






'''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()
SCREAMING_SNAKE_CASE_				      =							logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_				      =							"Hello world! cécé herlolip"






def   lowerCAmelCase__			(					SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__		):
 __a			,    __a    :	List[Any]									=						os.path.split(SCREAMING_SNAKE_CASE__		)
 __a    :	Optional[int]									=						DataaVecTextModel.from_pretrained(
     SCREAMING_SNAKE_CASE__       ,		checkpoint_file=SCREAMING_SNAKE_CASE__		)
 dataavec.eval()  # disable dropout
 __a    :	Optional[Any]									=						dataavec.models[0]
 __a    :	int									=						dataavec_model.encoder.sentence_encoder
 __a    :	Optional[int]									=						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:
  __a    :	Union[str, Any]									=						dataavec.model.classification_heads['mnli'].out_proj.weight.shape[0]
 print('Our BERT config:'       ,		SCREAMING_SNAKE_CASE__		)

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

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

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

  # self attention
  __a    :	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)		)}'''
  )

  __a    :	Tuple									=						dataavec_layer.self_attn.q_proj.weight
  __a    :	str									=						dataavec_layer.self_attn.q_proj.bias
  __a    :	Dict									=						dataavec_layer.self_attn.k_proj.weight
  __a    :	Union[str, Any]									=						dataavec_layer.self_attn.k_proj.bias
  __a    :	str									=						dataavec_layer.self_attn.v_proj.weight
  __a    :	Tuple									=						dataavec_layer.self_attn.v_proj.bias

  # self-attention output
  __a    :	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}'''
  __a    :	Tuple									=						dataavec_layer.self_attn.out_proj.weight
  __a    :	List[str]									=						dataavec_layer.self_attn.out_proj.bias
  __a    :	List[Any]									=						dataavec_layer.self_attn_layer_norm.weight
  __a    :	Optional[Any]									=						dataavec_layer.self_attn_layer_norm.bias

  # intermediate
  __a    :	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}'''
  __a    :	str									=						dataavec_layer.fca.weight
  __a    :	int									=						dataavec_layer.fca.bias

  # output
  __a    :	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}'''
  __a    :	Union[str, Any]									=						dataavec_layer.fca.weight
  __a    :	str									=						dataavec_layer.fca.bias
  __a    :	Dict									=						dataavec_layer.final_layer_norm.weight
  __a    :	List[Any]									=						dataavec_layer.final_layer_norm.bias
  # end of layer

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

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

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

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


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE_				      =							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."
  )
  SCREAMING_SNAKE_CASE_				      =							parser.parse_args()
  convert_dataavec_checkpoint_to_pytorch(
      args.checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
  )












'''simple docstring'''


from __future__ import annotations

import random

from matplotlib import animation
from matplotlib import pyplot as plt

# Frame rate of the animation
SCREAMING_SNAKE_CASE_				      =							2_0

# Time between time steps in seconds
SCREAMING_SNAKE_CASE_				      =							INTERVAL / 1_0_0_0




class 							lowerCAmelCase					:







   """simple docstring"""







   def __init__( self   ,   _A   ,   _A   ,   _A   ,   _A   ,   _A = 1.0   ,   _A = 1.0   ,   _A = "blue"   ,   )    ->   None:
    __a    :	str									=						position_x
    __a    :	Optional[Any]									=						position_y
    __a    :	List[Any]									=						velocity_x
    __a    :	Tuple									=						velocity_y
    __a    :	Dict									=						mass
    __a    :	int									=						size
    __a    :	Tuple									=						color



   @property
   def    __magic_name__    ( self			)    ->   tuple[float, float]:
    return self.position_x, self.position_y



   @property
   def    __magic_name__    ( self			)    ->   tuple[float, float]:
    return self.velocity_x, self.velocity_y



   def    __magic_name__    ( self   ,   _A   ,   _A   ,   _A			)    ->   None:
    self.velocity_x += force_x * delta_time
    self.velocity_y += force_y * delta_time



   def    __magic_name__    ( self   ,   _A			)    ->   None:
    self.position_x += self.velocity_x * delta_time
    self.position_y += self.velocity_y * delta_time




class 							lowerCAmelCase					:







   """simple docstring"""







   def __init__( self   ,   _A   ,   _A = 1.0   ,   _A = 1.0   ,   _A = 0.0   ,   )    ->   None:
    __a    :	List[str]									=						bodies
    __a    :	List[str]									=						gravitation_constant
    __a    :	int									=						time_factor
    __a    :	List[str]									=						softening_factor



   def __len__( self			)    ->   int:
    return len(self.bodies			)



   def    __magic_name__    ( self   ,   _A			)    ->   None:
    for bodya in self.bodies:
     __a    :	Any									=						0.0
     __a    :	str									=						0.0
     for bodya in self.bodies:
      if bodya != bodya:
       __a    :	Optional[Any]									=						bodya.position_x - bodya.position_x
       __a    :	Dict									=						bodya.position_y - bodya.position_y

       # Calculation of the distance using Pythagoras's theorem
       # Extra factor due to the softening technique
       __a    :	Optional[Any]									=						(dif_x**2 + dif_y**2 + self.softening_factor) ** (
           1 / 2
       )

