
from __future__ import annotations

import unittest

import numpy as np

from transformers import LayoutLMConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
    import tensorflow as tf

    from transformers.models.layoutlm.modeling_tf_layoutlm import (
        TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFLayoutLMForMaskedLM,
        TFLayoutLMForQuestionAnswering,
        TFLayoutLMForSequenceClassification,
        TFLayoutLMForTokenClassification,
        TFLayoutLMModel,
    )




class      __A:

  """simple docstring"""







  def __init__(self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_=13	,      SCREAMING_SNAKE_CASE_=7	,      SCREAMING_SNAKE_CASE_=True	,      SCREAMING_SNAKE_CASE_=True	,      SCREAMING_SNAKE_CASE_=True	,      SCREAMING_SNAKE_CASE_=True	,      SCREAMING_SNAKE_CASE_=99	,      SCREAMING_SNAKE_CASE_=32	,      SCREAMING_SNAKE_CASE_=2	,      SCREAMING_SNAKE_CASE_=4	,      SCREAMING_SNAKE_CASE_=37	,      SCREAMING_SNAKE_CASE_="gelu"	,      SCREAMING_SNAKE_CASE_=0.1	,      SCREAMING_SNAKE_CASE_=0.1	,      SCREAMING_SNAKE_CASE_=5_12	,      SCREAMING_SNAKE_CASE_=16	,      SCREAMING_SNAKE_CASE_=2	,      SCREAMING_SNAKE_CASE_=0.02	,      SCREAMING_SNAKE_CASE_=3	,      SCREAMING_SNAKE_CASE_=4	,      SCREAMING_SNAKE_CASE_=None	,      SCREAMING_SNAKE_CASE_=10_00	,      ):
       UpperCamelCase__				=    parent
       UpperCamelCase__				=    batch_size
       UpperCamelCase__				=    seq_length
       UpperCamelCase__				=    is_training
       UpperCamelCase__				=    use_input_mask
       UpperCamelCase__				=    use_token_type_ids
       UpperCamelCase__				=    use_labels
       UpperCamelCase__				=    vocab_size
       UpperCamelCase__				=    hidden_size
       UpperCamelCase__				=    num_hidden_layers
       UpperCamelCase__				=    num_attention_heads
       UpperCamelCase__				=    intermediate_size
       UpperCamelCase__				=    hidden_act
       UpperCamelCase__				=    hidden_dropout_prob
       UpperCamelCase__				=    attention_probs_dropout_prob
       UpperCamelCase__				=    max_position_embeddings
       UpperCamelCase__				=    type_vocab_size
       UpperCamelCase__				=    type_sequence_label_size
       UpperCamelCase__				=    initializer_range
       UpperCamelCase__				=    num_labels
       UpperCamelCase__				=    num_choices
       UpperCamelCase__				=    scope
       UpperCamelCase__				=    range_bbox







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    ids_tensor([self.batch_size, self.seq_length]	,      self.vocab_size						)

       # convert bbox to numpy since TF does not support item assignment
       UpperCamelCase__				=    ids_tensor([self.batch_size, self.seq_length, 4]	,      self.range_bbox						).numpy()
       # Ensure that bbox is legal
       for i in range(bbox.shape[0]						):
            for j in range(bbox.shape[1]						):
                 if bbox[i, j, 3] < bbox[i, j, 1]:
                      UpperCamelCase__				=    bbox[i, j, 3]
                      UpperCamelCase__				=    bbox[i, j, 1]
                      UpperCamelCase__				=    t
                 if bbox[i, j, 2] < bbox[i, j, 0]:
                      UpperCamelCase__				=    bbox[i, j, 2]
                      UpperCamelCase__				=    bbox[i, j, 0]
                      UpperCamelCase__				=    t
       UpperCamelCase__				=    tf.convert_to_tensor(SCREAMING_SNAKE_CASE_						)

       UpperCamelCase__				=    None
       if self.use_input_mask:
            UpperCamelCase__				=    random_attention_mask([self.batch_size, self.seq_length]						)

       UpperCamelCase__				=    None
       if self.use_token_type_ids:
            UpperCamelCase__				=    ids_tensor([self.batch_size, self.seq_length]	,      self.type_vocab_size						)

       UpperCamelCase__				=    None
       UpperCamelCase__				=    None
       UpperCamelCase__				=    None
       if self.use_labels:
            UpperCamelCase__				=    ids_tensor([self.batch_size]	,      self.type_sequence_label_size						)
            UpperCamelCase__				=    ids_tensor([self.batch_size, self.seq_length]	,      self.num_labels						)
            UpperCamelCase__				=    ids_tensor([self.batch_size]	,      self.num_choices						)

