


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()
UpperCAmelCase   :    int     =		logging.get_logger(__name__)

UpperCAmelCase   :    Dict     =		"Hello, World!"
UpperCAmelCase   :    Tuple     =		"en_XX"






def 	__lowerCamelCase						(							lowerCamelCase__		:						str  ,							lowerCamelCase__		:						str  ,							lowerCamelCase__		:						bool      ):





			'''simple docstring'''




			lowerCamelCase    		=  Path("""data_bin"""      )
			lowerCamelCase    		=  FairseqXmodModel.from_pretrained(
			    model_name_or_path=str(Path(__A      ).parent      )  ,							checkpoint_file=Path(__A      ).name  ,							_name="""xmod_base"""  ,							arch="""xmod_base"""  ,							task="""multilingual_masked_lm"""  ,							data_name_or_path=str(__A      )  ,							bpe="""sentencepiece"""  ,							sentencepiece_model=str(Path(__A      ).parent / """sentencepiece.bpe.model"""      )  ,							src_dict=str(data_dir / """dict.txt"""      )  ,							)
			xmod.eval()  # disable dropout
			print(__A      )

			lowerCamelCase    		=  xmod.model.encoder.sentence_encoder
			lowerCamelCase    		=  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:
						lowerCamelCase    		=  xmod.model.classification_heads['''mnli'''].out_proj.weight.shape[0]

			print("""Our X-MOD config:"""  ,							__A      )

			lowerCamelCase    		=  XmodForSequenceClassification(__A      ) if classification_head else XmodForMaskedLM(__A      )
			model.eval()

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

			lowerCamelCase    		=  xmod_sent_encoder.layernorm_embedding.weight
			lowerCamelCase    		=  xmod_sent_encoder.layernorm_embedding.bias

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

						# self attention
						lowerCamelCase    		=  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."""      )

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

						# self-attention output
						lowerCamelCase    		=  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."""      )
						lowerCamelCase    		=  xmod_layer.self_attn.out_proj.weight
						lowerCamelCase    		=  xmod_layer.self_attn.out_proj.bias
						lowerCamelCase    		=  xmod_layer.self_attn_layer_norm.weight
						lowerCamelCase    		=  xmod_layer.self_attn_layer_norm.bias

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

						# output
						lowerCamelCase    		=  layer.output
						if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
									raise AssertionError("""Dimensions of feed-forward weights do not match."""      )
						lowerCamelCase    		=  xmod_layer.fca.weight
						lowerCamelCase    		=  xmod_layer.fca.bias
						lowerCamelCase    		=  xmod_layer.final_layer_norm.weight
						lowerCamelCase    		=  xmod_layer.final_layer_norm.bias
						if bert_output.adapter_layer_norm is not None:
									lowerCamelCase    		=  xmod_layer.adapter_layer_norm.weight
									lowerCamelCase    		=  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():
									lowerCamelCase    		=  bert_output.adapter_modules[lang_code]
									lowerCamelCase    		=  xmod_layer.adapter_modules[lang_code]
									lowerCamelCase    		=  from_adapter.fca.weight
									lowerCamelCase    		=  from_adapter.fca.bias
									lowerCamelCase    		=  from_adapter.fca.weight
									lowerCamelCase    		=  from_adapter.fca.bias

						# end of layer

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

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

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

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

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


if __name__ == "__main__":
		UpperCAmelCase   :    Optional[int]     =		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."
		)
		UpperCAmelCase   :    Union[str, Any]     =		parser.parse_args()
		convert_xmod_checkpoint_to_pytorch(
		    args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
		)









'''simple docstring'''







import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


__lowerCAmelCase									=   {
    'sample_size': 32,
    'in_channels': 3,
    'out_channels': 3,
    'layers_per_block': 2,
    'num_class_embeds': 1_000,
    'block_out_channels': [32, 64],
    'attention_head_dim': 8,
    'down_block_types': [
        'ResnetDownsampleBlock2D',
        'AttnDownBlock2D',
    ],
    'up_block_types': [
        'AttnUpBlock2D',
        'ResnetUpsampleBlock2D',
    ],
    'resnet_time_scale_shift': 'scale_shift',
    'upsample_type': 'resnet',
    'downsample_type': 'resnet',
}

