





import itertools
import json
import os
import unittest

from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin




@require_tokenizers
class   UpperCamelCase_       (     UpperCamelCase__				,  unittest.TestCase    ):
       lowerCamelCase_           =					RobertaTokenizer
       lowerCamelCase_           =					RobertaTokenizerFast
       lowerCamelCase_           =					True
       lowerCamelCase_           =					{"cls_token": "<s>"}


       def      _snake_case  (							self    :List[str]		)   ->   Dict:



         """simple docstring"""



         super().setUp()

         # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
         SCREAMING_SNAKE_CASE__			=      [
             """l""",
             """o""",
             """w""",
             """e""",
             """r""",
             """s""",
             """t""",
             """i""",
             """d""",
             """n""",
             """\u0120""",
             """\u0120l""",
             """\u0120n""",
             """\u0120lo""",
             """\u0120low""",
             """er""",
             """\u0120lowest""",
             """\u0120newer""",
             """\u0120wider""",
             """<unk>""",
         ]
         SCREAMING_SNAKE_CASE__			=      dict(zip(__A							,				range(len(__A		)		)		)		)
         SCREAMING_SNAKE_CASE__			=      ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
         SCREAMING_SNAKE_CASE__			=      {"""unk_token""": """<unk>"""}

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


       def      _snake_case  (							self    :List[str]							,				**__A    :List[Any]		)   ->   List[str]:



         """simple docstring"""



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


       def      _snake_case  (							self    :Union[str, Any]							,				**__A    :int		)   ->   List[Any]:



         """simple docstring"""



         kwargs.update(self.special_tokens_map		)
         return RobertaTokenizerFast.from_pretrained(self.tmpdirname							,				**__A		)


       def      _snake_case  (							self    :str							,				__A    :Dict		)   ->   Any:



         """simple docstring"""



         SCREAMING_SNAKE_CASE__			=      """lower newer"""
         SCREAMING_SNAKE_CASE__			=      """lower newer"""
         return input_text, output_text


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



         """simple docstring"""



         SCREAMING_SNAKE_CASE__			=      self.tokenizer_class(self.vocab_file							,				self.merges_file							,				**self.special_tokens_map		)
         SCREAMING_SNAKE_CASE__			=      """lower newer"""
         SCREAMING_SNAKE_CASE__			=      ["""l""", """o""", """w""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
         SCREAMING_SNAKE_CASE__			=      tokenizer.tokenize(__A		)  # , add_prefix_space=True)
         self.assertListEqual(__A							,				__A		)

         SCREAMING_SNAKE_CASE__			=      tokens + [tokenizer.unk_token]
         SCREAMING_SNAKE_CASE__			=      [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(__A		)							,				__A		)


       def      _snake_case  (							self    :Optional[Any]		)   ->   Dict:



         """simple docstring"""



         SCREAMING_SNAKE_CASE__			=      self.get_tokenizer()

         self.assertListEqual(tokenizer.encode("""Hello world!"""							,				add_special_tokens=__A		)							,				[0, 3_1414, 232, 328, 2]		)
         self.assertListEqual(
             tokenizer.encode("""Hello world! cécé herlolip 418"""							,				add_special_tokens=__A		)							,				[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]							,				)


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



         """simple docstring"""



         SCREAMING_SNAKE_CASE__			=      self.tokenizer_class.from_pretrained("""roberta-base"""		)

         SCREAMING_SNAKE_CASE__			=      tokenizer.encode("""sequence builders"""							,				add_special_tokens=__A		)
         SCREAMING_SNAKE_CASE__			=      tokenizer.encode("""multi-sequence build"""							,				add_special_tokens=__A		)

         SCREAMING_SNAKE_CASE__			=      tokenizer.encode(
             """sequence builders"""							,				add_special_tokens=__A							,				add_prefix_space=__A		)
         SCREAMING_SNAKE_CASE__			=      tokenizer.encode(
             """sequence builders"""							,				"""multi-sequence build"""							,				add_special_tokens=__A							,				add_prefix_space=__A		)

         SCREAMING_SNAKE_CASE__			=      tokenizer.build_inputs_with_special_tokens(__A		)
         SCREAMING_SNAKE_CASE__			=      tokenizer.build_inputs_with_special_tokens(__A							,				__A		)

         assert encoded_sentence == encoded_text_from_decode
         assert encoded_pair == encoded_pair_from_decode


       def      _snake_case  (							self    :Any		)   ->   Tuple:



         """simple docstring"""



         SCREAMING_SNAKE_CASE__			=      self.get_tokenizer()