       # Newton's law of universal gravitation.
       force_x += (
           self.gravitation_constant * bodya.mass * dif_x / distance**3
       )
       force_y += (
           self.gravitation_constant * bodya.mass * dif_y / distance**3
       )

            # Update the body's velocity once all the force components have been added
     bodya.update_velocity(_A   ,   _A   ,   delta_time * self.time_factor			)

    # Update the positions only after all the velocities have been updated
    for body in self.bodies:
     body.update_position(delta_time * self.time_factor			)






def   lowerCAmelCase__			(					SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__		):
 body_system.update_system(SCREAMING_SNAKE_CASE__		)

 # Update the positions of the patches
 for patch, body in zip(SCREAMING_SNAKE_CASE__       ,		body_system.bodies		):
  __a    :	Dict									=						(body.position_x, body.position_y)






def   lowerCAmelCase__			(					SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__ = -1       ,		SCREAMING_SNAKE_CASE__ = 1       ,		SCREAMING_SNAKE_CASE__ = -1       ,		SCREAMING_SNAKE_CASE__ = 1       ,		):
 __a    :	int									=						plt.figure()
 fig.canvas.set_window_title(SCREAMING_SNAKE_CASE__		)
 __a    :	int									=						plt.axes(
     xlim=(x_start, x_end)       ,		ylim=(y_start, y_end)		)  # Set section to be plotted
 plt.gca().set_aspect('equal'		)  # Fix aspect ratio

 # Each body is drawn as a patch by the plt-function
 __a    :	str									=						[
     plt.Circle((body.position_x, body.position_y)       ,		body.size       ,		fc=body.color		)
     for body in body_system.bodies
 ]

 for patch in patches:
  ax.add_patch(SCREAMING_SNAKE_CASE__		)

 # Function called at each step of the animation
 def update(SCREAMING_SNAKE_CASE__		) -> list[plt.Circle]:
  update_step(SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__		)
  return patches

 __a    :	Dict									=						animation.FuncAnimation(  # noqa: F841
     SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__       ,		interval=SCREAMING_SNAKE_CASE__       ,		blit=SCREAMING_SNAKE_CASE__		)

 plt.show()






def   lowerCAmelCase__			(					):
 __a    :	Dict									=						0.970_0436
 __a    :	List[Any]									=						-0.2430_8753
 __a    :	Union[str, Any]									=						0.4_6620_3685
 __a    :	Any									=						0.4323_6573

 __a    :	Optional[int]									=						[
     Body(SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__       ,		size=0.2       ,		color='red'		),
     Body(-position_x       ,		-position_y       ,		SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__       ,		size=0.2       ,		color='green'		),
     Body(0       ,		0       ,		-2 * velocity_x       ,		-2 * velocity_y       ,		size=0.2       ,		color='blue'		),
 ]
 return BodySystem(SCREAMING_SNAKE_CASE__       ,		time_factor=3		)






def   lowerCAmelCase__			(					):
 __a    :	Optional[int]									=						7.34_76E22
 __a    :	List[Any]									=						5.9_72E24
 __a    :	List[Any]									=						1022
 __a    :	str									=						384399000
 __a    :	Optional[int]									=						6.6_74E-11

 # Calculation of the respective velocities so that total impulse is zero,
 # i.e. the two bodies together don't move
 __a    :	str									=						earth_mass * velocity_dif / (earth_mass + moon_mass)
 __a    :	Optional[int]									=						moon_velocity - velocity_dif

 __a    :	Union[str, Any]									=						Body(-earth_moon_distance       ,		0       ,		0       ,		SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__       ,		10000000       ,		'grey'		)
 __a    :	Union[str, Any]									=						Body(0       ,		0       ,		0       ,		SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__       ,		50000000       ,		'blue'		)
 return BodySystem([earth, moon]       ,		SCREAMING_SNAKE_CASE__       ,		time_factor=1000000		)






def   lowerCAmelCase__			(					):
 __a    :	Tuple									=						[]
 for _ in range(10		):
  __a    :	Dict									=						random.uniform(-0.5       ,		0.5		)
  __a    :	Optional[int]									=						random.uniform(-0.5       ,		0.5		)

  # Bodies are created pairwise with opposite velocities so that the
  # total impulse remains zero
  bodies.append(
      Body(
          random.uniform(-0.5       ,		0.5		)       ,		random.uniform(-0.5       ,		0.5		)       ,		SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__       ,		size=0.05       ,		)		)
  bodies.append(
      Body(
          random.uniform(-0.5       ,		0.5		)       ,		random.uniform(-0.5       ,		0.5		)       ,		-velocity_x       ,		-velocity_y       ,		size=0.05       ,		)		)
 return BodySystem(SCREAMING_SNAKE_CASE__       ,		0.01       ,		10       ,		0.1		)


if __name__ == "__main__":
  plot("Figure-8 solution to the 3-body-problem", example_a(), -2, 2, -2, 2)
  plot(
      "Moon's orbit around the earth",
      example_a(),
      -4_3_0_0_0_0_0_0_0,
      4_3_0_0_0_0_0_0_0,
      -4_3_0_0_0_0_0_0_0,
      4_3_0_0_0_0_0_0_0,
  )
  plot("Random system with many bodies", example_a(), -1.5, 1.5, -1.5, 1.5)




