
"""simple docstring"""


import argparse
from pathlib import Path

import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version

from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse('''0.12.2'''):
		raise Exception('''requires fairseq >= 0.12.2''')
if version.parse(fairseq.__version__) > version.parse('''2'''):
		raise Exception('''requires fairseq < v2''')

logging.set_verbosity_info()
_lowercase							   =				logging.get_logger(__name__)

_lowercase							   =				'Hello, World!'
_lowercase							   =				'en_XX'






def       _snake_case				(		snake_case__   :     str	,      snake_case__   :     str	,      snake_case__   :     bool     ):
	A						   =   Path('data_bin'     )
	A						   =   FairseqXmodModel.from_pretrained(
	    model_name_or_path=str(Path(_lowerCAmelCase     ).parent     )	,      checkpoint_file=Path(_lowerCAmelCase     ).name	,      _name='xmod_base'	,      arch='xmod_base'	,      task='multilingual_masked_lm'	,      data_name_or_path=str(_lowerCAmelCase     )	,      bpe='sentencepiece'	,      sentencepiece_model=str(Path(_lowerCAmelCase     ).parent / 'sentencepiece.bpe.model'     )	,      src_dict=str(data_dir / 'dict.txt'     )	,      )
	xmod.eval()  # disable dropout
	print(_lowerCAmelCase     )

	A						   =   xmod.model.encoder.sentence_encoder
	A						   =   XmodConfig(
	    vocab_size=xmod_sent_encoder.embed_tokens.num_embeddings	,      hidden_size=xmod.cfg.model.encoder_embed_dim	,      num_hidden_layers=xmod.cfg.model.encoder_layers	,      num_attention_heads=xmod.cfg.model.encoder_attention_heads	,      intermediate_size=xmod.cfg.model.encoder_ffn_embed_dim	,      max_position_embeddings=514	,      type_vocab_size=1	,      layer_norm_eps=1e-5	,      pre_norm=xmod.cfg.model.encoder_normalize_before	,      adapter_reduction_factor=getattr(xmod.cfg.model	,      'bottleneck'	,      2     )	,      adapter_layer_norm=xmod.cfg.model.adapter_layer_norm	,      adapter_reuse_layer_norm=xmod.cfg.model.adapter_reuse_layer_norm	,      ln_before_adapter=xmod.cfg.model.ln_before_adapter	,      languages=xmod.cfg.model.languages	,      )
	if classification_head:
		A						   =   xmod.model.classification_heads["mnli"].out_proj.weight.shape[0]

	print('Our X-MOD config:'	,      _lowerCAmelCase     )

	A						   =   XmodForSequenceClassification(_lowerCAmelCase     ) if classification_head else XmodForMaskedLM(_lowerCAmelCase     )
	model.eval()

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

	A						   =   xmod_sent_encoder.layernorm_embedding.weight
	A						   =   xmod_sent_encoder.layernorm_embedding.bias

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

		# self attention
		A						   =   layer.attention.self
		if not (
		    xmod_layer.self_attn.k_proj.weight.data.shape
		    == xmod_layer.self_attn.q_proj.weight.data.shape
		    == xmod_layer.self_attn.v_proj.weight.data.shape
		    == torch.Size((config.hidden_size, config.hidden_size)     )
		):
			raise AssertionError('Dimensions of self-attention weights do not match.'     )

		A						   =   xmod_layer.self_attn.q_proj.weight
		A						   =   xmod_layer.self_attn.q_proj.bias
		A						   =   xmod_layer.self_attn.k_proj.weight
		A						   =   xmod_layer.self_attn.k_proj.bias
		A						   =   xmod_layer.self_attn.v_proj.weight
		A						   =   xmod_layer.self_attn.v_proj.bias

