





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

from tokenizers import pre_tokenizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
    from transformers.pipelines.conversational import Conversation


lowercase				:	List[Any]      						=  logging.get_logger(__name__)

lowercase				:	List[Any]      						=  {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

lowercase				:	Optional[Any]      						=  {
    """tokenizer_file""": {
        """EleutherAI/gpt-neox-20b""": """https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json""",
    },
}

lowercase				:	str      						=  {
    """gpt-neox-20b""": 2_0_4_8,
}





class 			A__   (							__UpperCAmelCase     ):




   """simple docstring"""



   __A       :    Optional[Any]	  =  VOCAB_FILES_NAMES
   __A       :    str	  =  PRETRAINED_VOCAB_FILES_MAP
   __A       :    Union[str, Any]	  =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __A       :    Any	  =  ['''input_ids''', '''attention_mask''']

   def __init__( self ,  lowercase=None ,  lowercase=None ,  lowercase=None ,  lowercase="<|endoftext|>" ,  lowercase="<|endoftext|>" ,  lowercase="<|endoftext|>" ,  lowercase=False ,  **lowercase ,  )      ->						Dict:







          '''simple docstring'''

          super().__init__(
              lowercase ,  lowercase ,  tokenizer_file=lowercase ,  unk_token=lowercase ,  bos_token=lowercase ,  eos_token=lowercase ,  add_prefix_space=lowercase ,  **lowercase ,  )

          a__						:     Dict			=    json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
          if pre_tok_state.get('add_prefix_space' ,  lowercase) != add_prefix_space:
                 a__						:     int			=    getattr(lowercase ,  pre_tok_state.pop('type'))
                 a__						:     Union[str, Any]			=    add_prefix_space
                 a__						:     Optional[Any]			=    pre_tok_class(**lowercase)

          a__						:     Tuple			=    add_prefix_space

   def 							__lowercase		( self ,  lowercase ,  lowercase = None)      ->						Tuple[str]:







          '''simple docstring'''

          a__						:     Optional[Any]			=    self._tokenizer.model.save(lowercase ,  name=lowercase)
          return tuple(lowercase)

   def 							__lowercase		( self ,  lowercase)      ->						List[int]:







          '''simple docstring'''

          a__						:     Optional[Any]			=    []
          for is_user, text in conversation.iter_texts():
                 input_ids.extend(self.encode(lowercase ,  add_special_tokens=lowercase) + [self.eos_token_id])

          if len(lowercase) > self.model_max_length:
                 a__						:     List[Any]			=    input_ids[-self.model_max_length :]
          return input_ids









import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
    import tensorflow as tf
    from tensorflow.python.eager import context
    from tensorflow.python.framework import ops

    from transformers import GradientAccumulator, create_optimizer





@require_tf
class 			A__   (							unittest.TestCase     ):




   """simple docstring"""



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







          '''simple docstring'''

          self.assertEqual(len(lowercase) ,  len(lowercase))
          for a, b in zip(lowercase ,  lowercase):
                 self.assertAlmostEqual(lowercase ,  lowercase ,  delta=lowercase)

   def 							__lowercase		( self)      ->						Any:







          '''simple docstring'''

          a__						:     Tuple			=    GradientAccumulator()
          accumulator([tf.constant([1.0, 2.0])])
          accumulator([tf.constant([-2.0, 1.0])])
          accumulator([tf.constant([-1.0, 2.0])])
          with self.assertRaises(lowercase):
                 accumulator([tf.constant([1.0, 1.0]), tf.constant([2.0, 2.0])])
          self.assertEqual(accumulator.step ,  3)
          self.assertEqual(len(accumulator.gradients) ,  1)
          self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist() ,  [-2.0, 5.0] ,  tol=1e-2)
          accumulator.reset()
          self.assertEqual(accumulator.step ,  0)
          self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist() ,  [0.0, 0.0] ,  tol=1e-2)

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







          '''simple docstring'''

          a__						:     Tuple			=    None
          ops.enable_eager_execution_internal()
          a__						:     Optional[int]			=    tf.config.list_physical_devices('CPU')
          if len(lowercase) == 1:
                 tf.config.set_logical_device_configuration(
                     physical_devices[0] ,  [tf.config.LogicalDeviceConfiguration(), tf.config.LogicalDeviceConfiguration()])
          a__						:     int			=    tf.config.list_logical_devices(device_type='CPU')
          a__						:     Union[str, Any]			=    tf.distribute.MirroredStrategy(devices=devices[:2])

          with strategy.scope():
                 a__						:     str			=    GradientAccumulator()
                 a__						:     Tuple			=    tf.Variable([4.0, 3.0])
                 a__			,							a__						:     Tuple			=    create_optimizer(5e-5 ,  10 ,  5)
                 a__						:     Tuple			=    tf.Variable([0.0, 0.0] ,  trainable=lowercase)

          def accumulate_on_replica(lowercase):
                 accumulator([gradient])

          def apply_on_replica():
                 optimizer.apply_gradients(list(zip(accumulator.gradients ,  [variable])))

          @tf.function
          def accumulate(lowercase ,  lowercase):
                 with strategy.scope():
                        a__						:     Union[str, Any]			=    strategy.experimental_local_results(lowercase)
                        local_variables[0].assign(lowercase)
                        local_variables[1].assign(lowercase)
                        strategy.run(lowercase ,  args=(gradient_placeholder,))

          @tf.function
          def apply_grad():
                 with strategy.scope():
                        strategy.run(lowercase)

          def _check_local_values(lowercase ,  lowercase):
                 a__						:     Union[str, Any]			=    strategy.experimental_local_results(accumulator._gradients[0])
                 self.assertListAlmostEqual(values[0].value() ,  lowercase ,  tol=1e-2)
                 self.assertListAlmostEqual(values[1].value() ,  lowercase ,  tol=1e-2)

          accumulate([1.0, 2.0] ,  [-1.0, 1.0])
          accumulate([3.0, -1.0] ,  [-1.0, -1.0])
          accumulate([-2.0, 2.0] ,  [3.0, -2.0])
          self.assertEqual(accumulator.step ,  3)
          _check_local_values([2.0, 3.0] ,  [1.0, -2.0])
          apply_grad()
          self.assertListAlmostEqual(variable.value() ,  [4.0, 3.0] ,  tol=1e-2)
          accumulator.reset()
          self.assertEqual(accumulator.step ,  0)
          _check_local_values([0.0, 0.0] ,  [0.0, 0.0])


