

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__  :      Any			=    logging.get_logger(__name__)

UpperCamelCase__  :      Union[str, Any]			=    "Hello, World!"
UpperCamelCase__  :      str			=    "en_XX"


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		str ,   _SCREAMING_SNAKE_CASE					:		str ,   _SCREAMING_SNAKE_CASE					:		bool       ):





    """simple docstring"""


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

    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=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:
        SCREAMING_SNAKE_CASE_									=						xmod.model.classification_heads['mnli'].out_proj.weight.shape[0]

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

    SCREAMING_SNAKE_CASE_									=						XmodForSequenceClassification(_SCREAMING_SNAKE_CASE       ) if classification_head else XmodForMaskedLM(_SCREAMING_SNAKE_CASE       )
    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(_SCREAMING_SNAKE_CASE       ).unsqueeze(0       )  # batch of size 1
    model.roberta.set_default_language(_SCREAMING_SNAKE_CASE       )

    SCREAMING_SNAKE_CASE_									=						model(_SCREAMING_SNAKE_CASE       )[0]
    if classification_head:
        SCREAMING_SNAKE_CASE_									=						xmod.model.classification_heads['mnli'](xmod.extract_features(_SCREAMING_SNAKE_CASE       )       )
    else:
        SCREAMING_SNAKE_CASE_									=						xmod.model(_SCREAMING_SNAKE_CASE ,   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(_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'       )

    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__":
       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__  :      Dict			=    parser.parse_args()
       convert_xmod_checkpoint_to_pytorch(
           args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
       )





from typing import Dict, List, Optional, Tuple, Union

import torch

from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput



class       __snake_case  (    lowerCAmelCase__       ):

  def __init__(					self						,		_A						,		_A						,		_A						,		_A = None						,		):
      super().__init__()
      self.register_modules(transformer=_A						,		vae=_A						,		scheduler=_A)

      # create a imagenet -> id dictionary for easier use
      SCREAMING_SNAKE_CASE_									=						{}
      if idalabel is not None:
          for key, value in idalabel.items():
              for label in value.split(','):
                  SCREAMING_SNAKE_CASE_									=						int(_A)
          SCREAMING_SNAKE_CASE_									=						dict(sorted(self.labels.items()))

  def 						lowerCAmelCase__     (					self						,		_A):

      if not isinstance(_A						,		_A):
          SCREAMING_SNAKE_CASE_									=						list(_A)

      for l in label:
          if l not in self.labels:
              raise ValueError(
                  f"""{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}.""")

      return [self.labels[l] for l in label]


  @torch.no_grad()
  def __call__(					self						,		_A						,		_A = 4.0						,		_A = None						,		_A = 50						,		_A = "pil"						,		_A = True						,		):
      SCREAMING_SNAKE_CASE_									=						len(_A)
      SCREAMING_SNAKE_CASE_									=						self.transformer.config.sample_size
      SCREAMING_SNAKE_CASE_									=						self.transformer.config.in_channels

      SCREAMING_SNAKE_CASE_									=						randn_tensor(
          shape=(batch_size, latent_channels, latent_size, latent_size)						,		generator=_A						,		device=self.device						,		dtype=self.transformer.dtype						,		)
      SCREAMING_SNAKE_CASE_									=						torch.cat([latents] * 2) if guidance_scale > 1 else latents

      SCREAMING_SNAKE_CASE_									=						torch.tensor(_A						,		device=self.device).reshape(-1)
      SCREAMING_SNAKE_CASE_									=						torch.tensor([1000] * batch_size						,		device=self.device)
      SCREAMING_SNAKE_CASE_									=						torch.cat([class_labels, class_null]						,		0) if guidance_scale > 1 else class_labels

      # set step values
      self.scheduler.set_timesteps(_A)

      for t in self.progress_bar(self.scheduler.timesteps):
          if guidance_scale > 1:
              SCREAMING_SNAKE_CASE_									=						latent_model_input[: len(_A) // 2]
              SCREAMING_SNAKE_CASE_									=						torch.cat([half, half]						,		dim=0)
          SCREAMING_SNAKE_CASE_									=						self.scheduler.scale_model_input(_A						,		_A)

          SCREAMING_SNAKE_CASE_									=						t
          if not torch.is_tensor(_A):
              # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
              # This would be a good case for the `match` statement (Python 3.10+)
              SCREAMING_SNAKE_CASE_									=						latent_model_input.device.type == 'mps'
              if isinstance(_A						,		_A):
                  SCREAMING_SNAKE_CASE_									=						torch.floataa if is_mps else torch.floataa
              else:
                  SCREAMING_SNAKE_CASE_									=						torch.intaa if is_mps else torch.intaa
              SCREAMING_SNAKE_CASE_									=						torch.tensor([timesteps]						,		dtype=_A						,		device=latent_model_input.device)
          elif len(timesteps.shape) == 0:
              SCREAMING_SNAKE_CASE_									=						timesteps[None].to(latent_model_input.device)
          # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
          SCREAMING_SNAKE_CASE_									=						timesteps.expand(latent_model_input.shape[0])
          # predict noise model_output
          SCREAMING_SNAKE_CASE_									=						self.transformer(
              _A						,		timestep=_A						,		class_labels=_A).sample

          # perform guidance
          if guidance_scale > 1:
              SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_									=						noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
              SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_									=						torch.split(_A						,		len(_A) // 2						,		dim=0)

              SCREAMING_SNAKE_CASE_									=						uncond_eps + guidance_scale * (cond_eps - uncond_eps)
              SCREAMING_SNAKE_CASE_									=						torch.cat([half_eps, half_eps]						,		dim=0)

              SCREAMING_SNAKE_CASE_									=						torch.cat([eps, rest]						,		dim=1)

          # learned sigma
          if self.transformer.config.out_channels // 2 == latent_channels:
              SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_									=						torch.split(_A						,		_A						,		dim=1)
          else:
              SCREAMING_SNAKE_CASE_									=						noise_pred

          # compute previous image: x_t -> x_t-1
          SCREAMING_SNAKE_CASE_									=						self.scheduler.step(_A						,		_A						,		_A).prev_sample

      if guidance_scale > 1:
          SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_									=						latent_model_input.chunk(2						,		dim=0)
      else:
          SCREAMING_SNAKE_CASE_									=						latent_model_input

      SCREAMING_SNAKE_CASE_									=						1 / self.vae.config.scaling_factor * latents
      SCREAMING_SNAKE_CASE_									=						self.vae.decode(_A).sample

      SCREAMING_SNAKE_CASE_									=						(samples / 2 + 0.5).clamp(0						,		1)

      # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
      SCREAMING_SNAKE_CASE_									=						samples.cpu().permute(0						,		2						,		3						,		1).float().numpy()

      if output_type == "pil":
          SCREAMING_SNAKE_CASE_									=						self.numpy_to_pil(_A)

      if not return_dict:
          return (samples,)

      return ImagePipelineOutput(images=_A)


