




import os
import pathlib
import tempfile
import uuid

import numpy as np

from ..utils import is_soundfile_availble, is_torch_available, is_vision_available, logging


UpperCamelCase__						=		logging.get_logger(__name__)

if is_vision_available():
							import PIL.Image
							from PIL import Image
							from PIL.Image import Image as ImageType
else:
							UpperCamelCase__						=		object

if is_torch_available():
							import torch

if is_soundfile_availble():
							import soundfile as sf


class 							__SCREAMING_SNAKE_CASE				:

					def __init__(							self					,			__lowercase   ):







						'''simple docstring'''




						lowerCamelCase_				:						List[Any]				  =				value

					def __str__(							self   ):







						'''simple docstring'''




						return self.to_string()

					def       lowercase_				(							self   ):







						'''simple docstring'''




						logger.error(
						    'This is a raw AgentType of unknown type. Display in notebooks and string conversion will be unreliable'   )
						return self._value





					def       lowercase_				(							self   ):







						'''simple docstring'''




						logger.error(
						    'This is a raw AgentType of unknown type. Display in notebooks and string conversion will be unreliable'   )
						return str(self._value   )


class 							__SCREAMING_SNAKE_CASE				(						lowerCamelCase  ,	lowerCamelCase    ):

					def       lowercase_				(							self   ):







						'''simple docstring'''




						return self._value





					def       lowercase_				(							self   ):







						'''simple docstring'''




						return self._value


class 							__SCREAMING_SNAKE_CASE				(						lowerCamelCase  ,	lowerCamelCase    ):

					def __init__(							self					,			__lowercase   ):







						'''simple docstring'''




						super().__init__(__lowercase   )

						if not is_vision_available():
							raise ImportError('PIL must be installed in order to handle images.'   )

						lowerCamelCase_				:						str				  =				None
						lowerCamelCase_				:						Any				  =				None
						lowerCamelCase_				:						str				  =				None

						if isinstance(__lowercase					,			__lowercase   ):
							lowerCamelCase_				:						int				  =				value
						elif isinstance(__lowercase					,			(str, pathlib.Path)   ):
							lowerCamelCase_				:						Optional[Any]				  =				value
						elif isinstance(__lowercase					,			torch.Tensor   ):
							lowerCamelCase_				:						Any				  =				value
						else:
							raise ValueError(f"Unsupported type for {self.__class__.__name__}: {type(__lowercase   )}"   )

					def       lowercase_				(							self					,			__lowercase=None					,			__lowercase=None   ):







						'''simple docstring'''




						from IPython.display import Image, display

						display(Image(self.to_string()   )   )

					def       lowercase_				(							self   ):







						'''simple docstring'''




						if self._raw is not None:
							return self._raw

						if self._path is not None:
							lowerCamelCase_				:						int				  =				Image.open(self._path   )
							return self._raw





					def       lowercase_				(							self   ):







						'''simple docstring'''




						if self._path is not None:
							return self._path

						if self._raw is not None:
							lowerCamelCase_				:						List[str]				  =				tempfile.mkdtemp()
							lowerCamelCase_				:						Optional[Any]				  =				os.path.join(__lowercase					,			str(uuid.uuida()   ) + '.png'   )
							self._raw.save(self._path   )

							return self._path

						if self._tensor is not None:
							lowerCamelCase_				:						Optional[int]				  =				self._tensor.cpu().detach().numpy()

							# There is likely simpler than load into image into save
							lowerCamelCase_				:						Optional[int]				  =				Image.fromarray((array * 255).astype(np.uinta   )   )

							lowerCamelCase_				:						str				  =				tempfile.mkdtemp()
							lowerCamelCase_				:						Optional[Any]				  =				os.path.join(__lowercase					,			str(uuid.uuida()   ) + '.png'   )

							img.save(self._path   )

							return self._path


class 							__SCREAMING_SNAKE_CASE				(						lowerCamelCase    ):

					def __init__(							self					,			__lowercase					,			__lowercase=16_000   ):







