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()
snake_case__						      =			logging.get_logger(__name__)

snake_case__						      =			'Hello world! cécé herlolip'
def    __magic_name__(	__UpperCAmelCase			,    __UpperCAmelCase			,    __UpperCAmelCase		)    ->     List[str]:



      '''simple docstring'''

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

      _lowerCamelCase           =					DataaVecTextForSequenceClassification(__lowerCAmelCase		) if classification_head else DataaVecTextForMaskedLM(__lowerCAmelCase		)
      model.eval()

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

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

            # self attention
            _lowerCamelCase           =					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)		)}'
            )

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

            # self-attention output
            _lowerCamelCase           =					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}'
            _lowerCamelCase           =					dataavec_layer.self_attn.out_proj.weight
            _lowerCamelCase           =					dataavec_layer.self_attn.out_proj.bias
            _lowerCamelCase           =					dataavec_layer.self_attn_layer_norm.weight
            _lowerCamelCase           =					dataavec_layer.self_attn_layer_norm.bias

            # intermediate
            _lowerCamelCase           =					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}'
            _lowerCamelCase           =					dataavec_layer.fca.weight
            _lowerCamelCase           =					dataavec_layer.fca.bias

            # output
            _lowerCamelCase           =					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}'
            _lowerCamelCase           =					dataavec_layer.fca.weight
            _lowerCamelCase           =					dataavec_layer.fca.bias
            _lowerCamelCase           =					dataavec_layer.final_layer_norm.weight
            _lowerCamelCase           =					dataavec_layer.final_layer_norm.bias
            # end of layer

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

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

      _lowerCamelCase           =					model(__lowerCAmelCase		)[0]
      if classification_head:
            _lowerCamelCase           =					dataavec.model.classification_heads['''mnli'''](dataavec.extract_features(__lowerCAmelCase		)		)
      else:
            _lowerCamelCase           =					dataavec_model(__lowerCAmelCase		)[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(__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__":
     snake_case__						      =			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.'
     )
     snake_case__						      =			parser.parse_args()
     convert_dataavec_checkpoint_to_pytorch(
         args.checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
     )







import numpy as np


def __A       ( __lowerCAmelCase					,						__lowerCAmelCase					,						__lowerCAmelCase      )->    str:





						"""simple docstring"""



						_UpperCAmelCase							   =     n_draws * class_counts / class_counts.sum()
						# floored means we don't overshoot n_samples, but probably undershoot
						_UpperCAmelCase							   =     np.floor(__lowerCAmelCase      )
						# we add samples according to how much "left over" probability
						# they had, until we arrive at n_samples
						_UpperCAmelCase							   =     int(n_draws - floored.sum()      )
						if need_to_add > 0:
												_UpperCAmelCase							   =     continuous - floored
												_UpperCAmelCase							   =     np.sort(np.unique(__lowerCAmelCase      )      )[::-1]
												# add according to remainder, but break ties
												# randomly to avoid biases
												for value in values:
																		((_UpperCAmelCase)   ,					)							   =     np.where(remainder == value      )
																		# if we need_to_add less than what's in inds
																		# we draw randomly from them.
																		# if we need to add more, we add them all and
																		# go to the next value
																		_UpperCAmelCase							   =     min(len(__lowerCAmelCase      )					,						__lowerCAmelCase      )
																		_UpperCAmelCase							   =     rng.choice(__lowerCAmelCase					,						size=__lowerCAmelCase					,						replace=__lowerCAmelCase      )
																		floored[inds] += 1
																		need_to_add -= add_now
																		if need_to_add == 0:
																								break
						return floored.astype(np.intaa      )


def __A       ( __lowerCAmelCase					,						__lowerCAmelCase					,						__lowerCAmelCase					,						__lowerCAmelCase					,						__lowerCAmelCase=10      )->    int:





						"""simple docstring"""



						_UpperCAmelCase   ,					_UpperCAmelCase							   =     np.unique(__lowerCAmelCase					,						return_inverse=__lowerCAmelCase      )
						_UpperCAmelCase							   =     classes.shape[0]
						_UpperCAmelCase							   =     np.bincount(__lowerCAmelCase      )
						if np.min(__lowerCAmelCase      ) < 2:
												raise ValueError('Minimum class count error'      )
						if n_train < n_classes:
												raise ValueError(
												    'The train_size = %d should be greater or ' 'equal to the number of classes = %d' % (n_train, n_classes)      )
						if n_test < n_classes:
												raise ValueError(
												    'The test_size = %d should be greater or ' 'equal to the number of classes = %d' % (n_test, n_classes)      )
						_UpperCAmelCase							   =     np.split(np.argsort(__lowerCAmelCase					,						kind='mergesort'      )					,						np.cumsum(__lowerCAmelCase      )[:-1]      )
						for _ in range(__lowerCAmelCase      ):
												_UpperCAmelCase							   =     approximate_mode(__lowerCAmelCase					,						__lowerCAmelCase					,						__lowerCAmelCase      )
												_UpperCAmelCase							   =     class_counts - n_i
												_UpperCAmelCase							   =     approximate_mode(__lowerCAmelCase					,						__lowerCAmelCase					,						__lowerCAmelCase      )

												_UpperCAmelCase							   =     []
												_UpperCAmelCase							   =     []

												for i in range(__lowerCAmelCase      ):
																		_UpperCAmelCase							   =     rng.permutation(class_counts[i]      )
																		_UpperCAmelCase							   =     class_indices[i].take(__lowerCAmelCase					,						mode='clip'      )
																		train.extend(perm_indices_class_i[: n_i[i]]      )
																		test.extend(perm_indices_class_i[n_i[i] : n_i[i] + t_i[i]]      )
												_UpperCAmelCase							   =     rng.permutation(__lowerCAmelCase      )
												_UpperCAmelCase							   =     rng.permutation(__lowerCAmelCase      )

												yield train, test
