





'''simple docstring'''



import collections.abc
from typing import Optional, Tuple, Union

import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithNoAttention, ImageClassifierOutputWithNoAttention
from ...modeling_utils import PreTrainedModel
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_poolformer import PoolFormerConfig


__magic_name__   :						Optional[int]    =			logging.get_logger(__name__)

# General docstring
__magic_name__   :						Dict    =			"""PoolFormerConfig"""

# Base docstring
__magic_name__   :						Tuple    =			"""sail/poolformer_s12"""
__magic_name__   :						Any    =			[1, 512, 7, 7]

# Image classification docstring
__magic_name__   :						Union[str, Any]    =			"""sail/poolformer_s12"""
__magic_name__   :						Any    =			"""tabby, tabby cat"""

__magic_name__   :						Optional[Any]    =			[
    """sail/poolformer_s12""",
    # See all PoolFormer models at https://huggingface.co/models?filter=poolformer
]




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__ = 0.0					,	SCREAMING_SNAKE_CASE__ = False       ):







  '''simple docstring'''



  if drop_prob == 0.0 or not training:
    return input
  _snake_case     		=				1 - drop_prob
  _snake_case     		=				(input.shape[0],) + (1,) * (input.ndim - 1)  # work with diff dim tensors, not just 2D ConvNets
  _snake_case     		=				keep_prob + torch.rand(SCREAMING_SNAKE_CASE__					,	dtype=input.dtype					,	device=input.device       )
  random_tensor.floor_()  # binarize
  _snake_case     		=				input.div(SCREAMING_SNAKE_CASE__       ) * random_tensor
  return output


class       __SCREAMING_SNAKE_CASE	(						nn.Module			):



     '''simple docstring'''






     def __init__(					self						,   lowerCamelCase = None	):
       super().__init__()
       _snake_case     		=				drop_prob

     def 	UpperCamelCase(					self						,   lowerCamelCase	):
       return drop_path(lowerCamelCase						,   self.drop_prob						,   self.training	)






     def 	UpperCamelCase(					self	):
       return "p={}".format(self.drop_prob	)







class       __SCREAMING_SNAKE_CASE	(						nn.Module			):



     '''simple docstring'''






     def __init__(					self						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase=None	):
       super().__init__()
       _snake_case     		=				patch_size if isinstance(lowerCamelCase						,   collections.abc.Iterable	) else (patch_size, patch_size)
       _snake_case     		=				stride if isinstance(lowerCamelCase						,   collections.abc.Iterable	) else (stride, stride)
       _snake_case     		=				padding if isinstance(lowerCamelCase						,   collections.abc.Iterable	) else (padding, padding)

       _snake_case     		=				nn.Convad(lowerCamelCase						,   lowerCamelCase						,   kernel_size=lowerCamelCase						,   stride=lowerCamelCase						,   padding=lowerCamelCase	)
       _snake_case     		=				norm_layer(lowerCamelCase	) if norm_layer else nn.Identity()






     def 	UpperCamelCase(					self						,   lowerCamelCase	):
       _snake_case     		=				self.projection(lowerCamelCase	)
       _snake_case     		=				self.norm(lowerCamelCase	)
       return embeddings







class       __SCREAMING_SNAKE_CASE	(						nn.GroupNorm			):



     '''simple docstring'''






     def __init__(					self						,   lowerCamelCase						,   **lowerCamelCase	):
       super().__init__(1						,   lowerCamelCase						,   **lowerCamelCase	)







class       __SCREAMING_SNAKE_CASE	(						nn.Module			):



     '''simple docstring'''






     def __init__(					self						,   lowerCamelCase	):
       super().__init__()
       _snake_case     		=				nn.AvgPoolad(lowerCamelCase						,   stride=1						,   padding=pool_size // 2						,   count_include_pad=lowerCamelCase	)






     def 	UpperCamelCase(					self						,   lowerCamelCase	):
       return self.pool(lowerCamelCase	) - hidden_states







class       __SCREAMING_SNAKE_CASE	(						nn.Module			):



     '''simple docstring'''






     def __init__(					self						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase	):
       super().__init__()
       _snake_case     		=				nn.Convad(lowerCamelCase						,   lowerCamelCase						,   1	)
       _snake_case     		=				nn.Convad(lowerCamelCase						,   lowerCamelCase						,   1	)
       _snake_case     		=				PoolFormerDropPath(lowerCamelCase	)
       if isinstance(config.hidden_act						,   lowerCamelCase	):
         _snake_case     		=				ACTaFN[config.hidden_act]
       else:
         _snake_case     		=				config.hidden_act






     def 	UpperCamelCase(					self						,   lowerCamelCase	):
       _snake_case     		=				self.conva(lowerCamelCase	)
       _snake_case     		=				self.act_fn(lowerCamelCase	)
       _snake_case     		=				self.drop(lowerCamelCase	)
       _snake_case     		=				self.conva(lowerCamelCase	)
       _snake_case     		=				self.drop(lowerCamelCase	)

       return hidden_states







class       __SCREAMING_SNAKE_CASE	(						nn.Module			):



