
'''simple docstring'''
import unittest

from parameterized import parameterized

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

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch

  from transformers import OpenLlamaForCausalLM, OpenLlamaForSequenceClassification, OpenLlamaModel






class    UpperCAmelCase_				:





 def __init__(    self ,       lowercase_ ,       lowercase_=13 ,       lowercase_=7 ,       lowercase_=True ,       lowercase_=True ,       lowercase_=False ,       lowercase_=True ,       lowercase_=99 ,       lowercase_=32 ,       lowercase_=5 ,       lowercase_=4 ,       lowercase_=37 ,       lowercase_="gelu" ,       lowercase_=0.1 ,       lowercase_=0.1 ,       lowercase_=5_12 ,       lowercase_=16 ,       lowercase_=2 ,       lowercase_=0.02 ,       lowercase_=3 ,       lowercase_=4 ,       lowercase_=None ,       ):
     snake_case_ :    Any     			=						parent
     snake_case_ :    Any     			=						batch_size
     snake_case_ :    List[Any]     			=						seq_length
     snake_case_ :    int     			=						is_training
     snake_case_ :    Dict     			=						use_input_mask
     snake_case_ :    Union[str, Any]     			=						use_token_type_ids
     snake_case_ :    Dict     			=						use_labels
     snake_case_ :    str     			=						vocab_size
     snake_case_ :    List[Any]     			=						hidden_size
     snake_case_ :    int     			=						num_hidden_layers
     snake_case_ :    List[Any]     			=						num_attention_heads
     snake_case_ :    List[Any]     			=						intermediate_size
     snake_case_ :    Any     			=						hidden_act
     snake_case_ :    List[Any]     			=						hidden_dropout_prob
     snake_case_ :    Optional[Any]     			=						attention_probs_dropout_prob
     snake_case_ :    Optional[Any]     			=						max_position_embeddings
     snake_case_ :    int     			=						type_vocab_size
     snake_case_ :    Any     			=						type_sequence_label_size
     snake_case_ :    Dict     			=						initializer_range
     snake_case_ :    List[str]     			=						num_labels
     snake_case_ :    Any     			=						num_choices
     snake_case_ :    int     			=						scope





 def        snake_case__		(    self):
     snake_case_ :    int     			=						ids_tensor([self.batch_size, self.seq_length] ,       self.vocab_size)

     snake_case_ :    Any     			=						None
     if self.use_input_mask:
         snake_case_ :    Dict     			=						random_attention_mask([self.batch_size, self.seq_length])

     snake_case_ :    Dict     			=						None
     if self.use_token_type_ids:
         snake_case_ :    Union[str, Any]     			=						ids_tensor([self.batch_size, self.seq_length] ,       self.type_vocab_size)

     snake_case_ :    str     			=						None
     snake_case_ :    int     			=						None
     snake_case_ :    Dict     			=						None
     if self.use_labels:
         snake_case_ :    int     			=						ids_tensor([self.batch_size] ,       self.type_sequence_label_size)
         snake_case_ :    str     			=						ids_tensor([self.batch_size, self.seq_length] ,       self.num_labels)
         snake_case_ :    List[Any]     			=						ids_tensor([self.batch_size] ,       self.num_choices)

     snake_case_ :    Tuple     			=						self.get_config()

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





 def        snake_case__		(    self):
     return OpenLlamaConfig(
         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=lowercase_ ,       initializer_range=self.initializer_range ,       use_stable_embedding=lowercase_ ,       )





 def        snake_case__		(    self ,       lowercase_ ,       lowercase_ ,       lowercase_ ,       lowercase_ ,       lowercase_ ,       lowercase_ ,       lowercase_):
     snake_case_ :    List[str]     			=						OpenLlamaModel(config=lowercase_)
     model.to(lowercase_)
     model.eval()
     snake_case_ :    Union[str, Any]     			=						model(lowercase_ ,       attention_mask=lowercase_)
     snake_case_ :    List[str]     			=						model(lowercase_)
     self.parent.assertEqual(result.last_hidden_state.shape ,       (self.batch_size, self.seq_length, self.hidden_size))





