
'''simple docstring'''




import copy
import tempfile
import unittest

from transformers import MaMaaaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from transformers.utils import cached_property

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


if is_torch_available():
      import torch

      from transformers import MaMaaaForConditionalGeneration, MaMaaaModel, MaMaaaTokenizer
      from transformers.models.mam_aaa.modeling_mam_aaa import MaMaaaDecoder, MaMaaaEncoder







def 						lowerCAmelCase_							(  snake_case__						,							snake_case__						,							snake_case__						,							snake_case__=None						,							snake_case__=None						,							snake_case__=None						,							snake_case__=None						,							snake_case__=None						,							):




    '''simple docstring'''



    if attention_mask is None:
        A			:  Any   =		input_ids.ne(config.pad_token_id		)
    if decoder_attention_mask is None:
        A			:  Optional[int]   =		decoder_input_ids.ne(config.pad_token_id		)
    if head_mask is None:
        A			:  Optional[int]   =		torch.ones(config.encoder_layers						,							config.encoder_attention_heads						,							device=snake_case__		)
    if decoder_head_mask is None:
        A			:  Any   =		torch.ones(config.decoder_layers						,							config.decoder_attention_heads						,							device=snake_case__		)
    if cross_attn_head_mask is None:
        A			:  List[Any]   =		torch.ones(config.decoder_layers						,							config.decoder_attention_heads						,							device=snake_case__		)
    return {
        "input_ids": input_ids,
        "decoder_input_ids": decoder_input_ids,
        "attention_mask": attention_mask,
        "decoder_attention_mask": attention_mask,
        "head_mask": head_mask,
        "decoder_head_mask": decoder_head_mask,
        "cross_attn_head_mask": cross_attn_head_mask,
    }





class 					A							:





     def __init__(				self		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE=13		,     SCREAMING_SNAKE_CASE=7		,     SCREAMING_SNAKE_CASE=True		,     SCREAMING_SNAKE_CASE=False		,     SCREAMING_SNAKE_CASE=99		,     SCREAMING_SNAKE_CASE=16		,     SCREAMING_SNAKE_CASE=2		,     SCREAMING_SNAKE_CASE=4		,     SCREAMING_SNAKE_CASE=4		,     SCREAMING_SNAKE_CASE="relu"		,     SCREAMING_SNAKE_CASE=0.1		,     SCREAMING_SNAKE_CASE=0.1		,     SCREAMING_SNAKE_CASE=0.0		,     SCREAMING_SNAKE_CASE=0.0		,     SCREAMING_SNAKE_CASE=20		,     SCREAMING_SNAKE_CASE=2		,     SCREAMING_SNAKE_CASE=1		,     SCREAMING_SNAKE_CASE=0		,     )					->							int:






         """simple docstring"""






         A			:  Dict   =		parent
         A			:  Tuple   =		batch_size
         A			:  List[Any]   =		seq_length
         A			:  str   =		is_training
         A			:  str   =		use_labels
         A			:  List[Any]   =		vocab_size
         A			:  str   =		hidden_size
         A			:  Union[str, Any]   =		num_hidden_layers
         A			:  Dict   =		num_attention_heads
         A			:  Dict   =		intermediate_size
         A			:  str   =		hidden_act
         A			:  Any   =		hidden_dropout_prob
         A			:  str   =		attention_probs_dropout_prob
         A			:  str   =		encoder_layerdrop
         A			:  Dict   =		decoder_layerdrop
         A			:  int   =		max_position_embeddings
         A			:  Union[str, Any]   =		eos_token_id
         A			:  List[Any]   =		pad_token_id
         A			:  Union[str, Any]   =		bos_token_id





     def 							__lowerCAmelCase				(				self      )					->							List[str]:






         """simple docstring"""






         A			:  List[str]   =		ids_tensor([self.batch_size, self.seq_length]		,     self.vocab_size      )
         A			:  Dict   =		self.eos_token_id  # Eos Token
         A			:  Union[str, Any]   =		ids_tensor([self.batch_size, self.seq_length]		,     self.vocab_size      )

