





"""simple docstring"""





import argparse
from pathlib import Path

import torch

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


logging.set_verbosity_info()
__A											=						logging.get_logger(__name__)
def 					__A  (_SCREAMING_SNAKE_CASE     )   ->Optional[int]:





  """simple docstring"""


  lowerCAmelCase__     :List[Any]											=				torch.load(_SCREAMING_SNAKE_CASE	,  map_location='cpu'     )
  if "model" in sd.keys():
    lowerCAmelCase__     :Optional[int]											=				torch.load(_SCREAMING_SNAKE_CASE	,  map_location='cpu'     )['model']

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

  lowerCAmelCase__     :Optional[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:
      lowerCAmelCase__     :Union[str, Any]											=				sd.pop(_SCREAMING_SNAKE_CASE     )

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

      lowerCAmelCase__     :Any											=				key.replace('.qkv_proj.'	,  '.q_proj.'     )
      lowerCAmelCase__     :Optional[Any]											=				key.replace('.qkv_proj.'	,  '.k_proj.'     )
      lowerCAmelCase__     :List[str]											=				key.replace('.qkv_proj.'	,  '.v_proj.'     )

      lowerCAmelCase__     :Optional[int]											=				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
      lowerCAmelCase__				,							lowerCAmelCase__				,							lowerCAmelCase__     :List[str]											=				torch.split(_SCREAMING_SNAKE_CASE	,  depth // 3	,  dim=0     )

      lowerCAmelCase__     :Optional[int]											=				q
      lowerCAmelCase__     :Optional[Any]											=				k
      lowerCAmelCase__     :Union[str, Any]											=				v
      del sd[key]

  return sd
@torch.no_grad()
def 					__A  (_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE=None     )   ->Union[str, Any]:





  """simple docstring"""


  lowerCAmelCase__     :Dict											=				load_checkpoint(_SCREAMING_SNAKE_CASE     )

  if config is not None:
    lowerCAmelCase__     :str											=				OPTConfig.from_pretrained(_SCREAMING_SNAKE_CASE     )
  else:
    lowerCAmelCase__     :List[str]											=				OPTConfig()

  lowerCAmelCase__     :Tuple											=				OPTModel(_SCREAMING_SNAKE_CASE     ).half().eval()
  model.load_state_dict(_SCREAMING_SNAKE_CASE     )

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


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




'''simple docstring'''





from __future__ import annotations

import unittest

from transformers import is_tf_available, is_torch_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, SMALL_MODEL_IDENTIFIER, is_pt_tf_cross_test, slow


if is_tf_available():
					from transformers import (
					    AutoConfig,
					    BertConfig,
					    GPTaConfig,
					    TaConfig,
					    TFAutoModel,
					    TFAutoModelForCausalLM,
					    TFAutoModelForMaskedLM,
					    TFAutoModelForPreTraining,
					    TFAutoModelForQuestionAnswering,
					    TFAutoModelForSeqaSeqLM,
					    TFAutoModelForSequenceClassification,
					    TFAutoModelWithLMHead,
					    TFBertForMaskedLM,
					    TFBertForPreTraining,
					    TFBertForQuestionAnswering,
					    TFBertForSequenceClassification,
					    TFBertModel,
					    TFGPTaLMHeadModel,
					    TFRobertaForMaskedLM,
					    TFTaForConditionalGeneration,
					)
					from transformers.models.bert.modeling_tf_bert import TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST
					from transformers.models.gpta.modeling_tf_gpta import TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST
					from transformers.models.ta.modeling_tf_ta import TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST

if is_torch_available():
					from transformers import (
					    AutoModel,
					    AutoModelForCausalLM,
					    AutoModelForMaskedLM,
					    AutoModelForPreTraining,
					    AutoModelForQuestionAnswering,
					    AutoModelForSeqaSeqLM,
					    AutoModelForSequenceClassification,
					    AutoModelWithLMHead,
					    BertForMaskedLM,
					    BertForPreTraining,
					    BertForQuestionAnswering,
					    BertForSequenceClassification,
					    BertModel,
					    GPTaLMHeadModel,
					    RobertaForMaskedLM,
					    TaForConditionalGeneration,
					)
@is_pt_tf_cross_test
class 						a__					(					unittest.TestCase   ):





			'''simple docstring'''