 def        snake_case__		(    self ,       lowercase_ ,       lowercase_ ,       lowercase_ ,       lowercase_ ,       lowercase_ ,       lowercase_ ,       lowercase_ ,       lowercase_ ,       lowercase_ ,       ):
     snake_case_ :    Optional[Any]     			=						True
     snake_case_ :    Dict     			=						OpenLlamaModel(lowercase_)
     model.to(lowercase_)
     model.eval()
     snake_case_ :    Any     			=						model(
         lowercase_ ,       attention_mask=lowercase_ ,       encoder_hidden_states=lowercase_ ,       encoder_attention_mask=lowercase_ ,       )
     snake_case_ :    List[Any]     			=						model(
         lowercase_ ,       attention_mask=lowercase_ ,       encoder_hidden_states=lowercase_ ,       )
     snake_case_ :    Any     			=						model(lowercase_ ,       attention_mask=lowercase_)
     self.parent.assertEqual(result.last_hidden_state.shape ,       (self.batch_size, self.seq_length, self.hidden_size))





 def        snake_case__		(    self ,       lowercase_ ,       lowercase_ ,       lowercase_ ,       lowercase_ ,       lowercase_ ,       lowercase_ ,       lowercase_ ,       lowercase_ ,       lowercase_ ,       ):
     snake_case_ :    Optional[Any]     			=						OpenLlamaForCausalLM(config=lowercase_)
     model.to(lowercase_)
     model.eval()
     snake_case_ :    List[Any]     			=						model(lowercase_ ,       attention_mask=lowercase_ ,       labels=lowercase_)
     self.parent.assertEqual(result.logits.shape ,       (self.batch_size, self.seq_length, self.vocab_size))





 def        snake_case__		(    self ,       lowercase_ ,       lowercase_ ,       lowercase_ ,       lowercase_ ,       lowercase_ ,       lowercase_ ,       lowercase_ ,       lowercase_ ,       lowercase_ ,       ):
     snake_case_ :    int     			=						True
     snake_case_ :    Dict     			=						True
     snake_case_ :    int     			=						OpenLlamaForCausalLM(config=lowercase_)
     model.to(lowercase_)
     model.eval()

     # first forward pass
     snake_case_ :    Union[str, Any]     			=						model(
         lowercase_ ,       attention_mask=lowercase_ ,       encoder_hidden_states=lowercase_ ,       encoder_attention_mask=lowercase_ ,       use_cache=lowercase_ ,       )
     snake_case_ :    Optional[Any]     			=						outputs.past_key_values

     # create hypothetical multiple next token and extent to next_input_ids
     snake_case_ :    Dict     			=						ids_tensor((self.batch_size, 3) ,       config.vocab_size)
     snake_case_ :    Dict     			=						ids_tensor((self.batch_size, 3) ,       vocab_size=2)

     # append to next input_ids and
     snake_case_ :    int     			=						torch.cat([input_ids, next_tokens] ,       dim=-1)
     snake_case_ :    Dict     			=						torch.cat([input_mask, next_mask] ,       dim=-1)

     snake_case_ :    str     			=						model(
         lowercase_ ,       attention_mask=lowercase_ ,       encoder_hidden_states=lowercase_ ,       encoder_attention_mask=lowercase_ ,       output_hidden_states=lowercase_ ,       )["hidden_states"][0]
     snake_case_ :    str     			=						model(
         lowercase_ ,       attention_mask=lowercase_ ,       encoder_hidden_states=lowercase_ ,       encoder_attention_mask=lowercase_ ,       past_key_values=lowercase_ ,       output_hidden_states=lowercase_ ,       )["hidden_states"][0]

     # select random slice
     snake_case_ :    Union[str, Any]     			=						ids_tensor((1,) ,       output_from_past.shape[-1]).item()
     snake_case_ :    Optional[Any]     			=						output_from_no_past[:, -3:, random_slice_idx].detach()
     snake_case_ :    int     			=						output_from_past[:, :, random_slice_idx].detach()

     self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1])