__lowerCAmelCase									=   {
    'sample_size': 64,
    'in_channels': 3,
    'out_channels': 3,
    'layers_per_block': 3,
    'num_class_embeds': 1_000,
    'block_out_channels': [192, 192 * 2, 192 * 3, 192 * 4],
    'attention_head_dim': 64,
    'down_block_types': [
        'ResnetDownsampleBlock2D',
        'AttnDownBlock2D',
        'AttnDownBlock2D',
        'AttnDownBlock2D',
    ],
    'up_block_types': [
        'AttnUpBlock2D',
        'AttnUpBlock2D',
        'AttnUpBlock2D',
        'ResnetUpsampleBlock2D',
    ],
    'resnet_time_scale_shift': 'scale_shift',
    'upsample_type': 'resnet',
    'downsample_type': 'resnet',
}

__lowerCAmelCase									=   {
    'sample_size': 256,
    'in_channels': 3,
    'out_channels': 3,
    'layers_per_block': 2,
    'num_class_embeds': None,
    'block_out_channels': [256, 256, 256 * 2, 256 * 2, 256 * 4, 256 * 4],
    'attention_head_dim': 64,
    'down_block_types': [
        'ResnetDownsampleBlock2D',
        'ResnetDownsampleBlock2D',
        'ResnetDownsampleBlock2D',
        'AttnDownBlock2D',
        'AttnDownBlock2D',
        'AttnDownBlock2D',
    ],
    'up_block_types': [
        'AttnUpBlock2D',
        'AttnUpBlock2D',
        'AttnUpBlock2D',
        'ResnetUpsampleBlock2D',
        'ResnetUpsampleBlock2D',
        'ResnetUpsampleBlock2D',
    ],
    'resnet_time_scale_shift': 'default',
    'upsample_type': 'resnet',
    'downsample_type': 'resnet',
}

__lowerCAmelCase									=   {
    'num_train_timesteps': 40,
    'sigma_min': 0.002,
    'sigma_max': 80.0,
}

__lowerCAmelCase									=   {
    'num_train_timesteps': 201,
    'sigma_min': 0.002,
    'sigma_max': 80.0,
}

__lowerCAmelCase									=   {
    'num_train_timesteps': 151,
    'sigma_min': 0.002,
    'sigma_max': 80.0,
}





def 		_UpperCAmelCase (				__A				: Tuple   ):
				if isinstance(__A    ,		__A   ):
								return v
				if v.lower() in ("yes", "true", "t", "y", "1"):
								return True
				elif v.lower() in ("no", "false", "f", "n", "0"):
								return False
				else:
								raise argparse.ArgumentTypeError('''boolean value expected'''   )





def 		_UpperCAmelCase (				__A				: str    ,		__A				: Tuple    ,		__A				: Optional[int]    ,		__A				: int    ,		__A				: Optional[Any]=False   ):
				a_		:	str									=   checkpoint[f'{old_prefix}.in_layers.0.weight']
				a_		:	Dict									=   checkpoint[f'{old_prefix}.in_layers.0.bias']
				a_		:	Optional[Any]									=   checkpoint[f'{old_prefix}.in_layers.2.weight']
				a_		:	Any									=   checkpoint[f'{old_prefix}.in_layers.2.bias']
				a_		:	Any									=   checkpoint[f'{old_prefix}.emb_layers.1.weight']
				a_		:	int									=   checkpoint[f'{old_prefix}.emb_layers.1.bias']
				a_		:	Union[str, Any]									=   checkpoint[f'{old_prefix}.out_layers.0.weight']
				a_		:	Optional[Any]									=   checkpoint[f'{old_prefix}.out_layers.0.bias']
				a_		:	Optional[Any]									=   checkpoint[f'{old_prefix}.out_layers.3.weight']
				a_		:	List[Any]									=   checkpoint[f'{old_prefix}.out_layers.3.bias']

				if has_skip:
								a_		:	int									=   checkpoint[f'{old_prefix}.skip_connection.weight']
								a_		:	List[Any]									=   checkpoint[f'{old_prefix}.skip_connection.bias']

				return new_checkpoint





def 		_UpperCAmelCase (				__A				: int    ,		__A				: int    ,		__A				: Union[str, Any]    ,		__A				: Optional[int]    ,		__A				: List[str]=None   ):
				a_					,      a_					,      a_		:	Any									=   checkpoint[f'{old_prefix}.qkv.weight'].chunk(3    ,		dim=0   )
				a_					,      a_					,      a_		:	List[Any]									=   checkpoint[f'{old_prefix}.qkv.bias'].chunk(3    ,		dim=0   )

				a_		:	List[str]									=   checkpoint[f'{old_prefix}.norm.weight']
				a_		:	Optional[Any]									=   checkpoint[f'{old_prefix}.norm.bias']

