



'''simple docstring'''


import argparse

import torch

from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()
_lowerCamelCase	:       Tuple      	=	logging.get_logger(__name__)

_lowerCamelCase	:       Any      	=	[
    ["attention", "attn"],
    ["encoder_attention", "encoder_attn"],
    ["q_lin", "q_proj"],
    ["k_lin", "k_proj"],
    ["v_lin", "v_proj"],
    ["out_lin", "out_proj"],
    ["norm_embeddings", "layernorm_embedding"],
    ["position_embeddings", "embed_positions"],
    ["embeddings", "embed_tokens"],
    ["ffn.lin", "fc"],
]







def 				__lowerCamelCase				(   A__    )       ->	List[str]:






   """simple docstring"""
   if k == "embeddings.weight":
      return "shared.weight"

   for parlai_name, hf_name in PATTERNS:
      UpperCamelCase					=  k.replace(A__    ,						A__    )

   if k.startswith('encoder'    ):
      UpperCamelCase					=  k.replace('.attn'    ,						'.self_attn'    )
      UpperCamelCase					=  k.replace('norm1'    ,						'self_attn_layer_norm'    )
      UpperCamelCase					=  k.replace('norm2'    ,						'final_layer_norm'    )
   elif k.startswith('decoder'    ):
      UpperCamelCase					=  k.replace('norm1'    ,						'self_attn_layer_norm'    )
      UpperCamelCase					=  k.replace('norm2'    ,						'encoder_attn_layer_norm'    )
      UpperCamelCase					=  k.replace('norm3'    ,						'final_layer_norm'    )
   return k







def 				__lowerCamelCase				(   A__    )       ->	Any:






   """simple docstring"""
   UpperCamelCase					=  [
       'model.encoder.layernorm_embedding.weight',
       'model.encoder.layernorm_embedding.bias',
       'model.decoder.layernorm_embedding.weight',
       'model.decoder.layernorm_embedding.bias',
   ]
   for k in keys:
      UpperCamelCase					=  sd.pop(A__    )
      UpperCamelCase					=  k.replace('layernorm_embedding'    ,						'layer_norm'    )
      assert new_k not in sd
      UpperCamelCase					=  v


_lowerCamelCase	:       Any      	=	["START"]







@torch.no_grad()
def 				__lowerCamelCase				(   A__    ,						A__    ,						A__    )       ->	int:






   """simple docstring"""
   UpperCamelCase					=  torch.load(A__    ,						map_location='cpu'    )
   UpperCamelCase					=  model['model']
   UpperCamelCase					=  BlenderbotConfig.from_json_file(A__    )
   UpperCamelCase					=  BlenderbotForConditionalGeneration(A__    )
   UpperCamelCase					=  m.model.state_dict().keys()
   UpperCamelCase					=  []
   UpperCamelCase					=  {}
   for k, v in sd.items():
      if k in IGNORE_KEYS:
         continue

      UpperCamelCase					=  rename_state_dict_key(A__    )
      if new_k not in valid_keys:
         failures.append([k, new_k]    )
      else:
         UpperCamelCase					=  v
   if cfg.normalize_before:  # Blenderbot-3B checkpoints. Rename layernorm_embedding -> layer_norm
      rename_layernorm_keys(A__    )
   m.model.load_state_dict(A__    ,						strict=A__    )
   m.half()
   m.save_pretrained(A__    )


if __name__ == "__main__":
   _lowerCamelCase	:       List[str]      	=	argparse.ArgumentParser()
   # Required parameters
   parser.add_argument("--src_path", type=str, help="like blenderbot-model.bin")
   parser.add_argument("--save_dir", default="hf_blenderbot", type=str, help="Where to save converted model.")
   parser.add_argument(
       "--hf_config_json", default="blenderbot-3b-config.json", type=str, help="Path to config to use"
   )
   _lowerCamelCase	:       str      	=	parser.parse_args()
   convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)






