





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

import numpy as np

from ...utils import is_tf_available, is_torch_available, logging


if TYPE_CHECKING:
					if is_torch_available():
										import torch
					if is_tf_available():
										import tensorflow as tf

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_codegen import CodeGenTokenizer


__A     =    logging.get_logger(__name__)

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

__A     =    {
    "vocab_file": {
        "Salesforce/codegen-350M-mono": "https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/vocab.json",
    },
    "merges_file": {
        "Salesforce/codegen-350M-mono": "https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/merges.txt",
    },
    "tokenizer_file": {
        "Salesforce/codegen-350M-mono": (
            "https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/tokenizer.json"
        ),
    },
}

__A     =    {
    "Salesforce/codegen-350M-mono": 2048,
}



class        _SCREAMING_SNAKE_CASE						(     _SCREAMING_SNAKE_CASE   ):







	'''simple docstring'''
	lowercase_		=      VOCAB_FILES_NAMES
	lowercase_		=      PRETRAINED_VOCAB_FILES_MAP
	lowercase_		=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	lowercase_		=      ["input_ids", "attention_mask"]
	lowercase_		=      CodeGenTokenizer

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



			'''simple docstring'''



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

			if kwargs.pop("add_bos_token"	,			UpperCAmelCase_):
					lowerCamelCase__:				int        =kwargs.pop("name_or_path"	,			"")
					raise ValueError(
					    "Currenty GPT2\'s fast tokenizer does NOT support adding a BOS token."
					    "Instead you should use GPT2\'s slow tokenizer class `CodeGenTokenizer` as follows: \n"
					    F"""`CodeGenTokenizer.from_pretrained(\'{model_id}\')`\nor\n"""
					    F"""`AutoTokenizer.from_pretrained(\'{model_id}\', use_fast=False)`\n"""
					    "This issue will be fixed soon, see: https://github.com/huggingface/tokenizers/pull/1005."
					    " so that the fast tokenizer works correctly.")

			lowerCamelCase__:				Any        =json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
			if pre_tok_state.get("add_prefix_space"	,			UpperCAmelCase_) != add_prefix_space:
					lowerCamelCase__:				str        =getattr(UpperCAmelCase_	,			pre_tok_state.pop("type"))
					lowerCamelCase__:				Any        =add_prefix_space
					lowerCamelCase__:				Any        =pre_tok_class(**UpperCAmelCase_)

			lowerCamelCase__:				List[Any]        =add_prefix_space

	def 							SCREAMING_SNAKE_CASE_ (self    :       str	,			*UpperCAmelCase_    :       int	,			**UpperCAmelCase_    :       Optional[Any])	->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()._batch_encode_plus(*UpperCAmelCase_	,			**UpperCAmelCase_)

	def 							SCREAMING_SNAKE_CASE_ (self    :       Union[str, Any]	,			*UpperCAmelCase_    :       Dict	,			**UpperCAmelCase_    :       List[Any])	->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    :       List[Any]	,			UpperCAmelCase_    :       str	,			UpperCAmelCase_    :       Optional[Any] = None)	->Tuple[str]:



			'''simple docstring'''



			lowerCamelCase__:				List[Any]        =self._tokenizer.model.save(UpperCAmelCase_	,			name=UpperCAmelCase_)
			return tuple(UpperCAmelCase_)

	def 							SCREAMING_SNAKE_CASE_ (self    :       int	,			UpperCAmelCase_    :       Tuple	,			UpperCAmelCase_    :       List[str] = False	,			UpperCAmelCase_    :       Optional[Any] = None	,			UpperCAmelCase_    :       int = None	,			**UpperCAmelCase_    :       Tuple	,			)	->str:



			'''simple docstring'''



			lowerCamelCase__:				Union[str, Any]        =super().decode(
			    token_ids=UpperCAmelCase_	,			skip_special_tokens=UpperCAmelCase_	,			clean_up_tokenization_spaces=UpperCAmelCase_	,			**UpperCAmelCase_	,			)

			if truncate_before_pattern is not None and len(UpperCAmelCase_) > 0:
					lowerCamelCase__:				List[str]        =self.truncate(UpperCAmelCase_	,			UpperCAmelCase_)

			return decoded_text







	def 							SCREAMING_SNAKE_CASE_ (self    :       Optional[Any]	,			UpperCAmelCase_    :       Any	,			UpperCAmelCase_    :       Tuple)	->int:



