





import inspect
import unittest

from transformers import RegNetConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
					import torch
					from torch import nn

					from transformers import RegNetForImageClassification, RegNetModel
					from transformers.models.regnet.modeling_regnet import REGNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
					from PIL import Image

					from transformers import AutoImageProcessor

class 							__SCREAMING_SNAKE_CASE       :






							def __init__(    self	,			__lowerCAmelCase	,			__lowerCAmelCase=3	,			__lowerCAmelCase=32	,			__lowerCAmelCase=3	,			__lowerCAmelCase=10	,			__lowerCAmelCase=[10, 20, 30, 40]	,			__lowerCAmelCase=[1, 1, 2, 1]	,			__lowerCAmelCase=True	,			__lowerCAmelCase=True	,			__lowerCAmelCase="relu"	,			__lowerCAmelCase=3	,			__lowerCAmelCase=None	,			):
														UpperCamelCase__					  =      parent
														UpperCamelCase__					  =      batch_size
														UpperCamelCase__					  =      image_size
														UpperCamelCase__					  =      num_channels
														UpperCamelCase__					  =      embeddings_size
														UpperCamelCase__					  =      hidden_sizes
														UpperCamelCase__					  =      depths
														UpperCamelCase__					  =      is_training
														UpperCamelCase__					  =      use_labels
														UpperCamelCase__					  =      hidden_act
														UpperCamelCase__					  =      num_labels
														UpperCamelCase__					  =      scope
														UpperCamelCase__					  =      len(UpperCamelCase_	)






							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__					  =      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

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

														UpperCamelCase__					  =      self.get_config()

														return config, pixel_values, labels






							def 					_lowerCamelCase		(    self	):
														return RegNetConfig(
														    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	,			)






							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase	):
														UpperCamelCase__					  =      RegNetModel(config=UpperCamelCase_	)
														model.to(UpperCamelCase_	)
														model.eval()
														UpperCamelCase__					  =      model(UpperCamelCase_	)
														# expected last hidden states: B, C, H // 32, W // 32
														self.parent.assertEqual(
														    result.last_hidden_state.shape	,			(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)	,			)






							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase	):
														UpperCamelCase__					  =      self.num_labels
														UpperCamelCase__					  =      RegNetForImageClassification(UpperCamelCase_	)
														model.to(UpperCamelCase_	)
														model.eval()
														UpperCamelCase__					  =      model(UpperCamelCase_	,			labels=UpperCamelCase_	)
														self.parent.assertEqual(result.logits.shape	,			(self.batch_size, self.num_labels)	)







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






@require_torch
class 							__SCREAMING_SNAKE_CASE       (  A__ ,		A__ ,		unittest.TestCase							):
							snake_case  :   Tuple      =							(RegNetModel, RegNetForImageClassification) if is_torch_available() else ()
							snake_case  :   Any      =							(
							    {"""feature-extraction""": RegNetModel, """image-classification""": RegNetForImageClassification}
							    if is_torch_available()
							    else {}
							)

							snake_case  :   Any      =							False
							snake_case  :   Union[str, Any]      =							False
							snake_case  :   Dict      =							False
							snake_case  :   Optional[Any]      =							False






							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__					  =      RegNetModelTester(self	)
														UpperCamelCase__					  =      ConfigTester(self	,			config_class=UpperCamelCase_	,			has_text_modality=UpperCamelCase_	)






							def 					_lowerCamelCase		(    self	):
														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 					_lowerCamelCase		(    self	):
														return






							@unittest.skip(reason="""RegNet does not use inputs_embeds"""	)
							def 					_lowerCamelCase		(    self	):
														pass






							@unittest.skip(reason="""RegNet does not support input and output embeddings"""	)
							def 					_lowerCamelCase		(    self	):
														pass






							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__					  =      self.model_tester.prepare_config_and_inputs_for_common()

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

																					UpperCamelCase__					  =      ['''pixel_values''']
																					self.assertListEqual(arg_names[:1]	,			UpperCamelCase_	)






							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__					  =      self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_model(*UpperCamelCase_	)