						'''simple docstring'''




						super().__init__(__lowercase   )

						if not is_soundfile_availble():
							raise ImportError('soundfile must be installed in order to handle audio.'   )

						lowerCamelCase_				:						Optional[int]				  =				None
						lowerCamelCase_				:						List[str]				  =				None

						lowerCamelCase_				:						Union[str, Any]				  =				samplerate

						if isinstance(__lowercase					,			(str, pathlib.Path)   ):
							lowerCamelCase_				:						Optional[int]				  =				value
						elif isinstance(__lowercase					,			torch.Tensor   ):
							lowerCamelCase_				:						Optional[int]				  =				value
						else:
							raise ValueError(f"Unsupported audio type: {type(__lowercase   )}"   )

					def       lowercase_				(							self					,			__lowercase=None					,			__lowercase=None   ):







						'''simple docstring'''




						from IPython.display import Audio, display

						display(Audio(self.to_string()					,			rate=self.samplerate   )   )

					def       lowercase_				(							self   ):







						'''simple docstring'''




						if self._tensor is not None:
							return self._tensor

						if self._path is not None:
							lowerCamelCase_  ,     lowerCamelCase_				:						str				  =				sf.read(self._path   )
							lowerCamelCase_				:						List[str]				  =				torch.tensor(__lowercase   )
							return self._tensor





					def       lowercase_				(							self   ):







						'''simple docstring'''




						if self._path is not None:
							return self._path

						if self._tensor is not None:
							lowerCamelCase_				:						List[Any]				  =				tempfile.mkdtemp()
							lowerCamelCase_				:						str				  =				os.path.join(__lowercase					,			str(uuid.uuida()   ) + '.wav'   )
							sf.write(self._path					,			self._tensor					,			samplerate=self.samplerate   )
							return self._path
UpperCamelCase__						=		{'text': AgentText, 'image': AgentImage, 'audio': AgentAudio}
UpperCamelCase__						=		{str: AgentText}

if is_vision_available():
							UpperCamelCase__						=		AgentImage






def    snake_case   (				*A		,  **A     )				->				int:




	'''simple docstring'''


	lowerCamelCase_				:						Dict				  =				[(arg.to_raw() if isinstance(A		,  A     ) else arg) for arg in args]
	lowerCamelCase_				:						int				  =				{k: (v.to_raw() if isinstance(A		,  A     ) else v) for k, v in kwargs.items()}
	return args, kwargs






def    snake_case   (				A		,  A=None     )				->				Tuple:




	'''simple docstring'''


	if isinstance(A		,  A     ):
		lowerCamelCase_				:						Any				  =				{}
		for i, (k, v) in enumerate(outputs.items()     ):
			if output_types is not None:
				# If the class has defined outputs, we can map directly according to the class definition
				if output_types[i] in AGENT_TYPE_MAPPING:
					lowerCamelCase_				:						int				  =				AGENT_TYPE_MAPPING[output_types[i]](A     )
				else:
					lowerCamelCase_				:						str				  =				AgentType(A     )

			else:
				# If the class does not have defined output, then we map according to the type
				for _k, _v in INSTANCE_TYPE_MAPPING.items():
					if isinstance(A		,  _k     ):
						lowerCamelCase_				:						str				  =				_v(A     )
				if k not in decoded_outputs:
					lowerCamelCase_				:						str				  =				AgentType[v]

	elif isinstance(A		,  (list, tuple)     ):
		lowerCamelCase_				:						Optional[int]				  =				type(A     )()
		for i, v in enumerate(A     ):
			if output_types is not None:
				# If the class has defined outputs, we can map directly according to the class definition
				if output_types[i] in AGENT_TYPE_MAPPING:
					decoded_outputs.append(AGENT_TYPE_MAPPING[output_types[i]](A     )     )
				else:
					decoded_outputs.append(AgentType(A     )     )
			else:
				# If the class does not have defined output, then we map according to the type
				lowerCamelCase_				:						List[Any]				  =				False
				for _k, _v in INSTANCE_TYPE_MAPPING.items():
					if isinstance(A		,  _k     ):
						decoded_outputs.append(_v(A     )     )
						lowerCamelCase_				:						Tuple				  =				True

				if not found:
					decoded_outputs.append(AgentType(A     )     )

	else:
		if output_types[0] in AGENT_TYPE_MAPPING:
			# If the class has defined outputs, we can map directly according to the class definition
			lowerCamelCase_				:						Any				  =				AGENT_TYPE_MAPPING[output_types[0]](A     )

		else:
			# If the class does not have defined output, then we map according to the type
			for _k, _v in INSTANCE_TYPE_MAPPING.items():
				if isinstance(A		,  _k     ):
					return _v(A     )
			return AgentType(A     )

	return decoded_outputs





"""simple docstring"""







import inspect
import unittest

from transformers import ResNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available

