





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

a__:      Union[str, Any]									=		'Hello, World!'
a__:      Any									=		'en_XX'


def 	UpperCamelCase__(       UpperCamelCase__			:       str ,     UpperCamelCase__			:       str ,     UpperCamelCase__			:       bool  )->Union[str, Any]:
       A__											=						Path('''data_bin'''  )
       A__											=						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__  )

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

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

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

       A__											=						model(UpperCamelCase__  )[0]
       if classification_head:
              A__											=						xmod.model.classification_heads['''mnli'''](xmod.extract_features(UpperCamelCase__  )  )
       else:
              A__											=						xmod.model(UpperCamelCase__ ,     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(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__:      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.'
       )
       a__:      List[str]									=		parser.parse_args()
       convert_xmod_checkpoint_to_pytorch(
           args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
       )












import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device


a__:      List[str]									=		False




class     SCREAMING_SNAKE_CASE__ (						unittest.TestCase      ):
 pass




@nightly
@require_torch_gpu
class     SCREAMING_SNAKE_CASE__ (						unittest.TestCase      ):



 def 				UpperCamelCase			(							self ):
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()



 def 				UpperCamelCase			(							self ):
        A__											=						VersatileDiffusionTextToImagePipeline.from_pretrained('''shi-labs/versatile-diffusion''' )
        # remove text_unet
        pipe.remove_unused_weights()
        pipe.to(__lowerCamelCase )
        pipe.set_progress_bar_config(disable=__lowerCamelCase )

        A__											=						'''A painting of a squirrel eating a burger '''
        A__											=						torch.manual_seed(0 )
        A__											=						pipe(
            prompt=__lowerCamelCase,generator=__lowerCamelCase,guidance_scale=7.5,num_inference_steps=2,output_type='''numpy''' ).images

        with tempfile.TemporaryDirectory() as tmpdirname:
               pipe.save_pretrained(__lowerCamelCase )
               A__											=						VersatileDiffusionTextToImagePipeline.from_pretrained(__lowerCamelCase )
        pipe.to(__lowerCamelCase )
        pipe.set_progress_bar_config(disable=__lowerCamelCase )

        A__											=						generator.manual_seed(0 )
        A__											=						pipe(
            prompt=__lowerCamelCase,generator=__lowerCamelCase,guidance_scale=7.5,num_inference_steps=2,output_type='''numpy''' ).images

        assert np.abs(image - new_image ).sum() < 1E-5, "Models don't have the same forward pass"




 def 				UpperCamelCase			(							self ):
        A__											=						VersatileDiffusionTextToImagePipeline.from_pretrained(
            '''shi-labs/versatile-diffusion''',torch_dtype=torch.floataa )
        pipe.to(__lowerCamelCase )
        pipe.set_progress_bar_config(disable=__lowerCamelCase )

        A__											=						'''A painting of a squirrel eating a burger '''
        A__											=						torch.manual_seed(0 )
        A__											=						pipe(
            prompt=__lowerCamelCase,generator=__lowerCamelCase,guidance_scale=7.5,num_inference_steps=50,output_type='''numpy''' ).images

        A__											=						image[0, 253:256, 253:256, -1]

        assert image.shape == (1, 512, 512, 3)
        A__											=						np.array([0.3367, 0.3169, 0.2656, 0.3870, 0.4790, 0.3796, 0.4009, 0.4878, 0.4778] )

        assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2





