





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 SCREAMING_SNAKE_CASE_    (       snake_case_       ):
						__magic_name__:      Tuple			   =							CONFIG_NAME






						@register_to_config
						def __init__(   self   :   Any		, _A   :   bool = True		, _A   :   int = 8		, _A   :   str = "lanczos"		, _A   :   bool = True		, _A   :   bool = False		, )				->     int:







							"""simple docstring"""


							super().__init__()






						@staticmethod
						def UpperCAmelCase_					(   _A   :   np.ndarray						)				->     PIL.Image.Image:







							"""simple docstring"""


							if images.ndim == 3:
								snake_case_		:      Optional[int]           =						images[None, ...]
							snake_case_		:      Any           =						(images * 255).round().astype('uint8'						)
							if images.shape[-1] == 1:
								# special case for grayscale (single channel) images
								snake_case_		:      List[str]           =						[Image.fromarray(image.squeeze()		, mode='L'						) for image in images]
							else:
								snake_case_		:      Any           =						[Image.fromarray(_A						) for image in images]

							return pil_images






						@staticmethod
						def UpperCAmelCase_					(   _A   :   Union[List[PIL.Image.Image], PIL.Image.Image]						)				->     np.ndarray:







							"""simple docstring"""


							if not isinstance(_A		, _A						):
								snake_case_		:      Tuple           =						[images]
							snake_case_		:      int           =						[np.array(_A						).astype(np.floataa						) / 2_5_5.0 for image in images]
							snake_case_		:      Dict           =						np.stack(_A		, axis=0						)

							return images






						@staticmethod
						def UpperCAmelCase_					(   _A   :   np.ndarray						)				->     torch.FloatTensor:







							"""simple docstring"""


							if images.ndim == 3:
								snake_case_		:      Union[str, Any]           =						images[..., None]

							snake_case_		:      Any           =						torch.from_numpy(images.transpose(0		, 3		, 1		, 2						)						)
							return images






						@staticmethod
						def UpperCAmelCase_					(   _A   :   torch.FloatTensor						)				->     np.ndarray:







							"""simple docstring"""


							snake_case_		:      Union[str, Any]           =						images.cpu().permute(0		, 2		, 3		, 1						).float().numpy()
							return images






						@staticmethod
						def UpperCAmelCase_					(   _A   :   Optional[int]						)				->     Optional[Any]:







							"""simple docstring"""


							return 2.0 * images - 1.0






						@staticmethod
						def UpperCAmelCase_					(   _A   :   Optional[int]						)				->     Tuple:







							"""simple docstring"""


							return (images / 2 + 0.5).clamp(0		, 1						)






						@staticmethod
						def UpperCAmelCase_					(   _A   :   PIL.Image.Image						)				->     PIL.Image.Image:







							"""simple docstring"""


							snake_case_		:      Optional[int]           =						image.convert('RGB'						)
							return image






						def UpperCAmelCase_					(   self   :   List[str]		, _A   :   PIL.Image.Image		, _A   :   Optional[int] = None		, _A   :   Optional[int] = None		, )				->     PIL.Image.Image:







							"""simple docstring"""


							if height is None:
								snake_case_		:      Union[str, Any]           =						image.height
							if width is None:
								snake_case_		:      int           =						image.width

							snake_case_    ,snake_case_		:      str           =						(
							    x - x % self.config.vae_scale_factor for x in (width, height)
							)  # resize to integer multiple of vae_scale_factor
							snake_case_		:      Optional[Any]           =						image.resize((width, height)		, resample=PIL_INTERPOLATION[self.config.resample]						)
							return image






						def UpperCAmelCase_					(   self   :   Dict		, _A   :   Union[torch.FloatTensor, PIL.Image.Image, np.ndarray]		, _A   :   Optional[int] = None		, _A   :   Optional[int] = None		, )				->     torch.Tensor:







							"""simple docstring"""


							snake_case_		:      List[Any]           =						(PIL.Image.Image, np.ndarray, torch.Tensor)
							if isinstance(_A		, _A						):
								snake_case_		:      Optional[int]           =						[image]
							elif not (isinstance(_A		, _A						) and all(isinstance(_A		, _A						) for i in image						)):
								raise ValueError(
								    F"""Input is in incorrect format: {[type(_A						) for i in image]}. Currently, we only support {', '.join(_A						)}"""						)

							if isinstance(image[0]		, PIL.Image.Image						):
								if self.config.do_convert_rgb:
									snake_case_		:      str           =						[self.convert_to_rgb(_A						) for i in image]
								if self.config.do_resize:
									snake_case_		:      Union[str, Any]           =						[self.resize(_A		, _A		, _A						) for i in image]
								snake_case_		:      int           =						self.pil_to_numpy(_A						)  # to np
								snake_case_		:      str           =						self.numpy_to_pt(_A						)  # to pt

