"""simple docstring"""







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_    :    List[Any]											=						logging.get_logger(__name__)

UpperCamelCase_    :    str											=						"""Hello world! cécé herlolip"""


def 					a__ (						UpperCAmelCase__    :     str						,     UpperCAmelCase__    :     str						,     UpperCAmelCase__    :     bool     )->		Optional[Any]:
						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=5_1_4						,     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_    :    Optional[int]											=						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_    :    List[Any]											=						parser.parse_args()
						convert_dataavec_checkpoint_to_pytorch(
						    args.checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
						)


"""simple docstring"""







from collections import namedtuple

UpperCamelCase_    :    Any											=						namedtuple("""from_to""", """from_ to""")

UpperCamelCase_    :    Union[str, Any]											=						{
    """millimeter""": """mm""",
    """centimeter""": """cm""",
    """meter""": """m""",
    """kilometer""": """km""",
    """inch""": """in""",
    """inche""": """in""",  # Trailing 's' has been stripped off
    """feet""": """ft""",
    """foot""": """ft""",
    """yard""": """yd""",
    """mile""": """mi""",
}

UpperCamelCase_    :    str											=						{
    """mm""": from_to(0.001, 1000),
    """cm""": from_to(0.01, 100),
    """m""": from_to(1, 1),
    """km""": from_to(1000, 0.001),
    """in""": from_to(0.0254, 39.3701),
    """ft""": from_to(0.3048, 3.2_8084),
    """yd""": from_to(0.9144, 1.0_9361),
    """mi""": from_to(1609.34, 0.0_0062_1371),
}


def 					a__ (						UpperCAmelCase__    :     float						,     UpperCAmelCase__    :     str						,     UpperCAmelCase__    :     str     )->		float:
						UpperCAmelCase__   		=						from_type.lower().rstrip("""s"""     )
						UpperCAmelCase__   		=						TYPE_CONVERSION.get(UpperCAmelCase__						,     UpperCAmelCase__     )
						UpperCAmelCase__   		=						to_type.lower().rstrip("""s"""     )
						UpperCAmelCase__   		=						TYPE_CONVERSION.get(UpperCAmelCase__						,     UpperCAmelCase__     )
						if new_from not in METRIC_CONVERSION:
												UpperCAmelCase__   		=						(
												    f'Invalid \'from_type\' value: {from_type!r}.\n'
												    f'Conversion abbreviations are: {", ".join(UpperCAmelCase__     )}'
												)
												raise ValueError(UpperCAmelCase__     )
						if new_to not in METRIC_CONVERSION:
												UpperCAmelCase__   		=						(
												    f'Invalid \'to_type\' value: {to_type!r}.\n'
												    f'Conversion abbreviations are: {", ".join(UpperCAmelCase__     )}'
												)
												raise ValueError(UpperCAmelCase__     )
						return value * METRIC_CONVERSION[new_from].from_ * METRIC_CONVERSION[new_to].to


if __name__ == "__main__":
						import doctest

						doctest.testmod()