         # we need to clamp the input ids here to avoid having pad token in between
         # this is because for M2M100 the position_ids are prepared such that
         # all pad tokens have pos id = 2 and rest are between 2..seq_length
         # and the seq_length here is seq_length - num_pad_tokens
         # but when using past, there is no way of knowing if the past input ids had
         # pad tokens in them, which results in incorrect seq_lenth and which in turn results in
         # position_ids being off by num_pad_tokens in past input
         A			:  int   =		input_ids.clamp(self.pad_token_id + 1      )
         A			:  Dict   =		decoder_input_ids.clamp(self.pad_token_id + 1      )

         A			:  Tuple   =		self.get_config()
         A			:  Union[str, Any]   =		prepare_mam_aaa_inputs_dict(SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      )
         return config, inputs_dict





     def 							__lowerCAmelCase				(				self      )					->							Union[str, Any]:






         """simple docstring"""






         return MaMaaaConfig(
             vocab_size=self.vocab_size		,     d_model=self.hidden_size		,     encoder_layers=self.num_hidden_layers		,     decoder_layers=self.num_hidden_layers		,     encoder_attention_heads=self.num_attention_heads		,     decoder_attention_heads=self.num_attention_heads		,     encoder_ffn_dim=self.intermediate_size		,     decoder_ffn_dim=self.intermediate_size		,     dropout=self.hidden_dropout_prob		,     attention_dropout=self.attention_probs_dropout_prob		,     encoder_layerdrop=self.encoder_layerdrop		,     decoder_layerdrop=self.decoder_layerdrop		,     max_position_embeddings=self.max_position_embeddings		,     eos_token_id=self.eos_token_id		,     bos_token_id=self.bos_token_id		,     pad_token_id=self.pad_token_id		,     )





     def 							__lowerCAmelCase				(				self      )					->							List[str]:






         """simple docstring"""






         A,    A			:  Dict   =		self.prepare_config_and_inputs()
         return config, inputs_dict





     def 							__lowerCAmelCase				(				self		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      )					->							Dict:






         """simple docstring"""






         A			:  List[str]   =		MaMaaaModel(config=SCREAMING_SNAKE_CASE      ).get_decoder().to(SCREAMING_SNAKE_CASE      ).eval()
         A			:  Optional[int]   =		inputs_dict['''input_ids''']
         A			:  List[Any]   =		inputs_dict['''attention_mask''']
         A			:  Dict   =		inputs_dict['''head_mask''']

         # first forward pass
         A			:  Tuple   =		model(SCREAMING_SNAKE_CASE		,     attention_mask=SCREAMING_SNAKE_CASE		,     head_mask=SCREAMING_SNAKE_CASE		,     use_cache=SCREAMING_SNAKE_CASE      )

         A,    A			:  Union[str, Any]   =		outputs.to_tuple()

         # create hypothetical multiple next token and extent to next_input_ids
         A			:  Optional[Any]   =		ids_tensor((self.batch_size, 3)		,     config.vocab_size      )
         A			:  Optional[Any]   =		ids_tensor((self.batch_size, 3)		,     2      )

         # append to next input_ids and
         A			:  Optional[int]   =		torch.cat([input_ids, next_tokens]		,     dim=-1      )
         A			:  str   =		torch.cat([attention_mask, next_attn_mask]		,     dim=-1      )

         A			:  List[Any]   =		model(SCREAMING_SNAKE_CASE		,     attention_mask=SCREAMING_SNAKE_CASE      )['''last_hidden_state''']
         A			:  Dict   =		model(SCREAMING_SNAKE_CASE		,     attention_mask=SCREAMING_SNAKE_CASE		,     past_key_values=SCREAMING_SNAKE_CASE      )[
             '''last_hidden_state'''
         ]

         # select random slice
         A			:  int   =		ids_tensor((1,)		,     output_from_past.shape[-1]      ).item()
         A			:  List[Any]   =		output_from_no_past[:, -3:, random_slice_idx].detach()
         A			:  Any   =		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(SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     atol=1e-2      )      )





     def 							__lowerCAmelCase				(				self		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      )					->							List[Any]:






         """simple docstring"""






         A			:  str   =		MaMaaaModel(config=SCREAMING_SNAKE_CASE      ).to(SCREAMING_SNAKE_CASE      ).eval()
         A			:  str   =		model(**SCREAMING_SNAKE_CASE      )

