

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()
a_         =      logging.get_logger(__name__)

a_         =      'Hello world! cécé herlolip'

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

	SCREAMING_SNAKE_CASE				: Optional[int]			       =    DataaVecTextForSequenceClassification(_a) if classification_head else DataaVecTextForMaskedLM(_a)
	model.eval()

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

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

		# self attention
		SCREAMING_SNAKE_CASE				: 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))}"
		)

		SCREAMING_SNAKE_CASE				: List[str]			       =    dataavec_layer.self_attn.q_proj.weight
		SCREAMING_SNAKE_CASE				: Tuple			       =    dataavec_layer.self_attn.q_proj.bias
		SCREAMING_SNAKE_CASE				: Tuple			       =    dataavec_layer.self_attn.k_proj.weight
		SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    dataavec_layer.self_attn.k_proj.bias
		SCREAMING_SNAKE_CASE				: int			       =    dataavec_layer.self_attn.v_proj.weight
		SCREAMING_SNAKE_CASE				: Optional[Any]			       =    dataavec_layer.self_attn.v_proj.bias

		# self-attention output
		SCREAMING_SNAKE_CASE				: 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}"
		SCREAMING_SNAKE_CASE				: int			       =    dataavec_layer.self_attn.out_proj.weight
		SCREAMING_SNAKE_CASE				: Optional[Any]			       =    dataavec_layer.self_attn.out_proj.bias
		SCREAMING_SNAKE_CASE				: str			       =    dataavec_layer.self_attn_layer_norm.weight
		SCREAMING_SNAKE_CASE				: int			       =    dataavec_layer.self_attn_layer_norm.bias

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

		# output
		SCREAMING_SNAKE_CASE				: 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}"
		SCREAMING_SNAKE_CASE				: Tuple			       =    dataavec_layer.fca.weight
		SCREAMING_SNAKE_CASE				: Any			       =    dataavec_layer.fca.bias
		SCREAMING_SNAKE_CASE				: int			       =    dataavec_layer.final_layer_norm.weight
		SCREAMING_SNAKE_CASE				: Tuple			       =    dataavec_layer.final_layer_norm.bias
		# end of layer

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

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

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

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


if __name__ == "__main__":
			a_         =      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.'
			)
			a_         =      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_tokenizers_available, is_torch_available


a_         =      {
    'configuration_codegen': ['CODEGEN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CodeGenConfig', 'CodeGenOnnxConfig'],
    'tokenization_codegen': ['CodeGenTokenizer'],
}

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

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a_         =      [
			    'CODEGEN_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'CodeGenForCausalLM',
			    'CodeGenModel',
			    'CodeGenPreTrainedModel',
			]

if TYPE_CHECKING:
			from .configuration_codegen import CODEGEN_PRETRAINED_CONFIG_ARCHIVE_MAP, CodeGenConfig, CodeGenOnnxConfig
			from .tokenization_codegen import CodeGenTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_codegen_fast import CodeGenTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_codegen import (
						    CODEGEN_PRETRAINED_MODEL_ARCHIVE_LIST,
						    CodeGenForCausalLM,
						    CodeGenModel,
						    CodeGenPreTrainedModel,
						)

else:
			import sys

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