		# self-attention output
		A						   =   layer.attention.output
		if self_output.dense.weight.shape != xmod_layer.self_attn.out_proj.weight.shape:
			raise AssertionError('Dimensions of self-attention output weights do not match.'     )
		A						   =   xmod_layer.self_attn.out_proj.weight
		A						   =   xmod_layer.self_attn.out_proj.bias
		A						   =   xmod_layer.self_attn_layer_norm.weight
		A						   =   xmod_layer.self_attn_layer_norm.bias

		# intermediate
		A						   =   layer.intermediate
		if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
			raise AssertionError('Dimensions of intermediate weights do not match.'     )
		A						   =   xmod_layer.fca.weight
		A						   =   xmod_layer.fca.bias

		# output
		A						   =   layer.output
		if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
			raise AssertionError('Dimensions of feed-forward weights do not match.'     )
		A						   =   xmod_layer.fca.weight
		A						   =   xmod_layer.fca.bias
		A						   =   xmod_layer.final_layer_norm.weight
		A						   =   xmod_layer.final_layer_norm.bias
		if bert_output.adapter_layer_norm is not None:
			A						   =   xmod_layer.adapter_layer_norm.weight
			A						   =   xmod_layer.adapter_layer_norm.bias

		if sorted(bert_output.adapter_modules.keys()     ) != sorted(xmod_layer.adapter_modules.keys()     ):
			raise AssertionError('Lists of language adapters do not match.'     )
		for lang_code, adapter in xmod_layer.adapter_modules.items():
			A						   =   bert_output.adapter_modules[lang_code]
			A						   =   xmod_layer.adapter_modules[lang_code]
			A						   =   from_adapter.fca.weight
			A						   =   from_adapter.fca.bias
			A						   =   from_adapter.fca.weight
			A						   =   from_adapter.fca.bias

		# end of layer

	if xmod_sent_encoder.layer_norm is not None:
		A						   =   xmod_sent_encoder.layer_norm.weight
		A						   =   xmod_sent_encoder.layer_norm.bias

	if classification_head:
		A						   =   xmod.model.classification_heads["mnli"].dense.weight
		A						   =   xmod.model.classification_heads["mnli"].dense.bias
		A						   =   xmod.model.classification_heads["mnli"].out_proj.weight
		A						   =   xmod.model.classification_heads["mnli"].out_proj.bias
	else:
		# LM Head
		A						   =   xmod.model.encoder.lm_head.dense.weight
		A						   =   xmod.model.encoder.lm_head.dense.bias
		A						   =   xmod.model.encoder.lm_head.layer_norm.weight
		A						   =   xmod.model.encoder.lm_head.layer_norm.bias
		A						   =   xmod.model.encoder.lm_head.weight
		A						   =   xmod.model.encoder.lm_head.bias

	# Let's check that we get the same results.
	A						   =   xmod.encode(_lowerCAmelCase     ).unsqueeze(0     )  # batch of size 1
	model.roberta.set_default_language(_lowerCAmelCase     )

	A						   =   model(_lowerCAmelCase     )[0]
	if classification_head:
		A						   =   xmod.model.classification_heads["mnli"](xmod.extract_features(_lowerCAmelCase     )     )
	else:
		A						   =   xmod.model(_lowerCAmelCase	,      lang_id=[SAMPLE_LANGUAGE]     )[0]
	print(our_output.shape	,      their_output.shape     )
	A						   =   torch.max(torch.abs(our_output - their_output     )     ).item()
	print(F'max_absolute_diff = {max_absolute_diff}'     )  # ~ 1e-7
	A						   =   torch.allclose(_lowerCAmelCase	,      _lowerCAmelCase	,      atol=1e-3     )
	print('Do both models output the same tensors?'	,      '🔥' if success else '💩'     )
	if not success:
		raise Exception('Something went wRoNg'     )