         A			:  Optional[int]   =		outputs.encoder_last_hidden_state
         A			:  Any   =		outputs.last_hidden_state

         with tempfile.TemporaryDirectory() as tmpdirname:
             A			:  List[str]   =		model.get_encoder()
             encoder.save_pretrained(SCREAMING_SNAKE_CASE      )
             A			:  Optional[Any]   =		MaMaaaEncoder.from_pretrained(SCREAMING_SNAKE_CASE      ).to(SCREAMING_SNAKE_CASE      )

         A			:  List[Any]   =		encoder(inputs_dict['''input_ids''']		,     attention_mask=inputs_dict['''attention_mask''']      )[
             0
         ]

         self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1e-3      )

         with tempfile.TemporaryDirectory() as tmpdirname:
             A			:  Tuple   =		model.get_decoder()
             decoder.save_pretrained(SCREAMING_SNAKE_CASE      )
             A			:  Any   =		MaMaaaDecoder.from_pretrained(SCREAMING_SNAKE_CASE      ).to(SCREAMING_SNAKE_CASE      )

         A			:  Dict   =		decoder(
             input_ids=inputs_dict['''decoder_input_ids''']		,     attention_mask=inputs_dict['''decoder_attention_mask''']		,     encoder_hidden_states=SCREAMING_SNAKE_CASE		,     encoder_attention_mask=inputs_dict['''attention_mask''']		,     )[0]

         self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1e-3      )





@require_torch
class 					A							(				__snake_case       ,     __snake_case       ,     __snake_case       ,     unittest.TestCase		):
     __magic_name__          =						(
         (
             MaMaaaModel,
             MaMaaaForConditionalGeneration,
         )
         if is_torch_available()
         else ()
     )
     __magic_name__          =						(MaMaaaForConditionalGeneration,) if is_torch_available() else ()
     __magic_name__          =						(
         {
             '''conversational''': MaMaaaForConditionalGeneration,
             '''feature-extraction''': MaMaaaModel,
             '''summarization''': MaMaaaForConditionalGeneration,
             '''text2text-generation''': MaMaaaForConditionalGeneration,
             '''translation''': MaMaaaForConditionalGeneration,
         }
         if is_torch_available()
         else {}
     )
     __magic_name__          =						True
     __magic_name__          =						True
     __magic_name__          =						False
     __magic_name__          =						False





     def 							__lowerCAmelCase				(				self		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      )					->							Optional[int]:






         """simple docstring"""






         if pipeline_test_casse_name == "TranslationPipelineTests":
             # Get `ValueError: Translation requires a `src_lang` and a `tgt_lang` for this model`.
             # `M2M100Config` was never used in pipeline tests: cannot create a simple tokenizer.
             return True

         return False





     def 							__lowerCAmelCase				(				self      )					->							Optional[int]:






         """simple docstring"""






         A			:  Any   =		MaMaaaModelTester(self      )
         A			:  Optional[int]   =		ConfigTester(self		,     config_class=SCREAMING_SNAKE_CASE      )





     def 							__lowerCAmelCase				(				self      )					->							Any:






         """simple docstring"""






         self.config_tester.run_common_tests()





     def 							__lowerCAmelCase				(				self      )					->							Dict:






         """simple docstring"""






         A,    A			:  List[str]   =		self.model_tester.prepare_config_and_inputs()
         for model_class in self.all_model_classes:
             A			:  Dict   =		model_class(SCREAMING_SNAKE_CASE      )

             with tempfile.TemporaryDirectory() as tmpdirname:
                 model.save_pretrained(SCREAMING_SNAKE_CASE      )
                 A,    A			:  Dict   =		model_class.from_pretrained(SCREAMING_SNAKE_CASE		,     output_loading_info=SCREAMING_SNAKE_CASE      )
             self.assertEqual(info['''missing_keys''']		,     []      )





     def 							__lowerCAmelCase				(				self      )					->							Union[str, Any]:






         """simple docstring"""






         A			:  Dict   =		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_decoder_model_past_large_inputs(*SCREAMING_SNAKE_CASE      )





     def 							__lowerCAmelCase				(				self      )					->							Union[str, Any]:






         """simple docstring"""






         A			:  Optional[Any]   =		self.model_tester.prepare_config_and_inputs_for_common()
         self.model_tester.check_encoder_decoder_model_standalone(*SCREAMING_SNAKE_CASE      )





     def 							__lowerCAmelCase				(				self      )					->							List[Any]:






         """simple docstring"""






         A,    A			:  List[Any]   =		self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in (MaMaaaModel, MaMaaaForConditionalGeneration):
             A			:  Dict   =		model_class(SCREAMING_SNAKE_CASE      )
             model.to(SCREAMING_SNAKE_CASE      )
             model.eval()

             A			:  Optional[Any]   =		copy.deepcopy(self._prepare_for_class(SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      )      )

             if not self.is_encoder_decoder:
                 A			:  Tuple   =		inputs['''input_ids''']
                 del inputs["input_ids"]
             else:
                 A			:  Dict   =		inputs['''input_ids''']
                 A			:  Union[str, Any]   =		inputs.get('''decoder_input_ids'''		,     SCREAMING_SNAKE_CASE      )
                 del inputs["input_ids"]
                 inputs.pop('''decoder_input_ids'''		,     SCREAMING_SNAKE_CASE      )

             A			:  Dict   =		model.get_input_embeddings()
             if not self.is_encoder_decoder:
                 A			:  str   =		wte(SCREAMING_SNAKE_CASE      )
             else:
                 A			:  Tuple   =		wte(SCREAMING_SNAKE_CASE      )
                 A			:  Union[str, Any]   =		wte(SCREAMING_SNAKE_CASE      )

             with torch.no_grad():
                 model(**SCREAMING_SNAKE_CASE      )[0]





     def 							__lowerCAmelCase				(				self      )					->							Tuple:






         """simple docstring"""






         A,    A			:  Dict   =		self.model_tester.prepare_config_and_inputs()
         A			:  List[str]   =		input_dict['''input_ids''']
         A			:  Union[str, Any]   =		input_ids.ne(1      ).to(SCREAMING_SNAKE_CASE      )
         A			:  List[str]   =		MaMaaaForConditionalGeneration(SCREAMING_SNAKE_CASE      ).eval().to(SCREAMING_SNAKE_CASE      )
         if torch_device == "cuda":
             model.half()
         model.generate(SCREAMING_SNAKE_CASE		,     attention_mask=SCREAMING_SNAKE_CASE      )
         model.generate(num_beams=4		,     do_sample=SCREAMING_SNAKE_CASE		,     early_stopping=SCREAMING_SNAKE_CASE		,     num_return_sequences=3      )







def 						lowerCAmelCase_							(  snake_case__		):




    '''simple docstring'''



    return torch.tensor(snake_case__						,							dtype=torch.long						,							device=snake_case__		)


lowercase     :							Any       						=     1e-4







@require_torch
@require_sentencepiece
@require_tokenizers
@slow
class 					A							(				unittest.TestCase		):





     @cached_property
     def 							__lowerCAmelCase				(				self      )					->							int:






         """simple docstring"""






         return MaMaaaTokenizer.from_pretrained('''facebook/m2m100_418M'''      )





     def 							__lowerCAmelCase				(				self      )					->							Optional[Any]:






         """simple docstring"""






         A			:  List[str]   =		MaMaaaModel.from_pretrained('''facebook/m2m100_418M'''      ).to(SCREAMING_SNAKE_CASE      )
         A			:  List[Any]   =		_long_tensor([[128028, 98, 12, 30527, 2732, 159, 7755, 61904, 39144, 38, 2]]      )
         A			:  List[Any]   =		_long_tensor([[2, 128028, 98, 12, 30527, 2732, 159, 7755, 61904, 39144, 38]]      )
         A			:  Tuple   =		prepare_mam_aaa_inputs_dict(model.config		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      )
         with torch.no_grad():
             A			:  Optional[int]   =		model(**SCREAMING_SNAKE_CASE      )[0]
         A			:  Union[str, Any]   =		torch.Size((1, 11, 1024)      )
         self.assertEqual(output.shape		,     SCREAMING_SNAKE_CASE      )
         # change to expected output here
         A			:  Tuple   =		torch.tensor(
             [[-0.7_780, -0.1_676, 0.1_038], [-6.7_556, -1.3_992, 0.0_567], [-7.5_383, -0.5_920, -0.2_779]]		,     device=SCREAMING_SNAKE_CASE      )
         self.assertTrue(torch.allclose(output[:, :3, :3]		,     SCREAMING_SNAKE_CASE		,     atol=SCREAMING_SNAKE_CASE      )      )





     def 							__lowerCAmelCase				(				self      )					->							int:






         """simple docstring"""






         A			:  str   =		MaMaaaForConditionalGeneration.from_pretrained('''facebook/m2m100_418M'''      ).to(SCREAMING_SNAKE_CASE      )

         # change to intended input
         A			:  str   =		_long_tensor([[128028, 98, 12, 30527, 2732, 159, 7755, 61904, 39144, 38, 2]]      )
         A			:  Tuple   =		_long_tensor([[2, 128028, 98, 12, 30527, 2732, 159, 7755, 61904, 39144, 38]]      )
         A			:  str   =		prepare_mam_aaa_inputs_dict(model.config		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      )
         with torch.no_grad():
             A			:  Tuple   =		model(**SCREAMING_SNAKE_CASE      )[0]
         A			:  List[str]   =		torch.Size((1, 11, model.config.vocab_size)      )
         self.assertEqual(output.shape		,     SCREAMING_SNAKE_CASE      )
         # change to expected output here
         A			:  List[str]   =		torch.tensor(
             [[-1.0_448, -1.0_411, 3.7_992], [-3.2_191, -3.2_386, -1.3_451], [-3.6_210, -3.5_993, 0.4_925]]		,     device=SCREAMING_SNAKE_CASE      )
         self.assertTrue(torch.allclose(output[:, :3, :3]		,     SCREAMING_SNAKE_CASE		,     atol=SCREAMING_SNAKE_CASE      )      )





     def 							__lowerCAmelCase				(				self      )					->							Optional[Any]:






         """simple docstring"""






         A			:  Any   =		MaMaaaForConditionalGeneration.from_pretrained('''facebook/m2m100_418M'''      ).to(SCREAMING_SNAKE_CASE      )
         A			:  List[str]   =		MaMaaaTokenizer.from_pretrained('''facebook/m2m100_418M'''		,     src_lang='''fr'''		,     tgt_lang='''en'''      )

         A			:  Optional[Any]   =		[
             '''L\'affaire NSA souligne l\'absence totale de débat sur le renseignement''',
             '''Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.''',
             '''Lorsque François Hollande téléphone à Barack Obama ou quand le ministre des affaires étrangères Laurent'''
             ''' Fabius convoque l\'ambassadeur des Etats-Unis, ils réagissent à une vraie découverte, qui est celle de'''
             ''' l\'ampleur de la surveillance américaine sur l\'ensemble des communications en France.''',
         ]

         # The below article tests that we don't add any hypotheses outside of the top n_beams
         A			:  Union[str, Any]   =		tokenizer(SCREAMING_SNAKE_CASE		,     padding=SCREAMING_SNAKE_CASE		,     return_tensors='''pt'''      )

         A			:  int   =		model.generate(
             input_ids=dct['''input_ids'''].to(SCREAMING_SNAKE_CASE      )		,     attention_mask=dct['''attention_mask'''].to(SCREAMING_SNAKE_CASE      )		,     num_beams=5		,     forced_bos_token_id=tokenizer.get_lang_id('''en'''      )		,     )

         A			:  Any   =		[
             '''The NSA case highlights the total absence of intelligence debate''',
             '''I think there are two levels of response from the French government.''',
             '''When François Hollande calls Barack Obama or when Foreign Minister Laurent Fabius calls the U.S.'''
             ''' Ambassador, they respond to a real discovery, which is that of the scale of U.S. surveillance on all'''
             ''' communications in France.''',
         ]

         A			:  int   =		tokenizer.batch_decode(
             hypotheses_batch.tolist()		,     clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE		,     skip_special_tokens=SCREAMING_SNAKE_CASE      )
         assert generated == expected_en






