


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()
snake_case__     							= logging.get_logger(__name__)

snake_case__     							= 'Hello world! cécé herlolip'
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :str      ,						lowercase__  :str      ,						lowercase__  :bool				)       ->  Optional[Any]:
						__magic_name__		, __magic_name__				:							Optional[int]				=      os.path.split(lowercase__				)
						__magic_name__				:							Union[str, Any]				=      DataaVecTextModel.from_pretrained(
						    lowercase__      ,						checkpoint_file=lowercase__				)
						dataavec.eval()  # disable dropout
						__magic_name__				:							List[Any]				=      dataavec.models[0]
						__magic_name__				:							int				=      dataavec_model.encoder.sentence_encoder
						__magic_name__				:							Dict				=      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:
												__magic_name__				:							List[Any]				=      dataavec.model.classification_heads['mnli'].out_proj.weight.shape[0]
						print('Our BERT config:'      ,						lowercase__				)

						__magic_name__				:							Tuple				=      DataaVecTextForSequenceClassification(lowercase__				) if classification_head else DataaVecTextForMaskedLM(lowercase__				)
						model.eval()

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

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

												# self attention
												__magic_name__				:							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)				)}"
												)

												__magic_name__				:							List[str]				=      dataavec_layer.self_attn.q_proj.weight
												__magic_name__				:							Any				=      dataavec_layer.self_attn.q_proj.bias
												__magic_name__				:							List[Any]				=      dataavec_layer.self_attn.k_proj.weight
												__magic_name__				:							Union[str, Any]				=      dataavec_layer.self_attn.k_proj.bias
												__magic_name__				:							Dict				=      dataavec_layer.self_attn.v_proj.weight
												__magic_name__				:							Tuple				=      dataavec_layer.self_attn.v_proj.bias

												# self-attention output
												__magic_name__				:							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}"
												__magic_name__				:							Dict				=      dataavec_layer.self_attn.out_proj.weight
												__magic_name__				:							List[str]				=      dataavec_layer.self_attn.out_proj.bias
												__magic_name__				:							str				=      dataavec_layer.self_attn_layer_norm.weight
												__magic_name__				:							Tuple				=      dataavec_layer.self_attn_layer_norm.bias

												# intermediate
												__magic_name__				:							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}"
												__magic_name__				:							List[str]				=      dataavec_layer.fca.weight
												__magic_name__				:							Dict				=      dataavec_layer.fca.bias

												# output
												__magic_name__				:							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}"
												__magic_name__				:							Optional[Any]				=      dataavec_layer.fca.weight
												__magic_name__				:							Union[str, Any]				=      dataavec_layer.fca.bias
												__magic_name__				:							List[str]				=      dataavec_layer.final_layer_norm.weight
												__magic_name__				:							Dict				=      dataavec_layer.final_layer_norm.bias
												# end of layer

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

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

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

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


if __name__ == "__main__":
			snake_case__     							= 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.'
			)
			snake_case__     							= parser.parse_args()
			convert_dataavec_checkpoint_to_pytorch(
			    args.checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
			)






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


snake_case__     							= {
    'configuration_flaubert': ['FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FlaubertConfig', 'FlaubertOnnxConfig'],
    'tokenization_flaubert': ['FlaubertTokenizer'],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			snake_case__     							= [
			    'FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'FlaubertForMultipleChoice',
			    'FlaubertForQuestionAnswering',
			    'FlaubertForQuestionAnsweringSimple',
			    'FlaubertForSequenceClassification',
			    'FlaubertForTokenClassification',
			    'FlaubertModel',
			    'FlaubertWithLMHeadModel',
			    'FlaubertPreTrainedModel',
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			snake_case__     							= [
			    'TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'TFFlaubertForMultipleChoice',
			    'TFFlaubertForQuestionAnsweringSimple',
			    'TFFlaubertForSequenceClassification',
			    'TFFlaubertForTokenClassification',
			    'TFFlaubertModel',
			    'TFFlaubertPreTrainedModel',
			    'TFFlaubertWithLMHeadModel',
			]


if TYPE_CHECKING:
			from .configuration_flaubert import FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, FlaubertConfig, FlaubertOnnxConfig
			from .tokenization_flaubert import FlaubertTokenizer

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_flaubert import (
						    FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    FlaubertForMultipleChoice,
						    FlaubertForQuestionAnswering,
						    FlaubertForQuestionAnsweringSimple,
						    FlaubertForSequenceClassification,
						    FlaubertForTokenClassification,
						    FlaubertModel,
						    FlaubertPreTrainedModel,
						    FlaubertWithLMHeadModel,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_flaubert import (
						    TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFFlaubertForMultipleChoice,
						    TFFlaubertForQuestionAnsweringSimple,
						    TFFlaubertForSequenceClassification,
						    TFFlaubertForTokenClassification,
						    TFFlaubertModel,
						    TFFlaubertPreTrainedModel,
						    TFFlaubertWithLMHeadModel,
						)

else:
			import sys

			snake_case__     							= _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


