






'''simple docstring'''




import inspect
import unittest
from typing import List

import numpy as np

from transformers import EfficientFormerConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
		import tensorflow as tf

		from transformers import (
		    TFEfficientFormerForImageClassification,
		    TFEfficientFormerForImageClassificationWithTeacher,
		    TFEfficientFormerModel,
		)
		from transformers.models.efficientformer.modeling_tf_efficientformer import (
		    TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		)


if is_vision_available():
		from PIL import Image

		from transformers import EfficientFormerImageProcessor







class    A_	:
							"""simple docstring"""




							def __init__(		self			:str       ,							lowerCAmelCase__			:Dict       ,							lowerCAmelCase__			:int = 13       ,							lowerCAmelCase__			:int = 64       ,							lowerCAmelCase__			:int = 2       ,							lowerCAmelCase__			:int = 3       ,							lowerCAmelCase__			:int = 3       ,							lowerCAmelCase__			:bool = True       ,							lowerCAmelCase__			:bool = True       ,							lowerCAmelCase__			:int = 128       ,							lowerCAmelCase__			:List[str]=[16, 32, 64, 128]       ,							lowerCAmelCase__			:int = 7       ,							lowerCAmelCase__			:int = 4       ,							lowerCAmelCase__			:int = 37       ,							lowerCAmelCase__			:str = "gelu"       ,							lowerCAmelCase__			:float = 0.1       ,							lowerCAmelCase__			:float = 0.1       ,							lowerCAmelCase__			:int = 10       ,							lowerCAmelCase__			:float = 0.0_2       ,							lowerCAmelCase__			:int = 2       ,							lowerCAmelCase__			:int = 1       ,							lowerCAmelCase__			:int = 128       ,							lowerCAmelCase__			:List[int] = [2, 2, 2, 2]       ,							lowerCAmelCase__			:int = 2       ,							lowerCAmelCase__			:int = 2       ,							)				->  Tuple:


										'''simple docstring'''




										snake_case_				:					Union[str, Any]   =       parent
										snake_case_				:					Dict   =       batch_size
										snake_case_				:					Dict   =       image_size
										snake_case_				:					List[str]   =       patch_size
										snake_case_				:					Tuple   =       num_channels
										snake_case_				:					List[Any]   =       is_training
										snake_case_				:					Union[str, Any]   =       use_labels
										snake_case_				:					Tuple   =       hidden_size
										snake_case_				:					Tuple   =       num_hidden_layers
										snake_case_				:					Optional[int]   =       num_attention_heads
										snake_case_				:					Tuple   =       intermediate_size
										snake_case_				:					Tuple   =       hidden_act
										snake_case_				:					Union[str, Any]   =       hidden_dropout_prob
										snake_case_				:					int   =       attention_probs_dropout_prob
										snake_case_				:					Union[str, Any]   =       type_sequence_label_size
										snake_case_				:					Optional[int]   =       initializer_range
										snake_case_				:					int   =       encoder_stride
										snake_case_				:					Dict   =       num_attention_outputs
										snake_case_				:					Union[str, Any]   =       embed_dim
										snake_case_				:					str   =       embed_dim + 1
										snake_case_				:					Optional[int]   =       resolution
										snake_case_				:					List[Any]   =       depths
										snake_case_				:					Union[str, Any]   =       hidden_sizes
										snake_case_				:					List[str]   =       dim
										snake_case_				:					int   =       mlp_expansion_ratio

							def 							_A	(		self			:Optional[int]				)				->  Union[str, Any]:


										'''simple docstring'''




										snake_case_				:					List[str]   =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

										snake_case_				:					Tuple   =       None
										if self.use_labels:
													snake_case_				:					Any   =       ids_tensor([self.batch_size]       ,							self.type_sequence_label_size				)

										snake_case_				:					Tuple   =       self.get_config()

										return config, pixel_values, labels

							def 							_A	(		self			:int				)				->  int:


										'''simple docstring'''




										return EfficientFormerConfig(
										    image_size=self.image_size       ,							patch_size=self.patch_size       ,							num_channels=self.num_channels       ,							hidden_size=self.hidden_size       ,							num_hidden_layers=self.num_hidden_layers       ,							num_attention_heads=self.num_attention_heads       ,							intermediate_size=self.intermediate_size       ,							hidden_act=self.hidden_act       ,							hidden_dropout_prob=self.hidden_dropout_prob       ,							attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,							is_decoder=lowerCAmelCase__       ,							initializer_range=self.initializer_range       ,							encoder_stride=self.encoder_stride       ,							resolution=self.resolution       ,							depths=self.depths       ,							hidden_sizes=self.hidden_sizes       ,							dim=self.dim       ,							mlp_expansion_ratio=self.mlp_expansion_ratio       ,							)

							def 							_A	(		self			:Tuple       ,							lowerCAmelCase__			:Optional[int]       ,							lowerCAmelCase__			:List[str]       ,							lowerCAmelCase__			:int				)				->  Any:


										'''simple docstring'''




										snake_case_				:					List[Any]   =       TFEfficientFormerModel(config=lowerCAmelCase__				)
										snake_case_				:					Tuple   =       model(lowerCAmelCase__       ,							training=lowerCAmelCase__				)
										self.parent.assertEqual(result.last_hidden_state.shape       ,							(self.batch_size, self.seq_length, self.hidden_size)				)

							def 							_A	(		self			:str       ,							lowerCAmelCase__			:int       ,							lowerCAmelCase__			:str       ,							lowerCAmelCase__			:Union[str, Any]				)				->  List[str]:


										'''simple docstring'''




										snake_case_				:					str   =       self.type_sequence_label_size
										snake_case_				:					str   =       TFEfficientFormerForImageClassification(lowerCAmelCase__				)
										snake_case_				:					Tuple   =       model(lowerCAmelCase__       ,							labels=lowerCAmelCase__       ,							training=lowerCAmelCase__				)
										self.parent.assertEqual(result.logits.shape       ,							(self.batch_size, self.type_sequence_label_size)				)

										# test greyscale images
										snake_case_				:					str   =       1
										snake_case_				:					Tuple   =       TFEfficientFormerForImageClassification(lowerCAmelCase__				)

										snake_case_				:					Union[str, Any]   =       floats_tensor([self.batch_size, 1, self.image_size, self.image_size]				)
										snake_case_				:					Dict   =       model(lowerCAmelCase__       ,							labels=lowerCAmelCase__				)
										self.parent.assertEqual(result.logits.shape       ,							(self.batch_size, self.type_sequence_label_size)				)






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


										'''simple docstring'''




										snake_case_				:					Any   =       self.prepare_config_and_inputs()
										snake_case_,	snake_case_,	snake_case_				:					Union[str, Any]   =       config_and_inputs
										snake_case_				:					Any   =       {"pixel_values": pixel_values}
										return config, inputs_dict







@require_tf
class    A_	(a_						,					a_						,					unittest.TestCase							):
							"""simple docstring"""




							a__   		=	(
							    (
							        TFEfficientFormerModel,
							        TFEfficientFormerForImageClassificationWithTeacher,
							        TFEfficientFormerForImageClassification,
							    )
							    if is_tf_available()
							    else ()
							)
							a__   		=	(
							    {
							        '''feature-extraction''': TFEfficientFormerModel,
							        '''image-classification''': (
							            TFEfficientFormerForImageClassification,
							            TFEfficientFormerForImageClassificationWithTeacher,
							        ),
							    }
							    if is_tf_available()
							    else {}
							)

							a__   		=	False

							a__   		=	False
							a__   		=	False
							a__   		=	False
							a__   		=	False

							def 							_A	(		self			:Optional[Any]				)				->  Tuple:


										'''simple docstring'''




										snake_case_				:					Dict   =       TFEfficientFormerModelTester(self				)
										snake_case_				:					Union[str, Any]   =       ConfigTester(
										    self       ,							config_class=lowerCAmelCase__       ,							has_text_modality=lowerCAmelCase__       ,							hidden_size=37				)

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


										'''simple docstring'''




										self.config_tester.run_common_tests()

							@unittest.skip(reason="EfficientFormer does not use inputs_embeds"				)
							def 							_A	(		self			:int				)				->  Optional[int]:


										'''simple docstring'''




										pass

							@unittest.skip(reason="EfficientFormer does not support input and output embeddings"				)
							def 							_A	(		self			:Union[str, Any]				)				->  Union[str, Any]:


										'''simple docstring'''




										pass

							def 							_A	(		self			:List[Any]				)				->  Tuple:


										'''simple docstring'''




										snake_case_,	snake_case_				:					Dict   =       self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
													snake_case_				:					Tuple   =       model_class(lowerCAmelCase__				)
													snake_case_				:					Optional[Any]   =       inspect.signature(model.call				)
													# signature.parameters is an OrderedDict => so arg_names order is deterministic
													snake_case_				:					List[Any]   =       [*signature.parameters.keys()]

													snake_case_				:					Optional[int]   =       ["pixel_values"]
													self.assertListEqual(arg_names[:1]       ,							lowerCAmelCase__				)

							def 							_A	(		self			:Dict				)				->  Any:


										'''simple docstring'''




										def check_hidden_states_output(lowerCAmelCase__			:Any       ,							lowerCAmelCase__			:List[str]       ,							lowerCAmelCase__			:List[str]				):
													snake_case_				:					Dict   =       model_class(lowerCAmelCase__				)

													snake_case_				:					Union[str, Any]   =       model(**self._prepare_for_class(lowerCAmelCase__       ,							lowerCAmelCase__				)       ,							training=lowerCAmelCase__				)
													snake_case_				:					List[str]   =       outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

													snake_case_				:					List[str]   =       getattr(
													    self.model_tester       ,							"expected_num_hidden_layers"       ,							self.model_tester.num_hidden_layers + 1				)
													self.assertEqual(len(lowerCAmelCase__				)       ,							lowerCAmelCase__				)

													if hasattr(self.model_tester       ,							"encoder_seq_length"				):
																snake_case_				:					Dict   =       self.model_tester.encoder_seq_length
																if hasattr(self.model_tester       ,							"chunk_length"				) and self.model_tester.chunk_length > 1:
																			snake_case_				:					List[Any]   =       seq_length * self.model_tester.chunk_length
													else:
																snake_case_				:					List[Any]   =       self.model_tester.seq_length

													self.assertListEqual(
													    list(hidden_states[-1].shape[-2:]				)       ,							[seq_length, self.model_tester.hidden_size]       ,							)

													if config.is_encoder_decoder:
																snake_case_				:					List[str]   =       outputs.decoder_hidden_states

																self.asseretIsInstance(lowerCAmelCase__       ,							(list, tuple)				)
																self.assertEqual(len(lowerCAmelCase__				)       ,							lowerCAmelCase__				)
																snake_case_				:					Any   =       getattr(self.model_tester       ,							"seq_length"       ,							lowerCAmelCase__				)
																snake_case_				:					Any   =       getattr(self.model_tester       ,							"decoder_seq_length"       ,							lowerCAmelCase__				)

																self.assertListEqual(
																    list(hidden_states[-1].shape[-2:]				)       ,							[decoder_seq_length, self.model_tester.hidden_size]       ,							)

										snake_case_,	snake_case_				:					Union[str, Any]   =       self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
													snake_case_				:					Optional[Any]   =       True
													check_hidden_states_output(lowerCAmelCase__       ,							lowerCAmelCase__       ,							lowerCAmelCase__				)

													# check that output_hidden_states also work using config
													del inputs_dict["output_hidden_states"]
													snake_case_				:					Tuple   =       True

													check_hidden_states_output(lowerCAmelCase__       ,							lowerCAmelCase__       ,							lowerCAmelCase__				)

							def 							_A	(		self			:Union[str, Any]       ,							lowerCAmelCase__			:int       ,							lowerCAmelCase__			:Any       ,							lowerCAmelCase__			:str=False				)				->  Dict:


										'''simple docstring'''




										snake_case_				:					int   =       super()._prepare_for_class(lowerCAmelCase__       ,							lowerCAmelCase__       ,							return_labels=lowerCAmelCase__				)

										if return_labels:
													if model_class.__name__ == "TFEfficientFormerForImageClassificationWithTeacher":
																del inputs_dict["labels"]

										return inputs_dict

							def 							_A	(		self			:Union[str, Any]				)				->  Optional[int]:


										'''simple docstring'''




										snake_case_				:					Dict   =       self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*lowerCAmelCase__				)