       UpperCamelCase__				=    LayoutLMConfig(
           vocab_size=self.vocab_size	,      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	,      max_position_embeddings=self.max_position_embeddings	,      type_vocab_size=self.type_vocab_size	,      initializer_range=self.initializer_range	,      )

       return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						):
       UpperCamelCase__				=    TFLayoutLMModel(config=SCREAMING_SNAKE_CASE_						)

       UpperCamelCase__				=    model(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      attention_mask=SCREAMING_SNAKE_CASE_	,      token_type_ids=SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    model(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      token_type_ids=SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    model(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						)
       self.parent.assertEqual(result.last_hidden_state.shape	,      (self.batch_size, self.seq_length, self.hidden_size)						)
       self.parent.assertEqual(result.pooler_output.shape	,      (self.batch_size, self.hidden_size)						)







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						):
       UpperCamelCase__				=    TFLayoutLMForMaskedLM(config=SCREAMING_SNAKE_CASE_						)

       UpperCamelCase__				=    model(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      attention_mask=SCREAMING_SNAKE_CASE_	,      token_type_ids=SCREAMING_SNAKE_CASE_	,      labels=SCREAMING_SNAKE_CASE_						)
       self.parent.assertEqual(result.logits.shape	,      (self.batch_size, self.seq_length, self.vocab_size)						)







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						):
       UpperCamelCase__				=    self.num_labels
       UpperCamelCase__				=    TFLayoutLMForSequenceClassification(config=SCREAMING_SNAKE_CASE_						)

       UpperCamelCase__				=    model(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      attention_mask=SCREAMING_SNAKE_CASE_	,      token_type_ids=SCREAMING_SNAKE_CASE_						)
       self.parent.assertEqual(result.logits.shape	,      (self.batch_size, self.num_labels)						)







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						):
       UpperCamelCase__				=    self.num_labels
       UpperCamelCase__				=    TFLayoutLMForTokenClassification(config=SCREAMING_SNAKE_CASE_						)

       UpperCamelCase__				=    model(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      attention_mask=SCREAMING_SNAKE_CASE_	,      token_type_ids=SCREAMING_SNAKE_CASE_	,      labels=SCREAMING_SNAKE_CASE_						)
       self.parent.assertEqual(result.logits.shape	,      (self.batch_size, self.seq_length, self.num_labels)						)







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						):
       UpperCamelCase__				=    TFLayoutLMForQuestionAnswering(config=SCREAMING_SNAKE_CASE_						)

       UpperCamelCase__				=    model(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      attention_mask=SCREAMING_SNAKE_CASE_	,      token_type_ids=SCREAMING_SNAKE_CASE_						)
       self.parent.assertEqual(result.start_logits.shape	,      (self.batch_size, self.seq_length)						)
       self.parent.assertEqual(result.end_logits.shape	,      (self.batch_size, self.seq_length)						)







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    self.prepare_config_and_inputs()
       (
           (
           UpperCamelCase__
       )		,	(
           UpperCamelCase__
       )		,	(
           UpperCamelCase__
       )		,	(
           UpperCamelCase__
       )		,	(
           UpperCamelCase__
       )		,	(
           UpperCamelCase__
       )		,	(
           UpperCamelCase__
       )		,	(
           UpperCamelCase__
       )		,	
       )				=    config_and_inputs
       UpperCamelCase__				=    {
           """input_ids""": input_ids,
           """bbox""": bbox,
           """token_type_ids""": token_type_ids,
           """attention_mask""": input_mask,
       }
       return config, inputs_dict






@require_tf
class      __A(    __lowerCamelCase    ,				__lowerCamelCase    ,				unittest.TestCase			):

  """simple docstring"""
  SCREAMING_SNAKE_CASE__         =      (
      (
          TFLayoutLMModel,
          TFLayoutLMForMaskedLM,
          TFLayoutLMForTokenClassification,
          TFLayoutLMForSequenceClassification,
          TFLayoutLMForQuestionAnswering,
      )
      if is_tf_available()
      else ()
  )
  SCREAMING_SNAKE_CASE__         =      (
      {
          """feature-extraction""": TFLayoutLMModel,
          """fill-mask""": TFLayoutLMForMaskedLM,
          """text-classification""": TFLayoutLMForSequenceClassification,
          """token-classification""": TFLayoutLMForTokenClassification,
          """zero-shot""": TFLayoutLMForSequenceClassification,
      }
      if is_tf_available()
      else {}
  )
  SCREAMING_SNAKE_CASE__         =      False
  SCREAMING_SNAKE_CASE__         =      True
  SCREAMING_SNAKE_CASE__         =      10







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    TFLayoutLMModelTester(self						)
       UpperCamelCase__				=    ConfigTester(self	,      config_class=SCREAMING_SNAKE_CASE_	,      hidden_size=37						)







  def 	UpperCAmelCase_ (self						):
       self.config_tester.run_common_tests()