         SCREAMING_SNAKE_CASE__			=      """Encode this sequence."""
         SCREAMING_SNAKE_CASE__			=      tokenizer.byte_encoder[""" """.encode("""utf-8"""		)[0]]

         # Testing encoder arguments
         SCREAMING_SNAKE_CASE__			=      tokenizer.encode(__A							,				add_special_tokens=__A							,				add_prefix_space=__A		)
         SCREAMING_SNAKE_CASE__			=      tokenizer.convert_ids_to_tokens(encoded[0]		)[0]
         self.assertNotEqual(__A							,				__A		)

         SCREAMING_SNAKE_CASE__			=      tokenizer.encode(__A							,				add_special_tokens=__A							,				add_prefix_space=__A		)
         SCREAMING_SNAKE_CASE__			=      tokenizer.convert_ids_to_tokens(encoded[0]		)[0]
         self.assertEqual(__A							,				__A		)

         tokenizer.add_special_tokens({"""bos_token""": """<s>"""}		)
         SCREAMING_SNAKE_CASE__			=      tokenizer.encode(__A							,				add_special_tokens=__A		)
         SCREAMING_SNAKE_CASE__			=      tokenizer.convert_ids_to_tokens(encoded[1]		)[0]
         self.assertNotEqual(__A							,				__A		)

         # Testing spaces after special tokens
         SCREAMING_SNAKE_CASE__			=      """<mask>"""
         tokenizer.add_special_tokens(
             {"""mask_token""": AddedToken(__A							,				lstrip=__A							,				rstrip=__A		)}		)  # mask token has a left space
         SCREAMING_SNAKE_CASE__			=      tokenizer.convert_tokens_to_ids(__A		)

         SCREAMING_SNAKE_CASE__			=      """Encode <mask> sequence"""
         SCREAMING_SNAKE_CASE__			=      """Encode <mask>sequence"""

         SCREAMING_SNAKE_CASE__			=      tokenizer.encode(__A		)
         SCREAMING_SNAKE_CASE__			=      encoded.index(__A		)
         SCREAMING_SNAKE_CASE__			=      tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]		)[0]
         self.assertEqual(__A							,				__A		)

         SCREAMING_SNAKE_CASE__			=      tokenizer.encode(__A		)
         SCREAMING_SNAKE_CASE__			=      encoded.index(__A		)
         SCREAMING_SNAKE_CASE__			=      tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]		)[0]
         self.assertNotEqual(__A							,				__A		)


       def      _snake_case  (							self    :List[Any]		)   ->   List[str]:



         """simple docstring"""



         pass


       def      _snake_case  (							self    :Any		)   ->   Optional[Any]:



         """simple docstring"""



         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
           with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''		):
             SCREAMING_SNAKE_CASE__			=      self.rust_tokenizer_class.from_pretrained(__A							,				**__A		)
             SCREAMING_SNAKE_CASE__			=      self.tokenizer_class.from_pretrained(__A							,				**__A		)
             SCREAMING_SNAKE_CASE__			=      """A, <mask> AllenNLP sentence."""
             SCREAMING_SNAKE_CASE__			=      tokenizer_r.encode_plus(__A							,				add_special_tokens=__A							,				return_token_type_ids=__A		)
             SCREAMING_SNAKE_CASE__			=      tokenizer_p.encode_plus(__A							,				add_special_tokens=__A							,				return_token_type_ids=__A		)

             # 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"""]		)							,				)

             SCREAMING_SNAKE_CASE__			=      tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""]		)
             SCREAMING_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(
                 __A							,				["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]		)
             self.assertSequenceEqual(
                 __A							,				["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]		)


       def      _snake_case  (							self    :List[str]		)   ->   List[Any]:



         """simple docstring"""



         for trim_offsets, add_prefix_space in itertools.product([True, False]							,				repeat=2		):
           SCREAMING_SNAKE_CASE__			=      self.rust_tokenizer_class.from_pretrained(
               self.tmpdirname							,				use_fast=__A							,				add_prefix_space=__A							,				trim_offsets=__A		)

           SCREAMING_SNAKE_CASE__			=      json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()		)
           SCREAMING_SNAKE_CASE__			=      json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()		)

           self.assertEqual(pre_tokenizer_state["""add_prefix_space"""]							,				__A		)

           self.assertEqual(post_processor_state["""add_prefix_space"""]							,				__A		)
           self.assertEqual(post_processor_state["""trim_offsets"""]							,				__A		)


       def      _snake_case  (							self    :Optional[int]		)   ->   Any:



