
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 	__A(				unittest.TestCase ):

  """simple docstring"""







  def __init__(self       ,			SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_=2       ,			SCREAMING_SNAKE_CASE_=56       ,			SCREAMING_SNAKE_CASE_=True       ,			SCREAMING_SNAKE_CASE_=True       ,			SCREAMING_SNAKE_CASE_=True       ,			SCREAMING_SNAKE_CASE_=True       ,			SCREAMING_SNAKE_CASE_=99       ,			SCREAMING_SNAKE_CASE_=32       ,			SCREAMING_SNAKE_CASE_=2       ,			SCREAMING_SNAKE_CASE_=2       ,			SCREAMING_SNAKE_CASE_=7       ,			SCREAMING_SNAKE_CASE_="gelu_new"       ,			SCREAMING_SNAKE_CASE_=0.1       ,			SCREAMING_SNAKE_CASE_=0.1       ,			SCREAMING_SNAKE_CASE_=5_12       ,			SCREAMING_SNAKE_CASE_=16       ,			SCREAMING_SNAKE_CASE_=2       ,			SCREAMING_SNAKE_CASE_=0.02       ,			SCREAMING_SNAKE_CASE_=4       ,			SCREAMING_SNAKE_CASE_="block_sparse"       ,			SCREAMING_SNAKE_CASE_=True       ,			SCREAMING_SNAKE_CASE_=False       ,			SCREAMING_SNAKE_CASE_=2       ,			SCREAMING_SNAKE_CASE_=3       ,			):
       UpperCamelCase__				=				parent
       UpperCamelCase__				=				batch_size
       UpperCamelCase__				=				seq_length
       UpperCamelCase__				=				is_training
       UpperCamelCase__				=				use_attention_mask
       UpperCamelCase__				=				use_token_type_ids
       UpperCamelCase__				=				use_labels
       UpperCamelCase__				=				vocab_size
       UpperCamelCase__				=				hidden_size
       UpperCamelCase__				=				num_hidden_layers
       UpperCamelCase__				=				num_attention_heads
       UpperCamelCase__				=				intermediate_size
       UpperCamelCase__				=				hidden_act
       UpperCamelCase__				=				hidden_dropout_prob
       UpperCamelCase__				=				attention_probs_dropout_prob
       UpperCamelCase__				=				max_position_embeddings
       UpperCamelCase__				=				type_vocab_size
       UpperCamelCase__				=				type_sequence_label_size
       UpperCamelCase__				=				initializer_range
       UpperCamelCase__				=				num_choices

       UpperCamelCase__				=				rescale_embeddings
       UpperCamelCase__				=				attention_type
       UpperCamelCase__				=				use_bias
       UpperCamelCase__				=				block_size
       UpperCamelCase__				=				num_random_blocks







  def        UpperCAmelCase_					(self						):
       UpperCamelCase__				=				ids_tensor([self.batch_size, self.seq_length]       ,			self.vocab_size						)

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

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

       UpperCamelCase__				=				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=SCREAMING_SNAKE_CASE_       ,			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						):
       UpperCamelCase__				=				self.prepare_config_and_inputs()
       UpperCamelCase__				=				config_and_inputs
       UpperCamelCase__				=				{
           'input_ids': input_ids,
           'token_type_ids': token_type_ids,
           'attention_mask': attention_mask,
       }
       return config, inputs_dict






@require_flax
class 	__A(				__UpperCAmelCase				,      unittest.TestCase ):

  """simple docstring"""
  SCREAMING_SNAKE_CASE__			    =			(
      (
          FlaxBigBirdForCausalLM,
          FlaxBigBirdModel,
          FlaxBigBirdForPreTraining,
          FlaxBigBirdForMaskedLM,
          FlaxBigBirdForMultipleChoice,
          FlaxBigBirdForQuestionAnswering,
          FlaxBigBirdForSequenceClassification,
          FlaxBigBirdForTokenClassification,
      )
      if is_flax_available()
      else ()
  )

