





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()
A_												=     logging.get_logger(__name__)

A_												=     "Hello, World!"
A_												=     "en_XX"





def    __UpperCamelCase      (    a,     a,     a) ->Tuple:
					lowerCamelCase__       =       Path("data_bin")
					lowerCamelCase__       =       FairseqXmodModel.from_pretrained(
					    model_name_or_path=str(Path(a).parent),     checkpoint_file=Path(a).name,     _name="xmod_base",     arch="xmod_base",     task="multilingual_masked_lm",     data_name_or_path=str(a),     bpe="sentencepiece",     sentencepiece_model=str(Path(a).parent / "sentencepiece.bpe.model"),     src_dict=str(data_dir / "dict.txt"),     )
					xmod.eval()  # disable dropout
					print(a)

					lowerCamelCase__       =       xmod.model.encoder.sentence_encoder
					lowerCamelCase__       =       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:
										lowerCamelCase__       =       xmod.model.classification_heads["mnli"].out_proj.weight.shape[0]

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

					lowerCamelCase__       =       XmodForSequenceClassification(a) if classification_head else XmodForMaskedLM(a)
					model.eval()

					# Now let's copy all the weights.
					# Embeddings
					lowerCamelCase__       =       xmod_sent_encoder.embed_tokens.weight
					lowerCamelCase__       =       xmod_sent_encoder.embed_positions.weight
					lowerCamelCase__       =       torch.zeros_like(
					    model.roberta.embeddings.token_type_embeddings.weight)  # just zero them out b/c xmod doesn't use them.

					lowerCamelCase__       =       xmod_sent_encoder.layernorm_embedding.weight
					lowerCamelCase__       =       xmod_sent_encoder.layernorm_embedding.bias

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

										# self attention
										lowerCamelCase__       =       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.")

										lowerCamelCase__       =       xmod_layer.self_attn.q_proj.weight
										lowerCamelCase__       =       xmod_layer.self_attn.q_proj.bias
										lowerCamelCase__       =       xmod_layer.self_attn.k_proj.weight
										lowerCamelCase__       =       xmod_layer.self_attn.k_proj.bias
										lowerCamelCase__       =       xmod_layer.self_attn.v_proj.weight
										lowerCamelCase__       =       xmod_layer.self_attn.v_proj.bias

										# self-attention output
										lowerCamelCase__       =       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.")
										lowerCamelCase__       =       xmod_layer.self_attn.out_proj.weight
										lowerCamelCase__       =       xmod_layer.self_attn.out_proj.bias
										lowerCamelCase__       =       xmod_layer.self_attn_layer_norm.weight
										lowerCamelCase__       =       xmod_layer.self_attn_layer_norm.bias

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

										# output
										lowerCamelCase__       =       layer.output
										if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
															raise AssertionError("Dimensions of feed-forward weights do not match.")
										lowerCamelCase__       =       xmod_layer.fca.weight
										lowerCamelCase__       =       xmod_layer.fca.bias
										lowerCamelCase__       =       xmod_layer.final_layer_norm.weight
										lowerCamelCase__       =       xmod_layer.final_layer_norm.bias
										if bert_output.adapter_layer_norm is not None:
															lowerCamelCase__       =       xmod_layer.adapter_layer_norm.weight
															lowerCamelCase__       =       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():
															lowerCamelCase__       =       bert_output.adapter_modules[lang_code]
															lowerCamelCase__       =       xmod_layer.adapter_modules[lang_code]
															lowerCamelCase__       =       from_adapter.fca.weight
															lowerCamelCase__       =       from_adapter.fca.bias
															lowerCamelCase__       =       from_adapter.fca.weight
															lowerCamelCase__       =       from_adapter.fca.bias

										# end of layer

					if xmod_sent_encoder.layer_norm is not None:
										lowerCamelCase__       =       xmod_sent_encoder.layer_norm.weight
										lowerCamelCase__       =       xmod_sent_encoder.layer_norm.bias

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

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

					lowerCamelCase__       =       model(a)[0]
					if classification_head:
										lowerCamelCase__       =       xmod.model.classification_heads["mnli"](xmod.extract_features(a))
					else:
										lowerCamelCase__       =       xmod.model(a,     lang_id=[SAMPLE_LANGUAGE])[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(a,     a,     atol=1E-3)
					print("Do both models output the same tensors?",     "🔥" if success else "💩")
					if not success:
										raise Exception("Something went wRoNg")

