code: 
  
  
  '''simple docstring'''
  
  
  
  import gc
  import random
  import unittest
  
  import numpy as np
  import torch
  from PIL import Image
  from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModelWithProjection
  
  from diffusers import (
      AutoencoderKL,
      DPMSolverMultistepScheduler,
      PNDMScheduler,
      StableDiffusionImageVariationPipeline,
      UNetaDConditionModel,
  )
  from diffusers.utils import floats_tensor, load_image, load_numpy, nightly, slow, torch_device
  from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
  
  from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
  from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
  
  
  enable_full_determinism()
  
  
  
  
  
  class      UpperCAmelCase_						(_UpperCAmelCase      ,	_UpperCAmelCase      ,	_UpperCAmelCase      ,	unittest.TestCase						):
  
  
  
  
  
      """simple docstring"""
  
  
  
  
  
  
      lowerCamelCase						:      Optional[Any]							  =    StableDiffusionImageVariationPipeline
      lowerCamelCase						:      Optional[Any]							  =    IMAGE_VARIATION_PARAMS
      lowerCamelCase						:      List[Any]							  =    IMAGE_VARIATION_BATCH_PARAMS
      lowerCamelCase						:      Optional[int]							  =    frozenset([]						)
      # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
      lowerCamelCase						:      Optional[Any]							  =    frozenset([]						)
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  Union[str, Any]:
           torch.manual_seed(0			)
           __lowerCamelCase     :			List[str]              =	UNetaDConditionModel(
               block_out_channels=(32, 64)    ,  layers_per_block=2    ,  sample_size=32    ,  in_channels=4    ,  out_channels=4    ,  down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')    ,  up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')    ,  cross_attention_dim=32    ,  )
           __lowerCamelCase     :			Optional[int]              =	PNDMScheduler(skip_prk_steps=SCREAMING_SNAKE_CASE_			)
           torch.manual_seed(0			)
           __lowerCamelCase     :			Optional[int]              =	AutoencoderKL(
               block_out_channels=[32, 64]    ,  in_channels=3    ,  out_channels=3    ,  down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']    ,  up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']    ,  latent_channels=4    ,  )
           torch.manual_seed(0			)
           __lowerCamelCase     :			Optional[int]              =	CLIPVisionConfig(
               hidden_size=32    ,  projection_dim=32    ,  intermediate_size=37    ,  layer_norm_eps=1E-05    ,  num_attention_heads=4    ,  num_hidden_layers=5    ,  image_size=32    ,  patch_size=4    ,  )
           __lowerCamelCase     :			List[str]              =	CLIPVisionModelWithProjection(SCREAMING_SNAKE_CASE_			)
           __lowerCamelCase     :			Tuple              =	CLIPImageProcessor(crop_size=32    ,  size=32			)
  
           __lowerCamelCase     :			List[str]              =	{
               'unet': unet,
               'scheduler': scheduler,
               'vae': vae,
               'image_encoder': image_encoder,
               'feature_extractor': feature_extractor,
               'safety_checker': None,
           }
           return components
  
  
  
  
  
  
  
      def    lowercase_       (							self    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_=0			)       ->  str:
           __lowerCamelCase     :			Any              =	floats_tensor((1, 3, 32, 32)    ,  rng=random.Random(SCREAMING_SNAKE_CASE_			)			)
           __lowerCamelCase     :			Union[str, Any]              =	image.cpu().permute(0    ,  2    ,  3    ,  1			)[0]
           __lowerCamelCase     :			Dict              =	Image.fromarray(np.uinta(SCREAMING_SNAKE_CASE_			)			).convert('RGB'			).resize((32, 32)			)
           if str(SCREAMING_SNAKE_CASE_			).startswith('mps'			):
                __lowerCamelCase     :			Dict              =	torch.manual_seed(SCREAMING_SNAKE_CASE_			)
           else:
                __lowerCamelCase     :			List[str]              =	torch.Generator(device=SCREAMING_SNAKE_CASE_			).manual_seed(SCREAMING_SNAKE_CASE_			)
           __lowerCamelCase     :			Union[str, Any]              =	{
               'image': image,
               'generator': generator,
               'num_inference_steps': 2,
               'guidance_scale': 6.0,
               'output_type': 'numpy',
           }
           return inputs
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  Optional[int]:
           __lowerCamelCase     :			str              =	'cpu'  # ensure determinism for the device-dependent torch.Generator
           __lowerCamelCase     :			Dict              =	self.get_dummy_components()
           __lowerCamelCase     :			List[str]              =	StableDiffusionImageVariationPipeline(**SCREAMING_SNAKE_CASE_			)
           __lowerCamelCase     :			int              =	sd_pipe.to(SCREAMING_SNAKE_CASE_			)
           sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_			)
  
