
import unittest

from datasets import load_dataset

from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
class   lowerCamelCase	(						A_							,				unittest.TestCase						):
     UpperCAmelCase__       :							Optional[Any]     =	None
     UpperCAmelCase__       :							Optional[int]     =	BloomTokenizerFast
     UpperCAmelCase__       :							Optional[Any]     =	BloomTokenizerFast
     UpperCAmelCase__       :							int     =	True
     UpperCAmelCase__       :							str     =	False
     UpperCAmelCase__       :							Dict     =	"tokenizer_file"
     UpperCAmelCase__       :							str     =	{"bos_token": "<s>", "eos_token": "</s>", "unk_token": "<unk>", "pad_token": "<pad>"}





     def 	UpperCAmelCase(self   :					Any     )		->		int:
         super().setUp()
         snake_case				  =      BloomTokenizerFast.from_pretrained("bigscience/tokenizer"     )
         tokenizer.save_pretrained(self.tmpdirname     )





     def 	UpperCAmelCase(self   :					Optional[int]       ,					**_A   :					Any     )		->		str:
         kwargs.update(self.special_tokens_map     )
         return BloomTokenizerFast.from_pretrained(self.tmpdirname       ,					**_A     )





     def 	UpperCAmelCase(self   :					Tuple     )		->		Dict:
         snake_case				  =      self.get_rust_tokenizer()

         snake_case				  =      ["The quick brown fox</s>", "jumps over the lazy dog</s>"]
         snake_case				  =      [[2_1_7_5, 2_3_7_1_4, 7_3_1_7_3, 1_4_4_2_5_2, 2], [7_7, 1_3_2_6_1_9, 3_4_7_8, 3_6_8, 1_0_9_5_8_6, 3_5_4_3_3, 2]]

         snake_case				  =      tokenizer.batch_encode_plus(_A     )["input_ids"]
         self.assertListEqual(_A       ,					_A     )

         snake_case				  =      tokenizer.batch_decode(_A     )
         self.assertListEqual(_A       ,					_A     )





     def 	UpperCAmelCase(self   :					str       ,					_A   :					Optional[int]=6     )		->		Optional[int]:
         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(_A       ,					**_A     )
                 # tokenizer_r.pad_token = None # Hotfixing padding = None
                 # 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
                 try:
                     tokenizer_r.encode(_A       ,					max_length=_A     )
                     tokenizer_r.encode_plus(_A       ,					max_length=_A     )

                     tokenizer_r.batch_encode_plus(_A       ,					max_length=_A     )
                     tokenizer_r.encode(_A       ,					max_length=_A     )
                     tokenizer_r.batch_encode_plus(_A       ,					max_length=_A     )
                 except ValueError:
                     self.fail("Bloom Tokenizer should be able to deal with padding"     )

                 snake_case				  =      None  # Hotfixing padding = None
                 self.assertRaises(_A       ,					tokenizer_r.encode       ,					_A       ,					max_length=_A       ,					padding="max_length"     )

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

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

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

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

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





     def 	UpperCAmelCase(self   :					Any     )		->		Union[str, Any]:
         snake_case				  =      self.get_rust_tokenizer()
         snake_case				  =      load_dataset("xnli"       ,					"all_languages"       ,					split="test"       ,					streaming=_A     )

         snake_case				  =      next(iter(_A     )     )["premise"]  # pick up one data
         snake_case				  =      list(sample_data.values()     )

         snake_case				  =      list(map(tokenizer.encode       ,					_A     )     )
         snake_case				  =      [tokenizer.decode(_A       ,					clean_up_tokenization_spaces=_A     ) for x in output_tokens]
         self.assertListEqual(_A       ,					_A     )





     def 	UpperCAmelCase(self   :					Dict     )		->		List[Any]:
         # The test has to be overriden because BLOOM uses ALiBi positional embeddings that does not have
         # any sequence length constraints. This test of the parent class will fail since it relies on the
         # maximum sequence length of the positoonal embeddings.
         self.assertGreaterEqual(len(self.tokenizer_class.pretrained_vocab_files_map     )       ,					1     )
         self.assertGreaterEqual(len(list(self.tokenizer_class.pretrained_vocab_files_map.values()     )[0]     )       ,					1     )



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   lowerCamelCase	(						unittest.TestCase						):





     def 	UpperCAmelCase(self   :					Dict     )		->		Dict:
         snake_case				  =      inspect.getfile(accelerate.test_utils     )
         snake_case				  =      os.path.sep.join(mod_file.split(os.path.sep     )[:-1] + ["scripts", "test_script.py"]     )
         snake_case				  =      os.path.sep.join(
             mod_file.split(os.path.sep     )[:-1] + ["scripts", "test_distributed_data_loop.py"]     )
         snake_case				  =      os.path.sep.join(mod_file.split(os.path.sep     )[:-1] + ["scripts", "test_ops.py"]     )





     @require_multi_gpu
     def 	UpperCAmelCase(self   :					List[Any]     )		->		int:
         print(f'Found {torch.cuda.device_count()} devices.'     )
         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 	UpperCAmelCase(self   :					Any     )		->		Optional[Any]:
         print(f'Found {torch.cuda.device_count()} devices.'     )
         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 	UpperCAmelCase(self   :					Union[str, Any]     )		->		Dict:
         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 	UpperCAmelCase(self   :					List[str]     )		->		Dict:
         print(f'Found {torch.cuda.device_count()} devices, using 2 devices only'     )
         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__":
   _A						      =				Accelerator()
   _A						      =				(accelerator.state.process_index + 2, 10)
   _A						      =				torch.randint(0, 10, shape).to(accelerator.device)

   _A						      =				""

   _A						      =				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)."

   _A						      =				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."
   _A						      =				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)

