






"""simple docstring"""



import gc
import importlib.metadata
import tempfile
import unittest

from packaging import version

from transformers import (
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    BitsAndBytesConfig,
    pipeline,
)
from transformers.testing_utils import (
    is_torch_available,
    require_accelerate,
    require_bitsandbytes,
    require_torch,
    require_torch_gpu,
    require_torch_multi_gpu,
    slow,
)


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : Union[str, Any]				)->		str:






     '''simple docstring'''



     if model.config.model_type == "gpt2":
          return model.transformer.h[0].mlp.c_fc
     return model.transformer.h[0].mlp.dense_ah_to_h


if is_torch_available():
 import torch
 import torch.nn as nn




 class 			lowerCAmelCase__			(    nn.Module   ):
    def __init__(		self      :	Dict							,			snake_case__      :	nn.Module							,			snake_case__      :	int							):


         '''simple docstring'''
         super().__init__()
         UpperCAmelCase__						:    Optional[int]							=							module
         UpperCAmelCase__						:    Any							=							nn.Sequential(
             nn.Linear(module.in_features							,			snake_case__							,			bias=snake_case__							)							,			nn.Linear(snake_case__							,			module.out_features							,			bias=snake_case__							)							,			)
         UpperCAmelCase__						:    Dict							=							(2.0 / (5 * min(module.in_features							,			module.out_features							))) ** 0.5
         nn.init.normal_(self.adapter[0].weight							,			std=snake_case__							)
         nn.init.zeros_(self.adapter[1].weight							)
         self.adapter.to(module.weight.device							)




    def       __a							(		self      :	Any							,			snake_case__      :	Union[str, Any]							,			*snake_case__      :	Optional[Any]							,			**snake_case__      :	int							):


         '''simple docstring'''
         return self.module(snake_case__							,			*snake_case__							,			**snake_case__							) + self.adapter(snake_case__							)




@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class 			lowerCAmelCase__			(    unittest.TestCase   ):
   # We keep the constants inside the init function and model loading inside setUp function

   # We need to test on relatively large models (aka >1b parameters otherwise the quantiztion may not work as expected)
   # Therefore here we use only bloom-1b3 to test our module
   SCREAMING_SNAKE_CASE_    ='''bigscience/bloom-1b7'''

   # Constant values
   SCREAMING_SNAKE_CASE_    =2.1_09_65_95_52_69_25_74

   SCREAMING_SNAKE_CASE_    ='''Hello my name is'''
   SCREAMING_SNAKE_CASE_    =set()
   EXPECTED_OUTPUTS.add('''Hello my name is John and I am a professional photographer. I'''   )
   EXPECTED_OUTPUTS.add('''Hello my name is John.\nI am a friend of your father.\n'''   )
   EXPECTED_OUTPUTS.add('''Hello my name is John Doe, I am a student at the University'''   )
   SCREAMING_SNAKE_CASE_    =10
   def       __a							(		self      :	Any							):


        '''simple docstring'''
        # Models and tokenizer
        UpperCAmelCase__						:    Dict							=							AutoTokenizer.from_pretrained(self.model_name							)




class 			lowerCAmelCase__			(    __magic_name__   ):
   def       __a							(		self      :	int							):


        '''simple docstring'''
        super().setUp()

        # Models and tokenizer
        UpperCAmelCase__						:    Optional[Any]							=							AutoModelForCausalLM.from_pretrained(
            self.model_name							,			torch_dtype=torch.floataa							,			device_map="auto"							)
        UpperCAmelCase__						:    Dict							=							AutoModelForCausalLM.from_pretrained(self.model_name							,			load_in_abit=snake_case__							,			device_map="auto"							)
   def       __a							(		self      :	Dict							):


        '''simple docstring'''
        del self.model_fpaa
        del self.model_abit

        gc.collect()
        torch.cuda.empty_cache()
   def       __a							(		self      :	Optional[Any]							):