							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__					  =      self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in self.all_model_classes:
																					UpperCamelCase__					  =      model_class(config=UpperCamelCase_	)
																					for name, module in model.named_modules():
																												if isinstance(UpperCamelCase_	,			(nn.BatchNormad, nn.GroupNorm)	):
																																			self.assertTrue(
																																			    torch.all(module.weight == 1	)	,			msg=f"""Parameter {name} of model {model_class} seems not properly initialized"""	,			)
																																			self.assertTrue(
																																			    torch.all(module.bias == 0	)	,			msg=f"""Parameter {name} of model {model_class} seems not properly initialized"""	,			)






							def 					_lowerCamelCase		(    self	):


														def check_hidden_states_output(__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase	):
																					UpperCamelCase__					  =      model_class(UpperCamelCase_	)
																					model.to(UpperCamelCase_	)
																					model.eval()

																					with torch.no_grad():
																												UpperCamelCase__					  =      model(**self._prepare_for_class(UpperCamelCase_	,			UpperCamelCase_	)	)

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

																					UpperCamelCase__					  =      self.model_tester.num_stages
																					self.assertEqual(len(UpperCamelCase_	)	,			expected_num_stages + 1	)

																					# RegNet's feature maps are of shape (batch_size, num_channels, height, width)
																					self.assertListEqual(
																					    list(hidden_states[0].shape[-2:]	)	,			[self.model_tester.image_size // 2, self.model_tester.image_size // 2]	,			)

														UpperCamelCase__					  =      self.model_tester.prepare_config_and_inputs_for_common()
														UpperCamelCase__					  =      ['''basic''', '''bottleneck''']
														for model_class in self.all_model_classes:
																					for layer_type in layers_type:
																												UpperCamelCase__					  =      layer_type
																												UpperCamelCase__					  =      True
																												check_hidden_states_output(UpperCamelCase_	,			UpperCamelCase_	,			UpperCamelCase_	)

																												# check that output_hidden_states also work using config
																												del inputs_dict["output_hidden_states"]
																												UpperCamelCase__					  =      True

																												check_hidden_states_output(UpperCamelCase_	,			UpperCamelCase_	,			UpperCamelCase_	)






							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__					  =      self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_image_classification(*UpperCamelCase_	)







							@slow
							def 					_lowerCamelCase		(    self	):


														for model_name in REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																					UpperCamelCase__					  =      RegNetModel.from_pretrained(UpperCamelCase_	)
																					self.assertIsNotNone(UpperCamelCase_	)






def      _UpperCamelCase  ():






							"""simple docstring"""





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

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






							@cached_property
							def 					_lowerCamelCase		(    self	):
														return (
														    AutoImageProcessor.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]	)
														    if is_vision_available()
														    else None
														)







							@slow
							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__					  =      RegNetForImageClassification.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]	).to(UpperCamelCase_	)

														UpperCamelCase__					  =      self.default_image_processor
														UpperCamelCase__					  =      prepare_img()
														UpperCamelCase__					  =      image_processor(images=UpperCamelCase_	,			return_tensors="""pt"""	).to(UpperCamelCase_	)

														# forward pass
														with torch.no_grad():
																					UpperCamelCase__					  =      model(**UpperCamelCase_	)

														# verify the logits
														UpperCamelCase__					  =      torch.Size((1, 1000)	)
														self.assertEqual(outputs.logits.shape	,			UpperCamelCase_	)

														UpperCamelCase__					  =      torch.tensor([-0.4180, -1.5051, -3.4836]	).to(UpperCamelCase_	)

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








'''simple docstring'''
import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


__snake_case				 =	logging.getLogger(__name__)
@dataclass
class   lowercase      (				A__   ):

		"""simple docstring"""

		_a       					=							field(
		    default=0.0      ,						metadata={'help': 'The label smoothing epsilon to apply (if not zero).'}   )
		_a       					=							field(default=A__      ,						metadata={'help': 'Whether to SortishSamler or not.'}   )
		_a       					=							field(
		    default=A__      ,						metadata={'help': 'Whether to use generate to calculate generative metrics (ROUGE, BLEU).'}   )
		_a       					=							field(default=A__      ,						metadata={'help': 'whether to use adafactor'}   )
		_a       					=							field(
		    default=A__      ,						metadata={'help': 'Encoder layer dropout probability. Goes into model.config.'}   )
		_a       					=							field(
		    default=A__      ,						metadata={'help': 'Decoder layer dropout probability. Goes into model.config.'}   )
		_a       					=							field(default=A__      ,						metadata={'help': 'Dropout probability. Goes into model.config.'}   )
		_a       					=							field(
		    default=A__      ,						metadata={'help': 'Attention dropout probability. Goes into model.config.'}   )
		_a       					=							field(
		    default='linear'      ,						metadata={'help': f'''Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()   )}'''}      ,						)