





'''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()
__UpperCAmelCase         =   logging.get_logger(__name__)

__UpperCAmelCase         =   """Hello world! cécé herlolip"""



def 						__A						(       lowerCamelCase_	,	lowerCamelCase_	,	lowerCamelCase_					):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE				:							Tuple						=	FairseqRobertaModel.from_pretrained(lowerCamelCase_					)
		roberta.eval()  # disable dropout
		SCREAMING_SNAKE_CASE				:							int						=	roberta.model.encoder.sentence_encoder
		SCREAMING_SNAKE_CASE				:							Dict						=	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=5_14	,	type_vocab_size=1	,	layer_norm_eps=1E-5	,	)
		if classification_head:
				SCREAMING_SNAKE_CASE				:							Dict						=	roberta.model.classification_heads["""mnli"""].out_proj.weight.shape[0]
		print("""Our BERT config:"""	,	lowerCamelCase_					)

		SCREAMING_SNAKE_CASE				:							Tuple						=	RobertaForSequenceClassification(lowerCamelCase_					) if classification_head else RobertaForMaskedLM(lowerCamelCase_					)
		model.eval()

		# Now let's copy all the weights.
		# Embeddings
		SCREAMING_SNAKE_CASE				:							Any						=	roberta_sent_encoder.embed_tokens.weight
		SCREAMING_SNAKE_CASE				:							List[Any]						=	roberta_sent_encoder.embed_positions.weight
		SCREAMING_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.
		SCREAMING_SNAKE_CASE				:							Any						=	roberta_sent_encoder.emb_layer_norm.weight
		SCREAMING_SNAKE_CASE				:							Optional[Any]						=	roberta_sent_encoder.emb_layer_norm.bias

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

				# self attention
				SCREAMING_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)					)
				)

				SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	roberta_layer.self_attn.q_proj.weight
				SCREAMING_SNAKE_CASE				:							Optional[Any]						=	roberta_layer.self_attn.q_proj.bias
				SCREAMING_SNAKE_CASE				:							Dict						=	roberta_layer.self_attn.k_proj.weight
				SCREAMING_SNAKE_CASE				:							int						=	roberta_layer.self_attn.k_proj.bias
				SCREAMING_SNAKE_CASE				:							List[Any]						=	roberta_layer.self_attn.v_proj.weight
				SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	roberta_layer.self_attn.v_proj.bias

				# self-attention output
				SCREAMING_SNAKE_CASE				:							BertSelfOutput						=	layer.attention.output
				assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
				SCREAMING_SNAKE_CASE				:							List[str]						=	roberta_layer.self_attn.out_proj.weight
				SCREAMING_SNAKE_CASE				:							str						=	roberta_layer.self_attn.out_proj.bias
				SCREAMING_SNAKE_CASE				:							List[Any]						=	roberta_layer.self_attn_layer_norm.weight
				SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	roberta_layer.self_attn_layer_norm.bias

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

				# output
				SCREAMING_SNAKE_CASE				:							BertOutput						=	layer.output
				assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
				SCREAMING_SNAKE_CASE				:							Optional[Any]						=	roberta_layer.fca.weight
				SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	roberta_layer.fca.bias
				SCREAMING_SNAKE_CASE				:							List[str]						=	roberta_layer.final_layer_norm.weight
				SCREAMING_SNAKE_CASE				:							Tuple						=	roberta_layer.final_layer_norm.bias
				# end of layer

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

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

		SCREAMING_SNAKE_CASE				:							Dict						=	model(lowerCamelCase_					)[0]
		if classification_head:
				SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	roberta.model.classification_heads["""mnli"""](roberta.extract_features(lowerCamelCase_					)					)
		else:
				SCREAMING_SNAKE_CASE				:							Tuple						=	roberta.model(lowerCamelCase_					)[0]
		print(our_output.shape	,	their_output.shape					)
		SCREAMING_SNAKE_CASE				:							Optional[Any]						=	torch.max(torch.abs(our_output - their_output					)					).item()
		print(f'''max_absolute_diff = {max_absolute_diff}'''					)  # ~ 1e-7
		SCREAMING_SNAKE_CASE				:							Optional[int]						=	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__":
							__UpperCAmelCase         =   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."""
							)
							__UpperCAmelCase         =   parser.parse_args()
							convert_roberta_checkpoint_to_pytorch(
							    args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
							)










'''simple docstring'''




import argparse

import torch

from transformers import ImageGPTConfig, ImageGPTForCausalLM, load_tf_weights_in_imagegpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()



def 						__A						(       lowerCamelCase_	,	lowerCamelCase_	,	lowerCamelCase_					):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE				:							Optional[int]						=	{"""small""": (5_12, 8, 24), """medium""": (10_24, 8, 36), """large""": (15_36, 16, 48)}
		SCREAMING_SNAKE_CASE,    SCREAMING_SNAKE_CASE,    SCREAMING_SNAKE_CASE				:							Tuple						=	MODELS[model_size]  # set model hyperparameters
		SCREAMING_SNAKE_CASE				:							Any						=	ImageGPTConfig(n_embd=lowerCamelCase_	,	n_layer=lowerCamelCase_	,	n_head=lowerCamelCase_					)
		SCREAMING_SNAKE_CASE				:							str						=	ImageGPTForCausalLM(lowerCamelCase_					)

		# Load weights from numpy
		load_tf_weights_in_imagegpt(lowerCamelCase_	,	lowerCamelCase_	,	lowerCamelCase_					)

		# Save pytorch-model
		SCREAMING_SNAKE_CASE				:							Any						=	pytorch_dump_folder_path + """/""" + WEIGHTS_NAME
		SCREAMING_SNAKE_CASE				:							Optional[int]						=	pytorch_dump_folder_path + """/""" + CONFIG_NAME
		print(f'''Save PyTorch model to {pytorch_weights_dump_path}'''					)
		torch.save(model.state_dict()	,	lowerCamelCase_					)
		print(f'''Save configuration file to {pytorch_config_dump_path}'''					)
		with open(lowerCamelCase_	,	"""w"""	,	encoding="""utf-8"""					) as f:
				f.write(config.to_json_string()					)


if __name__ == "__main__":
							__UpperCAmelCase         =   argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    """--imagegpt_checkpoint_path""",
							    default=None,
							    type=str,
							    required=True,
							    help="""Path to the TensorFlow checkpoint path.""",
							)
							parser.add_argument(
							    """--model_size""",
							    default=None,
							    type=str,
							    required=True,
							    help="""Size of the model (can be either 'small', 'medium' or 'large').""",
							)
							parser.add_argument(
							    """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
							)
							__UpperCAmelCase         =   parser.parse_args()
							convert_imagegpt_checkpoint_to_pytorch(
							    args.imagegpt_checkpoint_path, args.model_size, args.pytorch_dump_folder_path
							)