'''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 						lowerCAmelCase_							(  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 					A							(				nn.Module		):





           def __init__(				self		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      )					->							Tuple:






               """simple docstring"""






               super().__init__()
               A			:  List[Any]   =		module
               A			:  Any   =		nn.Sequential(
                   nn.Linear(module.in_features		,     SCREAMING_SNAKE_CASE		,     bias=SCREAMING_SNAKE_CASE      )		,     nn.Linear(SCREAMING_SNAKE_CASE		,     module.out_features		,     bias=SCREAMING_SNAKE_CASE      )		,     )
               A			:  List[str]   =		(2.0 / (5 * min(module.in_features		,     module.out_features      ))) ** 0.5
               nn.init.normal_(self.adapter[0].weight		,     std=SCREAMING_SNAKE_CASE      )
               nn.init.zeros_(self.adapter[1].weight      )
               self.adapter.to(module.weight.device      )





           def 							__lowerCAmelCase				(				self		,     SCREAMING_SNAKE_CASE		,     *SCREAMING_SNAKE_CASE		,     **SCREAMING_SNAKE_CASE      )					->							str:






               """simple docstring"""






               return self.module(SCREAMING_SNAKE_CASE		,     *SCREAMING_SNAKE_CASE		,     **SCREAMING_SNAKE_CASE      ) + self.adapter(SCREAMING_SNAKE_CASE      )





@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class 					A							(				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
     __magic_name__          =						'''bigscience/bloom-1b7'''

     # Constant values
     __magic_name__          =						2.1_09_65_95_52_69_25_74

     __magic_name__          =						'''Hello my name is'''
     __magic_name__          =						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'''		)
     __magic_name__          =						10





     def 							__lowerCAmelCase				(				self      )					->							str:






         """simple docstring"""






         A			:  Any   =		AutoTokenizer.from_pretrained(self.model_name      )





class 					A							(				__snake_case		):





     def 							__lowerCAmelCase				(				self      )					->							Union[str, Any]:






         """simple docstring"""






         super().setUp()

         # Models and tokenizer
         A			:  Optional[Any]   =		AutoModelForCausalLM.from_pretrained(
             self.model_name		,     torch_dtype=torch.floataa		,     device_map='''auto'''      )
         A			:  List[Any]   =		AutoModelForCausalLM.from_pretrained(self.model_name		,     load_in_abit=SCREAMING_SNAKE_CASE		,     device_map='''auto'''      )





     def 							__lowerCAmelCase				(				self      )					->							Tuple:






         """simple docstring"""






         del self.model_fpaa
         del self.model_abit

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





     def 							__lowerCAmelCase				(				self      )					->							Optional[Any]:






         """simple docstring"""






         A			:  Any   =		self.model_abit.config

         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE		,     '''quantization_config'''      )      )

         A			:  List[str]   =		config.to_dict()
         A			:  List[Any]   =		config.to_diff_dict()

         A			:  Union[str, Any]   =		config.to_json_string()





     def 							__lowerCAmelCase				(				self      )					->							int:






         """simple docstring"""






         from bitsandbytes.nn import Paramsabit

         A			:  Optional[Any]   =		self.model_fpaa.get_memory_footprint()
         A			:  Any   =		self.model_abit.get_memory_footprint()

         self.assertAlmostEqual(mem_fpaa / mem_abit		,     self.EXPECTED_RELATIVE_DIFFERENCE      )
         A			:  Union[str, Any]   =		get_some_linear_layer(self.model_abit      )
         self.assertTrue(linear.weight.__class__ == Paramsabit      )





     def 							__lowerCAmelCase				(				self      )					->							Any:






         """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(SCREAMING_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 							__lowerCAmelCase				(				self      )					->							Any:






         """simple docstring"""






         A			:  List[Any]   =		self.tokenizer(self.input_text		,     return_tensors='''pt'''      )
         A			:  List[str]   =		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=SCREAMING_SNAKE_CASE      )		,     self.EXPECTED_OUTPUTS      )





     def 							__lowerCAmelCase				(				self      )					->							List[str]:






         """simple docstring"""






