


'''simple docstring'''


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()
__magic_name__			=      logging.get_logger(__name__)

__magic_name__			=      'Hello, World!'
__magic_name__			=      'en_XX'




def      lowerCamelCase					(      lowerCamelCase  :      str							,						lowerCamelCase  :      str							,						lowerCamelCase  :      bool):
						A_						:  Dict          =				Path("""data_bin""")
						A_						:  Union[str, Any]          =				FairseqXmodModel.from_pretrained(
						    model_name_or_path=str(Path(_lowerCAmelCase).parent)							,						checkpoint_file=Path(_lowerCAmelCase).name							,						_name="""xmod_base"""							,						arch="""xmod_base"""							,						task="""multilingual_masked_lm"""							,						data_name_or_path=str(_lowerCAmelCase)							,						bpe="""sentencepiece"""							,						sentencepiece_model=str(Path(_lowerCAmelCase).parent / """sentencepiece.bpe.model""")							,						src_dict=str(data_dir / """dict.txt""")							,						)
						xmod.eval()  # disable dropout
						print(_lowerCAmelCase)

						A_						:  Tuple          =				xmod.model.encoder.sentence_encoder
						A_						:  List[str]          =				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=514							,						type_vocab_size=1							,						layer_norm_eps=1E-5							,						pre_norm=xmod.cfg.model.encoder_normalize_before							,						adapter_reduction_factor=getattr(xmod.cfg.model							,						"""bottleneck"""							,						2)							,						adapter_layer_norm=xmod.cfg.model.adapter_layer_norm							,						adapter_reuse_layer_norm=xmod.cfg.model.adapter_reuse_layer_norm							,						ln_before_adapter=xmod.cfg.model.ln_before_adapter							,						languages=xmod.cfg.model.languages							,						)
						if classification_head:
												A_						:  List[Any]          =				xmod.model.classification_heads["mnli"].out_proj.weight.shape[0]

						print("""Our X-MOD config:"""							,						_lowerCAmelCase)

						A_						:  Optional[Any]          =				XmodForSequenceClassification(_lowerCAmelCase) if classification_head else XmodForMaskedLM(_lowerCAmelCase)
						model.eval()

						# Now let's copy all the weights.
						# Embeddings
						A_						:  str          =				xmod_sent_encoder.embed_tokens.weight
						A_						:  List[Any]          =				xmod_sent_encoder.embed_positions.weight
						A_						:  Optional[int]          =				torch.zeros_like(
						    model.roberta.embeddings.token_type_embeddings.weight)  # just zero them out b/c xmod doesn't use them.

						A_						:  Any          =				xmod_sent_encoder.layernorm_embedding.weight
						A_						:  str          =				xmod_sent_encoder.layernorm_embedding.bias

						for i in range(config.num_hidden_layers):
												# Encoder: start of layer
												A_						:  Optional[Any]          =				model.roberta.encoder.layer[i]
												A_						:  str          =				xmod_sent_encoder.layers[i]

												# self attention
												A_						:  Any          =				layer.attention.self
												if not (
												    xmod_layer.self_attn.k_proj.weight.data.shape
												    == xmod_layer.self_attn.q_proj.weight.data.shape
												    == xmod_layer.self_attn.v_proj.weight.data.shape
												    == torch.Size((config.hidden_size, config.hidden_size))
												):
																		raise AssertionError("""Dimensions of self-attention weights do not match.""")

												A_						:  Tuple          =				xmod_layer.self_attn.q_proj.weight
												A_						:  Optional[int]          =				xmod_layer.self_attn.q_proj.bias
												A_						:  Tuple          =				xmod_layer.self_attn.k_proj.weight
												A_						:  Optional[int]          =				xmod_layer.self_attn.k_proj.bias
												A_						:  Union[str, Any]          =				xmod_layer.self_attn.v_proj.weight
												A_						:  Dict          =				xmod_layer.self_attn.v_proj.bias