							@unittest.skip(reason="EfficientFormer does not implement masked image modeling yet"				)
							def 							_A	(		self			:Optional[int]				)				->  List[str]:


										'''simple docstring'''




										snake_case_				:					Dict   =       self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_masked_image_modeling(*lowerCAmelCase__				)

							def 							_A	(		self			:Dict				)				->  int:


										'''simple docstring'''




										snake_case_				:					Tuple   =       self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase__				)

							@slow
							def 							_A	(		self			:Optional[int]				)				->  List[str]:


										'''simple docstring'''




										for model_name in TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													snake_case_				:					str   =       TFEfficientFormerModel.from_pretrained(lowerCAmelCase__				)
													self.assertIsNotNone(lowerCAmelCase__				)

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


										'''simple docstring'''




										snake_case_,	snake_case_				:					Tuple   =       self.model_tester.prepare_config_and_inputs_for_common()
										snake_case_				:					Union[str, Any]   =       True

										snake_case_				:					Tuple   =       getattr(self.model_tester       ,							"seq_length"       ,							lowerCAmelCase__				)
										snake_case_				:					Tuple   =       getattr(self.model_tester       ,							"encoder_seq_length"       ,							lowerCAmelCase__				)
										snake_case_				:					List[str]   =       getattr(self.model_tester       ,							"key_length"       ,							lowerCAmelCase__				)
										snake_case_				:					Optional[Any]   =       getattr(self.model_tester       ,							"chunk_length"       ,							lowerCAmelCase__				)

										if chunk_length is not None and hasattr(self.model_tester       ,							"num_hashes"				):
													snake_case_				:					Optional[int]   =       encoder_seq_length * self.model_tester.num_hashes

										for model_class in self.all_model_classes:
													snake_case_				:					Optional[int]   =       True
													snake_case_				:					List[Any]   =       False
													snake_case_				:					Tuple   =       True
													snake_case_				:					Any   =       model_class(lowerCAmelCase__				)

													snake_case_				:					int   =       model(**self._prepare_for_class(lowerCAmelCase__       ,							lowerCAmelCase__				)       ,							training=lowerCAmelCase__				)
													snake_case_				:					Any   =       outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
													self.assertEqual(len(lowerCAmelCase__				)       ,							self.model_tester.num_attention_outputs				)

													# check that output_attentions also work using config
													del inputs_dict["output_attentions"]
													snake_case_				:					Optional[Any]   =       True
													snake_case_				:					Tuple   =       model_class(lowerCAmelCase__				)
													snake_case_				:					Optional[int]   =       model(**self._prepare_for_class(lowerCAmelCase__       ,							lowerCAmelCase__				)       ,							training=lowerCAmelCase__				)

													snake_case_				:					List[str]   =       outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
													self.assertEqual(len(lowerCAmelCase__				)       ,							self.model_tester.num_attention_outputs				)

													if chunk_length is not None:
																self.assertListEqual(
																    list(attentions[0].shape[-4:]				)       ,							[self.model_tester.num_attention_heads, encoder_seq_length, chunk_length, encoder_key_length]       ,							)
													else:
																self.assertListEqual(
																    list(attentions[0].shape[-3:]				)       ,							[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length]       ,							)






							def 							_A	(		self			:Tuple				)				->  List[str]:


										'''simple docstring'''




										snake_case_,	snake_case_				:					Tuple   =       self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
													# Prepare our model
													snake_case_				:					str   =       model_class(lowerCAmelCase__				)
													# These are maximally general inputs for the model, with multiple None dimensions
													# Hopefully this will catch any conditionals that fail for flexible shapes
													snake_case_				:					int   =       {
													    key: tf.keras.Input(shape=val.shape[1:]       ,							dtype=val.dtype       ,							name=lowerCAmelCase__				)
													    for key, val in model.input_signature.items()
													    if key in model.dummy_inputs
													}
													snake_case_				:					int   =       model(lowerCAmelCase__				)
													self.assertTrue(outputs_dict is not None				)

def 	__UpperCAmelCase       ( )->    Tuple:



			"""simple docstring"""



			snake_case_				:					List[str]   =       Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"						)
			return image







@require_tf
@require_vision
class    A_	(unittest.TestCase							):
							"""simple docstring"""




