
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


lowerCamelCase__     =	logging.get_logger(__name__)

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

if is_torch_available():
						import torch

if is_soundfile_availble():
						import soundfile as sf






class 				SCREAMING_SNAKE_CASE     :





							def __init__(				self						:						str ,			__lowercase						:						Optional[int]		):

											'''simple docstring'''
											__a					       =						value





							def __str__(				self						:						Dict		):

											'''simple docstring'''
											return self.to_string()





							def   UpperCamelCase_   (				self						:						Tuple		):

											'''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   UpperCamelCase_   (				self						:						Union[str, Any]		):

											'''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     (							a_     , a_   ):





							def   UpperCamelCase_   (				self						:						List[str]		):

											'''simple docstring'''
											return self._value







							def   UpperCamelCase_   (				self						:						List[Any]		):

											'''simple docstring'''
											return self._value






class 				SCREAMING_SNAKE_CASE     (							a_     , a_   ):





							def __init__(				self						:						Tuple ,			__lowercase						:						Optional[int]		):

											'''simple docstring'''
											super().__init__(snake_case__		)

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

											__a					       =						None
											__a					       =						None
											__a					       =						None

											if isinstance(snake_case__ ,			snake_case__		):
															__a					       =						value
											elif isinstance(snake_case__ ,			(str, pathlib.Path)		):
															__a					       =						value
											elif isinstance(snake_case__ ,			torch.Tensor		):
															__a					       =						value
											else:
															raise ValueError(F"Unsupported type for {self.__class__.__name__}: {type(snake_case__		)}"		)





							def   UpperCamelCase_   (				self						:						int ,			__lowercase						:						List[Any]=None ,			__lowercase						:						int=None		):

											'''simple docstring'''
											from IPython.display import Image, display

											display(Image(self.to_string()		)		)





							def   UpperCamelCase_   (				self						:						List[Any]		):

											'''simple docstring'''


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

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







							def   UpperCamelCase_   (				self						:						List[Any]		):

											'''simple docstring'''


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

											if self._raw is not None:
															__a					       =						tempfile.mkdtemp()
															__a					       =						os.path.join(snake_case__ ,			str(uuid.uuida()		) + """.png"""		)
															self._raw.save(self._path		)

															return self._path

											if self._tensor is not None:
															__a					       =						self._tensor.cpu().detach().numpy()

															# There is likely simpler than load into image into save
															__a					       =						Image.fromarray((array * 255).astype(np.uinta		)		)

															__a					       =						tempfile.mkdtemp()
															__a					       =						os.path.join(snake_case__ ,			str(uuid.uuida()		) + """.png"""		)

															img.save(self._path		)

															return self._path






class 				SCREAMING_SNAKE_CASE     (							a_   ):





							def __init__(				self						:						int ,			__lowercase						:						Optional[Any] ,			__lowercase						:						str=16000		):

											'''simple docstring'''
											super().__init__(snake_case__		)

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

											__a					       =						None
											__a					       =						None

											__a					       =						samplerate

											if isinstance(snake_case__ ,			(str, pathlib.Path)		):
															__a					       =						value
											elif isinstance(snake_case__ ,			torch.Tensor		):
															__a					       =						value
											else:
															raise ValueError(F"Unsupported audio type: {type(snake_case__		)}"		)





							def   UpperCamelCase_   (				self						:						Tuple ,			__lowercase						:						Dict=None ,			__lowercase						:						int=None		):

											'''simple docstring'''
											from IPython.display import Audio, display

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





							def   UpperCamelCase_   (				self						:						Optional[Any]		):

											'''simple docstring'''


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

											if self._path is not None:
															__a       ,   __a					       =						sf.read(self._path		)
															__a					       =						torch.tensor(snake_case__		)
															return self._tensor







							def   UpperCamelCase_   (				self						:						Optional[int]		):

											'''simple docstring'''


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

											if self._tensor is not None:
															__a					       =						tempfile.mkdtemp()
															__a					       =						os.path.join(snake_case__ ,			str(uuid.uuida()		) + """.wav"""		)
															sf.write(self._path ,			self._tensor ,			samplerate=self.samplerate		)
															return self._path



lowerCamelCase__     =	{"""text""": AgentText, """image""": AgentImage, """audio""": AgentAudio}
lowerCamelCase__     =	{str: AgentText}

if is_vision_available():
						lowerCamelCase__     =	AgentImage

def 					lowerCAmelCase__  (							*_SCREAMING_SNAKE_CASE    :     List[Any]			,       **_SCREAMING_SNAKE_CASE    :     Union[str, Any]      ):
				"""simple docstring"""







				__a					       =						[(arg.to_raw() if isinstance(_lowerCAmelCase			,       _lowerCAmelCase      ) else arg) for arg in args]
				__a					       =						{k: (v.to_raw() if isinstance(_lowerCAmelCase			,       _lowerCAmelCase      ) else v) for k, v in kwargs.items()}
				return args, kwargs

def 					lowerCAmelCase__  (							_SCREAMING_SNAKE_CASE    :     Optional[int]			,       _SCREAMING_SNAKE_CASE    :     str=None      ):
				"""simple docstring"""







				if isinstance(_lowerCAmelCase			,       _lowerCAmelCase      ):
								__a					       =						{}
								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:
																				__a					       =						AGENT_TYPE_MAPPING[output_types[i]](_lowerCAmelCase      )
																else:
																				__a					       =						AgentType(_lowerCAmelCase      )

