




import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging


__A						:							List[Any]							=  logging.get_logger(__name__)

__A						:							Dict							=  """▁"""

__A						:							Tuple							=  {"""vocab_file""": """sentencepiece.bpe.model"""}

__A						:							Union[str, Any]							=  {
    """vocab_file""": {
        """facebook/mbart-large-50-one-to-many-mmt""": (
            """https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/sentencepiece.bpe.model"""
        ),
    }
}

__A						:							List[str]							=  {
    """facebook/mbart-large-50-one-to-many-mmt""": 1_0_2_4,
}

# fmt: off
__A						:							List[Any]							=  ["""ar_AR""", """cs_CZ""", """de_DE""", """en_XX""", """es_XX""", """et_EE""", """fi_FI""", """fr_XX""", """gu_IN""", """hi_IN""", """it_IT""", """ja_XX""", """kk_KZ""", """ko_KR""", """lt_LT""", """lv_LV""", """my_MM""", """ne_NP""", """nl_XX""", """ro_RO""", """ru_RU""", """si_LK""", """tr_TR""", """vi_VN""", """zh_CN""", """af_ZA""", """az_AZ""", """bn_IN""", """fa_IR""", """he_IL""", """hr_HR""", """id_ID""", """ka_GE""", """km_KH""", """mk_MK""", """ml_IN""", """mn_MN""", """mr_IN""", """pl_PL""", """ps_AF""", """pt_XX""", """sv_SE""", """sw_KE""", """ta_IN""", """te_IN""", """th_TH""", """tl_XX""", """uk_UA""", """ur_PK""", """xh_ZA""", """gl_ES""", """sl_SI"""]




class 			UpperCAmelCase_      (   A       ):




							'''simple docstring'''



							a__						  =     VOCAB_FILES_NAMES
							a__						  =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							a__						  =     PRETRAINED_VOCAB_FILES_MAP
							a__						  =     ['''input_ids''', '''attention_mask''']

							a__						  =     []
							a__						  =     []





							def __init__(					self       :	str       ,     a       :	Optional[Any]       ,     a       :	str=None       ,     a       :	Optional[Any]=None       ,     a       :	List[str]="</s>"       ,     a       :	Any="</s>"       ,     a       :	int="<s>"       ,     a       :	str="<unk>"       ,     a       :	Optional[Any]="<pad>"       ,     a       :	str="<mask>"       ,     a       :	Optional[Dict[str, Any]] = None       ,     **a       :	List[str]       ,     )			->					None:
										# Mask token behave like a normal word, i.e. include the space before it
										SCREAMING_SNAKE_CASE           =					AddedToken(a       ,     lstrip=a       ,     rstrip=a			) if isinstance(a       ,     a			) else mask_token

										SCREAMING_SNAKE_CASE           =					{} if sp_model_kwargs is None else sp_model_kwargs

										SCREAMING_SNAKE_CASE           =					kwargs.get("""additional_special_tokens"""       ,     []			)
										kwargs["additional_special_tokens"] += [
										    code for code in FAIRSEQ_LANGUAGE_CODES if code not in kwargs["additional_special_tokens"]
										]

										super().__init__(
										    src_lang=a       ,     tgt_lang=a       ,     eos_token=a       ,     unk_token=a       ,     sep_token=a       ,     cls_token=a       ,     pad_token=a       ,     mask_token=a       ,     sp_model_kwargs=self.sp_model_kwargs       ,     **a       ,     )

										SCREAMING_SNAKE_CASE           =					spm.SentencePieceProcessor(**self.sp_model_kwargs			)
										self.sp_model.Load(str(a			)			)
										SCREAMING_SNAKE_CASE           =					vocab_file

										# Original fairseq vocab and spm vocab must be "aligned":
										# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
										# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
										# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
										# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

										# Mimic fairseq token-to-id alignment for the first 4 token
										SCREAMING_SNAKE_CASE           =					{"""<s>""": 0, """<pad>""": 1, """</s>""": 2, """<unk>""": 3}

										# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
										SCREAMING_SNAKE_CASE           =					1

										SCREAMING_SNAKE_CASE           =					len(self.sp_model			)
										SCREAMING_SNAKE_CASE           =					{
										    code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(a			)
										}
										SCREAMING_SNAKE_CASE           =					{v: k for k, v in self.lang_code_to_id.items()}
										SCREAMING_SNAKE_CASE           =					len(self.sp_model			) + len(self.lang_code_to_id			) + self.fairseq_offset

										self.fairseq_tokens_to_ids.update(self.lang_code_to_id			)
										SCREAMING_SNAKE_CASE           =					{v: k for k, v in self.fairseq_tokens_to_ids.items()}