	Path(_lowerCAmelCase     ).mkdir(parents=_lowerCAmelCase	,      exist_ok=_lowerCAmelCase     )
	print(F'Saving model to {pytorch_dump_folder_path}'     )
	model.save_pretrained(_lowerCAmelCase     )


if __name__ == "__main__":
		_lowercase							   =				argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--xmod_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.'''
		)
		_lowercase							   =				parser.parse_args()
		convert_xmod_checkpoint_to_pytorch(
		    args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
		)

"""simple docstring"""


from dataclasses import dataclass
from typing import Optional

import numpy as np
import torch
import torch.nn as nn

from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_up_block







@dataclass
class 				lowerCAmelCase_				(      _lowercase    ):



						'''simple docstring'''


						_lowerCamelCase:    torch.FloatTensor







class 				lowerCAmelCase_				(      nn.Module    ):



						'''simple docstring'''


						def __init__( self     :      List[str]  ,A_     :      Dict=3  ,A_     :      int=3  ,A_     :      str=("DownEncoderBlock2D",)  ,A_     :      Dict=(64,)  ,A_     :      str=2  ,A_     :      Union[str, Any]=32  ,A_     :      Optional[int]="silu"  ,A_     :      str=True  ,)     ->   Union[str, Any]:
							super().__init__()
							A						   =   layers_per_block

							A						   =   torch.nn.Convad(
							    A_  ,block_out_channels[0]  ,kernel_size=3  ,stride=1  ,padding=1  ,)

							A						   =   None
							A						   =   nn.ModuleList([]      )

							# down
							A						   =   block_out_channels[0]
							for i, down_block_type in enumerate(A_      ):
								A						   =   output_channel
								A						   =   block_out_channels[i]
								A						   =   i == len(A_      ) - 1

								A						   =   get_down_block(
								    A_  ,num_layers=self.layers_per_block  ,in_channels=A_  ,out_channels=A_  ,add_downsample=not is_final_block  ,resnet_eps=1e-6  ,downsample_padding=0  ,resnet_act_fn=A_  ,resnet_groups=A_  ,attention_head_dim=A_  ,temb_channels=A_  ,)
								self.down_blocks.append(A_      )

							# mid
							A						   =   UNetMidBlockaD(
							    in_channels=block_out_channels[-1]  ,resnet_eps=1e-6  ,resnet_act_fn=A_  ,output_scale_factor=1  ,resnet_time_scale_shift='default'  ,attention_head_dim=block_out_channels[-1]  ,resnet_groups=A_  ,temb_channels=A_  ,)

							# out
							A						   =   nn.GroupNorm(num_channels=block_out_channels[-1]  ,num_groups=A_  ,eps=1e-6      )
							A						   =   nn.SiLU()

							A						   =   2 * out_channels if double_z else out_channels
							A						   =   nn.Convad(block_out_channels[-1]  ,A_  ,3  ,padding=1      )

							A						   =   False




						def 						_SCREAMING_SNAKE_CASE      ( self     :      Union[str, Any]  ,A_     :      Optional[int]      )     ->   Union[str, Any]:
							A						   =   x
							A						   =   self.conv_in(A_      )

							if self.training and self.gradient_checkpointing:

								def create_custom_forward(A_     :      Dict      ):
									def custom_forward(*A_     :      Tuple      ):
										return module(*A_      )

									return custom_forward

								# down
								if is_torch_version('>='  ,'1.11.0'      ):
									for down_block in self.down_blocks:
										A						   =   torch.utils.checkpoint.checkpoint(
										    create_custom_forward(A_      )  ,A_  ,use_reentrant=A_      )
									# middle
									A						   =   torch.utils.checkpoint.checkpoint(
									    create_custom_forward(self.mid_block      )  ,A_  ,use_reentrant=A_      )
								else:
									for down_block in self.down_blocks:
										A						   =   torch.utils.checkpoint.checkpoint(create_custom_forward(A_      )  ,A_      )
									# middle
									A						   =   torch.utils.checkpoint.checkpoint(create_custom_forward(self.mid_block      )  ,A_      )

							else:
								# down
								for down_block in self.down_blocks:
									A						   =   down_block(A_      )

