import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableDiffusionUpscalePipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()





class 					lowercase__						(   unittest.TestCase):


						def        __A       (       self	:				Optional[int]						):





												'''simple docstring'''





												super().tearDown()
												gc.collect()
												torch.cuda.empty_cache()


						@property
						def        __A       (       self	:				List[Any]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Tuple							=							1
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							3
												SCREAMING_SNAKE_CASE							:    int							=							(32, 32)

												SCREAMING_SNAKE_CASE							:    int							=							floats_tensor((batch_size, num_channels) + sizes    ,		rng=random.Random(0						)						).to(_lowercase						)
												return image


						@property
						def        __A       (       self	:				str						):





												'''simple docstring'''





												torch.manual_seed(0						)
												SCREAMING_SNAKE_CASE							:    Dict							=							UNetaDConditionModel(
												    block_out_channels=(32, 32, 64)    ,		layers_per_block=2    ,		sample_size=32    ,		in_channels=7    ,		out_channels=4    ,		down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''', '''CrossAttnDownBlock2D''')    ,		up_block_types=('''CrossAttnUpBlock2D''', '''CrossAttnUpBlock2D''', '''UpBlock2D''')    ,		cross_attention_dim=32    ,		attention_head_dim=8    ,		use_linear_projection=_lowercase    ,		only_cross_attention=(True, True, False)    ,		num_class_embeds=100    ,		)
												return model


						@property
						def        __A       (       self	:				Optional[int]						):





												'''simple docstring'''





												torch.manual_seed(0						)
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							AutoencoderKL(
												    block_out_channels=[32, 32, 64]    ,		in_channels=3    ,		out_channels=3    ,		down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''', '''DownEncoderBlock2D''']    ,		up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''', '''UpDecoderBlock2D''']    ,		latent_channels=4    ,		)
												return model


						@property
						def        __A       (       self	:				int						):





												'''simple docstring'''





												torch.manual_seed(0						)
												SCREAMING_SNAKE_CASE							:    Any							=							CLIPTextConfig(
												    bos_token_id=0    ,		eos_token_id=2    ,		hidden_size=32    ,		intermediate_size=37    ,		layer_norm_eps=1E-05    ,		num_attention_heads=4    ,		num_hidden_layers=5    ,		pad_token_id=1    ,		vocab_size=1000    ,		hidden_act='''gelu'''    ,		projection_dim=512    ,		)
												return CLIPTextModel(_lowercase						)


						def        __A       (       self	:				Optional[int]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							'''cpu'''  # ensure determinism for the device-dependent torch.Generator
												SCREAMING_SNAKE_CASE							:    int							=							self.dummy_cond_unet_upscale
												SCREAMING_SNAKE_CASE							:    Dict							=							DDPMScheduler()
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							DDIMScheduler(prediction_type='''v_prediction'''						)
												SCREAMING_SNAKE_CASE							:    Any							=							self.dummy_vae
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							self.dummy_text_encoder
												SCREAMING_SNAKE_CASE							:    int							=							CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''						)

												SCREAMING_SNAKE_CASE							:    str							=							self.dummy_image.cpu().permute(0    ,		2    ,		3    ,		1						)[0]
												SCREAMING_SNAKE_CASE							:    Tuple							=							Image.fromarray(np.uinta(_lowercase						)						).convert('''RGB'''						).resize((64, 64)						)

												# make sure here that pndm scheduler skips prk
												SCREAMING_SNAKE_CASE							:    List[str]							=							StableDiffusionUpscalePipeline(
												    unet=_lowercase    ,		low_res_scheduler=_lowercase    ,		scheduler=_lowercase    ,		vae=_lowercase    ,		text_encoder=_lowercase    ,		tokenizer=_lowercase    ,		max_noise_level=350    ,		)
												SCREAMING_SNAKE_CASE							:    Any							=							sd_pipe.to(_lowercase						)
												sd_pipe.set_progress_bar_config(disable=_lowercase						)

												SCREAMING_SNAKE_CASE							:    Dict							=							'''A painting of a squirrel eating a burger'''
												SCREAMING_SNAKE_CASE							:    List[Any]							=							torch.Generator(device=_lowercase						).manual_seed(0						)
												SCREAMING_SNAKE_CASE							:    int							=							sd_pipe(
												    [prompt]    ,		image=_lowercase    ,		generator=_lowercase    ,		guidance_scale=6.0    ,		noise_level=20    ,		num_inference_steps=2    ,		output_type='''np'''    ,		)

												SCREAMING_SNAKE_CASE							:    Any							=							output.images

												SCREAMING_SNAKE_CASE							:    Any							=							torch.Generator(device=_lowercase						).manual_seed(0						)
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							sd_pipe(
												    [prompt]    ,		image=_lowercase    ,		generator=_lowercase    ,		guidance_scale=6.0    ,		noise_level=20    ,		num_inference_steps=2    ,		output_type='''np'''    ,		return_dict=_lowercase    ,		)[0]

