

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()
_UpperCamelCase 			=		logging.get_logger(__name__)

_UpperCamelCase 			=		"Hello, World!"
_UpperCamelCase 			=		"en_XX"







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

      __lowerCAmelCase					:						Dict					=					xmod.model.encoder.sentence_encoder
      __lowerCAmelCase					:						int					=					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=5_1_4   ,	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					:						Any					=					xmod.model.classification_heads['''mnli'''].out_proj.weight.shape[0]

      print('''Our X-MOD config:'''   ,	lowercase__						)

      __lowerCAmelCase					:						Any					=					XmodForSequenceClassification(lowercase__						) if classification_head else XmodForMaskedLM(lowercase__						)
      model.eval()

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

      __lowerCAmelCase					:						List[Any]					=					xmod_sent_encoder.layernorm_embedding.weight
      __lowerCAmelCase					:						Dict					=					xmod_sent_encoder.layernorm_embedding.bias

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

            # self attention
            __lowerCAmelCase					:						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.'''						)

            __lowerCAmelCase					:						List[str]					=					xmod_layer.self_attn.q_proj.weight
            __lowerCAmelCase					:						int					=					xmod_layer.self_attn.q_proj.bias
            __lowerCAmelCase					:						Optional[int]					=					xmod_layer.self_attn.k_proj.weight
            __lowerCAmelCase					:						List[str]					=					xmod_layer.self_attn.k_proj.bias
            __lowerCAmelCase					:						Optional[int]					=					xmod_layer.self_attn.v_proj.weight
            __lowerCAmelCase					:						List[Any]					=					xmod_layer.self_attn.v_proj.bias

            # self-attention output
            __lowerCAmelCase					:						Tuple					=					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					:						Dict					=					xmod_layer.self_attn.out_proj.weight
            __lowerCAmelCase					:						Optional[int]					=					xmod_layer.self_attn.out_proj.bias
            __lowerCAmelCase					:						Optional[int]					=					xmod_layer.self_attn_layer_norm.weight
            __lowerCAmelCase					:						Tuple					=					xmod_layer.self_attn_layer_norm.bias

            # intermediate
            __lowerCAmelCase					:						List[Any]					=					layer.intermediate
            if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
                  raise AssertionError('''Dimensions of intermediate weights do not match.'''						)
            __lowerCAmelCase					:						Tuple					=					xmod_layer.fca.weight
            __lowerCAmelCase					:						Optional[int]					=					xmod_layer.fca.bias

            # output
            __lowerCAmelCase					:						Tuple					=					layer.output
            if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
                  raise AssertionError('''Dimensions of feed-forward weights do not match.'''						)
            __lowerCAmelCase					:						Dict					=					xmod_layer.fca.weight
            __lowerCAmelCase					:						str					=					xmod_layer.fca.bias
            __lowerCAmelCase					:						str					=					xmod_layer.final_layer_norm.weight
            __lowerCAmelCase					:						List[Any]					=					xmod_layer.final_layer_norm.bias
            if bert_output.adapter_layer_norm is not None:
                  __lowerCAmelCase					:						Tuple					=					xmod_layer.adapter_layer_norm.weight
                  __lowerCAmelCase					:						int					=					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					:						List[str]					=					bert_output.adapter_modules[lang_code]
                  __lowerCAmelCase					:						Optional[int]					=					xmod_layer.adapter_modules[lang_code]
                  __lowerCAmelCase					:						Tuple					=					from_adapter.fca.weight
                  __lowerCAmelCase					:						Dict					=					from_adapter.fca.bias
                  __lowerCAmelCase					:						Tuple					=					from_adapter.fca.weight
                  __lowerCAmelCase					:						Optional[int]					=					from_adapter.fca.bias

            # end of layer

      if xmod_sent_encoder.layer_norm is not None:
            __lowerCAmelCase					:						List[Any]					=					xmod_sent_encoder.layer_norm.weight
            __lowerCAmelCase					:						Dict					=					xmod_sent_encoder.layer_norm.bias

