



"""simple docstring"""







import argparse

import torch
from torch import nn

from transformers import PLBartConfig, PLBartForConditionalGeneration, PLBartForSequenceClassification

def 	lowerCAmelCase__      (  lowerCamelCase__      )			->				Dict:
							A    =       [
							    'encoder.version',
							    'decoder.version',
							    'model.encoder.version',
							    'model.decoder.version',
							    '_float_tensor',
							    'decoder.output_projection.weight',
							]
							for k in ignore_keys:
														state_dict.pop(lowerCamelCase__					,   lowerCamelCase__      )

def 	lowerCAmelCase__      (  lowerCamelCase__      )			->				Any:
							A   ,	A    =       emb.weight.shape
							A    =       nn.Linear(lowerCamelCase__					,   lowerCamelCase__					,   bias=lowerCamelCase__      )
							A    =       emb.weight.data
							return lin_layer

def 	lowerCAmelCase__      (  lowerCamelCase__					,   lowerCamelCase__="uclanlp/plbart-base"					,   lowerCamelCase__=False					,   lowerCamelCase__=False      )			->				Optional[Any]:
							A    =       torch.load(lowerCamelCase__					,   map_location='cpu'      )['model']
							remove_ignore_keys_(lowerCamelCase__      )
							A    =       state_dict['encoder.embed_tokens.weight'].shape[0]

							A    =       PLBartConfig.from_pretrained(lowerCamelCase__					,   vocab_size=lowerCamelCase__      )

							A    =       state_dict['decoder.embed_tokens.weight']
							if not classification:
														A    =       PLBartForConditionalGeneration(lowerCamelCase__      )
														model.model.load_state_dict(lowerCamelCase__      )
														if finetuned:
																					A    =       make_linear_from_emb(model.model.shared      )

							else:
														A    =       {}
														for key, value in state_dict.copy().items():
																					if key.startswith('classification_heads.sentence_classification_head'      ):
																												A    =       value
																												state_dict.pop(lowerCamelCase__      )
														A    =       PLBartForSequenceClassification(lowerCamelCase__      )
														model.model.load_state_dict(lowerCamelCase__      )
														model.classification_head.load_state_dict(lowerCamelCase__      )

							return model


if __name__ == "__main__":
							A     =      argparse.ArgumentParser()
							# Required parameters
							parser.add_argument('fairseq_path', type=str, help='model.pt on local filesystem.')
							parser.add_argument('pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
							parser.add_argument(
							    '--hf_config',
							    default='uclanlp/plbart-base',
							    type=str,
							    help='Which huggingface architecture to use: plbart-base',
							)
							parser.add_argument('--finetuned', action='store_true', help='whether the model is a fine-tuned checkpoint')
							parser.add_argument(
							    '--classification', action='store_true', help='whether the model is a classification checkpoint'
							)
							A     =      parser.parse_args()
							A     =      convert_fairseq_plbart_checkpoint_from_disk(
							    args.fairseq_path,
							    hf_config_path=args.hf_config,
							    finetuned=args.finetuned,
							    classification=args.classification,
							)
							model.save_pretrained(args.pytorch_dump_folder_path)








"""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					)					)



