





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()
UpperCAmelCase__  :	Any     =logging.get_logger(__name__)

UpperCAmelCase__  :	List[str]     ='''Hello world! cécé herlolip'''





def    _lowercase    (  _UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase )  ->     str:
				lowerCamelCase       ,     lowerCamelCase		       =os.path.split(_UpperCAmelCase )
				lowerCamelCase		       =DataaVecTextModel.from_pretrained(
				    _UpperCAmelCase	,					checkpoint_file=_UpperCAmelCase )
				dataavec.eval()  # disable dropout
				lowerCamelCase		       =dataavec.models[0]
				lowerCamelCase		       =dataavec_model.encoder.sentence_encoder
				lowerCamelCase		       =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_14	,					type_vocab_size=1	,					layer_norm_eps=1e-5	,					)
				if classification_head:
								lowerCamelCase		       =dataavec.model.classification_heads["""mnli"""].out_proj.weight.shape[0]
				print("""Our BERT config:"""	,					_UpperCAmelCase )

				lowerCamelCase		       =DataaVecTextForSequenceClassification(_UpperCAmelCase ) if classification_head else DataaVecTextForMaskedLM(_UpperCAmelCase )
				model.eval()

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

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

								# self attention
								lowerCamelCase		       =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		       =dataavec_layer.self_attn.q_proj.weight
								lowerCamelCase		       =dataavec_layer.self_attn.q_proj.bias
								lowerCamelCase		       =dataavec_layer.self_attn.k_proj.weight
								lowerCamelCase		       =dataavec_layer.self_attn.k_proj.bias
								lowerCamelCase		       =dataavec_layer.self_attn.v_proj.weight
								lowerCamelCase		       =dataavec_layer.self_attn.v_proj.bias

								# self-attention output
								lowerCamelCase		       =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		       =dataavec_layer.self_attn.out_proj.weight
								lowerCamelCase		       =dataavec_layer.self_attn.out_proj.bias
								lowerCamelCase		       =dataavec_layer.self_attn_layer_norm.weight
								lowerCamelCase		       =dataavec_layer.self_attn_layer_norm.bias

								# intermediate
								lowerCamelCase		       =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		       =dataavec_layer.fca.weight
								lowerCamelCase		       =dataavec_layer.fca.bias

								# output
								lowerCamelCase		       =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		       =dataavec_layer.fca.weight
								lowerCamelCase		       =dataavec_layer.fca.bias
								lowerCamelCase		       =dataavec_layer.final_layer_norm.weight
								lowerCamelCase		       =dataavec_layer.final_layer_norm.bias
								# end of layer

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

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

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

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


if __name__ == "__main__":
							UpperCAmelCase__  :	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.'''
							)
							UpperCAmelCase__  :	Dict     =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_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase__  :	Optional[Any]     ={'''configuration_pegasus''': ['''PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''PegasusConfig''']}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__  :	Union[str, Any]     =['''PegasusTokenizer''']

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__  :	int     =['''PegasusTokenizerFast''']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__  :	str     =[
							    '''PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''PegasusForCausalLM''',
							    '''PegasusForConditionalGeneration''',
							    '''PegasusModel''',
							    '''PegasusPreTrainedModel''',
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__  :	Union[str, Any]     =[
							    '''TFPegasusForConditionalGeneration''',
							    '''TFPegasusModel''',
							    '''TFPegasusPreTrainedModel''',
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__  :	Dict     =[
							    '''FlaxPegasusForConditionalGeneration''',
							    '''FlaxPegasusModel''',
							    '''FlaxPegasusPreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_pegasus import PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP, PegasusConfig

							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_pegasus import PegasusTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_pegasus_fast import PegasusTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_pegasus import (
														    PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
														    PegasusForCausalLM,
														    PegasusForConditionalGeneration,
														    PegasusModel,
														    PegasusPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_pegasus import TFPegasusForConditionalGeneration, TFPegasusModel, TFPegasusPreTrainedModel

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_pegasus import (
														    FlaxPegasusForConditionalGeneration,
														    FlaxPegasusModel,
														    FlaxPegasusPreTrainedModel,
														)

else:
							import sys

							UpperCAmelCase__  :	Union[str, Any]     =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





