



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_	       =   logging.get_logger(__name__)

UpperCamelCase_	       =   "Hello world! cécé herlolip"







def 				A			(		__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase	)       ->       Any:



						'''simple docstring'''






						UpperCAmelCase_							,      UpperCAmelCase_     							=     os.path.split(__UpperCAmelCase	)
						UpperCAmelCase_     							=     DataaVecTextModel.from_pretrained(
						    __UpperCAmelCase							,					checkpoint_file=__UpperCAmelCase	)
						dataavec.eval()  # disable dropout
						UpperCAmelCase_     							=     dataavec.models[0]
						UpperCAmelCase_     							=     dataavec_model.encoder.sentence_encoder
						UpperCAmelCase_     							=     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:
												UpperCAmelCase_     							=     dataavec.model.classification_heads['''mnli'''].out_proj.weight.shape[0]
						print('''Our BERT config:'''							,					__UpperCAmelCase	)

						UpperCAmelCase_     							=     DataaVecTextForSequenceClassification(__UpperCAmelCase	) if classification_head else DataaVecTextForMaskedLM(__UpperCAmelCase	)
						model.eval()

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

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

												# self attention
												UpperCAmelCase_     							=     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)	)}"
												)

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

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

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

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

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

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

						UpperCAmelCase_     							=     model(__UpperCAmelCase	)[0]
						if classification_head:
												UpperCAmelCase_     							=     dataavec.model.classification_heads['''mnli'''](dataavec.extract_features(__UpperCAmelCase	)	)
						else:
												UpperCAmelCase_     							=     dataavec_model(__UpperCAmelCase	)[0]
						print(our_output.shape							,					their_output.shape	)
						UpperCAmelCase_     							=     torch.max(torch.abs(our_output - their_output	)	).item()
						print(f"max_absolute_diff = {max_absolute_diff}"	)  # ~ 1e-7
						UpperCAmelCase_     							=     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_	       =   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_	       =   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_torch_available, is_vision_available


UpperCamelCase_	       =   {"configuration_vit_hybrid": ["VIT_HYBRID_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViTHybridConfig"]}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase_	       =   [
							    "VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "ViTHybridForImageClassification",
							    "ViTHybridModel",
							    "ViTHybridPreTrainedModel",
							]

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase_	       =   ["ViTHybridImageProcessor"]


if TYPE_CHECKING:
							from .configuration_vit_hybrid import VIT_HYBRID_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTHybridConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_vit_hybrid import (
														    VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST,
														    ViTHybridForImageClassification,
														    ViTHybridModel,
														    ViTHybridPreTrainedModel,
														)

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .image_processing_vit_hybrid import ViTHybridImageProcessor


else:
							import sys

							UpperCamelCase_	       =   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