				a_		:	Any									=   weight_q.squeeze(-1   ).squeeze(-1   )
				a_		:	int									=   bias_q.squeeze(-1   ).squeeze(-1   )
				a_		:	Any									=   weight_k.squeeze(-1   ).squeeze(-1   )
				a_		:	Optional[int]									=   bias_k.squeeze(-1   ).squeeze(-1   )
				a_		:	str									=   weight_v.squeeze(-1   ).squeeze(-1   )
				a_		:	Optional[Any]									=   bias_v.squeeze(-1   ).squeeze(-1   )

				a_		:	Union[str, Any]									=   (
				    checkpoint[f'{old_prefix}.proj_out.weight'].squeeze(-1   ).squeeze(-1   )
				)
				a_		:	Any									=   checkpoint[f'{old_prefix}.proj_out.bias'].squeeze(-1   ).squeeze(-1   )

				return new_checkpoint





def 		_UpperCAmelCase (				__A				: str    ,		__A				: str   ):
				a_		:	int									=   torch.load(__A    ,		map_location='''cpu'''   )
				a_		:	Union[str, Any]									=   {}

				a_		:	int									=   checkpoint['''time_embed.0.weight''']
				a_		:	Tuple									=   checkpoint['''time_embed.0.bias''']
				a_		:	List[Any]									=   checkpoint['''time_embed.2.weight''']
				a_		:	Union[str, Any]									=   checkpoint['''time_embed.2.bias''']

				if unet_config["num_class_embeds"] is not None:
								a_		:	List[Any]									=   checkpoint['''label_emb.weight''']

				a_		:	Optional[int]									=   checkpoint['''input_blocks.0.0.weight''']
				a_		:	int									=   checkpoint['''input_blocks.0.0.bias''']

				a_		:	Optional[int]									=   unet_config['''down_block_types''']
				a_		:	Union[str, Any]									=   unet_config['''layers_per_block''']
				a_		:	Optional[Any]									=   unet_config['''attention_head_dim''']
				a_		:	str									=   unet_config['''block_out_channels''']
				a_		:	Any									=   1
				a_		:	Union[str, Any]									=   channels_list[0]

				for i, layer_type in enumerate(__A   ):
								a_		:	Any									=   channels_list[i]
								a_		:	Tuple									=   current_channels != prev_channels
								if layer_type == "ResnetDownsampleBlock2D":
												for j in range(__A   ):
																a_		:	Union[str, Any]									=   f'down_blocks.{i}.resnets.{j}'
																a_		:	int									=   f'input_blocks.{current_layer}.0'
																a_		:	List[Any]									=   True if j == 0 and downsample_block_has_skip else False
																a_		:	List[str]									=   convert_resnet(__A    ,		__A    ,		__A    ,		__A    ,		has_skip=__A   )
																current_layer += 1

								elif layer_type == "AttnDownBlock2D":
												for j in range(__A   ):
																a_		:	Union[str, Any]									=   f'down_blocks.{i}.resnets.{j}'
																a_		:	Optional[int]									=   f'input_blocks.{current_layer}.0'
																a_		:	Optional[int]									=   True if j == 0 and downsample_block_has_skip else False
																a_		:	Any									=   convert_resnet(__A    ,		__A    ,		__A    ,		__A    ,		has_skip=__A   )
																a_		:	Optional[int]									=   f'down_blocks.{i}.attentions.{j}'
																a_		:	List[Any]									=   f'input_blocks.{current_layer}.1'
																a_		:	int									=   convert_attention(
																    __A    ,		__A    ,		__A    ,		__A    ,		__A   )
																current_layer += 1

								if i != len(__A   ) - 1:
												a_		:	Dict									=   f'down_blocks.{i}.downsamplers.0'
												a_		:	Optional[int]									=   f'input_blocks.{current_layer}.0'
												a_		:	Union[str, Any]									=   convert_resnet(__A    ,		__A    ,		__A    ,		__A   )
												current_layer += 1

								a_		:	Any									=   current_channels

				# hardcoded the mid-block for now
				a_		:	Tuple									=   '''mid_block.resnets.0'''
				a_		:	int									=   '''middle_block.0'''
				a_		:	Tuple									=   convert_resnet(__A    ,		__A    ,		__A    ,		__A   )
				a_		:	List[Any]									=   '''mid_block.attentions.0'''
				a_		:	Optional[int]									=   '''middle_block.1'''
				a_		:	Optional[int]									=   convert_attention(__A    ,		__A    ,		__A    ,		__A    ,		__A   )
				a_		:	Any									=   '''mid_block.resnets.1'''
				a_		:	Tuple									=   '''middle_block.2'''
				a_		:	Tuple									=   convert_resnet(__A    ,		__A    ,		__A    ,		__A   )

