



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 __future__ import annotations

from math import pi, pow


def     _UpperCAmelCase				(     _SCREAMING_SNAKE_CASE    :						int | float				):





    """simple docstring"""


    if side_length < 0:
        raise ValueError('vol_cube() only accepts non-negative values'				)
    return pow(_SCREAMING_SNAKE_CASE       ,      3				)


def     _UpperCAmelCase				(     _SCREAMING_SNAKE_CASE    :						float       ,      _SCREAMING_SNAKE_CASE    :						float				):





    """simple docstring"""


    if height < 0 or radius < 0:
        raise ValueError('vol_spherical_cap() only accepts non-negative values'				)
    # Volume is 1/3 pi * height squared * (3 * radius - height)
    return 1 / 3 * pi * pow(_SCREAMING_SNAKE_CASE       ,      2				) * (3 * radius - height)


def     _UpperCAmelCase				(     _SCREAMING_SNAKE_CASE    :						float       ,      _SCREAMING_SNAKE_CASE    :						float       ,      _SCREAMING_SNAKE_CASE    :						float				):





    """simple docstring"""


    if radius_a < 0 or radius_a < 0 or centers_distance < 0:
        raise ValueError('vol_spheres_intersect() only accepts non-negative values'				)
    if centers_distance == 0:
        return vol_sphere(min(_SCREAMING_SNAKE_CASE       ,      _SCREAMING_SNAKE_CASE				)				)

    SCREAMING_SNAKE_CASE_													=  (
        (radius_a - radius_a + centers_distance)
        * (radius_a + radius_a - centers_distance)
        / (2 * centers_distance)
    )
    SCREAMING_SNAKE_CASE_													=  (
        (radius_a - radius_a + centers_distance)
        * (radius_a + radius_a - centers_distance)
        / (2 * centers_distance)
    )

    return vol_spherical_cap(_SCREAMING_SNAKE_CASE       ,      _SCREAMING_SNAKE_CASE				) + vol_spherical_cap(_SCREAMING_SNAKE_CASE       ,      _SCREAMING_SNAKE_CASE				)


def     _UpperCAmelCase				(     _SCREAMING_SNAKE_CASE    :						float       ,      _SCREAMING_SNAKE_CASE    :						float       ,      _SCREAMING_SNAKE_CASE    :						float				):





    """simple docstring"""


    if radius_a <= 0 or radius_a <= 0 or centers_distance < 0:
        raise ValueError(
            'vol_spheres_union() only accepts non-negative values, non-zero radius'				)

    if centers_distance == 0:
        return vol_sphere(max(_SCREAMING_SNAKE_CASE       ,      _SCREAMING_SNAKE_CASE				)				)

    return (
        vol_sphere(_SCREAMING_SNAKE_CASE				)
        + vol_sphere(_SCREAMING_SNAKE_CASE				)
        - vol_spheres_intersect(_SCREAMING_SNAKE_CASE       ,      _SCREAMING_SNAKE_CASE       ,      _SCREAMING_SNAKE_CASE				)
    )


def     _UpperCAmelCase				(     _SCREAMING_SNAKE_CASE    :						float       ,      _SCREAMING_SNAKE_CASE    :						float       ,      _SCREAMING_SNAKE_CASE    :						float				):





    """simple docstring"""


    if width < 0 or height < 0 or length < 0:
        raise ValueError('vol_cuboid() only accepts non-negative values'				)
    return float(width * height * length				)


def     _UpperCAmelCase				(     _SCREAMING_SNAKE_CASE    :						float       ,      _SCREAMING_SNAKE_CASE    :						float				):





    """simple docstring"""


    if height < 0 or area_of_base < 0:
        raise ValueError('vol_cone() only accepts non-negative values'				)
    return area_of_base * height / 3.0


def     _UpperCAmelCase				(     _SCREAMING_SNAKE_CASE    :						float       ,      _SCREAMING_SNAKE_CASE    :						float				):





    """simple docstring"""


    if height < 0 or radius < 0:
        raise ValueError('vol_right_circ_cone() only accepts non-negative values'				)
    return pi * pow(_SCREAMING_SNAKE_CASE       ,      2				) * height / 3.0


def     _UpperCAmelCase				(     _SCREAMING_SNAKE_CASE    :						float       ,      _SCREAMING_SNAKE_CASE    :						float				):





    """simple docstring"""


    if height < 0 or area_of_base < 0:
        raise ValueError('vol_prism() only accepts non-negative values'				)
    return float(area_of_base * height				)


def     _UpperCAmelCase				(     _SCREAMING_SNAKE_CASE    :						float       ,      _SCREAMING_SNAKE_CASE    :						float				):





    """simple docstring"""


    if height < 0 or area_of_base < 0:
        raise ValueError('vol_pyramid() only accepts non-negative values'				)
    return area_of_base * height / 3.0


def     _UpperCAmelCase				(     _SCREAMING_SNAKE_CASE    :						float				):