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


if is_flax_available():
							import jax
							import jax.numpy as jnp

							from transformers.models.resnet.modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel

if is_vision_available():
							from PIL import Image

							from transformers import AutoImageProcessor






class   UpperCAmelCase__		(   unittest.TestCase  ):



		def __init__(			self	:     Dict   ,				snake_case	:     Dict   ,				snake_case	:     Optional[Any]=3   ,				snake_case	:     Union[str, Any]=32   ,				snake_case	:     int=3   ,				snake_case	:     Any=10   ,				snake_case	:     int=[10, 20, 30, 40]   ,				snake_case	:     List[Any]=[1, 1, 2, 1]   ,				snake_case	:     List[str]=True   ,				snake_case	:     Any=True   ,				snake_case	:     str="relu"   ,				snake_case	:     List[str]=3   ,				snake_case	:     List[str]=None   ,				)      ->    int:






									'''simple docstring'''

									A    =       parent
									A    =       batch_size
									A    =       image_size
									A    =       num_channels
									A    =       embeddings_size
									A    =       hidden_sizes
									A    =       depths
									A    =       is_training
									A    =       use_labels
									A    =       hidden_act
									A    =       num_labels
									A    =       scope
									A    =       len(snake_case					)



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






									'''simple docstring'''

									A    =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

									A    =       self.get_config()

									return config, pixel_values



		def        A_		(			self	:     int					)      ->    List[Any]:






									'''simple docstring'''

									return ResNetConfig(
									    num_channels=self.num_channels   ,				embeddings_size=self.embeddings_size   ,				hidden_sizes=self.hidden_sizes   ,				depths=self.depths   ,				hidden_act=self.hidden_act   ,				num_labels=self.num_labels   ,				image_size=self.image_size   ,				)



		def        A_		(			self	:     Optional[Any]   ,				snake_case	:     str   ,				snake_case	:     Any					)      ->    int:






									'''simple docstring'''

									A    =       FlaxResNetModel(config=snake_case					)
									A    =       model(snake_case					)

									# Output shape (b, c, h, w)
									self.parent.assertEqual(
									    result.last_hidden_state.shape   ,				(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)   ,				)



		def        A_		(			self	:     Dict   ,				snake_case	:     Tuple   ,				snake_case	:     Optional[Any]					)      ->    Tuple:






									'''simple docstring'''

									A    =       self.num_labels
									A    =       FlaxResNetForImageClassification(config=snake_case					)
									A    =       model(snake_case					)
									self.parent.assertEqual(result.logits.shape   ,				(self.batch_size, self.num_labels)					)



		def        A_		(			self	:     List[str]					)      ->    Optional[Any]:






									'''simple docstring'''

									A    =       self.prepare_config_and_inputs()
									A   ,	A    =       config_and_inputs
									A    =       {'pixel_values': pixel_values}
									return config, inputs_dict






@require_flax
class   UpperCAmelCase__		(   UpperCamelCase ,unittest.TestCase  ):
		lowerCAmelCase_							:						List[str]							=  (FlaxResNetModel, FlaxResNetForImageClassification) if is_flax_available() else ()

		lowerCAmelCase_							:						Tuple							=  False
		lowerCAmelCase_							:						List[str]							=  False
		lowerCAmelCase_							:						List[str]							=  False



		def        A_		(			self	:     Optional[Any]					)      ->    None:






									'''simple docstring'''

									A    =       FlaxResNetModelTester(self					)
									A    =       ConfigTester(self   ,				config_class=snake_case   ,				has_text_modality=snake_case					)



		def        A_		(			self	:     List[str]					)      ->    Dict:






