


'''simple docstring'''







import itertools
import random
import unittest

import numpy as np

from transformers import BatchFeature, SpeechTaFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
   import torch


A				:					List[str]    						=			random.Random()






def 				lowerCAmelCase__	(    lowerCamelCase					:      List[Any]  ,lowerCamelCase					:      Optional[int]=1.0  ,lowerCamelCase					:      Union[str, Any]=None  ,lowerCamelCase					:      List[Any]=None       ):
       if rng is None:
              _A					:     Optional[Any]     = global_rng

       _A					:     Dict     = []
       for batch_idx in range(shape[0]       ):
              values.append([]       )
              for _ in range(shape[1]       ):
                     values[-1].append(rng.random() * scale       )

       return values


@require_torch
class 			__lowerCamelCase			(	unittest.TestCase	):
    """simple docstring"""


    def __init__(  self      :							Tuple				,							SCREAMING_SNAKE_CASE      :							Tuple				,							SCREAMING_SNAKE_CASE      :							str=7				,							SCREAMING_SNAKE_CASE      :							int=400				,							SCREAMING_SNAKE_CASE      :							Union[str, Any]=2000				,							SCREAMING_SNAKE_CASE      :							List[Any]=1				,							SCREAMING_SNAKE_CASE      :							Union[str, Any]=0.0				,							SCREAMING_SNAKE_CASE      :							List[Any]=16000				,							SCREAMING_SNAKE_CASE      :							Tuple=True				,							SCREAMING_SNAKE_CASE      :							int=80				,							SCREAMING_SNAKE_CASE      :							Optional[Any]=16				,							SCREAMING_SNAKE_CASE      :							Optional[Any]=64				,							SCREAMING_SNAKE_CASE      :							str="hann_window"				,							SCREAMING_SNAKE_CASE      :							str=80				,							SCREAMING_SNAKE_CASE      :							Optional[Any]=7600				,							SCREAMING_SNAKE_CASE      :							Tuple=1e-10				,							SCREAMING_SNAKE_CASE      :							Union[str, Any]=True				,							):
           _A					:     str     = parent
           _A					:     Tuple     = batch_size
           _A					:     Optional[Any]     = min_seq_length
           _A					:     str     = max_seq_length
           _A					:     Union[str, Any]     = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
           _A					:     Dict     = feature_size
           _A					:     Optional[Any]     = padding_value
           _A					:     Dict     = sampling_rate
           _A					:     Optional[int]     = do_normalize
           _A					:     int     = num_mel_bins
           _A					:     str     = hop_length
           _A					:     Union[str, Any]     = win_length
           _A					:     List[str]     = win_function
           _A					:     int     = fmin
           _A					:     Any     = fmax
           _A					:     List[str]     = mel_floor
           _A					:     List[str]     = return_attention_mask


    def        A   (  self      :							List[Any]):
           return {
               "feature_size": self.feature_size,
               "padding_value": self.padding_value,
               "sampling_rate": self.sampling_rate,
               "do_normalize": self.do_normalize,
               "num_mel_bins": self.num_mel_bins,
               "hop_length": self.hop_length,
               "win_length": self.win_length,
               "win_function": self.win_function,
               "fmin": self.fmin,
               "fmax": self.fmax,
               "mel_floor": self.mel_floor,
               "return_attention_mask": self.return_attention_mask,
           }


    def        A   (  self      :							Union[str, Any]				,							SCREAMING_SNAKE_CASE      :							Any=False				,							SCREAMING_SNAKE_CASE      :							Any=False):
           def _flatten(SCREAMING_SNAKE_CASE      :							int):
                  return list(itertools.chain(*SCREAMING_SNAKE_CASE))

           if equal_length:
                  _A					:     List[Any]     = floats_list((self.batch_size, self.max_seq_length))
           else:
                  # make sure that inputs increase in size
                  _A					:     List[Any]     = [
                      _flatten(floats_list((x, self.feature_size)))
                      for x in range(self.min_seq_length				,							self.max_seq_length				,							self.seq_length_diff)
                  ]

           if numpify:
                  _A					:     Dict     = [np.asarray(SCREAMING_SNAKE_CASE) for x in speech_inputs]

           return speech_inputs


    def        A   (  self      :							List[Any]				,							SCREAMING_SNAKE_CASE      :							str=False				,							SCREAMING_SNAKE_CASE      :							Optional[int]=False):
           if equal_length:
                  _A					:     List[str]     = [floats_list((self.max_seq_length, self.num_mel_bins)) for _ in range(self.batch_size)]
           else:
                  # make sure that inputs increase in size
                  _A					:     Dict     = [
                      floats_list((x, self.num_mel_bins))
                      for x in range(self.min_seq_length				,							self.max_seq_length				,							self.seq_length_diff)
                  ]

           if numpify:
                  _A					:     List[Any]     = [np.asarray(SCREAMING_SNAKE_CASE) for x in speech_inputs]

           return speech_inputs




@require_torch
class 			__lowerCamelCase			(	a_    , unittest.TestCase	):
    """simple docstring"""
    a													=   SpeechTaFeatureExtractor


    def        A   (  self      :							List[Any]):
           _A					:     Optional[int]     = SpeechTaFeatureExtractionTester(self)


    def        A   (  self      :							Any				,							SCREAMING_SNAKE_CASE      :							List[Any]):
           self.assertTrue(np.all(np.mean(SCREAMING_SNAKE_CASE				,							axis=0) < 1e-3))
           self.assertTrue(np.all(np.abs(np.var(SCREAMING_SNAKE_CASE				,							axis=0) - 1) < 1e-3))


    def        A   (  self      :							Optional[Any]):
           # Tests that all call wrap to encode_plus and batch_encode_plus
           _A					:     Union[str, Any]     = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
           # create three inputs of length 800, 1000, and 1200
           _A					:     Optional[int]     = [floats_list((1, x))[0] for x in range(800				,							1400				,							200)]
           _A					:     str     = [np.asarray(SCREAMING_SNAKE_CASE) for speech_input in speech_inputs]

