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

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_gpta import GPTaTokenizer


if TYPE_CHECKING:
    from transformers.pipelines.conversational import Conversation


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

lowerCAmelCase			:							str     ={"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

lowerCAmelCase			:							Optional[int]     ={
    "vocab_file": {
        "gpt2": "https://huggingface.co/gpt2/resolve/main/vocab.json",
        "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/vocab.json",
        "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/vocab.json",
        "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/vocab.json",
        "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/vocab.json",
    },
    "merges_file": {
        "gpt2": "https://huggingface.co/gpt2/resolve/main/merges.txt",
        "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/merges.txt",
        "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/merges.txt",
        "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/merges.txt",
        "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/merges.txt",
    },
    "tokenizer_file": {
        "gpt2": "https://huggingface.co/gpt2/resolve/main/tokenizer.json",
        "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/tokenizer.json",
        "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/tokenizer.json",
        "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/tokenizer.json",
        "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/tokenizer.json",
    },
}

lowerCAmelCase			:							Optional[Any]     ={
    "gpt2": 1024,
    "gpt2-medium": 1024,
    "gpt2-large": 1024,
    "gpt2-xl": 1024,
    "distilgpt2": 1024,
}




class 	__snake_case	( __lowerCAmelCase		):





      '''simple docstring'''






      _snake_case    		=       VOCAB_FILES_NAMES
      _snake_case    		=       PRETRAINED_VOCAB_FILES_MAP
      _snake_case    		=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _snake_case    		=       ['input_ids', 'attention_mask']
      _snake_case    		=       GPTaTokenizer



      def __init__(					self     :				Any    ,  _UpperCamelCase     :				Union[str, Any]=None    ,  _UpperCamelCase     :				Union[str, Any]=None    ,  _UpperCamelCase     :				int=None    ,  _UpperCamelCase     :				Tuple="<|endoftext|>"    ,  _UpperCamelCase     :				Optional[Any]="<|endoftext|>"    ,  _UpperCamelCase     :				List[str]="<|endoftext|>"    ,  _UpperCamelCase     :				Optional[int]=False    ,  **_UpperCamelCase     :				Dict    ,  )				->List[str]:


          """simple docstring"""


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

          _lowerCamelCase     :		Optional[Any] 						=						kwargs.pop("""add_bos_token"""    ,  _UpperCamelCase)

          _lowerCamelCase     :		Optional[Any] 						=						json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
          if pre_tok_state.get("""add_prefix_space"""    ,  _UpperCamelCase) != add_prefix_space:
              _lowerCamelCase     :		Optional[int] 						=						getattr(_UpperCamelCase    ,  pre_tok_state.pop("""type"""))
              _lowerCamelCase     :		Dict 						=						add_prefix_space
              _lowerCamelCase     :		List[Any] 						=						pre_tok_class(**_UpperCamelCase)

          _lowerCamelCase     :		Optional[Any] 						=						add_prefix_space



      def 	_SCREAMING_SNAKE_CASE    (					self     :				List[Any]    ,  *_UpperCamelCase     :				Optional[int]    ,  **_UpperCamelCase     :				Optional[int])				->BatchEncoding:


          """simple docstring"""


          _lowerCamelCase     :		int 						=						kwargs.get("""is_split_into_words"""    ,  _UpperCamelCase)
          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(*_UpperCamelCase    ,  **_UpperCamelCase)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Dict    ,  *_UpperCamelCase     :				Dict    ,  **_UpperCamelCase     :				Dict)				->BatchEncoding:


          """simple docstring"""


          _lowerCamelCase     :		List[Any] 						=						kwargs.get("""is_split_into_words"""    ,  _UpperCamelCase)

          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(*_UpperCamelCase    ,  **_UpperCamelCase)



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


          """simple docstring"""


          _lowerCamelCase     :		Any 						=						self._tokenizer.model.save(_UpperCamelCase    ,  name=_UpperCamelCase)
          return tuple(_UpperCamelCase)




      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[int]    ,  _UpperCamelCase     :				"Conversation")				->List[int]:


          """simple docstring"""


          _lowerCamelCase     :		List[str] 						=						[]
          for is_user, text in conversation.iter_texts():
              input_ids.extend(self.encode(_UpperCamelCase    ,  add_special_tokens=_UpperCamelCase) + [self.eos_token_id])

          if len(_UpperCamelCase) > self.model_max_length:
              _lowerCamelCase     :		str 						=						input_ids[-self.model_max_length :]
          return input_ids





import unittest

from transformers.utils.backbone_utils import (
    BackboneMixin,
    get_aligned_output_features_output_indices,
    verify_out_features_out_indices,
)




class 	__snake_case	( unittest.TestCase		):





