import pickle
import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


SCREAMING_SNAKE_CASE__		:					str						=					get_tests_dir("fixtures/test_sentencepiece.model")

@require_sentencepiece
@require_tokenizers
class       snake_case					(      UpperCamelCase_   ,						unittest.TestCase						):
  lowercase_			=     XLMRobertaTokenizer
  lowercase_			=     XLMRobertaTokenizerFast
  lowercase_			=     True
  lowercase_			=     True




  def 	__lowercase(      self     :	Any				)->       str:




       """simple docstring"""
       super().setUp()

       # We have a SentencePiece fixture for testing
       SCREAMING_SNAKE_CASE__				:				Tuple			=		XLMRobertaTokenizer(a_     , keep_accents=a_				)
       tokenizer.save_pretrained(self.tmpdirname				)




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




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		'<pad>'
       SCREAMING_SNAKE_CASE__				:				Dict			=		1

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




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




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Dict			=		list(self.get_tokenizer().get_vocab().keys()				)

       self.assertEqual(vocab_keys[0]     , '<s>'				)
       self.assertEqual(vocab_keys[1]     , '<pad>'				)
       self.assertEqual(vocab_keys[-1]     , '<mask>'				)
       self.assertEqual(len(a_				)     , 1002				)




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




       """simple docstring"""
       self.assertEqual(self.get_tokenizer().vocab_size     , 1002				)




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




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Tuple			=		XLMRobertaTokenizer(a_     , keep_accents=a_				)

       SCREAMING_SNAKE_CASE__				:				List[str]			=		tokenizer.tokenize('This is a test'				)
       self.assertListEqual(a_     , ['▁This', '▁is', '▁a', '▁t', 'est']				)

       self.assertListEqual(
           tokenizer.convert_tokens_to_ids(a_				)     , [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]     , )

       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		tokenizer.tokenize('I was born in 92000, and this is falsé.'				)
       self.assertListEqual(
           a_     , [
               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',
               '.',
           ]     , )
       SCREAMING_SNAKE_CASE__				:				int			=		tokenizer.convert_tokens_to_ids(a_				)
       self.assertListEqual(
           a_     , [
               value + tokenizer.fairseq_offset
               for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
               #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
           ]     , )

       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		tokenizer.convert_ids_to_tokens(a_				)
       self.assertListEqual(
           a_     , [
               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>',
               '.',
           ]     , )




  def 	__lowercase(      self     :	Tuple				)->       Optional[int]:




       """simple docstring"""
       if not self.test_slow_tokenizer:
            # as we don't have a slow version, we can't compare the outputs between slow and fast versions
            return

       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		(self.rust_tokenizer_class, 'hf-internal-testing/tiny-xlm-roberta', {})
       for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
            with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''				):
                 SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		self.rust_tokenizer_class.from_pretrained(a_     , **a_				)
                 SCREAMING_SNAKE_CASE__				:				List[Any]			=		self.tokenizer_class.from_pretrained(a_     , **a_				)

                 SCREAMING_SNAKE_CASE__				:				List[str]			=		tempfile.mkdtemp()

                 SCREAMING_SNAKE_CASE__				:				Any			=		tokenizer_r.save_pretrained(a_				)
                 SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		tokenizer_p.save_pretrained(a_				)

                 # Checks it save with the same files + the tokenizer.json file for the fast one
                 self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files				)				)
                 SCREAMING_SNAKE_CASE__				:				List[str]			=		tuple(f for f in tokenizer_r_files if 'tokenizer.json' not in f				)
                 self.assertSequenceEqual(a_     , a_				)

                 # Checks everything loads correctly in the same way
                 SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		tokenizer_r.from_pretrained(a_				)
                 SCREAMING_SNAKE_CASE__				:				List[str]			=		tokenizer_p.from_pretrained(a_				)

                 # Check special tokens are set accordingly on Rust and Python
                 for key in tokenizer_pp.special_tokens_map:
                      self.assertTrue(hasattr(a_     , a_				)				)
                      # self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
                      # self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

                 shutil.rmtree(a_				)

                 # Save tokenizer rust, legacy_format=True
                 SCREAMING_SNAKE_CASE__				:				Tuple			=		tempfile.mkdtemp()

                 SCREAMING_SNAKE_CASE__				:				Any			=		tokenizer_r.save_pretrained(a_     , legacy_format=a_				)
                 SCREAMING_SNAKE_CASE__				:				str			=		tokenizer_p.save_pretrained(a_				)

                 # Checks it save with the same files
                 self.assertSequenceEqual(a_     , a_				)

                 # Checks everything loads correctly in the same way
                 SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		tokenizer_r.from_pretrained(a_				)
                 SCREAMING_SNAKE_CASE__				:				int			=		tokenizer_p.from_pretrained(a_				)

                 # Check special tokens are set accordingly on Rust and Python
                 for key in tokenizer_pp.special_tokens_map:
                      self.assertTrue(hasattr(a_     , a_				)				)

                 shutil.rmtree(a_				)

