



"""simple docstring"""
import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union

import tqdm
from filelock import FileLock

from transformers import (
    BartTokenizer,
    BartTokenizerFast,
    DataProcessor,
    PreTrainedTokenizer,
    RobertaTokenizer,
    RobertaTokenizerFast,
    XLMRobertaTokenizer,
    is_tf_available,
    is_torch_available,
)


_UpperCAmelCase     =			logging.getLogger(__name__)
@dataclass(frozen=UpperCAmelCase__       )
class 					a	:
  UpperCamelCase	:				str
  UpperCamelCase	:				str
  UpperCamelCase	:				Optional[str]     						=					None
  UpperCamelCase	:				Optional[str]     						=					None
  UpperCamelCase	:				Optional[str]     						=					None







@dataclass(frozen=UpperCAmelCase__       )
class 					a	:
  UpperCamelCase	:				List[int]
  UpperCamelCase	:				Optional[List[int]]     						=					None
  UpperCamelCase	:				Optional[List[int]]     						=					None
  UpperCamelCase	:				Optional[Union[int, float]]     						=					None
  UpperCamelCase	:				Optional[int]     						=					None


if is_torch_available():
   import torch
   from torch.utils.data import Dataset
   class 					a	(  UpperCAmelCase__       ):
     UpperCamelCase	:				List[InputFeatures]



     def __init__(						self :					Any       ,		lowerCAmelCase :					str       ,		lowerCAmelCase :					PreTrainedTokenizer       ,		lowerCAmelCase :					str       ,		lowerCAmelCase :					Optional[int] = None       ,		lowerCAmelCase :					str=False       ,		lowerCAmelCase :					bool = False       ,		)     ->					Dict:


        '''simple docstring'''




        SCREAMING_SNAKE_CASE_: str     			=hans_processors[task]()

        SCREAMING_SNAKE_CASE_: List[Any]     			=os.path.join(
            lowerCAmelCase       ,		"""cached_{}_{}_{}_{}""".format(
                """dev""" if evaluate else """train"""       ,		tokenizer.__class__.__name__       ,		str(lowerCAmelCase		)       ,		lowerCAmelCase       ,		)       ,		)
        SCREAMING_SNAKE_CASE_: List[str]     			=processor.get_labels()
        if tokenizer.__class__ in (
            RobertaTokenizer,
            RobertaTokenizerFast,
            XLMRobertaTokenizer,
            BartTokenizer,
            BartTokenizerFast,
        ):
           # HACK(label indices are swapped in RoBERTa pretrained model)
           SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: Optional[Any]     			=label_list[2], label_list[1]
        SCREAMING_SNAKE_CASE_: List[str]     			=label_list

        # Make sure only the first process in distributed training processes the dataset,
        # and the others will use the cache.
        SCREAMING_SNAKE_CASE_: List[Any]     			=cached_features_file + """.lock"""
        with FileLock(lowerCAmelCase		):
           if os.path.exists(lowerCAmelCase		) and not overwrite_cache:
              logger.info(f'''Loading features from cached file {cached_features_file}'''		)
              SCREAMING_SNAKE_CASE_: Any     			=torch.load(lowerCAmelCase		)
           else:
              logger.info(f'''Creating features from dataset file at {data_dir}'''		)

              SCREAMING_SNAKE_CASE_: Optional[Any]     			=(
                  processor.get_dev_examples(lowerCAmelCase		) if evaluate else processor.get_train_examples(lowerCAmelCase		)
              )

              logger.info("""Training examples: %s"""       ,		len(lowerCAmelCase		)		)
              SCREAMING_SNAKE_CASE_: List[Any]     			=hans_convert_examples_to_features(lowerCAmelCase       ,		lowerCAmelCase       ,		lowerCAmelCase       ,		lowerCAmelCase		)
              logger.info("""Saving features into cached file %s"""       ,		lowerCAmelCase		)
              torch.save(self.features       ,		lowerCAmelCase		)



     def __len__(						self :					Any		)     ->					str:


        '''simple docstring'''




        return len(self.features		)



     def __getitem__(						self :					Any       ,		lowerCAmelCase :					Any		)     ->					InputFeatures:


        '''simple docstring'''




        return self.features[i]





     def 	lowerCamelCase__       (						self :					str		)     ->					str:


