

import argparse
import os
from pathlib import Path

import fairseq
import torch
from packaging import version
from torch import nn

from transformers import (
    BartConfig,
    BartForConditionalGeneration,
    BartForSequenceClassification,
    BartModel,
    BartTokenizer,
)
from transformers.utils import logging


_a							=							['bart.large', 'bart.large.mnli', 'bart.large.cnn', 'bart_xsum/model.pt']
_a							=							{'bart.large': BartModel, 'bart.large.mnli': BartForSequenceClassification}
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'

_a							=							[
    ('model.classification_heads.mnli.dense.weight', 'classification_head.dense.weight'),
    ('model.classification_heads.mnli.dense.bias', 'classification_head.dense.bias'),
    ('model.classification_heads.mnli.out_proj.weight', 'classification_head.out_proj.weight'),
    ('model.classification_heads.mnli.out_proj.bias', 'classification_head.out_proj.bias'),
]




def   _A  (  UpperCamelCase_					:	Any)     ->						Optional[Any]:
   '''simple docstring'''


   __lowercase								=						[
       "encoder.version",
       "decoder.version",
       "model.encoder.version",
       "model.decoder.version",
       "_float_tensor",
   ]
   for k in ignore_keys:
      state_dict.pop(UpperCamelCase_,      UpperCamelCase_)




def   _A  (  UpperCamelCase_					:	Optional[Any],      UpperCamelCase_					:	int,      UpperCamelCase_					:	Optional[int])     ->						Dict:
   '''simple docstring'''


   __lowercase								=						dct.pop(UpperCamelCase_)
   __lowercase								=						val




def   _A  (  UpperCamelCase_					:	Union[str, Any])     ->						List[Any]:
   '''simple docstring'''


   __lowercase								=						torch.load(UpperCamelCase_,      map_location="cpu")
   __lowercase								=						torch.hub.load("pytorch/fairseq",      "bart.large.cnn").eval()
   hub_interface.model.load_state_dict(sd["model"])
   return hub_interface




def   _A  (  UpperCamelCase_					:	Union[str, Any])     ->						Dict:
   '''simple docstring'''


   __lowercase		,__lowercase								=						emb.weight.shape
   __lowercase								=						nn.Linear(UpperCamelCase_,      UpperCamelCase_,      bias=UpperCamelCase_)
   __lowercase								=						emb.weight.data
   return lin_layer




@torch.no_grad()
def   _A  (  UpperCamelCase_					:	Any,      UpperCamelCase_					:	Optional[int],      UpperCamelCase_					:	Optional[int]=None)     ->						int:
   '''simple docstring'''


   if not os.path.exists(UpperCamelCase_):
      __lowercase								=						torch.hub.load("pytorch/fairseq",      UpperCamelCase_).eval()
   else:
      __lowercase								=						load_xsum_checkpoint(UpperCamelCase_)

   bart.model.upgrade_state_dict(bart.model.state_dict())
   if hf_checkpoint_name is None:
      __lowercase								=						checkpoint_path.replace(".",      "-")
   __lowercase								=						BartConfig.from_pretrained(UpperCamelCase_)
   __lowercase								=						bart.encode(UpperCamelCase_).unsqueeze(0)
   __lowercase								=						BartTokenizer.from_pretrained(UpperCamelCase_).encode(UpperCamelCase_,      return_tensors="pt").unsqueeze(0)
   if not torch.eq(UpperCamelCase_,      UpperCamelCase_).all():
      raise ValueError(
          F"""converted tokenizer and pretrained tokenizer returned different output: {tokens} != {tokensa}""")

   if checkpoint_path == "bart.large.mnli":
      __lowercase								=						bart.state_dict()
      remove_ignore_keys_(UpperCamelCase_)
      __lowercase								=						state_dict["model.decoder.embed_tokens.weight"]
      for src, dest in mnli_rename_keys:
         rename_key(UpperCamelCase_,      UpperCamelCase_,      UpperCamelCase_)
      __lowercase								=						BartForSequenceClassification(UpperCamelCase_).eval()
      model.load_state_dict(UpperCamelCase_)
      __lowercase								=						bart.predict("mnli",      UpperCamelCase_,      return_logits=UpperCamelCase_)
      __lowercase								=						model(UpperCamelCase_)[0]  # logits
   else:  # no classification heads to worry about
      __lowercase								=						bart.model.state_dict()
      remove_ignore_keys_(UpperCamelCase_)
      __lowercase								=						state_dict["decoder.embed_tokens.weight"]
      __lowercase								=						bart.extract_features(UpperCamelCase_)
      if hf_checkpoint_name == "facebook/bart-large":
         __lowercase								=						BartModel(UpperCamelCase_).eval()
         model.load_state_dict(UpperCamelCase_)
         __lowercase								=						model(UpperCamelCase_).model[0]
      else:
         __lowercase								=						BartForConditionalGeneration(UpperCamelCase_).eval()  # an existing summarization ckpt
         model.model.load_state_dict(UpperCamelCase_)
         if hasattr(UpperCamelCase_,      "lm_head"):
            __lowercase								=						make_linear_from_emb(model.model.shared)
         __lowercase								=						model.model(UpperCamelCase_)[0]