							elif isinstance(image[0]		, np.ndarray						):
								snake_case_		:      List[Any]           =						np.concatenate(_A		, axis=0						) if image[0].ndim == 4 else np.stack(_A		, axis=0						)
								snake_case_		:      Any           =						self.numpy_to_pt(_A						)
								snake_case_    ,snake_case_    ,snake_case_    ,snake_case_		:      Tuple           =						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						):
								snake_case_		:      List[Any]           =						torch.cat(_A		, axis=0						) if image[0].ndim == 4 else torch.stack(_A		, axis=0						)
								snake_case_    ,snake_case_    ,snake_case_    ,snake_case_		:      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]
							snake_case_		:      Tuple           =						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()}]"""		, _A		, )
								snake_case_		:      Optional[Any]           =						False

							if do_normalize:
								snake_case_		:      Dict           =						self.normalize(_A						)

							return image






						def UpperCAmelCase_					(   self   :   Any		, _A   :   torch.FloatTensor		, _A   :   str = "pil"		, _A   :   Optional[List[bool]] = None		, )				->     int:







							"""simple docstring"""


							if not isinstance(_A		, torch.Tensor						):
								raise ValueError(
								    F"""Input for postprocessing is in incorrect format: {type(_A						)}. We only support pytorch tensor"""						)
							if output_type not in ["latent", "pt", "np", "pil"]:
								snake_case_		:      List[Any]           =						(
								    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'		, _A		, standard_warn=_A						)
								snake_case_		:      Optional[int]           =						'np'

							if output_type == "latent":
								return image

							if do_denormalize is None:
								snake_case_		:      Optional[Any]           =						[self.config.do_normalize] * image.shape[0]

							snake_case_		:      Dict           =						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

							snake_case_		:      Dict           =						self.pt_to_numpy(_A						)

							if output_type == "np":
								return image

							if output_type == "pil":
								return self.numpy_to_pil(_A						)
class SCREAMING_SNAKE_CASE_    (       snake_case_       ):
						__magic_name__:      Union[str, Any]			   =							CONFIG_NAME






						@register_to_config
						def __init__(   self   :   List[str]		, _A   :   bool = True		, _A   :   int = 8		, _A   :   str = "lanczos"		, _A   :   bool = True		, )				->     Optional[Any]:







							"""simple docstring"""


							super().__init__()






						@staticmethod
						def UpperCAmelCase_					(   _A   :   int						)				->     Dict:







							"""simple docstring"""


							if images.ndim == 3:
								snake_case_		:      Dict           =						images[None, ...]
							snake_case_		:      Optional[int]           =						(images * 255).round().astype('uint8'						)
							if images.shape[-1] == 1:
								# special case for grayscale (single channel) images
								snake_case_		:      Union[str, Any]           =						[Image.fromarray(image.squeeze()		, mode='L'						) for image in images]
							else:
								snake_case_		:      Any           =						[Image.fromarray(image[:, :, :3]						) for image in images]

							return pil_images






						@staticmethod
						def UpperCAmelCase_					(   _A   :   Optional[Any]						)				->     Optional[int]:







							"""simple docstring"""


							return image[:, :, 1] * 2**8 + image[:, :, 2]






						def UpperCAmelCase_					(   self   :   List[Any]		, _A   :   Optional[int]						)				->     Any:







							"""simple docstring"""


							if images.ndim == 3:
								snake_case_		:      int           =						images[None, ...]
							snake_case_		:      Any           =						images[:, :, :, 3:]
							if images.shape[-1] == 6:
								snake_case_		:      Optional[int]           =						(images_depth * 255).round().astype('uint8'						)
								snake_case_		:      List[str]           =						[
								    Image.fromarray(self.rgblike_to_depthmap(_A						)		, mode='I;16'						) for image_depth in images_depth
								]
							elif images.shape[-1] == 4:
								snake_case_		:      Dict           =						(images_depth * 6_5_5_3_5.0).astype(np.uintaa						)
								snake_case_		:      Union[str, Any]           =						[Image.fromarray(_A		, mode='I;16'						) for image_depth in images_depth]
							else:
								raise Exception('Not supported'						)

							return pil_images






						def UpperCAmelCase_					(   self   :   Any		, _A   :   torch.FloatTensor		, _A   :   str = "pil"		, _A   :   Optional[List[bool]] = None		, )				->     Dict:







							"""simple docstring"""


							if not isinstance(_A		, torch.Tensor						):
								raise ValueError(
								    F"""Input for postprocessing is in incorrect format: {type(_A						)}. We only support pytorch tensor"""						)
							if output_type not in ["latent", "pt", "np", "pil"]:
								snake_case_		:      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'		, _A		, standard_warn=_A						)
								snake_case_		:      str           =						'np'

							if do_denormalize is None:
								snake_case_		:      Union[str, Any]           =						[self.config.do_normalize] * image.shape[0]

							snake_case_		:      List[Any]           =						torch.stack(
							    [self.denormalize(image[i]						) if do_denormalize[i] else image[i] for i in range(image.shape[0]						)]						)

							snake_case_		:      int           =						self.pt_to_numpy(_A						)

							if output_type == "np":
								if image.shape[-1] == 6:
									snake_case_		:      Tuple           =						np.stack([self.rgblike_to_depthmap(im[:, :, 3:]						) for im in image]		, axis=0						)
								else:
									snake_case_		:      Any           =						image[:, :, :, 3:]
								return image[:, :, :, :3], image_depth

							if output_type == "pil":
								return self.numpy_to_pil(_A						), self.numpy_to_depth(_A						)
							else:
								raise Exception(F"""This type {output_type} is not supported"""						)



"""simple docstring"""


import argparse
import os
import pathlib

import fairseq
import torch
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import (
    DataaVecTextConfig,
    DataaVecTextForMaskedLM,
    DataaVecTextForSequenceClassification,
    DataaVecTextModel,
)
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)

# IMPORTANT: In order for this script to run, please make sure to download the dictionary: `dict.txt` from wget https://dl.fbaipublicfiles.com/fairseq/models/roberta.large.tar.gz
# File copied from https://github.com/pytorch/fairseq/blob/main/examples/data2vec/models/data2vec_text.py
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					=			logging.get_logger(__name__)

__A					=			'Hello world! cécé herlolip'



def        _lowerCamelCase(__UpperCamelCase       ,					__UpperCamelCase       ,					__UpperCamelCase			)							->					List[Any]:
	_lowerCAmelCase		,				_lowerCAmelCase								=os.path.split(__UpperCamelCase			)
	_lowerCAmelCase								=DataaVecTextModel.from_pretrained(
	    __UpperCamelCase       ,					checkpoint_file=__UpperCamelCase			)
	dataavec.eval()  # disable dropout
	_lowerCAmelCase								=dataavec.models[0]
	_lowerCAmelCase								=dataavec_model.encoder.sentence_encoder
	_lowerCAmelCase								=DataaVecTextConfig(
	    vocab_size=dataavec_sent_encoder.embed_tokens.num_embeddings       ,					hidden_size=dataavec_model.args.encoder_embed_dim       ,					num_hidden_layers=dataavec_model.args.encoder_layers       ,					num_attention_heads=dataavec_model.args.encoder_attention_heads       ,					intermediate_size=dataavec_model.args.encoder_ffn_embed_dim       ,					max_position_embeddings=514       ,					type_vocab_size=1       ,					layer_norm_eps=1E-5       ,					)
	if classification_head:
		_lowerCAmelCase								=dataavec.model.classification_heads["""mnli"""].out_proj.weight.shape[0]
	print("""Our BERT config:"""       ,					__UpperCamelCase			)

	_lowerCAmelCase								=DataaVecTextForSequenceClassification(__UpperCamelCase			) if classification_head else DataaVecTextForMaskedLM(__UpperCamelCase			)
	model.eval()

	# Now let's copy all the weights.
	# Embeddings
	_lowerCAmelCase								=dataavec_sent_encoder.embed_tokens.weight
	_lowerCAmelCase								=dataavec_sent_encoder.embed_positions.weight
	_lowerCAmelCase								=torch.zeros_like(
	    model.dataavec_text.embeddings.token_type_embeddings.weight			)  # just zero them out b/c data2vec doesn't use them.
	_lowerCAmelCase								=dataavec_sent_encoder.layernorm_embedding.weight
	_lowerCAmelCase								=dataavec_sent_encoder.layernorm_embedding.bias

	for i in range(config.num_hidden_layers			):
		# Encoder: start of layer
		_lowerCAmelCase								=model.dataavec_text.encoder.layer[i]
		_lowerCAmelCase								=dataavec_sent_encoder.layers[i]