     '''simple docstring'''






     def __init__(					self						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase	):
       super().__init__()
       _snake_case     		=				PoolFormerPooling(lowerCamelCase	)
       _snake_case     		=				PoolFormerOutput(lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase	)
       _snake_case     		=				PoolFormerGroupNorm(lowerCamelCase	)
       _snake_case     		=				PoolFormerGroupNorm(lowerCamelCase	)

       # Useful for training neural nets
       _snake_case     		=				PoolFormerDropPath(lowerCamelCase	) if drop_path > 0.0 else nn.Identity()
       _snake_case     		=				config.use_layer_scale
       if config.use_layer_scale:
         _snake_case     		=				nn.Parameter(
             config.layer_scale_init_value * torch.ones((lowerCamelCase)	)						,   requires_grad=lowerCamelCase	)
         _snake_case     		=				nn.Parameter(
             config.layer_scale_init_value * torch.ones((lowerCamelCase)	)						,   requires_grad=lowerCamelCase	)






     def 	UpperCamelCase(					self						,   lowerCamelCase	):
       if self.use_layer_scale:
         _snake_case     		=				self.pooling(self.before_norm(lowerCamelCase	)	)
         _snake_case     		=				self.layer_scale_a.unsqueeze(-1	).unsqueeze(-1	) * pooling_output
         # First residual connection
         _snake_case     		=				hidden_states + self.drop_path(lowerCamelCase	)
         _snake_case     		=				()

         _snake_case     		=				self.output(self.after_norm(lowerCamelCase	)	)
         _snake_case     		=				self.layer_scale_a.unsqueeze(-1	).unsqueeze(-1	) * layer_output
         # Second residual connection
         _snake_case     		=				hidden_states + self.drop_path(lowerCamelCase	)

         _snake_case     		=				(output,) + outputs
         return outputs

       else:
         _snake_case     		=				self.drop_path(self.pooling(self.before_norm(lowerCamelCase	)	)	)
         # First residual connection
         _snake_case     		=				pooling_output + hidden_states
         _snake_case     		=				()

         # Second residual connection inside the PoolFormerOutput block
         _snake_case     		=				self.drop_path(self.output(self.after_norm(lowerCamelCase	)	)	)
         _snake_case     		=				hidden_states + layer_output

         _snake_case     		=				(output,) + outputs
         return outputs







class       __SCREAMING_SNAKE_CASE	(						nn.Module			):



     '''simple docstring'''






     def __init__(					self						,   lowerCamelCase	):
       super().__init__()
       _snake_case     		=				config
       # stochastic depth decay rule
       _snake_case     		=				[x.item() for x in torch.linspace(0						,   config.drop_path_rate						,   sum(config.depths	)	)]

       # patch embeddings
       _snake_case     		=				[]
       for i in range(config.num_encoder_blocks	):
         embeddings.append(
             PoolFormerEmbeddings(
                 patch_size=config.patch_sizes[i]						,   stride=config.strides[i]						,   padding=config.padding[i]						,   num_channels=config.num_channels if i == 0 else config.hidden_sizes[i - 1]						,   hidden_size=config.hidden_sizes[i]						,   )	)
       _snake_case     		=				nn.ModuleList(lowerCamelCase	)

       # Transformer blocks
       _snake_case     		=				[]
       _snake_case     		=				0
       for i in range(config.num_encoder_blocks	):
         # each block consists of layers
         _snake_case     		=				[]
         if i != 0:
           cur += config.depths[i - 1]
         for j in range(config.depths[i]	):
           layers.append(
               PoolFormerLayer(
                   lowerCamelCase						,   num_channels=config.hidden_sizes[i]						,   pool_size=config.pool_size						,   hidden_size=config.hidden_sizes[i]						,   intermediate_size=int(config.hidden_sizes[i] * config.mlp_ratio	)						,   drop_path=dpr[cur + j]						,   )	)
         blocks.append(nn.ModuleList(lowerCamelCase	)	)

       _snake_case     		=				nn.ModuleList(lowerCamelCase	)






     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase=False						,   lowerCamelCase=True	):
       _snake_case     		=				() if output_hidden_states else None

