
import argparse

import torch

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


logging.set_verbosity_info()
lowerCamelCase__    :		str          =   logging.get_logger(__name__)

lowerCamelCase__    :		str          =   [
    ["""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 		UpperCamelCase	(      lowercase_   )		->	Tuple:


  '''simple docstring'''






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

  for parlai_name, hf_name in PATTERNS:
    lowercase__  :   List[Any]    		=      k.replace(lowercase_ , lowercase_   )

  if k.startswith("""encoder"""   ):
    lowercase__  :   Dict    		=      k.replace(""".attn""" , """.self_attn"""   )
    lowercase__  :   str    		=      k.replace("""norm1""" , """self_attn_layer_norm"""   )
    lowercase__  :   List[Any]    		=      k.replace("""norm2""" , """final_layer_norm"""   )
  elif k.startswith("""decoder"""   ):
    lowercase__  :   Optional[Any]    		=      k.replace("""norm1""" , """self_attn_layer_norm"""   )
    lowercase__  :   Optional[int]    		=      k.replace("""norm2""" , """encoder_attn_layer_norm"""   )
    lowercase__  :   List[str]    		=      k.replace("""norm3""" , """final_layer_norm"""   )
  return k

def 		UpperCamelCase	(      lowercase_   )		->	str:


  '''simple docstring'''






  lowercase__  :   List[Any]    		=      [
      """model.encoder.layernorm_embedding.weight""",
      """model.encoder.layernorm_embedding.bias""",
      """model.decoder.layernorm_embedding.weight""",
      """model.decoder.layernorm_embedding.bias""",
  ]
  for k in keys:
    lowercase__  :   str    		=      sd.pop(lowercase_   )
    lowercase__  :   List[Any]    		=      k.replace("""layernorm_embedding""" , """layer_norm"""   )
    assert new_k not in sd
    lowercase__  :   Dict    		=      v


lowerCamelCase__    :		Union[str, Any]          =   ["""START"""]

@torch.no_grad()
def 		UpperCamelCase	(      lowercase_ , lowercase_ , lowercase_   )		->	Union[str, Any]:


  '''simple docstring'''






  lowercase__  :   Union[str, Any]    		=      torch.load(lowercase_ , map_location="""cpu"""   )
  lowercase__  :   Optional[int]    		=      model["""model"""]
  lowercase__  :   Optional[Any]    		=      BlenderbotConfig.from_json_file(lowercase_   )
  lowercase__  :   Optional[int]    		=      BlenderbotForConditionalGeneration(lowercase_   )
  lowercase__  :   Optional[Any]    		=      m.model.state_dict().keys()
  lowercase__  :   Optional[int]    		=      []
  lowercase__  :   Tuple    		=      {}
  for k, v in sd.items():
    if k in IGNORE_KEYS:
      continue

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


if __name__ == "__main__":
     lowerCamelCase__    :		Optional[int]          =   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__    :		List[str]          =   parser.parse_args()
     convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)








import unittest

from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
     import os

     # The slow tests are often failing with OOM error on GPU
     # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
     # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
     lowerCamelCase__    :		Optional[int]          =   """platform"""
     import jax
     import jax.numpy as jnp
     import numpy as np

     from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel



@require_flax
class       _snake_case   :
 __lowerCAmelCase  :     Optional[int]        =			PegasusConfig
 __lowerCAmelCase  :     str        =			{}
 __lowerCAmelCase  :     int        =			'gelu'


 def __init__(					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_=13					,  SCREAMING_SNAKE_CASE_=7					,  SCREAMING_SNAKE_CASE_=True					,  SCREAMING_SNAKE_CASE_=False					,  SCREAMING_SNAKE_CASE_=99					,  SCREAMING_SNAKE_CASE_=32					,  SCREAMING_SNAKE_CASE_=5					,  SCREAMING_SNAKE_CASE_=4					,  SCREAMING_SNAKE_CASE_=37					,  SCREAMING_SNAKE_CASE_=0.1					,  SCREAMING_SNAKE_CASE_=0.1					,  SCREAMING_SNAKE_CASE_=20					,  SCREAMING_SNAKE_CASE_=2					,  SCREAMING_SNAKE_CASE_=1					,  SCREAMING_SNAKE_CASE_=0					,  ):