								# middle
								A						   =   self.mid_block(A_      )

							# post-process
							A						   =   self.conv_norm_out(A_      )
							A						   =   self.conv_act(A_      )
							A						   =   self.conv_out(A_      )

							return sample







class 				lowerCAmelCase_				(      nn.Module    ):



						'''simple docstring'''


						def __init__( self     :      List[Any]  ,A_     :      Optional[Any]=3  ,A_     :      Optional[int]=3  ,A_     :      str=("UpDecoderBlock2D",)  ,A_     :      Any=(64,)  ,A_     :      Optional[int]=2  ,A_     :      Optional[int]=32  ,A_     :      Tuple="silu"  ,A_     :      Optional[int]="group"  ,)     ->   Any:
							super().__init__()
							A						   =   layers_per_block

							A						   =   nn.Convad(
							    A_  ,block_out_channels[-1]  ,kernel_size=3  ,stride=1  ,padding=1  ,)

							A						   =   None
							A						   =   nn.ModuleList([]      )

							A						   =   in_channels if norm_type == 'spatial' else None

							# mid
							A						   =   UNetMidBlockaD(
							    in_channels=block_out_channels[-1]  ,resnet_eps=1e-6  ,resnet_act_fn=A_  ,output_scale_factor=1  ,resnet_time_scale_shift='default' if norm_type == 'group' else norm_type  ,attention_head_dim=block_out_channels[-1]  ,resnet_groups=A_  ,temb_channels=A_  ,)

							# up
							A						   =   list(reversed(A_      )      )
							A						   =   reversed_block_out_channels[0]
							for i, up_block_type in enumerate(A_      ):
								A						   =   output_channel
								A						   =   reversed_block_out_channels[i]

								A						   =   i == len(A_      ) - 1

								A						   =   get_up_block(
								    A_  ,num_layers=self.layers_per_block + 1  ,in_channels=A_  ,out_channels=A_  ,prev_output_channel=A_  ,add_upsample=not is_final_block  ,resnet_eps=1e-6  ,resnet_act_fn=A_  ,resnet_groups=A_  ,attention_head_dim=A_  ,temb_channels=A_  ,resnet_time_scale_shift=A_  ,)
								self.up_blocks.append(A_      )
								A						   =   output_channel

							# out
							if norm_type == "spatial":
								A						   =   SpatialNorm(block_out_channels[0]  ,A_      )
							else:
								A						   =   nn.GroupNorm(num_channels=block_out_channels[0]  ,num_groups=A_  ,eps=1e-6      )
							A						   =   nn.SiLU()
							A						   =   nn.Convad(block_out_channels[0]  ,A_  ,3  ,padding=1      )

							A						   =   False




						def 						_SCREAMING_SNAKE_CASE      ( self     :      Union[str, Any]  ,A_     :      str  ,A_     :      Union[str, Any]=None      )     ->   Any:
							A						   =   z
							A						   =   self.conv_in(A_      )

							A						   =   next(iter(self.up_blocks.parameters()      )      ).dtype
							if self.training and self.gradient_checkpointing:

								def create_custom_forward(A_     :      List[Any]      ):
									def custom_forward(*A_     :      Tuple      ):
										return module(*A_      )

									return custom_forward

								if is_torch_version('>='  ,'1.11.0'      ):
									# middle
									A						   =   torch.utils.checkpoint.checkpoint(
									    create_custom_forward(self.mid_block      )  ,A_  ,A_  ,use_reentrant=A_      )
									A						   =   sample.to(A_      )

									# up
									for up_block in self.up_blocks:
										A						   =   torch.utils.checkpoint.checkpoint(
										    create_custom_forward(A_      )  ,A_  ,A_  ,use_reentrant=A_      )
								else:
									# middle
									A						   =   torch.utils.checkpoint.checkpoint(
									    create_custom_forward(self.mid_block      )  ,A_  ,A_      )
									A						   =   sample.to(A_      )

