
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


__lowerCAmelCase     :	Union[str, Any]       			=  ['text', 'image', 'audio']

def a__       (			A_   ):






  '''simple docstring'''





  __magic_name__								=			[]

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

  return inputs

def a__       (			A_   ):






  '''simple docstring'''





  __magic_name__								=			[]

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

  return output_types





@is_tool_test
class 			UpperCAmelCase_     :


  '''simple docstring'''







  def 				_lowercase				(					self     :					Any       )     ->      Any:







    """simple docstring"""






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

    __magic_name__								=			self.tool.inputs
    for _input in inputs:
      if isinstance(_input     ,				a__       ):
        for __input in _input:
          self.assertTrue(__input in authorized_types       )
      else:
        self.assertTrue(_input in authorized_types       )

    __magic_name__								=			self.tool.outputs
    for _output in outputs:
      self.assertTrue(_output in authorized_types       )
  def 				_lowercase				(					self     :					Any       )     ->      int:







    """simple docstring"""






    __magic_name__								=			create_inputs(self.tool.inputs       )
    __magic_name__								=			self.tool(*a__       )

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

    self.assertListEqual(output_types(a__       )     ,				self.tool.outputs       )
  def 				_lowercase				(					self     :					List[str]       )     ->      Union[str, 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 				_lowercase				(					self     :					Union[str, Any]       )     ->      Tuple:







    """simple docstring"""






    __magic_name__								=			create_inputs(self.tool.inputs       )
    __magic_name__								=			self.tool(*a__       )

    if not isinstance(a__     ,				a__       ):
      __magic_name__								=			[outputs]

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

    for output, output_type in zip(a__     ,				self.tool.outputs       ):
      __magic_name__								=			AGENT_TYPE_MAPPING[output_type]
      self.assertTrue(isinstance(a__     ,				a__       )       )





  def 				_lowercase				(					self     :					Optional[Any]       )     ->      int:







    """simple docstring"""






    __magic_name__								=			create_inputs(self.tool.inputs       )

    __magic_name__								=			[]

    for _input, input_type in zip(a__     ,				self.tool.inputs       ):
      if isinstance(a__     ,				a__       ):
        _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
    __magic_name__								=			self.tool(*a__       )

    if not isinstance(a__     ,				a__       ):
      __magic_name__								=			[outputs]

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



'''simple docstring'''

import inspect
import unittest

import numpy as np

from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
     import jax

     from transformers.models.vit.modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel




class    lowerCAmelCase       (     unittest.TestCase  ):


 def __init__(    self  : Optional[Any]			,							a__  : List[Any]			,							a__  : Any=13			,							a__  : Optional[int]=30			,							a__  : List[Any]=2			,							a__  : Union[str, Any]=3			,							a__  : Tuple=True			,							a__  : Tuple=True			,							a__  : List[str]=32			,							a__  : Tuple=5			,							a__  : List[str]=4			,							a__  : str=37			,							a__  : Dict="gelu"			,							a__  : Union[str, Any]=0.1			,							a__  : Union[str, Any]=0.1			,							a__  : List[Any]=10			,							a__  : List[Any]=0.02			,							):
  '''simple docstring'''

  lowerCAmelCase__     :   Union[str, Any]     		=	parent
  lowerCAmelCase__     :   Union[str, Any]     		=	batch_size
  lowerCAmelCase__     :   List[Any]     		=	image_size
  lowerCAmelCase__     :   int     		=	patch_size
  lowerCAmelCase__     :   Tuple     		=	num_channels
  lowerCAmelCase__     :   List[str]     		=	is_training
  lowerCAmelCase__     :   str     		=	use_labels
  lowerCAmelCase__     :   Dict     		=	hidden_size
  lowerCAmelCase__     :   Any     		=	num_hidden_layers
  lowerCAmelCase__     :   Any     		=	num_attention_heads
  lowerCAmelCase__     :   Any     		=	intermediate_size
  lowerCAmelCase__     :   Union[str, Any]     		=	hidden_act
  lowerCAmelCase__     :   Optional[int]     		=	hidden_dropout_prob
  lowerCAmelCase__     :   str     		=	attention_probs_dropout_prob
  lowerCAmelCase__     :   Optional[int]     		=	type_sequence_label_size
  lowerCAmelCase__     :   List[str]     		=	initializer_range

  # in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
  lowerCAmelCase__     :   Tuple     		=	(image_size // patch_size) ** 2
  lowerCAmelCase__     :   Dict     		=	num_patches + 1


 def    _A	(    self  : int    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Dict     		=	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

  lowerCAmelCase__     :   Any     		=	ViTConfig(
      image_size=self.image_size			,							patch_size=self.patch_size			,							num_channels=self.num_channels			,							hidden_size=self.hidden_size			,							num_hidden_layers=self.num_hidden_layers			,							num_attention_heads=self.num_attention_heads			,							intermediate_size=self.intermediate_size			,							hidden_act=self.hidden_act			,							hidden_dropout_prob=self.hidden_dropout_prob			,							attention_probs_dropout_prob=self.attention_probs_dropout_prob			,							is_decoder=a__			,							initializer_range=self.initializer_range			,							)