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_						)







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_masked_lm(*SCREAMING_SNAKE_CASE_						)







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_sequence_classification(*SCREAMING_SNAKE_CASE_						)







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_token_classification(*SCREAMING_SNAKE_CASE_						)







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_question_answering(*SCREAMING_SNAKE_CASE_						)







  @slow
  def 	UpperCAmelCase_ (self						):
       for model_name in TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            UpperCamelCase__				=    TFLayoutLMModel.from_pretrained(SCREAMING_SNAKE_CASE_						)
            self.assertIsNotNone(SCREAMING_SNAKE_CASE_						)







  @unittest.skip("""Onnx compliancy broke with TF 2.10"""						)
  def 	UpperCAmelCase_ (self						):
       pass






def 						__magic_name__			(   ):



     '''simple docstring'''




     UpperCamelCase__				=    tf.convert_to_tensor([[101,1_019,1_014,1_016,1_037,12_849,4_747,1_004,14_246,2_278,5_439,4_524,5_002,2_930,2_193,2_930,4_341,3_208,1_005,1_055,2_171,2_848,11_300,3_531,102],[101,4_070,4_034,7_020,1_024,3_058,1_015,1_013,2_861,1_013,6_070,19_274,2_772,6_205,27_814,16_147,16_147,4_343,2_047,10_283,10_969,14_389,1_012,2_338,102]]						)  # noqa: E231
     UpperCamelCase__				=    tf.convert_to_tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],]						)  # noqa: E231
     UpperCamelCase__				=    tf.convert_to_tensor([[[0,0,0,0],[423,237,440,251],[427,272,441,287],[419,115,437,129],[961,885,992,912],[256,38,330,58],[256,38,330,58],[336,42,353,57],[360,39,401,56],[360,39,401,56],[411,39,471,59],[479,41,528,59],[533,39,630,60],[67,113,134,131],[141,115,209,132],[68,149,133,166],[141,149,187,164],[195,148,287,165],[195,148,287,165],[195,148,287,165],[295,148,349,165],[441,149,492,166],[497,149,546,164],[64,201,125,218],[1_000,1_000,1_000,1_000]],[[0,0,0,0],[662,150,754,166],[665,199,742,211],[519,213,554,228],[519,213,554,228],[134,433,187,454],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[314,469,376,482],[504,684,582,706],[941,825,973,900],[941,825,973,900],[941,825,973,900],[941,825,973,900],[610,749,652,765],[130,659,168,672],[176,657,237,672],[238,657,312,672],[443,653,628,672],[443,653,628,672],[716,301,825,317],[1_000,1_000,1_000,1_000]]]						)  # noqa: E231
     UpperCamelCase__				=    tf.convert_to_tensor([[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]]						)  # noqa: E231
     # these are sequence labels (i.e. at the token level)
     UpperCamelCase__				=    tf.convert_to_tensor([[-100,10,10,10,9,1,-100,7,7,-100,7,7,4,2,5,2,8,8,-100,-100,5,0,3,2,-100],[-100,12,12,12,-100,12,10,-100,-100,-100,-100,10,12,9,-100,-100,-100,10,10,10,9,12,-100,10,-100]]						)  # noqa: E231
     # fmt: on

     return input_ids, attention_mask, bbox, token_type_ids, labels




@require_tf
class      __A(    unittest.TestCase			):

  """simple docstring"""







  @slow
  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    TFLayoutLMModel.from_pretrained("""microsoft/layoutlm-base-uncased"""						)

       UpperCamelCase__		,	UpperCamelCase__		,	UpperCamelCase__		,	UpperCamelCase__		,	UpperCamelCase__				=    prepare_layoutlm_batch_inputs()

       # forward pass
       UpperCamelCase__				=    model(input_ids=SCREAMING_SNAKE_CASE_	,      bbox=SCREAMING_SNAKE_CASE_	,      attention_mask=SCREAMING_SNAKE_CASE_	,      token_type_ids=SCREAMING_SNAKE_CASE_						)

       # test the sequence output on [0, :3, :3]
       UpperCamelCase__				=    tf.convert_to_tensor(
           [[0.1785, -0.1947, -0.0425], [-0.3254, -0.2807, 0.2553], [-0.5391, -0.3322, 0.3364]]	,      )

       self.assertTrue(np.allclose(outputs.last_hidden_state[0, :3, :3]	,      SCREAMING_SNAKE_CASE_	,      atol=1E-3						)						)

       # test the pooled output on [1, :3]
       UpperCamelCase__				=    tf.convert_to_tensor([-0.6580, -0.0214, 0.8552]						)

       self.assertTrue(np.allclose(outputs.pooler_output[1, :3]	,      SCREAMING_SNAKE_CASE_	,      atol=1E-3						)						)