           __lowerCamelCase     :			List[str]              =	self.get_dummy_inputs(SCREAMING_SNAKE_CASE_			)
           __lowerCamelCase     :			Optional[int]              =	sd_pipe(**SCREAMING_SNAKE_CASE_			).images
           __lowerCamelCase     :			int              =	image[0, -3:, -3:, -1]
  
           assert image.shape == (1, 64, 64, 3)
           __lowerCamelCase     :			List[Any]              =	np.array([0.5_2_3_9, 0.5_7_2_3, 0.4_7_9_6, 0.5_0_4_9, 0.5_5_5_0, 0.4_6_8_5, 0.5_3_2_9, 0.4_8_9_1, 0.4_9_2_1]			)
  
           assert np.abs(image_slice.flatten() - expected_slice			).max() < 1E-3
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  List[str]:
           __lowerCamelCase     :			Any              =	'cpu'  # ensure determinism for the device-dependent torch.Generator
           __lowerCamelCase     :			Optional[Any]              =	self.get_dummy_components()
           __lowerCamelCase     :			List[str]              =	StableDiffusionImageVariationPipeline(**SCREAMING_SNAKE_CASE_			)
           __lowerCamelCase     :			Union[str, Any]              =	sd_pipe.to(SCREAMING_SNAKE_CASE_			)
           sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_			)
  
           __lowerCamelCase     :			Tuple              =	self.get_dummy_inputs(SCREAMING_SNAKE_CASE_			)
           __lowerCamelCase     :			Dict              =	2 * [inputs['image']]
           __lowerCamelCase     :			int              =	sd_pipe(**SCREAMING_SNAKE_CASE_			)
  
           __lowerCamelCase     :			int              =	output.images
  
           __lowerCamelCase     :			Dict              =	image[-1, -3:, -3:, -1]
  
           assert image.shape == (2, 64, 64, 3)
           __lowerCamelCase     :			int              =	np.array([0.6_8_9_2, 0.5_6_3_7, 0.5_8_3_6, 0.5_7_7_1, 0.6_2_5_4, 0.6_4_0_9, 0.5_5_8_0, 0.5_5_6_9, 0.5_2_8_9]			)
  
           assert np.abs(image_slice.flatten() - expected_slice			).max() < 1E-3
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  str:
           super().test_inference_batch_single_identical(expected_max_diff=3E-3			)
  
  
  
  
  
  
  
  @slow
  @require_torch_gpu
  class      UpperCAmelCase_						(unittest.TestCase						):
  
  
  
  
  
      """simple docstring"""
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  str:
           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()
  
  
  
  
  
  
  
      def    lowercase_       (							self    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_="cpu"    ,  SCREAMING_SNAKE_CASE_=torch.floataa    ,  SCREAMING_SNAKE_CASE_=0			)       ->  Dict:
           __lowerCamelCase     :			Optional[Any]              =	torch.Generator(device=SCREAMING_SNAKE_CASE_			).manual_seed(SCREAMING_SNAKE_CASE_			)
           __lowerCamelCase     :			int              =	load_image(
               'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'
               '/stable_diffusion_imgvar/input_image_vermeer.png'			)
           __lowerCamelCase     :			Union[str, Any]              =	np.random.RandomState(SCREAMING_SNAKE_CASE_			).standard_normal((1, 4, 64, 64)			)
           __lowerCamelCase     :			List[str]              =	torch.from_numpy(SCREAMING_SNAKE_CASE_			).to(device=SCREAMING_SNAKE_CASE_    ,  dtype=SCREAMING_SNAKE_CASE_			)
           __lowerCamelCase     :			str              =	{
               'image': init_image,
               'latents': latents,
               'generator': generator,
               'num_inference_steps': 3,
               'guidance_scale': 7.5,
               'output_type': 'numpy',
           }
           return inputs
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  Optional[int]:
           __lowerCamelCase     :			Dict              =	StableDiffusionImageVariationPipeline.from_pretrained(
               'lambdalabs/sd-image-variations-diffusers'    ,  safety_checker=SCREAMING_SNAKE_CASE_			)
           __lowerCamelCase     :			int              =	sd_pipe.to(SCREAMING_SNAKE_CASE_			)
           sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_			)
  