												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(_lowerCAmelCase			,       _k      ):
																								__a					       =						_v(_lowerCAmelCase      )
																if k not in decoded_outputs:
																				__a					       =						AgentType[v]

				elif isinstance(_lowerCAmelCase			,       (list, tuple)      ):
								__a					       =						type(_lowerCAmelCase      )()
								for i, v in enumerate(_lowerCAmelCase      ):
												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]](_lowerCAmelCase      )      )
																else:
																				decoded_outputs.append(AgentType(_lowerCAmelCase      )      )
												else:
																# If the class does not have defined output, then we map according to the type
																__a					       =						False
																for _k, _v in INSTANCE_TYPE_MAPPING.items():
																				if isinstance(_lowerCAmelCase			,       _k      ):
																								decoded_outputs.append(_v(_lowerCAmelCase      )      )
																								__a					       =						True

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

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

								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(_lowerCAmelCase			,       _k      ):
																				return _v(_lowerCAmelCase      )
												return AgentType(_lowerCAmelCase      )

				return decoded_outputs





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 				SCREAMING_SNAKE_CASE     (							unittest.TestCase   ):





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

											'''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(__lowercase		)





							def   UpperCamelCase_   (				self						:						List[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   UpperCamelCase_   (				self						:						Optional[int]		):

											'''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   UpperCamelCase_   (				self						:						List[str] ,			__lowercase						:						Union[str, Any] ,			__lowercase						:						str		):

											'''simple docstring'''
											__a					       =						FlaxResNetModel(config=__lowercase		)
											__a					       =						model(__lowercase		)

											# 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   UpperCamelCase_   (				self						:						List[str] ,			__lowercase						:						Dict ,			__lowercase						:						Optional[int]		):

											'''simple docstring'''
											__a					       =						self.num_labels
											__a					       =						FlaxResNetForImageClassification(config=__lowercase		)
											__a					       =						model(__lowercase		)
											self.parent.assertEqual(result.logits.shape ,			(self.batch_size, self.num_labels)		)







							def   UpperCamelCase_   (				self						:						List[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 				SCREAMING_SNAKE_CASE     (							lowerCamelCase__     , unittest.TestCase   ):
							__lowerCamelCase		:		int      =(FlaxResNetModel, FlaxResNetForImageClassification) if is_flax_available() else ()

							__lowerCamelCase		:		Union[str, Any]      =False
							__lowerCamelCase		:		Optional[Any]      =False
							__lowerCamelCase		:		str      =False





							def   UpperCamelCase_   (				self						:						Optional[Any]		):

											'''simple docstring'''
											__a					       =						FlaxResNetModelTester(self		)
											__a					       =						ConfigTester(self ,			config_class=__lowercase ,			has_text_modality=__lowercase		)





							def   UpperCamelCase_   (				self						:						str		):

											'''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   UpperCamelCase_   (				self						:						str		):

											'''simple docstring'''
											return





							def   UpperCamelCase_   (				self						:						Any		):

											'''simple docstring'''
											__a					       =						self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*__lowercase		)





							def   UpperCamelCase_   (				self						:						Dict		):

											'''simple docstring'''
											__a					       =						self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_image_classification(*__lowercase		)





							@unittest.skip(reason="""ResNet does not use inputs_embeds"""		)
							def   UpperCamelCase_   (				self						:						Optional[Any]		):

											'''simple docstring'''
											pass





							@unittest.skip(reason="""ResNet does not support input and output embeddings"""		)
							def   UpperCamelCase_   (				self						:						int		):

											'''simple docstring'''
											pass





							def   UpperCamelCase_   (				self						:						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(__lowercase		)
															__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] ,			__lowercase		)





							def   UpperCamelCase_   (				self						:						Union[str, Any]		):

											'''simple docstring'''
											def check_hidden_states_output(__lowercase						:						Tuple ,			__lowercase						:						List[str] ,			__lowercase						:						str		):
															__a					       =						model_class(__lowercase		)

															__a					       =						model(**self._prepare_for_class(__lowercase ,			__lowercase		)		)

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

															__a					       =						self.model_tester.num_stages
															self.assertEqual(len(__lowercase		) ,			expected_num_stages + 1		)





							@unittest.skip(reason="""ResNet does not use feedforward chunking"""		)
							def   UpperCamelCase_   (				self						:						Optional[Any]		):

											'''simple docstring'''
											pass







							def   UpperCamelCase_   (				self						:						List[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(__lowercase ,			__lowercase		)
																			__a					       =						model_class(__lowercase		)

																			@jax.jit
																			def model_jitted(__lowercase						:						List[str] ,			**__lowercase						:						Optional[int]		):
																							return model(pixel_values=__lowercase ,			**__lowercase		)

																			with self.subTest("""JIT Enabled"""		):
																							__a					       =						model_jitted(**__lowercase		).to_tuple()

																			with self.subTest("""JIT Disabled"""		):
																							with jax.disable_jit():
																											__a					       =						model_jitted(**__lowercase		).to_tuple()

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



def 					lowerCAmelCase__  (							):
				"""simple docstring"""







				__a					       =						Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""      )
				return image






@require_flax
class 				SCREAMING_SNAKE_CASE     (							unittest.TestCase   ):





							@cached_property
							def   UpperCamelCase_   (				self						:						Union[str, Any]		):

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







							@slow
							def   UpperCamelCase_   (				self						:						int		):

											'''simple docstring'''
											__a					       =						FlaxResNetForImageClassification.from_pretrained("""microsoft/resnet-50"""		)

											__a					       =						self.default_image_processor
											__a					       =						prepare_img()
											__a					       =						image_processor(images=__lowercase ,			return_tensors="""np"""		)

											__a					       =						model(**__lowercase		)

											# verify the logits
											__a					       =						(1, 1000)
											self.assertEqual(outputs.logits.shape ,			__lowercase		)

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

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