  @slow
  def 	UpperCAmelCase_ (self						):
       # initialize model with randomly initialized sequence classification head
       UpperCamelCase__				=    TFLayoutLMForSequenceClassification.from_pretrained("""microsoft/layoutlm-base-uncased"""	,      num_labels=2						)

       UpperCamelCase__		,	UpperCamelCase__		,	UpperCamelCase__		,	UpperCamelCase__		,	UpperCamelCase__				=    prepare_layoutlm_batch_inputs()

       # forward pass
       UpperCamelCase__				=    model(
           input_ids=SCREAMING_SNAKE_CASE_	,      bbox=SCREAMING_SNAKE_CASE_	,      attention_mask=SCREAMING_SNAKE_CASE_	,      token_type_ids=SCREAMING_SNAKE_CASE_	,      labels=tf.convert_to_tensor([1, 1]						)	,      )

       # test whether we get a loss as a scalar
       UpperCamelCase__				=    outputs.loss
       UpperCamelCase__				=    (2,)
       self.assertEqual(loss.shape	,      SCREAMING_SNAKE_CASE_						)

       # test the shape of the logits
       UpperCamelCase__				=    outputs.logits
       UpperCamelCase__				=    (2, 2)
       self.assertEqual(logits.shape	,      SCREAMING_SNAKE_CASE_						)







  @slow
  def 	UpperCAmelCase_ (self						):
       # initialize model with randomly initialized token classification head
       UpperCamelCase__				=    TFLayoutLMForTokenClassification.from_pretrained("""microsoft/layoutlm-base-uncased"""	,      num_labels=13						)

       UpperCamelCase__		,	UpperCamelCase__		,	UpperCamelCase__		,	UpperCamelCase__		,	UpperCamelCase__				=    prepare_layoutlm_batch_inputs()

       # forward pass
       UpperCamelCase__				=    model(
           input_ids=SCREAMING_SNAKE_CASE_	,      bbox=SCREAMING_SNAKE_CASE_	,      attention_mask=SCREAMING_SNAKE_CASE_	,      token_type_ids=SCREAMING_SNAKE_CASE_	,      labels=SCREAMING_SNAKE_CASE_						)

       # test the shape of the logits
       UpperCamelCase__				=    outputs.logits
       UpperCamelCase__				=    tf.convert_to_tensor((2, 25, 13)						)
       self.assertEqual(logits.shape	,      SCREAMING_SNAKE_CASE_						)







  @slow
  def 	UpperCAmelCase_ (self						):
       # initialize model with randomly initialized token classification head
       UpperCamelCase__				=    TFLayoutLMForQuestionAnswering.from_pretrained("""microsoft/layoutlm-base-uncased"""						)

       UpperCamelCase__		,	UpperCamelCase__		,	UpperCamelCase__		,	UpperCamelCase__		,	UpperCamelCase__				=    prepare_layoutlm_batch_inputs()

       # forward pass
       UpperCamelCase__				=    model(input_ids=SCREAMING_SNAKE_CASE_	,      bbox=SCREAMING_SNAKE_CASE_	,      attention_mask=SCREAMING_SNAKE_CASE_	,      token_type_ids=SCREAMING_SNAKE_CASE_						)

       # test the shape of the logits
       UpperCamelCase__				=    tf.convert_to_tensor((2, 25)						)
       self.assertEqual(outputs.start_logits.shape	,      SCREAMING_SNAKE_CASE_						)
       self.assertEqual(outputs.end_logits.shape	,      SCREAMING_SNAKE_CASE_						)







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_						     =					['''text''', '''image''', '''audio''']






def 						__magic_name__			(   __a      :	List[str]						):



     '''simple docstring'''




     UpperCamelCase__				=    []

     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(3_000						)						)
          elif isinstance(__a	,		__a						):
               inputs.append(create_inputs(__a						)						)
          else:
               raise ValueError(f"Invalid type requested: {input_type}"						)

     return inputs






def 						__magic_name__			(   __a      :	List						):



     '''simple docstring'''




     UpperCamelCase__				=    []

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

       UpperCamelCase__				=    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						)

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







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    create_inputs(self.tool.inputs						)
       UpperCamelCase__				=    self.tool(*SCREAMING_SNAKE_CASE_						)

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

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







  def 	UpperCAmelCase_ (self						):
       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 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    create_inputs(self.tool.inputs						)
       UpperCamelCase__				=    self.tool(*SCREAMING_SNAKE_CASE_						)

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

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

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







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    create_inputs(self.tool.inputs						)

       UpperCamelCase__				=    []

       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
       UpperCamelCase__				=    self.tool(*SCREAMING_SNAKE_CASE_						)

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

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