			'''simple docstring'''



			def find_re(UpperCAmelCase_    :       Any	,			UpperCAmelCase_    :       Optional[int]	,			UpperCAmelCase_    :       List[Any]):
					lowerCamelCase__:				Union[str, Any]        =pattern.search(UpperCAmelCase_	,			UpperCAmelCase_)
					return m.start() if m else -1

			lowerCamelCase__:				Union[str, Any]        =[re.compile(UpperCAmelCase_	,			re.MULTILINE) for pattern in truncate_before_pattern]

			lowerCamelCase__:				Optional[int]        =list(re.finditer("^print"	,			UpperCAmelCase_	,			re.MULTILINE))

			if len(UpperCAmelCase_) > 1:
					lowerCamelCase__:				Optional[Any]        =completion[: prints[1].start()]

			lowerCamelCase__:				int        =list(re.finditer("^def"	,			UpperCAmelCase_	,			re.MULTILINE))

			if len(UpperCAmelCase_) > 1:
					lowerCamelCase__:				Any        =completion[: defs[1].start()]

			lowerCamelCase__:				int        =0

			lowerCamelCase__:				Optional[int]        =[
			    pos for pos in [find_re(UpperCAmelCase_	,			UpperCAmelCase_	,			UpperCAmelCase_) for terminal in terminals] if pos != -1
			]

			if len(UpperCAmelCase_) > 0:
					return completion[: min(UpperCAmelCase_)]
			else:
					return completion








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


__A     =    ["text", "image", "audio"]





def 					lowerCAmelCase_			(	__a		)					->		Optional[Any]:







  """simple docstring"""





  lowerCamelCase__:				Tuple        =[]

  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((512, 512)		)		)
    elif input_type == "audio":
      inputs.append(torch.ones(3000		)		)
    elif isinstance(__a     ,   __a		):
      inputs.append(create_inputs(__a		)		)
    else:
      raise ValueError(F"""Invalid type requested: {input_type}"""		)

  return inputs





def 					lowerCAmelCase_			(	__a		)					->		Union[str, Any]:







  """simple docstring"""





  lowerCamelCase__:				Union[str, Any]        =[]

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

  return output_types






@is_tool_test
class        _SCREAMING_SNAKE_CASE						:







 '''simple docstring'''

 def 							SCREAMING_SNAKE_CASE_ (self    :       List[str])	->Dict:



   '''simple docstring'''



   self.assertTrue(hasattr(self.tool	,			"inputs"))
   self.assertTrue(hasattr(self.tool	,			"outputs"))

   lowerCamelCase__:				Tuple        =self.tool.inputs
   for _input in inputs:
     if isinstance(_input	,			UpperCAmelCase_):
       for __input in _input:
         self.assertTrue(__input in authorized_types)
     else:
       self.assertTrue(_input in authorized_types)

   lowerCamelCase__:				Optional[Any]        =self.tool.outputs
   for _output in outputs:
     self.assertTrue(_output in authorized_types)

 def 							SCREAMING_SNAKE_CASE_ (self    :       Optional[Any])	->str:



   '''simple docstring'''



   lowerCamelCase__:				List[str]        =create_inputs(self.tool.inputs)
   lowerCamelCase__:				str        =self.tool(*UpperCAmelCase_)

   # There is a single output
   if len(self.tool.outputs) == 1:
     lowerCamelCase__:				Optional[Any]        =[outputs]

   self.assertListEqual(output_types(UpperCAmelCase_)	,			self.tool.outputs)

 def 							SCREAMING_SNAKE_CASE_ (self    :       Dict)	->Any:



   '''simple docstring'''



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



   '''simple docstring'''



   lowerCamelCase__:				str        =create_inputs(self.tool.inputs)
   lowerCamelCase__:				Dict        =self.tool(*UpperCAmelCase_)

   if not isinstance(UpperCAmelCase_	,			UpperCAmelCase_):
     lowerCamelCase__:				Tuple        =[outputs]

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

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







 def 							SCREAMING_SNAKE_CASE_ (self    :       Dict)	->str:



   '''simple docstring'''



   lowerCamelCase__:				Any        =create_inputs(self.tool.inputs)

   lowerCamelCase__:				int        =[]

   for _input, input_type in zip(UpperCAmelCase_	,			self.tool.inputs):
     if isinstance(UpperCAmelCase_	,			UpperCAmelCase_):
       _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
   lowerCamelCase__:				Union[str, Any]        =self.tool(*UpperCAmelCase_)

   if not isinstance(UpperCAmelCase_	,			UpperCAmelCase_):
     lowerCamelCase__:				str        =[outputs]

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