										SCREAMING_SNAKE_CASE           =					src_lang if src_lang is not None else """en_XX"""
										SCREAMING_SNAKE_CASE           =					self.lang_code_to_id[self._src_lang]
										SCREAMING_SNAKE_CASE           =					tgt_lang
										self.set_src_lang_special_tokens(self._src_lang			)





							@property
							def    _UpperCAmelCase     (					self       :	Union[str, Any]			)			->					int:
										return len(self.sp_model			) + len(self.lang_code_to_id			) + self.fairseq_offset + 1  # Plus 1 for the mask token





							@property
							def    _UpperCAmelCase     (					self       :	int			)			->					str:
										return self._src_lang





							@src_lang.setter
							def    _UpperCAmelCase     (					self       :	Any       ,     a       :	str			)			->					None:
										SCREAMING_SNAKE_CASE           =					new_src_lang
										self.set_src_lang_special_tokens(self._src_lang			)





							def __getstate__(					self       :	List[Any]			)			->					Dict:
										SCREAMING_SNAKE_CASE           =					self.__dict__.copy()
										SCREAMING_SNAKE_CASE           =					None
										return state





							def __setstate__(					self       :	int       ,     a       :	Dict			)			->					None:
										SCREAMING_SNAKE_CASE           =					d

										# for backward compatibility
										if not hasattr(self       ,     """sp_model_kwargs"""			):
													SCREAMING_SNAKE_CASE           =					{}

										SCREAMING_SNAKE_CASE           =					spm.SentencePieceProcessor(**self.sp_model_kwargs			)
										self.sp_model.Load(self.vocab_file			)





							def    _UpperCAmelCase     (					self       :	List[Any]			)			->					Dict:
										SCREAMING_SNAKE_CASE           =					{self.convert_ids_to_tokens(a			): i for i in range(self.vocab_size			)}
										vocab.update(self.added_tokens_encoder			)
										return vocab





							def    _UpperCAmelCase     (					self       :	Union[str, Any]       ,     a       :	str			)			->					List[str]:
										return self.sp_model.encode(a       ,     out_type=a			)





							def    _UpperCAmelCase     (					self       :	Tuple       ,     a       :	str			)			->					int:
										if token in self.fairseq_tokens_to_ids:
													return self.fairseq_tokens_to_ids[token]
										SCREAMING_SNAKE_CASE           =					self.sp_model.PieceToId(a			)

										# Need to return unknown token if the SP model returned 0
										return spm_id + self.fairseq_offset if spm_id else self.unk_token_id





							def    _UpperCAmelCase     (					self       :	Optional[int]       ,     a       :	int			)			->					str:
										if index in self.fairseq_ids_to_tokens:
													return self.fairseq_ids_to_tokens[index]
										return self.sp_model.IdToPiece(index - self.fairseq_offset			)





							def    _UpperCAmelCase     (					self       :	Optional[Any]       ,     a       :	Union[str, Any]			)			->					Any:
										SCREAMING_SNAKE_CASE           =					[]
										SCREAMING_SNAKE_CASE           =					""""""
										SCREAMING_SNAKE_CASE           =					False
										for token in tokens:
													# make sure that special tokens are not decoded using sentencepiece model
													if token in self.all_special_tokens:
																if not prev_is_special:
																			out_string += " "
																out_string += self.sp_model.decode(a			) + token
																SCREAMING_SNAKE_CASE           =					True
																SCREAMING_SNAKE_CASE           =					[]
													else:
																current_sub_tokens.append(a			)
																SCREAMING_SNAKE_CASE           =					False
										out_string += self.sp_model.decode(a			)
										return out_string.strip()





							def    _UpperCAmelCase     (					self       :	List[Any]       ,     a       :	str       ,     a       :	Optional[str] = None			)			->					Tuple[str]:
										if not os.path.isdir(a			):
													logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""			)
													return
										SCREAMING_SNAKE_CASE           =					os.path.join(
										    a       ,     (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]			)

										if os.path.abspath(self.vocab_file			) != os.path.abspath(a			) and os.path.isfile(self.vocab_file			):
													copyfile(self.vocab_file       ,     a			)
										elif not os.path.isfile(self.vocab_file			):
													with open(a       ,     """wb"""			) as fi:
																SCREAMING_SNAKE_CASE           =					self.sp_model.serialized_model_proto()
																fi.write(a			)

										return (out_vocab_file,)





							def    _UpperCAmelCase     (					self       :	Dict       ,     a       :	List[int]       ,     a       :	Optional[List[int]] = None       ,     a       :	bool = False			)			->					List[int]:

										if already_has_special_tokens:
													return super().get_special_tokens_mask(
													    token_ids_a=a       ,     token_ids_a=a       ,     already_has_special_tokens=a			)