      '''simple docstring'''






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


          """simple docstring"""


          _lowerCamelCase     :		Tuple 						=						["""a""", """b""", """c"""]

          # Defaults to last layer if both are None
          _lowerCamelCase						,							_lowerCamelCase     :		int 						=						get_aligned_output_features_output_indices(_UpperCamelCase    ,  _UpperCamelCase    ,  _UpperCamelCase)
          self.assertEqual(_UpperCamelCase    ,  ["""c"""])
          self.assertEqual(_UpperCamelCase    ,  [2])

          # Out indices set to match out features
          _lowerCamelCase						,							_lowerCamelCase     :		int 						=						get_aligned_output_features_output_indices(["""a""", """c"""]    ,  _UpperCamelCase    ,  _UpperCamelCase)
          self.assertEqual(_UpperCamelCase    ,  ["""a""", """c"""])
          self.assertEqual(_UpperCamelCase    ,  [0, 2])

          # Out features set to match out indices
          _lowerCamelCase						,							_lowerCamelCase     :		Tuple 						=						get_aligned_output_features_output_indices(_UpperCamelCase    ,  [0, 2]    ,  _UpperCamelCase)
          self.assertEqual(_UpperCamelCase    ,  ["""a""", """c"""])
          self.assertEqual(_UpperCamelCase    ,  [0, 2])

          # Out features selected from negative indices
          _lowerCamelCase						,							_lowerCamelCase     :		str 						=						get_aligned_output_features_output_indices(_UpperCamelCase    ,  [-3, -1]    ,  _UpperCamelCase)
          self.assertEqual(_UpperCamelCase    ,  ["""a""", """c"""])
          self.assertEqual(_UpperCamelCase    ,  [-3, -1])



      def 	_SCREAMING_SNAKE_CASE    (					self     :				int)				->int:


          """simple docstring"""


          with self.assertRaises(_UpperCamelCase):
              verify_out_features_out_indices(["""a""", """b"""]    ,  (0, 1)    ,  _UpperCamelCase)

          # Out features must be a list
          with self.assertRaises(_UpperCamelCase):
              verify_out_features_out_indices(("""a""", """b""")    ,  (0, 1)    ,  ["""a""", """b"""])

          # Out features must be a subset of stage names
          with self.assertRaises(_UpperCamelCase):
              verify_out_features_out_indices(["""a""", """b"""]    ,  (0, 1)    ,  ["""a"""])

          # Out indices must be a list or tuple
          with self.assertRaises(_UpperCamelCase):
              verify_out_features_out_indices(_UpperCamelCase    ,  0    ,  ["""a""", """b"""])

          # Out indices must be a subset of stage names
          with self.assertRaises(_UpperCamelCase):
              verify_out_features_out_indices(_UpperCamelCase    ,  (0, 1)    ,  ["""a"""])

          # Out features and out indices must be the same length
          with self.assertRaises(_UpperCamelCase):
              verify_out_features_out_indices(["""a""", """b"""]    ,  (0,)    ,  ["""a""", """b""", """c"""])

          # Out features should match out indices
          with self.assertRaises(_UpperCamelCase):
              verify_out_features_out_indices(["""a""", """b"""]    ,  (0, 2)    ,  ["""a""", """b""", """c"""])

          # Out features and out indices should be in order
          with self.assertRaises(_UpperCamelCase):
              verify_out_features_out_indices(["""b""", """a"""]    ,  (0, 1)    ,  ["""a""", """b"""])

          # Check passes with valid inputs
          verify_out_features_out_indices(["""a""", """b""", """d"""]    ,  (0, 1, -1)    ,  ["""a""", """b""", """c""", """d"""])




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


          """simple docstring"""


          _lowerCamelCase     :		int 						=						BackboneMixin()

          _lowerCamelCase     :		Union[str, Any] 						=						["""a""", """b""", """c"""]
          _lowerCamelCase     :		Tuple 						=						["""a""", """c"""]
          _lowerCamelCase     :		List[Any] 						=						[0, 2]

          # Check that the output features and indices are set correctly
          self.assertEqual(backbone.out_features    ,  ["""a""", """c"""])
          self.assertEqual(backbone.out_indices    ,  [0, 2])

          # Check out features and indices are updated correctly
          _lowerCamelCase     :		str 						=						["""a""", """b"""]
          self.assertEqual(backbone.out_features    ,  ["""a""", """b"""])
          self.assertEqual(backbone.out_indices    ,  [0, 1])

          _lowerCamelCase     :		Optional[int] 						=						[-3, -1]
          self.assertEqual(backbone.out_features    ,  ["""a""", """c"""])
          self.assertEqual(backbone.out_indices    ,  [-3, -1])




