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()
A   :     str       =				logging.get_logger(__name__)

A   :     List[Any]       =				"Hello, World!"
A   :     Tuple       =				"en_XX"


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

      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_1_4   ,					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:"   ,					__UpperCamelCase )

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

      SCREAMING_SNAKE_CASE_      =     model(__UpperCamelCase )[0]
      if classification_head:
            SCREAMING_SNAKE_CASE_      =     xmod.model.classification_heads["mnli"](xmod.extract_features(__UpperCamelCase ) )
      else:
            SCREAMING_SNAKE_CASE_      =     xmod.model(__UpperCamelCase   ,					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(__UpperCamelCase   ,					__UpperCamelCase   ,					atol=1E-3 )
      print("Do both models output the same tensors?"   ,					"🔥" if success else "💩" )
      if not success:
            raise Exception("Something went wRoNg" )

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


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



from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available


@dataclass
class      lowerCamelCase    (SCREAMING_SNAKE_CASE__    ):
  """simple docstring"""







  lowerCamelCase__   =  42
  lowerCamelCase__   =  42


if is_transformers_available() and is_torch_available():
      from .pipeline_semantic_stable_diffusion import SemanticStableDiffusionPipeline