   '''simple docstring'''




   lowercase__  :   Any    		=      parent
   lowercase__  :   int    		=      batch_size
   lowercase__  :   Optional[int]    		=      seq_length
   lowercase__  :   str    		=      is_training
   lowercase__  :   str    		=      use_labels
   lowercase__  :   List[str]    		=      vocab_size
   lowercase__  :   Dict    		=      hidden_size
   lowercase__  :   int    		=      num_hidden_layers
   lowercase__  :   int    		=      num_attention_heads
   lowercase__  :   int    		=      intermediate_size

   lowercase__  :   List[str]    		=      hidden_dropout_prob
   lowercase__  :   List[str]    		=      attention_probs_dropout_prob
   lowercase__  :   Tuple    		=      max_position_embeddings
   lowercase__  :   Dict    		=      eos_token_id
   lowercase__  :   Any    		=      pad_token_id
   lowercase__  :   Any    		=      bos_token_id


 def      lowercase__     (					self):


   '''simple docstring'''




   lowercase__  :   List[str]    		=      ids_tensor([self.batch_size, self.seq_length - 1]					,  self.vocab_size).clip(3					,  self.vocab_size)
   lowercase__  :   str    		=      np.expand_dims(np.array([self.eos_token_id] * self.batch_size)					,  1)
   lowercase__  :   Union[str, Any]    		=      np.concatenate([input_ids, eos_tensor]					,  axis=1)

   lowercase__  :   Optional[int]    		=      ids_tensor([self.batch_size, self.seq_length]					,  self.vocab_size)

   lowercase__  :   str    		=      self.config_cls(
       vocab_size=self.vocab_size					,  d_model=self.hidden_size					,  encoder_layers=self.num_hidden_layers					,  decoder_layers=self.num_hidden_layers					,  encoder_attention_heads=self.num_attention_heads					,  decoder_attention_heads=self.num_attention_heads					,  encoder_ffn_dim=self.intermediate_size					,  decoder_ffn_dim=self.intermediate_size					,  dropout=self.hidden_dropout_prob					,  attention_dropout=self.attention_probs_dropout_prob					,  max_position_embeddings=self.max_position_embeddings					,  eos_token_ids=[2]					,  bos_token_id=self.bos_token_id					,  pad_token_id=self.pad_token_id					,  decoder_start_token_id=self.pad_token_id					,  **self.config_updates					,  )
   lowercase__  :   Any    		=      prepare_pegasus_inputs_dict(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_)
   return config, inputs_dict


 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__  :   List[Any]    		=      20
   lowercase__  :   str    		=      model_class_name(SCREAMING_SNAKE_CASE_)

   lowercase__  :   str    		=      model.encode(inputs_dict["""input_ids"""])

   lowercase__		,    lowercase__  :   Dict    		=      (
       inputs_dict["""decoder_input_ids"""],
       inputs_dict["""decoder_attention_mask"""],
   )

   lowercase__  :   int    		=      model.init_cache(decoder_input_ids.shape[0]					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_)
   lowercase__  :   Tuple    		=      jnp.ones((decoder_input_ids.shape[0], max_decoder_length)					,  dtype="""i4""")

   lowercase__  :   Union[str, Any]    		=      jnp.broadcast_to(
       jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :]					,  (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)					,  )
   lowercase__  :   str    		=      model.decode(
       decoder_input_ids[:, :-1]					,  SCREAMING_SNAKE_CASE_					,  decoder_attention_mask=SCREAMING_SNAKE_CASE_					,  past_key_values=SCREAMING_SNAKE_CASE_					,  decoder_position_ids=SCREAMING_SNAKE_CASE_					,  )

   lowercase__  :   Any    		=      jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]					,  dtype="""i4""")
   lowercase__  :   List[Any]    		=      model.decode(
       decoder_input_ids[:, -1:]					,  SCREAMING_SNAKE_CASE_					,  decoder_attention_mask=SCREAMING_SNAKE_CASE_					,  past_key_values=outputs_cache.past_key_values					,  decoder_position_ids=SCREAMING_SNAKE_CASE_					,  )

   lowercase__  :   Any    		=      model.decode(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_)

   lowercase__  :   int    		=      np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
   self.parent.assertTrue(diff < 1E-3					,  msg=f'Max diff is {diff}')



