






import argparse
import pathlib

import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import XLMRobertaConfig, XLMRobertaXLForMaskedLM, XLMRobertaXLForSequenceClassification
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.models.roberta.modeling_roberta import RobertaAttention
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse("""1.0.0a"""):
 raise Exception("""requires fairseq >= 1.0.0a""")

logging.set_verbosity_info()
snake_case__   :						Any													=   logging.get_logger(__name__)

snake_case__   :						Dict													=   """Hello world! cécé herlolip"""






def      _snake_case     (__lowercase     ,   __lowercase     ,   __lowercase):
   UpperCamelCase_						=  FairseqRobertaModel.from_pretrained(__lowercase)
   roberta.eval()  # disable dropout
   UpperCamelCase_						=  roberta.model.encoder.sentence_encoder
   UpperCamelCase_						=  XLMRobertaConfig(
       vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings     ,   hidden_size=roberta.cfg.model.encoder_embed_dim     ,   num_hidden_layers=roberta.cfg.model.encoder_layers     ,   num_attention_heads=roberta.cfg.model.encoder_attention_heads     ,   intermediate_size=roberta.cfg.model.encoder_ffn_embed_dim     ,   max_position_embeddings=514     ,   type_vocab_size=1     ,   layer_norm_eps=1e-5     ,   )
   if classification_head:
      UpperCamelCase_						=  roberta.model.classification_heads['mnli'].out_proj.weight.shape[0]

   print('Our RoBERTa config:'     ,   __lowercase)

   UpperCamelCase_						=  XLMRobertaXLForSequenceClassification(__lowercase) if classification_head else XLMRobertaXLForMaskedLM(__lowercase)
   model.eval()

   # Now let's copy all the weights.
   # Embeddings
   UpperCamelCase_						=  roberta_sent_encoder.embed_tokens.weight
   UpperCamelCase_						=  roberta_sent_encoder.embed_positions.weight
   UpperCamelCase_						=  torch.zeros_like(
       model.roberta.embeddings.token_type_embeddings.weight)  # just zero them out b/c RoBERTa doesn't use them.

   UpperCamelCase_						=  roberta_sent_encoder.layer_norm.weight
   UpperCamelCase_						=  roberta_sent_encoder.layer_norm.bias

   for i in range(config.num_hidden_layers):
      # Encoder: start of layer
      UpperCamelCase_						=  model.roberta.encoder.layer[i]
      UpperCamelCase_						=  roberta_sent_encoder.layers[i]

      UpperCamelCase_						=  layer.attention
      UpperCamelCase_						=  roberta_layer.self_attn_layer_norm.weight
      UpperCamelCase_						=  roberta_layer.self_attn_layer_norm.bias

      # self attention
      UpperCamelCase_						=  layer.attention.self
      assert (
          roberta_layer.self_attn.k_proj.weight.data.shape
          == roberta_layer.self_attn.q_proj.weight.data.shape
          == roberta_layer.self_attn.v_proj.weight.data.shape
          == torch.Size((config.hidden_size, config.hidden_size))
      )

      UpperCamelCase_						=  roberta_layer.self_attn.q_proj.weight
      UpperCamelCase_						=  roberta_layer.self_attn.q_proj.bias
      UpperCamelCase_						=  roberta_layer.self_attn.k_proj.weight
      UpperCamelCase_						=  roberta_layer.self_attn.k_proj.bias
      UpperCamelCase_						=  roberta_layer.self_attn.v_proj.weight
      UpperCamelCase_						=  roberta_layer.self_attn.v_proj.bias

      # self-attention output
      UpperCamelCase_						=  layer.attention.output
      assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
      UpperCamelCase_						=  roberta_layer.self_attn.out_proj.weight
      UpperCamelCase_						=  roberta_layer.self_attn.out_proj.bias

      # this one is final layer norm
      UpperCamelCase_						=  roberta_layer.final_layer_norm.weight
      UpperCamelCase_						=  roberta_layer.final_layer_norm.bias

      # intermediate
      UpperCamelCase_						=  layer.intermediate
      assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
      UpperCamelCase_						=  roberta_layer.fca.weight
      UpperCamelCase_						=  roberta_layer.fca.bias

