import os
import pickle
import unittest
from collections import UserDict, namedtuple

import torch

from accelerate.test_utils.testing import require_cuda, require_torch_min_version
from accelerate.test_utils.training import RegressionModel
from accelerate.utils import (
    convert_outputs_to_fpaa,
    extract_model_from_parallel,
    find_device,
    listify,
    patch_environment,
    recursively_apply,
    send_to_device,
)


a_					      = namedtuple('ExampleNamedTuple', 'a b c')





class 	_lowercase		(      unittest.TestCase ):

 def 		SCREAMING_SNAKE_CASE__     (				self			:							Any    )     ->		Dict:

  """simple docstring"""





  UpperCamelCase_      :		Any				=      torch.randn(5     ,   2    )
  UpperCamelCase_      :		List[Any]				=      torch.device('cuda'    ) if torch.cuda.is_available() else torch.device('cpu'    )

  UpperCamelCase_      :		List[Any]				=      send_to_device(snake_case     ,   snake_case    )
  self.assertTrue(torch.equal(resulta.cpu()     ,   snake_case    )    )

  UpperCamelCase_      :		List[Any]				=      send_to_device((tensor, [tensor, tensor], 1)     ,   snake_case    )
  self.assertIsInstance(snake_case     ,   snake_case    )
  self.assertTrue(torch.equal(resulta[0].cpu()     ,   snake_case    )    )
  self.assertIsInstance(resulta[1]     ,   snake_case    )
  self.assertTrue(torch.equal(resulta[1][0].cpu()     ,   snake_case    )    )
  self.assertTrue(torch.equal(resulta[1][1].cpu()     ,   snake_case    )    )
  self.assertEqual(resulta[2]     ,   1    )

  UpperCamelCase_      :		List[Any]				=      send_to_device({'a': tensor, 'b': [tensor, tensor], 'c': 1}     ,   snake_case    )
  self.assertIsInstance(snake_case     ,   snake_case    )
  self.assertTrue(torch.equal(resulta['a'].cpu()     ,   snake_case    )    )
  self.assertIsInstance(resulta['b']     ,   snake_case    )
  self.assertTrue(torch.equal(resulta['b'][0].cpu()     ,   snake_case    )    )
  self.assertTrue(torch.equal(resulta['b'][1].cpu()     ,   snake_case    )    )
  self.assertEqual(resulta['c']     ,   1    )

  UpperCamelCase_      :		List[Any]				=      send_to_device(ExampleNamedTuple(a=snake_case     ,   b=[tensor, tensor]     ,   c=1    )     ,   snake_case    )
  self.assertIsInstance(snake_case     ,   snake_case    )
  self.assertTrue(torch.equal(resulta.a.cpu()     ,   snake_case    )    )
  self.assertIsInstance(resulta.b     ,   snake_case    )
  self.assertTrue(torch.equal(resulta.b[0].cpu()     ,   snake_case    )    )
  self.assertTrue(torch.equal(resulta.b[1].cpu()     ,   snake_case    )    )
  self.assertEqual(resulta.c     ,   1    )

  UpperCamelCase_      :		List[Any]				=      send_to_device(UserDict({'a': tensor, 'b': [tensor, tensor], 'c': 1}    )     ,   snake_case    )
  self.assertIsInstance(snake_case     ,   snake_case    )
  self.assertTrue(torch.equal(resulta['a'].cpu()     ,   snake_case    )    )
  self.assertIsInstance(resulta['b']     ,   snake_case    )
  self.assertTrue(torch.equal(resulta['b'][0].cpu()     ,   snake_case    )    )
  self.assertTrue(torch.equal(resulta['b'][1].cpu()     ,   snake_case    )    )
  self.assertEqual(resulta['c']     ,   1    )

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

  """simple docstring"""





  with self.assertRaises(snake_case    ) as cm:
   UpperCamelCase_      :		Optional[Any]				=      recursively_apply(torch.tensor     ,   (torch.tensor(1    ), 1)     ,   error_on_other_type=snake_case    )
  self.assertEqual(
      str(cm.exception    )     ,   'Unsupported types (<class \'int\'>) passed to `tensor`. Only nested list/tuple/dicts of objects that are valid for `is_torch_tensor` should be passed.'     ,   )

 def 		SCREAMING_SNAKE_CASE__     (				self			:							List[Any]    )     ->		int:

  """simple docstring"""