       _snake_case     		=				pixel_values
       for idx, layers in enumerate(zip(self.patch_embeddings						,   self.block	)	):
         _snake_case		,		_snake_case     		=				layers
         # Get patch embeddings from hidden_states
         _snake_case     		=				embedding_layer(lowerCamelCase	)
         # Send the embeddings through the blocks
         for _, blk in enumerate(lowerCamelCase	):
           _snake_case     		=				blk(lowerCamelCase	)
           _snake_case     		=				layer_outputs[0]

         if output_hidden_states:
           _snake_case     		=				all_hidden_states + (hidden_states,)

       if not return_dict:
         return tuple(v for v in [hidden_states, all_hidden_states] if v is not None	)

       return BaseModelOutputWithNoAttention(last_hidden_state=lowerCamelCase						,   hidden_states=lowerCamelCase	)







class       __SCREAMING_SNAKE_CASE	(						__UpperCamelCase			):



     '''simple docstring'''






     UpperCAmelCase__       :						Union[str, Any]      =   PoolFormerConfig
     UpperCAmelCase__       :						Any      =   '''poolformer'''
     UpperCAmelCase__       :						Union[str, Any]      =   '''pixel_values'''
     UpperCAmelCase__       :						int      =   True

     def 	UpperCamelCase(					self						,   lowerCamelCase	):
       if isinstance(lowerCamelCase						,   (nn.Linear, nn.Convad)	):
         module.weight.data.normal_(mean=0.0						,   std=self.config.initializer_range	)
         if module.bias is not None:
           module.bias.data.zero_()
       elif isinstance(lowerCamelCase						,   nn.LayerNorm	):
         module.bias.data.zero_()
         module.weight.data.fill_(1.0	)






     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase=False	):
       if isinstance(lowerCamelCase						,   lowerCamelCase	):
         _snake_case     		=				value







__magic_name__   :						Dict    =			r"""
    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
    it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
    behavior.

    Parameters:
        config ([`PoolFormerConfig`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""

__magic_name__   :						Tuple    =			r"""
    Args:
        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
            [`PoolFormerImageProcessor.__call__`] for details.
"""


@add_start_docstrings(
    '''The bare PoolFormer Model transformer outputting raw hidden-states without any specific head on top.'''					,    __UpperCamelCase					,    )
class       __SCREAMING_SNAKE_CASE	(						__UpperCamelCase			):



     '''simple docstring'''






     def __init__(					self						,   lowerCamelCase	):
       super().__init__(lowerCamelCase	)
       _snake_case     		=				config

       _snake_case     		=				PoolFormerEncoder(lowerCamelCase	)

       # Initialize weights and apply final processing
       self.post_init()

     def 	UpperCamelCase(					self	):
       return self.embeddings.patch_embeddings






     @add_start_docstrings_to_model_forward(lowerCamelCase	)
     @add_code_sample_docstrings(
         checkpoint=_CHECKPOINT_FOR_DOC						,   output_type=lowerCamelCase						,   config_class=_CONFIG_FOR_DOC						,   modality="vision"						,   expected_output=_EXPECTED_OUTPUT_SHAPE						,   )
     def 	UpperCamelCase(					self						,   lowerCamelCase = None						,   lowerCamelCase = None						,   lowerCamelCase = None						,   ):
       _snake_case     		=				(
           output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
       )
       _snake_case     		=				return_dict if return_dict is not None else self.config.use_return_dict

       if pixel_values is None:
         raise ValueError("You have to specify pixel_values"	)

       _snake_case     		=				self.encoder(
           lowerCamelCase						,   output_hidden_states=lowerCamelCase						,   return_dict=lowerCamelCase						,   )
       _snake_case     		=				encoder_outputs[0]

       if not return_dict:
         return (sequence_output, None) + encoder_outputs[1:]

       return BaseModelOutputWithNoAttention(
           last_hidden_state=lowerCamelCase						,   hidden_states=encoder_outputs.hidden_states						,   )







class       __SCREAMING_SNAKE_CASE	(						nn.Module			):



     '''simple docstring'''






     def __init__(					self						,   lowerCamelCase	):
       super().__init__()
       _snake_case     		=				nn.Linear(config.hidden_size						,   config.hidden_size	)






     def 	UpperCamelCase(					self						,   lowerCamelCase	):
       _snake_case     		=				self.dense(lowerCamelCase	)
       return output







@add_start_docstrings(
    '''
    PoolFormer Model transformer with an image classification head on top
    '''					,    __UpperCamelCase					,    )
class       __SCREAMING_SNAKE_CASE	(						__UpperCamelCase			):



     '''simple docstring'''






     def __init__(					self						,   lowerCamelCase	):
       super().__init__(lowerCamelCase	)
       _snake_case     		=				config.num_labels
       _snake_case     		=				PoolFormerModel(lowerCamelCase	)

