
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


lowercase__       =logging.get_logger(__name__)

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

lowercase__       ={
    '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',
    },
}

lowercase__       ={
    'gpt2': 1024,
    'gpt2-medium': 1024,
    'gpt2-large': 1024,
    'gpt2-xl': 1024,
    'distilgpt2': 1024,
}



class 		UpperCamelCase__  (       __lowercase    ):
   _SCREAMING_SNAKE_CASE      :							int	       =      VOCAB_FILES_NAMES
   _SCREAMING_SNAKE_CASE      :							Optional[int]	       =      PRETRAINED_VOCAB_FILES_MAP
   _SCREAMING_SNAKE_CASE      :							List[Any]	       =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _SCREAMING_SNAKE_CASE      :							int	       =      ["input_ids", "attention_mask"]
   _SCREAMING_SNAKE_CASE      :							Union[str, Any]	       =      GPTaTokenizer
   def __init__(self :					Optional[int]					,				snake_case_ :					List[Any]=None					,				snake_case_ :					List[str]=None					,				snake_case_ :					Dict=None					,				snake_case_ :					Dict="<|endoftext|>"					,				snake_case_ :					List[str]="<|endoftext|>"					,				snake_case_ :					str="<|endoftext|>"					,				snake_case_ :					List[Any]=False					,				**snake_case_ :					Dict					,				):
    super().__init__(
        snake_case_					,				snake_case_					,				tokenizer_file=snake_case_					,				unk_token=snake_case_					,				bos_token=snake_case_					,				eos_token=snake_case_					,				add_prefix_space=snake_case_					,				**snake_case_					,				)

    __a :  Dict						=  kwargs.pop('''add_bos_token'''					,				snake_case_   )

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

    __a :  Optional[Any]						=  add_prefix_space
   def       lowerCAmelCase			(self :					int					,				*snake_case_ :					Optional[int]					,				**snake_case_ :					Optional[int]   ):
    __a :  Optional[int]						=  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       lowerCAmelCase			(self :					List[Any]					,				*snake_case_ :					Tuple					,				**snake_case_ :					List[Any]   ):
    __a :  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       lowerCAmelCase			(self :					Union[str, Any]					,				snake_case_ :					str					,				snake_case_ :					Optional[str] = None   ):
    __a :  Union[str, Any]						=  self._tokenizer.model.save(snake_case_					,				name=snake_case_   )
    return tuple(snake_case_   )







   def       lowerCAmelCase			(self :					Optional[Any]					,				snake_case_ :					"Conversation"   ):
    __a :  Union[str, Any]						=  []
    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:
     __a :  Tuple						=  input_ids[-self.model_max_length :]
    return input_ids


from pathlib import Path
from typing import List

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText


if is_torch_available():
      import torch

if is_vision_available():
      from PIL import Image


lowercase__       =['text', 'image', 'audio']




def    __UpperCamelCase     (				lowerCAmelCase__							:				List[str]  ):
 __a :  Optional[int]						=  []

 for input_type in input_types:
  if input_type == "text":
   inputs.append('''Text input'''  )
  elif input_type == "image":
   inputs.append(
       Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO'''  )  ) / '''000000039769.png'''  ).resize((5_1_2, 5_1_2)  )  )
  elif input_type == "audio":
   inputs.append(torch.ones(3_0_0_0  )  )
  elif isinstance(lowerCAmelCase__  ,   lowerCAmelCase__  ):
   inputs.append(create_inputs(lowerCAmelCase__  )  )
  else:
   raise ValueError(f"Invalid type requested: {input_type}"  )

 return inputs




def    __UpperCamelCase     (				lowerCAmelCase__							:				List  ):
 __a :  List[str]						=  []

 for output in outputs:
  if isinstance(lowerCAmelCase__  ,   (str, AgentText)  ):
   output_types.append('''text'''  )
  elif isinstance(lowerCAmelCase__  ,   (Image.Image, AgentImage)  ):
   output_types.append('''image'''  )
  elif isinstance(lowerCAmelCase__  ,   (torch.Tensor, AgentAudio)  ):
   output_types.append('''audio'''  )
  else:
   raise ValueError(f"Invalid output: {output}"  )

 return output_types



@is_tool_test
class 		UpperCamelCase__  :
   def       lowerCAmelCase			(self :					Any   ):
    self.assertTrue(hasattr(self.tool					,				'''inputs'''   )   )
    self.assertTrue(hasattr(self.tool					,				'''outputs'''   )   )

    __a :  Any						=  self.tool.inputs
    for _input in inputs:
     if isinstance(_input					,				snake_case_   ):
      for __input in _input:
       self.assertTrue(__input in authorized_types   )
     else:
      self.assertTrue(_input in authorized_types   )

    __a :  Optional[int]						=  self.tool.outputs
    for _output in outputs:
     self.assertTrue(_output in authorized_types   )
   def       lowerCAmelCase			(self :					List[Any]   ):
    __a :  Union[str, Any]						=  create_inputs(self.tool.inputs   )
    __a :  List[Any]						=  self.tool(*snake_case_   )

    # There is a single output
    if len(self.tool.outputs   ) == 1:
     __a :  Tuple						=  [outputs]

    self.assertListEqual(output_types(snake_case_   )					,				self.tool.outputs   )
   def       lowerCAmelCase			(self :					List[Any]   ):
    self.assertTrue(hasattr(self.tool					,				'''description'''   )   )
    self.assertTrue(hasattr(self.tool					,				'''default_checkpoint'''   )   )
    self.assertTrue(self.tool.description.startswith('''This is a tool that'''   )   )
   def       lowerCAmelCase			(self :					Any   ):
    __a :  Any						=  create_inputs(self.tool.inputs   )
    __a :  Union[str, Any]						=  self.tool(*snake_case_   )

    if not isinstance(snake_case_					,				snake_case_   ):
     __a :  Tuple						=  [outputs]

    self.assertEqual(len(snake_case_   )					,				len(self.tool.outputs   )   )

    for output, output_type in zip(snake_case_					,				self.tool.outputs   ):
     __a :  List[Any]						=  AGENT_TYPE_MAPPING[output_type]
     self.assertTrue(isinstance(snake_case_					,				snake_case_   )   )







   def       lowerCAmelCase			(self :					Optional[int]   ):
    __a :  Any						=  create_inputs(self.tool.inputs   )

    __a :  Dict						=  []

    for _input, input_type in zip(snake_case_					,				self.tool.inputs   ):
     if isinstance(snake_case_					,				snake_case_   ):
      _inputs.append([AGENT_TYPE_MAPPING[_input_type](_input   ) for _input_type in input_type]   )
     else:
      _inputs.append(AGENT_TYPE_MAPPING[input_type](_input   )   )

        # Should not raise an error
    __a :  Optional[Any]						=  self.tool(*snake_case_   )

    if not isinstance(snake_case_					,				snake_case_   ):
     __a :  Dict						=  [outputs]

    self.assertEqual(len(snake_case_   )					,				len(self.tool.outputs   )   )
