






"""simple docstring"""






from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


SCREAMING_SNAKE_CASE						:						int					   =       logging.get_logger(__name__)

SCREAMING_SNAKE_CASE						:						int					   =       {
    '''EleutherAI/gpt-j-6B''': '''https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json''',
    # See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}





class       snake_case_       (    _lowerCamelCase       ):



 """simple docstring"""

 SCREAMING_SNAKE_CASE_:							Union[str, Any]   			=					"""gptj"""
 SCREAMING_SNAKE_CASE_:							Union[str, Any]   			=					{
     """max_position_embeddings""": """n_positions""",
     """hidden_size""": """n_embd""",
     """num_attention_heads""": """n_head""",
     """num_hidden_layers""": """n_layer""",
 }


 def __init__(      self  ,					__a=5_0400  ,					__a=2048  ,					__a=4096  ,					__a=28  ,					__a=16  ,					__a=64  ,					__a=None  ,					__a="gelu_new"  ,					__a=0.0  ,					__a=0.0  ,					__a=0.0  ,					__a=1E-5  ,					__a=0.02  ,					__a=True  ,					__a=5_0256  ,					__a=5_0256  ,					__a=False  ,					**__a  ,					):


     """simple docstring"""

     A__							 =       vocab_size
     A__							 =       n_positions
     A__							 =       n_embd
     A__							 =       n_layer
     A__							 =       n_head
     A__							 =       n_inner
     A__							 =       rotary_dim
     A__							 =       activation_function
     A__							 =       resid_pdrop
     A__							 =       embd_pdrop
     A__							 =       attn_pdrop
     A__							 =       layer_norm_epsilon
     A__							 =       initializer_range
     A__							 =       use_cache

     A__							 =       bos_token_id
     A__							 =       eos_token_id

     super().__init__(
         bos_token_id=__a  ,					eos_token_id=__a  ,					tie_word_embeddings=__a  ,					**__a						)





class       snake_case_       (    _lowerCamelCase       ):



 """simple docstring"""

 def __init__(      self  ,					__a  ,					__a = "default"  ,					__a = None  ,					__a = False  ,					):


     """simple docstring"""

     super().__init__(__a  ,					task=__a  ,					patching_specs=__a  ,					use_past=__a						)
     if not getattr(self._config  ,					'pad_token_id'  ,					__a						):
         # TODO: how to do that better?
         A__							 =       0


 @property
 def       _UpperCAmelCase						(      self						):


     """simple docstring"""

     A__							 =       OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}}						)
     if self.use_past:
         self.fill_with_past_key_values_(__a  ,					direction='inputs'						)
         A__							 =       {0: 'batch', 1: 'past_sequence + sequence'}
     else:
         A__							 =       {0: 'batch', 1: 'sequence'}

     return common_inputs


 @property
 def       _UpperCAmelCase						(      self						):


     """simple docstring"""

     return self._config.n_layer


 @property
 def       _UpperCAmelCase						(      self						):


     """simple docstring"""

     return self._config.n_head


 def       _UpperCAmelCase						(      self  ,					__a  ,					__a = -1  ,					__a = -1  ,					__a = False  ,					__a = None  ,					):


     """simple docstring"""

     A__							 =       super(__a  ,					self						).generate_dummy_inputs(
         __a  ,					batch_size=__a  ,					seq_length=__a  ,					is_pair=__a  ,					framework=__a						)

     # We need to order the input in the way they appears in the forward()
     A__							 =       OrderedDict({'input_ids': common_inputs['input_ids']}						)

     # Need to add the past_keys
     if self.use_past:
         if not is_torch_available():
             raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'						)
         else:
             import torch

             A__ ,			A__							 =       common_inputs['input_ids'].shape
             # Not using the same length for past_key_values
             A__							 =       seqlen + 2
             A__							 =       (
                 batch,
                 self.num_attention_heads,
                 past_key_values_length,
                 self._config.hidden_size // self.num_attention_heads,
             )
             A__							 =       [
                 (torch.zeros(__a						), torch.zeros(__a						)) for _ in range(self.num_layers						)
             ]

     A__							 =       common_inputs['attention_mask']
     if self.use_past:
         A__							 =       ordered_inputs['attention_mask'].dtype
         A__							 =       torch.cat(
             [ordered_inputs['attention_mask'], torch.ones(__a  ,					__a  ,					dtype=__a						)]  ,					dim=1						)

     return ordered_inputs






 @property
 def       _UpperCAmelCase						(      self						):


     """simple docstring"""

     return 13










"""simple docstring"""






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


SCREAMING_SNAKE_CASE						:						Optional[int]					   =       logging.get_logger(__name__)

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

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

SCREAMING_SNAKE_CASE						:						str					   =       {
    '''gpt-neox-20b''': 2_048,
}





class       snake_case_       (    _lowerCamelCase       ):



	"""simple docstring"""

	SCREAMING_SNAKE_CASE_:							Union[str, Any]   			=					VOCAB_FILES_NAMES
	SCREAMING_SNAKE_CASE_:							Any   			=					PRETRAINED_VOCAB_FILES_MAP
	SCREAMING_SNAKE_CASE_:							List[Any]   			=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	SCREAMING_SNAKE_CASE_:							Optional[Any]   			=					["""input_ids""", """attention_mask"""]


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


					"""simple docstring"""

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

					A__							 =       json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()						)
					if pre_tok_state.get('add_prefix_space'  ,					__a						) != add_prefix_space:
									A__							 =       getattr(__a  ,					pre_tok_state.pop('type'						)						)
									A__							 =       add_prefix_space
									A__							 =       pre_tok_class(**__a						)

					A__							 =       add_prefix_space


	def       _UpperCAmelCase						(      self  ,					__a  ,					__a = None						):


					"""simple docstring"""

					A__							 =       self._tokenizer.model.save(__a  ,					name=__a						)
					return tuple(__a						)






	def       _UpperCAmelCase						(      self  ,					__a						):


					"""simple docstring"""

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

					if len(__a						) > self.model_max_length:
									A__							 =       input_ids[-self.model_max_length :]
					return input_ids