  SCREAMING_SNAKE_CASE__			    =			False
  SCREAMING_SNAKE_CASE__			    =			False







  def        UpperCAmelCase_					(self						):
       UpperCamelCase__				=				FlaxBigBirdModelTester(self						)







  @slow
  # copied from `test_modeling_flax_common` because it takes much longer than other models
  def        UpperCAmelCase_					(self						):
       super().test_from_pretrained_save_pretrained()







  @slow
  # copied from `test_modeling_flax_common` because it takes much longer than other models
  def        UpperCAmelCase_					(self						):
       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						):
       super().test_no_automatic_init()







  @slow
  # copied from `test_modeling_flax_common` because it takes much longer than other models
  def        UpperCAmelCase_					(self						):
       super().test_hidden_states_output()







  @slow
  def        UpperCAmelCase_					(self						):

       for model_class_name in self.all_model_classes:
            UpperCamelCase__				=				model_class_name.from_pretrained("""google/bigbird-roberta-base"""						)
            self.assertIsNotNone(SCREAMING_SNAKE_CASE_						)







  def        UpperCAmelCase_					(self						):

       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						):
       UpperCamelCase__				=				self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
            with self.subTest(model_class.__name__						):
                 UpperCamelCase__				=				self._prepare_for_class(SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_						)
                 UpperCamelCase__				=				model_class(SCREAMING_SNAKE_CASE_						)

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

                 with self.subTest("""JIT Enabled"""						):
                      UpperCamelCase__				=				model_jitted(**SCREAMING_SNAKE_CASE_						).to_tuple()

                 with self.subTest("""JIT Disabled"""						):
                      with jax.disable_jit():
                           UpperCamelCase__				=				model_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        UpperCAmelCase_					(self       ,			SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_=1E-5       ,			SCREAMING_SNAKE_CASE_="outputs"       ,			SCREAMING_SNAKE_CASE_=None						):

       if name.startswith("""outputs.attentions"""						):
            return
       else:
            super().check_pt_flax_outputs(SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_						)












import argparse
from pathlib import Path

import torch

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


logging.set_verbosity_info()
lowercase      :       Any									=  logging.get_logger(__name__)



def A_	(		A__				)  ->		List[str]:
       a__						:	Union[str, Any] =				torch.load(A__		,      map_location='cpu'				)
       if "model" in sd.keys():
              a__						:	Tuple =				torch.load(A__		,      map_location='cpu'				)['model']

       # pop unnecessary weights
       a__						:	Optional[Any] =				[
           'decoder.version',
           'decoder.output_projection.weight',
       ]
       for key in keys_to_delete:
              if key in sd:
                     sd.pop(A__				)

       a__						:	Union[str, Any] =				{
           '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__						:	Any =				sd.pop(A__				)

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

                     a__						:	Union[str, Any] =				key.replace('.qkv_proj.'		,      '.q_proj.'				)
                     a__						:	int =				key.replace('.qkv_proj.'		,      '.k_proj.'				)
                     a__						:	Optional[int] =				key.replace('.qkv_proj.'		,      '.v_proj.'				)

                     a__						:	List[Any] =				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__						:	Any =				torch.split(A__		,      depth // 3		,      dim=0				)

                     a__						:	int =				q
                     a__						:	List[Any] =				k
                     a__						:	int =				v
                     del sd[key]

       return sd



@torch.no_grad()
def A_	(		A__		,      A__		,      A__=None				)  ->		Union[str, Any]:
       a__						:	Union[str, Any] =				load_checkpoint(A__				)

       if config is not None:
              a__						:	List[Any] =				OPTConfig.from_pretrained(A__				)
       else:
              a__						:	int =				OPTConfig()

       a__						:	Optional[int] =				OPTModel(A__				).half().eval()
       model.load_state_dict(A__				)

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


if __name__ == "__main__":
    lowercase      :       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.""")
    lowercase      :       Any									=  parser.parse_args()
    convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)