        '''simple docstring'''
        UpperCAmelCase__						:    Tuple							=							self.model_abit.config

        self.assertTrue(hasattr(snake_case__							,			"quantization_config"							)							)

        UpperCAmelCase__						:    Dict							=							config.to_dict()
        UpperCAmelCase__						:    Optional[int]							=							config.to_diff_dict()

        UpperCAmelCase__						:    str							=							config.to_json_string()
   def       __a							(		self      :	List[str]							):


        '''simple docstring'''
        from bitsandbytes.nn import Paramsabit

        UpperCAmelCase__						:    Union[str, Any]							=							self.model_fpaa.get_memory_footprint()
        UpperCAmelCase__						:    Union[str, Any]							=							self.model_abit.get_memory_footprint()

        self.assertAlmostEqual(mem_fpaa / mem_abit							,			self.EXPECTED_RELATIVE_DIFFERENCE							)
        UpperCAmelCase__						:    int							=							get_some_linear_layer(self.model_abit							)
        self.assertTrue(linear.weight.__class__ == Paramsabit							)
   def       __a							(		self      :	Optional[int]							):


        '''simple docstring'''
        from transformers import TaPreTrainedModel

        self.model_fpaa.get_memory_footprint()
        self.model_abit.get_memory_footprint()

        for name, module in self.model_abit.named_modules():
             if isinstance(snake_case__							,			torch.nn.Linear							):
                  if name not in ["lm_head"] + TaPreTrainedModel._keep_in_fpaa_modules:
                       # 4-bit parameters are packed in uint8 variables
                       self.assertTrue(module.weight.dtype == torch.uinta							)
   def       __a							(		self      :	Dict							):


        '''simple docstring'''
        UpperCAmelCase__						:    Optional[int]							=							self.tokenizer(self.input_text							,			return_tensors="pt"							)
        UpperCAmelCase__						:    Tuple							=							self.model_abit.generate(input_ids=encoded_input["input_ids"].to(0							)							,			max_new_tokens=1_0							)

        self.assertIn(self.tokenizer.decode(output_sequences[0]							,			skip_special_tokens=snake_case__							)							,			self.EXPECTED_OUTPUTS							)
   def       __a							(		self      :	Optional[Any]							):


        '''simple docstring'''
        UpperCAmelCase__						:    str							=							BitsAndBytesConfig()
        UpperCAmelCase__						:    Any							=							True

        UpperCAmelCase__						:    Optional[int]							=							AutoModelForCausalLM.from_pretrained(
            self.model_name							,			quantization_config=snake_case__							,			device_map="auto"							)

        UpperCAmelCase__						:    List[str]							=							self.tokenizer(self.input_text							,			return_tensors="pt"							)
        UpperCAmelCase__						:    Union[str, Any]							=							model_abit_from_config.generate(
            input_ids=encoded_input["input_ids"].to(0							)							,			max_new_tokens=1_0							)

        self.assertIn(self.tokenizer.decode(output_sequences[0]							,			skip_special_tokens=snake_case__							)							,			self.EXPECTED_OUTPUTS							)
   def       __a							(		self      :	Any							):


        '''simple docstring'''
        with self.assertRaises(snake_case__							), tempfile.TemporaryDirectory() as tmpdirname:
             self.model_abit.save_pretrained(snake_case__							)
   def       __a							(		self      :	Optional[Any]							):


        '''simple docstring'''
        UpperCAmelCase__						:    List[str]							=							BitsAndBytesConfig()

        with self.assertRaises(snake_case__							):
             UpperCAmelCase__						:    List[Any]							=							AutoModelForCausalLM.from_pretrained(
                 self.model_name							,			quantization_config=snake_case__							,			load_in_abit=snake_case__							,			device_map="auto"							,			bnb_abit_quant_type="nf4"							,			)
   def       __a							(		self      :	Optional[int]							):


