code: 
  
  
  '''simple docstring'''
  
  
  
  import warnings
  from typing import List, Optional, Union
  
  import numpy as np
  import PIL
  import torch
  from PIL import Image
  
  from .configuration_utils import ConfigMixin, register_to_config
  from .utils import CONFIG_NAME, PIL_INTERPOLATION, deprecate
  
  
  
  
  
  class      UpperCAmelCase_						(_UpperCAmelCase						):
  
  
  
  
  
      """simple docstring"""
  
  
  
  
  
  
      lowerCamelCase						:      Optional[int]							  =    CONFIG_NAME
  
  
  
  
  
  
  
      @register_to_config
      def __init__(							self    ,  SCREAMING_SNAKE_CASE_ = True    ,  SCREAMING_SNAKE_CASE_ = 8    ,  SCREAMING_SNAKE_CASE_ = "lanczos"    ,  SCREAMING_SNAKE_CASE_ = True    ,  SCREAMING_SNAKE_CASE_ = False    ,  )       ->  Dict:
           super().__init__()
  
  
  
  
  
  
  
      @staticmethod
      def    lowercase_       (							SCREAMING_SNAKE_CASE_			)       ->  PIL.Image.Image:
           if images.ndim == 3:
                __lowerCamelCase     :			Optional[Any]              =	images[None, ...]
           __lowerCamelCase     :			Any              =	(images * 2_55).round().astype('uint8'			)
           if images.shape[-1] == 1:
                # special case for grayscale (single channel) images
                __lowerCamelCase     :			int              =	[Image.fromarray(image.squeeze()    ,  mode='L'			) for image in images]
           else:
                __lowerCamelCase     :			Optional[Any]              =	[Image.fromarray(SCREAMING_SNAKE_CASE_			) for image in images]
  
           return pil_images
  
  
  
  
  
  
  
      @staticmethod
      def    lowercase_       (							SCREAMING_SNAKE_CASE_			)       ->  np.ndarray:
           if not isinstance(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			):
                __lowerCamelCase     :			Dict              =	[images]
           __lowerCamelCase     :			List[Any]              =	[np.array(SCREAMING_SNAKE_CASE_			).astype(np.floataa			) / 2_5_5.0 for image in images]
           __lowerCamelCase     :			str              =	np.stack(SCREAMING_SNAKE_CASE_    ,  axis=0			)
  
           return images
  
  
  
  
  
  
  
      @staticmethod
      def    lowercase_       (							SCREAMING_SNAKE_CASE_			)       ->  torch.FloatTensor:
           if images.ndim == 3:
                __lowerCamelCase     :			Dict              =	images[..., None]
  
           __lowerCamelCase     :			Any              =	torch.from_numpy(images.transpose(0    ,  3    ,  1    ,  2			)			)
           return images
  
  
  
  
  
  
  
      @staticmethod
      def    lowercase_       (							SCREAMING_SNAKE_CASE_			)       ->  np.ndarray:
           __lowerCamelCase     :			Optional[Any]              =	images.cpu().permute(0    ,  2    ,  3    ,  1			).float().numpy()
           return images
  
  
  
  
  
  
  
      @staticmethod
      def    lowercase_       (							SCREAMING_SNAKE_CASE_			)       ->  Union[str, Any]:
           return 2.0 * images - 1.0
  
  
  
  
  
  
  
      @staticmethod
      def    lowercase_       (							SCREAMING_SNAKE_CASE_			)       ->  int:
           return (images / 2 + 0.5).clamp(0    ,  1			)
  
  
  
  
  
  
  
      @staticmethod
      def    lowercase_       (							SCREAMING_SNAKE_CASE_			)       ->  PIL.Image.Image:
           __lowerCamelCase     :			str              =	image.convert('RGB'			)
           return image
  
  
  
  
  
  
  
      def    lowercase_       (							self    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_ = None    ,  SCREAMING_SNAKE_CASE_ = None    ,  )       ->  PIL.Image.Image:
           if height is None:
                __lowerCamelCase     :			Dict              =	image.height
           if width is None:
                __lowerCamelCase     :			List[Any]              =	image.width
  
           __lowerCamelCase       , __lowerCamelCase     :			Tuple              =	(
               x - x % self.config.vae_scale_factor for x in (width, height)
           )  # resize to integer multiple of vae_scale_factor
           __lowerCamelCase     :			Union[str, Any]              =	image.resize((width, height)    ,  resample=PIL_INTERPOLATION[self.config.resample]			)
           return image
  
  
  
  
  