					Path(a).mkdir(parents=a,     exist_ok=a)
					print(f"Saving model to {pytorch_dump_folder_path}")
					model.save_pretrained(a)


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








from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
						import PIL

A_												=     logging.get_logger(__name__)





def    __UpperCamelCase      (    a) ->List[List[ImageInput]]:
					if isinstance(a,     (list, tuple)) and isinstance(videos[0],     (list, tuple)) and is_valid_image(videos[0][0]):
										return videos

					elif isinstance(a,     (list, tuple)) and is_valid_image(videos[0]):
										return [videos]

					elif is_valid_image(a):
										return [[videos]]

					raise ValueError(f"Could not make batched video from {videos}")







class SCREAMING_SNAKE_CASE_ (					lowercase_   ):





					"""simple docstring"""


					A__				 =    ["pixel_values"]





					def __init__( self	,  _lowerCAmelCase = True	,  _lowerCAmelCase = None	,  _lowerCAmelCase = PILImageResampling.BILINEAR	,  _lowerCAmelCase = True	,  _lowerCAmelCase = None	,  _lowerCAmelCase = True	,  _lowerCAmelCase = 1 / 255	,  _lowerCAmelCase = True	,  _lowerCAmelCase = True	,  _lowerCAmelCase = None	,  _lowerCAmelCase = None	,  **_lowerCAmelCase	,  ):
										super().__init__(**_lowerCAmelCase	)
										lowerCamelCase__       =       size if size is not None else {"shortest_edge": 256}
										lowerCamelCase__       =       get_size_dict(_lowerCAmelCase	,  default_to_square=_lowerCAmelCase	)
										lowerCamelCase__       =       crop_size if crop_size is not None else {"height": 224, "width": 224}
										lowerCamelCase__       =       get_size_dict(_lowerCAmelCase	,  param_name="crop_size"	)

										lowerCamelCase__       =       do_resize
										lowerCamelCase__       =       size
										lowerCamelCase__       =       do_center_crop
										lowerCamelCase__       =       crop_size
										lowerCamelCase__       =       resample
										lowerCamelCase__       =       do_rescale
										lowerCamelCase__       =       rescale_factor
										lowerCamelCase__       =       offset
										lowerCamelCase__       =       do_normalize
										lowerCamelCase__       =       image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
										lowerCamelCase__       =       image_std if image_std is not None else IMAGENET_STANDARD_STD





					def 					__magic_name__ ( self	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase = PILImageResampling.BILINEAR	,  _lowerCAmelCase = None	,  **_lowerCAmelCase	,  ):
										lowerCamelCase__       =       get_size_dict(_lowerCAmelCase	,  default_to_square=_lowerCAmelCase	)
										if "shortest_edge" in size:
															lowerCamelCase__       =       get_resize_output_image_size(_lowerCAmelCase	,  size["shortest_edge"]	,  default_to_square=_lowerCAmelCase	)
										elif "height" in size and "width" in size:
															lowerCamelCase__       =       (size["height"], size["width"])
										else:
															raise ValueError(F"Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"	)
										return resize(_lowerCAmelCase	,  size=_lowerCAmelCase	,  resample=_lowerCAmelCase	,  data_format=_lowerCAmelCase	,  **_lowerCAmelCase	)





					def 					__magic_name__ ( self	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase = None	,  **_lowerCAmelCase	,  ):
										lowerCamelCase__       =       get_size_dict(_lowerCAmelCase	)
										if "height" not in size or "width" not in size:
															raise ValueError(F"Size must have 'height' and 'width' as keys. Got {size.keys()}"	)
										return center_crop(_lowerCAmelCase	,  size=(size["height"], size["width"])	,  data_format=_lowerCAmelCase	,  **_lowerCAmelCase	)





					def 					__magic_name__ ( self	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase = True	,  _lowerCAmelCase = None	,  **_lowerCAmelCase	,  ):
										lowerCamelCase__       =       image.astype(np.floataa	)
										if offset:
															lowerCamelCase__       =       image - (scale / 2)
										return rescale(_lowerCAmelCase	,  scale=_lowerCAmelCase	,  data_format=_lowerCAmelCase	,  **_lowerCAmelCase	)





					def 					__magic_name__ ( self	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase = None	,  **_lowerCAmelCase	,  ):
										return normalize(_lowerCAmelCase	,  mean=_lowerCAmelCase	,  std=_lowerCAmelCase	,  data_format=_lowerCAmelCase	,  **_lowerCAmelCase	)