												SCREAMING_SNAKE_CASE							:    Any							=							image[0, -3:, -3:, -1]
												SCREAMING_SNAKE_CASE							:    Tuple							=							image_from_tuple[0, -3:, -3:, -1]

												SCREAMING_SNAKE_CASE							:    Optional[int]							=							low_res_image.size[0] * 4
												assert image.shape == (1, expected_height_width, expected_height_width, 3)
												SCREAMING_SNAKE_CASE							:    List[Any]							=							np.array([0.3113, 0.3910, 0.4272, 0.4859, 0.5061, 0.4652, 0.5362, 0.5715, 0.5661]						)

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


						def        __A       (       self	:				int						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Optional[int]							=							'''cpu'''  # ensure determinism for the device-dependent torch.Generator
												SCREAMING_SNAKE_CASE							:    int							=							self.dummy_cond_unet_upscale
												SCREAMING_SNAKE_CASE							:    Dict							=							DDPMScheduler()
												SCREAMING_SNAKE_CASE							:    List[Any]							=							DDIMScheduler(prediction_type='''v_prediction'''						)
												SCREAMING_SNAKE_CASE							:    Any							=							self.dummy_vae
												SCREAMING_SNAKE_CASE							:    List[str]							=							self.dummy_text_encoder
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''						)

												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							self.dummy_image.cpu().permute(0    ,		2    ,		3    ,		1						)[0]
												SCREAMING_SNAKE_CASE							:    Tuple							=							Image.fromarray(np.uinta(_lowercase						)						).convert('''RGB'''						).resize((64, 64)						)

												# make sure here that pndm scheduler skips prk
												SCREAMING_SNAKE_CASE							:    Tuple							=							StableDiffusionUpscalePipeline(
												    unet=_lowercase    ,		low_res_scheduler=_lowercase    ,		scheduler=_lowercase    ,		vae=_lowercase    ,		text_encoder=_lowercase    ,		tokenizer=_lowercase    ,		max_noise_level=350    ,		)
												SCREAMING_SNAKE_CASE							:    List[Any]							=							sd_pipe.to(_lowercase						)
												sd_pipe.set_progress_bar_config(disable=_lowercase						)

												SCREAMING_SNAKE_CASE							:    List[Any]							=							'''A painting of a squirrel eating a burger'''
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							sd_pipe(
												    2 * [prompt]    ,		image=2 * [low_res_image]    ,		guidance_scale=6.0    ,		noise_level=20    ,		num_inference_steps=2    ,		output_type='''np'''    ,		)
												SCREAMING_SNAKE_CASE							:    List[str]							=							output.images
												assert image.shape[0] == 2

												SCREAMING_SNAKE_CASE							:    List[Any]							=							torch.Generator(device=_lowercase						).manual_seed(0						)
												SCREAMING_SNAKE_CASE							:    List[Any]							=							sd_pipe(
												    [prompt]    ,		image=_lowercase    ,		generator=_lowercase    ,		num_images_per_prompt=2    ,		guidance_scale=6.0    ,		noise_level=20    ,		num_inference_steps=2    ,		output_type='''np'''    ,		)
												SCREAMING_SNAKE_CASE							:    List[str]							=							output.images
												assert image.shape[0] == 2



						@unittest.skipIf(torch_device != '''cuda'''    ,		'''This test requires a GPU'''						)
						def        __A       (       self	:				str						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    List[Any]							=							self.dummy_cond_unet_upscale
												SCREAMING_SNAKE_CASE							:    str							=							DDPMScheduler()
												SCREAMING_SNAKE_CASE							:    List[str]							=							DDIMScheduler(prediction_type='''v_prediction'''						)
												SCREAMING_SNAKE_CASE							:    List[str]							=							self.dummy_vae
												SCREAMING_SNAKE_CASE							:    List[Any]							=							self.dummy_text_encoder
												SCREAMING_SNAKE_CASE							:    Dict							=							CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''						)

												SCREAMING_SNAKE_CASE							:    Tuple							=							self.dummy_image.cpu().permute(0    ,		2    ,		3    ,		1						)[0]
												SCREAMING_SNAKE_CASE							:    List[Any]							=							Image.fromarray(np.uinta(_lowercase						)						).convert('''RGB'''						).resize((64, 64)						)

												# put models in fp16, except vae as it overflows in fp16
												SCREAMING_SNAKE_CASE							:    List[Any]							=							unet.half()
												SCREAMING_SNAKE_CASE							:    str							=							text_encoder.half()

												# make sure here that pndm scheduler skips prk
												SCREAMING_SNAKE_CASE							:    List[str]							=							StableDiffusionUpscalePipeline(
												    unet=_lowercase    ,		low_res_scheduler=_lowercase    ,		scheduler=_lowercase    ,		vae=_lowercase    ,		text_encoder=_lowercase    ,		tokenizer=_lowercase    ,		max_noise_level=350    ,		)
												SCREAMING_SNAKE_CASE							:    Tuple							=							sd_pipe.to(_lowercase						)
												sd_pipe.set_progress_bar_config(disable=_lowercase						)

