


"""simple docstring"""



import os
import tempfile
import unittest
import uuid
from pathlib import Path

from transformers.testing_utils import get_tests_dir, require_soundfile, require_torch, require_vision
from transformers.tools.agent_types import AgentAudio, AgentImage, AgentText
from transformers.utils import is_soundfile_availble, is_torch_available, is_vision_available


if is_torch_available():
 import torch

if is_soundfile_availble():
 import soundfile as sf

if is_vision_available():
 from PIL import Image






def       lowerCamelCase					(a_						:Union[str, Any]="")     ->							str:
       lowercase		:Optional[int]       							=				tempfile.mkdtemp()
       return os.path.join(a_				,				str(uuid.uuida()) + suffix)
@require_soundfile
@require_torch
class 					__magic_name__		(			unittest.TestCase     ):






       def  __snake_case						(   self  :    Any				):

              '''simple docstring'''






              lowercase		:Optional[int]       							=				torch.rand(1_2  ,     dtype=torch.floataa				) - 0.5
              lowercase		:Union[str, Any]       							=				AgentAudio(snake_case__				)
              lowercase		:Tuple       							=				str(agent_type.to_string()				)

              # Ensure that the tensor and the agent_type's tensor are the same
              self.assertTrue(torch.allclose(snake_case__  ,     agent_type.to_raw()  ,     atol=1e-4				)				)

              del agent_type

              # Ensure the path remains even after the object deletion
              self.assertTrue(os.path.exists(snake_case__				)				)

              # Ensure that the file contains the same value as the original tensor
              lowercase		:Union[str, Any]       							=				sf.read(snake_case__				)
              self.assertTrue(torch.allclose(snake_case__  ,     torch.tensor(snake_case__				)  ,     atol=1e-4				)				)







       def  __snake_case						(   self  :    Any				):

              '''simple docstring'''






              lowercase		:List[Any]       							=				torch.rand(1_2  ,     dtype=torch.floataa				) - 0.5
              lowercase		:List[str]       							=				get_new_path(suffix='''.wav'''				)
              sf.write(snake_case__  ,     snake_case__  ,     1_6_0_0_0				)

              lowercase		:Tuple       							=				AgentAudio(snake_case__				)

              self.assertTrue(torch.allclose(snake_case__  ,     agent_type.to_raw()  ,     atol=1e-4				)				)
              self.assertEqual(agent_type.to_string()  ,     snake_case__				)







@require_vision
@require_torch
class 					__magic_name__		(			unittest.TestCase     ):






       def  __snake_case						(   self  :    Any				):

              '''simple docstring'''






              lowercase		:Optional[int]       							=				torch.randint(0  ,     2_5_6  ,     (6_4, 6_4, 3)				)
              lowercase		:List[Any]       							=				AgentImage(snake_case__				)
              lowercase		:Any       							=				str(agent_type.to_string()				)

              # Ensure that the tensor and the agent_type's tensor are the same
              self.assertTrue(torch.allclose(snake_case__  ,     agent_type._tensor  ,     atol=1e-4				)				)

              self.assertIsInstance(agent_type.to_raw()  ,     Image.Image				)

              # Ensure the path remains even after the object deletion
              del agent_type
              self.assertTrue(os.path.exists(snake_case__				)				)






       def  __snake_case						(   self  :    Optional[int]				):

              '''simple docstring'''






              lowercase		:List[str]       							=				Path(get_tests_dir('''fixtures/tests_samples/COCO'''				)				) / '''000000039769.png'''
              lowercase		:Optional[int]       							=				Image.open(snake_case__				)
              lowercase		:List[Any]       							=				AgentImage(snake_case__				)

              self.assertTrue(path.samefile(agent_type.to_string()				)				)
              self.assertTrue(image == agent_type.to_raw()				)

              # Ensure the path remains even after the object deletion
              del agent_type
              self.assertTrue(os.path.exists(snake_case__				)				)







       def  __snake_case						(   self  :    int				):

              '''simple docstring'''






              lowercase		:Optional[Any]       							=				Path(get_tests_dir('''fixtures/tests_samples/COCO'''				)				) / '''000000039769.png'''
              lowercase		:Any       							=				Image.open(snake_case__				)
              lowercase		:str       							=				AgentImage(snake_case__				)

              self.assertFalse(path.samefile(agent_type.to_string()				)				)
              self.assertTrue(image == agent_type.to_raw()				)