					def 					__magic_name__ ( self	,  _lowerCAmelCase	,  _lowerCAmelCase = None	,  _lowerCAmelCase = None	,  _lowerCAmelCase = None	,  _lowerCAmelCase = None	,  _lowerCAmelCase = None	,  _lowerCAmelCase = None	,  _lowerCAmelCase = None	,  _lowerCAmelCase = None	,  _lowerCAmelCase = None	,  _lowerCAmelCase = None	,  _lowerCAmelCase = None	,  _lowerCAmelCase = ChannelDimension.FIRST	,  ):
										if do_resize and size is None or resample is None:
															raise ValueError("Size and resample 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."	)

										if offset and not do_rescale:
															raise ValueError("For offset, do_rescale must also be set to True."	)

										# All transformations expect numpy arrays.
										lowerCamelCase__       =       to_numpy_array(_lowerCAmelCase	)

										if do_resize:
															lowerCamelCase__       =       self.resize(image=_lowerCAmelCase	,  size=_lowerCAmelCase	,  resample=_lowerCAmelCase	)

										if do_center_crop:
															lowerCamelCase__       =       self.center_crop(_lowerCAmelCase	,  size=_lowerCAmelCase	)

										if do_rescale:
															lowerCamelCase__       =       self.rescale(image=_lowerCAmelCase	,  scale=_lowerCAmelCase	,  offset=_lowerCAmelCase	)

										if do_normalize:
															lowerCamelCase__       =       self.normalize(image=_lowerCAmelCase	,  mean=_lowerCAmelCase	,  std=_lowerCAmelCase	)

										lowerCamelCase__       =       to_channel_dimension_format(_lowerCAmelCase	,  _lowerCAmelCase	)
										return image






					def 					__magic_name__ ( self	,  _lowerCAmelCase	,  _lowerCAmelCase = None	,  _lowerCAmelCase = None	,  _lowerCAmelCase = None	,  _lowerCAmelCase = None	,  _lowerCAmelCase = None	,  _lowerCAmelCase = None	,  _lowerCAmelCase = None	,  _lowerCAmelCase = None	,  _lowerCAmelCase = None	,  _lowerCAmelCase = None	,  _lowerCAmelCase = None	,  _lowerCAmelCase = None	,  _lowerCAmelCase = ChannelDimension.FIRST	,  **_lowerCAmelCase	,  ):
										lowerCamelCase__       =       do_resize if do_resize is not None else self.do_resize
										lowerCamelCase__       =       resample if resample is not None else self.resample
										lowerCamelCase__       =       do_center_crop if do_center_crop is not None else self.do_center_crop
										lowerCamelCase__       =       do_rescale if do_rescale is not None else self.do_rescale
										lowerCamelCase__       =       rescale_factor if rescale_factor is not None else self.rescale_factor
										lowerCamelCase__       =       offset if offset is not None else self.offset
										lowerCamelCase__       =       do_normalize if do_normalize is not None else self.do_normalize
										lowerCamelCase__       =       image_mean if image_mean is not None else self.image_mean
										lowerCamelCase__       =       image_std if image_std is not None else self.image_std

										lowerCamelCase__       =       size if size is not None else self.size
										lowerCamelCase__       =       get_size_dict(_lowerCAmelCase	,  default_to_square=_lowerCAmelCase	)
										lowerCamelCase__       =       crop_size if crop_size is not None else self.crop_size
										lowerCamelCase__       =       get_size_dict(_lowerCAmelCase	,  param_name="crop_size"	)

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

										lowerCamelCase__       =       make_batched(_lowerCAmelCase	)

										lowerCamelCase__       =       [
										    [
										        self._preprocess_image(
										            image=_lowerCAmelCase	,  do_resize=_lowerCAmelCase	,  size=_lowerCAmelCase	,  resample=_lowerCAmelCase	,  do_center_crop=_lowerCAmelCase	,  crop_size=_lowerCAmelCase	,  do_rescale=_lowerCAmelCase	,  rescale_factor=_lowerCAmelCase	,  offset=_lowerCAmelCase	,  do_normalize=_lowerCAmelCase	,  image_mean=_lowerCAmelCase	,  image_std=_lowerCAmelCase	,  data_format=_lowerCAmelCase	,  )
										        for img in video
										    ]
										    for video in videos
										]

										lowerCamelCase__       =       {"pixel_values": videos}
										return BatchFeature(data=_lowerCAmelCase	,  tensor_type=_lowerCAmelCase	)

