






import time
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
							import torch

							from transformers.generation import (
							    MaxLengthCriteria,
							    MaxNewTokensCriteria,
							    MaxTimeCriteria,
							    StoppingCriteriaList,
							    validate_stopping_criteria,
							)






@require_torch
class 			_UpperCamelCase(       unittest.TestCase		):

		def    __lowerCAmelCase  (					self		:    Tuple    ,       SCREAMING_SNAKE_CASE__		:    Dict    ):



					'''simple docstring'''



					__a						:  Any									=     3
					__a						:  int									=     2_5_0

					__a						:  Union[str, Any]									=     ids_tensor((batch_size, length)    ,       SCREAMING_SNAKE_CASE__    )
					__a						:  Optional[int]									=     torch.ones((batch_size, length)    ,       device=SCREAMING_SNAKE_CASE__    ,       dtype=torch.float    ) / length
					return input_ids, scores

		def    __lowerCAmelCase  (					self		:    Optional[int]    ):



					'''simple docstring'''



					__a						,					__a						:  List[str]									=     self._get_tensors(5    )

					__a						:  List[str]									=     StoppingCriteriaList(
					    [
					        MaxLengthCriteria(max_length=1_0    ),
					        MaxTimeCriteria(max_time=0.1    ),
					    ]    )

					self.assertFalse(criteria(SCREAMING_SNAKE_CASE__    ,       SCREAMING_SNAKE_CASE__    )    )

					__a						,					__a						:  str									=     self._get_tensors(9    )
					self.assertFalse(criteria(SCREAMING_SNAKE_CASE__    ,       SCREAMING_SNAKE_CASE__    )    )

					__a						,					__a						:  List[Any]									=     self._get_tensors(1_0    )
					self.assertTrue(criteria(SCREAMING_SNAKE_CASE__    ,       SCREAMING_SNAKE_CASE__    )    )

		def    __lowerCAmelCase  (					self		:    List[Any]    ):



					'''simple docstring'''



					__a						:  Any									=     MaxLengthCriteria(max_length=1_0    )

					__a						,					__a						:  Optional[Any]									=     self._get_tensors(5    )
					self.assertFalse(criteria(SCREAMING_SNAKE_CASE__    ,       SCREAMING_SNAKE_CASE__    )    )

					__a						,					__a						:  Any									=     self._get_tensors(9    )
					self.assertFalse(criteria(SCREAMING_SNAKE_CASE__    ,       SCREAMING_SNAKE_CASE__    )    )

					__a						,					__a						:  List[str]									=     self._get_tensors(1_0    )
					self.assertTrue(criteria(SCREAMING_SNAKE_CASE__    ,       SCREAMING_SNAKE_CASE__    )    )

		def    __lowerCAmelCase  (					self		:    int    ):



					'''simple docstring'''



					__a						:  Dict									=     MaxNewTokensCriteria(start_length=5    ,       max_new_tokens=5    )

					__a						,					__a						:  List[Any]									=     self._get_tensors(5    )
					self.assertFalse(criteria(SCREAMING_SNAKE_CASE__    ,       SCREAMING_SNAKE_CASE__    )    )

					__a						,					__a						:  Union[str, Any]									=     self._get_tensors(9    )
					self.assertFalse(criteria(SCREAMING_SNAKE_CASE__    ,       SCREAMING_SNAKE_CASE__    )    )

					__a						,					__a						:  Any									=     self._get_tensors(1_0    )
					self.assertTrue(criteria(SCREAMING_SNAKE_CASE__    ,       SCREAMING_SNAKE_CASE__    )    )

					__a						:  Union[str, Any]									=     StoppingCriteriaList([criteria]    )
					self.assertEqual(criteria_list.max_length    ,       1_0    )

		def    __lowerCAmelCase  (					self		:    Union[str, Any]    ):



					'''simple docstring'''



					__a						,					__a						:  Tuple									=     self._get_tensors(5    )

					__a						:  Dict									=     MaxTimeCriteria(max_time=0.1    )
					self.assertFalse(criteria(SCREAMING_SNAKE_CASE__    ,       SCREAMING_SNAKE_CASE__    )    )

					__a						:  List[Any]									=     MaxTimeCriteria(max_time=0.1    ,       initial_timestamp=time.time() - 0.2    )
					self.assertTrue(criteria(SCREAMING_SNAKE_CASE__    ,       SCREAMING_SNAKE_CASE__    )    )



		def    __lowerCAmelCase  (					self		:    Tuple    ):



					'''simple docstring'''



					validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(1_0    )]    )    ,       1_0    )

					with self.assertWarns(SCREAMING_SNAKE_CASE__    ):
								validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(1_0    )]    )    ,       1_1    )

					__a						:  List[str]									=     validate_stopping_criteria(StoppingCriteriaList()    ,       1_1    )

					self.assertEqual(len(SCREAMING_SNAKE_CASE__    )    ,       1    )













import argparse
from pathlib import Path

import torch

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


logging.set_verbosity_info()
__magic_name__   :  str        =							logging.get_logger(__name__)
def 		lowercase__  (				_UpperCamelCase) ->			int:
      """simple docstring"""

      UpperCamelCase					       =  torch.load(_UpperCamelCase       ,							map_location='cpu')
      if "model" in sd.keys():
            UpperCamelCase					       =  torch.load(_UpperCamelCase       ,							map_location='cpu')['model']

      # pop unnecessary weights
      UpperCamelCase					       =  [
          'decoder.version',
          'decoder.output_projection.weight',
      ]
      for key in keys_to_delete:
            if key in sd:
                  sd.pop(_UpperCamelCase)

      UpperCamelCase					       =  {
          '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:
                  UpperCamelCase					       =  sd.pop(_UpperCamelCase)

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

                  UpperCamelCase					       =  key.replace('.qkv_proj.'       ,							'.q_proj.')
                  UpperCamelCase					       =  key.replace('.qkv_proj.'       ,							'.k_proj.')
                  UpperCamelCase					       =  key.replace('.qkv_proj.'       ,							'.v_proj.')

                  UpperCamelCase					       =  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
                  UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase					       =  torch.split(_UpperCamelCase       ,							depth // 3       ,							dim=0)

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

      return sd
@torch.no_grad()
def 		lowercase__  (				_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase=None) ->			Optional[int]:
      """simple docstring"""

      UpperCamelCase					       =  load_checkpoint(_UpperCamelCase)

      if config is not None:
            UpperCamelCase					       =  OPTConfig.from_pretrained(_UpperCamelCase)
      else:
            UpperCamelCase					       =  OPTConfig()

      UpperCamelCase					       =  OPTModel(_UpperCamelCase).half().eval()
      model.load_state_dict(_UpperCamelCase)

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


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