        '''simple docstring'''




        return self.label_list


if is_tf_available():
   import tensorflow as tf
   class 					a	:
     UpperCamelCase	:				List[InputFeatures]



     def __init__(						self :					Optional[Any]       ,		lowerCAmelCase :					str       ,		lowerCAmelCase :					PreTrainedTokenizer       ,		lowerCAmelCase :					str       ,		lowerCAmelCase :					Optional[int] = 128       ,		lowerCAmelCase :					Tuple=False       ,		lowerCAmelCase :					bool = False       ,		)     ->					Any:


        '''simple docstring'''




        SCREAMING_SNAKE_CASE_: Dict     			=hans_processors[task]()
        SCREAMING_SNAKE_CASE_: int     			=processor.get_labels()
        if tokenizer.__class__ in (
            RobertaTokenizer,
            RobertaTokenizerFast,
            XLMRobertaTokenizer,
            BartTokenizer,
            BartTokenizerFast,
        ):
           # HACK(label indices are swapped in RoBERTa pretrained model)
           SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: List[str]     			=label_list[2], label_list[1]
        SCREAMING_SNAKE_CASE_: Optional[Any]     			=label_list

        SCREAMING_SNAKE_CASE_: Tuple     			=processor.get_dev_examples(lowerCAmelCase		) if evaluate else processor.get_train_examples(lowerCAmelCase		)
        SCREAMING_SNAKE_CASE_: Any     			=hans_convert_examples_to_features(lowerCAmelCase       ,		lowerCAmelCase       ,		lowerCAmelCase       ,		lowerCAmelCase		)

        def gen():
           for ex_index, ex in tqdm.tqdm(enumerate(self.features		)       ,		desc="""convert examples to features"""		):
              if ex_index % 1_0000 == 0:
                 logger.info("""Writing example %d of %d""" % (ex_index, len(lowerCAmelCase		))		)

              yield (
                  {
                      "example_id": 0,
                      "input_ids": ex.input_ids,
                      "attention_mask": ex.attention_mask,
                      "token_type_ids": ex.token_type_ids,
                  },
                  ex.label,
              )

        SCREAMING_SNAKE_CASE_: int     			=tf.data.Dataset.from_generator(
            lowerCAmelCase       ,		(
                {
                    """example_id""": tf.intaa,
                    """input_ids""": tf.intaa,
                    """attention_mask""": tf.intaa,
                    """token_type_ids""": tf.intaa,
                },
                tf.intaa,
            )       ,		(
                {
                    """example_id""": tf.TensorShape([]		),
                    """input_ids""": tf.TensorShape([None, None]		),
                    """attention_mask""": tf.TensorShape([None, None]		),
                    """token_type_ids""": tf.TensorShape([None, None]		),
                },
                tf.TensorShape([]		),
            )       ,		)



     def 	lowerCamelCase__       (						self :					Any		)     ->					Dict:


        '''simple docstring'''




        return self.dataset



     def __len__(						self :					str		)     ->					Dict:


        '''simple docstring'''




        return len(self.features		)



     def __getitem__(						self :					List[str]       ,		lowerCAmelCase :					Optional[Any]		)     ->					InputFeatures:


        '''simple docstring'''




        return self.features[i]





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


        '''simple docstring'''




        return self.label_list
class 					a	(  UpperCAmelCase__       ):



  def 	lowerCamelCase__       (						self :					str       ,		lowerCAmelCase :					List[Any]		)     ->					int:


     '''simple docstring'''




     return self._create_examples(self._read_tsv(os.path.join(lowerCAmelCase       ,		"""heuristics_train_set.txt"""		)		)       ,		"""train"""		)



  def 	lowerCamelCase__       (						self :					int       ,		lowerCAmelCase :					Tuple		)     ->					Optional[Any]:


     '''simple docstring'''




     return self._create_examples(self._read_tsv(os.path.join(lowerCAmelCase       ,		"""heuristics_evaluation_set.txt"""		)		)       ,		"""dev"""		)



  def 	lowerCamelCase__       (						self :					Optional[Any]		)     ->					Dict:


     '''simple docstring'''




     return ["contradiction", "entailment", "neutral"]





  def 	lowerCamelCase__       (						self :					Dict       ,		lowerCAmelCase :					Dict       ,		lowerCAmelCase :					List[Any]		)     ->					Tuple:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=[]
     for i, line in enumerate(lowerCAmelCase		):
        if i == 0:
           continue
        SCREAMING_SNAKE_CASE_: Dict     			="""%s-%s""" % (set_type, line[0])
        SCREAMING_SNAKE_CASE_: List[str]     			=line[5]
        SCREAMING_SNAKE_CASE_: List[Any]     			=line[6]
        SCREAMING_SNAKE_CASE_: Tuple     			=line[7][2:] if line[7].startswith("""ex"""		) else line[7]
        SCREAMING_SNAKE_CASE_: Union[str, Any]     			=line[0]
        examples.append(InputExample(guid=lowerCAmelCase       ,		text_a=lowerCAmelCase       ,		text_b=lowerCAmelCase       ,		label=lowerCAmelCase       ,		pairID=lowerCAmelCase		)		)
     return examples







def      __magic_name__				(							lowercase							,			lowercase							,			lowercase							,			lowercase							,			):
   SCREAMING_SNAKE_CASE_: str     			={label: i for i, label in enumerate(lowercase )}