        '''simple docstring'''
        with self.assertRaises(snake_case__							):
             # Tries with `str`
             self.model_abit.to("cpu"							)

        with self.assertRaises(snake_case__							):
             # Tries with a `dtype``
             self.model_abit.to(torch.floataa							)

        with self.assertRaises(snake_case__							):
             # Tries with a `device`
             self.model_abit.to(torch.device("cuda:0"							)							)

        with self.assertRaises(snake_case__							):
             # Tries with a `device`
             self.model_abit.float()

        with self.assertRaises(snake_case__							):
             # Tries with a `device`
             self.model_abit.half()

        # Test if we did not break anything
        UpperCAmelCase__						:    Tuple							=							self.tokenizer(self.input_text							,			return_tensors="pt"							)

        UpperCAmelCase__						:    Dict							=							self.model_fpaa.to(torch.floataa							)
        UpperCAmelCase__						:    Tuple							=							self.model_fpaa.generate(input_ids=encoded_input["input_ids"].to(0							)							,			max_new_tokens=1_0							)

        # Check this does not throw an error
        UpperCAmelCase__						:    List[str]							=							self.model_fpaa.to("cpu"							)

        # Check this does not throw an error
        UpperCAmelCase__						:    Optional[Any]							=							self.model_fpaa.half()

        # Check this does not throw an error
        UpperCAmelCase__						:    List[Any]							=							self.model_fpaa.float()




   def       __a							(		self      :	Optional[Any]							):


        '''simple docstring'''
        UpperCAmelCase__						:    List[Any]							=							AutoModelForSeqaSeqLM.from_pretrained("t5-small"							,			load_in_abit=snake_case__							,			device_map="auto"							)
        self.assertTrue(model.decoder.block[0].layer[2].DenseReluDense.wo.weight.dtype == torch.floataa							)




@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class 			lowerCAmelCase__			(    unittest.TestCase   ):
   @classmethod
   def       __a							(		cls      :	List[str]							):


        '''simple docstring'''
        UpperCAmelCase__						:    str							=							"t5-small"
        UpperCAmelCase__						:    Optional[int]							=							"google/flan-t5-small"  # flan-t5 uses dense-act instead of dense-relu-dense
        UpperCAmelCase__						:    List[str]							=							AutoTokenizer.from_pretrained(cls.model_name							)
        UpperCAmelCase__						:    Dict							=							"Translate in German: Hello, my dog is cute"
   def       __a							(		self      :	Dict							):


        '''simple docstring'''
        gc.collect()
        torch.cuda.empty_cache()
   def       __a							(		self      :	Optional[int]							):


        '''simple docstring'''
        from transformers import TaForConditionalGeneration

        UpperCAmelCase__						:    Any							=							TaForConditionalGeneration._keep_in_fpaa_modules
        UpperCAmelCase__						:    int							=							None

        # test with `t5-small`
        UpperCAmelCase__						:    int							=							TaForConditionalGeneration.from_pretrained(self.model_name							,			load_in_abit=snake_case__							,			device_map="auto"							)
        UpperCAmelCase__						:    Union[str, Any]							=							self.tokenizer(self.input_text							,			return_tensors="pt"							).to(0							)
        UpperCAmelCase__						:    Dict							=							model.generate(**snake_case__							)

        # test with `flan-t5-small`
        UpperCAmelCase__						:    Tuple							=							TaForConditionalGeneration.from_pretrained(
            self.dense_act_model_name							,			load_in_abit=snake_case__							,			device_map="auto"							)
        UpperCAmelCase__						:    Optional[int]							=							self.tokenizer(self.input_text							,			return_tensors="pt"							).to(0							)
        UpperCAmelCase__						:    int							=							model.generate(**snake_case__							)
        UpperCAmelCase__						:    Union[str, Any]							=							modules




   def       __a							(		self      :	Dict							):


        '''simple docstring'''
        import bitsandbytes as bnb

        from transformers import TaForConditionalGeneration

        # test with `t5-small`
        UpperCAmelCase__						:    Dict							=							TaForConditionalGeneration.from_pretrained(self.model_name							,			load_in_abit=snake_case__							,			device_map="auto"							)

