





import random
import unittest

import numpy as np

import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax


if is_flax_available():
    import os

    import jax.numpy as jnp
    from jax import jit

    from transformers import AutoTokenizer, FlaxAutoModelForCausalLM
    from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model

    lowercase      :       Optional[Any]									=  """0.12"""  # assumed parallelism: 8


if is_torch_available():
    import torch



def A_	(		A__		,      A__		,      A__=None				)  ->		Optional[int]:
       if rng is None:
              a__						:	Optional[Any] =				random.Random()

       a__						:	int =				1
       for dim in shape:
              total_dims *= dim

       a__						:	Union[str, Any] =				[]
       for _ in range(A__				):
              values.append(rng.randint(0		,      vocab_size - 1				)				)

       a__						:	List[Any] =				np.array(A__		,      dtype=jnp.intaa				).reshape(A__				)

       return output



def A_	(		A__		,      A__=None				)  ->		Union[str, Any]:
       a__						:	str =				ids_tensor(A__		,      vocab_size=2		,      rng=A__				)
       # make sure that at least one token is attended to for each batch
       a__						:	str =				1
       return attn_mask





@require_flax
class  A__    :




   """simple docstring"""



   __A   :				str         =  None
   __A   :				Tuple         =  ()

   def 							__lowercase		(					self)			->						int:







          '''simple docstring'''

          a__ ,							a__						:	Tuple =				self.model_tester.prepare_config_and_inputs_for_common()

          # cut to half length & take max batch_size 3
          a__						:	Optional[Any] =				2
          a__						:	Dict =				inputs['input_ids'].shape[-1] // 2
          a__						:	Optional[int] =				inputs['input_ids'][:max_batch_size, :sequence_length]

          a__						:	Optional[Any] =				jnp.ones_like(lowercase)
          a__						:	Optional[int] =				attention_mask[:max_batch_size, :sequence_length]

          # generate max 5 tokens
          a__						:	Optional[Any] =				input_ids.shape[-1] + 5
          if config.eos_token_id is not None and config.pad_token_id is None:
                 # hack to allow generate for models such as GPT2 as is done in `generate()`
                 a__						:	Tuple =				config.eos_token_id
          return config, input_ids, attention_mask, max_length

   @is_pt_flax_cross_test
   def 							__lowercase		(					self)			->						Dict:







          '''simple docstring'''

          a__ ,							a__ ,							a__ ,							a__						:	List[str] =				self._get_input_ids_and_config()
          a__						:	int =				False
          a__						:	Optional[int] =				max_length
          a__						:	Optional[int] =				0

          for model_class in self.all_generative_model_classes:
                 a__						:	Union[str, Any] =				model_class(lowercase)

                 a__						:	Union[str, Any] =				model_class.__name__[4:]  # Skip the "Flax" at the beginning
                 a__						:	int =				getattr(lowercase					,  lowercase)
                 a__						:	List[Any] =				pt_model_class(lowercase).eval()
                 a__						:	Tuple =				load_flax_weights_in_pytorch_model(lowercase					,  flax_model.params)

                 a__						:	Optional[Any] =				flax_model.generate(lowercase).sequences
                 a__						:	List[Any] =				pt_model.generate(torch.tensor(lowercase					,  dtype=torch.long))

                 if flax_generation_outputs.shape[-1] > pt_generation_outputs.shape[-1]:
                        a__						:	Optional[Any] =				flax_generation_outputs[:, : pt_generation_outputs.shape[-1]]

                 self.assertListEqual(pt_generation_outputs.numpy().tolist()					,  flax_generation_outputs.tolist())

   def 							__lowercase		(					self)			->						Any:







          '''simple docstring'''

          a__ ,							a__ ,							a__ ,							a__						:	Tuple =				self._get_input_ids_and_config()
          a__						:	Optional[int] =				False
          a__						:	Union[str, Any] =				max_length

          for model_class in self.all_generative_model_classes:
                 a__						:	Optional[int] =				model_class(lowercase)

                 a__						:	Optional[int] =				model.generate(lowercase).sequences
                 self.assertEqual(generation_outputs.shape[-1]					,  lowercase)

                 a__						:	List[Any] =				jit(model.generate)
                 a__						:	Any =				jit_generate(lowercase).sequences

                 self.assertListEqual(generation_outputs.tolist()					,  jit_generation_outputs.tolist())

   def 							__lowercase		(					self)			->						Union[str, Any]:







          '''simple docstring'''

          a__ ,							a__ ,							a__ ,							a__						:	List[str] =				self._get_input_ids_and_config()
          a__						:	Dict =				True
          a__						:	Union[str, Any] =				max_length

          for model_class in self.all_generative_model_classes:
                 a__						:	Union[str, Any] =				model_class(lowercase)

                 a__						:	List[Any] =				model.generate(lowercase).sequences
                 self.assertEqual(generation_outputs.shape[-1]					,  lowercase)

                 a__						:	str =				jit(model.generate)
                 a__						:	str =				jit_generate(lowercase).sequences

                 self.assertListEqual(generation_outputs.tolist()					,  jit_generation_outputs.tolist())

   def 							__lowercase		(					self)			->						str:







          '''simple docstring'''

          a__ ,							a__ ,							a__ ,							a__						:	Optional[int] =				self._get_input_ids_and_config()
          a__						:	Optional[int] =				False
          a__						:	str =				max_length
          a__						:	Optional[int] =				2

          for model_class in self.all_generative_model_classes:
                 a__						:	Union[str, Any] =				model_class(lowercase)

                 a__						:	str =				model.generate(lowercase).sequences
                 self.assertEqual(generation_outputs.shape[-1]					,  lowercase)

                 a__						:	List[Any] =				jit(model.generate)
                 a__						:	Dict =				jit_generate(lowercase).sequences

                 self.assertListEqual(generation_outputs.tolist()					,  jit_generation_outputs.tolist())

   def 							__lowercase		(					self)			->						List[str]:







          '''simple docstring'''

          a__ ,							a__ ,							a__ ,							a__						:	Optional[Any] =				self._get_input_ids_and_config()
          a__						:	List[str] =				False
          a__						:	Optional[int] =				max_length
          a__						:	Optional[Any] =				2
          a__						:	int =				2

          for model_class in self.all_generative_model_classes:
                 a__						:	Dict =				model_class(lowercase)

                 a__						:	Optional[Any] =				model.generate(lowercase).sequences
                 self.assertEqual(generation_outputs.shape[0]					,  input_ids.shape[0] * config.num_return_sequences)

   def 							__lowercase		(					self)			->						Any:







          '''simple docstring'''

          a__ ,							a__ ,							a__ ,							a__						:	Union[str, Any] =				self._get_input_ids_and_config()
          a__						:	int =				True
          a__						:	Optional[Any] =				max_length
          a__						:	List[Any] =				0.8
          a__						:	List[str] =				10
          a__						:	Tuple =				0.3
          a__						:	List[Any] =				1
          a__						:	Any =				8
          a__						:	Union[str, Any] =				9

          for model_class in self.all_generative_model_classes:
                 a__						:	str =				model_class(lowercase)

                 a__						:	str =				model.generate(lowercase).sequences
                 self.assertEqual(generation_outputs.shape[-1]					,  lowercase)

                 a__						:	List[str] =				jit(model.generate)
                 a__						:	Dict =				jit_generate(lowercase).sequences

                 self.assertListEqual(generation_outputs.tolist()					,  jit_generation_outputs.tolist())

   def 							__lowercase		(					self)			->						Optional[Any]:







          '''simple docstring'''

          a__ ,							a__ ,							a__ ,							a__						:	str =				self._get_input_ids_and_config()
          a__						:	int =				max_length
          a__						:	Dict =				1
          a__						:	Optional[Any] =				8
          a__						:	Union[str, Any] =				9

          for model_class in self.all_generative_model_classes:
                 a__						:	Union[str, Any] =				model_class(lowercase)

                 a__						:	Optional[int] =				model.generate(lowercase).sequences
                 self.assertEqual(generation_outputs.shape[-1]					,  lowercase)

                 a__						:	Tuple =				jit(model.generate)
                 a__						:	Optional[int] =				jit_generate(lowercase).sequences

                 self.assertListEqual(generation_outputs.tolist()					,  jit_generation_outputs.tolist())

   def 							__lowercase		(					self)			->						Union[str, Any]:







          '''simple docstring'''

          a__ ,							a__ ,							a__ ,							a__						:	int =				self._get_input_ids_and_config()
          a__						:	int =				max_length
          a__						:	Optional[int] =				2
          a__						:	int =				1
          a__						:	Tuple =				8
          a__						:	Optional[int] =				9

          for model_class in self.all_generative_model_classes:
                 a__						:	List[str] =				model_class(lowercase)

                 a__						:	List[Any] =				model.generate(lowercase).sequences
                 self.assertEqual(generation_outputs.shape[-1]					,  lowercase)

                 a__						:	Any =				jit(model.generate)
                 a__						:	Dict =				jit_generate(lowercase).sequences

                 self.assertListEqual(generation_outputs.tolist()					,  jit_generation_outputs.tolist())

   def 							__lowercase		(					self)			->						str:







          '''simple docstring'''

          a__ ,							a__ ,							a__ ,							a__						:	Optional[int] =				self._get_input_ids_and_config()

          # pad attention mask on the left
          a__						:	int =				attention_mask.at[(0, 0)].set(0)

          a__						:	Union[str, Any] =				False
          a__						:	Dict =				max_length

          for model_class in self.all_generative_model_classes:
                 a__						:	Optional[Any] =				model_class(lowercase)

                 a__						:	str =				model.generate(lowercase					,  attention_mask=lowercase).sequences
                 self.assertEqual(generation_outputs.shape[-1]					,  lowercase)

                 a__						:	Union[str, Any] =				jit(model.generate)
                 a__						:	Union[str, Any] =				jit_generate(lowercase					,  attention_mask=lowercase).sequences

                 self.assertListEqual(generation_outputs.tolist()					,  jit_generation_outputs.tolist())

   def 							__lowercase		(					self)			->						Union[str, Any]:







          '''simple docstring'''

          a__ ,							a__ ,							a__ ,							a__						:	Dict =				self._get_input_ids_and_config()

          # pad attention mask on the left
          a__						:	Union[str, Any] =				attention_mask.at[(0, 0)].set(0)

          a__						:	Union[str, Any] =				True
          a__						:	Tuple =				max_length

          for model_class in self.all_generative_model_classes:
                 a__						:	List[str] =				model_class(lowercase)

                 a__						:	int =				model.generate(lowercase					,  attention_mask=lowercase).sequences
                 self.assertEqual(generation_outputs.shape[-1]					,  lowercase)

                 a__						:	Optional[int] =				jit(model.generate)
                 a__						:	Any =				jit_generate(lowercase					,  attention_mask=lowercase).sequences

                 self.assertListEqual(generation_outputs.tolist()					,  jit_generation_outputs.tolist())

   def 							__lowercase		(					self)			->						Optional[int]:







          '''simple docstring'''

          a__ ,							a__ ,							a__ ,							a__						:	str =				self._get_input_ids_and_config()

          # pad attention mask on the left
          a__						:	List[Any] =				attention_mask.at[(0, 0)].set(0)

          a__						:	int =				2
          a__						:	Tuple =				max_length

          for model_class in self.all_generative_model_classes:
                 a__						:	int =				model_class(lowercase)

                 a__						:	Optional[int] =				model.generate(lowercase					,  attention_mask=lowercase).sequences
                 self.assertEqual(generation_outputs.shape[-1]					,  lowercase)

                 a__						:	Any =				jit(model.generate)
                 a__						:	Union[str, Any] =				jit_generate(lowercase					,  attention_mask=lowercase).sequences

                 self.assertListEqual(generation_outputs.tolist()					,  jit_generation_outputs.tolist())





@require_flax
class  A__    (							unittest.TestCase	):




   """simple docstring"""



   def 							__lowercase		(					self)			->						Any:







          '''simple docstring'''

          a__						:	Tuple =				AutoTokenizer.from_pretrained('hf-internal-testing/tiny-bert')
          a__						:	int =				FlaxAutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-bert-flax-only')

          a__						:	Optional[int] =				'Hello world'
          a__						:	Any =				tokenizer(lowercase					,  return_tensors='np').input_ids

          # typos are quickly detected (the correct argument is `do_sample`)
          with self.assertRaisesRegex(lowercase					,  'do_samples'):
                 model.generate(lowercase					,  do_samples=lowercase)

          # arbitrary arguments that will not be used anywhere are also not accepted
          with self.assertRaisesRegex(lowercase					,  'foo'):
                 a__						:	Dict =				{'foo': 'bar'}
                 model.generate(lowercase					,  **lowercase)









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)