      if classification_head:
            __lowerCAmelCase					:						Optional[int]					=					xmod.model.classification_heads['''mnli'''].dense.weight
            __lowerCAmelCase					:						Optional[Any]					=					xmod.model.classification_heads['''mnli'''].dense.bias
            __lowerCAmelCase					:						Dict					=					xmod.model.classification_heads['''mnli'''].out_proj.weight
            __lowerCAmelCase					:						Optional[int]					=					xmod.model.classification_heads['''mnli'''].out_proj.bias
      else:
            # LM Head
            __lowerCAmelCase					:						Union[str, Any]					=					xmod.model.encoder.lm_head.dense.weight
            __lowerCAmelCase					:						Union[str, Any]					=					xmod.model.encoder.lm_head.dense.bias
            __lowerCAmelCase					:						int					=					xmod.model.encoder.lm_head.layer_norm.weight
            __lowerCAmelCase					:						List[str]					=					xmod.model.encoder.lm_head.layer_norm.bias
            __lowerCAmelCase					:						Union[str, Any]					=					xmod.model.encoder.lm_head.weight
            __lowerCAmelCase					:						Any					=					xmod.model.encoder.lm_head.bias

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

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

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


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





import math
from typing import Dict, Iterable, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    is_torch_available,
    is_torch_tensor,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_torch_available():
 import torch

if is_vision_available():
 import PIL


_UpperCamelCase 			=		logging.get_logger(__name__)







def 		_lowercase (     lowercase__   ,	lowercase__   ,	lowercase__   ,	lowercase__						):
      def constraint_to_multiple_of(lowercase__   ,	lowercase__   ,	lowercase__=0   ,	lowercase__=None						):
            __lowerCAmelCase					:						int					=					round(val / multiple						) * multiple

            if max_val is not None and x > max_val:
                  __lowerCAmelCase					:						Optional[int]					=					math.floor(val / multiple						) * multiple

            if x < min_val:
                  __lowerCAmelCase					:						Any					=					math.ceil(val / multiple						) * multiple

            return x

      __lowerCAmelCase					:						Dict					=					(output_size, output_size) if isinstance(lowercase__   ,	lowercase__						) else output_size

      __lowerCAmelCase,							__lowerCAmelCase					:						Optional[Any]					=					get_image_size(lowercase__						)
      __lowerCAmelCase,							__lowerCAmelCase					:						int					=					output_size

      # determine new height and width
      __lowerCAmelCase					:						Optional[Any]					=					output_height / input_height
      __lowerCAmelCase					:						List[Any]					=					output_width / input_width

      if keep_aspect_ratio:
            # scale as little as possible
            if abs(1 - scale_width						) < abs(1 - scale_height						):
                  # fit width
                  __lowerCAmelCase					:						str					=					scale_width
            else:
                  # fit height
                  __lowerCAmelCase					:						str					=					scale_height

      __lowerCAmelCase					:						Any					=					constraint_to_multiple_of(scale_height * input_height   ,	multiple=lowercase__						)
      __lowerCAmelCase					:						Union[str, Any]					=					constraint_to_multiple_of(scale_width * input_width   ,	multiple=lowercase__						)

      return (new_height, new_width)







class   __lowercase      (_UpperCAmelCase ):
 _UpperCamelCase			  =  ["""pixel_values"""]
 def __init__(							self      ,							A_ = True      ,							A_ = None      ,							A_ = PILImageResampling.BILINEAR      ,							A_ = False      ,							A_ = 1      ,							A_ = True      ,							A_ = 1 / 255      ,							A_ = True      ,							A_ = None      ,							A_ = None      ,							**A_      ,							)					->None:



       '''simple docstring'''





       super().__init__(**A_					)
       __lowerCAmelCase					:						Union[str, Any]					=					size if size is not None else {'''height''': 384, '''width''': 384}
       __lowerCAmelCase					:						Dict					=					get_size_dict(A_					)
       __lowerCAmelCase					:						Optional[Any]					=					do_resize
       __lowerCAmelCase					:						int					=					size
       __lowerCAmelCase					:						Dict					=					keep_aspect_ratio
       __lowerCAmelCase					:						List[Any]					=					ensure_multiple_of
       __lowerCAmelCase					:						Tuple					=					resample
       __lowerCAmelCase					:						Dict					=					do_rescale
       __lowerCAmelCase					:						Any					=					rescale_factor
       __lowerCAmelCase					:						List[Any]					=					do_normalize
       __lowerCAmelCase					:						Optional[int]					=					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
       __lowerCAmelCase					:						Optional[int]					=					image_std if image_std is not None else IMAGENET_STANDARD_STD
 def 	UpperCamelCase__  (							self      ,							A_      ,							A_      ,							A_ = False      ,							A_ = 1      ,							A_ = PILImageResampling.BICUBIC      ,							A_ = None      ,							**A_      ,							)					->np.ndarray:



       '''simple docstring'''