        # there was a bug with decoders - this test checks that it is fixed
        self.assertTrue(isinstance(model.decoder.block[0].layer[0].SelfAttention.q							,			bnb.nn.Linearabit							)							)

        UpperCAmelCase__						:    List[Any]							=							self.tokenizer(self.input_text							,			return_tensors="pt"							).to(0							)
        UpperCAmelCase__						:    List[Any]							=							model.generate(**snake_case__							)

        # test with `flan-t5-small`
        UpperCAmelCase__						:    int							=							TaForConditionalGeneration.from_pretrained(
            self.dense_act_model_name							,			load_in_abit=snake_case__							,			device_map="auto"							)
        UpperCAmelCase__						:    Optional[int]							=							self.tokenizer(self.input_text							,			return_tensors="pt"							).to(0							)
        UpperCAmelCase__						:    Tuple							=							model.generate(**snake_case__							)




class 			lowerCAmelCase__			(    __magic_name__   ):
   def       __a							(		self      :	Dict							):


        '''simple docstring'''
        super().setUp()
        # model_name
        UpperCAmelCase__						:    Any							=							"bigscience/bloom-560m"
        UpperCAmelCase__						:    Optional[int]							=							"t5-small"

        # Different types of model

        UpperCAmelCase__						:    Dict							=							AutoModel.from_pretrained(self.model_name							,			load_in_abit=snake_case__							,			device_map="auto"							)
        # Sequence classification model
        UpperCAmelCase__						:    List[str]							=							AutoModelForSequenceClassification.from_pretrained(
            self.model_name							,			load_in_abit=snake_case__							,			device_map="auto"							)
        # CausalLM model
        UpperCAmelCase__						:    List[Any]							=							AutoModelForCausalLM.from_pretrained(self.model_name							,			load_in_abit=snake_case__							,			device_map="auto"							)
        # Seq2seq model
        UpperCAmelCase__						:    Any							=							AutoModelForSeqaSeqLM.from_pretrained(
            self.seq_to_seq_name							,			load_in_abit=snake_case__							,			device_map="auto"							)
   def       __a							(		self      :	Optional[Any]							):


        '''simple docstring'''
        del self.base_model
        del self.sequence_model
        del self.model_abit
        del self.seq_to_seq_model

        gc.collect()
        torch.cuda.empty_cache()




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


        '''simple docstring'''
        from bitsandbytes.nn import Paramsabit

        self.assertTrue(self.base_model.h[-1].mlp.dense_ah_to_h.weight.__class__ == Paramsabit							)

        # Other heads should be nn.Parameter
        self.assertTrue(self.model_abit.lm_head.weight.__class__ == torch.nn.Parameter							)
        self.assertTrue(self.sequence_model.score.weight.__class__ == torch.nn.Parameter							)
        self.assertTrue(self.seq_to_seq_model.lm_head.weight.__class__ == torch.nn.Parameter							)




class 			lowerCAmelCase__			(    __magic_name__   ):
   def       __a							(		self      :	List[Any]							):


        '''simple docstring'''
        super().setUp()
   def       __a							(		self      :	List[Any]							):


        '''simple docstring'''
        del self.pipe

        gc.collect()
        torch.cuda.empty_cache()




   def       __a							(		self      :	str							):


        '''simple docstring'''
        UpperCAmelCase__						:    str							=							pipeline(
            "text-generation"							,			model=self.model_name							,			model_kwargs={"device_map": "auto", "load_in_4bit": True, "torch_dtype": torch.floataa}							,			max_new_tokens=self.MAX_NEW_TOKENS							,			)

        # Real second forward pass
        UpperCAmelCase__						:    Tuple							=							self.pipe(self.input_text							)
        self.assertIn(pipeline_output[0]["generated_text"]							,			self.EXPECTED_OUTPUTS							)