  UpperCamelCase_      :		Tuple				=      torch.tensor([1, 2, 3, 4, 5]    )
  self.assertEqual(listify(snake_case    )     ,   [1, 2, 3, 4, 5]    )

  UpperCamelCase_      :		List[Any]				=      torch.tensor([[1, 2, 3, 4, 5], [6, 7, 8, 9, 1_0]]    )
  self.assertEqual(listify(snake_case    )     ,   [[1, 2, 3, 4, 5], [6, 7, 8, 9, 1_0]]    )

  UpperCamelCase_      :		int				=      torch.tensor([[[1, 2, 3, 4, 5], [6, 7, 8, 9, 1_0]], [[1_1, 1_2, 1_3, 1_4, 1_5], [1_6, 1_7, 1_8, 1_9, 2_0]]]    )
  self.assertEqual(
      listify(snake_case    )     ,   [[[1, 2, 3, 4, 5], [6, 7, 8, 9, 1_0]], [[1_1, 1_2, 1_3, 1_4, 1_5], [1_6, 1_7, 1_8, 1_9, 2_0]]]    )

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

  """simple docstring"""





  with patch_environment(aa=1     ,   BB=2    ):
   self.assertEqual(os.environ.get('AA'    )     ,   '1'    )
   self.assertEqual(os.environ.get('BB'    )     ,   '2'    )

  self.assertNotIn('AA'     ,   os.environ    )
  self.assertNotIn('BB'     ,   os.environ    )

 def 		SCREAMING_SNAKE_CASE__     (				self			:							List[Any]    )     ->		int:

  """simple docstring"""





  UpperCamelCase_      :		Any				=      RegressionModel()
  UpperCamelCase_      :		List[str]				=      model.forward
  UpperCamelCase_      :		Tuple				=      convert_outputs_to_fpaa(model.forward    )
  UpperCamelCase_      :		str				=      extract_model_from_parallel(snake_case     ,   keep_fpaa_wrapper=snake_case    )
  UpperCamelCase_      :		Union[str, Any]				=      pickle.dumps(snake_case    )

 @require_cuda
 def 		SCREAMING_SNAKE_CASE__     (				self			:							int    )     ->		Union[str, Any]:

  """simple docstring"""





  UpperCamelCase_      :		int				=      RegressionModel()
  UpperCamelCase_      :		str				=      model.forward
  UpperCamelCase_      :		Tuple				=      torch.cuda.amp.autocast(dtype=torch.floataa    )(model.forward    )
  UpperCamelCase_      :		Optional[int]				=      convert_outputs_to_fpaa(model.forward    )
  UpperCamelCase_      :		int				=      extract_model_from_parallel(snake_case     ,   keep_fpaa_wrapper=snake_case    )
  UpperCamelCase_      :		Union[str, Any]				=      pickle.dumps(snake_case    )

 @require_cuda
 @require_torch_min_version(version='2.0'    )
 def 		SCREAMING_SNAKE_CASE__     (				self			:							int    )     ->		int:

  """simple docstring"""





  UpperCamelCase_      :		Union[str, Any]				=      RegressionModel()
  UpperCamelCase_      :		Tuple				=      model.forward
  UpperCamelCase_      :		str				=      torch.cuda.amp.autocast(dtype=torch.floataa    )(model.forward    )
  UpperCamelCase_      :		Union[str, Any]				=      convert_outputs_to_fpaa(model.forward    )
  UpperCamelCase_      :		List[str]				=      torch.compile(model.forward     ,   backend='inductor'    )
  UpperCamelCase_      :		Union[str, Any]				=      torch.randn(4     ,   1_0    ).cuda()
  UpperCamelCase_      :		str				=      model(snake_case    )

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

  """simple docstring"""





  UpperCamelCase_      :		int				=      RegressionModel()
  # could also do a test with DistributedDataParallel, but difficult to run on CPU or single GPU
  UpperCamelCase_      :		Optional[int]				=      torch.nn.parallel.DataParallel(snake_case    )
  UpperCamelCase_      :		Tuple				=      extract_model_from_parallel(snake_case    )

  self.assertEqual(snake_case     ,   snake_case    )

 @require_torch_min_version(version='2.0'    )
 def 		SCREAMING_SNAKE_CASE__     (				self			:							Dict    )     ->		Union[str, Any]:

  """simple docstring"""





  UpperCamelCase_      :		Optional[Any]				=      RegressionModel()
  UpperCamelCase_      :		Optional[int]				=      torch.compile(snake_case    )