       __lowerCAmelCase					:						int					=					get_size_dict(A_					)
       if "height" not in size or "width" not in size:
             raise ValueError(f"""The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}"""					)
       __lowerCAmelCase					:						Union[str, Any]					=					get_resize_output_image_size(
           A_      ,							output_size=(size['''height'''], size['''width'''])      ,							keep_aspect_ratio=A_      ,							multiple=A_      ,							)
       return resize(A_      ,							size=A_      ,							resample=A_      ,							data_format=A_      ,							**A_					)
 def 	UpperCamelCase__  (							self      ,							A_      ,							A_      ,							A_ = None      ,							**A_      ,							)					->Dict:



       '''simple docstring'''





       return rescale(A_      ,							scale=A_      ,							data_format=A_      ,							**A_					)
 def 	UpperCamelCase__  (							self      ,							A_      ,							A_      ,							A_      ,							A_ = None      ,							**A_      ,							)					->np.ndarray:



       '''simple docstring'''





       return normalize(A_      ,							mean=A_      ,							std=A_      ,							data_format=A_      ,							**A_					)
 def 	UpperCamelCase__  (							self      ,							A_      ,							A_ = None      ,							A_ = None      ,							A_ = None      ,							A_ = None      ,							A_ = None      ,							A_ = None      ,							A_ = None      ,							A_ = None      ,							A_ = None      ,							A_ = None      ,							A_ = None      ,							A_ = ChannelDimension.FIRST      ,							**A_      ,							)					->PIL.Image.Image:



       '''simple docstring'''





       __lowerCAmelCase					:						int					=					do_resize if do_resize is not None else self.do_resize
       __lowerCAmelCase					:						Optional[int]					=					size if size is not None else self.size
       __lowerCAmelCase					:						Union[str, Any]					=					get_size_dict(A_					)
       __lowerCAmelCase					:						List[Any]					=					keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
       __lowerCAmelCase					:						Optional[int]					=					ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
       __lowerCAmelCase					:						Tuple					=					resample if resample is not None else self.resample
       __lowerCAmelCase					:						Optional[int]					=					do_rescale if do_rescale is not None else self.do_rescale
       __lowerCAmelCase					:						Union[str, Any]					=					rescale_factor if rescale_factor is not None else self.rescale_factor
       __lowerCAmelCase					:						Tuple					=					do_normalize if do_normalize is not None else self.do_normalize
       __lowerCAmelCase					:						str					=					image_mean if image_mean is not None else self.image_mean
       __lowerCAmelCase					:						Optional[Any]					=					image_std if image_std is not None else self.image_std

       __lowerCAmelCase					:						Optional[Any]					=					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 or resample is None:
             raise ValueError('''Size and resample must be specified if do_resize 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.'''					)

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

       if do_resize:
             __lowerCAmelCase					:						Optional[Any]					=					[self.resize(image=A_      ,							size=A_      ,							resample=A_					) for image in images]

       if do_rescale:
             __lowerCAmelCase					:						Tuple					=					[self.rescale(image=A_      ,							scale=A_					) for image in images]

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

       __lowerCAmelCase					:						Union[str, Any]					=					[to_channel_dimension_format(A_      ,							A_					) for image in images]

       __lowerCAmelCase					:						Dict					=					{'''pixel_values''': images}
       return BatchFeature(data=A_      ,							tensor_type=A_					)





 def 	UpperCamelCase__  (							self      ,							A_      ,							A_ = None					)					->Any:



       '''simple docstring'''





       __lowerCAmelCase					:						Any					=					outputs.logits

       # Resize logits and compute semantic segmentation maps
       if target_sizes is not None:
             if len(A_					) != len(A_					):
                   raise ValueError(
                       '''Make sure that you pass in as many target sizes as the batch dimension of the logits'''					)

             if is_torch_tensor(A_					):
                   __lowerCAmelCase					:						Optional[int]					=					target_sizes.numpy()

             __lowerCAmelCase					:						List[str]					=					[]

             for idx in range(len(A_					)					):
                   __lowerCAmelCase					:						Any					=					torch.nn.functional.interpolate(
                       logits[idx].unsqueeze(dim=0					)      ,							size=target_sizes[idx]      ,							mode='''bilinear'''      ,							align_corners=A_					)
                   __lowerCAmelCase					:						str					=					resized_logits[0].argmax(dim=0					)
                   semantic_segmentation.append(A_					)
       else:
             __lowerCAmelCase					:						Any					=					logits.argmax(dim=1					)
             __lowerCAmelCase					:						List[Any]					=					[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]					)]

       return semantic_segmentation