  
  
      def    lowercase_       (							self    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_ = None    ,  SCREAMING_SNAKE_CASE_ = None    ,  )       ->  torch.Tensor:
           __lowerCamelCase     :			str              =	(PIL.Image.Image, np.ndarray, torch.Tensor)
           if isinstance(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			):
                __lowerCamelCase     :			Any              =	[image]
           elif not (isinstance(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			) and all(isinstance(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			) for i in image			)):
                raise ValueError(
                    f'Input is in incorrect format: {[type(SCREAMING_SNAKE_CASE_			) for i in image]}. Currently, we only support {", ".join(SCREAMING_SNAKE_CASE_			)}'			)
  
           if isinstance(image[0]    ,  PIL.Image.Image			):
                if self.config.do_convert_rgb:
                     __lowerCamelCase     :			Dict              =	[self.convert_to_rgb(SCREAMING_SNAKE_CASE_			) for i in image]
                if self.config.do_resize:
                     __lowerCamelCase     :			Optional[int]              =	[self.resize(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			) for i in image]
                __lowerCamelCase     :			List[str]              =	self.pil_to_numpy(SCREAMING_SNAKE_CASE_			)  # to np
                __lowerCamelCase     :			Optional[int]              =	self.numpy_to_pt(SCREAMING_SNAKE_CASE_			)  # to pt
  
           elif isinstance(image[0]    ,  np.ndarray			):
                __lowerCamelCase     :			Dict              =	np.concatenate(SCREAMING_SNAKE_CASE_    ,  axis=0			) if image[0].ndim == 4 else np.stack(SCREAMING_SNAKE_CASE_    ,  axis=0			)
                __lowerCamelCase     :			int              =	self.numpy_to_pt(SCREAMING_SNAKE_CASE_			)
                __lowerCamelCase       , __lowerCamelCase       , __lowerCamelCase       , __lowerCamelCase     :			Optional[int]              =	image.shape
                if self.config.do_resize and (
                    height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
                ):
                     raise ValueError(
                         f'Currently we only support resizing for PIL image - please resize your numpy array to be divisible by {self.config.vae_scale_factor}'
                         f'currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor'			)
  
           elif isinstance(image[0]    ,  torch.Tensor			):
                __lowerCamelCase     :			Any              =	torch.cat(SCREAMING_SNAKE_CASE_    ,  axis=0			) if image[0].ndim == 4 else torch.stack(SCREAMING_SNAKE_CASE_    ,  axis=0			)
                __lowerCamelCase       , __lowerCamelCase       , __lowerCamelCase       , __lowerCamelCase     :			List[Any]              =	image.shape
  
                # don't need any preprocess if the image is latents
                if channel == 4:
                     return image
  
                if self.config.do_resize and (
                    height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
                ):
                     raise ValueError(
                         f'Currently we only support resizing for PIL image - please resize your pytorch tensor to be divisible by {self.config.vae_scale_factor}'
                         f'currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor'			)
  
          # expected range [0,1], normalize to [-1,1]
           __lowerCamelCase     :			Optional[Any]              =	self.config.do_normalize
           if image.min() < 0:
                warnings.warn(
                    'Passing `image` as torch tensor with value range in [-1,1] is deprecated. The expected value range for image tensor is [0,1] '
                    f'when passing as pytorch tensor or numpy Array. You passed `image` with value range [{image.min()},{image.max()}]'    ,  SCREAMING_SNAKE_CASE_    ,  )
                __lowerCamelCase     :			Tuple              =	False
  
           if do_normalize:
                __lowerCamelCase     :			Dict              =	self.normalize(SCREAMING_SNAKE_CASE_			)
  
           return image
  
  
  
  
  
  
  
      def    lowercase_       (							self    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_ = "pil"    ,  SCREAMING_SNAKE_CASE_ = None    ,  )       ->  Optional[int]:
           if not isinstance(SCREAMING_SNAKE_CASE_    ,  torch.Tensor			):
                raise ValueError(
                    f'Input for postprocessing is in incorrect format: {type(SCREAMING_SNAKE_CASE_			)}. We only support pytorch tensor'			)
           if output_type not in ["latent", "pt", "np", "pil"]:
                __lowerCamelCase     :			Optional[int]              =	(
                    f'the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: '
                    '`pil`, `np`, `pt`, `latent`'
                )
                deprecate('Unsupported output_type'    ,  '1.0.0'    ,  SCREAMING_SNAKE_CASE_    ,  standard_warn=SCREAMING_SNAKE_CASE_			)
                __lowerCamelCase     :			Any              =	'np'
  
           if output_type == "latent":
                return image
  
           if do_denormalize is None:
                __lowerCamelCase     :			Dict              =	[self.config.do_normalize] * image.shape[0]
  
           __lowerCamelCase     :			Optional[int]              =	torch.stack(
               [self.denormalize(image[i]			) if do_denormalize[i] else image[i] for i in range(image.shape[0]			)]			)
  
           if output_type == "pt":
                return image
  
           __lowerCamelCase     :			Tuple              =	self.pt_to_numpy(SCREAMING_SNAKE_CASE_			)
  
           if output_type == "np":
                return image
  
           if output_type == "pil":
                return self.numpy_to_pil(SCREAMING_SNAKE_CASE_			)
  