												# self-attention output
												A_						:  Dict          =				layer.attention.output
												if self_output.dense.weight.shape != xmod_layer.self_attn.out_proj.weight.shape:
																		raise AssertionError("""Dimensions of self-attention output weights do not match.""")
												A_						:  List[Any]          =				xmod_layer.self_attn.out_proj.weight
												A_						:  Union[str, Any]          =				xmod_layer.self_attn.out_proj.bias
												A_						:  str          =				xmod_layer.self_attn_layer_norm.weight
												A_						:  Tuple          =				xmod_layer.self_attn_layer_norm.bias

												# intermediate
												A_						:  int          =				layer.intermediate
												if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
																		raise AssertionError("""Dimensions of intermediate weights do not match.""")
												A_						:  Any          =				xmod_layer.fca.weight
												A_						:  Dict          =				xmod_layer.fca.bias

												# output
												A_						:  Tuple          =				layer.output
												if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
																		raise AssertionError("""Dimensions of feed-forward weights do not match.""")
												A_						:  str          =				xmod_layer.fca.weight
												A_						:  Any          =				xmod_layer.fca.bias
												A_						:  List[str]          =				xmod_layer.final_layer_norm.weight
												A_						:  List[Any]          =				xmod_layer.final_layer_norm.bias
												if bert_output.adapter_layer_norm is not None:
																		A_						:  Union[str, Any]          =				xmod_layer.adapter_layer_norm.weight
																		A_						:  Tuple          =				xmod_layer.adapter_layer_norm.bias

												if sorted(bert_output.adapter_modules.keys()) != sorted(xmod_layer.adapter_modules.keys()):
																		raise AssertionError("""Lists of language adapters do not match.""")
												for lang_code, adapter in xmod_layer.adapter_modules.items():
																		A_						:  Optional[int]          =				bert_output.adapter_modules[lang_code]
																		A_						:  List[Any]          =				xmod_layer.adapter_modules[lang_code]
																		A_						:  int          =				from_adapter.fca.weight
																		A_						:  Dict          =				from_adapter.fca.bias
																		A_						:  int          =				from_adapter.fca.weight
																		A_						:  Any          =				from_adapter.fca.bias

												# end of layer

						if xmod_sent_encoder.layer_norm is not None:
												A_						:  str          =				xmod_sent_encoder.layer_norm.weight
												A_						:  Optional[Any]          =				xmod_sent_encoder.layer_norm.bias

						if classification_head:
												A_						:  int          =				xmod.model.classification_heads["mnli"].dense.weight
												A_						:  Any          =				xmod.model.classification_heads["mnli"].dense.bias
												A_						:  List[Any]          =				xmod.model.classification_heads["mnli"].out_proj.weight
												A_						:  Optional[Any]          =				xmod.model.classification_heads["mnli"].out_proj.bias
						else:
												# LM Head
												A_						:  Any          =				xmod.model.encoder.lm_head.dense.weight
												A_						:  Any          =				xmod.model.encoder.lm_head.dense.bias
												A_						:  Optional[int]          =				xmod.model.encoder.lm_head.layer_norm.weight
												A_						:  Optional[int]          =				xmod.model.encoder.lm_head.layer_norm.bias
												A_						:  Optional[int]          =				xmod.model.encoder.lm_head.weight
												A_						:  List[Any]          =				xmod.model.encoder.lm_head.bias

						# Let's check that we get the same results.
						A_						:  Any          =				xmod.encode(_lowerCAmelCase).unsqueeze(0)  # batch of size 1
						model.roberta.set_default_language(_lowerCAmelCase)

						A_						:  int          =				model(_lowerCAmelCase)[0]
						if classification_head:
												A_						:  List[str]          =				xmod.model.classification_heads["mnli"](xmod.extract_features(_lowerCAmelCase))
						else:
												A_						:  str          =				xmod.model(_lowerCAmelCase							,						lang_id=[SAMPLE_LANGUAGE])[0]
						print(our_output.shape							,						their_output.shape)
						A_						:  Dict          =				torch.max(torch.abs(our_output - their_output)).item()
						print(F'max_absolute_diff = {max_absolute_diff}')  # ~ 1e-7
						A_						:  Dict          =				torch.allclose(_lowerCAmelCase							,						_lowerCAmelCase							,						atol=1E-3)
						print("""Do both models output the same tensors?"""							,						"""🔥""" if success else """💩""")
						if not success:
												raise Exception("""Something went wRoNg""")