       # Final norm
       _snake_case     		=				PoolFormerGroupNorm(config.hidden_sizes[-1]	)
       # Classifier head
       _snake_case     		=				(
           nn.Linear(config.hidden_sizes[-1]						,   config.num_labels	) if config.num_labels > 0 else nn.Identity()
       )

       # Initialize weights and apply final processing
       self.post_init()






     @add_start_docstrings_to_model_forward(lowerCamelCase	)
     @add_code_sample_docstrings(
         checkpoint=_IMAGE_CLASS_CHECKPOINT						,   output_type=lowerCamelCase						,   config_class=_CONFIG_FOR_DOC						,   expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT						,   )
     def 	UpperCamelCase(					self						,   lowerCamelCase = None						,   lowerCamelCase = None						,   lowerCamelCase = None						,   lowerCamelCase = None						,   ):
       _snake_case     		=				return_dict if return_dict is not None else self.config.use_return_dict

       _snake_case     		=				self.poolformer(
           lowerCamelCase						,   output_hidden_states=lowerCamelCase						,   return_dict=lowerCamelCase						,   )

       _snake_case     		=				outputs[0]

       _snake_case     		=				self.classifier(self.norm(lowerCamelCase	).mean([-2, -1]	)	)

       _snake_case     		=				None
       if labels is not None:
         if self.config.problem_type is None:
           if self.num_labels == 1:
             _snake_case     		=				"regression"
           elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
             _snake_case     		=				"single_label_classification"
           else:
             _snake_case     		=				"multi_label_classification"

         if self.config.problem_type == "regression":
           _snake_case     		=				MSELoss()
           if self.num_labels == 1:
             _snake_case     		=				loss_fct(logits.squeeze()						,   labels.squeeze()	)
           else:
             _snake_case     		=				loss_fct(lowerCamelCase						,   lowerCamelCase	)
         elif self.config.problem_type == "single_label_classification":
           _snake_case     		=				CrossEntropyLoss()
           _snake_case     		=				loss_fct(logits.view(-1						,   self.num_labels	)						,   labels.view(-1	)	)
         elif self.config.problem_type == "multi_label_classification":
           _snake_case     		=				BCEWithLogitsLoss()
           _snake_case     		=				loss_fct(lowerCamelCase						,   lowerCamelCase	)

       if not return_dict:
         _snake_case     		=				(logits,) + outputs[2:]
         return ((loss,) + output) if loss is not None else output

       return ImageClassifierOutputWithNoAttention(loss=lowerCamelCase						,   logits=lowerCamelCase						,   hidden_states=outputs.hidden_states	)









'''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__   :						Union[str, Any]    =			logging.get_logger(__name__)

__magic_name__   :						Tuple    =			"""Hello, World!"""
__magic_name__   :						Union[str, Any]    =			"""en_XX"""




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  _snake_case     		=				Path("data_bin"       )
  _snake_case     		=				FairseqXmodModel.from_pretrained(
      model_name_or_path=str(Path(SCREAMING_SNAKE_CASE__       ).parent       )					,	checkpoint_file=Path(SCREAMING_SNAKE_CASE__       ).name					,	_name="xmod_base"					,	arch="xmod_base"					,	task="multilingual_masked_lm"					,	data_name_or_path=str(SCREAMING_SNAKE_CASE__       )					,	bpe="sentencepiece"					,	sentencepiece_model=str(Path(SCREAMING_SNAKE_CASE__       ).parent / "sentencepiece.bpe.model"       )					,	src_dict=str(data_dir / "dict.txt"       )					,	)
  xmod.eval()  # disable dropout
  print(SCREAMING_SNAKE_CASE__       )

  _snake_case     		=				xmod.model.encoder.sentence_encoder
  _snake_case     		=				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_14					,	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:
    _snake_case     		=				xmod.model.classification_heads["mnli"].out_proj.weight.shape[0]

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

  _snake_case     		=				XmodForSequenceClassification(SCREAMING_SNAKE_CASE__       ) if classification_head else XmodForMaskedLM(SCREAMING_SNAKE_CASE__       )
  model.eval()

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

  _snake_case     		=				xmod_sent_encoder.layernorm_embedding.weight
  _snake_case     		=				xmod_sent_encoder.layernorm_embedding.bias

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

    # self attention
    _snake_case     		=				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."       )

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

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

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

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

    # end of layer

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

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

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

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

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


if __name__ == "__main__":
 __magic_name__   :						str    =			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__   :						List[Any]    =			parser.parse_args()
 convert_xmod_checkpoint_to_pytorch(
     args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
 )