									# up
									for up_block in self.up_blocks:
										A						   =   torch.utils.checkpoint.checkpoint(create_custom_forward(A_      )  ,A_  ,A_      )
							else:
								# middle
								A						   =   self.mid_block(A_  ,A_      )
								A						   =   sample.to(A_      )

								# up
								for up_block in self.up_blocks:
									A						   =   up_block(A_  ,A_      )

        # post-process
							if latent_embeds is None:
								A						   =   self.conv_norm_out(A_      )
							else:
								A						   =   self.conv_norm_out(A_  ,A_      )
							A						   =   self.conv_act(A_      )
							A						   =   self.conv_out(A_      )

							return sample







class 				lowerCAmelCase_				(      nn.Module    ):



						'''simple docstring'''


						def __init__( self     :      Optional[int]  ,A_     :      Optional[int]  ,A_     :      Any  ,A_     :      str  ,A_     :      Dict=None  ,A_     :      List[Any]="random"  ,A_     :      Optional[int]=False  ,A_     :      str=True      )     ->   List[str]:
							super().__init__()
							A						   =   n_e
							A						   =   vq_embed_dim
							A						   =   beta
							A						   =   legacy

							A						   =   nn.Embedding(self.n_e  ,self.vq_embed_dim      )
							self.embedding.weight.data.uniform_(-1.0 / self.n_e  ,1.0 / self.n_e      )

							A						   =   remap
							if self.remap is not None:
								self.register_buffer('used'  ,torch.tensor(np.load(self.remap      )      )      )
								A						   =   self.used.shape[0]
								A						   =   unknown_index  # "random" or "extra" or integer
								if self.unknown_index == "extra":
									A						   =   self.re_embed
									A						   =   self.re_embed + 1
								print(
								    F'Remapping {self.n_e} indices to {self.re_embed} indices. '
								    F'Using {self.unknown_index} for unknown indices.'      )
							else:
								A						   =   n_e

							A						   =   sane_index_shape


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[str]  ,A_     :      Optional[int]      )     ->   Any:
							A						   =   inds.shape
							assert len(A_      ) > 1
							A						   =   inds.reshape(ishape[0]  ,-1      )
							A						   =   self.used.to(A_      )
							A						   =   (inds[:, :, None] == used[None, None, ...]).long()
							A						   =   match.argmax(-1      )
							A						   =   match.sum(2      ) < 1
							if self.unknown_index == "random":
								A						   =   torch.randint(0  ,self.re_embed  ,size=new[unknown].shape      ).to(device=new.device      )
							else:
								A						   =   self.unknown_index
							return new.reshape(A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[str]  ,A_     :      Optional[Any]      )     ->   List[Any]:
							A						   =   inds.shape
							assert len(A_      ) > 1
							A						   =   inds.reshape(ishape[0]  ,-1      )
							A						   =   self.used.to(A_      )
							if self.re_embed > self.used.shape[0]:  # extra token
								A						   =   0  # simply set to zero
							A						   =   torch.gather(used[None, :][inds.shape[0] * [0], :]  ,1  ,A_      )
							return back.reshape(A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[int]  ,A_     :      List[Any]      )     ->   str:
							# reshape z -> (batch, height, width, channel) and flatten
							A						   =   z.permute(0  ,2  ,3  ,1      ).contiguous()
							A						   =   z.view(-1  ,self.vq_embed_dim      )

							# distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
							A						   =   torch.argmin(torch.cdist(A_  ,self.embedding.weight      )  ,dim=1      )

							A						   =   self.embedding(A_      ).view(z.shape      )
							A						   =   None
							A						   =   None