  return config, pixel_values


 def    _A	(    self  : Optional[Any]			,							a__  : Optional[int]			,							a__  : Optional[Any]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   List[str]     		=	FlaxViTModel(config=a__    )
  lowerCAmelCase__     :   Any     		=	model(a__    )
  # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
  lowerCAmelCase__     :   List[str]     		=	(self.image_size, self.image_size)
  lowerCAmelCase__     :   List[str]     		=	(self.patch_size, self.patch_size)
  lowerCAmelCase__     :   int     		=	(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
  self.parent.assertEqual(result.last_hidden_state.shape			,							(self.batch_size, num_patches + 1, self.hidden_size)    )


 def    _A	(    self  : str			,							a__  : Optional[int]			,							a__  : Dict    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Optional[Any]     		=	self.type_sequence_label_size
  lowerCAmelCase__     :   Any     		=	FlaxViTForImageClassification(config=a__    )
  lowerCAmelCase__     :   Dict     		=	model(a__    )
  self.parent.assertEqual(result.logits.shape			,							(self.batch_size, self.type_sequence_label_size)    )

  # test greyscale images
  lowerCAmelCase__     :   Tuple     		=	1
  lowerCAmelCase__     :   List[Any]     		=	FlaxViTForImageClassification(a__    )

  lowerCAmelCase__     :   Union[str, Any]     		=	floats_tensor([self.batch_size, 1, self.image_size, self.image_size]    )
  lowerCAmelCase__     :   int     		=	model(a__    )


 def    _A	(    self  : List[str]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   str     		=	self.prepare_config_and_inputs()
  (
      (
      lowerCAmelCase__
  )		,     (
      lowerCAmelCase__
  )		,     
  )     :   Tuple     		=	config_and_inputs
  lowerCAmelCase__     :   Optional[Any]     		=	{"pixel_values": pixel_values}
  return config, inputs_dict




@require_flax
class    lowerCAmelCase       (     UpperCamelCase_    ,  unittest.TestCase  ):
 A_				:				Union[str, Any]	=			(FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()


 def    _A	(    self  : Any    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Optional[Any]     		=	FlaxViTModelTester(self    )
  lowerCAmelCase__     :   Union[str, Any]     		=	ConfigTester(self			,							config_class=a__			,							has_text_modality=a__			,							hidden_size=37    )


 def    _A	(    self  : List[str]    ):
  '''simple docstring'''

  self.config_tester.run_common_tests()


 def    _A	(    self  : str    ):
  '''simple docstring'''

  lowerCAmelCase__     :   str     		=	self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_model(*a__    )


 def    _A	(    self  : Optional[Any]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   str     		=	self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_for_image_classification(*a__    )


 def    _A	(    self  : Union[str, Any]    ):
  '''simple docstring'''

  lowerCAmelCase__		,     lowerCAmelCase__     :   List[Any]     		=	self.model_tester.prepare_config_and_inputs_for_common()

  for model_class in self.all_model_classes:
   lowerCAmelCase__     :   Dict     		=	model_class(a__    )
   lowerCAmelCase__     :   str     		=	inspect.signature(model.__call__    )
   # signature.parameters is an OrderedDict => so arg_names order is deterministic
   lowerCAmelCase__     :   List[Any]     		=	[*signature.parameters.keys()]

   lowerCAmelCase__     :   List[str]     		=	["pixel_values"]
   self.assertListEqual(arg_names[:1]			,							a__    )


 def    _A	(    self  : str    ):
  '''simple docstring'''

  lowerCAmelCase__		,     lowerCAmelCase__     :   int     		=	self.model_tester.prepare_config_and_inputs_for_common()

  for model_class in self.all_model_classes:
   with self.subTest(model_class.__name__    ):
    lowerCAmelCase__     :   List[Any]     		=	self._prepare_for_class(a__			,							a__    )
    lowerCAmelCase__     :   Optional[int]     		=	model_class(a__    )

    @jax.jit
    def model_jitted(a__  : Optional[int]			,							**a__  : Optional[Any]    ):
     return model(pixel_values=a__			,							**a__    )

    with self.subTest("JIT Enabled"    ):
     lowerCAmelCase__     :   Any     		=	model_jitted(**a__    ).to_tuple()

    with self.subTest("JIT Disabled"    ):
     with jax.disable_jit():
      lowerCAmelCase__     :   str     		=	model_jitted(**a__    ).to_tuple()

    self.assertEqual(len(a__    )			,							len(a__    )    )
    for jitted_output, output in zip(a__			,							a__    ):
     self.assertEqual(jitted_output.shape			,							output.shape    )


 @slow
 def    _A	(    self  : Any    ):
  '''simple docstring'''

  for model_class_name in self.all_model_classes:
   lowerCAmelCase__     :   Optional[Any]     		=	model_class_name.from_pretrained("google/vit-base-patch16-224"    )
   lowerCAmelCase__     :   Tuple     		=	model(np.ones((1, 3, 224, 224)    )    )
   self.assertIsNotNone(a__    )





