'''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()
_UpperCamelCase				:		Tuple       			=		logging.get_logger(__name__)

_UpperCamelCase				:		Optional[int]       			=		'Hello world! cécé herlolip'



def 						__snake_case (       lowerCAmelCase       :     str      ,  lowerCAmelCase       :     str      ,  lowerCAmelCase       :     bool			):
 __UpperCAmelCase  ,			__UpperCAmelCase         =					os.path.split(lowerCAmelCase			)
 __UpperCAmelCase         =					DataaVecTextModel.from_pretrained(
     lowerCAmelCase      ,  checkpoint_file=lowerCAmelCase			)
 dataavec.eval()  # disable dropout
 __UpperCAmelCase         =					dataavec.models[0]
 __UpperCAmelCase         =					dataavec_model.encoder.sentence_encoder
 __UpperCAmelCase         =					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:
  __UpperCAmelCase         =					dataavec.model.classification_heads['mnli'].out_proj.weight.shape[0]
 print('Our BERT config:'      ,  lowerCAmelCase			)

 __UpperCAmelCase         =					DataaVecTextForSequenceClassification(lowerCAmelCase			) if classification_head else DataaVecTextForMaskedLM(lowerCAmelCase			)
 model.eval()

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

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

  # self attention
  __UpperCAmelCase         =					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)			)}"""
  )

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

  # self-attention output
  __UpperCAmelCase         =					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}"""
  __UpperCAmelCase         =					dataavec_layer.self_attn.out_proj.weight
  __UpperCAmelCase         =					dataavec_layer.self_attn.out_proj.bias
  __UpperCAmelCase         =					dataavec_layer.self_attn_layer_norm.weight
  __UpperCAmelCase         =					dataavec_layer.self_attn_layer_norm.bias

  # intermediate
  __UpperCAmelCase         =					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}"""
  __UpperCAmelCase         =					dataavec_layer.fca.weight
  __UpperCAmelCase         =					dataavec_layer.fca.bias

  # output
  __UpperCAmelCase         =					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}"""
  __UpperCAmelCase         =					dataavec_layer.fca.weight
  __UpperCAmelCase         =					dataavec_layer.fca.bias
  __UpperCAmelCase         =					dataavec_layer.final_layer_norm.weight
  __UpperCAmelCase         =					dataavec_layer.final_layer_norm.bias
  # end of layer

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

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

 __UpperCAmelCase         =					model(lowerCAmelCase			)[0]
 if classification_head:
  __UpperCAmelCase         =					dataavec.model.classification_heads['mnli'](dataavec.extract_features(lowerCAmelCase			)			)
 else:
  __UpperCAmelCase         =					dataavec_model(lowerCAmelCase			)[0]
 print(our_output.shape      ,  their_output.shape			)
 __UpperCAmelCase         =					torch.max(torch.abs(our_output - their_output			)			).item()
 print(F"""max_absolute_diff = {max_absolute_diff}"""			)  # ~ 1e-7
 __UpperCAmelCase         =					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'			)

 pathlib.Path(lowerCAmelCase			).mkdir(parents=lowerCAmelCase      ,  exist_ok=lowerCAmelCase			)
 print(F"""Saving model to {pytorch_dump_folder_path}"""			)
 model.save_pretrained(lowerCAmelCase			)


if __name__ == "__main__":
     _UpperCamelCase				:		Optional[int]       			=		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.'
     )
     _UpperCamelCase				:		Optional[int]       			=		parser.parse_args()
     convert_dataavec_checkpoint_to_pytorch(
         args.checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
     )




'''simple docstring'''




from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_UpperCamelCase				:		Any       			=		logging.get_logger(__name__)

_UpperCamelCase				:		Any       			=		{
    'apple/mobilevitv2-1.0': 'https://huggingface.co/apple/mobilevitv2-1.0/resolve/main/config.json',
}

class   _lowercase(     _lowerCamelCase				):



   """simple docstring"""





   __lowerCamelCase    =  '''mobilevitv2'''





   def __init__(							self:    Union[str, Any]			,a:    Optional[int]=3			,a:    Any=256			,a:    str=2			,a:    Any=2.0			,a:    Union[str, Any]="swish"			,a:    List[str]=3			,a:    List[str]=32			,a:    int=0.1			,a:    List[Any]=0.02			,a:    List[Any]=1e-5			,a:    Tuple=512			,a:    Optional[Any]=[6, 12, 18]			,a:    Dict=0.1			,a:    List[Any]=255			,a:    Union[str, Any]=[2, 4, 3]			,a:    str=[128, 192, 256]			,a:    int=1.0			,a:    Optional[int]=2			,a:    Optional[Any]=0.0			,a:    int=0.0			,**a:    Optional[Any]			,):
    super().__init__(**a	)

    __UpperCAmelCase         =					num_channels
    __UpperCAmelCase         =					image_size
    __UpperCAmelCase         =					patch_size
    __UpperCAmelCase         =					expand_ratio
    __UpperCAmelCase         =					hidden_act
    __UpperCAmelCase         =					conv_kernel_size
    __UpperCAmelCase         =					output_stride
    __UpperCAmelCase         =					initializer_range
    __UpperCAmelCase         =					layer_norm_eps
    __UpperCAmelCase         =					n_attn_blocks
    __UpperCAmelCase         =					base_attn_unit_dims
    __UpperCAmelCase         =					width_multiplier
    __UpperCAmelCase         =					ffn_multiplier
    __UpperCAmelCase         =					ffn_dropout
    __UpperCAmelCase         =					attn_dropout
    __UpperCAmelCase         =					classifier_dropout_prob

    # decode head attributes for semantic segmentation
    __UpperCAmelCase         =					aspp_out_channels
    __UpperCAmelCase         =					atrous_rates
    __UpperCAmelCase         =					aspp_dropout_prob
    __UpperCAmelCase         =					semantic_loss_ignore_index




class   _lowercase(     _lowerCamelCase				):



   """simple docstring"""





   __lowerCamelCase    =  version.parse('''1.11'''				)





   @property
   def 			snake_case	(							self:    Optional[int]	):
    return OrderedDict([('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'})]	)





   @property
   def 			snake_case	(							self:    Tuple	):
    if self.task == "image-classification":
     return OrderedDict([('logits', {0: 'batch'})]	)
    else:
     return OrderedDict([('last_hidden_state', {0: 'batch'}), ('pooler_output', {0: 'batch'})]	)





   @property
   def 			snake_case	(							self:    Union[str, Any]	):
    return 1e-4



