



import argparse
import os
import pathlib

import fairseq
import torch
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import (
    DataaVecTextConfig,
    DataaVecTextForMaskedLM,
    DataaVecTextForSequenceClassification,
    DataaVecTextModel,
)
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)

# IMPORTANT: In order for this script to run, please make sure to download the dictionary: `dict.txt` from wget https://dl.fbaipublicfiles.com/fairseq/models/roberta.large.tar.gz
# File copied from https://github.com/pytorch/fairseq/blob/main/examples/data2vec/models/data2vec_text.py
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()
lowerCAmelCase					:				str   	=logging.get_logger(__name__)

lowerCAmelCase					:				Dict   	='Hello world! cécé herlolip'







def  _UpperCAmelCase  (				SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__  ):


						'''simple docstring'''




						lowerCAmelCase     ,	lowerCAmelCase	:   str					     =					os.path.split(SCREAMING_SNAKE_CASE__  )
						lowerCAmelCase	:   Any					     =					DataaVecTextModel.from_pretrained(
						    SCREAMING_SNAKE_CASE__			,checkpoint_file=SCREAMING_SNAKE_CASE__  )
						dataavec.eval()  # disable dropout
						lowerCAmelCase	:   Any					     =					dataavec.models[0]
						lowerCAmelCase	:   str					     =					dataavec_model.encoder.sentence_encoder
						lowerCAmelCase	:   List[str]					     =					DataaVecTextConfig(
						    vocab_size=dataavec_sent_encoder.embed_tokens.num_embeddings			,hidden_size=dataavec_model.args.encoder_embed_dim			,num_hidden_layers=dataavec_model.args.encoder_layers			,num_attention_heads=dataavec_model.args.encoder_attention_heads			,intermediate_size=dataavec_model.args.encoder_ffn_embed_dim			,max_position_embeddings=5_1_4			,type_vocab_size=1			,layer_norm_eps=1e-5			,)
						if classification_head:
												lowerCAmelCase	:   Tuple					     =					dataavec.model.classification_heads["""mnli"""].out_proj.weight.shape[0]
						print("""Our BERT config:"""			,SCREAMING_SNAKE_CASE__  )

						lowerCAmelCase	:   str					     =					DataaVecTextForSequenceClassification(SCREAMING_SNAKE_CASE__  ) if classification_head else DataaVecTextForMaskedLM(SCREAMING_SNAKE_CASE__  )
						model.eval()

						# Now let's copy all the weights.
						# Embeddings
						lowerCAmelCase	:   List[str]					     =					dataavec_sent_encoder.embed_tokens.weight
						lowerCAmelCase	:   List[Any]					     =					dataavec_sent_encoder.embed_positions.weight
						lowerCAmelCase	:   List[Any]					     =					torch.zeros_like(
						    model.dataavec_text.embeddings.token_type_embeddings.weight  )  # just zero them out b/c data2vec doesn't use them.
						lowerCAmelCase	:   Dict					     =					dataavec_sent_encoder.layernorm_embedding.weight
						lowerCAmelCase	:   str					     =					dataavec_sent_encoder.layernorm_embedding.bias

						for i in range(config.num_hidden_layers  ):
												# Encoder: start of layer
												lowerCAmelCase	:   BertLayer					     =					model.dataavec_text.encoder.layer[i]
												lowerCAmelCase	:   TransformerSentenceEncoderLayer					     =					dataavec_sent_encoder.layers[i]

												# self attention
												lowerCAmelCase	:   BertSelfAttention					     =					layer.attention.self
												assert dataavec_layer.self_attn.k_proj.weight.data.shape == torch.Size(
												    (config.hidden_size, config.hidden_size)  ), (
												    "Shape for data2vec_layer.self_attn.k_proj.weight.data should be"
												    F""" {torch.Size((config.hidden_size, config.hidden_size)  )}"""
												)
												assert dataavec_layer.self_attn.q_proj.weight.data.shape == torch.Size(
												    (config.hidden_size, config.hidden_size)  ), (
												    "Shape for data2vec_layer.self_attn.q_proj.weight.data should be"
												    F""" {torch.Size((config.hidden_size, config.hidden_size)  )}"""
												)
												assert dataavec_layer.self_attn.v_proj.weight.data.shape == torch.Size(
												    (config.hidden_size, config.hidden_size)  ), (
												    "Shape for data2vec_layer.self_attn.v_proj.weight.data should be"
												    F""" {torch.Size((config.hidden_size, config.hidden_size)  )}"""
												)

												lowerCAmelCase	:   int					     =					dataavec_layer.self_attn.q_proj.weight
												lowerCAmelCase	:   List[Any]					     =					dataavec_layer.self_attn.q_proj.bias
												lowerCAmelCase	:   str					     =					dataavec_layer.self_attn.k_proj.weight
												lowerCAmelCase	:   int					     =					dataavec_layer.self_attn.k_proj.bias
												lowerCAmelCase	:   Dict					     =					dataavec_layer.self_attn.v_proj.weight
												lowerCAmelCase	:   Dict					     =					dataavec_layer.self_attn.v_proj.bias