										SCREAMING_SNAKE_CASE           =					[1] * len(self.prefix_tokens			)
										SCREAMING_SNAKE_CASE           =					[1] * len(self.suffix_tokens			)
										if token_ids_a is None:
													return prefix_ones + ([0] * len(a			)) + suffix_ones
										return prefix_ones + ([0] * len(a			)) + ([0] * len(a			)) + suffix_ones





							def    _UpperCAmelCase     (					self       :	List[Any]       ,     a       :	List[int]       ,     a       :	Optional[List[int]] = None			)			->					List[int]:
										if token_ids_a is None:
													return self.prefix_tokens + token_ids_a + self.suffix_tokens
										# We don't expect to process pairs, but leave the pair logic for API consistency
										return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens





							def    _UpperCAmelCase     (					self       :	List[str]       ,     a       :	List[Any]       ,     a       :	str       ,     a       :	Optional[str]       ,     a       :	Optional[str]       ,     **a       :	Tuple			)			->					int:
										if src_lang is None or tgt_lang is None:
													raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model"""			)
										SCREAMING_SNAKE_CASE           =					src_lang
										SCREAMING_SNAKE_CASE           =					self(a       ,     add_special_tokens=a       ,     return_tensors=a       ,     **a			)
										SCREAMING_SNAKE_CASE           =					self.convert_tokens_to_ids(a			)
										SCREAMING_SNAKE_CASE           =					tgt_lang_id
										return inputs





							def    _UpperCAmelCase     (					self       :	Optional[Any]       ,     a       :	List[str]       ,     a       :	str = "en_XX"       ,     a       :	Optional[List[str]] = None       ,     a       :	str = "ro_RO"       ,     **a       :	Optional[Any]       ,     )			->					BatchEncoding:
										SCREAMING_SNAKE_CASE           =					src_lang
										SCREAMING_SNAKE_CASE           =					tgt_lang
										return super().prepare_seqaseq_batch(a       ,     a       ,     **a			)





							def    _UpperCAmelCase     (					self       :	Any			)			->					Optional[Any]:
										return self.set_src_lang_special_tokens(self.src_lang			)





							def    _UpperCAmelCase     (					self       :	List[Any]			)			->					int:
										return self.set_tgt_lang_special_tokens(self.tgt_lang			)





							def    _UpperCAmelCase     (					self       :	Any       ,     a       :	str			)			->					None:
										SCREAMING_SNAKE_CASE           =					self.lang_code_to_id[src_lang]
										SCREAMING_SNAKE_CASE           =					[self.cur_lang_code_id]
										SCREAMING_SNAKE_CASE           =					[self.eos_token_id]







							def    _UpperCAmelCase     (					self       :	Optional[Any]       ,     a       :	str			)			->					None:
										SCREAMING_SNAKE_CASE           =					self.lang_code_to_id[tgt_lang]
										SCREAMING_SNAKE_CASE           =					[self.cur_lang_code_id]
										SCREAMING_SNAKE_CASE           =					[self.eos_token_id]












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 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE   ):
			'''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 			UpperCAmelCase_      (   nn.Module       ):




									'''simple docstring'''



									def __init__(					self       :	Optional[int]       ,     a       :	nn.Module       ,     a       :	int			)			->					Optional[Any]:
												super().__init__()
												SCREAMING_SNAKE_CASE           =					module
												SCREAMING_SNAKE_CASE           =					nn.Sequential(
												    nn.Linear(module.in_features       ,     a       ,     bias=a			)       ,     nn.Linear(a       ,     module.out_features       ,     bias=a			)       ,     )
												SCREAMING_SNAKE_CASE           =					(2.0 / (5 * min(module.in_features       ,     module.out_features			))) ** 0.5
												nn.init.normal_(self.adapter[0].weight       ,     std=a			)
												nn.init.zeros_(self.adapter[1].weight			)
												self.adapter.to(module.weight.device			)







									def    _UpperCAmelCase     (					self       :	Tuple       ,     a       :	str       ,     *a       :	List[Any]       ,     **a       :	List[Any]			)			->					List[Any]:
												return self.module(a       ,     *a       ,     **a			) + self.adapter(a			)