      # output
      UpperCamelCase_						=  layer.output
      assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
      UpperCamelCase_						=  roberta_layer.fca.weight
      UpperCamelCase_						=  roberta_layer.fca.bias
      # end of layer

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

   # Let's check that we get the same results.
   UpperCamelCase_						=  roberta.encode(__lowercase).unsqueeze(0)  # batch of size 1

   UpperCamelCase_						=  model(__lowercase)[0]
   if classification_head:
      UpperCamelCase_						=  roberta.model.classification_heads['mnli'](roberta.extract_features(__lowercase))
   else:
      UpperCamelCase_						=  roberta.model(__lowercase)[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(__lowercase     ,   __lowercase     ,   atol=1e-3)
   print('Do both models output the same tensors?'     ,   '🔥' if success else '💩')
   if not success:
      raise Exception('Something went wRoNg')

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


if __name__ == "__main__":
 snake_case__   :						str													=   argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     """--roberta_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."""
 )
 snake_case__   :						int													=   parser.parse_args()
 convert_xlm_roberta_xl_checkpoint_to_pytorch(
     args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
 )









'''simple docstring'''
from manim import *




class        _UpperCamelCase	(    SCREAMING_SNAKE_CASE):


				'''simple docstring'''


				def 	a__		(     self							)    ->  List[str]:
					lowercase						:		List[Any]   					=		Rectangle(height=0.5							,      width=0.5							)
					lowercase						:		str   					=		Rectangle(height=0.25							,      width=0.25							)
					lowercase						:		Union[str, Any]   					=		Rectangle(height=0.46							,      width=0.46							).set_stroke(width=0							)

					lowercase						:		List[str]   					=		[mem.copy() for i in range(6							)]
					lowercase						:		Any   					=		[mem.copy() for i in range(6							)]
					lowercase						:		List[str]   					=		VGroup(*a_							).arrange(a_							,      buff=0							)
					lowercase						:		List[Any]   					=		VGroup(*a_							).arrange(a_							,      buff=0							)
					lowercase						:		List[Any]   					=		VGroup(a_							,      a_							).arrange(a_							,      buff=0							)
					lowercase						:		Union[str, Any]   					=		Text("CPU"							,      font_size=2_4							)
					lowercase						:		List[Any]   					=		Group(a_							,      a_							).arrange(a_							,      buff=0.5							,      aligned_edge=a_							)
					cpu.move_to([-2.5, -0.5, 0]							)
					self.add(a_							)

					lowercase						:		List[Any]   					=		[mem.copy() for i in range(4							)]
					lowercase						:		Union[str, Any]   					=		VGroup(*a_							).arrange(a_							,      buff=0							)
					lowercase						:		Dict   					=		Text("GPU"							,      font_size=2_4							)
					lowercase						:		Tuple   					=		Group(a_							,      a_							).arrange(a_							,      buff=0.5							,      aligned_edge=a_							)
					gpu.move_to([-1, -1, 0]							)
					self.add(a_							)

					lowercase						:		Tuple   					=		[mem.copy() for i in range(6							)]
					lowercase						:		Optional[int]   					=		VGroup(*a_							).arrange(a_							,      buff=0							)

					lowercase						:		Any   					=		Text("Model"							,      font_size=2_4							)
					lowercase						:		str   					=		Group(a_							,      a_							).arrange(a_							,      buff=0.5							,      aligned_edge=a_							)
					model.move_to([3, -1.0, 0]							)
					self.add(a_							)

					lowercase						:		Dict   					=		[]
					lowercase						:		Tuple   					=		[]
					lowercase						:		List[Any]   					=		[]

					for i, rect in enumerate(a_							):
						rect.set_stroke(a_							)

						lowercase						:		Tuple   					=		Rectangle(height=0.46 / 4							,      width=0.46 / 3							).set_stroke(width=0.0							).set_fill(a_							,      opacity=0.7							)