						Path(_lowerCAmelCase).mkdir(parents=_lowerCAmelCase							,						exist_ok=_lowerCAmelCase)
						print(F'Saving model to {pytorch_dump_folder_path}')
						model.save_pretrained(_lowerCAmelCase)


if __name__ == "__main__":
					__magic_name__			=      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.'
					)
					__magic_name__			=      parser.parse_args()
					convert_xmod_checkpoint_to_pytorch(
					    args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
					)




'''simple docstring'''


from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


__magic_name__			=      logging.get_logger(__name__)


if is_vision_available():
     import PIL



class 	__lowerCAmelCase		(							__SCREAMING_SNAKE_CASE    ):


  '''simple docstring'''







  a_  					=							["""pixel_values"""]






  def __init__(							self :       Optional[Any]	,_a :       bool = True	,_a :       Dict[str, int] = None	,_a :       PILImageResampling = PILImageResampling.BICUBIC	,_a :       bool = True	,_a :       Dict[str, int] = None	,_a :       bool = True	,_a :       Union[int, float] = 1 / 255	,_a :       bool = True	,_a :       Optional[Union[float, List[float]]] = None	,_a :       Optional[Union[float, List[float]]] = None	,_a :       bool = True	,**_a :       Dict	,):

        '''simple docstring'''




        super().__init__(**_a )
        A_						:  Tuple          =				size if size is not None else {"""shortest_edge""": 224}
        A_						:  Optional[Any]          =				get_size_dict(_a	,default_to_square=_a )
        A_						:  Tuple          =				crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
        A_						:  Optional[Any]          =				get_size_dict(_a	,default_to_square=_a	,param_name="""crop_size""" )

        A_						:  Any          =				do_resize
        A_						:  List[str]          =				size
        A_						:  Union[str, Any]          =				resample
        A_						:  Dict          =				do_center_crop
        A_						:  List[str]          =				crop_size
        A_						:  Any          =				do_rescale
        A_						:  Union[str, Any]          =				rescale_factor
        A_						:  Any          =				do_normalize
        A_						:  List[str]          =				image_mean if image_mean is not None else OPENAI_CLIP_MEAN
        A_						:  List[Any]          =				image_std if image_std is not None else OPENAI_CLIP_STD
        A_						:  Tuple          =				do_convert_rgb






  def        _a							(							self :       Optional[int]	,_a :       np.ndarray	,_a :       Dict[str, int]	,_a :       PILImageResampling = PILImageResampling.BICUBIC	,_a :       Optional[Union[str, ChannelDimension]] = None	,**_a :       Optional[Any]	,):

        '''simple docstring'''




        A_						:  Optional[Any]          =				get_size_dict(_a	,default_to_square=_a )
        if "shortest_edge" not in size:
              raise ValueError(f'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}' )
        A_						:  Tuple          =				get_resize_output_image_size(_a	,size=size["""shortest_edge"""]	,default_to_square=_a )
        return resize(_a	,size=_a	,resample=_a	,data_format=_a	,**_a )






  def        _a							(							self :       List[Any]	,_a :       np.ndarray	,_a :       Dict[str, int]	,_a :       Optional[Union[str, ChannelDimension]] = None	,**_a :       Optional[int]	,):

        '''simple docstring'''




        A_						:  Optional[int]          =				get_size_dict(_a )
        if "height" not in size or "width" not in size:
              raise ValueError(f'The `size` parameter must contain the keys (height, width). Got {size.keys()}' )
        return center_crop(_a	,size=(size["""height"""], size["""width"""])	,data_format=_a	,**_a )






  def        _a							(							self :       Any	,_a :       np.ndarray	,_a :       Union[int, float]	,_a :       Optional[Union[str, ChannelDimension]] = None	,**_a :       Any	,):

