code: 
  
  
  
  
  
  
  from typing import Any
  
  import numpy as np
  
  
  
  
  
  
  
  def 			lowercase__  (				A_:	np.ndarray		)  ->							bool:
  
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
         assert np.shape(A_		)[0] == np.shape(A_		)[1]
  
         # If matrix not symmetric, exit right away.
         if np.allclose(A_     ,			matrix.T		) is False:
                return False
  
         # Get eigenvalues and eignevectors for a symmetric matrix.
         __UpperCAmelCase							,   __UpperCAmelCase    							=np.linalg.eigh(A_		)
  
         # Check sign of all eigenvalues.
         # np.all returns a value of type np.bool_
         return bool(np.all(eigen_values > 0		)		)
  
  
  
  
  
  
  
  def 			lowercase__  (				A_:	int		)  ->							Any:
  
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
         __UpperCAmelCase    							=np.random.randn(A_     ,			A_		)
         __UpperCAmelCase    							=np.dot(A_     ,			random_matrix.T		)
         assert _is_matrix_spd(A_		)
         return spd_matrix
  
  
  
  
  
  
  
  def 			lowercase__  (				A_:	np.ndarray     ,			A_:	np.ndarray     ,			A_:	int = 1000     ,			A_:	float = 1e-8     ,			)  ->							Any:
  
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
         assert np.shape(A_		)[0] == np.shape(A_		)[1]
         assert np.shape(A_		)[0] == np.shape(A_		)[0]
         assert _is_matrix_spd(A_		)
  
         # Initialize solution guess, residual, search direction.
         __UpperCAmelCase    							=np.zeros((np.shape(A_		)[0], 1)		)
         __UpperCAmelCase    							=np.copy(A_		)
         __UpperCAmelCase    							=np.copy(A_		)
  
         # Set initial errors in solution guess and residual.
         __UpperCAmelCase    							=1e9
         __UpperCAmelCase    							=1e9
         __UpperCAmelCase    							=1e9
  
         # Set iteration counter to threshold number of iterations.
         __UpperCAmelCase    							=0
  
         while error > tol:
                # Save this value so we only calculate the matrix-vector product once.
                __UpperCAmelCase    							=np.dot(A_     ,			A_		)
  
                # The main algorithm.
  
                # Update search direction magnitude.
                __UpperCAmelCase    							=np.dot(ra.T     ,			A_		) / np.dot(pa.T     ,			A_		)
                # Update solution guess.
                __UpperCAmelCase    							=xa + alpha * pa
                # Calculate new residual.
                __UpperCAmelCase    							=ra - alpha * w
                # Calculate new Krylov subspace scale.
                __UpperCAmelCase    							=np.dot(r.T     ,			A_		) / np.dot(ra.T     ,			A_		)
                # Calculate new A conjuage search direction.
                __UpperCAmelCase    							=r + beta * pa
  
                # Calculate errors.
                __UpperCAmelCase    							=np.linalg.norm(r - ra		)
                __UpperCAmelCase    							=np.linalg.norm(x - xa		)
                __UpperCAmelCase    							=np.maximum(A_     ,			A_		)
  
                # Update variables.
                __UpperCAmelCase    							=np.copy(A_		)
                __UpperCAmelCase    							=np.copy(A_		)
                __UpperCAmelCase    							=np.copy(A_		)
  
                # Update number of iterations.
                iterations += 1
                if iterations > max_iterations:
                       break
  
         return x
  
  
  
  
  
  
  
  def 			lowercase__  (				)  ->							None:
  
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
         __UpperCAmelCase    							=3
         __UpperCAmelCase    							=_create_spd_matrix(A_		)
         __UpperCAmelCase    							=np.random.randn(A_     ,			1		)
         __UpperCAmelCase    							=np.dot(A_     ,			A_		)
  
         # Numpy solution.
         __UpperCAmelCase    							=np.linalg.solve(A_     ,			A_		)
  
         # Our implementation.
         __UpperCAmelCase    							=conjugate_gradient(A_     ,			A_		)
  
         # Ensure both solutions are close to x_true (and therefore one another).
         assert np.linalg.norm(x_numpy - x_true		) <= 1e-6
         assert np.linalg.norm(x_conjugate_gradient - x_true		) <= 1e-6
  
  
  if __name__ == "__main__":
     import doctest
  
     doctest.testmod()
     test_conjugate_gradient()
  
  
  
  
  
style_context: 
  
  
  
  
  
  
  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()
  __A  							=  logging.get_logger(__name__)
  
  __A  							=  "Hello world! cécé herlolip"
  
  
  
  
  
  
  
  def 			lowercase__  (				A_:	str     ,			A_:	str     ,			A_:	bool		)  ->							str:
  
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
         __UpperCAmelCase							,   __UpperCAmelCase    							=os.path.split(A_		)
         __UpperCAmelCase    							=DataaVecTextModel.from_pretrained(
             A_     ,			checkpoint_file=A_		)
         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:"""     ,			A_		)
  
         __UpperCAmelCase    							=DataaVecTextForSequenceClassification(A_		) if classification_head else DataaVecTextForMaskedLM(A_		)
         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(A_		).unsqueeze(0		)  # batch of size 1
  
         __UpperCAmelCase    							=model(A_		)[0]
         if classification_head:
                __UpperCAmelCase    							=dataavec.model.classification_heads["""mnli"""](dataavec.extract_features(A_		)		)
         else:
                __UpperCAmelCase    							=dataavec_model(A_		)[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(A_     ,			A_     ,			atol=1e-3		)
         print("""Do both models output the same tensors?"""     ,			"""🔥""" if success else """💩"""		)
         if not success:
                raise Exception("""Something went wRoNg"""		)
  
         pathlib.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(
         "--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_dataavec_checkpoint_to_pytorch(
         args.checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
     )
  
  
  
  
  