           __lowerCamelCase     :			Optional[Any]              =	self.get_inputs(SCREAMING_SNAKE_CASE_			)
           __lowerCamelCase     :			Union[str, Any]              =	sd_pipe(**SCREAMING_SNAKE_CASE_			).images
           __lowerCamelCase     :			str              =	image[0, -3:, -3:, -1].flatten()
  
           assert image.shape == (1, 5_12, 5_12, 3)
           __lowerCamelCase     :			Dict              =	np.array([0.8_4_4_9_1, 0.9_0_7_8_9, 0.7_5_7_0_8, 0.7_8_7_3_4, 0.8_3_4_8_5, 0.7_0_0_9_9, 0.6_6_9_3_8, 0.6_8_7_2_7, 0.6_1_3_7_9]			)
           assert np.abs(image_slice - expected_slice			).max() < 6E-3
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  Optional[int]:
           __lowerCamelCase     :			Optional[Any]              =	0
  
           def callback_fn(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			) -> None:
                __lowerCamelCase     :			List[Any]              =	True
                nonlocal number_of_steps
                number_of_steps += 1
                if step == 1:
                     __lowerCamelCase     :			List[Any]              =	latents.detach().cpu().numpy()
                     assert latents.shape == (1, 4, 64, 64)
                     __lowerCamelCase     :			Tuple              =	latents[0, -3:, -3:, -1]
                     __lowerCamelCase     :			Any              =	np.array(
                         [-0.1_6_2_1, 0.2_8_3_7, -0.7_9_7_9, -0.1_2_2_1, -1.3_0_5_7, 0.7_6_8_1, -2.1_1_9_1, 0.0_4_6_4, 1.6_3_0_9]			)
  
                     assert np.abs(latents_slice.flatten() - expected_slice			).max() < 5E-2
                elif step == 2:
                     __lowerCamelCase     :			int              =	latents.detach().cpu().numpy()
                     assert latents.shape == (1, 4, 64, 64)
                     __lowerCamelCase     :			Union[str, Any]              =	latents[0, -3:, -3:, -1]
                     __lowerCamelCase     :			Tuple              =	np.array([0.6_2_9_9, 1.7_5_0_0, 1.1_9_9_2, -2.1_5_8_2, -1.8_9_9_4, 0.7_3_3_4, -0.7_0_9_0, 1.0_1_3_7, 1.5_2_7_3]			)
  
                     assert np.abs(latents_slice.flatten() - expected_slice			).max() < 5E-2
  
           __lowerCamelCase     :			Tuple              =	False
  
           __lowerCamelCase     :			int              =	StableDiffusionImageVariationPipeline.from_pretrained(
               'fusing/sd-image-variations-diffusers'    ,  safety_checker=SCREAMING_SNAKE_CASE_    ,  torch_dtype=torch.floataa    ,  )
           pipe.to(SCREAMING_SNAKE_CASE_			)
           pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_			)
           pipe.enable_attention_slicing()
  
           __lowerCamelCase     :			Tuple              =	self.get_inputs(SCREAMING_SNAKE_CASE_    ,  dtype=torch.floataa			)
           pipe(**SCREAMING_SNAKE_CASE_    ,  callback=SCREAMING_SNAKE_CASE_    ,  callback_steps=1			)
           assert callback_fn.has_been_called
           assert number_of_steps == inputs["num_inference_steps"]
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  str:
           torch.cuda.empty_cache()
           torch.cuda.reset_max_memory_allocated()
           torch.cuda.reset_peak_memory_stats()
  