						if i == 0:
							cpu_target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT							)							,      buff=0.02							,      direction=a_							)
							cpu_target.set_x(cpu_target.get_x() + 0.1							)
						elif i == 3:
							cpu_target.next_to(model_cpu_arr[0]							,      direction=a_							,      buff=0.0							)
						else:
							cpu_target.next_to(model_cpu_arr[i - 1]							,      direction=a_							,      buff=0.0							)
						self.add(a_							)
						model_cpu_arr.append(a_							)

					self.add(*a_							,      *a_							,      *a_							)

					lowercase						:		Any   					=		[mem.copy() for i in range(6							)]
					lowercase						:		Dict   					=		VGroup(*a_							).arrange(a_							,      buff=0							)

					lowercase						:		List[str]   					=		Text("Loaded Checkpoint"							,      font_size=2_4							)
					lowercase						:		Optional[Any]   					=		Group(a_							,      a_							).arrange(a_							,      buff=0.5							,      aligned_edge=a_							)
					checkpoint.move_to([3, 0.5, 0]							)

					self.add(a_							)

					lowercase						:		Any   					=		[]
					lowercase						:		int   					=		[]

					for i, rect in enumerate(a_							):
						lowercase						:		str   					=		fill.copy().set_fill(a_							,      opacity=0.7							)
						target.move_to(a_							)
						ckpt_arr.append(a_							)

						lowercase						:		Optional[Any]   					=		target.copy()
						if i < 5:
							cpu_target.move_to(cpu_left_col_base[i + 1]							)
						else:
							cpu_target.move_to(cpu_right_col_base[i - 5]							)
						ckpt_cpu_arr.append(a_							)
					self.add(*a_							,      *a_							)

					lowercase						:		Optional[Any]   					=		Square(side_length=2.2							)
					key.move_to([-5, 2, 0]							)

					lowercase						:		str   					=		MarkupText(
					    F'''<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model'''							,      font_size=1_8							,      )

					key_text.move_to([-5, 2.4, 0]							)

					self.add(a_							,      a_							)

					lowercase						:		Any   					=		MarkupText(
					    F'''<span fgcolor=\'{BLUE}\'>●</span> Checkpoint'''							,      font_size=1_8							,      )

					blue_text.next_to(a_							,      DOWN * 2.4							,      aligned_edge=key_text.get_left()							)
					self.add(a_							)

					lowercase						:		List[Any]   					=		MarkupText(
					    F'''Based on the passed in configuration, weights are stored in\na variety of np.memmaps on disk or to a particular device.'''							,      font_size=2_4							,      )
					step_a.move_to([2, 2, 0]							)

					lowercase						:		Any   					=		[meta_mem.copy() for i in range(6							)]
					lowercase						:		Dict   					=		[meta_mem.copy() for i in range(6							)]
					lowercase						:		Union[str, Any]   					=		VGroup(*a_							).arrange(a_							,      buff=0							)
					lowercase						:		Dict   					=		VGroup(*a_							).arrange(a_							,      buff=0							)
					lowercase						:		Any   					=		VGroup(a_							,      a_							).arrange(a_							,      buff=0							)
					lowercase						:		Optional[Any]   					=		Text("Disk"							,      font_size=2_4							)
					lowercase						:		List[str]   					=		Group(a_							,      a_							).arrange(a_							,      buff=0.5							,      aligned_edge=a_							)
					disk.move_to([-4.0, -1.25, 0]							)
					self.play(Write(a_							,      run_time=3							)							,      Write(a_							,      run_time=1							)							,      Create(a_							,      run_time=1							)							)

					lowercase						:		Optional[Any]   					=		[]
					for i, rect in enumerate(a_							):
						lowercase						:		int   					=		rect.copy()
						target.generate_target()
						target.target.move_to(disk_left_col_base[i]							).scale(0.5							)
						animations.append(MoveToTarget(a_							,      run_time=1.5							)							)
					self.play(*a_							)

					self.play(FadeOut(a_							)							)

					lowercase						:		List[Any]   					=		MarkupText(F'''Then, the checkpoint is removed from memory\nthrough garbage collection.'''							,      font_size=2_4							)
					step_a.move_to([2, 2, 0]							)

					self.play(Write(a_							,      run_time=3							)							)

					self.play(
					    FadeOut(a_							,      a_							,      *a_							,      *a_							)							,      )

					self.wait()