  # could also do a test with DistributedDataParallel, but difficult to run on CPU or single GPU
  UpperCamelCase_      :		Dict				=      torch.nn.parallel.DataParallel(snake_case    )
  UpperCamelCase_      :		Tuple				=      torch.compile(snake_case    )
  UpperCamelCase_      :		Dict				=      extract_model_from_parallel(snake_case    )

  self.assertEqual(compiled_model._orig_mod     ,   compiled_model_unwrapped._orig_mod    )


 def 		SCREAMING_SNAKE_CASE__     (				self			:							Any    )     ->		Tuple:

  """simple docstring"""





  self.assertEqual(find_device([1, 'a', torch.tensor([1, 2, 3]    )]    )     ,   torch.device('cpu'    )    )
  self.assertEqual(find_device({'a': 1, 'b': torch.tensor([1, 2, 3]    )}    )     ,   torch.device('cpu'    )    )
  self.assertIsNone(find_device([1, 'a']    )    )




import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_deberta import DebertaTokenizer


if TYPE_CHECKING:
   from transformers.pipelines.conversational import Conversation


a_					      = logging.get_logger(__name__)

a_					      = {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

a_					      = {
    'vocab_file': {
        'microsoft/deberta-base': 'https://huggingface.co/microsoft/deberta-base/resolve/main/vocab.json',
        'microsoft/deberta-large': 'https://huggingface.co/microsoft/deberta-large/resolve/main/vocab.json',
        'microsoft/deberta-xlarge': 'https://huggingface.co/microsoft/deberta-xlarge/resolve/main/vocab.json',
        'microsoft/deberta-base-mnli': 'https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/vocab.json',
        'microsoft/deberta-large-mnli': 'https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/vocab.json',
        'microsoft/deberta-xlarge-mnli': (
            'https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/vocab.json'
        ),
    },
    'merges_file': {
        'microsoft/deberta-base': 'https://huggingface.co/microsoft/deberta-base/resolve/main/merges.txt',
        'microsoft/deberta-large': 'https://huggingface.co/microsoft/deberta-large/resolve/main/merges.txt',
        'microsoft/deberta-xlarge': 'https://huggingface.co/microsoft/deberta-xlarge/resolve/main/merges.txt',
        'microsoft/deberta-base-mnli': 'https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/merges.txt',
        'microsoft/deberta-large-mnli': 'https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/merges.txt',
        'microsoft/deberta-xlarge-mnli': (
            'https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/merges.txt'
        ),
    },
}

a_					      = {
    'microsoft/deberta-base': 512,
    'microsoft/deberta-large': 512,
    'microsoft/deberta-xlarge': 512,
    'microsoft/deberta-base-mnli': 512,
    'microsoft/deberta-large-mnli': 512,
    'microsoft/deberta-xlarge-mnli': 512,
}

a_					      = {
    'microsoft/deberta-base': {'do_lower_case': False},
    'microsoft/deberta-large': {'do_lower_case': False},
}





class 	_lowercase		(      snake_case_ ):
 lowercase         =     VOCAB_FILES_NAMES
 lowercase         =     PRETRAINED_VOCAB_FILES_MAP
 lowercase         =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 lowercase         =     ['input_ids', 'attention_mask', 'token_type_ids']
 lowercase         =     DebertaTokenizer

 def __init__(				self			:							Optional[int]     ,   snake_case			:							Any=None     ,   snake_case			:							Union[str, Any]=None     ,   snake_case			:							Optional[Any]=None     ,   snake_case			:							List[str]="replace"     ,   snake_case			:							int="[CLS]"     ,   snake_case			:							Any="[SEP]"     ,   snake_case			:							int="[SEP]"     ,   snake_case			:							Union[str, Any]="[CLS]"     ,   snake_case			:							List[Any]="[UNK]"     ,   snake_case			:							List[str]="[PAD]"     ,   snake_case			:							Tuple="[MASK]"     ,   snake_case			:							int=False     ,   **snake_case			:							List[str]     ,   )     ->		Any:

  """simple docstring"""





  super().__init__(
      snake_case     ,   snake_case     ,   tokenizer_file=snake_case     ,   errors=snake_case     ,   bos_token=snake_case     ,   eos_token=snake_case     ,   unk_token=snake_case     ,   sep_token=snake_case     ,   cls_token=snake_case     ,   pad_token=snake_case     ,   mask_token=snake_case     ,   add_prefix_space=snake_case     ,   **snake_case     ,   )
  UpperCamelCase_      :		str				=      kwargs.pop('add_bos_token'     ,   snake_case    )