@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class 			UpperCAmelCase_      (   unittest.TestCase       ):




							'''simple docstring'''



							a__						  =     '''bigscience/bloom-1b7'''

							# Constant values
							a__						  =     2.1_0_9_6_5_9_5_5_2_6_9_2_5_7_4

							a__						  =     '''Hello my name is'''
							a__						  =     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'''       )
							a__						  =     1_0





							def    _UpperCAmelCase     (					self       :	Dict			)			->					str:
										# Models and tokenizer
										SCREAMING_SNAKE_CASE           =					AutoTokenizer.from_pretrained(self.model_name			)





class 			UpperCAmelCase_      (   A       ):




							'''simple docstring'''



							def    _UpperCAmelCase     (					self       :	Optional[Any]			)			->					Optional[int]:
										super().setUp()

										# Models and tokenizer
										SCREAMING_SNAKE_CASE           =					AutoModelForCausalLM.from_pretrained(
										    self.model_name       ,     torch_dtype=torch.floataa       ,     device_map="""auto"""			)
										SCREAMING_SNAKE_CASE           =					AutoModelForCausalLM.from_pretrained(self.model_name       ,     load_in_abit=a       ,     device_map="""auto"""			)





							def    _UpperCAmelCase     (					self       :	Tuple			)			->					int:
										del self.model_fpaa
										del self.model_abit

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





							def    _UpperCAmelCase     (					self       :	str			)			->					Union[str, Any]:
										SCREAMING_SNAKE_CASE           =					self.model_abit.config

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

										SCREAMING_SNAKE_CASE           =					config.to_dict()
										SCREAMING_SNAKE_CASE           =					config.to_diff_dict()

										SCREAMING_SNAKE_CASE           =					config.to_json_string()





							def    _UpperCAmelCase     (					self       :	Any			)			->					Optional[int]:
										from bitsandbytes.nn import Paramsabit

										SCREAMING_SNAKE_CASE           =					self.model_fpaa.get_memory_footprint()
										SCREAMING_SNAKE_CASE           =					self.model_abit.get_memory_footprint()

										self.assertAlmostEqual(mem_fpaa / mem_abit       ,     self.EXPECTED_RELATIVE_DIFFERENCE			)
										SCREAMING_SNAKE_CASE           =					get_some_linear_layer(self.model_abit			)
										self.assertTrue(linear.weight.__class__ == Paramsabit			)





							def    _UpperCAmelCase     (					self       :	Tuple			)			->					List[Any]:
										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(a       ,     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    _UpperCAmelCase     (					self       :	Dict			)			->					Dict:
										SCREAMING_SNAKE_CASE           =					self.tokenizer(self.input_text       ,     return_tensors="""pt"""			)
										SCREAMING_SNAKE_CASE           =					self.model_abit.generate(input_ids=encoded_input["""input_ids"""].to(0			)       ,     max_new_tokens=10			)

										self.assertIn(self.tokenizer.decode(output_sequences[0]       ,     skip_special_tokens=a			)       ,     self.EXPECTED_OUTPUTS			)





							def    _UpperCAmelCase     (					self       :	str			)			->					List[str]:
										SCREAMING_SNAKE_CASE           =					BitsAndBytesConfig()
										SCREAMING_SNAKE_CASE           =					True

										SCREAMING_SNAKE_CASE           =					AutoModelForCausalLM.from_pretrained(
										    self.model_name       ,     quantization_config=a       ,     device_map="""auto"""			)

										SCREAMING_SNAKE_CASE           =					self.tokenizer(self.input_text       ,     return_tensors="""pt"""			)
										SCREAMING_SNAKE_CASE           =					model_abit_from_config.generate(
										    input_ids=encoded_input["""input_ids"""].to(0			)       ,     max_new_tokens=10			)

										self.assertIn(self.tokenizer.decode(output_sequences[0]       ,     skip_special_tokens=a			)       ,     self.EXPECTED_OUTPUTS			)





							def    _UpperCAmelCase     (					self       :	str			)			->					Optional[int]:
										with self.assertRaises(a			), tempfile.TemporaryDirectory() as tmpdirname:
													self.model_abit.save_pretrained(a			)





							def    _UpperCAmelCase     (					self       :	Tuple			)			->					Dict:
										SCREAMING_SNAKE_CASE           =					BitsAndBytesConfig()

										with self.assertRaises(a			):
													SCREAMING_SNAKE_CASE           =					AutoModelForCausalLM.from_pretrained(
													    self.model_name       ,     quantization_config=a       ,     load_in_abit=a       ,     device_map="""auto"""       ,     bnb_abit_quant_type="""nf4"""       ,     )





							def    _UpperCAmelCase     (					self       :	Optional[Any]			)			->					int:
										with self.assertRaises(a			):
													# Tries with `str`
													self.model_abit.to("""cpu"""			)