              # Ensure the path remains even after the object deletion
              del agent_type
              self.assertTrue(os.path.exists(snake_case__				)				)







class 					__magic_name__		(			unittest.TestCase     ):






       def  __snake_case						(   self  :    Optional[int]				):

              '''simple docstring'''






              lowercase		:Union[str, Any]       							=				'''Hey!'''
              lowercase		:str       							=				AgentText(snake_case__				)

              self.assertEqual(snake_case__  ,     agent_type.to_string()				)
              self.assertEqual(snake_case__  ,     agent_type.to_raw()				)
              self.assertEqual(snake_case__  ,     snake_case__				)





"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase      		=			{
    '''configuration_xlm_roberta''': [
        '''XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''XLMRobertaConfig''',
        '''XLMRobertaOnnxConfig''',
    ],
}

try:
 if not is_sentencepiece_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase      		=			['''XLMRobertaTokenizer''']

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase      		=			['''XLMRobertaTokenizerFast''']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase      		=			[
     '''XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''XLMRobertaForCausalLM''',
     '''XLMRobertaForMaskedLM''',
     '''XLMRobertaForMultipleChoice''',
     '''XLMRobertaForQuestionAnswering''',
     '''XLMRobertaForSequenceClassification''',
     '''XLMRobertaForTokenClassification''',
     '''XLMRobertaModel''',
     '''XLMRobertaPreTrainedModel''',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase      		=			[
     '''TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''TFXLMRobertaForCausalLM''',
     '''TFXLMRobertaForMaskedLM''',
     '''TFXLMRobertaForMultipleChoice''',
     '''TFXLMRobertaForQuestionAnswering''',
     '''TFXLMRobertaForSequenceClassification''',
     '''TFXLMRobertaForTokenClassification''',
     '''TFXLMRobertaModel''',
     '''TFXLMRobertaPreTrainedModel''',
 ]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase      		=			[
     '''FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''FlaxXLMRobertaForMaskedLM''',
     '''FlaxXLMRobertaForCausalLM''',
     '''FlaxXLMRobertaForMultipleChoice''',
     '''FlaxXLMRobertaForQuestionAnswering''',
     '''FlaxXLMRobertaForSequenceClassification''',
     '''FlaxXLMRobertaForTokenClassification''',
     '''FlaxXLMRobertaModel''',
     '''FlaxXLMRobertaPreTrainedModel''',
 ]

if TYPE_CHECKING:
 from .configuration_xlm_roberta import (
     XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
     XLMRobertaConfig,
     XLMRobertaOnnxConfig,
 )

 try:
  if not is_sentencepiece_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_xlm_roberta import XLMRobertaTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_xlm_roberta_fast import XLMRobertaTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_xlm_roberta import (
      XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
      XLMRobertaForCausalLM,
      XLMRobertaForMaskedLM,
      XLMRobertaForMultipleChoice,
      XLMRobertaForQuestionAnswering,
      XLMRobertaForSequenceClassification,
      XLMRobertaForTokenClassification,
      XLMRobertaModel,
      XLMRobertaPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_xlm_roberta import (
      TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFXLMRobertaForCausalLM,
      TFXLMRobertaForMaskedLM,
      TFXLMRobertaForMultipleChoice,
      TFXLMRobertaForQuestionAnswering,
      TFXLMRobertaForSequenceClassification,
      TFXLMRobertaForTokenClassification,
      TFXLMRobertaModel,
      TFXLMRobertaPreTrainedModel,
  )

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_xlm_roberta import (
      FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
      FlaxXLMRobertaForCausalLM,
      FlaxXLMRobertaForMaskedLM,
      FlaxXLMRobertaForMultipleChoice,
      FlaxXLMRobertaForQuestionAnswering,
      FlaxXLMRobertaForSequenceClassification,
      FlaxXLMRobertaForTokenClassification,
      FlaxXLMRobertaModel,
      FlaxXLMRobertaPreTrainedModel,
  )

else:
 import sys

 UpperCAmelCase      		=			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