         """simple docstring"""



         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
           with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''		):
             SCREAMING_SNAKE_CASE__			=      """hello"""  # `hello` is a token in the vocabulary of `pretrained_name`
             SCREAMING_SNAKE_CASE__			=      f'''{text_of_1_token} {text_of_1_token}'''

             SCREAMING_SNAKE_CASE__			=      self.rust_tokenizer_class.from_pretrained(
                 __A							,				use_fast=__A							,				add_prefix_space=__A							,				trim_offsets=__A		)
             SCREAMING_SNAKE_CASE__			=      tokenizer_r(__A							,				return_offsets_mapping=__A							,				add_special_tokens=__A		)
             self.assertEqual(encoding.offset_mapping[0]							,				(0, len(__A		))		)
             self.assertEqual(
                 encoding.offset_mapping[1]							,				(len(__A		) + 1, len(__A		) + 1 + len(__A		))							,				)

             SCREAMING_SNAKE_CASE__			=      self.rust_tokenizer_class.from_pretrained(
                 __A							,				use_fast=__A							,				add_prefix_space=__A							,				trim_offsets=__A		)
             SCREAMING_SNAKE_CASE__			=      tokenizer_r(__A							,				return_offsets_mapping=__A							,				add_special_tokens=__A		)
             self.assertEqual(encoding.offset_mapping[0]							,				(0, len(__A		))		)
             self.assertEqual(
                 encoding.offset_mapping[1]							,				(len(__A		) + 1, len(__A		) + 1 + len(__A		))							,				)

             SCREAMING_SNAKE_CASE__			=      self.rust_tokenizer_class.from_pretrained(
                 __A							,				use_fast=__A							,				add_prefix_space=__A							,				trim_offsets=__A		)
             SCREAMING_SNAKE_CASE__			=      tokenizer_r(__A							,				return_offsets_mapping=__A							,				add_special_tokens=__A		)
             self.assertEqual(encoding.offset_mapping[0]							,				(0, len(__A		))		)
             self.assertEqual(
                 encoding.offset_mapping[1]							,				(len(__A		), len(__A		) + 1 + len(__A		))							,				)

             SCREAMING_SNAKE_CASE__			=      self.rust_tokenizer_class.from_pretrained(
                 __A							,				use_fast=__A							,				add_prefix_space=__A							,				trim_offsets=__A		)
             SCREAMING_SNAKE_CASE__			=      tokenizer_r(__A							,				return_offsets_mapping=__A							,				add_special_tokens=__A		)
             self.assertEqual(encoding.offset_mapping[0]							,				(0, len(__A		))		)
             self.assertEqual(
                 encoding.offset_mapping[1]							,				(len(__A		), len(__A		) + 1 + len(__A		))							,				)

             SCREAMING_SNAKE_CASE__			=      f''' {text}'''

             # tokenizer_r = self.rust_tokenizer_class.from_pretrained(
             #     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
             # )
             # encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
             # self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
             # self.assertEqual(
             #     encoding.offset_mapping[1],
             #     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
             # )

             SCREAMING_SNAKE_CASE__			=      self.rust_tokenizer_class.from_pretrained(
                 __A							,				use_fast=__A							,				add_prefix_space=__A							,				trim_offsets=__A		)
             SCREAMING_SNAKE_CASE__			=      tokenizer_r(__A							,				return_offsets_mapping=__A							,				add_special_tokens=__A		)
             self.assertEqual(encoding.offset_mapping[0]							,				(1, 1 + len(__A		))		)
             self.assertEqual(
                 encoding.offset_mapping[1]							,				(1 + len(__A		) + 1, 1 + len(__A		) + 1 + len(__A		))							,				)

             SCREAMING_SNAKE_CASE__			=      self.rust_tokenizer_class.from_pretrained(
                 __A							,				use_fast=__A							,				add_prefix_space=__A							,				trim_offsets=__A		)
             SCREAMING_SNAKE_CASE__			=      tokenizer_r(__A							,				return_offsets_mapping=__A							,				add_special_tokens=__A		)
             self.assertEqual(encoding.offset_mapping[0]							,				(0, 1 + len(__A		))		)
             self.assertEqual(
                 encoding.offset_mapping[1]							,				(1 + len(__A		), 1 + len(__A		) + 1 + len(__A		))							,				)

