
import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


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

						parser.add_argument(
						    '--checkpoint_path', default=None, type=str, required=True, help='Path to the checkpoint to convert.'
						)
						parser.add_argument(
						    '--original_config_file',
						    type=str,
						    required=True,
						    help='The YAML config file corresponding to the original architecture.',
						)
						parser.add_argument(
						    '--num_in_channels',
						    default=None,
						    type=int,
						    help='The number of input channels. If `None` number of input channels will be automatically inferred.',
						)
						parser.add_argument(
						    '--image_size',
						    default=5_1_2,
						    type=int,
						    help=(
						        'The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2'
						        ' Base. Use 768 for Stable Diffusion v2.'
						    ),
						)
						parser.add_argument(
						    '--extract_ema',
						    action='store_true',
						    help=(
						        'Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights'
						        ' or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield'
						        ' higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning.'
						    ),
						)
						parser.add_argument(
						    '--upcast_attention',
						    action='store_true',
						    help=(
						        'Whether the attention computation should always be upcasted. This is necessary when running stable'
						        ' diffusion 2.1.'
						    ),
						)
						parser.add_argument(
						    '--from_safetensors',
						    action='store_true',
						    help='If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.',
						)
						parser.add_argument(
						    '--to_safetensors',
						    action='store_true',
						    help='Whether to store pipeline in safetensors format or not.',
						)
						parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')
						parser.add_argument('--device', type=str, help='Device to use (e.g. cpu, cuda:0, cuda:1, etc.)')




						def 							a		(				snake_case__:   Optional[int]   ):


										'''simple docstring'''
										if string == "True":
														return True
										elif string == "False":
														return False
										else:
														raise ValueError(F'''could not parse string as bool {string}'''   )

						parser.add_argument(
						    '--use_linear_projection', help='Override for use linear projection', required=False, type=parse_bool
						)

						parser.add_argument('--cross_attention_dim', help='Override for cross attention_dim', required=False, type=int)

						__a         =   parser.parse_args()

						__a         =   download_controlnet_from_original_ckpt(
						    checkpoint_path=args.checkpoint_path,
						    original_config_file=args.original_config_file,
						    image_size=args.image_size,
						    extract_ema=args.extract_ema,
						    num_in_channels=args.num_in_channels,
						    upcast_attention=args.upcast_attention,
						    from_safetensors=args.from_safetensors,
						    device=args.device,
						    use_linear_projection=args.use_linear_projection,
						    cross_attention_dim=args.cross_attention_dim,
						)


						controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)







'''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()
UpperCAmelCase					:			List[str]       =	logging.get_logger(__name__)

UpperCAmelCase					:			Tuple       =	'Hello, World!'
UpperCAmelCase					:			Any       =	'en_XX'







def   a__			(    a__ ,       a__ ,       a__		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = Path("""data_bin"""		)
     __SCREAMING_SNAKE_CASE      = 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__		)

     __SCREAMING_SNAKE_CASE      = xmod.model.encoder.sentence_encoder
     __SCREAMING_SNAKE_CASE      = 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=5_14 ,       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:
          __SCREAMING_SNAKE_CASE      = xmod.model.classification_heads["""mnli"""].out_proj.weight.shape[0]

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

     __SCREAMING_SNAKE_CASE      = XmodForSequenceClassification(a__		) if classification_head else XmodForMaskedLM(a__		)
     model.eval()

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

     __SCREAMING_SNAKE_CASE      = xmod_sent_encoder.layernorm_embedding.weight
     __SCREAMING_SNAKE_CASE      = xmod_sent_encoder.layernorm_embedding.bias

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

          # self attention
          __SCREAMING_SNAKE_CASE      = 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."""		)

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

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

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

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

          # end of layer

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

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

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

     __SCREAMING_SNAKE_CASE      = model(a__		)[0]
     if classification_head:
          __SCREAMING_SNAKE_CASE      = xmod.model.classification_heads["""mnli"""](xmod.extract_features(a__		)		)
     else:
          __SCREAMING_SNAKE_CASE      = xmod.model(a__ ,       lang_id=[SAMPLE_LANGUAGE]		)[0]
     print(our_output.shape ,       their_output.shape		)
     __SCREAMING_SNAKE_CASE      = torch.max(torch.abs(our_output - their_output		)		).item()
     print(F'max_absolute_diff = {max_absolute_diff}'		)  # ~ 1e-7
     __SCREAMING_SNAKE_CASE      = 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					:			Tuple       =	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					:			Optional[int]       =	parser.parse_args()
  convert_xmod_checkpoint_to_pytorch(
      args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
  )