           __lowerCamelCase     :			Any              =	'fusing/sd-image-variations-diffusers'
           __lowerCamelCase     :			int              =	StableDiffusionImageVariationPipeline.from_pretrained(
               SCREAMING_SNAKE_CASE_    ,  safety_checker=SCREAMING_SNAKE_CASE_    ,  torch_dtype=torch.floataa			)
           __lowerCamelCase     :			int              =	pipe.to(SCREAMING_SNAKE_CASE_			)
           pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_			)
           pipe.enable_attention_slicing(1			)
           pipe.enable_sequential_cpu_offload()
  
           __lowerCamelCase     :			Optional[Any]              =	self.get_inputs(SCREAMING_SNAKE_CASE_    ,  dtype=torch.floataa			)
           __lowerCamelCase     :			Optional[Any]              =	pipe(**SCREAMING_SNAKE_CASE_			)
  
           __lowerCamelCase     :			Optional[Any]              =	torch.cuda.max_memory_allocated()
           # make sure that less than 2.6 GB is allocated
           assert mem_bytes < 2.6 * 10**9
  
  
  
  
  
  
  
  @nightly
  @require_torch_gpu
  class      UpperCAmelCase_						(unittest.TestCase						):
  
  
  
  
  
      """simple docstring"""
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  List[str]:
           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()
  
  
  
  
  
  
  
      def    lowercase_       (							self    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_="cpu"    ,  SCREAMING_SNAKE_CASE_=torch.floataa    ,  SCREAMING_SNAKE_CASE_=0			)       ->  Optional[Any]:
           __lowerCamelCase     :			Tuple              =	torch.Generator(device=SCREAMING_SNAKE_CASE_			).manual_seed(SCREAMING_SNAKE_CASE_			)
           __lowerCamelCase     :			Optional[Any]              =	load_image(
               'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'
               '/stable_diffusion_imgvar/input_image_vermeer.png'			)
           __lowerCamelCase     :			Union[str, Any]              =	np.random.RandomState(SCREAMING_SNAKE_CASE_			).standard_normal((1, 4, 64, 64)			)
           __lowerCamelCase     :			Dict              =	torch.from_numpy(SCREAMING_SNAKE_CASE_			).to(device=SCREAMING_SNAKE_CASE_    ,  dtype=SCREAMING_SNAKE_CASE_			)
           __lowerCamelCase     :			List[str]              =	{
               'image': init_image,
               'latents': latents,
               'generator': generator,
               'num_inference_steps': 50,
               'guidance_scale': 7.5,
               'output_type': 'numpy',
           }
           return inputs
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  Dict:
           __lowerCamelCase     :			Dict              =	StableDiffusionImageVariationPipeline.from_pretrained('fusing/sd-image-variations-diffusers'			)
           sd_pipe.to(SCREAMING_SNAKE_CASE_			)
           sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_			)
  
           __lowerCamelCase     :			Dict              =	self.get_inputs(SCREAMING_SNAKE_CASE_			)
           __lowerCamelCase     :			str              =	sd_pipe(**SCREAMING_SNAKE_CASE_			).images[0]
  
           __lowerCamelCase     :			List[str]              =	load_numpy(
               'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'
               '/stable_diffusion_imgvar/lambdalabs_variations_pndm.npy'			)
           __lowerCamelCase     :			Dict              =	np.abs(expected_image - image			).max()
           assert max_diff < 1E-3
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  Union[str, Any]:
           __lowerCamelCase     :			int              =	StableDiffusionImageVariationPipeline.from_pretrained('fusing/sd-image-variations-diffusers'			)
           __lowerCamelCase     :			Optional[Any]              =	DPMSolverMultistepScheduler.from_config(sd_pipe.scheduler.config			)
           sd_pipe.to(SCREAMING_SNAKE_CASE_			)
           sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_			)
  
           __lowerCamelCase     :			Tuple              =	self.get_inputs(SCREAMING_SNAKE_CASE_			)
           __lowerCamelCase     :			Optional[int]              =	25
           __lowerCamelCase     :			str              =	sd_pipe(**SCREAMING_SNAKE_CASE_			).images[0]
  