             SCREAMING_SNAKE_CASE__			=      self.rust_tokenizer_class.from_pretrained(
                 __A							,				use_fast=__A							,				add_prefix_space=__A							,				trim_offsets=__A		)
             SCREAMING_SNAKE_CASE__			=      tokenizer_r(__A							,				return_offsets_mapping=__A							,				add_special_tokens=__A		)
             self.assertEqual(encoding.offset_mapping[0]							,				(0, 1 + len(__A		))		)
             self.assertEqual(
                 encoding.offset_mapping[1]							,				(1 + len(__A		), 1 + len(__A		) + 1 + len(__A		))							,				)






import inspect
import os
import unittest

import torch

import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment




class   UpperCamelCase_       (     unittest.TestCase    ):


							def      _snake_case  (							self    :Any		)   ->   int:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      inspect.getfile(accelerate.test_utils		)
									SCREAMING_SNAKE_CASE__			=      os.path.sep.join(mod_file.split(os.path.sep		)[:-1] + ["""scripts""", """test_script.py"""]		)
									SCREAMING_SNAKE_CASE__			=      os.path.sep.join(
									    mod_file.split(os.path.sep		)[:-1] + ["""scripts""", """test_distributed_data_loop.py"""]		)
									SCREAMING_SNAKE_CASE__			=      os.path.sep.join(mod_file.split(os.path.sep		)[:-1] + ["""scripts""", """test_ops.py"""]		)


							@require_multi_gpu
							def      _snake_case  (							self    :Optional[Any]		)   ->   Tuple:



									"""simple docstring"""



									print(f'''Found {torch.cuda.device_count()} devices.'''		)
									SCREAMING_SNAKE_CASE__			=      ["""torchrun""", f'''--nproc_per_node={torch.cuda.device_count()}''', self.test_file_path]
									with patch_environment(omp_num_threads=1		):
											execute_subprocess_async(__A							,				env=os.environ.copy()		)


							@require_multi_gpu
							def      _snake_case  (							self    :Tuple		)   ->   Optional[Any]:



									"""simple docstring"""



									print(f'''Found {torch.cuda.device_count()} devices.'''		)
									SCREAMING_SNAKE_CASE__			=      ["""torchrun""", f'''--nproc_per_node={torch.cuda.device_count()}''', self.operation_file_path]
									print(f'''Command: {cmd}'''		)
									with patch_environment(omp_num_threads=1		):
											execute_subprocess_async(__A							,				env=os.environ.copy()		)


							@require_multi_gpu
							def      _snake_case  (							self    :Optional[Any]		)   ->   Union[str, Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      ["""torchrun""", f'''--nproc_per_node={torch.cuda.device_count()}''', inspect.getfile(self.__class__		)]
									with patch_environment(omp_num_threads=1		):
											execute_subprocess_async(__A							,				env=os.environ.copy()		)


							@require_multi_gpu
							def      _snake_case  (							self    :Optional[int]		)   ->   str:



									"""simple docstring"""



									print(f'''Found {torch.cuda.device_count()} devices, using 2 devices only'''		)
									SCREAMING_SNAKE_CASE__			=      ["""torchrun""", f'''--nproc_per_node={torch.cuda.device_count()}''', self.data_loop_file_path]
									with patch_environment(omp_num_threads=1							,				cuda_visible_devices="""0,1"""		):
											execute_subprocess_async(__A							,				env=os.environ.copy()		)


if __name__ == "__main__":
	_lowerCamelCase   				=			Accelerator()
	_lowerCamelCase   				=			(accelerator.state.process_index + 2, 10)
	_lowerCamelCase   				=			torch.randint(0, 10, shape).to(accelerator.device)

	_lowerCamelCase   				=			''

	_lowerCamelCase   				=			accelerator.pad_across_processes(tensor)
	if tensora.shape[0] != accelerator.state.num_processes + 1:
		error_msg += F"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
	if not torch.equal(tensora[: accelerator.state.process_index + 2], tensor):
		error_msg += "Tensors have different values."
	if not torch.all(tensora[accelerator.state.process_index + 2 :] == 0):
		error_msg += "Padding was not done with the right value (0)."

	_lowerCamelCase   				=			accelerator.pad_across_processes(tensor, pad_first=True)
	if tensora.shape[0] != accelerator.state.num_processes + 1:
		error_msg += F"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
	_lowerCamelCase   				=			accelerator.state.num_processes - accelerator.state.process_index - 1
	if not torch.equal(tensora[index:], tensor):
		error_msg += "Tensors have different values."
	if not torch.all(tensora[:index] == 0):
		error_msg += "Padding was not done with the right value (0)."

	# Raise error at the end to make sure we don't stop at the first failure.
	if len(error_msg) > 0:
		raise ValueError(error_msg)