							@cached_property
							def 							_A	(		self			:List[Any]				)				->  Optional[int]:


										'''simple docstring'''




										return (
										    EfficientFormerImageProcessor.from_pretrained("snap-research/efficientformer-l1-300"				)
										    if is_vision_available()
										    else None
										)

							@slow
							def 							_A	(		self			:Optional[Any]				)				->  Tuple:


										'''simple docstring'''




										snake_case_				:					Any   =       TFEfficientFormerForImageClassification.from_pretrained("snap-research/efficientformer-l1-300"				)
										snake_case_				:					Dict   =       self.default_image_processor
										snake_case_				:					Optional[Any]   =       prepare_img()
										snake_case_				:					Any   =       image_processor(images=lowerCAmelCase__       ,							return_tensors="tf"				)
										# forward pass
										snake_case_				:					int   =       model(**lowerCAmelCase__       ,							training=lowerCAmelCase__				)
										# verify the logits
										snake_case_				:					int   =       tf.TensorShape((1, 1_000)				)
										self.assertEqual(outputs.logits.shape       ,							lowerCAmelCase__				)
										snake_case_				:					Union[str, Any]   =       tf.constant([-0.0_5_5_5, 0.4_8_2_5, -0.0_8_5_2]				)
										self.assertTrue(np.allclose(outputs.logits[0, :3]       ,							lowerCAmelCase__       ,							atol=1E-4				)				)






							@slow
							def 							_A	(		self			:Tuple				)				->  Dict:


										'''simple docstring'''




										snake_case_				:					Optional[int]   =       TFEfficientFormerForImageClassificationWithTeacher.from_pretrained(
										    "snap-research/efficientformer-l1-300"				)
										snake_case_				:					Optional[int]   =       self.default_image_processor
										snake_case_				:					str   =       prepare_img()
										snake_case_				:					Union[str, Any]   =       image_processor(images=lowerCAmelCase__       ,							return_tensors="tf"				)
										# forward pass
										snake_case_				:					List[Any]   =       model(**lowerCAmelCase__       ,							training=lowerCAmelCase__				)
										# verify the logits
										snake_case_				:					Optional[int]   =       tf.TensorShape((1, 1_000)				)
										self.assertEqual(outputs.logits.shape       ,							lowerCAmelCase__				)
										snake_case_				:					Any   =       tf.constant([-0.1_3_1_2, 0.4_3_5_3, -1.0_4_9_9]				)
										self.assertTrue(np.allclose(outputs.logits[0, :3]       ,							lowerCAmelCase__       ,							atol=1E-4				)				)












'''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 XLMRobertaConfig, XLMRobertaXLForMaskedLM, XLMRobertaXLForSequenceClassification
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.models.roberta.modeling_roberta import RobertaAttention
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse('''1.0.0a'''):
		raise Exception('''requires fairseq >= 1.0.0a''')

logging.set_verbosity_info()
__lowerCamelCase   :					Union[str, Any]   							=					logging.get_logger(__name__)

__lowerCamelCase   :					Union[str, Any]   							=					'''Hello world! cécé herlolip'''

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__     ,__magic_name__						)->    Optional[Any]:



			"""simple docstring"""



			snake_case_				:					str   =       FairseqRobertaModel.from_pretrained(__magic_name__						)
			roberta.eval()  # disable dropout
			snake_case_				:					Dict   =       roberta.model.encoder.sentence_encoder
			snake_case_				:					List[str]   =       XLMRobertaConfig(
			    vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings     ,hidden_size=roberta.cfg.model.encoder_embed_dim     ,num_hidden_layers=roberta.cfg.model.encoder_layers     ,num_attention_heads=roberta.cfg.model.encoder_attention_heads     ,intermediate_size=roberta.cfg.model.encoder_ffn_embed_dim     ,max_position_embeddings=514     ,type_vocab_size=1     ,layer_norm_eps=1E-5     ,)
			if classification_head:
						snake_case_				:					List[str]   =       roberta.model.classification_heads["mnli"].out_proj.weight.shape[0]

			print("Our RoBERTa config:"     ,__magic_name__						)

			snake_case_				:					List[str]   =       XLMRobertaXLForSequenceClassification(__magic_name__						) if classification_head else XLMRobertaXLForMaskedLM(__magic_name__						)
			model.eval()