  
  
  
  
  
  
  class      UpperCAmelCase_						(_UpperCAmelCase						):
  
  
  
  
  
      """simple docstring"""
  
  
  
  
  
  
      lowerCamelCase						:      Optional[int]							  =    CONFIG_NAME
  
  
  
  
  
  
  
      @register_to_config
      def __init__(							self    ,  SCREAMING_SNAKE_CASE_ = True    ,  SCREAMING_SNAKE_CASE_ = 8    ,  SCREAMING_SNAKE_CASE_ = "lanczos"    ,  SCREAMING_SNAKE_CASE_ = True    ,  )       ->  List[str]:
           super().__init__()
  
  
  
  
  
  
  
      @staticmethod
      def    lowercase_       (							SCREAMING_SNAKE_CASE_			)       ->  Any:
           if images.ndim == 3:
                __lowerCamelCase     :			int              =	images[None, ...]
           __lowerCamelCase     :			str              =	(images * 2_55).round().astype('uint8'			)
           if images.shape[-1] == 1:
                # special case for grayscale (single channel) images
                __lowerCamelCase     :			Dict              =	[Image.fromarray(image.squeeze()    ,  mode='L'			) for image in images]
           else:
                __lowerCamelCase     :			Dict              =	[Image.fromarray(image[:, :, :3]			) for image in images]
  
           return pil_images
  
  
  
  
  
  
  
      @staticmethod
      def    lowercase_       (							SCREAMING_SNAKE_CASE_			)       ->  Optional[Any]:
           return image[:, :, 1] * 2**8 + image[:, :, 2]
  
  
  
  
  
  
  
      def    lowercase_       (							self    ,  SCREAMING_SNAKE_CASE_			)       ->  Union[str, Any]:
           if images.ndim == 3:
                __lowerCamelCase     :			Optional[Any]              =	images[None, ...]
           __lowerCamelCase     :			str              =	images[:, :, :, 3:]
           if images.shape[-1] == 6:
                __lowerCamelCase     :			Dict              =	(images_depth * 2_55).round().astype('uint8'			)
                __lowerCamelCase     :			int              =	[
                    Image.fromarray(self.rgblike_to_depthmap(SCREAMING_SNAKE_CASE_			)    ,  mode='I;16'			) for image_depth in images_depth
                ]
           elif images.shape[-1] == 4:
                __lowerCamelCase     :			Any              =	(images_depth * 6_5_5_3_5.0).astype(np.uintaa			)
                __lowerCamelCase     :			Any              =	[Image.fromarray(SCREAMING_SNAKE_CASE_    ,  mode='I;16'			) for image_depth in images_depth]
           else:
                raise Exception('Not supported'			)
  
           return pil_images
  
  
  
  
  
  
  
      def    lowercase_       (							self    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_ = "pil"    ,  SCREAMING_SNAKE_CASE_ = None    ,  )       ->  Any:
           if not isinstance(SCREAMING_SNAKE_CASE_    ,  torch.Tensor			):
                raise ValueError(
                    f'Input for postprocessing is in incorrect format: {type(SCREAMING_SNAKE_CASE_			)}. We only support pytorch tensor'			)
           if output_type not in ["latent", "pt", "np", "pil"]:
                __lowerCamelCase     :			int              =	(
                    f'the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: '
                    '`pil`, `np`, `pt`, `latent`'
                )
                deprecate('Unsupported output_type'    ,  '1.0.0'    ,  SCREAMING_SNAKE_CASE_    ,  standard_warn=SCREAMING_SNAKE_CASE_			)
                __lowerCamelCase     :			Union[str, Any]              =	'np'
  
           if do_denormalize is None:
                __lowerCamelCase     :			Union[str, Any]              =	[self.config.do_normalize] * image.shape[0]
  
           __lowerCamelCase     :			str              =	torch.stack(
               [self.denormalize(image[i]			) if do_denormalize[i] else image[i] for i in range(image.shape[0]			)]			)
  
           __lowerCamelCase     :			Optional[int]              =	self.pt_to_numpy(SCREAMING_SNAKE_CASE_			)
  
           if output_type == "np":
                if image.shape[-1] == 6:
                     __lowerCamelCase     :			Dict              =	np.stack([self.rgblike_to_depthmap(im[:, :, 3:]			) for im in image]    ,  axis=0			)
                else:
                     __lowerCamelCase     :			Dict              =	image[:, :, :, 3:]
                return image[:, :, :, :3], image_depth
  
           if output_type == "pil":
                return self.numpy_to_pil(SCREAMING_SNAKE_CASE_			), self.numpy_to_depth(SCREAMING_SNAKE_CASE_			)
           else:
                raise Exception(f'This type {output_type} is not supported'			)
  
  
  
  
  
  
  
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
        )
  
  
  
  
  
  
  