           __lowerCamelCase     :			Tuple              =	load_numpy(
               'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'
               '/stable_diffusion_imgvar/lambdalabs_variations_dpm_multi.npy'			)
           __lowerCamelCase     :			int              =	np.abs(expected_image - image			).max()
           assert max_diff < 1E-3
  
  
  
  
  
  
  
style_context: 
  
  
  '''simple docstring'''
  
  
  
  import argparse
  import pathlib
  
  import fairseq
  import torch
  from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
  from fairseq.modules import TransformerSentenceEncoderLayer
  from packaging import version
  
  from transformers import RobertaConfig, RobertaForMaskedLM, RobertaForSequenceClassification
  from transformers.models.bert.modeling_bert import (
      BertIntermediate,
      BertLayer,
      BertOutput,
      BertSelfAttention,
      BertSelfOutput,
  )
  from transformers.utils import logging
  
  
  if version.parse(fairseq.__version__) < version.parse("""0.9.0"""):
        raise Exception("""requires fairseq >= 0.9.0""")
  
  
  logging.set_verbosity_info()
  A__	:      List[str]				=						logging.get_logger(__name__)
  
  A__	:      Union[str, Any]				=						"""Hello world! cécé herlolip"""
  
  
  
  def     UpperCAmelCase__      (			UpperCAmelCase_					:       str   ,					UpperCAmelCase_					:       str   ,					UpperCAmelCase_					:       bool							)  ->	Optional[Any]:
       __lowerCamelCase     :			Any              =	FairseqRobertaModel.from_pretrained(UpperCAmelCase_							)
       roberta.eval()  # disable dropout
       __lowerCamelCase     :			Optional[int]              =	roberta.model.encoder.sentence_encoder
       __lowerCamelCase     :			Optional[Any]              =	RobertaConfig(
           vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings   ,					hidden_size=roberta.args.encoder_embed_dim   ,					num_hidden_layers=roberta.args.encoder_layers   ,					num_attention_heads=roberta.args.encoder_attention_heads   ,					intermediate_size=roberta.args.encoder_ffn_embed_dim   ,					max_position_embeddings=5_14   ,					type_vocab_size=1   ,					layer_norm_eps=1e-5   ,					)
       if classification_head:
            __lowerCamelCase     :			Optional[Any]              =	roberta.model.classification_heads['mnli'].out_proj.weight.shape[0]
       print('Our BERT config:'   ,					UpperCAmelCase_							)
  
       __lowerCamelCase     :			Tuple              =	RobertaForSequenceClassification(UpperCAmelCase_							) if classification_head else RobertaForMaskedLM(UpperCAmelCase_							)
       model.eval()
  
       # Now let's copy all the weights.
       # Embeddings
       __lowerCamelCase     :			Optional[Any]              =	roberta_sent_encoder.embed_tokens.weight
       __lowerCamelCase     :			List[str]              =	roberta_sent_encoder.embed_positions.weight
       __lowerCamelCase     :			Tuple              =	torch.zeros_like(
           model.roberta.embeddings.token_type_embeddings.weight							)  # just zero them out b/c RoBERTa doesn't use them.
       __lowerCamelCase     :			Any              =	roberta_sent_encoder.emb_layer_norm.weight
       __lowerCamelCase     :			Tuple              =	roberta_sent_encoder.emb_layer_norm.bias
  
       for i in range(config.num_hidden_layers							):
            # Encoder: start of layer
            __lowerCamelCase     :			BertLayer              =	model.roberta.encoder.layer[i]
            __lowerCamelCase     :			TransformerSentenceEncoderLayer              =	roberta_sent_encoder.layers[i]
  
            # self attention
            __lowerCamelCase     :			BertSelfAttention              =	layer.attention.self
            assert (
                roberta_layer.self_attn.k_proj.weight.data.shape
                == roberta_layer.self_attn.q_proj.weight.data.shape
                == roberta_layer.self_attn.v_proj.weight.data.shape
                == torch.Size((config.hidden_size, config.hidden_size)							)
            )
  
