






"""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							=  logging.get_logger(__name__)



def   a    (     __UpperCAmelCase      :    Dict  )    ->							Dict:
						__magic_name__:	List[Any]							=       torch.load(__UpperCAmelCase      ,   map_location="""cpu"""  )
						if "model" in sd.keys():
												__magic_name__:	str							=       torch.load(__UpperCAmelCase      ,   map_location="""cpu"""  )["""model"""]

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

						__magic_name__:	List[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:
																		__magic_name__:	Tuple							=       sd.pop(__UpperCAmelCase  )

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

																		__magic_name__:	Union[str, Any]							=       key.replace(""".qkv_proj."""      ,   """.q_proj."""  )
																		__magic_name__:	int							=       key.replace(""".qkv_proj."""      ,   """.k_proj."""  )
																		__magic_name__:	List[str]							=       key.replace(""".qkv_proj."""      ,   """.v_proj."""  )

																		__magic_name__:	str							=       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
																		__magic_name__,			__magic_name__,			__magic_name__:	int							=       torch.split(__UpperCAmelCase      ,   depth // 3      ,   dim=0  )

																		__magic_name__:	int							=       q
																		__magic_name__:	Optional[Any]							=       k
																		__magic_name__:	str							=       v
																		del sd[key]

						return sd



@torch.no_grad()
def   a    (     __UpperCAmelCase      :    List[str]      ,   __UpperCAmelCase      :    int      ,   __UpperCAmelCase      :    int=None  )    ->							Any:
						__magic_name__:	str							=       load_checkpoint(__UpperCAmelCase  )

						if config is not None:
												__magic_name__:	Tuple							=       OPTConfig.from_pretrained(__UpperCAmelCase  )
						else:
												__magic_name__:	List[Any]							=       OPTConfig()

						__magic_name__:	Tuple							=       OPTModel(__UpperCAmelCase  ).half().eval()
						model.load_state_dict(__UpperCAmelCase  )

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


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










from __future__ import annotations

import unittest

from transformers import EsmConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
 import numpy
 import tensorflow as tf

 from transformers.models.esm.modeling_tf_esm import (
     TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
     TFEsmForMaskedLM,
     TFEsmForSequenceClassification,
     TFEsmForTokenClassification,
     TFEsmModel,
 )







class      lowerCamelCase:
     '''simple docstring'''


     def __init__(    self				,     snake_case_				,     ):
         _A     =							parent
         _A     =							13
         _A     =							7
         _A     =							True
         _A     =							True
         _A     =							True
         _A     =							99
         _A     =							32
         _A     =							2
         _A     =							4
         _A     =							37
         _A     =							'gelu'
         _A     =							0.1
         _A     =							0.1
         _A     =							512
         _A     =							16
         _A     =							2
         _A     =							0.02
         _A     =							3
         _A     =							4
         _A     =							None




     def        lowerCAmelCase__		(    self							):
         _A     =							ids_tensor([self.batch_size, self.seq_length]				,     self.vocab_size							)

         _A     =							None
         if self.use_input_mask:
             _A     =							random_attention_mask([self.batch_size, self.seq_length]							)

         _A     =							None
         _A     =							None
         _A     =							None
         if self.use_labels:
             _A     =							ids_tensor([self.batch_size]				,     self.type_sequence_label_size							)
             _A     =							ids_tensor([self.batch_size, self.seq_length]				,     self.num_labels							)
             _A     =							ids_tensor([self.batch_size]				,     self.num_choices							)

         _A     =							EsmConfig(
             vocab_size=self.vocab_size				,     hidden_size=self.hidden_size				,     num_hidden_layers=self.num_hidden_layers				,     pad_token_id=1				,     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				,     initializer_range=self.initializer_range				,     )

         return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels




     def        lowerCAmelCase__		(    self							):
         (
             (
             _A
         ),			(
             _A
         ),			(
             _A
         ),			(
             _A
         ),			(
             _A
         ),			(
             _A
         ),			
         )     =							self.prepare_config_and_inputs()

         _A     =							True
         _A     =							floats_tensor([self.batch_size, self.seq_length, self.hidden_size]							)
         _A     =							ids_tensor([self.batch_size, self.seq_length]				,     vocab_size=2							)

         return (
             config,
             input_ids,
             input_mask,
             sequence_labels,
             token_labels,
             choice_labels,
             encoder_hidden_states,
             encoder_attention_mask,
         )




     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_							):
         _A     =							TFEsmModel(config=snake_case_							)
         _A     =							{'input_ids': input_ids, 'attention_mask': input_mask}
         _A     =							model(snake_case_							)

         _A     =							[input_ids, input_mask]
         _A     =							model(snake_case_							)

         _A     =							model(snake_case_							)

         self.parent.assertEqual(result.last_hidden_state.shape				,     (self.batch_size, self.seq_length, self.hidden_size)							)




     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     ):
         _A     =							True

         _A     =							TFEsmModel(config=snake_case_							)
         _A     =							{
             'input_ids': input_ids,
             'attention_mask': input_mask,
             'encoder_hidden_states': encoder_hidden_states,
             'encoder_attention_mask': encoder_attention_mask,
         }
         _A     =							model(snake_case_							)

         _A     =							[input_ids, input_mask]
         _A     =							model(snake_case_				,     encoder_hidden_states=snake_case_							)

         # Also check the case where encoder outputs are not passed
         _A     =							model(snake_case_				,     attention_mask=snake_case_							)

         self.parent.assertEqual(result.last_hidden_state.shape				,     (self.batch_size, self.seq_length, self.hidden_size)							)