                 # Save tokenizer rust, legacy_format=False
                 SCREAMING_SNAKE_CASE__				:				Tuple			=		tempfile.mkdtemp()

                 SCREAMING_SNAKE_CASE__				:				Optional[int]			=		tokenizer_r.save_pretrained(a_     , legacy_format=a_				)
                 SCREAMING_SNAKE_CASE__				:				int			=		tokenizer_p.save_pretrained(a_				)

                 # Checks it saved the tokenizer.json file
                 self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files				)				)

                 # Checks everything loads correctly in the same way
                 SCREAMING_SNAKE_CASE__				:				List[Any]			=		tokenizer_r.from_pretrained(a_				)
                 SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		tokenizer_p.from_pretrained(a_				)

                 # Check special tokens are set accordingly on Rust and Python
                 for key in tokenizer_pp.special_tokens_map:
                      self.assertTrue(hasattr(a_     , a_				)				)

                 shutil.rmtree(a_				)




  @cached_property
  def 	__lowercase(      self     :	Optional[int]				)->       Union[str, Any]:




       """simple docstring"""
       return XLMRobertaTokenizer.from_pretrained('xlm-roberta-base'				)




  def 	__lowercase(      self     :	int				)->       Optional[int]:




       """simple docstring"""
       with tempfile.NamedTemporaryFile() as f:
            shutil.copyfile(a_     , f.name				)
            SCREAMING_SNAKE_CASE__				:				int			=		XLMRobertaTokenizer(f.name     , keep_accents=a_				)
            SCREAMING_SNAKE_CASE__				:				Dict			=		pickle.dumps(a_				)
       pickle.loads(a_				)




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




       """simple docstring"""
       if not self.test_rust_tokenizer:
            return

       SCREAMING_SNAKE_CASE__				:				int			=		self.get_tokenizer()
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		self.get_rust_tokenizer()

       SCREAMING_SNAKE_CASE__				:				List[Any]			=		'I was born in 92000, and this is falsé.'

       SCREAMING_SNAKE_CASE__				:				int			=		tokenizer.tokenize(a_				)
       SCREAMING_SNAKE_CASE__				:				int			=		rust_tokenizer.tokenize(a_				)
       self.assertListEqual(a_     , a_				)

       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		tokenizer.encode(a_     , add_special_tokens=a_				)
       SCREAMING_SNAKE_CASE__				:				int			=		rust_tokenizer.encode(a_     , add_special_tokens=a_				)
       self.assertListEqual(a_     , a_				)

       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		self.get_rust_tokenizer()
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		tokenizer.encode(a_				)
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		rust_tokenizer.encode(a_				)
       self.assertListEqual(a_     , a_				)




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




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		'Hello World!'
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		[0, 3_5378, 6661, 38, 2]
       # xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
       # xlmr.eval()
       # xlmr.encode(symbols)

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




  @slow
  def 	__lowercase(      self     :	List[str]				)->       Dict:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				List[str]			=		(
           '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'
       )
       SCREAMING_SNAKE_CASE__				:				List[str]			=		[
           0,
           3293,
           83,
           10,
           4552,
           4989,
           7986,
           678,
           10,
           5915,
           111,
           17_9459,
           12_4850,
           4,
           6044,
           237,
           12,
           6,
           5,
           6,
           4,
           6780,
           705,
           15,
           1388,
           44,
           378,
           1_0114,
           711,
           152,
           20,
           6,
           5,
           2_2376,
           642,
           1221,
           1_5190,
           3_4153,
           450,
           5608,
           959,
           1119,
           5_7702,
           136,
           186,
           47,
           1098,
           2_9367,
           47,
           # 4426, # What fairseq tokenizes from "<unk>": "_<"
           # 3678, # What fairseq tokenizes from "<unk>": "unk"
           # 2740, # What fairseq tokenizes from "<unk>": ">"
           3,  # What we tokenize from "<unk>": "<unk>"
           6,  # Residue from the tokenization: an extra sentencepiece underline
           4,
           6044,
           237,
           6284,
           5_0901,
           528,
           31,
           90,
           34,
           927,
           2,
       ]
       # xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
       # xlmr.eval()
       # xlmr.encode(symbols)