     # test that outputs are equal for slice
     self.parent.assertTrue(torch.allclose(lowercase_ ,       lowercase_ ,       atol=1E-3))





 def        snake_case__		(    self):
     snake_case_ :    List[Any]     			=						self.prepare_config_and_inputs()
     (
         (
         snake_case_
     )			, (
         snake_case_
     )			, (
         snake_case_
     )			, (
         snake_case_
     )			, (
         snake_case_
     )			, (
         snake_case_
     )			, (
         snake_case_
     )			, 
     ) :    List[str]     			=						config_and_inputs
     snake_case_ :    Optional[int]     			=						{"input_ids": input_ids, "attention_mask": input_mask}
     return config, inputs_dict






@require_torch
class    UpperCAmelCase_				(	snake_case__    ,						snake_case__    ,						snake_case__    ,						unittest.TestCase					):
 UpperCAmelCase_		    =						(
     (OpenLlamaModel, OpenLlamaForCausalLM, OpenLlamaForSequenceClassification) if is_torch_available() else ()
 )
 UpperCAmelCase_		    =						(OpenLlamaForCausalLM,) if is_torch_available() else ()
 UpperCAmelCase_		    =						(
     {
         """feature-extraction""": OpenLlamaModel,
         """text-classification""": OpenLlamaForSequenceClassification,
         """text-generation""": OpenLlamaForCausalLM,
         """zero-shot""": OpenLlamaForSequenceClassification,
     }
     if is_torch_available()
     else {}
 )
 UpperCAmelCase_		    =						False
 UpperCAmelCase_		    =						False





 def        snake_case__		(    self):
     snake_case_ :    Optional[Any]     			=						OpenLlamaModelTester(self)
     snake_case_ :    List[str]     			=						ConfigTester(self ,       config_class=lowercase_ ,       hidden_size=37)





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





 def        snake_case__		(    self):
     snake_case_ :    str     			=						self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*lowercase_)





 def        snake_case__		(    self):
     snake_case_ :    List[str]     			=						self.model_tester.prepare_config_and_inputs()
     for type in ["absolute", "relative_key", "relative_key_query"]:
         snake_case_ :    int     			=						type
         self.model_tester.create_and_check_model(*lowercase_)





 def        snake_case__		(    self):
     snake_case_			, snake_case_ :    Tuple     			=						self.model_tester.prepare_config_and_inputs_for_common()
     snake_case_ :    Dict     			=						3
     snake_case_ :    List[str]     			=						input_dict["input_ids"]
     snake_case_ :    Union[str, Any]     			=						input_ids.ne(1).to(lowercase_)
     snake_case_ :    List[Any]     			=						ids_tensor([self.model_tester.batch_size] ,       self.model_tester.type_sequence_label_size)
     snake_case_ :    List[str]     			=						OpenLlamaForSequenceClassification(lowercase_)
     model.to(lowercase_)
     model.eval()
     snake_case_ :    Optional[Any]     			=						model(lowercase_ ,       attention_mask=lowercase_ ,       labels=lowercase_)
     self.assertEqual(result.logits.shape ,       (self.model_tester.batch_size, self.model_tester.num_labels))





 def        snake_case__		(    self):
     snake_case_			, snake_case_ :    Dict     			=						self.model_tester.prepare_config_and_inputs_for_common()
     snake_case_ :    Union[str, Any]     			=						3
     snake_case_ :    str     			=						"single_label_classification"
     snake_case_ :    int     			=						input_dict["input_ids"]
     snake_case_ :    Dict     			=						input_ids.ne(1).to(lowercase_)
     snake_case_ :    Any     			=						ids_tensor([self.model_tester.batch_size] ,       self.model_tester.type_sequence_label_size)
     snake_case_ :    Tuple     			=						OpenLlamaForSequenceClassification(lowercase_)
     model.to(lowercase_)
     model.eval()
     snake_case_ :    str     			=						model(lowercase_ ,       attention_mask=lowercase_ ,       labels=lowercase_)
     self.assertEqual(result.logits.shape ,       (self.model_tester.batch_size, self.model_tester.num_labels))