     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_							):
         _A     =							TFEsmForMaskedLM(config=snake_case_							)
         _A     =							model([input_ids, input_mask]							)
         self.parent.assertEqual(result.logits.shape				,     (self.batch_size, self.seq_length, self.vocab_size)							)




     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_							):
         _A     =							self.num_labels
         _A     =							TFEsmForTokenClassification(config=snake_case_							)
         _A     =							{'input_ids': input_ids, 'attention_mask': input_mask}
         _A     =							model(snake_case_							)
         self.parent.assertEqual(result.logits.shape				,     (self.batch_size, self.seq_length, self.num_labels)							)




     def        lowerCAmelCase__		(    self							):
         _A     =							self.prepare_config_and_inputs()
         (
             (
             _A
         ),			(
             _A
         ),			(
             _A
         ),			(
             _A
         ),			(
             _A
         ),			(
             _A
         ),			
         )     =							config_and_inputs
         _A     =							{'input_ids': input_ids, 'attention_mask': input_mask}
         return config, inputs_dict







@require_tf
class      lowerCamelCase(						__snake_case			, __snake_case			, unittest.TestCase	):
     '''simple docstring'''


     __magic_name__   =			(
         (
             TFEsmModel,
             TFEsmForMaskedLM,
             TFEsmForSequenceClassification,
             TFEsmForTokenClassification,
         )
         if is_tf_available()
         else ()
     )
     __magic_name__   =			(
         {
             'feature-extraction': TFEsmModel,
             'fill-mask': TFEsmForMaskedLM,
             'text-classification': TFEsmForSequenceClassification,
             'token-classification': TFEsmForTokenClassification,
             'zero-shot': TFEsmForSequenceClassification,
         }
         if is_tf_available()
         else {}
     )
     __magic_name__   =			False
     __magic_name__   =			False




     def        lowerCAmelCase__		(    self							):
         _A     =							TFEsmModelTester(self							)
         _A     =							ConfigTester(self				,     config_class=snake_case_				,     hidden_size=37							)




     def        lowerCAmelCase__		(    self							):
         self.config_tester.run_common_tests()




     def        lowerCAmelCase__		(    self							):
         _A     =							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*snake_case_							)




     def        lowerCAmelCase__		(    self							):
         _A     =							self.model_tester.prepare_config_and_inputs_for_decoder()
         self.model_tester.create_and_check_model_as_decoder(*snake_case_							)




     def        lowerCAmelCase__		(    self							):
         _A     =							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_masked_lm(*snake_case_							)




     def        lowerCAmelCase__		(    self							):
         _A     =							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_token_classification(*snake_case_							)




     @slow
     def        lowerCAmelCase__		(    self							):
         for model_name in TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             _A     =							TFEsmModel.from_pretrained(snake_case_							)
             self.assertIsNotNone(snake_case_							)




     @unittest.skip('Protein models do not support embedding resizing.'							)
     def        lowerCAmelCase__		(    self							):
         pass




     @unittest.skip('Protein models do not support embedding resizing.'							)
     def        lowerCAmelCase__		(    self							):
         pass




     def        lowerCAmelCase__		(    self							):
         _A,			_A     =							self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
             _A     =							model_class(snake_case_							)
             assert isinstance(model.get_input_embeddings()				,     tf.keras.layers.Layer							)
             if model_class is TFEsmForMaskedLM:
                 # Output embedding test differs from the main test because they're a matrix, not a layer
                 _A     =							model.get_bias()
                 assert isinstance(snake_case_				,     snake_case_							)
                 for k, v in name.items():
                     assert isinstance(snake_case_				,     tf.Variable							)
             else:
                 _A     =							model.get_output_embeddings()
                 assert x is None
                 _A     =							model.get_bias()
                 assert name is None







@require_tf
class      lowerCamelCase(						unittest.TestCase	):
     '''simple docstring'''


     @slow
     def        lowerCAmelCase__		(    self							):
         _A     =							TFEsmForMaskedLM.from_pretrained('facebook/esm2_t6_8M_UR50D'							)

         _A     =							tf.constant([[0, 1, 2, 3, 4, 5]]							)
         _A     =							model(snake_case_							)[0]
         _A     =							[1, 6, 33]
         self.assertEqual(list(output.numpy().shape							)				,     snake_case_							)
         # compare the actual values for a slice.
         _A     =							tf.constant(
             [
                 [
                     [8.92_1518, -10.58_9814, -6.467_1307],
                     [-6.396_7156, -13.91_1377, -1.121_1915],
                     [-7.78_1247, -13.95_1557, -3.74_0592],
                 ]
             ]							)
         self.assertTrue(numpy.allclose(output[:, :3, :3].numpy()				,     expected_slice.numpy()				,     atol=1E-2							)							)




     @slow
     def        lowerCAmelCase__		(    self							):
         _A     =							TFEsmModel.from_pretrained('facebook/esm2_t6_8M_UR50D'							)

         _A     =							tf.constant([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]]							)
         _A     =							model(snake_case_							)[0]
         # compare the actual values for a slice.
         _A     =							tf.constant(
             [
                 [
                     [0.1444_3092, 0.5412_5327, 0.324_7739],
                     [0.3034_0484, 0.0052_6676, 0.3107_7722],
                     [0.3227_8043, -0.2498_7096, 0.341_4628],
                 ]
             ]							)
         self.assertTrue(numpy.allclose(output[:, :3, :3].numpy()				,     expected_slice.numpy()				,     atol=1E-4							)							)