    # Check results
   if fairseq_output.shape != new_model_outputs.shape:
      raise ValueError(
          F"""`fairseq_output` shape and `new_model_output` shape are different: {fairseq_output.shape=}, {new_model_outputs.shape}""")
   if (fairseq_output != new_model_outputs).any().item():
      raise ValueError("Some values in `fairseq_output` are different from `new_model_outputs`")
   Path(UpperCamelCase_).mkdir(exist_ok=UpperCamelCase_)
   model.save_pretrained(UpperCamelCase_)


if __name__ == "__main__":
   _a							=							argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       'fairseq_path', type=str, help='bart.large, bart.large.cnn or a path to a model.pt on local filesystem.'
   )
   parser.add_argument('pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
   parser.add_argument(
       '--hf_config', default=None, type=str, help='Which huggingface architecture to use: bart-large-xsum'
   )
   _a							=							parser.parse_args()
   convert_bart_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, hf_checkpoint_name=args.hf_config)







"""simple docstring"""





import numpy

# List of input, output pairs
_a							=							(
    ((5, 2, 3), 15),
    ((6, 5, 9), 25),
    ((11, 12, 13), 41),
    ((1, 1, 1), 8),
    ((11, 12, 13), 41),
)
_a							=							(((5_15, 22, 13), 5_55), ((61, 35, 49), 1_50))
_a							=							[2, 4, 1, 5]
_a							=							len(train_data)
_a							=							0.009




def   _A  (  UpperCamelCase_					:	str,      UpperCamelCase_					:	List[Any]="train")     ->						Optional[Any]:
   '''simple docstring'''


   return calculate_hypothesis_value(UpperCamelCase_,      UpperCamelCase_) - output(
       UpperCamelCase_,      UpperCamelCase_)




def   _A  (  UpperCamelCase_					:	List[Any])     ->						Union[str, Any]:
   '''simple docstring'''


   __lowercase								=						0
   for i in range(len(UpperCamelCase_) - 1):
      hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
   hyp_val += parameter_vector[0]
   return hyp_val




def   _A  (  UpperCamelCase_					:	Dict,      UpperCamelCase_					:	Optional[int])     ->						Dict:
   '''simple docstring'''


   if data_set == "train":
      return train_data[example_no][1]
   elif data_set == "test":
      return test_data[example_no][1]
   return None




def   _A  (  UpperCamelCase_					:	Dict,      UpperCamelCase_					:	List[str])     ->						int:
   '''simple docstring'''


   if data_set == "train":
      return _hypothesis_value(train_data[example_no][0])
   elif data_set == "test":
      return _hypothesis_value(test_data[example_no][0])
   return None




def   _A  (  UpperCamelCase_					:	Any,      UpperCamelCase_					:	Tuple=m)     ->						int:
   '''simple docstring'''


   __lowercase								=						0
   for i in range(UpperCamelCase_):
      if index == -1:
         summation_value += _error(UpperCamelCase_)
      else:
         summation_value += _error(UpperCamelCase_) * train_data[i][0][index]
   return summation_value




def   _A  (  UpperCamelCase_					:	str)     ->						str:
   '''simple docstring'''


   __lowercase								=						summation_of_cost_derivative(UpperCamelCase_,      UpperCamelCase_) / m
   return cost_derivative_value




def   _A  (  )     ->						List[str]:
   '''simple docstring'''


   global parameter_vector
   # Tune these values to set a tolerance value for predicted output
   __lowercase								=						0.000_002
   __lowercase								=						0
   __lowercase								=						0
   while True:
      j += 1
      __lowercase								=						[0, 0, 0, 0]
      for i in range(0,      len(UpperCamelCase_)):
         __lowercase								=						get_cost_derivative(i - 1)
         __lowercase								=						(
             parameter_vector[i] - LEARNING_RATE * cost_derivative
         )
      if numpy.allclose(
          UpperCamelCase_,      UpperCamelCase_,      atol=UpperCamelCase_,      rtol=UpperCamelCase_,      ):
         break
      __lowercase								=						temp_parameter_vector
   print(("Number of iterations:", j))




def   _A  (  )     ->						int:
   '''simple docstring'''


   for i in range(len(UpperCamelCase_)):
      print(("Actual output value:", output(UpperCamelCase_,      "test")))
      print(("Hypothesis output:", calculate_hypothesis_value(UpperCamelCase_,      "test")))


if __name__ == "__main__":
   run_gradient_descent()
   print('\nTesting gradient descent for a linear hypothesis function.\n')
   test_gradient_descent()