  UpperCamelCase_      :		Tuple				=      json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()    )
  if pre_tok_state.get('add_prefix_space'     ,   snake_case    ) != add_prefix_space:
   UpperCamelCase_      :		str				=      getattr(snake_case     ,   pre_tok_state.pop('type'    )    )
   UpperCamelCase_      :		List[Any]				=      add_prefix_space
   UpperCamelCase_      :		List[str]				=      pre_tok_class(**snake_case    )

  UpperCamelCase_      :		int				=      add_prefix_space

 @property
 def 		SCREAMING_SNAKE_CASE__     (				self			:							List[str]    )     ->		str:

  """simple docstring"""





  if self._mask_token is None:
   if self.verbose:
    logger.error('Using mask_token, but it is not set yet.'    )
   return None
  return str(self._mask_token    )

 @mask_token.setter
 def 		SCREAMING_SNAKE_CASE__     (				self			:							Any     ,   snake_case			:							Optional[int]    )     ->		str:

  """simple docstring"""





  UpperCamelCase_      :		List[Any]				=      AddedToken(snake_case     ,   lstrip=snake_case     ,   rstrip=snake_case    ) if isinstance(snake_case     ,   snake_case    ) else value
  UpperCamelCase_      :		Optional[int]				=      value

 def 		SCREAMING_SNAKE_CASE__     (				self			:							str     ,   snake_case			:							List[int]     ,   snake_case			:							Optional[List[int]] = None    )     ->		List[int]:

  """simple docstring"""





  if token_ids_a is None:
   return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
  UpperCamelCase_      :		Dict				=      [self.cls_token_id]
  UpperCamelCase_      :		Tuple				=      [self.sep_token_id]
  return cls + token_ids_a + sep + token_ids_a + sep

 def 		SCREAMING_SNAKE_CASE__     (				self			:							str     ,   snake_case			:							List[int]     ,   snake_case			:							Optional[List[int]] = None    )     ->		List[int]:

  """simple docstring"""





  UpperCamelCase_      :		Optional[int]				=      [self.sep_token_id]
  UpperCamelCase_      :		List[str]				=      [self.cls_token_id]

  if token_ids_a is None:
   return len(cls + token_ids_a + sep    ) * [0]
  return len(cls + token_ids_a + sep    ) * [0] + len(token_ids_a + sep    ) * [1]

 def 		SCREAMING_SNAKE_CASE__     (				self			:							Optional[int]     ,   *snake_case			:							Any     ,   **snake_case			:							Union[str, Any]    )     ->		BatchEncoding:

  """simple docstring"""





  UpperCamelCase_      :		Tuple				=      kwargs.get('is_split_into_words'     ,   snake_case    )
  assert self.add_prefix_space or not is_split_into_words, (
      f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
      "to use it with pretokenized inputs."
  )

  return super()._batch_encode_plus(*snake_case     ,   **snake_case    )

 def 		SCREAMING_SNAKE_CASE__     (				self			:							int     ,   *snake_case			:							List[str]     ,   **snake_case			:							Union[str, Any]    )     ->		BatchEncoding:

  """simple docstring"""





  UpperCamelCase_      :		List[Any]				=      kwargs.get('is_split_into_words'     ,   snake_case    )

  assert self.add_prefix_space or not is_split_into_words, (
      f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
      "to use it with pretokenized inputs."
  )

  return super()._encode_plus(*snake_case     ,   **snake_case    )

 def 		SCREAMING_SNAKE_CASE__     (				self			:							Optional[Any]     ,   snake_case			:							str     ,   snake_case			:							Optional[str] = None    )     ->		Tuple[str]:

  """simple docstring"""





  UpperCamelCase_      :		Dict				=      self._tokenizer.model.save(snake_case     ,   name=snake_case    )
  return tuple(snake_case    )


 def 		SCREAMING_SNAKE_CASE__     (				self			:							Any     ,   snake_case			:							"Conversation"    )     ->		List[int]:

  """simple docstring"""





  UpperCamelCase_      :		Tuple				=      []
  for is_user, text in conversation.iter_texts():
   input_ids.extend(self.encode(snake_case     ,   add_special_tokens=snake_case    ) + [self.eos_token_id]    )

  if len(snake_case    ) > self.model_max_length:
   UpperCamelCase_      :		Optional[int]				=      input_ids[-self.model_max_length :]
  return input_ids