@require_torch_multi_gpu
class 			lowerCAmelCase__			(    __magic_name__   ):
   def       __a							(		self      :	Optional[int]							):


        '''simple docstring'''
        super().setUp()




   def       __a							(		self      :	List[str]							):


        '''simple docstring'''
        UpperCAmelCase__						:    List[Any]							=							AutoModelForCausalLM.from_pretrained(
            self.model_name							,			load_in_abit=snake_case__							,			device_map="balanced"							)

        # Check correct device map
        self.assertEqual(set(model_parallel.hf_device_map.values()							)							,			{0, 1}							)

        # Check that inference pass works on the model
        UpperCAmelCase__						:    Tuple							=							self.tokenizer(self.input_text							,			return_tensors="pt"							)

        # Second real batch
        UpperCAmelCase__						:    Optional[Any]							=							model_parallel.generate(input_ids=encoded_input["input_ids"].to(0							)							,			max_new_tokens=1_0							)
        self.assertIn(self.tokenizer.decode(output_parallel[0]							,			skip_special_tokens=snake_case__							)							,			self.EXPECTED_OUTPUTS							)




class 			lowerCAmelCase__			(    __magic_name__   ):
   def       __a							(		self      :	Dict							):


        '''simple docstring'''
        UpperCAmelCase__						:    Tuple							=							"facebook/opt-350m"
        super().setUp()




   def       __a							(		self      :	int							):


        '''simple docstring'''
        if version.parse(importlib.metadata.version("bitsandbytes"							)							) < version.parse("0.37.0"							):
             return

        # Step 1: freeze all parameters
        UpperCAmelCase__						:    int							=							AutoModelForCausalLM.from_pretrained(self.model_name							,			load_in_abit=snake_case__							)

        self.assertEqual(set(model.hf_device_map.values()							)							,			{torch.cuda.current_device()}							)

        for param in model.parameters():
             UpperCAmelCase__						:    Optional[Any]							=							False  # freeze the model - train adapters later
             if param.ndim == 1:
                  # cast the small parameters (e.g. layernorm) to fp32 for stability
                  UpperCAmelCase__						:    List[str]							=							param.data.to(torch.floataa							)

        # Step 2: add adapters
        for _, module in model.named_modules():
             if "OPTAttention" in repr(type(snake_case__							)							):
                  UpperCAmelCase__						:    Tuple							=							LoRALayer(module.q_proj							,			rank=1_6							)
                  UpperCAmelCase__						:    List[Any]							=							LoRALayer(module.k_proj							,			rank=1_6							)
                  UpperCAmelCase__						:    Any							=							LoRALayer(module.v_proj							,			rank=1_6							)

        # Step 3: dummy batch
        UpperCAmelCase__						:    Any							=							self.tokenizer("Test batch "							,			return_tensors="pt"							).to(0							)

        # Step 4: Check if the gradient is not None
        with torch.cuda.amp.autocast():
             UpperCAmelCase__						:    Optional[Any]							=							model.forward(**snake_case__							)
             out.logits.norm().backward()

        for module in model.modules():
             if isinstance(snake_case__							,			snake_case__							):
                  self.assertTrue(module.adapter[1].weight.grad is not None							)
                  self.assertTrue(module.adapter[1].weight.grad.norm().item() > 0							)
             elif isinstance(snake_case__							,			nn.Embedding							):
                  self.assertTrue(module.weight.grad is None							)




class 			lowerCAmelCase__			(    __magic_name__   ):
   SCREAMING_SNAKE_CASE_    ='''gpt2-xl'''
   SCREAMING_SNAKE_CASE_    =3.31_91_85_48_54_15_21_87










"""simple docstring"""



import argparse
import json
import os
import re
import shutil

import torch

from transformers import BioGptConfig, BioGptForCausalLM
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEIGHTS_NAME, logging


logging.set_verbosity_warning()