'''simple docstring'''


import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


_lowerCamelCase	:       List[str]      	=	5_0000
_lowerCamelCase	:       Optional[int]      	=	5000

_lowerCamelCase	,_lowerCamelCase	:       int      	=	os.path.split(__file__)
_lowerCamelCase	:       str      	=	os.path.join(RESULTS_BASEPATH, "results", RESULTS_FILENAME.replace(".py", ".json"))







@get_duration
def 				__lowerCamelCase				(   A__    ,						A__    )       ->	Any:






   """simple docstring"""
   for i in range(A__    ):
      UpperCamelCase					=  dataset[i]







@get_duration
def 				__lowerCamelCase				(   A__    ,						A__    ,						A__    )       ->	int:






   """simple docstring"""
   for i in range(0    ,						len(A__    )    ,						A__    ):
      UpperCamelCase					=  dataset[i : i + batch_size]







@get_duration
def 				__lowerCamelCase				(   A__    ,						A__    ,						A__    )       ->	List[Any]:






   """simple docstring"""
   with dataset.formatted_as(type=A__    ):
      for i in range(A__    ):
         UpperCamelCase					=  dataset[i]







@get_duration
def 				__lowerCamelCase				(   A__    ,						A__    ,						A__    ,						A__    )       ->	int:






   """simple docstring"""
   with dataset.formatted_as(type=A__    ):
      for i in range(0    ,						A__    ,						A__    ):
         UpperCamelCase					=  dataset[i : i + batch_size]







def 				__lowerCamelCase				(   )       ->	List[str]:






   """simple docstring"""
   UpperCamelCase					=  {'num examples': SPEED_TEST_N_EXAMPLES}
   UpperCamelCase					=  [
       (read, {'length': SMALL_TEST}),
       (read, {'length': SPEED_TEST_N_EXAMPLES}),
       (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 10}),
       (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 100}),
       (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 1_000}),
       (read_formatted, {'type': 'numpy', 'length': SMALL_TEST}),
       (read_formatted, {'type': 'pandas', 'length': SMALL_TEST}),
       (read_formatted, {'type': 'torch', 'length': SMALL_TEST}),
       (read_formatted, {'type': 'tensorflow', 'length': SMALL_TEST}),
       (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 10}),
       (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 1_000}),
   ]

   UpperCamelCase					=  [
       (read, {'length': SMALL_TEST}),
       (read, {'length': SPEED_TEST_N_EXAMPLES}),
       (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 10}),
       (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 100}),
       (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 1_000}),
       (read_formatted, {'type': 'numpy', 'length': SMALL_TEST}),
       (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 10}),
       (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 1_000}),
   ]
   with tempfile.TemporaryDirectory() as tmp_dir:
      print('generating dataset'    )
      UpperCamelCase					=  datasets.Features(
          {'list': datasets.Sequence(datasets.Value('float32'    )    ), 'numbers': datasets.Value('float32'    )}    )
      UpperCamelCase					=  generate_example_dataset(
          os.path.join(A__    ,						'dataset.arrow'    )    ,						A__    ,						num_examples=A__    ,						seq_shapes={'list': (100,)}    ,						)
      print('first set of iterations'    )
      for func, kwargs in functions:
         print(func.__name__    ,						str(A__    )    )
         UpperCamelCase					=  func(A__    ,						**A__    )

      print('shuffling dataset'    )
      UpperCamelCase					=  dataset.shuffle()
      print('Second set of iterations (after shuffling'    )
      for func, kwargs in functions_shuffled:
         print('shuffled '    ,						func.__name__    ,						str(A__    )    )
         UpperCamelCase					=  func(
             A__    ,						**A__    )

   with open(A__    ,						'wb'    ) as f:
      f.write(json.dumps(A__    ).encode('utf-8'    )    )


if __name__ == "__main__":  # useful to run the profiler
   benchmark_iterating()

