

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     : List[str]       =						['''text''', '''image''', '''audio''']







def       lowercase					(	__A :					List[str]		)				->			List[str]:





    '''simple docstring'''




    snake_case							:		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((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       lowercase					(	__A :					List		)				->			Union[str, Any]:





    '''simple docstring'''




    snake_case							:		Dict        =		[]

    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 						_A     :





  '''simple docstring'''



  def    snake_case_       (  self    ):

      '''simple docstring'''
      self.assertTrue(hasattr(self.tool   ,"""inputs"""    )    )
      self.assertTrue(hasattr(self.tool   ,"""outputs"""    )    )

      snake_case							:		Dict        =		self.tool.inputs
      for _input in inputs:
          if isinstance(_input   ,SCREAMING_SNAKE_CASE_    ):
              for __input in _input:
                  self.assertTrue(__input in authorized_types    )
          else:
              self.assertTrue(_input in authorized_types    )

      snake_case							:		int        =		self.tool.outputs
      for _output in outputs:
          self.assertTrue(_output in authorized_types    )





  def    snake_case_       (  self    ):

      '''simple docstring'''
      snake_case							:		List[str]        =		create_inputs(self.tool.inputs    )
      snake_case							:		str        =		self.tool(*SCREAMING_SNAKE_CASE_    )

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

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





  def    snake_case_       (  self    ):

      '''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    snake_case_       (  self    ):

      '''simple docstring'''
      snake_case							:		List[Any]        =		create_inputs(self.tool.inputs    )
      snake_case							:		int        =		self.tool(*SCREAMING_SNAKE_CASE_    )

      if not isinstance(SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    ):
          snake_case							:		Any        =		[outputs]

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

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





  def    snake_case_       (  self    ):

      '''simple docstring'''
      snake_case							:		Tuple        =		create_inputs(self.tool.inputs    )

      snake_case							:		Any        =		[]

      for _input, input_type in zip(SCREAMING_SNAKE_CASE_   ,self.tool.inputs    ):
          if isinstance(SCREAMING_SNAKE_CASE_   ,SCREAMING_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
      snake_case							:		Tuple        =		self.tool(*SCREAMING_SNAKE_CASE_    )

      if not isinstance(SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    ):
          snake_case							:		Union[str, Any]        =		[outputs]

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








import os
import sys
import unittest


a_								=       os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))

import check_dummies  # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init  # noqa: E402


# Align TRANSFORMERS_PATH in check_dummies with the current path
a_								=       os.path.join(git_repo_path, """src""", """transformers""")

a_								=       """
{0} = None
"""

a_								=       """
class {0}(metaclass=DummyObject):
    _backends = {1}

    def __init__(self, *args, **kwargs):
        requires_backends(self, {1})
"""


a_								=       """
def {0}(*args, **kwargs):
    requires_backends({0}, {1})
"""



class 			__lowerCAmelCase  (						unittest.TestCase ):




 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    __lowerCamelCase    				=     find_backend('''    _import_structure["models.albert"].append("AlbertTokenizerFast")'''					)
    self.assertIsNone(__UpperCAmelCase					)

    __lowerCamelCase    				=     find_backend('''    if not is_tokenizers_available():'''					)
    self.assertEqual(__UpperCAmelCase						,					'''tokenizers'''					)

    __lowerCamelCase    				=     find_backend('''    if not is_tensorflow_text_available():'''					)
    self.assertEqual(__UpperCAmelCase						,					'''tensorflow_text'''					)

    __lowerCamelCase    				=     find_backend('''    if not (is_sentencepiece_available() and is_tokenizers_available()):'''					)
    self.assertEqual(__UpperCAmelCase						,					'''sentencepiece_and_tokenizers'''					)

    __lowerCamelCase    				=     find_backend(
        '''    if not (is_sentencepiece_available() and is_tensorflow_text_available()):'''					)
    self.assertEqual(__UpperCAmelCase						,					'''sentencepiece_and_tensorflow_text'''					)

    __lowerCamelCase    				=     find_backend(
        '''    if not (is_sentencepiece_available() and is_tokenizers_available() and is_vision_available()):'''					)
    self.assertEqual(__UpperCAmelCase						,					'''sentencepiece_and_tokenizers_and_vision'''					)




 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    __lowerCamelCase    				=     read_init()
    # We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
    self.assertIn('''torch'''						,					__UpperCAmelCase					)
    self.assertIn('''tensorflow_text'''						,					__UpperCAmelCase					)
    self.assertIn('''sentencepiece_and_tokenizers'''						,					__UpperCAmelCase					)

    # Likewise, we can't assert on the exact content of a key
    self.assertIn('''BertModel'''						,					objects['''torch''']					)
    self.assertIn('''TFBertModel'''						,					objects['''tf''']					)
    self.assertIn('''FlaxBertModel'''						,					objects['''flax''']					)
    self.assertIn('''BertModel'''						,					objects['''torch''']					)
    self.assertIn('''TFBertTokenizer'''						,					objects['''tensorflow_text''']					)
    self.assertIn('''convert_slow_tokenizer'''						,					objects['''sentencepiece_and_tokenizers''']					)




 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    __lowerCamelCase    				=     create_dummy_object('''CONSTANT'''						,					'''\'torch\''''					)
    self.assertEqual(__UpperCAmelCase						,					'''\nCONSTANT = None\n'''					)

    __lowerCamelCase    				=     create_dummy_object('''function'''						,					'''\'torch\''''					)
    self.assertEqual(
        __UpperCAmelCase						,					'''\ndef function(*args, **kwargs):\n    requires_backends(function, \'torch\')\n'''					)

    __lowerCamelCase    				=     '''
class FakeClass(metaclass=DummyObject):
    _backends = \'torch\'

    def __init__(self, *args, **kwargs):
        requires_backends(self, \'torch\')
'''
    __lowerCamelCase    				=     create_dummy_object('''FakeClass'''						,					'''\'torch\''''					)
    self.assertEqual(__UpperCAmelCase						,					__UpperCAmelCase					)




 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    __lowerCamelCase    				=     '''# This file is autogenerated by the command `make fix-copies`, do not edit.
from ..utils import DummyObject, requires_backends


CONSTANT = None


def function(*args, **kwargs):
    requires_backends(function, ["torch"])


class FakeClass(metaclass=DummyObject):
    _backends = ["torch"]

    def __init__(self, *args, **kwargs):
        requires_backends(self, ["torch"])
'''
    __lowerCamelCase    				=     create_dummy_files({'''torch''': ['''CONSTANT''', '''function''', '''FakeClass''']}					)
    self.assertEqual(dummy_files['''torch''']						,					__UpperCAmelCase					)