_lowerCAmelCase	:	Optional[int]				=      2




class 			lowerCAmelCase__			:
   def __init__(		self      :	Any							,			*,  # begin keyword-only arguments
       snake_case__      :	List[str]="<s>"							,			snake_case__      :	str="<pad>"							,			snake_case__      :	List[str]="</s>"							,			snake_case__      :	Any="<unk>"							,			snake_case__      :	List[Any]=None							,			):


        '''simple docstring'''
        UpperCAmelCase__							,				UpperCAmelCase__							,				UpperCAmelCase__							,				UpperCAmelCase__						:    str							=							bos, unk, pad, eos
        UpperCAmelCase__						:    str							=							[]
        UpperCAmelCase__						:    Tuple							=							[]
        UpperCAmelCase__						:    Optional[int]							=							{}
        UpperCAmelCase__						:    Optional[int]							=							self.add_symbol(snake_case__							)
        UpperCAmelCase__						:    Tuple							=							self.add_symbol(snake_case__							)
        UpperCAmelCase__						:    str							=							self.add_symbol(snake_case__							)
        UpperCAmelCase__						:    Optional[int]							=							self.add_symbol(snake_case__							)
        if extra_special_symbols:
             for s in extra_special_symbols:
                  self.add_symbol(snake_case__							)
        UpperCAmelCase__						:    List[str]							=							len(self.symbols							)
   def __eq__(		self      :	List[Any]							,			snake_case__      :	str							):


        '''simple docstring'''
        return self.indices == other.indices
   def __getitem__(		self      :	Dict							,			snake_case__      :	Union[str, Any]							):


        '''simple docstring'''
        if idx < len(self.symbols							):
             return self.symbols[idx]
        return self.unk_word
   def __len__(		self      :	int							):


        '''simple docstring'''
        return len(self.symbols							)
   def __contains__(		self      :	Dict							,			snake_case__      :	Optional[Any]							):


        '''simple docstring'''
        return sym in self.indices
   @classmethod
   def       __a							(		cls      :	Optional[int]							,			snake_case__      :	List[str]							):


        '''simple docstring'''
        UpperCAmelCase__						:    Dict							=							cls()
        d.add_from_file(snake_case__							)
        return d
   def       __a							(		self      :	Optional[Any]							,			snake_case__      :	str							,			snake_case__      :	Dict=1							,			snake_case__      :	Dict=False							):


        '''simple docstring'''
        if word in self.indices and not overwrite:
             UpperCAmelCase__						:    List[Any]							=							self.indices[word]
             UpperCAmelCase__						:    Dict							=							self.count[idx] + n
             return idx
        else:
             UpperCAmelCase__						:    Optional[Any]							=							len(self.symbols							)
             UpperCAmelCase__						:    List[str]							=							idx
             self.symbols.append(snake_case__							)
             self.count.append(snake_case__							)
             return idx
   def       __a							(		self      :	Union[str, Any]							,			snake_case__      :	List[Any]							):


        '''simple docstring'''
        return 0




   def       __a							(		self      :	Any							,			snake_case__      :	Dict							):


        '''simple docstring'''
        if isinstance(snake_case__							,			snake_case__							):
             try:
                  with open(snake_case__							,			"r"							,			encoding="utf-8"							) as fd:
                       self.add_from_file(snake_case__							)
             except FileNotFoundError as fnfe:
                  raise fnfe
             except UnicodeError:
                  raise Exception("Incorrect encoding detected in {}, please rebuild the dataset".format(snake_case__							)							)
             return

