





import argparse
import logging
import pickle
import random
import time

import numpy as np

from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer


logging.basicConfig(
    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO
)
__A     =    logging.getLogger(__name__)





def 					lowerCAmelCase_			(	)					->		Dict:







		"""simple docstring"""





		lowerCamelCase__:				Tuple        =argparse.ArgumentParser(
		    description="Preprocess the data to avoid re-doing it several times by (tokenization + token_to_ids)."		)
		parser.add_argument("--file_path"     ,   type=__a     ,   default="data/dump.txt"     ,   help="The path to the data."		)
		parser.add_argument("--tokenizer_type"     ,   type=__a     ,   default="bert"     ,   choices=["bert", "roberta", "gpt2"]		)
		parser.add_argument("--tokenizer_name"     ,   type=__a     ,   default="bert-base-uncased"     ,   help="The tokenizer to use."		)
		parser.add_argument("--dump_file"     ,   type=__a     ,   default="data/dump"     ,   help="The dump file prefix."		)
		lowerCamelCase__:				Any        =parser.parse_args()

		logger.info(F"""Loading Tokenizer ({args.tokenizer_name})"""		)
		if args.tokenizer_type == "bert":
				lowerCamelCase__:				List[str]        =BertTokenizer.from_pretrained(args.tokenizer_name		)
				lowerCamelCase__:				List[Any]        =tokenizer.special_tokens_map["cls_token"]  # `[CLS]`
				lowerCamelCase__:				Dict        =tokenizer.special_tokens_map["sep_token"]  # `[SEP]`
		elif args.tokenizer_type == "roberta":
				lowerCamelCase__:				Union[str, Any]        =RobertaTokenizer.from_pretrained(args.tokenizer_name		)
				lowerCamelCase__:				str        =tokenizer.special_tokens_map["cls_token"]  # `<s>`
				lowerCamelCase__:				Tuple        =tokenizer.special_tokens_map["sep_token"]  # `</s>`
		elif args.tokenizer_type == "gpt2":
				lowerCamelCase__:				str        =GPTaTokenizer.from_pretrained(args.tokenizer_name		)
				lowerCamelCase__:				Tuple        =tokenizer.special_tokens_map["bos_token"]  # `<|endoftext|>`
				lowerCamelCase__:				Optional[int]        =tokenizer.special_tokens_map["eos_token"]  # `<|endoftext|>`

		logger.info(F"""Loading text from {args.file_path}"""		)
		with open(args.file_path     ,   "r"     ,   encoding="utf8"		) as fp:
				lowerCamelCase__:				Dict        =fp.readlines()

		logger.info("Start encoding"		)
		logger.info(F"""{len(__a		)} examples to process."""		)

		lowerCamelCase__:				int        =[]
		lowerCamelCase__:				Any        =0
		lowerCamelCase__:				List[Any]        =10000
		lowerCamelCase__:				Optional[int]        =time.time()
		for text in data:
				lowerCamelCase__:				int        =F"""{bos} {text.strip()} {sep}"""
				lowerCamelCase__:				Optional[Any]        =tokenizer.encode(__a     ,   add_special_tokens=__a		)
				rslt.append(__a		)

				iter += 1
				if iter % interval == 0:
						lowerCamelCase__:				Any        =time.time()
						logger.info(F"""{iter} examples processed. - {(end-start):.2f}s/{interval}expl"""		)
						lowerCamelCase__:				Any        =time.time()
		logger.info("Finished binarization"		)
		logger.info(F"""{len(__a		)} examples processed."""		)

		lowerCamelCase__:				Optional[int]        =F"""{args.dump_file}.{args.tokenizer_name}.pickle"""
		lowerCamelCase__:				Optional[int]        =tokenizer.vocab_size
		if vocab_size < (1 << 16):
				lowerCamelCase__:				str        =[np.uintaa(__a		) for d in rslt]
		else:
				lowerCamelCase__:				str        =[np.intaa(__a		) for d in rslt]
		random.shuffle(rslt_		)
		logger.info(F"""Dump to {dp_file}"""		)
		with open(__a     ,   "wb"		) as handle:
				pickle.dump(rslt_     ,   __a     ,   protocol=pickle.HIGHEST_PROTOCOL		)


if __name__ == "__main__":
					main()








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))

