





'''simple docstring'''

import unittest

from transformers import BigBirdConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
  import jax

  from transformers.models.big_bird.modeling_flax_big_bird import (
      FlaxBigBirdForCausalLM,
      FlaxBigBirdForMaskedLM,
      FlaxBigBirdForMultipleChoice,
      FlaxBigBirdForPreTraining,
      FlaxBigBirdForQuestionAnswering,
      FlaxBigBirdForSequenceClassification,
      FlaxBigBirdForTokenClassification,
      FlaxBigBirdModel,
  )






class        UpperCAmelCase     (	unittest.TestCase    ):
 def __init__(    self				:Optional[Any]						,	lowercase_				:str						,	lowercase_				:List[str]=2						,	lowercase_				:Dict=56						,	lowercase_				:int=True						,	lowercase_				:Optional[Any]=True						,	lowercase_				:Union[str, Any]=True						,	lowercase_				:List[Any]=True						,	lowercase_				:List[str]=99						,	lowercase_				:List[str]=32						,	lowercase_				:int=2						,	lowercase_				:str=2						,	lowercase_				:Any=7						,	lowercase_				:Optional[int]="gelu_new"						,	lowercase_				:List[Any]=0.1						,	lowercase_				:str=0.1						,	lowercase_				:Optional[Any]=5_12						,	lowercase_				:int=16						,	lowercase_				:Any=2						,	lowercase_				:Union[str, Any]=0.0_2						,	lowercase_				:Optional[Any]=4						,	lowercase_				:Tuple="block_sparse"						,	lowercase_				:Optional[Any]=True						,	lowercase_				:List[str]=False						,	lowercase_				:Any=2						,	lowercase_				:str=3						,	)->							List[str]:
     A__    =      parent
     A__    =      batch_size
     A__    =      seq_length
     A__    =      is_training
     A__    =      use_attention_mask
     A__    =      use_token_type_ids
     A__    =      use_labels
     A__    =      vocab_size
     A__    =      hidden_size
     A__    =      num_hidden_layers
     A__    =      num_attention_heads
     A__    =      intermediate_size
     A__    =      hidden_act
     A__    =      hidden_dropout_prob
     A__    =      attention_probs_dropout_prob
     A__    =      max_position_embeddings
     A__    =      type_vocab_size
     A__    =      type_sequence_label_size
     A__    =      initializer_range
     A__    =      num_choices

     A__    =      rescale_embeddings
     A__    =      attention_type
     A__    =      use_bias
     A__    =      block_size
     A__    =      num_random_blocks
 def 					UpperCAmelCase_							(    self				:Optional[int]							)->							List[Any]:
     A__    =      ids_tensor([self.batch_size, self.seq_length]						,	self.vocab_size							)

     A__    =      None
     if self.use_attention_mask:
         A__    =      random_attention_mask([self.batch_size, self.seq_length]							)

     A__    =      None
     if self.use_token_type_ids:
         A__    =      ids_tensor([self.batch_size, self.seq_length]						,	self.type_vocab_size							)

     A__    =      BigBirdConfig(
         vocab_size=self.vocab_size						,	hidden_size=self.hidden_size						,	num_hidden_layers=self.num_hidden_layers						,	num_attention_heads=self.num_attention_heads						,	intermediate_size=self.intermediate_size						,	hidden_act=self.hidden_act						,	hidden_dropout_prob=self.hidden_dropout_prob						,	attention_probs_dropout_prob=self.attention_probs_dropout_prob						,	max_position_embeddings=self.max_position_embeddings						,	type_vocab_size=self.type_vocab_size						,	is_decoder=lowercase_						,	initializer_range=self.initializer_range						,	attention_type=self.attention_type						,	block_size=self.block_size						,	num_random_blocks=self.num_random_blocks						,	use_bias=self.use_bias						,	rescale_embeddings=self.rescale_embeddings						,	)

     return config, input_ids, token_type_ids, attention_mask



 def 					UpperCAmelCase_							(    self				:Tuple							)->							Dict:
     A__    =      self.prepare_config_and_inputs()
     A__,     A__,     A__,     A__    =      config_and_inputs
     A__    =      {
         "input_ids": input_ids,
         "token_type_ids": token_type_ids,
         "attention_mask": attention_mask,
     }
     return config, inputs_dict






@require_flax
class        UpperCAmelCase     (	UpperCamelCase__      ,	unittest.TestCase    ):
 __lowercase       		=    (
     (
         FlaxBigBirdForCausalLM,
         FlaxBigBirdModel,
         FlaxBigBirdForPreTraining,
         FlaxBigBirdForMaskedLM,
         FlaxBigBirdForMultipleChoice,
         FlaxBigBirdForQuestionAnswering,
         FlaxBigBirdForSequenceClassification,
         FlaxBigBirdForTokenClassification,
     )
     if is_flax_available()
     else ()
 )