			# Now let's copy all the weights.
			# Embeddings
			snake_case_				:					List[Any]   =       roberta_sent_encoder.embed_tokens.weight
			snake_case_				:					int   =       roberta_sent_encoder.embed_positions.weight
			snake_case_				:					Union[str, Any]   =       torch.zeros_like(
			    model.roberta.embeddings.token_type_embeddings.weight						)  # just zero them out b/c RoBERTa doesn't use them.

			snake_case_				:					Union[str, Any]   =       roberta_sent_encoder.layer_norm.weight
			snake_case_				:					str   =       roberta_sent_encoder.layer_norm.bias

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

						snake_case_				:					RobertaAttention   =       layer.attention
						snake_case_				:					Dict   =       roberta_layer.self_attn_layer_norm.weight
						snake_case_				:					Dict   =       roberta_layer.self_attn_layer_norm.bias

						# self attention
						snake_case_				:					BertSelfAttention   =       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)						)
						)

						snake_case_				:					Dict   =       roberta_layer.self_attn.q_proj.weight
						snake_case_				:					Any   =       roberta_layer.self_attn.q_proj.bias
						snake_case_				:					Optional[Any]   =       roberta_layer.self_attn.k_proj.weight
						snake_case_				:					Optional[Any]   =       roberta_layer.self_attn.k_proj.bias
						snake_case_				:					Optional[int]   =       roberta_layer.self_attn.v_proj.weight
						snake_case_				:					Any   =       roberta_layer.self_attn.v_proj.bias

						# self-attention output
						snake_case_				:					BertSelfOutput   =       layer.attention.output
						assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
						snake_case_				:					List[str]   =       roberta_layer.self_attn.out_proj.weight
						snake_case_				:					Optional[int]   =       roberta_layer.self_attn.out_proj.bias

						# this one is final layer norm
						snake_case_				:					int   =       roberta_layer.final_layer_norm.weight
						snake_case_				:					Union[str, Any]   =       roberta_layer.final_layer_norm.bias

						# intermediate
						snake_case_				:					BertIntermediate   =       layer.intermediate
						assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
						snake_case_				:					List[str]   =       roberta_layer.fca.weight
						snake_case_				:					List[Any]   =       roberta_layer.fca.bias

						# output
						snake_case_				:					BertOutput   =       layer.output
						assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
						snake_case_				:					Any   =       roberta_layer.fca.weight
						snake_case_				:					Any   =       roberta_layer.fca.bias
						# end of layer

			if classification_head:
						snake_case_				:					int   =       roberta.model.classification_heads["mnli"].dense.weight
						snake_case_				:					Union[str, Any]   =       roberta.model.classification_heads["mnli"].dense.bias
						snake_case_				:					Tuple   =       roberta.model.classification_heads["mnli"].out_proj.weight
						snake_case_				:					str   =       roberta.model.classification_heads["mnli"].out_proj.bias
			else:
						# LM Head
						snake_case_				:					Optional[Any]   =       roberta.model.encoder.lm_head.dense.weight
						snake_case_				:					int   =       roberta.model.encoder.lm_head.dense.bias
						snake_case_				:					Optional[Any]   =       roberta.model.encoder.lm_head.layer_norm.weight
						snake_case_				:					Optional[int]   =       roberta.model.encoder.lm_head.layer_norm.bias
						snake_case_				:					int   =       roberta.model.encoder.lm_head.weight
						snake_case_				:					List[str]   =       roberta.model.encoder.lm_head.bias

			# Let's check that we get the same results.
			snake_case_				:					torch.Tensor   =       roberta.encode(__magic_name__						).unsqueeze(0						)  # batch of size 1

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

			pathlib.Path(__magic_name__						).mkdir(parents=__magic_name__     ,exist_ok=__magic_name__						)
			print(F'''Saving model to {pytorch_dump_folder_path}'''						)
			model.save_pretrained(__magic_name__						)


if __name__ == "__main__":
		__lowerCamelCase   :					Any   							=					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.'''
		)
		__lowerCamelCase   :					Tuple   							=					parser.parse_args()
		convert_xlm_roberta_xl_checkpoint_to_pytorch(
		    args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
		)