         A			:  str   =		BitsAndBytesConfig()
         A			:  Optional[Any]   =		True

         A			:  int   =		AutoModelForCausalLM.from_pretrained(
             self.model_name		,     quantization_config=SCREAMING_SNAKE_CASE		,     device_map='''auto'''      )

         A			:  Union[str, Any]   =		self.tokenizer(self.input_text		,     return_tensors='''pt'''      )
         A			:  Tuple   =		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=SCREAMING_SNAKE_CASE      )		,     self.EXPECTED_OUTPUTS      )





     def 							__lowerCAmelCase				(				self      )					->							Union[str, Any]:






         """simple docstring"""






         with self.assertRaises(SCREAMING_SNAKE_CASE      ), tempfile.TemporaryDirectory() as tmpdirname:
             self.model_abit.save_pretrained(SCREAMING_SNAKE_CASE      )





     def 							__lowerCAmelCase				(				self      )					->							Any:






         """simple docstring"""






         A			:  Union[str, Any]   =		BitsAndBytesConfig()

         with self.assertRaises(SCREAMING_SNAKE_CASE      ):
             A			:  int   =		AutoModelForCausalLM.from_pretrained(
                 self.model_name		,     quantization_config=SCREAMING_SNAKE_CASE		,     load_in_abit=SCREAMING_SNAKE_CASE		,     device_map='''auto'''		,     bnb_abit_quant_type='''nf4'''		,     )





     def 							__lowerCAmelCase				(				self      )					->							Optional[Any]:






         """simple docstring"""






         with self.assertRaises(SCREAMING_SNAKE_CASE      ):
             # Tries with `str`
             self.model_abit.to('''cpu'''      )

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

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

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

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

         # Test if we did not break anything
         A			:  int   =		self.tokenizer(self.input_text		,     return_tensors='''pt'''      )

         A			:  Dict   =		self.model_fpaa.to(torch.floataa      )
         A			:  Union[str, Any]   =		self.model_fpaa.generate(input_ids=encoded_input['''input_ids'''].to(0      )		,     max_new_tokens=10      )

         # Check this does not throw an error
         A			:  str   =		self.model_fpaa.to('''cpu'''      )

         # Check this does not throw an error
         A			:  List[str]   =		self.model_fpaa.half()

         # Check this does not throw an error
         A			:  Tuple   =		self.model_fpaa.float()





     def 							__lowerCAmelCase				(				self      )					->							List[Any]:






         """simple docstring"""






         A			:  Tuple   =		AutoModelForSeqaSeqLM.from_pretrained('''t5-small'''		,     load_in_abit=SCREAMING_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 					A							(				unittest.TestCase		):





     @classmethod
     def 							__lowerCAmelCase				(				cls      )					->							Union[str, Any]:






         """simple docstring"""






         A			:  int   =		'''t5-small'''
         A			:  Any   =		'''google/flan-t5-small'''  # flan-t5 uses dense-act instead of dense-relu-dense
         A			:  List[Any]   =		AutoTokenizer.from_pretrained(cls.model_name      )
         A			:  List[str]   =		'''Translate in German: Hello, my dog is cute'''





     def 							__lowerCAmelCase				(				self      )					->							str:






         """simple docstring"""






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





     def 							__lowerCAmelCase				(				self      )					->							Union[str, Any]:






         """simple docstring"""






         from transformers import TaForConditionalGeneration

         A			:  int   =		TaForConditionalGeneration._keep_in_fpaa_modules
         A			:  Optional[Any]   =		None

         # test with `t5-small`
         A			:  Optional[Any]   =		TaForConditionalGeneration.from_pretrained(self.model_name		,     load_in_abit=SCREAMING_SNAKE_CASE		,     device_map='''auto'''      )
         A			:  str   =		self.tokenizer(self.input_text		,     return_tensors='''pt'''      ).to(0      )
         A			:  List[Any]   =		model.generate(**SCREAMING_SNAKE_CASE      )