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

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

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

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

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

										SCREAMING_SNAKE_CASE           =					self.model_fpaa.to(torch.floataa			)
										SCREAMING_SNAKE_CASE           =					self.model_fpaa.generate(input_ids=encoded_input["""input_ids"""].to(0			)       ,     max_new_tokens=10			)

										# Check this does not throw an error
										SCREAMING_SNAKE_CASE           =					self.model_fpaa.to("""cpu"""			)

										# Check this does not throw an error
										SCREAMING_SNAKE_CASE           =					self.model_fpaa.half()

										# Check this does not throw an error
										SCREAMING_SNAKE_CASE           =					self.model_fpaa.float()







							def    _UpperCAmelCase     (					self       :	List[Any]			)			->					Optional[int]:
										SCREAMING_SNAKE_CASE           =					AutoModelForSeqaSeqLM.from_pretrained("""t5-small"""       ,     load_in_abit=a       ,     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 			UpperCAmelCase_      (   unittest.TestCase       ):




							'''simple docstring'''



							@classmethod
							def    _UpperCAmelCase     (					cls       :	Union[str, Any]			)			->					Dict:
										SCREAMING_SNAKE_CASE           =					"""t5-small"""
										SCREAMING_SNAKE_CASE           =					"""google/flan-t5-small"""  # flan-t5 uses dense-act instead of dense-relu-dense
										SCREAMING_SNAKE_CASE           =					AutoTokenizer.from_pretrained(cls.model_name			)
										SCREAMING_SNAKE_CASE           =					"""Translate in German: Hello, my dog is cute"""





							def    _UpperCAmelCase     (					self       :	Any			)			->					List[str]:
										gc.collect()
										torch.cuda.empty_cache()





							def    _UpperCAmelCase     (					self       :	List[str]			)			->					int:
										from transformers import TaForConditionalGeneration

										SCREAMING_SNAKE_CASE           =					TaForConditionalGeneration._keep_in_fpaa_modules
										SCREAMING_SNAKE_CASE           =					None

										# test with `t5-small`
										SCREAMING_SNAKE_CASE           =					TaForConditionalGeneration.from_pretrained(self.model_name       ,     load_in_abit=a       ,     device_map="""auto"""			)
										SCREAMING_SNAKE_CASE           =					self.tokenizer(self.input_text       ,     return_tensors="""pt"""			).to(0			)
										SCREAMING_SNAKE_CASE           =					model.generate(**a			)

										# test with `flan-t5-small`
										SCREAMING_SNAKE_CASE           =					TaForConditionalGeneration.from_pretrained(
										    self.dense_act_model_name       ,     load_in_abit=a       ,     device_map="""auto"""			)
										SCREAMING_SNAKE_CASE           =					self.tokenizer(self.input_text       ,     return_tensors="""pt"""			).to(0			)
										SCREAMING_SNAKE_CASE           =					model.generate(**a			)
										SCREAMING_SNAKE_CASE           =					modules







							def    _UpperCAmelCase     (					self       :	int			)			->					int:
										import bitsandbytes as bnb

										from transformers import TaForConditionalGeneration

										# test with `t5-small`
										SCREAMING_SNAKE_CASE           =					TaForConditionalGeneration.from_pretrained(self.model_name       ,     load_in_abit=a       ,     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			)			)

										SCREAMING_SNAKE_CASE           =					self.tokenizer(self.input_text       ,     return_tensors="""pt"""			).to(0			)
										SCREAMING_SNAKE_CASE           =					model.generate(**a			)

										# test with `flan-t5-small`
										SCREAMING_SNAKE_CASE           =					TaForConditionalGeneration.from_pretrained(
										    self.dense_act_model_name       ,     load_in_abit=a       ,     device_map="""auto"""			)
										SCREAMING_SNAKE_CASE           =					self.tokenizer(self.input_text       ,     return_tensors="""pt"""			).to(0			)
										SCREAMING_SNAKE_CASE           =					model.generate(**a			)





class 			UpperCAmelCase_      (   A       ):