												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							'''A painting of a squirrel eating a burger'''
												SCREAMING_SNAKE_CASE							:    List[str]							=							torch.manual_seed(0						)
												SCREAMING_SNAKE_CASE							:    Any							=							sd_pipe(
												    [prompt]    ,		image=_lowercase    ,		generator=_lowercase    ,		num_inference_steps=2    ,		output_type='''np'''    ,		).images

												SCREAMING_SNAKE_CASE							:    Any							=							low_res_image.size[0] * 4
												assert image.shape == (1, expected_height_width, expected_height_width, 3)






@slow
@require_torch_gpu
class 					lowercase__						(   unittest.TestCase):


						def        __A       (       self	:				Dict						):





												'''simple docstring'''





												super().tearDown()
												gc.collect()
												torch.cuda.empty_cache()


						def        __A       (       self	:				Tuple						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    str							=							load_image(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
												    '''/sd2-upscale/low_res_cat.png'''						)
												SCREAMING_SNAKE_CASE							:    Dict							=							load_numpy(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale'''
												    '''/upsampled_cat.npy'''						)

												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							'''stabilityai/stable-diffusion-x4-upscaler'''
												SCREAMING_SNAKE_CASE							:    int							=							StableDiffusionUpscalePipeline.from_pretrained(_lowercase						)
												pipe.to(_lowercase						)
												pipe.set_progress_bar_config(disable=_lowercase						)
												pipe.enable_attention_slicing()

												SCREAMING_SNAKE_CASE							:    Tuple							=							'''a cat sitting on a park bench'''

												SCREAMING_SNAKE_CASE							:    List[Any]							=							torch.manual_seed(0						)
												SCREAMING_SNAKE_CASE							:    List[str]							=							pipe(
												    prompt=_lowercase    ,		image=_lowercase    ,		generator=_lowercase    ,		output_type='''np'''    ,		)
												SCREAMING_SNAKE_CASE							:    str							=							output.images[0]

												assert image.shape == (512, 512, 3)
												assert np.abs(expected_image - image						).max() < 1E-3


						def        __A       (       self	:				int						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Dict							=							load_image(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
												    '''/sd2-upscale/low_res_cat.png'''						)
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							load_numpy(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale'''
												    '''/upsampled_cat_fp16.npy'''						)

												SCREAMING_SNAKE_CASE							:    Any							=							'''stabilityai/stable-diffusion-x4-upscaler'''
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							StableDiffusionUpscalePipeline.from_pretrained(
												    _lowercase    ,		torch_dtype=torch.floataa    ,		)
												pipe.to(_lowercase						)
												pipe.set_progress_bar_config(disable=_lowercase						)
												pipe.enable_attention_slicing()

												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							'''a cat sitting on a park bench'''

												SCREAMING_SNAKE_CASE							:    Any							=							torch.manual_seed(0						)
												SCREAMING_SNAKE_CASE							:    List[str]							=							pipe(
												    prompt=_lowercase    ,		image=_lowercase    ,		generator=_lowercase    ,		output_type='''np'''    ,		)
												SCREAMING_SNAKE_CASE							:    Dict							=							output.images[0]

												assert image.shape == (512, 512, 3)
												assert np.abs(expected_image - image						).max() < 5E-1



						def        __A       (       self	:				Tuple						):





												'''simple docstring'''





												torch.cuda.empty_cache()
												torch.cuda.reset_max_memory_allocated()
												torch.cuda.reset_peak_memory_stats()

												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							load_image(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
												    '''/sd2-upscale/low_res_cat.png'''						)

												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							'''stabilityai/stable-diffusion-x4-upscaler'''
												SCREAMING_SNAKE_CASE							:    Any							=							StableDiffusionUpscalePipeline.from_pretrained(
												    _lowercase    ,		torch_dtype=torch.floataa    ,		)
												pipe.to(_lowercase						)
												pipe.set_progress_bar_config(disable=_lowercase						)
												pipe.enable_attention_slicing(1						)
												pipe.enable_sequential_cpu_offload()

												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							'''a cat sitting on a park bench'''

												SCREAMING_SNAKE_CASE							:    str							=							torch.manual_seed(0						)
												SCREAMING_SNAKE_CASE							:    List[str]							=							pipe(
												    prompt=_lowercase    ,		image=_lowercase    ,		generator=_lowercase    ,		num_inference_steps=5    ,		output_type='''np'''    ,		)

												SCREAMING_SNAKE_CASE							:    List[str]							=							torch.cuda.max_memory_allocated()
												# make sure that less than 2.9 GB is allocated
												assert mem_bytes < 2.9 * 10**9






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()
__lowerCamelCase			: Dict							   =		logging.get_logger(__name__)

__lowerCamelCase			: Dict							   =		'''Hello, World!'''
__lowerCamelCase			: Optional[Any]							   =		'''en_XX'''



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       str ,     __UpperCamelCase    :       str ,     __UpperCamelCase    :       bool	)							->					Any:







  """simple docstring"""





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