									'''simple docstring'''

									self.create_and_test_config_common_properties()
									self.config_tester.create_and_test_config_to_json_string()
									self.config_tester.create_and_test_config_to_json_file()
									self.config_tester.create_and_test_config_from_and_save_pretrained()
									self.config_tester.create_and_test_config_with_num_labels()
									self.config_tester.check_config_can_be_init_without_params()
									self.config_tester.check_config_arguments_init()



		def        A_		(			self	:     Dict					)      ->    List[Any]:






									'''simple docstring'''

									return



		def        A_		(			self	:     str					)      ->    List[str]:






									'''simple docstring'''

									A    =       self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*snake_case					)



		def        A_		(			self	:     Optional[int]					)      ->    List[str]:






									'''simple docstring'''

									A    =       self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*snake_case					)



		@unittest.skip(reason='ResNet does not use inputs_embeds'					)
		def        A_		(			self	:     Tuple					)      ->    str:






									'''simple docstring'''

									pass



		@unittest.skip(reason='ResNet does not support input and output embeddings'					)
		def        A_		(			self	:     Tuple					)      ->    Any:






									'''simple docstring'''

									pass



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






									'''simple docstring'''

									A   ,	A    =       self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
																A    =       model_class(snake_case					)
																A    =       inspect.signature(model.__call__					)
																# signature.parameters is an OrderedDict => so arg_names order is deterministic
																A    =       [*signature.parameters.keys()]

																A    =       ['pixel_values']
																self.assertListEqual(arg_names[:1]   ,				snake_case					)



		def        A_		(			self	:     Any					)      ->    Tuple:






									'''simple docstring'''

									def check_hidden_states_output(snake_case	:     Tuple   ,				snake_case	:     Union[str, Any]   ,				snake_case	:     Optional[int]					):
																A    =       model_class(snake_case					)

																A    =       model(**self._prepare_for_class(snake_case   ,				snake_case					)					)

																A    =       outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

																A    =       self.model_tester.num_stages
																self.assertEqual(len(snake_case					)   ,				expected_num_stages + 1					)



		@unittest.skip(reason='ResNet does not use feedforward chunking'					)
		def        A_		(			self	:     Optional[int]					)      ->    List[Any]:






									'''simple docstring'''

									pass



		def        A_		(			self	:     Union[str, Any]					)      ->    Union[str, Any]:






									'''simple docstring'''

									A   ,	A    =       self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
																with self.subTest(model_class.__name__					):
																							A    =       self._prepare_for_class(snake_case   ,				snake_case					)
																							A    =       model_class(snake_case					)

																							@jax.jit
																							def model_jitted(snake_case	:     Any   ,				**snake_case	:     int					):
																														return model(pixel_values=snake_case   ,				**snake_case					)

																							with self.subTest('JIT Enabled'					):
																														A    =       model_jitted(**snake_case					).to_tuple()

																							with self.subTest('JIT Disabled'					):
																														with jax.disable_jit():
																																					A    =       model_jitted(**snake_case					).to_tuple()

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



def 	lowerCAmelCase__      (  )			->				int:
							A    =       Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'      )
							return image






@require_flax
class   UpperCAmelCase__		(   unittest.TestCase  ):



		@cached_property
		def        A_		(			self	:     Union[str, Any]					)      ->    int:






									'''simple docstring'''

									return AutoImageProcessor.from_pretrained('microsoft/resnet-50'					) if is_vision_available() else None



		@slow
		def        A_		(			self	:     Any					)      ->    Tuple:






									'''simple docstring'''

									A    =       FlaxResNetForImageClassification.from_pretrained('microsoft/resnet-50'					)

									A    =       self.default_image_processor
									A    =       prepare_img()
									A    =       image_processor(images=snake_case   ,				return_tensors='np'					)

									A    =       model(**snake_case					)

									# verify the logits
									A    =       (1, 1_000)
									self.assertEqual(outputs.logits.shape   ,				snake_case					)

									A    =       jnp.array([-11.1069, -9.7877, -8.3777]					)

									self.assertTrue(jnp.allclose(outputs.logits[0, :3]   ,				snake_case   ,				atol=1E-4					)					)