							'''simple docstring'''



							def    _UpperCAmelCase     (					self       :	Optional[Any]			)			->					List[str]:
										super().setUp()
										# model_name
										SCREAMING_SNAKE_CASE           =					"""bigscience/bloom-560m"""
										SCREAMING_SNAKE_CASE           =					"""t5-small"""

										# Different types of model

										SCREAMING_SNAKE_CASE           =					AutoModel.from_pretrained(self.model_name       ,     load_in_abit=a       ,     device_map="""auto"""			)
										# Sequence classification model
										SCREAMING_SNAKE_CASE           =					AutoModelForSequenceClassification.from_pretrained(
										    self.model_name       ,     load_in_abit=a       ,     device_map="""auto"""			)
										# CausalLM model
										SCREAMING_SNAKE_CASE           =					AutoModelForCausalLM.from_pretrained(self.model_name       ,     load_in_abit=a       ,     device_map="""auto"""			)
										# Seq2seq model
										SCREAMING_SNAKE_CASE           =					AutoModelForSeqaSeqLM.from_pretrained(
										    self.seq_to_seq_name       ,     load_in_abit=a       ,     device_map="""auto"""			)





							def    _UpperCAmelCase     (					self       :	Union[str, Any]			)			->					Tuple:
										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    _UpperCAmelCase     (					self       :	Dict			)			->					List[Any]:
										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 			UpperCAmelCase_      (   A       ):




							'''simple docstring'''



							def    _UpperCAmelCase     (					self       :	Union[str, Any]			)			->					str:
										super().setUp()





							def    _UpperCAmelCase     (					self       :	Tuple			)			->					Optional[Any]:
										del self.pipe

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







							def    _UpperCAmelCase     (					self       :	List[str]			)			->					Optional[int]:
										SCREAMING_SNAKE_CASE           =					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
										SCREAMING_SNAKE_CASE           =					self.pipe(self.input_text			)
										self.assertIn(pipeline_output[0]["""generated_text"""]       ,     self.EXPECTED_OUTPUTS			)





@require_torch_multi_gpu
class 			UpperCAmelCase_      (   A       ):




							'''simple docstring'''



							def    _UpperCAmelCase     (					self       :	Dict			)			->					Optional[int]:
										super().setUp()







							def    _UpperCAmelCase     (					self       :	List[Any]			)			->					Optional[int]:
										SCREAMING_SNAKE_CASE           =					AutoModelForCausalLM.from_pretrained(
										    self.model_name       ,     load_in_abit=a       ,     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
										SCREAMING_SNAKE_CASE           =					self.tokenizer(self.input_text       ,     return_tensors="""pt"""			)

										# Second real batch
										SCREAMING_SNAKE_CASE           =					model_parallel.generate(input_ids=encoded_input["""input_ids"""].to(0			)       ,     max_new_tokens=10			)
										self.assertIn(self.tokenizer.decode(output_parallel[0]       ,     skip_special_tokens=a			)       ,     self.EXPECTED_OUTPUTS			)





class 			UpperCAmelCase_      (   A       ):




							'''simple docstring'''



							def    _UpperCAmelCase     (					self       :	int			)			->					Union[str, Any]:
										SCREAMING_SNAKE_CASE           =					"""facebook/opt-350m"""
										super().setUp()







							def    _UpperCAmelCase     (					self       :	Any			)			->					Tuple:
										if version.parse(importlib.metadata.version("""bitsandbytes"""			)			) < version.parse("""0.37.0"""			):
													return

										# Step 1: freeze all parameters
										SCREAMING_SNAKE_CASE           =					AutoModelForCausalLM.from_pretrained(self.model_name       ,     load_in_abit=a			)

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

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

        # Step 2: add adapters
										for _, module in model.named_modules():
													if "OPTAttention" in repr(type(a			)			):
																SCREAMING_SNAKE_CASE           =					LoRALayer(module.q_proj       ,     rank=16			)
																SCREAMING_SNAKE_CASE           =					LoRALayer(module.k_proj       ,     rank=16			)
																SCREAMING_SNAKE_CASE           =					LoRALayer(module.v_proj       ,     rank=16			)

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

										# Step 4: Check if the gradient is not None
										with torch.cuda.amp.autocast():
													SCREAMING_SNAKE_CASE           =					model.forward(**a			)
													out.logits.norm().backward()

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





class 			UpperCAmelCase_      (   A       ):




							'''simple docstring'''



							a__						  =     '''gpt2-xl'''
							a__						  =     3.3_1_9_1_8_5_4_8_5_4_1_5_2_1_8_7






