



"""simple docstring"""







import argparse
import pathlib

import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import RobertaConfig, RobertaForMaskedLM, RobertaForSequenceClassification
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse('0.9.0'):
							raise Exception('requires fairseq >= 0.9.0')


logging.set_verbosity_info()
A     =      logging.get_logger(__name__)

A     =      'Hello world! cécé herlolip'

def 	lowerCAmelCase__      (  lowerCamelCase__					,   lowerCamelCase__					,   lowerCamelCase__      )			->				List[Any]:
							A    =       FairseqRobertaModel.from_pretrained(lowerCamelCase__      )
							roberta.eval()  # disable dropout
							A    =       roberta.model.encoder.sentence_encoder
							A    =       RobertaConfig(
							    vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings					,   hidden_size=roberta.args.encoder_embed_dim					,   num_hidden_layers=roberta.args.encoder_layers					,   num_attention_heads=roberta.args.encoder_attention_heads					,   intermediate_size=roberta.args.encoder_ffn_embed_dim					,   max_position_embeddings=514					,   type_vocab_size=1					,   layer_norm_eps=1E-5					,   )
							if classification_head:
														A    =       roberta.model.classification_heads['mnli'].out_proj.weight.shape[0]
							print('Our BERT config:'					,   lowerCamelCase__      )

							A    =       RobertaForSequenceClassification(lowerCamelCase__      ) if classification_head else RobertaForMaskedLM(lowerCamelCase__      )
							model.eval()

							# Now let's copy all the weights.
							# Embeddings
							A    =       roberta_sent_encoder.embed_tokens.weight
							A    =       roberta_sent_encoder.embed_positions.weight
							A    =       torch.zeros_like(
							    model.roberta.embeddings.token_type_embeddings.weight      )  # just zero them out b/c RoBERTa doesn't use them.
							A    =       roberta_sent_encoder.emb_layer_norm.weight
							A    =       roberta_sent_encoder.emb_layer_norm.bias

							for i in range(config.num_hidden_layers      ):
														# Encoder: start of layer
														A    =       model.roberta.encoder.layer[i]
														A    =       roberta_sent_encoder.layers[i]

														# self attention
														A    =       layer.attention.self
														assert (
														    roberta_layer.self_attn.k_proj.weight.data.shape
														    == roberta_layer.self_attn.q_proj.weight.data.shape
														    == roberta_layer.self_attn.v_proj.weight.data.shape
														    == torch.Size((config.hidden_size, config.hidden_size)      )
														)

														A    =       roberta_layer.self_attn.q_proj.weight
														A    =       roberta_layer.self_attn.q_proj.bias
														A    =       roberta_layer.self_attn.k_proj.weight
														A    =       roberta_layer.self_attn.k_proj.bias
														A    =       roberta_layer.self_attn.v_proj.weight
														A    =       roberta_layer.self_attn.v_proj.bias

														# self-attention output
														A    =       layer.attention.output
														assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
														A    =       roberta_layer.self_attn.out_proj.weight
														A    =       roberta_layer.self_attn.out_proj.bias
														A    =       roberta_layer.self_attn_layer_norm.weight
														A    =       roberta_layer.self_attn_layer_norm.bias

														# intermediate
														A    =       layer.intermediate
														assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
														A    =       roberta_layer.fca.weight
														A    =       roberta_layer.fca.bias

														# output
														A    =       layer.output
														assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
														A    =       roberta_layer.fca.weight
														A    =       roberta_layer.fca.bias
														A    =       roberta_layer.final_layer_norm.weight
														A    =       roberta_layer.final_layer_norm.bias
														# end of layer

							if classification_head:
														A    =       roberta.model.classification_heads['mnli'].dense.weight
														A    =       roberta.model.classification_heads['mnli'].dense.bias
														A    =       roberta.model.classification_heads['mnli'].out_proj.weight
														A    =       roberta.model.classification_heads['mnli'].out_proj.bias
							else:
														# LM Head
														A    =       roberta.model.encoder.lm_head.dense.weight
														A    =       roberta.model.encoder.lm_head.dense.bias
														A    =       roberta.model.encoder.lm_head.layer_norm.weight
														A    =       roberta.model.encoder.lm_head.layer_norm.bias
														A    =       roberta.model.encoder.lm_head.weight
														A    =       roberta.model.encoder.lm_head.bias

							# Let's check that we get the same results.
							A    =       roberta.encode(lowerCamelCase__      ).unsqueeze(0      )  # batch of size 1

							A    =       model(lowerCamelCase__      )[0]
							if classification_head:
														A    =       roberta.model.classification_heads['mnli'](roberta.extract_features(lowerCamelCase__      )      )
							else:
														A    =       roberta.model(lowerCamelCase__      )[0]
							print(our_output.shape					,   their_output.shape      )
							A    =       torch.max(torch.abs(our_output - their_output      )      ).item()
							print(f"""max_absolute_diff = {max_absolute_diff}"""      )  # ~ 1e-7
							A    =       torch.allclose(lowerCamelCase__					,   lowerCamelCase__					,   atol=1E-3      )
							print('Do both models output the same tensors?'					,   '🔥' if success else '💩'      )
							if not success:
														raise Exception('Something went wRoNg'      )

							pathlib.Path(lowerCamelCase__      ).mkdir(parents=lowerCamelCase__					,   exist_ok=lowerCamelCase__      )
							print(f"""Saving model to {pytorch_dump_folder_path}"""      )
							model.save_pretrained(lowerCamelCase__      )


if __name__ == "__main__":
							A     =      argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '--roberta_checkpoint_path', default=None, type=str, required=True, help='Path the official PyTorch dump.'
							)
							parser.add_argument(
							    '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
							)
							parser.add_argument(
							    '--classification_head', action='store_true', help='Whether to convert a final classification head.'
							)
							A     =      parser.parse_args()
							convert_roberta_checkpoint_to_pytorch(
							    args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
							)








"""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					)					)



