





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))








import enum
import os
from hashlib import shaaaa
from typing import Optional

from .. import config
from .logging import get_logger


__A     =    get_logger(__name__)



class        _SCREAMING_SNAKE_CASE						(     enum.Enum   ):







 '''simple docstring'''
 lowercase_		=      "all_checks"
 lowercase_		=      "basic_checks"
 lowercase_		=      "no_checks"



class        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ):







 '''simple docstring'''



class        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ):







 '''simple docstring'''



class        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ):







 '''simple docstring'''



class        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ):







 '''simple docstring'''





def 					lowerCAmelCase_			(	__a     ,   __a     ,   __a=None		)					->		Optional[int]:







  """simple docstring"""





  if expected_checksums is None:
    logger.info("Unable to verify checksums."		)
    return
  if len(set(__a		) - set(__a		)		) > 0:
    raise ExpectedMoreDownloadedFiles(str(set(__a		) - set(__a		)		)		)
  if len(set(__a		) - set(__a		)		) > 0:
    raise UnexpectedDownloadedFile(str(set(__a		) - set(__a		)		)		)
  lowerCamelCase__:				List[Any]        =[url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]]
  lowerCamelCase__:				Union[str, Any]        =" for " + verification_name if verification_name is not None else ""
  if len(__a		) > 0:
    raise NonMatchingChecksumError(
        F"""Checksums didn't match{for_verification_name}:\n"""
        F"""{bad_urls}\n"""
        "Set `verification_mode='no_checks'` to skip checksums verification and ignore this error"		)
  logger.info("All the checksums matched successfully" + for_verification_name		)



class        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ):







 '''simple docstring'''



class        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ):







 '''simple docstring'''



class        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ):







 '''simple docstring'''



class        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ):







 '''simple docstring'''





def 					lowerCAmelCase_			(	__a     ,   __a		)					->		Any:







  """simple docstring"""





  if expected_splits is None:
    logger.info("Unable to verify splits sizes."		)
    return
  if len(set(__a		) - set(__a		)		) > 0:
    raise ExpectedMoreSplits(str(set(__a		) - set(__a		)		)		)
  if len(set(__a		) - set(__a		)		) > 0:
    raise UnexpectedSplits(str(set(__a		) - set(__a		)		)		)
  lowerCamelCase__:				Optional[int]        =[
      {"expected": expected_splits[name], "recorded": recorded_splits[name]}
      for name in expected_splits
      if expected_splits[name].num_examples != recorded_splits[name].num_examples
  ]
  if len(__a		) > 0:
    raise NonMatchingSplitsSizesError(str(__a		)		)
  logger.info("All the splits matched successfully."		)





def 					lowerCAmelCase_			(	__a     ,   __a = True		)					->		dict:







  """simple docstring"""





  if record_checksum:
    lowerCamelCase__:				str        =shaaaa()
    with open(__a     ,   "rb"		) as f:
      for chunk in iter(lambda: f.read(1 << 20		)     ,   b""		):
        m.update(__a		)
      lowerCamelCase__:				Dict        =m.hexdigest()
  else:
    lowerCamelCase__:				List[str]        =None
  return {"num_bytes": os.path.getsize(__a		), "checksum": checksum}






def 					lowerCAmelCase_			(	__a		)					->		int:







  """simple docstring"""





  if dataset_size and config.IN_MEMORY_MAX_SIZE:
    return dataset_size < config.IN_MEMORY_MAX_SIZE
  else:
    return False