		# self attention
		_lowerCAmelCase								=layer.attention.self
		assert dataavec_layer.self_attn.k_proj.weight.data.shape == torch.Size(
		    (config.hidden_size, config.hidden_size)			), (
		    "Shape for data2vec_layer.self_attn.k_proj.weight.data should be"
		    F''' {torch.Size((config.hidden_size, config.hidden_size)			)}'''
		)
		assert dataavec_layer.self_attn.q_proj.weight.data.shape == torch.Size(
		    (config.hidden_size, config.hidden_size)			), (
		    "Shape for data2vec_layer.self_attn.q_proj.weight.data should be"
		    F''' {torch.Size((config.hidden_size, config.hidden_size)			)}'''
		)
		assert dataavec_layer.self_attn.v_proj.weight.data.shape == torch.Size(
		    (config.hidden_size, config.hidden_size)			), (
		    "Shape for data2vec_layer.self_attn.v_proj.weight.data should be"
		    F''' {torch.Size((config.hidden_size, config.hidden_size)			)}'''
		)

		_lowerCAmelCase								=dataavec_layer.self_attn.q_proj.weight
		_lowerCAmelCase								=dataavec_layer.self_attn.q_proj.bias
		_lowerCAmelCase								=dataavec_layer.self_attn.k_proj.weight
		_lowerCAmelCase								=dataavec_layer.self_attn.k_proj.bias
		_lowerCAmelCase								=dataavec_layer.self_attn.v_proj.weight
		_lowerCAmelCase								=dataavec_layer.self_attn.v_proj.bias

		# self-attention output
		_lowerCAmelCase								=layer.attention.output
		assert (
		    self_output.dense.weight.shape == dataavec_layer.self_attn.out_proj.weight.shape
		), F'''Shape for self_output.dense.weight should be {dataavec_layer.self_attn.out_proj.weight.shape}'''
		_lowerCAmelCase								=dataavec_layer.self_attn.out_proj.weight
		_lowerCAmelCase								=dataavec_layer.self_attn.out_proj.bias
		_lowerCAmelCase								=dataavec_layer.self_attn_layer_norm.weight
		_lowerCAmelCase								=dataavec_layer.self_attn_layer_norm.bias

		# intermediate
		_lowerCAmelCase								=layer.intermediate
		assert (
		    intermediate.dense.weight.shape == dataavec_layer.fca.weight.shape
		), F'''Shape for intermediate.dense.weight should be {dataavec_layer.fca.weight.shape}'''
		_lowerCAmelCase								=dataavec_layer.fca.weight
		_lowerCAmelCase								=dataavec_layer.fca.bias

		# output
		_lowerCAmelCase								=layer.output
		assert (
		    bert_output.dense.weight.shape == dataavec_layer.fca.weight.shape
		), F'''Shape for bert_output.dense.weight should be {dataavec_layer.fca.weight.shape}'''
		_lowerCAmelCase								=dataavec_layer.fca.weight
		_lowerCAmelCase								=dataavec_layer.fca.bias
		_lowerCAmelCase								=dataavec_layer.final_layer_norm.weight
		_lowerCAmelCase								=dataavec_layer.final_layer_norm.bias
		# end of layer

	if classification_head:
		_lowerCAmelCase								=dataavec.model.classification_heads["""mnli"""].dense.weight
		_lowerCAmelCase								=dataavec.model.classification_heads["""mnli"""].dense.bias
		_lowerCAmelCase								=dataavec.model.classification_heads["""mnli"""].out_proj.weight
		_lowerCAmelCase								=dataavec.model.classification_heads["""mnli"""].out_proj.bias
	else:
		# LM Head
		_lowerCAmelCase								=dataavec_model.encoder.lm_head.dense.weight
		_lowerCAmelCase								=dataavec_model.encoder.lm_head.dense.bias
		_lowerCAmelCase								=dataavec_model.encoder.lm_head.layer_norm.weight
		_lowerCAmelCase								=dataavec_model.encoder.lm_head.layer_norm.bias
		_lowerCAmelCase								=dataavec_model.encoder.lm_head.weight
		_lowerCAmelCase								=dataavec_model.encoder.lm_head.bias

	# Let's check that we get the same results.
	_lowerCAmelCase								=dataavec.encode(__UpperCamelCase			).unsqueeze(0			)  # batch of size 1

	_lowerCAmelCase								=model(__UpperCamelCase			)[0]
	if classification_head:
		_lowerCAmelCase								=dataavec.model.classification_heads["""mnli"""](dataavec.extract_features(__UpperCamelCase			)			)
	else:
		_lowerCAmelCase								=dataavec_model(__UpperCamelCase			)[0]
	print(our_output.shape       ,					their_output.shape			)
	_lowerCAmelCase								=torch.max(torch.abs(our_output - their_output			)			).item()
	print(F'''max_absolute_diff = {max_absolute_diff}'''			)  # ~ 1e-7
	_lowerCAmelCase								=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					=			argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '--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					=			parser.parse_args()
			convert_dataavec_checkpoint_to_pytorch(
			    args.checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
			)