							# compute loss for embedding
							if not self.legacy:
								A						   =   self.beta * torch.mean((z_q.detach() - z) ** 2      ) + torch.mean((z_q - z.detach()) ** 2      )
							else:
								A						   =   torch.mean((z_q.detach() - z) ** 2      ) + self.beta * torch.mean((z_q - z.detach()) ** 2      )

							# preserve gradients
							A						   =   z + (z_q - z).detach()

							# reshape back to match original input shape
							A						   =   z_q.permute(0  ,3  ,1  ,2      ).contiguous()

							if self.remap is not None:
								A						   =   min_encoding_indices.reshape(z.shape[0]  ,-1      )  # add batch axis
								A						   =   self.remap_to_used(A_      )
								A						   =   min_encoding_indices.reshape(-1  ,1      )  # flatten

							if self.sane_index_shape:
								A						   =   min_encoding_indices.reshape(z_q.shape[0]  ,z_q.shape[2]  ,z_q.shape[3]      )

							return z_q, loss, (perplexity, min_encodings, min_encoding_indices)




						def 						_SCREAMING_SNAKE_CASE      ( self     :      Dict  ,A_     :      Dict  ,A_     :      str      )     ->   Union[str, Any]:
							# shape specifying (batch, height, width, channel)
							if self.remap is not None:
								A						   =   indices.reshape(shape[0]  ,-1      )  # add batch axis
								A						   =   self.unmap_to_all(A_      )
								A						   =   indices.reshape(-1      )  # flatten again

							# get quantized latent vectors
							A						   =   self.embedding(A_      )

							if shape is not None:
								A						   =   z_q.view(A_      )
								# reshape back to match original input shape
								A						   =   z_q.permute(0  ,3  ,1  ,2      ).contiguous()

							return z_q







class 				lowerCAmelCase_				(      _lowercase    ):



						'''simple docstring'''


						def __init__( self     :      str  ,A_     :      Tuple  ,A_     :      Dict=False      )     ->   List[str]:
							A						   =   parameters
							A   ,      A						   =   torch.chunk(A_  ,2  ,dim=1      )
							A						   =   torch.clamp(self.logvar  ,-30.0  ,20.0      )
							A						   =   deterministic
							A						   =   torch.exp(0.5 * self.logvar      )
							A						   =   torch.exp(self.logvar      )
							if self.deterministic:
								A						   =   A						   =   torch.zeros_like(
								    self.mean  ,device=self.parameters.device  ,dtype=self.parameters.dtype      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Any  ,A_     :      Optional[torch.Generator] = None      )     ->   torch.FloatTensor:
							# make sure sample is on the same device as the parameters and has same dtype
							A						   =   randn_tensor(
							    self.mean.shape  ,generator=A_  ,device=self.parameters.device  ,dtype=self.parameters.dtype      )
							A						   =   self.mean + self.std * sample
							return x


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Any  ,A_     :      Tuple=None      )     ->   int:
							if self.deterministic:
								return torch.Tensor([0.0]      )
							else:
								if other is None:
									return 0.5 * torch.sum(torch.pow(self.mean  ,2      ) + self.var - 1.0 - self.logvar  ,dim=[1, 2, 3]      )
								else:
									return 0.5 * torch.sum(
									    torch.pow(self.mean - other.mean  ,2      ) / other.var
									    + self.var / other.var
									    - 1.0
									    - self.logvar
									    + other.logvar  ,dim=[1, 2, 3]  ,)


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[str]  ,A_     :      List[str]  ,A_     :      Union[str, Any]=[1, 2, 3]      )     ->   List[str]:
							if self.deterministic:
								return torch.Tensor([0.0]      )
							A						   =   np.log(2.0 * np.pi      )
							return 0.5 * torch.sum(logtwopi + self.logvar + torch.pow(sample - self.mean  ,2      ) / self.var  ,dim=A_      )




						def 						_SCREAMING_SNAKE_CASE      ( self     :      Dict      )     ->   Optional[int]:
							return self.mean