        '''simple docstring'''




        return rescale(_a	,scale=_a	,data_format=_a	,**_a )






  def        _a							(							self :       Any	,_a :       np.ndarray	,_a :       Union[float, List[float]]	,_a :       Union[float, List[float]]	,_a :       Optional[Union[str, ChannelDimension]] = None	,**_a :       List[str]	,):

        '''simple docstring'''




        return normalize(_a	,mean=_a	,std=_a	,data_format=_a	,**_a )






  def        _a							(							self :       Optional[Any]	,_a :       ImageInput	,_a :       bool = None	,_a :       Dict[str, int] = None	,_a :       PILImageResampling = None	,_a :       bool = None	,_a :       int = None	,_a :       bool = None	,_a :       float = None	,_a :       bool = None	,_a :       Optional[Union[float, List[float]]] = None	,_a :       Optional[Union[float, List[float]]] = None	,_a :       bool = None	,_a :       Optional[Union[str, TensorType]] = None	,_a :       Optional[ChannelDimension] = ChannelDimension.FIRST	,**_a :       int	,):

        '''simple docstring'''




        A_						:  Union[str, Any]          =				do_resize if do_resize is not None else self.do_resize
        A_						:  Tuple          =				size if size is not None else self.size
        A_						:  Optional[int]          =				get_size_dict(_a	,param_name="""size"""	,default_to_square=_a )
        A_						:  List[str]          =				resample if resample is not None else self.resample
        A_						:  int          =				do_center_crop if do_center_crop is not None else self.do_center_crop
        A_						:  Any          =				crop_size if crop_size is not None else self.crop_size
        A_						:  int          =				get_size_dict(_a	,param_name="""crop_size"""	,default_to_square=_a )
        A_						:  List[Any]          =				do_rescale if do_rescale is not None else self.do_rescale
        A_						:  int          =				rescale_factor if rescale_factor is not None else self.rescale_factor
        A_						:  Any          =				do_normalize if do_normalize is not None else self.do_normalize
        A_						:  int          =				image_mean if image_mean is not None else self.image_mean
        A_						:  int          =				image_std if image_std is not None else self.image_std
        A_						:  List[str]          =				do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

        A_						:  int          =				make_list_of_images(_a )

        if not valid_images(_a ):
              raise ValueError(
                  """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
                  """torch.Tensor, tf.Tensor or jax.ndarray.""" )

        if do_resize and size is None:
              raise ValueError("""Size must be specified if do_resize is True.""" )

        if do_center_crop and crop_size is None:
              raise ValueError("""Crop size must be specified if do_center_crop is True.""" )

        if do_rescale and rescale_factor is None:
              raise ValueError("""Rescale factor must be specified if do_rescale is True.""" )

        if do_normalize and (image_mean is None or image_std is None):
              raise ValueError("""Image mean and std must be specified if do_normalize is True.""" )

        # PIL RGBA images are converted to RGB
        if do_convert_rgb:
              A_						:  Optional[int]          =				[convert_to_rgb(_a ) for image in images]

        # All transformations expect numpy arrays.
        A_						:  Dict          =				[to_numpy_array(_a ) for image in images]

        if do_resize:
              A_						:  int          =				[self.resize(image=_a	,size=_a	,resample=_a ) for image in images]

        if do_center_crop:
              A_						:  Tuple          =				[self.center_crop(image=_a	,size=_a ) for image in images]

        if do_rescale:
              A_						:  List[str]          =				[self.rescale(image=_a	,scale=_a ) for image in images]

        if do_normalize:
              A_						:  Any          =				[self.normalize(image=_a	,mean=_a	,std=_a ) for image in images]

        A_						:  List[str]          =				[to_channel_dimension_format(_a	,_a ) for image in images]

        A_						:  List[str]          =				{"""pixel_values""": images}
        return BatchFeature(data=_a	,tensor_type=_a )
