


'''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 			a      (			__a=""   )  ->	int:



					'''simple docstring'''






					UpperCamelCase__   :Optional[int]												= tempfile.mkdtemp()
					return os.path.join(lowerCAmelCase_ ,					str(uuid.uuida()   ) + suffix   )
@require_soundfile
@require_torch
class    lowercase	(							unittest.TestCase					):

		"""simple docstring"""

		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :str												= torch.rand(12    ,   dtype=torch.floataa							) - 0.5
							UpperCamelCase__   :Any												= AgentAudio(UpperCAmelCase__							)
							UpperCamelCase__   :Dict												= str(agent_type.to_string()							)

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

							del agent_type

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

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




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :List[Any]												= torch.rand(12    ,   dtype=torch.floataa							) - 0.5
							UpperCamelCase__   :int												= get_new_path(suffix='''.wav'''							)
							sf.write(UpperCAmelCase__    ,   UpperCAmelCase__    ,   16000							)

							UpperCamelCase__   :List[str]												= AgentAudio(UpperCAmelCase__							)

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



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

		"""simple docstring"""

		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :List[Any]												= torch.randint(0    ,   256    ,   (64, 64, 3)							)
							UpperCamelCase__   :str												= AgentImage(UpperCAmelCase__							)
							UpperCamelCase__   :str												= str(agent_type.to_string()							)

							# Ensure that the tensor and the agent_type's tensor are the same
							self.assertTrue(torch.allclose(UpperCAmelCase__    ,   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(UpperCAmelCase__							)							)




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


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

							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(UpperCAmelCase__							)							)




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :Tuple												= Path(get_tests_dir('''fixtures/tests_samples/COCO'''							)							) / '''000000039769.png'''
							UpperCamelCase__   :int												= Image.open(UpperCAmelCase__							)
							UpperCamelCase__   :List[Any]												= AgentImage(UpperCAmelCase__							)

							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(UpperCAmelCase__							)							)



class    lowercase	(							unittest.TestCase					):

		"""simple docstring"""

		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :List[str]												= '''Hey!'''
							UpperCamelCase__   :Optional[Any]												= AgentText(UpperCAmelCase__							)

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



"""simple docstring"""







from typing import TYPE_CHECKING

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


a__				:							str						=     {
    '''configuration_roformer''': ['''ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RoFormerConfig''', '''RoFormerOnnxConfig'''],
    '''tokenization_roformer''': ['''RoFormerTokenizer'''],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a__				:							int						=     ['''RoFormerTokenizerFast''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a__				:							int						=     [
		    '''ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''RoFormerForCausalLM''',
		    '''RoFormerForMaskedLM''',
		    '''RoFormerForMultipleChoice''',
		    '''RoFormerForQuestionAnswering''',
		    '''RoFormerForSequenceClassification''',
		    '''RoFormerForTokenClassification''',
		    '''RoFormerLayer''',
		    '''RoFormerModel''',
		    '''RoFormerPreTrainedModel''',
		    '''load_tf_weights_in_roformer''',
		]


try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a__				:							List[Any]						=     [
		    '''TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''TFRoFormerForCausalLM''',
		    '''TFRoFormerForMaskedLM''',
		    '''TFRoFormerForMultipleChoice''',
		    '''TFRoFormerForQuestionAnswering''',
		    '''TFRoFormerForSequenceClassification''',
		    '''TFRoFormerForTokenClassification''',
		    '''TFRoFormerLayer''',
		    '''TFRoFormerModel''',
		    '''TFRoFormerPreTrainedModel''',
		]


try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a__				:							Tuple						=     [
		    '''FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''FlaxRoFormerForMaskedLM''',
		    '''FlaxRoFormerForMultipleChoice''',
		    '''FlaxRoFormerForQuestionAnswering''',
		    '''FlaxRoFormerForSequenceClassification''',
		    '''FlaxRoFormerForTokenClassification''',
		    '''FlaxRoFormerModel''',
		    '''FlaxRoFormerPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
		from .tokenization_roformer import RoFormerTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_roformer_fast import RoFormerTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_roformer import (
				    ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    RoFormerForCausalLM,
				    RoFormerForMaskedLM,
				    RoFormerForMultipleChoice,
				    RoFormerForQuestionAnswering,
				    RoFormerForSequenceClassification,
				    RoFormerForTokenClassification,
				    RoFormerLayer,
				    RoFormerModel,
				    RoFormerPreTrainedModel,
				    load_tf_weights_in_roformer,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_roformer import (
				    TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFRoFormerForCausalLM,
				    TFRoFormerForMaskedLM,
				    TFRoFormerForMultipleChoice,
				    TFRoFormerForQuestionAnswering,
				    TFRoFormerForSequenceClassification,
				    TFRoFormerForTokenClassification,
				    TFRoFormerLayer,
				    TFRoFormerModel,
				    TFRoFormerPreTrainedModel,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_roformer import (
				    FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    FlaxRoFormerForMaskedLM,
				    FlaxRoFormerForMultipleChoice,
				    FlaxRoFormerForQuestionAnswering,
				    FlaxRoFormerForSequenceClassification,
				    FlaxRoFormerForTokenClassification,
				    FlaxRoFormerModel,
				    FlaxRoFormerPreTrainedModel,
				)


else:
		import sys

		a__				:							int						=     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