        UpperCAmelCase__						:    Optional[int]							=							f.readlines()
        UpperCAmelCase__						:    Dict							=							self._load_meta(snake_case__							)

        for line in lines[indices_start_line:]:
             try:
                  UpperCAmelCase__							,				UpperCAmelCase__						:    str							=							line.rstrip().rsplit(" "							,			1							)
                  if field == "#fairseq:overwrite":
                       UpperCAmelCase__						:    Union[str, Any]							=							True
                       UpperCAmelCase__							,				UpperCAmelCase__						:    str							=							line.rsplit(" "							,			1							)
                  else:
                       UpperCAmelCase__						:    Tuple							=							False
                  UpperCAmelCase__						:    Any							=							int(snake_case__							)
                  UpperCAmelCase__						:    List[Any]							=							line
                  if word in self and not overwrite:
                       raise RuntimeError(
                           "Duplicate word found when loading Dictionary: '{}'. "
                           "Duplicate words can overwrite earlier ones by adding the "
                           "#fairseq:overwrite flag at the end of the corresponding row "
                           "in the dictionary file. If using the Camembert model, please "
                           "download an updated copy of the model file.".format(snake_case__							)							)
                  self.add_symbol(snake_case__							,			n=snake_case__							,			overwrite=snake_case__							)
             except ValueError:
                  raise ValueError("Incorrect dictionary format, expected '<token> <cnt> [flags]'"							)




def 	SCREAMING_SNAKE_CASE__       (  snake_case       : List[Any]				)->		Tuple:






     '''simple docstring'''



     UpperCAmelCase__						:    Optional[int]							=							dict((re.sub(r"@@$"			,					""			,					snake_case				), v) if k.endswith("@@"				) else (re.sub(r"$"			,					"</w>"			,					snake_case				), v) for k, v in d.items()				)
     UpperCAmelCase__						:    Optional[int]							=							"<s> <pad> </s> <unk>".split()
     # restore the special tokens
     for k in keep_keys:
          del da[f'{k}</w>']
          UpperCAmelCase__						:    List[str]							=							d[k]  # restore
     return da


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : Optional[int]			,					snake_case       : int				)->		Union[str, Any]:






     '''simple docstring'''



     if not os.path.exists(snake_case				):
          raise ValueError(f'path {biogpt_checkpoint_path} does not exist!'				)
     os.makedirs(snake_case			,					exist_ok=snake_case				)
     print(f'Writing results to {pytorch_dump_folder_path}'				)

     # handle various types of models

     UpperCAmelCase__						:    Dict							=							os.path.join(snake_case			,					"checkpoint.pt"				)
     if not os.path.isfile(snake_case				):
          raise ValueError(f'path to the file {checkpoint_file} does not exist!'				)
     UpperCAmelCase__						:    Optional[int]							=							torch.load(snake_case			,					map_location="cpu"				)

     UpperCAmelCase__						:    Optional[int]							=							chkpt["cfg"]["model"]

     # dicts
     UpperCAmelCase__						:    Optional[int]							=							os.path.join(snake_case			,					"dict.txt"				)
     if not os.path.isfile(snake_case				):
          raise ValueError(f'path to the file {dict_file} does not exist!'				)
     UpperCAmelCase__						:    Dict							=							Dictionary.load(snake_case				)
     UpperCAmelCase__						:    Optional[Any]							=							rewrite_dict_keys(src_dict.indices				)
     UpperCAmelCase__						:    List[str]							=							len(snake_case				)
     UpperCAmelCase__						:    Tuple							=							os.path.join(snake_case			,					VOCAB_FILES_NAMES["vocab_file"]				)
     print(f'Generating {src_vocab_file} of {src_vocab_size} records'				)
     with open(snake_case			,					"w"			,					encoding="utf-8"				) as f:
          f.write(json.dumps(snake_case			,					ensure_ascii=snake_case			,					indent=snake_case				)				)

     # merges_file (bpecodes)
     UpperCAmelCase__						:    List[Any]							=							os.path.join(snake_case			,					"bpecodes"				)
     if not os.path.isfile(snake_case				):
          raise ValueError(f'path to the file {bpecodes_file} does not exist!'				)

     UpperCAmelCase__						:    int							=							os.path.join(snake_case			,					VOCAB_FILES_NAMES["merges_file"]				)
     shutil.copyfile(snake_case			,					snake_case				)