         # test with `flan-t5-small`
         A			:  Any   =		TaForConditionalGeneration.from_pretrained(
             self.dense_act_model_name		,     load_in_abit=SCREAMING_SNAKE_CASE		,     device_map='''auto'''      )
         A			:  Optional[Any]   =		self.tokenizer(self.input_text		,     return_tensors='''pt'''      ).to(0      )
         A			:  List[Any]   =		model.generate(**SCREAMING_SNAKE_CASE      )
         A			:  str   =		modules





     def 							__lowerCAmelCase				(				self      )					->							Union[str, Any]:






         """simple docstring"""






         import bitsandbytes as bnb

         from transformers import TaForConditionalGeneration

         # test with `t5-small`
         A			:  List[str]   =		TaForConditionalGeneration.from_pretrained(self.model_name		,     load_in_abit=SCREAMING_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      )      )

         A			:  List[Any]   =		self.tokenizer(self.input_text		,     return_tensors='''pt'''      ).to(0      )
         A			:  Any   =		model.generate(**SCREAMING_SNAKE_CASE      )

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





class 					A							(				__snake_case		):





     def 							__lowerCAmelCase				(				self      )					->							Dict:






         """simple docstring"""






         super().setUp()
         # model_name
         A			:  Tuple   =		'''bigscience/bloom-560m'''
         A			:  int   =		'''t5-small'''

         # Different types of model

         A			:  Optional[int]   =		AutoModel.from_pretrained(self.model_name		,     load_in_abit=SCREAMING_SNAKE_CASE		,     device_map='''auto'''      )
         # Sequence classification model
         A			:  Optional[Any]   =		AutoModelForSequenceClassification.from_pretrained(
             self.model_name		,     load_in_abit=SCREAMING_SNAKE_CASE		,     device_map='''auto'''      )
         # CausalLM model
         A			:  List[str]   =		AutoModelForCausalLM.from_pretrained(self.model_name		,     load_in_abit=SCREAMING_SNAKE_CASE		,     device_map='''auto'''      )
         # Seq2seq model
         A			:  Optional[int]   =		AutoModelForSeqaSeqLM.from_pretrained(
             self.seq_to_seq_name		,     load_in_abit=SCREAMING_SNAKE_CASE		,     device_map='''auto'''      )





     def 							__lowerCAmelCase				(				self      )					->							Tuple:






         """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 							__lowerCAmelCase				(				self      )					->							List[str]:






         """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 					A							(				__snake_case		):





     def 							__lowerCAmelCase				(				self      )					->							Any:






         """simple docstring"""






         super().setUp()





     def 							__lowerCAmelCase				(				self      )					->							Tuple:






         """simple docstring"""






         del self.pipe

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





     def 							__lowerCAmelCase				(				self      )					->							Tuple:






         """simple docstring"""






         A			:  Optional[Any]   =		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
         A			:  str   =		self.pipe(self.input_text      )
         self.assertIn(pipeline_output[0]['''generated_text''']		,     self.EXPECTED_OUTPUTS      )





@require_torch_multi_gpu
class 					A							(				__snake_case		):





     def 							__lowerCAmelCase				(				self      )					->							Dict:






         """simple docstring"""






         super().setUp()





     def 							__lowerCAmelCase				(				self      )					->							Optional[int]:






         """simple docstring"""






         A			:  Tuple   =		AutoModelForCausalLM.from_pretrained(
             self.model_name		,     load_in_abit=SCREAMING_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
         A			:  Optional[int]   =		self.tokenizer(self.input_text		,     return_tensors='''pt'''      )

         # Second real batch
         A			:  Optional[Any]   =		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=SCREAMING_SNAKE_CASE      )		,     self.EXPECTED_OUTPUTS      )





class 					A							(				__snake_case		):





     def 							__lowerCAmelCase				(				self      )					->							str:






         """simple docstring"""






         A			:  int   =		'''facebook/opt-350m'''
         super().setUp()





     def 							__lowerCAmelCase				(				self      )					->							List[str]:






         """simple docstring"""






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

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

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

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

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

        # Step 3: dummy batch
         A			:  Dict   =		self.tokenizer('''Test batch '''		,     return_tensors='''pt'''      ).to(0      )

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

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





class 					A							(				__snake_case		):
     __magic_name__          =						'''gpt2-xl'''
     __magic_name__          =						3.31_91_85_48_54_15_21_87




