
                  27,
                  49,
                  26,
                  2_82,
                  25,
                  2_64,
                  2_99,
                  19,
                  26,
                  0,
                  2_58,
                  2_77,
                  1_17,
                  86,
                  93,
                  1_76,
                  1_83,
                  2_70,
                  11,
                  2_62,
                  42,
                  61,
                  2_65,
              ]

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


       @require_torch
       @slow
       def 			a		(		self   )      ->							List[Any]:






              """simple docstring"""


              import torch

              from transformers import ReformerConfig, ReformerModel

              # Build sequence
              __snake_case       	=							list(self.big_tokenizer.get_vocab().keys()   )[:10]
              __snake_case       	=							""" """.join(_UpperCamelCase   )
              __snake_case       	=							self.big_tokenizer.encode_plus(_UpperCamelCase      , return_tensors="""pt"""   )
              __snake_case       	=							self.big_tokenizer.batch_encode_plus([sequence, sequence]      , return_tensors="""pt"""   )

              __snake_case       	=							ReformerConfig()
              # The input gets padded during training so adjust the axial position encodings from the pretrained model value of (512, 1024)
              __snake_case       	=							encoded_sequence["""input_ids"""].shape
              __snake_case       	=							ReformerModel(_UpperCamelCase   )

              # Reformer has config.vocab_size == tokenizer.vocab_size == len(tokenizer) - 1 = 320; len(tokenizer) is 321 (including a pad token with id 320)
              assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

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






       @slow
       def 			a		(		self   )      ->							int:






              """simple docstring"""


              __snake_case       	=							{"""input_ids""": [[1_08, 2_65, 24, 1_11, 4, 2_58, 1_56, 7, 51, 2_79, 58, 7, 76, 25, 69, 2_78], [1_40, 2_43, 2_64, 1_34, 17, 2_67, 77, 2_63, 22, 2_62, 2_97, 2_58, 3_04, 1_77, 2_79, 2_66, 14, 89, 13, 35, 2_61, 2_99, 2_72, 1_37, 2_75, 2_78]], """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]]}  # noqa: E501
              # fmt: on

              # This tokenizer does not know some characters like ")".
              # That is the reason why we use very simple texts here.
              # Also see https://github.com/huggingface/transformers/pull/11737#issuecomment-850769064
              __snake_case       	=							[
                  """This is a very simple sentence.""",
                  """The quick brown fox jumps over the lazy dog.""",
              ]

              self.tokenizer_integration_test_util(
                  expected_encoding=_UpperCamelCase      , model_name="""google/reformer-crime-and-punishment"""      , revision="""0e6c3decb8211d49bf881013425dc8b0448b3f5a"""      , padding=_UpperCamelCase      , sequences=_UpperCamelCase      , )




# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from pathlib import Path

import torch

from ...utils import is_npu_available, is_xpu_available
from .config_args import ClusterConfig, default_json_config_file
from .config_utils import SubcommandHelpFormatter


UpperCamelCase__		 =     '''Create a default config file for Accelerate with only a few flags set.'''







def 						lowerCamelCase__      ( __A    :List[Any]="no"	,__A    :str = default_json_config_file	,__A    :bool = False		):


       """simple docstring"""




       __snake_case       	=							Path(__A		)
       path.parent.mkdir(parents=__A	,exist_ok=__A		)
       if path.exists():
              print(
                  F'Configuration already exists at {save_location}, will not override. Run `accelerate config` manually or pass a different `save_location`.'		)
              return False
       __snake_case       	=							mixed_precision.lower()
       if mixed_precision not in ["no", "fp16", "bf16", "fp8"]:
              raise ValueError(
                  F'`mixed_precision` should be one of \'no\', \'fp16\', \'bf16\', or \'fp8\'. Received {mixed_precision}'		)
       __snake_case       	=							{
           """compute_environment""": """LOCAL_MACHINE""",
           """mixed_precision""": mixed_precision,
       }
       if torch.cuda.is_available():
              __snake_case       	=							torch.cuda.device_count()
              __snake_case       	=							num_gpus
              __snake_case       	=							False
              if num_gpus > 1:
                     __snake_case       	=							"""MULTI_GPU"""
              else:
                     __snake_case       	=							"""NO"""
       elif is_xpu_available() and use_xpu:
              __snake_case       	=							torch.xpu.device_count()
              __snake_case       	=							num_xpus
              __snake_case       	=							False
              if num_xpus > 1:
                     __snake_case       	=							"""MULTI_XPU"""
              else:
                     __snake_case       	=							"""NO"""
       elif is_npu_available():
              __snake_case       	=							torch.npu.device_count()
              __snake_case       	=							num_npus
              __snake_case       	=							False
              if num_npus > 1:
                     __snake_case       	=							"""MULTI_NPU"""
              else:
                     __snake_case       	=							"""NO"""
       else:
              __snake_case       	=							0
              __snake_case       	=							True
              __snake_case       	=							1
              __snake_case       	=							"""NO"""
       __snake_case       	=							ClusterConfig(**__A		)
       config.to_json_file(__A		)
       return path







def 						lowerCamelCase__      ( __A    :Dict	,__A    :List[Any]		):


       """simple docstring"""




       __snake_case       	=							parser.add_parser("""default"""	,parents=__A	,help=__A	,formatter_class=__A		)
       parser.add_argument(
           """--config_file"""	,default=__A	,help=(
               """The path to use to store the config file. Will default to a file named default_config.yaml in the cache """
               """location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have """
               """such an environment variable, your cache directory ('~/.cache' or the content of `XDG_CACHE_HOME`) suffixed """
               """with 'huggingface'."""
           )	,dest="""save_location"""	,)

       parser.add_argument(
           """--mixed_precision"""	,choices=["""no""", """fp16""", """bf16"""]	,type=__A	,help="""Whether or not to use mixed precision training. """
           """Choose between FP16 and BF16 (bfloat16) training. """
           """BF16 training is only supported on Nvidia Ampere GPUs and PyTorch 1.10 or later."""	,default="""no"""	,)
       parser.set_defaults(func=__A		)
       return parser







def 						lowerCamelCase__      ( __A    :Optional[Any]		):


       """simple docstring"""




       __snake_case       	=							write_basic_config(args.mixed_precision	,args.save_location		)
       if config_file:
              print(F'accelerate configuration saved at {config_file}'		)