 __lowercase       		=    False
 __lowercase       		=    False
 def 					UpperCAmelCase_							(    self				:Optional[int]							)->							Dict:
     A__    =      FlaxBigBirdModelTester(self							)
 @slow
 # copied from `test_modeling_flax_common` because it takes much longer than other models
 def 					UpperCAmelCase_							(    self				:Tuple							)->							str:
     super().test_from_pretrained_save_pretrained()
 @slow
 # copied from `test_modeling_flax_common` because it takes much longer than other models
 def 					UpperCAmelCase_							(    self				:Optional[Any]							)->							Union[str, Any]:
     super().test_from_pretrained_with_no_automatic_init()
 @slow
 # copied from `test_modeling_flax_common` because it takes much longer than other models
 def 					UpperCAmelCase_							(    self				:str							)->							int:
     super().test_no_automatic_init()
 @slow
 # copied from `test_modeling_flax_common` because it takes much longer than other models
 def 					UpperCAmelCase_							(    self				:Tuple							)->							Union[str, Any]:
     super().test_hidden_states_output()
 @slow
 def 					UpperCAmelCase_							(    self				:Any							)->							Union[str, Any]:
     for model_class_name in self.all_model_classes:
         A__    =      model_class_name.from_pretrained("google/bigbird-roberta-base"							)
         self.assertIsNotNone(lowercase_							)
 def 					UpperCAmelCase_							(    self				:Optional[int]							)->							str:
     if self.test_attn_probs:
         super().test_attention_outputs()
 @slow
 # copied from `test_modeling_flax_common` because it takes much longer than other models
 def 					UpperCAmelCase_							(    self				:int							)->							int:
     A__,     A__    =      self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
         with self.subTest(model_class.__name__							):
             A__    =      self._prepare_for_class(lowercase_						,	lowercase_							)
             A__    =      model_class(lowercase_							)

             @jax.jit
             def model_jitted(lowercase_				:Optional[int]						,	lowercase_				:List[Any]=None						,	**lowercase_				:Tuple							):
                 return model(input_ids=lowercase_						,	attention_mask=lowercase_						,	**lowercase_							)

             with self.subTest("JIT Enabled"							):
                 A__    =      model_jitted(**lowercase_							).to_tuple()

             with self.subTest("JIT Disabled"							):
                 with jax.disable_jit():
                     A__    =      model_jitted(**lowercase_							).to_tuple()

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



 def 					UpperCAmelCase_							(    self				:Optional[int]						,	lowercase_				:Optional[Any]						,	lowercase_				:List[str]						,	lowercase_				:Any						,	lowercase_				:int=1E-5						,	lowercase_				:Any="outputs"						,	lowercase_				:int=None							)->							str:
     # `bigbird_block_sparse_attention` in `FlaxBigBird` returns `attention_probs = None`, while in PyTorch version,
     # an effort was done to return `attention_probs` (yet to be verified).
     if name.startswith("outputs.attentions"							):
         return
     else:
         super().check_pt_flax_outputs(lowercase_						,	lowercase_						,	lowercase_						,	lowercase_						,	lowercase_						,	lowercase_							)










'''simple docstring'''

import argparse
from pathlib import Path

import torch

from transformers import OPTConfig, OPTModel
from transformers.utils import logging


logging.set_verbosity_info()
__lowerCAmelCase							:     List[str]		     =logging.get_logger(__name__)





def 						UpperCamelCase    (							_lowerCamelCase : str	):
    A__    =      torch.load(_lowerCamelCase						,	map_location="cpu"	)
    if "model" in sd.keys():
        A__    =      torch.load(_lowerCamelCase						,	map_location="cpu"	)["model"]

    # pop unnecessary weights
    A__    =      [
        "decoder.version",
        "decoder.output_projection.weight",
    ]
    for key in keys_to_delete:
        if key in sd:
            sd.pop(_lowerCamelCase	)

    A__    =      {
        "decoder.project_in_dim.weight": "decoder.project_in.weight",
        "decoder.project_out_dim.weight": "decoder.project_out.weight",
        "decoder.layer_norm.weight": "decoder.final_layer_norm.weight",
        "decoder.layer_norm.bias": "decoder.final_layer_norm.bias",
    }
    for old_key, new_key in keys_to_rename.items():
        if old_key in sd:
            A__    =      sd.pop(_lowerCamelCase	)

    A__    =      list(sd.keys()	)
    for key in keys:
        if ".qkv_proj." in key:
            A__    =      sd[key]
            # We split QKV in separate Q,K,V

            A__    =      key.replace(".qkv_proj."						,	".q_proj."	)
            A__    =      key.replace(".qkv_proj."						,	".k_proj."	)
            A__    =      key.replace(".qkv_proj."						,	".v_proj."	)

            A__    =      value.shape[0]
            assert depth % 3 == 0
            # `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
            # https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
            A__,     A__,     A__    =      torch.split(_lowerCamelCase						,	depth // 3						,	dim=0	)

            A__    =      q
            A__    =      k
            A__    =      v
            del sd[key]

    return sd





@torch.no_grad()
def 						UpperCamelCase    (							_lowerCamelCase : List[Any]						,	_lowerCamelCase : Any						,	_lowerCamelCase : Dict=None	):
    A__    =      load_checkpoint(_lowerCamelCase	)

    if config is not None:
        A__    =      OPTConfig.from_pretrained(_lowerCamelCase	)
    else:
        A__    =      OPTConfig()

    A__    =      OPTModel(_lowerCamelCase	).half().eval()
    model.load_state_dict(_lowerCamelCase	)

    # Check results
    Path(_lowerCamelCase	).mkdir(exist_ok=_lowerCamelCase	)
    model.save_pretrained(_lowerCamelCase	)


if __name__ == "__main__":
  __lowerCAmelCase							:     List[str]		     =argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--fairseq_path",
      type=str,
      help=(
          "path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:"
          " https://huggingface.co/models?other=opt_metasq"
      ),
  )
  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="Define HF config.")
  __lowerCAmelCase							:     List[Any]		     =parser.parse_args()
  convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)