   SCREAMING_SNAKE_CASE_: List[Any]     			=[]
   for ex_index, example in tqdm.tqdm(enumerate(lowercase )							,			desc="""convert examples to features""" ):
      if ex_index % 1_0000 == 0:
         logger.info("""Writing example %d""" % (ex_index) )

      SCREAMING_SNAKE_CASE_: List[Any]     			=tokenizer(
          example.text_a							,			example.text_b							,			add_special_tokens=lowercase							,			max_length=lowercase							,			padding="""max_length"""							,			truncation=lowercase							,			return_overflowing_tokens=lowercase							,			)

      SCREAMING_SNAKE_CASE_: Union[str, Any]     			=label_map[example.label] if example.label in label_map else 0

      SCREAMING_SNAKE_CASE_: str     			=int(example.pairID )

      features.append(InputFeatures(**lowercase							,			label=lowercase							,			pairID=lowercase ) )

   for i, example in enumerate(examples[:5] ):
      logger.info("""*** Example ***""" )
      logger.info(f'''guid: {example}''' )
      logger.info(f'''features: {features[i]}''' )

   return features


_UpperCAmelCase     =			{
    """hans""": 3,
}
_UpperCAmelCase     =			{
    """hans""": HansProcessor,
}








"""simple docstring"""
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


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





def      __magic_name__				(							lowercase ):
   SCREAMING_SNAKE_CASE_: str     			=[]

   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(lowercase							,			lowercase ):
         inputs.append(create_inputs(lowercase ) )
      else:
         raise ValueError(f'''Invalid type requested: {input_type}''' )

   return inputs





def      __magic_name__				(							lowercase ):
   SCREAMING_SNAKE_CASE_: int     			=[]

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

   return output_types
@is_tool_test
class 					a	:



  def 	lowerCamelCase__       (						self :					Tuple		)     ->					List[Any]:


     '''simple docstring'''




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

     SCREAMING_SNAKE_CASE_: Optional[int]     			=self.tool.inputs
     for _input in inputs:
        if isinstance(_input       ,		lowerCAmelCase		):
           for __input in _input:
              self.assertTrue(__input in authorized_types		)
        else:
           self.assertTrue(_input in authorized_types		)

     SCREAMING_SNAKE_CASE_: Any     			=self.tool.outputs
     for _output in outputs:
        self.assertTrue(_output in authorized_types		)



  def 	lowerCamelCase__       (						self :					str		)     ->					List[Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Dict     			=create_inputs(self.tool.inputs		)
     SCREAMING_SNAKE_CASE_: List[Any]     			=self.tool(*lowerCAmelCase		)

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

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



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


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: str     			=create_inputs(self.tool.inputs		)
     SCREAMING_SNAKE_CASE_: Tuple     			=self.tool(*lowerCAmelCase		)

     if not isinstance(lowerCAmelCase       ,		lowerCAmelCase		):
        SCREAMING_SNAKE_CASE_: int     			=[outputs]

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

     for output, output_type in zip(lowerCAmelCase       ,		self.tool.outputs		):
        SCREAMING_SNAKE_CASE_: int     			=AGENT_TYPE_MAPPING[output_type]
        self.assertTrue(isinstance(lowerCAmelCase       ,		lowerCAmelCase		)		)





  def 	lowerCamelCase__       (						self :					List[Any]		)     ->					List[Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Tuple     			=create_inputs(self.tool.inputs		)

     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=[]

     for _input, input_type in zip(lowerCAmelCase       ,		self.tool.inputs		):
        if isinstance(lowerCAmelCase       ,		lowerCAmelCase		):
           _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
     SCREAMING_SNAKE_CASE_: Dict     			=self.tool(*lowerCAmelCase		)

     if not isinstance(lowerCAmelCase       ,		lowerCAmelCase		):
        SCREAMING_SNAKE_CASE_: List[str]     			=[outputs]

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