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




  @slow
  def 	__lowercase(      self     :	str				)->       Dict:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				int			=		{'input_ids': [[0, 1_1062, 8_2772, 7, 15, 8_2772, 538, 5_1529, 237, 1_7198, 1290, 206, 9, 21_5175, 1314, 136, 1_7198, 1290, 206, 9, 5_6359, 42, 12_2009, 9, 1_6466, 16, 8_7344, 4537, 9, 4717, 7_8381, 6, 15_9958, 7, 15, 2_4480, 618, 4, 527, 2_2693, 5428, 4, 2777, 2_4480, 9874, 4, 4_3523, 594, 4, 803, 1_8392, 3_3189, 18, 4, 4_3523, 2_4447, 1_2399, 100, 2_4955, 8_3658, 9626, 14_4057, 15, 839, 2_2335, 16, 136, 2_4955, 8_3658, 8_3479, 15, 3_9102, 724, 16, 678, 645, 2789, 1328, 4589, 42, 12_2009, 11_5774, 23, 805, 1328, 4_6876, 7, 136, 5_3894, 1940, 4_2227, 4_1159, 1_7721, 823, 425, 4, 2_7512, 9_8722, 206, 136, 5531, 4970, 919, 1_7336, 5, 2], [0, 2_0080, 618, 83, 8_2775, 47, 479, 9, 1517, 73, 5_3894, 333, 8_0581, 11_0117, 1_8811, 5256, 1295, 51, 15_2526, 297, 7986, 390, 12_4416, 538, 3_5431, 214, 98, 1_5044, 2_5737, 136, 7108, 4_3701, 23, 756, 13_5355, 7, 5, 2, 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, 581, 6_3773, 11_9455, 6, 14_7797, 8_8203, 7, 645, 70, 21, 3285, 1_0269, 5, 2, 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]], '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, 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, 0, 0, 0, 0, 0], [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, 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=a_     , model_name='xlm-roberta-base'     , revision='d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3'     , )




# Copyright 2023 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.

import torch

from accelerate import PartialState
from accelerate.utils.operations import broadcast, gather, gather_object, pad_across_processes, reduce







def     _a				(      lowercase__  :			Any ):







     '''simple docstring'''


     return (torch.arange(state.num_processes ) + 1.0 + (state.num_processes * state.process_index)).to(state.device )







def     _a				(      lowercase__  :			Tuple ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				int			=		create_tensor(lowercase__ )
     SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		gather(lowercase__ )
     assert gathered_tensor.tolist() == list(range(1     ,  state.num_processes**2 + 1 ) )







def     _a				(      lowercase__  :			List[Any] ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				str			=		[state.process_index]
     SCREAMING_SNAKE_CASE__				:				Any			=		gather_object(lowercase__ )
     assert len(lowercase__ ) == state.num_processes, f'''{gathered_obj}, {len(lowercase__ )} != {state.num_processes}'''
     assert gathered_obj == list(range(state.num_processes ) ), f'''{gathered_obj} != {list(range(state.num_processes ) )}'''







def     _a				(      lowercase__  :			str ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		create_tensor(lowercase__ )
     SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		broadcast(lowercase__ )
     assert broadcasted_tensor.shape == torch.Size([state.num_processes] )
     assert broadcasted_tensor.tolist() == list(range(1     ,  state.num_processes + 1 ) )







def     _a				(      lowercase__  :			int ):







     '''simple docstring'''


     if state.is_main_process:
          SCREAMING_SNAKE_CASE__				:				Optional[int]			=		torch.arange(state.num_processes + 1 ).to(state.device )
     else:
          SCREAMING_SNAKE_CASE__				:				List[Any]			=		torch.arange(state.num_processes ).to(state.device )
     SCREAMING_SNAKE_CASE__				:				Any			=		pad_across_processes(lowercase__ )
     assert padded_tensor.shape == torch.Size([state.num_processes + 1] )
     if not state.is_main_process:
          assert padded_tensor.tolist() == list(range(0     ,  state.num_processes ) ) + [0]







def     _a				(      lowercase__  :			Optional[Any] ):







     '''simple docstring'''


     if state.num_processes != 2:
          return
     SCREAMING_SNAKE_CASE__				:				List[Any]			=		create_tensor(lowercase__ )
     SCREAMING_SNAKE_CASE__				:				str			=		reduce(lowercase__     ,  'sum' )
     SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		torch.tensor([4.0, 6] ).to(state.device )
     assert torch.allclose(lowercase__     ,  lowercase__ ), f'''{reduced_tensor} != {truth_tensor}'''







def     _a				(      lowercase__  :			int ):







     '''simple docstring'''


     if state.num_processes != 2:
          return
     SCREAMING_SNAKE_CASE__				:				Any			=		create_tensor(lowercase__ )
     SCREAMING_SNAKE_CASE__				:				List[Any]			=		reduce(lowercase__     ,  'mean' )
     SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		torch.tensor([2.0, 3] ).to(state.device )
     assert torch.allclose(lowercase__     ,  lowercase__ ), f'''{reduced_tensor} != {truth_tensor}'''







def     _a				(      lowercase__  :			int ):







     '''simple docstring'''


     main()







def     _a				(      ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				int			=		PartialState()
     state.print(f'''State: {state}''' )
     state.print('testing gather' )
     test_gather(lowercase__ )
     state.print('testing gather_object' )
     test_gather_object(lowercase__ )
     state.print('testing broadcast' )
     test_broadcast(lowercase__ )
     state.print('testing pad_across_processes' )
     test_pad_across_processes(lowercase__ )
     state.print('testing reduce_sum' )
     test_reduce_sum(lowercase__ )
     state.print('testing reduce_mean' )
     test_reduce_mean(lowercase__ )


if __name__ == "__main__":
      main()