 def        snake_case__		(    self):
     snake_case_			, snake_case_ :    Any     			=						self.model_tester.prepare_config_and_inputs_for_common()
     snake_case_ :    Optional[int]     			=						3
     snake_case_ :    Union[str, Any]     			=						"multi_label_classification"
     snake_case_ :    Tuple     			=						input_dict["input_ids"]
     snake_case_ :    Any     			=						input_ids.ne(1).to(lowercase_)
     snake_case_ :    Union[str, Any]     			=						ids_tensor(
         [self.model_tester.batch_size, config.num_labels] ,       self.model_tester.type_sequence_label_size).to(torch.float)
     snake_case_ :    List[str]     			=						OpenLlamaForSequenceClassification(lowercase_)
     model.to(lowercase_)
     model.eval()
     snake_case_ :    Optional[Any]     			=						model(lowercase_ ,       attention_mask=lowercase_ ,       labels=lowercase_)
     self.assertEqual(result.logits.shape ,       (self.model_tester.batch_size, self.model_tester.num_labels))





 @unittest.skip("Open-Llama buffers include complex numbers, which breaks this test")
 def        snake_case__		(    self):
     pass





 @parameterized.expand([("linear",), ("dynamic",)])
 def        snake_case__		(    self ,       lowercase_):
     snake_case_			, snake_case_ :    List[Any]     			=						self.model_tester.prepare_config_and_inputs_for_common()
     snake_case_ :    List[str]     			=						ids_tensor([1, 10] ,       config.vocab_size)
     snake_case_ :    Any     			=						ids_tensor([1, int(config.max_position_embeddings * 1.5)] ,       config.vocab_size)

     set_seed(42)  # Fixed seed at init time so the two models get the same random weights
     snake_case_ :    List[str]     			=						OpenLlamaModel(lowercase_)
     original_model.to(lowercase_)
     original_model.eval()
     snake_case_ :    Union[str, Any]     			=						original_model(lowercase_).last_hidden_state
     snake_case_ :    Tuple     			=						original_model(lowercase_).last_hidden_state

     set_seed(42)  # Fixed seed at init time so the two models get the same random weights
     snake_case_ :    Dict     			=						{"type": scaling_type, "factor": 10.0}
     snake_case_ :    int     			=						OpenLlamaModel(lowercase_)
     scaled_model.to(lowercase_)
     scaled_model.eval()
     snake_case_ :    Dict     			=						scaled_model(lowercase_).last_hidden_state
     snake_case_ :    Optional[int]     			=						scaled_model(lowercase_).last_hidden_state

     # Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
     # maximum sequence length, so the outputs for the short input should match.
     if scaling_type == "dynamic":
         self.assertTrue(torch.allclose(lowercase_ ,       lowercase_ ,       atol=1E-5))
     else:
         self.assertFalse(torch.allclose(lowercase_ ,       lowercase_ ,       atol=1E-5))

     # The output should be different for long inputs
     self.assertFalse(torch.allclose(lowercase_ ,       lowercase_ ,       atol=1E-5))






'''simple docstring'''
import argparse
import os

import torch

from transformers.utils import WEIGHTS_NAME


a_											=						["small", "medium", "large"]

a_											=						"lm_head.decoder.weight"
a_											=						"lm_head.weight"

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    snake_case_ :    List[Any]     			=						torch.load(__SCREAMING_SNAKE_CASE       )
    snake_case_ :    List[Any]     			=						d.pop(__SCREAMING_SNAKE_CASE       )
    os.makedirs(__SCREAMING_SNAKE_CASE,		exist_ok=__SCREAMING_SNAKE_CASE       )
    torch.save(__SCREAMING_SNAKE_CASE,		os.path.join(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       )       )


if __name__ == "__main__":
  a_											=						argparse.ArgumentParser()
  parser.add_argument("--dialogpt_path", default=".", type=str)
  a_											=						parser.parse_args()
  for MODEL in DIALOGPT_MODELS:
    a_											=						os.path.join(args.dialogpt_path, f"""{MODEL}_ft.pkl""")
    a_											=						f"""./DialoGPT-{MODEL}"""
    convert_dialogpt_checkpoint(
        checkpoint_path,
        pytorch_dump_folder_path,
    )