 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__  :   Tuple    		=      20
   lowercase__  :   Tuple    		=      model_class_name(SCREAMING_SNAKE_CASE_)

   lowercase__  :   List[Any]    		=      model.encode(inputs_dict["""input_ids"""])

   lowercase__		,    lowercase__  :   Dict    		=      (
       inputs_dict["""decoder_input_ids"""],
       inputs_dict["""decoder_attention_mask"""],
   )

   lowercase__  :   Optional[int]    		=      jnp.concatenate(
       [
           decoder_attention_mask,
           jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])),
       ]					,  axis=-1					,  )

   lowercase__  :   Tuple    		=      model.init_cache(decoder_input_ids.shape[0]					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_)
   lowercase__  :   List[Any]    		=      jnp.broadcast_to(
       jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :]					,  (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)					,  )

   lowercase__  :   Tuple    		=      model.decode(
       decoder_input_ids[:, :-1]					,  SCREAMING_SNAKE_CASE_					,  decoder_attention_mask=SCREAMING_SNAKE_CASE_					,  past_key_values=SCREAMING_SNAKE_CASE_					,  decoder_position_ids=SCREAMING_SNAKE_CASE_					,  )
   lowercase__  :   Union[str, Any]    		=      jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]					,  dtype="""i4""")
   lowercase__  :   Dict    		=      model.decode(
       decoder_input_ids[:, -1:]					,  SCREAMING_SNAKE_CASE_					,  past_key_values=outputs_cache.past_key_values					,  decoder_attention_mask=SCREAMING_SNAKE_CASE_					,  decoder_position_ids=SCREAMING_SNAKE_CASE_					,  )

   lowercase__  :   Dict    		=      model.decode(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  decoder_attention_mask=SCREAMING_SNAKE_CASE_)

   lowercase__  :   Any    		=      np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
   self.parent.assertTrue(diff < 1E-3					,  msg=f'Max diff is {diff}')

def 		UpperCamelCase	(      lowercase_ , lowercase_ , lowercase_ , lowercase_=None , lowercase_=None , )		->	List[str]:


  '''simple docstring'''






  if attention_mask is None:
    lowercase__  :   int    		=      np.not_equal(lowercase_ , config.pad_token_id   ).astype(np.inta   )
  if decoder_attention_mask is None:
    lowercase__  :   Dict    		=      np.concatenate(
        [
            np.ones(decoder_input_ids[:, :1].shape , dtype=np.inta   ),
            np.not_equal(decoder_input_ids[:, 1:] , config.pad_token_id   ).astype(np.inta   ),
        ] , axis=-1 , )
  return {
      "input_ids": input_ids,
      "decoder_input_ids": decoder_input_ids,
      "attention_mask": attention_mask,
      "decoder_attention_mask": decoder_attention_mask,
  }



@require_flax
class       _snake_case   (	UpperCAmelCase_  ,						unittest.TestCase							):
 __lowerCAmelCase  :     Tuple        =			(
     (
         FlaxPegasusForConditionalGeneration,
         FlaxPegasusModel,
     )
     if is_flax_available()
     else ()
 )
 __lowerCAmelCase  :     Optional[Any]        =			(FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
 __lowerCAmelCase  :     List[str]        =			True
 __lowerCAmelCase  :     Optional[Any]        =			False
 __lowerCAmelCase  :     List[str]        =			False
 __lowerCAmelCase  :     List[Any]        =			False


 def      lowercase__     (					self):


   '''simple docstring'''




   lowercase__  :   Any    		=      FlaxPegasusModelTester(self)
   lowercase__  :   int    		=      ConfigTester(self					,  config_class=SCREAMING_SNAKE_CASE_)


 def      lowercase__     (					self):


   '''simple docstring'''




   self.config_tester.run_common_tests()


 def      lowercase__     (					self):


   '''simple docstring'''




   lowercase__		,    lowercase__  :   Optional[Any]    		=      self.model_tester.prepare_config_and_inputs_for_common()
   for model_class in self.all_model_classes:
     self.model_tester.check_use_cache_forward(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_)


 def      lowercase__     (					self):


   '''simple docstring'''




   lowercase__		,    lowercase__  :   Optional[Any]    		=      self.model_tester.prepare_config_and_inputs_for_common()
   for model_class in self.all_model_classes:
     self.model_tester.check_use_cache_forward_with_attn_mask(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_)


 def      lowercase__     (					self):


   '''simple docstring'''




   lowercase__		,    lowercase__  :   int    		=      self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
     with self.subTest(model_class.__name__):
       lowercase__  :   Optional[int]    		=      self._prepare_for_class(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_)
       lowercase__  :   Dict    		=      model_class(SCREAMING_SNAKE_CASE_)

       @jax.jit
       def encode_jitted(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_=None					,  **SCREAMING_SNAKE_CASE_):
         return model.encode(input_ids=SCREAMING_SNAKE_CASE_					,  attention_mask=SCREAMING_SNAKE_CASE_)

       with self.subTest("""JIT Enabled"""):
         lowercase__  :   List[Any]    		=      encode_jitted(**SCREAMING_SNAKE_CASE_).to_tuple()

       with self.subTest("""JIT Disabled"""):
         with jax.disable_jit():
           lowercase__  :   Optional[int]    		=      encode_jitted(**SCREAMING_SNAKE_CASE_).to_tuple()

       self.assertEqual(len(SCREAMING_SNAKE_CASE_)					,  len(SCREAMING_SNAKE_CASE_))
       for jitted_output, output in zip(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_):
         self.assertEqual(jitted_output.shape					,  output.shape)


 def      lowercase__     (					self):