				a_		:	str									=   0
				a_		:	Optional[int]									=   unet_config['''up_block_types''']

				for i, layer_type in enumerate(__A   ):
								if layer_type == "ResnetUpsampleBlock2D":
												for j in range(layers_per_block + 1   ):
																a_		:	Tuple									=   f'up_blocks.{i}.resnets.{j}'
																a_		:	Optional[int]									=   f'output_blocks.{current_layer}.0'
																a_		:	Optional[Any]									=   convert_resnet(__A    ,		__A    ,		__A    ,		__A    ,		has_skip=__A   )
																current_layer += 1

												if i != len(__A   ) - 1:
																a_		:	int									=   f'up_blocks.{i}.upsamplers.0'
																a_		:	Union[str, Any]									=   f'output_blocks.{current_layer-1}.1'
																a_		:	int									=   convert_resnet(__A    ,		__A    ,		__A    ,		__A   )
								elif layer_type == "AttnUpBlock2D":
												for j in range(layers_per_block + 1   ):
																a_		:	Optional[Any]									=   f'up_blocks.{i}.resnets.{j}'
																a_		:	str									=   f'output_blocks.{current_layer}.0'
																a_		:	List[Any]									=   convert_resnet(__A    ,		__A    ,		__A    ,		__A    ,		has_skip=__A   )
																a_		:	List[Any]									=   f'up_blocks.{i}.attentions.{j}'
																a_		:	List[str]									=   f'output_blocks.{current_layer}.1'
																a_		:	str									=   convert_attention(
																    __A    ,		__A    ,		__A    ,		__A    ,		__A   )
																current_layer += 1

												if i != len(__A   ) - 1:
																a_		:	Dict									=   f'up_blocks.{i}.upsamplers.0'
																a_		:	Any									=   f'output_blocks.{current_layer-1}.2'
																a_		:	int									=   convert_resnet(__A    ,		__A    ,		__A    ,		__A   )

				a_		:	Dict									=   checkpoint['''out.0.weight''']
				a_		:	int									=   checkpoint['''out.0.bias''']
				a_		:	int									=   checkpoint['''out.2.weight''']
				a_		:	str									=   checkpoint['''out.2.bias''']

				return new_checkpoint


if __name__ == "__main__":
		__lowerCAmelCase									=   argparse.ArgumentParser()

		parser.add_argument('--unet_path', default=None, type=str, required=True, help='Path to the unet.pt to convert.')
		parser.add_argument(
		    '--dump_path', default=None, type=str, required=True, help='Path to output the converted UNet model.'
		)
		parser.add_argument('--class_cond', default=True, type=str, help='Whether the model is class-conditional.')

		__lowerCAmelCase									=   parser.parse_args()
		__lowerCAmelCase									=   strabool(args.class_cond)

		__lowerCAmelCase									=   os.path.basename(args.unet_path)
		print(F"""Checkpoint: {ckpt_name}""")

		# Get U-Net config
		if "imagenet64" in ckpt_name:
				__lowerCAmelCase									=   IMAGENET_64_UNET_CONFIG
		elif "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
				__lowerCAmelCase									=   LSUN_256_UNET_CONFIG
		elif "test" in ckpt_name:
				__lowerCAmelCase									=   TEST_UNET_CONFIG
		else:
				raise ValueError(F"""Checkpoint type {ckpt_name} is not currently supported.""")

		if not args.class_cond:
				__lowerCAmelCase									=   None

		__lowerCAmelCase									=   con_pt_to_diffuser(args.unet_path, unet_config)

		__lowerCAmelCase									=   UNetaDModel(**unet_config)
		image_unet.load_state_dict(converted_unet_ckpt)

		# Get scheduler config
		if "cd" in ckpt_name or "test" in ckpt_name:
				__lowerCAmelCase									=   CD_SCHEDULER_CONFIG
		elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
				__lowerCAmelCase									=   CT_IMAGENET_64_SCHEDULER_CONFIG
		elif "ct" in ckpt_name and "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
				__lowerCAmelCase									=   CT_LSUN_256_SCHEDULER_CONFIG
		else:
				raise ValueError(F"""Checkpoint type {ckpt_name} is not currently supported.""")

		__lowerCAmelCase									=   CMStochasticIterativeScheduler(**scheduler_config)

		__lowerCAmelCase									=   ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
		consistency_model.save_pretrained(args.dump_path)