    """simple docstring"""


    if radius < 0:
        raise ValueError('vol_sphere() only accepts non-negative values'				)
    # Volume is 4/3 * pi * radius cubed
    return 4 / 3 * pi * pow(_SCREAMING_SNAKE_CASE       ,      3				)


def     _UpperCAmelCase				(     _SCREAMING_SNAKE_CASE    :						float				):





    """simple docstring"""


    if radius < 0:
        raise ValueError('vol_hemisphere() only accepts non-negative values'				)
    # Volume is radius cubed * pi * 2/3
    return pow(_SCREAMING_SNAKE_CASE       ,      3				) * pi * 2 / 3


def     _UpperCAmelCase				(     _SCREAMING_SNAKE_CASE    :						float       ,      _SCREAMING_SNAKE_CASE    :						float				):





    """simple docstring"""


    if height < 0 or radius < 0:
        raise ValueError('vol_circular_cylinder() only accepts non-negative values'				)
    # Volume is radius squared * height * pi
    return pow(_SCREAMING_SNAKE_CASE       ,      2				) * height * pi


def     _UpperCAmelCase				(     _SCREAMING_SNAKE_CASE    :						float       ,      _SCREAMING_SNAKE_CASE    :						float       ,      _SCREAMING_SNAKE_CASE    :						float				):





    """simple docstring"""


    if inner_radius < 0 or outer_radius < 0 or height < 0:
        raise ValueError(
            'vol_hollow_circular_cylinder() only accepts non-negative values'				)
    if outer_radius <= inner_radius:
        raise ValueError('outer_radius must be greater than inner_radius'				)
    return pi * (pow(_SCREAMING_SNAKE_CASE       ,      2				) - pow(_SCREAMING_SNAKE_CASE       ,      2				)) * height


def     _UpperCAmelCase				(     _SCREAMING_SNAKE_CASE    :						float       ,      _SCREAMING_SNAKE_CASE    :						float       ,      _SCREAMING_SNAKE_CASE    :						float				):





    """simple docstring"""


    if radius_a < 0 or radius_a < 0 or height < 0:
        raise ValueError('vol_conical_frustum() only accepts non-negative values'				)
    return (
        1
        / 3
        * pi
        * height
        * (pow(_SCREAMING_SNAKE_CASE       ,      2				) + pow(_SCREAMING_SNAKE_CASE       ,      2				) + radius_a * radius_a)
    )


def     _UpperCAmelCase				(     _SCREAMING_SNAKE_CASE    :						float       ,      _SCREAMING_SNAKE_CASE    :						float				):





    """simple docstring"""


    if torus_radius < 0 or tube_radius < 0:
        raise ValueError('vol_torus() only accepts non-negative values'				)
    return 2 * pow(_SCREAMING_SNAKE_CASE       ,      2				) * torus_radius * pow(_SCREAMING_SNAKE_CASE       ,      2				)


def     _UpperCAmelCase				(     ):





    """simple docstring"""


    print('Volumes:'				)
    print(f"""Cube: {vol_cube(2				) = }"""				)  # = 8
    print(f"""Cuboid: {vol_cuboid(2       ,      2       ,      2				) = }"""				)  # = 8
    print(f"""Cone: {vol_cone(2       ,      2				) = }"""				)  # ~= 1.33
    print(f"""Right Circular Cone: {vol_right_circ_cone(2       ,      2				) = }"""				)  # ~= 8.38
    print(f"""Prism: {vol_prism(2       ,      2				) = }"""				)  # = 4
    print(f"""Pyramid: {vol_pyramid(2       ,      2				) = }"""				)  # ~= 1.33
    print(f"""Sphere: {vol_sphere(2				) = }"""				)  # ~= 33.5
    print(f"""Hemisphere: {vol_hemisphere(2				) = }"""				)  # ~= 16.75
    print(f"""Circular Cylinder: {vol_circular_cylinder(2       ,      2				) = }"""				)  # ~= 25.1
    print(f"""Torus: {vol_torus(2       ,      2				) = }"""				)  # ~= 157.9
    print(f"""Conical Frustum: {vol_conical_frustum(2       ,      2       ,      4				) = }"""				)  # ~= 58.6
    print(f"""Spherical cap: {vol_spherical_cap(1       ,      2				) = }"""				)  # ~= 5.24
    print(f"""Spheres intersetion: {vol_spheres_intersect(2       ,      2       ,      1				) = }"""				)  # ~= 21.21
    print(f"""Spheres union: {vol_spheres_union(2       ,      2       ,      1				) = }"""				)  # ~= 45.81
    print(
        f"""Hollow Circular Cylinder: {vol_hollow_circular_cylinder(1       ,      2       ,      3				) = }"""				)  # ~= 28.3


if __name__ == "__main__":
       main()