   '''simple docstring'''




   lowercase__		,    lowercase__  :   str    		=      self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
     with self.subTest(model_class.__name__):
       lowercase__  :   Dict    		=      model_class(SCREAMING_SNAKE_CASE_)
       lowercase__  :   Any    		=      model.encode(inputs_dict["""input_ids"""]					,  inputs_dict["""attention_mask"""])

       lowercase__  :   Any    		=      {
           """decoder_input_ids""": inputs_dict["""decoder_input_ids"""],
           """decoder_attention_mask""": inputs_dict["""decoder_attention_mask"""],
           """encoder_outputs""": encoder_outputs,
       }

       @jax.jit
       def decode_jitted(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_):
         return model.decode(
             decoder_input_ids=SCREAMING_SNAKE_CASE_					,  decoder_attention_mask=SCREAMING_SNAKE_CASE_					,  encoder_outputs=SCREAMING_SNAKE_CASE_					,  )

       with self.subTest("""JIT Enabled"""):
         lowercase__  :   List[str]    		=      decode_jitted(**SCREAMING_SNAKE_CASE_).to_tuple()

       with self.subTest("""JIT Disabled"""):
         with jax.disable_jit():
           lowercase__  :   Tuple    		=      decode_jitted(**SCREAMING_SNAKE_CASE_).to_tuple()

       self.assertEqual(len(SCREAMING_SNAKE_CASE_)					,  len(SCREAMING_SNAKE_CASE_))
       for jitted_output, output in zip(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_):
         self.assertEqual(jitted_output.shape					,  output.shape)


 @slow
 def      lowercase__     (					self):


   '''simple docstring'''




   for model_class_name in self.all_model_classes:
     lowercase__  :   Any    		=      model_class_name.from_pretrained("""google/pegasus-large"""					,  from_pt=SCREAMING_SNAKE_CASE_)
     lowercase__  :   Any    		=      np.ones((1, 1))
     lowercase__  :   Optional[int]    		=      model(SCREAMING_SNAKE_CASE_)
     self.assertIsNotNone(SCREAMING_SNAKE_CASE_)



 @slow
 def      lowercase__     (					self):


   '''simple docstring'''




   lowercase__  :   int    		=      FlaxPegasusForConditionalGeneration.from_pretrained("""google/pegasus-xsum""")
   lowercase__  :   Optional[int]    		=      PegasusTokenizer.from_pretrained("""google/pegasus-xsum""")

   lowercase__  :   Union[str, Any]    		=      [
       """ PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.""",
       """ The London trio are up for best UK act and best album, as well as getting two nominations in the best song category.\"We got told like this morning 'Oh I think you're nominated'\", said Dappy.\"And I was like 'Oh yeah, which one?' And now we've got nominated for four awards. I mean, wow!\"Bandmate Fazer added: \"We thought it's best of us to come down and mingle with everyone and say hello to the cameras. And now we find we've got four nominations.\"The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn't be too disappointed if they didn't win this time around.\"At the end of the day we're grateful to be where we are in our careers.\"If it don't happen then it don't happen - live to fight another day and keep on making albums and hits for the fans.\"Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a  re-working of The Killers' All These Things That I've Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year's Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border.\"We just done Edinburgh the other day,\" said Dappy.\"We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!\" """,
   ]

   lowercase__  :   Union[str, Any]    		=      [
       """California's largest electricity provider has turned off power to hundreds of thousands of customers.""",
       """Pop group N-Dubz have revealed they were surprised to get four nominations for this year's Mobo Awards.""",
   ]

   lowercase__  :   Dict    		=      tokenizer(SCREAMING_SNAKE_CASE_					,  return_tensors="""np"""					,  truncation=SCREAMING_SNAKE_CASE_					,  max_length=5_12					,  padding=SCREAMING_SNAKE_CASE_)
   lowercase__  :   Dict    		=      model.generate(**SCREAMING_SNAKE_CASE_					,  num_beams=2).sequences
   lowercase__  :   Dict    		=      tokenizer.batch_decode(SCREAMING_SNAKE_CASE_					,  skip_special_tokens=SCREAMING_SNAKE_CASE_)
   assert tgt_text == decoded