			@slow
			def 					lowerCAmelCase     (  self							:			List[str]	)      ->		List[Any]:
						# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						for model_name in ["bert-base-uncased"]:
									__A=	AutoConfig.from_pretrained(lowerCAmelCase_	)
									self.assertIsNotNone(lowerCAmelCase_	)
									self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)

									__A=	TFAutoModel.from_pretrained(lowerCAmelCase_			,    from_pt=lowerCAmelCase_	)
									self.assertIsNotNone(lowerCAmelCase_	)
									self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)

									__A=	AutoModel.from_pretrained(lowerCAmelCase_			,    from_tf=lowerCAmelCase_	)
									self.assertIsNotNone(lowerCAmelCase_	)
									self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)
			@slow
			def 					lowerCAmelCase     (  self							:			Tuple	)      ->		Optional[Any]:
						# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						for model_name in ["bert-base-uncased"]:
									__A=	AutoConfig.from_pretrained(lowerCAmelCase_	)
									self.assertIsNotNone(lowerCAmelCase_	)
									self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)

									__A=	TFAutoModelForPreTraining.from_pretrained(lowerCAmelCase_			,    from_pt=lowerCAmelCase_	)
									self.assertIsNotNone(lowerCAmelCase_	)
									self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)

									__A=	AutoModelForPreTraining.from_pretrained(lowerCAmelCase_			,    from_tf=lowerCAmelCase_	)
									self.assertIsNotNone(lowerCAmelCase_	)
									self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)
			@slow
			def 					lowerCAmelCase     (  self							:			int	)      ->		Optional[int]:
						for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									__A=	AutoConfig.from_pretrained(lowerCAmelCase_	)
									self.assertIsNotNone(lowerCAmelCase_	)
									self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)

									__A=	TFAutoModelForCausalLM.from_pretrained(lowerCAmelCase_			,    from_pt=lowerCAmelCase_	)
									__A,	__A=	TFAutoModelForCausalLM.from_pretrained(
									    lowerCAmelCase_			,    output_loading_info=lowerCAmelCase_			,    from_pt=lowerCAmelCase_	)
									self.assertIsNotNone(lowerCAmelCase_	)
									self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)

									__A=	AutoModelForCausalLM.from_pretrained(lowerCAmelCase_			,    from_tf=lowerCAmelCase_	)
									__A,	__A=	AutoModelForCausalLM.from_pretrained(
									    lowerCAmelCase_			,    output_loading_info=lowerCAmelCase_			,    from_tf=lowerCAmelCase_	)
									self.assertIsNotNone(lowerCAmelCase_	)
									self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)
			@slow
			def 					lowerCAmelCase     (  self							:			Union[str, Any]	)      ->		Optional[Any]:
						for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									__A=	AutoConfig.from_pretrained(lowerCAmelCase_	)
									self.assertIsNotNone(lowerCAmelCase_	)
									self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)

									__A=	TFAutoModelWithLMHead.from_pretrained(lowerCAmelCase_			,    from_pt=lowerCAmelCase_	)
									self.assertIsNotNone(lowerCAmelCase_	)
									self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)

									__A=	AutoModelWithLMHead.from_pretrained(lowerCAmelCase_			,    from_tf=lowerCAmelCase_	)
									self.assertIsNotNone(lowerCAmelCase_	)
									self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)
			@slow
			def 					lowerCAmelCase     (  self							:			Union[str, Any]	)      ->		Optional[int]:
						for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									__A=	AutoConfig.from_pretrained(lowerCAmelCase_	)
									self.assertIsNotNone(lowerCAmelCase_	)
									self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)

									__A=	TFAutoModelForMaskedLM.from_pretrained(lowerCAmelCase_			,    from_pt=lowerCAmelCase_	)
									__A,	__A=	TFAutoModelForMaskedLM.from_pretrained(
									    lowerCAmelCase_			,    output_loading_info=lowerCAmelCase_			,    from_pt=lowerCAmelCase_	)
									self.assertIsNotNone(lowerCAmelCase_	)
									self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)