            __lowerCamelCase     :			Optional[int]              =	roberta_layer.self_attn.q_proj.weight
            __lowerCamelCase     :			Any              =	roberta_layer.self_attn.q_proj.bias
            __lowerCamelCase     :			Tuple              =	roberta_layer.self_attn.k_proj.weight
            __lowerCamelCase     :			Dict              =	roberta_layer.self_attn.k_proj.bias
            __lowerCamelCase     :			Any              =	roberta_layer.self_attn.v_proj.weight
            __lowerCamelCase     :			List[Any]              =	roberta_layer.self_attn.v_proj.bias
  
            # self-attention output
            __lowerCamelCase     :			BertSelfOutput              =	layer.attention.output
            assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
            __lowerCamelCase     :			List[Any]              =	roberta_layer.self_attn.out_proj.weight
            __lowerCamelCase     :			Dict              =	roberta_layer.self_attn.out_proj.bias
            __lowerCamelCase     :			Tuple              =	roberta_layer.self_attn_layer_norm.weight
            __lowerCamelCase     :			List[str]              =	roberta_layer.self_attn_layer_norm.bias
  
            # intermediate
            __lowerCamelCase     :			BertIntermediate              =	layer.intermediate
            assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
            __lowerCamelCase     :			Union[str, Any]              =	roberta_layer.fca.weight
            __lowerCamelCase     :			Optional[Any]              =	roberta_layer.fca.bias
  
            # output
            __lowerCamelCase     :			BertOutput              =	layer.output
            assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
            __lowerCamelCase     :			str              =	roberta_layer.fca.weight
            __lowerCamelCase     :			Tuple              =	roberta_layer.fca.bias
            __lowerCamelCase     :			Optional[int]              =	roberta_layer.final_layer_norm.weight
            __lowerCamelCase     :			Optional[Any]              =	roberta_layer.final_layer_norm.bias
            # end of layer
  
       if classification_head:
            __lowerCamelCase     :			Dict              =	roberta.model.classification_heads['mnli'].dense.weight
            __lowerCamelCase     :			Union[str, Any]              =	roberta.model.classification_heads['mnli'].dense.bias
            __lowerCamelCase     :			Optional[int]              =	roberta.model.classification_heads['mnli'].out_proj.weight
            __lowerCamelCase     :			Any              =	roberta.model.classification_heads['mnli'].out_proj.bias
       else:
            # LM Head
            __lowerCamelCase     :			str              =	roberta.model.encoder.lm_head.dense.weight
            __lowerCamelCase     :			List[Any]              =	roberta.model.encoder.lm_head.dense.bias
            __lowerCamelCase     :			str              =	roberta.model.encoder.lm_head.layer_norm.weight
            __lowerCamelCase     :			int              =	roberta.model.encoder.lm_head.layer_norm.bias
            __lowerCamelCase     :			Optional[Any]              =	roberta.model.encoder.lm_head.weight
            __lowerCamelCase     :			Tuple              =	roberta.model.encoder.lm_head.bias
  
       # Let's check that we get the same results.
       __lowerCamelCase     :			torch.Tensor              =	roberta.encode(UpperCAmelCase_							).unsqueeze(0							)  # batch of size 1
  
       __lowerCamelCase     :			int              =	model(UpperCAmelCase_							)[0]
       if classification_head:
            __lowerCamelCase     :			List[str]              =	roberta.model.classification_heads['mnli'](roberta.extract_features(UpperCAmelCase_							)							)
       else:
            __lowerCamelCase     :			Dict              =	roberta.model(UpperCAmelCase_							)[0]
       print(our_output.shape   ,					their_output.shape							)
       __lowerCamelCase     :			Union[str, Any]              =	torch.max(torch.abs(our_output - their_output							)							).item()
       print(F'max_absolute_diff = {max_absolute_diff}'							)  # ~ 1e-7
       __lowerCamelCase     :			List[str]              =	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'							)
  
       pathlib.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[Any]				=						argparse.ArgumentParser()
        # Required parameters
        parser.add_argument(
            """--roberta_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__	:      Optional[Any]				=						parser.parse_args()
        convert_roberta_checkpoint_to_pytorch(
            args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
        )
  
  
  
  
  
  
  