
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()
SCREAMING_SNAKE_CASE__				=							logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__				=							"""Hello world! cécé herlolip"""




def 				__SCREAMING_SNAKE_CASE  (						SCREAMING_SNAKE_CASE			: Optional[Any]       ,				SCREAMING_SNAKE_CASE			: Dict       ,				SCREAMING_SNAKE_CASE			: int     )	->		Optional[Any]:
				__lowercase						,   __lowercase								=   os.path.split(SCREAMING_SNAKE_CASE     )
				__lowercase								=   DataaVecTextModel.from_pretrained(
				    SCREAMING_SNAKE_CASE       ,				checkpoint_file=SCREAMING_SNAKE_CASE     )
				dataavec.eval()  # disable dropout
				__lowercase								=   dataavec.models[0]
				__lowercase								=   dataavec_model.encoder.sentence_encoder
				__lowercase								=   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=514       ,				type_vocab_size=1       ,				layer_norm_eps=1E-5       ,				)
				if classification_head:
								__lowercase								=   dataavec.model.classification_heads['mnli'].out_proj.weight.shape[0]
				print('Our BERT config:'       ,				SCREAMING_SNAKE_CASE     )

				__lowercase								=   DataaVecTextForSequenceClassification(SCREAMING_SNAKE_CASE     ) if classification_head else DataaVecTextForMaskedLM(SCREAMING_SNAKE_CASE     )
				model.eval()

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

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

								# self attention
								__lowercase								=   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)     )}"""
								)

								__lowercase								=   dataavec_layer.self_attn.q_proj.weight
								__lowercase								=   dataavec_layer.self_attn.q_proj.bias
								__lowercase								=   dataavec_layer.self_attn.k_proj.weight
								__lowercase								=   dataavec_layer.self_attn.k_proj.bias
								__lowercase								=   dataavec_layer.self_attn.v_proj.weight
								__lowercase								=   dataavec_layer.self_attn.v_proj.bias

								# self-attention output
								__lowercase								=   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}"""
								__lowercase								=   dataavec_layer.self_attn.out_proj.weight
								__lowercase								=   dataavec_layer.self_attn.out_proj.bias
								__lowercase								=   dataavec_layer.self_attn_layer_norm.weight
								__lowercase								=   dataavec_layer.self_attn_layer_norm.bias

								# intermediate
								__lowercase								=   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}"""
								__lowercase								=   dataavec_layer.fca.weight
								__lowercase								=   dataavec_layer.fca.bias

								# output
								__lowercase								=   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}"""
								__lowercase								=   dataavec_layer.fca.weight
								__lowercase								=   dataavec_layer.fca.bias
								__lowercase								=   dataavec_layer.final_layer_norm.weight
								__lowercase								=   dataavec_layer.final_layer_norm.bias
								# end of layer

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

				# Let's check that we get the same results.
				__lowercase								=   dataavec.encode(SCREAMING_SNAKE_CASE     ).unsqueeze(0     )  # batch of size 1

				__lowercase								=   model(SCREAMING_SNAKE_CASE     )[0]
				if classification_head:
								__lowercase								=   dataavec.model.classification_heads['mnli'](dataavec.extract_features(SCREAMING_SNAKE_CASE     )     )
				else:
								__lowercase								=   dataavec_model(SCREAMING_SNAKE_CASE     )[0]
				print(our_output.shape       ,				their_output.shape     )
				__lowercase								=   torch.max(torch.abs(our_output - their_output     )     ).item()
				print(F"""max_absolute_diff = {max_absolute_diff}"""     )  # ~ 1e-7
				__lowercase								=   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__":
							SCREAMING_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."""
							)
							SCREAMING_SNAKE_CASE__				=							parser.parse_args()
							convert_dataavec_checkpoint_to_pytorch(
							    args.checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
							)






"""simple docstring"""


from ...configuration_utils import PretrainedConfig
from ...utils import logging


__UpperCamelCase										=     logging.get_logger(__name__)

__UpperCamelCase										=     {
    '''tau/splinter-base''': '''https://huggingface.co/tau/splinter-base/resolve/main/config.json''',
    '''tau/splinter-base-qass''': '''https://huggingface.co/tau/splinter-base-qass/resolve/main/config.json''',
    '''tau/splinter-large''': '''https://huggingface.co/tau/splinter-large/resolve/main/config.json''',
    '''tau/splinter-large-qass''': '''https://huggingface.co/tau/splinter-large-qass/resolve/main/config.json''',
    # See all Splinter models at https://huggingface.co/models?filter=splinter
}




class 					UpperCamelCase   (							lowerCAmelCase__  ):
       SCREAMING_SNAKE_CASE_						      = "splinter"







       def __init__(					self,				lowerCAmelCase__=3_0522,				lowerCAmelCase__=768,				lowerCAmelCase__=12,				lowerCAmelCase__=12,				lowerCAmelCase__=3072,				lowerCAmelCase__="gelu",				lowerCAmelCase__=0.1,				lowerCAmelCase__=0.1,				lowerCAmelCase__=512,				lowerCAmelCase__=2,				lowerCAmelCase__=0.02,				lowerCAmelCase__=1e-12,				lowerCAmelCase__=True,				lowerCAmelCase__=0,				lowerCAmelCase__=104,				**lowerCAmelCase__,				) -> Dict:
            super().__init__(pad_token_id=lowerCAmelCase__,				**lowerCAmelCase__)

            snake_case_   			=   vocab_size
            snake_case_   			=   max_position_embeddings
            snake_case_   			=   hidden_size
            snake_case_   			=   num_hidden_layers
            snake_case_   			=   num_attention_heads
            snake_case_   			=   intermediate_size
            snake_case_   			=   hidden_act
            snake_case_   			=   hidden_dropout_prob
            snake_case_   			=   attention_probs_dropout_prob
            snake_case_   			=   initializer_range
            snake_case_   			=   type_vocab_size
            snake_case_   			=   layer_norm_eps
            snake_case_   			=   use_cache
            snake_case_   			=   question_token_id