												# self-attention output
												lowerCAmelCase	:   BertSelfOutput					     =					layer.attention.output
												assert (
												    self_output.dense.weight.shape == dataavec_layer.self_attn.out_proj.weight.shape
												), F"""Shape for self_output.dense.weight should be {dataavec_layer.self_attn.out_proj.weight.shape}"""
												lowerCAmelCase	:   Any					     =					dataavec_layer.self_attn.out_proj.weight
												lowerCAmelCase	:   List[Any]					     =					dataavec_layer.self_attn.out_proj.bias
												lowerCAmelCase	:   List[str]					     =					dataavec_layer.self_attn_layer_norm.weight
												lowerCAmelCase	:   int					     =					dataavec_layer.self_attn_layer_norm.bias

												# intermediate
												lowerCAmelCase	:   BertIntermediate					     =					layer.intermediate
												assert (
												    intermediate.dense.weight.shape == dataavec_layer.fca.weight.shape
												), F"""Shape for intermediate.dense.weight should be {dataavec_layer.fca.weight.shape}"""
												lowerCAmelCase	:   Any					     =					dataavec_layer.fca.weight
												lowerCAmelCase	:   Optional[int]					     =					dataavec_layer.fca.bias

												# output
												lowerCAmelCase	:   BertOutput					     =					layer.output
												assert (
												    bert_output.dense.weight.shape == dataavec_layer.fca.weight.shape
												), F"""Shape for bert_output.dense.weight should be {dataavec_layer.fca.weight.shape}"""
												lowerCAmelCase	:   Dict					     =					dataavec_layer.fca.weight
												lowerCAmelCase	:   Dict					     =					dataavec_layer.fca.bias
												lowerCAmelCase	:   Union[str, Any]					     =					dataavec_layer.final_layer_norm.weight
												lowerCAmelCase	:   Dict					     =					dataavec_layer.final_layer_norm.bias
												# end of layer

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

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

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

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


if __name__ == "__main__":
		lowerCAmelCase					:				Optional[Any]   	=argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '--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					:				Union[str, Any]   	=parser.parse_args()
		convert_dataavec_checkpoint_to_pytorch(
		    args.checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
		)






import argparse
import json

from transformers import GPTNeoConfig, GPTNeoForCausalLM, load_tf_weights_in_gpt_neo
from transformers.utils import logging


logging.set_verbosity_info()







def  _UpperCAmelCase  (				SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__  ):


						'''simple docstring'''




						lowerCAmelCase	:   List[str]					     =					json.load(open(SCREAMING_SNAKE_CASE__			,"""r"""  )  )
						lowerCAmelCase	:   Tuple					     =					GPTNeoConfig(
						    hidden_size=config_json["""n_embd"""]			,num_layers=config_json["""n_layer"""]			,num_heads=config_json["""n_head"""]			,attention_types=config_json["""attention_types"""]			,max_position_embeddings=config_json["""n_positions"""]			,resid_dropout=config_json["""res_dropout"""]			,embed_dropout=config_json["""embed_dropout"""]			,attention_dropout=config_json["""attn_dropout"""]			,)
						print(F"""Building PyTorch model from configuration: {config}"""  )
						lowerCAmelCase	:   str					     =					GPTNeoForCausalLM(SCREAMING_SNAKE_CASE__  )

						# Load weights from tf checkpoint
						load_tf_weights_in_gpt_neo(SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__  )

						# Save pytorch-model
						print(F"""Save PyTorch model to {pytorch_dump_path}"""  )
						model.save_pretrained(SCREAMING_SNAKE_CASE__  )


if __name__ == "__main__":
		lowerCAmelCase					:				Optional[Any]   	=argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
		)
		parser.add_argument(
		    '--config_file',
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        'The config json file corresponding to the pre-trained mesh-tf model. \n'
		        'This specifies the model architecture.'
		    ),
		)
		parser.add_argument(
		    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
		)
		lowerCAmelCase					:				str   	=parser.parse_args()
		convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)