									__A=	AutoModelForMaskedLM.from_pretrained(lowerCAmelCase_			,    from_tf=lowerCAmelCase_	)
									__A,	__A=	AutoModelForMaskedLM.from_pretrained(
									    lowerCAmelCase_			,    output_loading_info=lowerCAmelCase_			,    from_tf=lowerCAmelCase_	)
									self.assertIsNotNone(lowerCAmelCase_	)
									self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)
			@slow
			def 					lowerCAmelCase     (  self							:			Tuple	)      ->		Dict:
						for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									__A=	AutoConfig.from_pretrained(lowerCAmelCase_	)
									self.assertIsNotNone(lowerCAmelCase_	)
									self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)

									__A=	TFAutoModelForSeqaSeqLM.from_pretrained(lowerCAmelCase_			,    from_pt=lowerCAmelCase_	)
									__A,	__A=	TFAutoModelForSeqaSeqLM.from_pretrained(
									    lowerCAmelCase_			,    output_loading_info=lowerCAmelCase_			,    from_pt=lowerCAmelCase_	)
									self.assertIsNotNone(lowerCAmelCase_	)
									self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)

									__A=	AutoModelForSeqaSeqLM.from_pretrained(lowerCAmelCase_			,    from_tf=lowerCAmelCase_	)
									__A,	__A=	AutoModelForSeqaSeqLM.from_pretrained(
									    lowerCAmelCase_			,    output_loading_info=lowerCAmelCase_			,    from_tf=lowerCAmelCase_	)
									self.assertIsNotNone(lowerCAmelCase_	)
									self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)
			@slow
			def 					lowerCAmelCase     (  self							:			Optional[Any]	)      ->		str:
						# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						for model_name in ["bert-base-uncased"]:
									__A=	AutoConfig.from_pretrained(lowerCAmelCase_	)
									self.assertIsNotNone(lowerCAmelCase_	)
									self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)

									__A=	TFAutoModelForSequenceClassification.from_pretrained(lowerCAmelCase_			,    from_pt=lowerCAmelCase_	)
									self.assertIsNotNone(lowerCAmelCase_	)
									self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)

									__A=	AutoModelForSequenceClassification.from_pretrained(lowerCAmelCase_			,    from_tf=lowerCAmelCase_	)
									self.assertIsNotNone(lowerCAmelCase_	)
									self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)
			@slow
			def 					lowerCAmelCase     (  self							:			Union[str, Any]	)      ->		List[Any]:
						# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						for model_name in ["bert-base-uncased"]:
									__A=	AutoConfig.from_pretrained(lowerCAmelCase_	)
									self.assertIsNotNone(lowerCAmelCase_	)
									self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)

									__A=	TFAutoModelForQuestionAnswering.from_pretrained(lowerCAmelCase_			,    from_pt=lowerCAmelCase_	)
									self.assertIsNotNone(lowerCAmelCase_	)
									self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)

									__A=	AutoModelForQuestionAnswering.from_pretrained(lowerCAmelCase_			,    from_tf=lowerCAmelCase_	)
									self.assertIsNotNone(lowerCAmelCase_	)
									self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)
			def 					lowerCAmelCase     (  self							:			int	)      ->		List[str]:
						__A=	TFAutoModelWithLMHead.from_pretrained(lowerCAmelCase_			,    from_pt=lowerCAmelCase_	)
						self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)
						self.assertEqual(model.num_parameters()			,    14_410	)
						self.assertEqual(model.num_parameters(only_trainable=lowerCAmelCase_	)			,    14_410	)

						__A=	AutoModelWithLMHead.from_pretrained(lowerCAmelCase_			,    from_tf=lowerCAmelCase_	)
						self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)
						self.assertEqual(model.num_parameters()			,    14_410	)
						self.assertEqual(model.num_parameters(only_trainable=lowerCAmelCase_	)			,    14_410	)

			def 					lowerCAmelCase     (  self							:			Optional[Any]	)      ->		Tuple:
						__A=	TFAutoModelWithLMHead.from_pretrained(lowerCAmelCase_			,    from_pt=lowerCAmelCase_	)
						self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)
						self.assertEqual(model.num_parameters()			,    14_410	)
						self.assertEqual(model.num_parameters(only_trainable=lowerCAmelCase_	)			,    14_410	)

						__A=	AutoModelWithLMHead.from_pretrained(lowerCAmelCase_			,    from_tf=lowerCAmelCase_	)
						self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)
						self.assertEqual(model.num_parameters()			,    14_410	)
						self.assertEqual(model.num_parameters(only_trainable=lowerCAmelCase_	)			,    14_410	)