     # model config
     UpperCAmelCase__						:    List[Any]							=							os.path.join(snake_case			,					"config.json"				)

     UpperCAmelCase__						:    Any							=							{
         "activation_dropout": args["activation_dropout"],
         "architectures": ["BioGptForCausalLM"],
         "attention_probs_dropout_prob": args["attention_dropout"],
         "bos_token_id": 0,
         "eos_token_id": 2,
         "hidden_act": args["activation_fn"],
         "hidden_dropout_prob": args["dropout"],
         "hidden_size": args["decoder_embed_dim"],
         "initializer_range": 0.02,
         "intermediate_size": args["decoder_ffn_embed_dim"],
         "layer_norm_eps": 1E-1_2,
         "layerdrop": args["decoder_layerdrop"],
         "max_position_embeddings": args["max_target_positions"],
         "model_type": "biogpt",
         "num_attention_heads": args["decoder_attention_heads"],
         "num_hidden_layers": args["decoder_layers"],
         "pad_token_id": 1,
         "scale_embedding": not args["no_scale_embedding"],
         "tie_word_embeddings": args["share_decoder_input_output_embed"],
         "vocab_size": src_vocab_size,
     }

     # good hparam defaults to start with

     print(f'Generating {biogpt_model_config_file}'				)
     with open(snake_case			,					"w"			,					encoding="utf-8"				) as f:
          f.write(json.dumps(snake_case			,					ensure_ascii=snake_case			,					indent=snake_case				)				)

     # tokenizer config
     UpperCAmelCase__						:    List[Any]							=							os.path.join(snake_case			,					snake_case				)

     UpperCAmelCase__						:    str							=							{
         "bos_token": "<s>",
         "eos_token": "</s>",
         "model_max_length": 1024,
         "pad_token": "<pad>",
         "special_tokens_map_file": None,
         "tokenizer_class": "BioGptTokenizer",
         "unk_token": "<unk>",
     }

     print(f'Generating {biogpt_tokenizer_config_file}'				)
     with open(snake_case			,					"w"			,					encoding="utf-8"				) as f:
          f.write(json.dumps(snake_case			,					ensure_ascii=snake_case			,					indent=snake_case				)				)

     # model
     UpperCAmelCase__						:    Union[str, Any]							=							chkpt["model"]

     # remove unneeded keys
     UpperCAmelCase__						:    Union[str, Any]							=							[
         "decoder.version",
     ]
     for k in ignore_keys:
          model_state_dict.pop(snake_case			,					snake_case				)

     UpperCAmelCase__						:    Union[str, Any]							=							list(model_state_dict.keys()				)
     for layer_name in layer_names:
          if layer_name.endswith("output_projection.weight"				):
               UpperCAmelCase__						:    int							=							model_state_dict.pop(snake_case				)
          else:
               UpperCAmelCase__						:    Tuple							=							model_state_dict.pop(snake_case				)

     UpperCAmelCase__						:    Any							=							BioGptConfig.from_pretrained(snake_case				)
     UpperCAmelCase__						:    List[Any]							=							BioGptForCausalLM(snake_case				)

     # check that it loads ok
     model_new.load_state_dict(snake_case				)

     # save
     UpperCAmelCase__						:    Union[str, Any]							=							os.path.join(snake_case			,					snake_case				)
     print(f'Generating {pytorch_weights_dump_path}'				)
     torch.save(snake_case			,					snake_case				)

     print("Conversion is done!"				)


if __name__ == "__main__":
 _lowerCAmelCase	:	List[Any]				=      argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     """--biogpt_checkpoint_path""",
     default=None,
     type=str,
     required=True,
     help=(
         """Path to the official PyTorch checkpoint file which is expected to reside in the dump dir with dicts,"""
         """ bpecodes, etc."""
     ),
 )
 parser.add_argument(
     """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
 )
 _lowerCAmelCase	:	List[str]				=      parser.parse_args()
 convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)


