"""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()
a							=logging.get_logger(__name__)

a							='Hello world! cécé herlolip'





def       lowerCamelCase_			( __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase    )							-> List[Any]:
						lowerCamelCase__ =FairseqRobertaModel.from_pretrained(__lowerCAmelCase    )
						roberta.eval()  # disable dropout
						lowerCamelCase__ =roberta.model.encoder.sentence_encoder
						lowerCamelCase__ =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=514       ,   type_vocab_size=1       ,   layer_norm_eps=1e-5       ,   )
						if classification_head:
												lowerCamelCase__ =roberta.model.classification_heads["mnli"].out_proj.weight.shape[0]
						print("Our BERT config:"       ,   __lowerCAmelCase    )

						lowerCamelCase__ =RobertaForSequenceClassification(__lowerCAmelCase    ) if classification_head else RobertaForMaskedLM(__lowerCAmelCase    )
						model.eval()

						# Now let's copy all the weights.
						# Embeddings
						lowerCamelCase__ =roberta_sent_encoder.embed_tokens.weight
						lowerCamelCase__ =roberta_sent_encoder.embed_positions.weight
						lowerCamelCase__ =torch.zeros_like(
						    model.roberta.embeddings.token_type_embeddings.weight    )  # just zero them out b/c RoBERTa doesn't use them.
						lowerCamelCase__ =roberta_sent_encoder.emb_layer_norm.weight
						lowerCamelCase__ =roberta_sent_encoder.emb_layer_norm.bias

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

												# self attention
												lowerCamelCase__ =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)    )
												)

												lowerCamelCase__ =roberta_layer.self_attn.q_proj.weight
												lowerCamelCase__ =roberta_layer.self_attn.q_proj.bias
												lowerCamelCase__ =roberta_layer.self_attn.k_proj.weight
												lowerCamelCase__ =roberta_layer.self_attn.k_proj.bias
												lowerCamelCase__ =roberta_layer.self_attn.v_proj.weight
												lowerCamelCase__ =roberta_layer.self_attn.v_proj.bias

												# self-attention output
												lowerCamelCase__ =layer.attention.output
												assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
												lowerCamelCase__ =roberta_layer.self_attn.out_proj.weight
												lowerCamelCase__ =roberta_layer.self_attn.out_proj.bias
												lowerCamelCase__ =roberta_layer.self_attn_layer_norm.weight
												lowerCamelCase__ =roberta_layer.self_attn_layer_norm.bias

												# intermediate
												lowerCamelCase__ =layer.intermediate
												assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
												lowerCamelCase__ =roberta_layer.fca.weight
												lowerCamelCase__ =roberta_layer.fca.bias

												# output
												lowerCamelCase__ =layer.output
												assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
												lowerCamelCase__ =roberta_layer.fca.weight
												lowerCamelCase__ =roberta_layer.fca.bias
												lowerCamelCase__ =roberta_layer.final_layer_norm.weight
												lowerCamelCase__ =roberta_layer.final_layer_norm.bias
												# end of layer

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

						# Let's check that we get the same results.
						lowerCamelCase__ =roberta.encode(__lowerCAmelCase    ).unsqueeze(0    )  # batch of size 1

						lowerCamelCase__ =model(__lowerCAmelCase    )[0]
						if classification_head:
												lowerCamelCase__ =roberta.model.classification_heads["mnli"](roberta.extract_features(__lowerCAmelCase    )    )
						else:
												lowerCamelCase__ =roberta.model(__lowerCAmelCase    )[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(__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__":
	a							=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.'
	)
	a							=parser.parse_args()
	convert_roberta_checkpoint_to_pytorch(
	    args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
	)



"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


a							={
    'configuration_openai': ['OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'OpenAIGPTConfig'],
    'tokenization_openai': ['OpenAIGPTTokenizer'],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a							=['OpenAIGPTTokenizerFast']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a							=[
	    'OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'OpenAIGPTDoubleHeadsModel',
	    'OpenAIGPTForSequenceClassification',
	    'OpenAIGPTLMHeadModel',
	    'OpenAIGPTModel',
	    'OpenAIGPTPreTrainedModel',
	    'load_tf_weights_in_openai_gpt',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a							=[
	    'TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'TFOpenAIGPTDoubleHeadsModel',
	    'TFOpenAIGPTForSequenceClassification',
	    'TFOpenAIGPTLMHeadModel',
	    'TFOpenAIGPTMainLayer',
	    'TFOpenAIGPTModel',
	    'TFOpenAIGPTPreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_openai import OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenAIGPTConfig
	from .tokenization_openai import OpenAIGPTTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_openai_fast import OpenAIGPTTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_openai import (
		    OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    OpenAIGPTDoubleHeadsModel,
		    OpenAIGPTForSequenceClassification,
		    OpenAIGPTLMHeadModel,
		    OpenAIGPTModel,
		    OpenAIGPTPreTrainedModel,
		    load_tf_weights_in_openai_gpt,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_openai import (
		    TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFOpenAIGPTDoubleHeadsModel,
		    TFOpenAIGPTForSequenceClassification,
		    TFOpenAIGPTLMHeadModel,
		    TFOpenAIGPTMainLayer,
		    TFOpenAIGPTModel,
		    TFOpenAIGPTPreTrainedModel,
		)

else:
	import sys

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