           # Test not batched input
           _A					:     List[str]     = feat_extract(speech_inputs[0]				,							return_tensors='np').input_values
           _A					:     str     = feat_extract(np_speech_inputs[0]				,							return_tensors='np').input_values
           self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE				,							atol=1e-3))

           # Test batched
           _A					:     str     = feat_extract(SCREAMING_SNAKE_CASE				,							return_tensors='np').input_values
           _A					:     Tuple     = feat_extract(SCREAMING_SNAKE_CASE				,							return_tensors='np').input_values
           for enc_seq_a, enc_seq_a in zip(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE):
                  self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE				,							atol=1e-3))


    def        A   (  self      :							List[str]):
           _A					:     List[Any]     = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
           _A					:     Optional[Any]     = [floats_list((1, x))[0] for x in range(800				,							1400				,							200)]

           _A					:     List[Any]     = ['longest', 'max_length', 'do_not_pad']
           _A					:     str     = [None, 1600, None]
           for max_length, padding in zip(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE):
                  _A					:     str     = feat_extract(SCREAMING_SNAKE_CASE				,							padding=SCREAMING_SNAKE_CASE				,							max_length=SCREAMING_SNAKE_CASE				,							return_tensors='np')
                  _A					:     Tuple     = processed.input_values

                  self._check_zero_mean_unit_variance(input_values[0][:800])
                  self.assertTrue(input_values[0][800:].sum() < 1e-6)
                  self._check_zero_mean_unit_variance(input_values[1][:1000])
                  self.assertTrue(input_values[0][1000:].sum() < 1e-6)
                  self._check_zero_mean_unit_variance(input_values[2][:1200])


    def        A   (  self      :							Any):
           _A					:     Dict     = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
           _A					:     Dict     = range(800				,							1400				,							200)
           _A					:     List[str]     = [floats_list((1, x))[0] for x in lengths]

           _A					:     Any     = ['longest', 'max_length', 'do_not_pad']
           _A					:     int     = [None, 1600, None]

           for max_length, padding in zip(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE):
                  _A					:     Dict     = feat_extract(SCREAMING_SNAKE_CASE				,							max_length=SCREAMING_SNAKE_CASE				,							padding=SCREAMING_SNAKE_CASE)
                  _A					:     Any     = processed.input_values

                  self._check_zero_mean_unit_variance(input_values[0][:800])
                  self._check_zero_mean_unit_variance(input_values[1][:1000])
                  self._check_zero_mean_unit_variance(input_values[2][:1200])


    def        A   (  self      :							Optional[Any]):
           _A					:     List[str]     = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
           _A					:     Tuple     = [floats_list((1, x))[0] for x in range(800				,							1400				,							200)]
           _A					:     List[Any]     = feat_extract(
               SCREAMING_SNAKE_CASE				,							truncation=SCREAMING_SNAKE_CASE				,							max_length=1000				,							padding='max_length'				,							return_tensors='np')
           _A					:     Optional[int]     = processed.input_values

           self._check_zero_mean_unit_variance(input_values[0, :800])
           self._check_zero_mean_unit_variance(input_values[1])
           self._check_zero_mean_unit_variance(input_values[2])


    def        A   (  self      :							Union[str, Any]):
           _A					:     List[Any]     = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
           _A					:     str     = [floats_list((1, x))[0] for x in range(800				,							1400				,							200)]
           _A					:     Optional[Any]     = feat_extract(
               SCREAMING_SNAKE_CASE				,							truncation=SCREAMING_SNAKE_CASE				,							max_length=1000				,							padding='longest'				,							return_tensors='np')
           _A					:     int     = processed.input_values

           self._check_zero_mean_unit_variance(input_values[0, :800])
           self._check_zero_mean_unit_variance(input_values[1, :1000])
           self._check_zero_mean_unit_variance(input_values[2])

           # make sure that if max_length < longest -> then pad to max_length
           self.assertTrue(input_values.shape == (3, 1000))

           _A					:     List[Any]     = [floats_list((1, x))[0] for x in range(800				,							1400				,							200)]
           _A					:     List[Any]     = feat_extract(
               SCREAMING_SNAKE_CASE				,							truncation=SCREAMING_SNAKE_CASE				,							max_length=2000				,							padding='longest'				,							return_tensors='np')
           _A					:     Union[str, Any]     = processed.input_values

           self._check_zero_mean_unit_variance(input_values[0, :800])
           self._check_zero_mean_unit_variance(input_values[1, :1000])
           self._check_zero_mean_unit_variance(input_values[2])

           # make sure that if max_length > longest -> then pad to longest
           self.assertTrue(input_values.shape == (3, 1200))


    def        A   (  self      :							List[str]):
           _A					:     Tuple     = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
           _A					:     Optional[int]     = np.random.rand(100).astype(np.floataa)
           _A					:     str     = np_speech_inputs.tolist()

           for inputs in [py_speech_inputs, np_speech_inputs]:
                  _A					:     List[Any]     = feature_extractor.pad([{'input_values': inputs}]				,							return_tensors='np')
                  self.assertTrue(np_processed.input_values.dtype == np.floataa)
                  _A					:     Optional[Any]     = feature_extractor.pad([{'input_values': inputs}]				,							return_tensors='pt')
                  self.assertTrue(pt_processed.input_values.dtype == torch.floataa)


    def        A   (  self      :							Any):
           # Tests that all call wrap to encode_plus and batch_encode_plus
           _A					:     Any     = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
           # create three inputs of length 800, 1000, and 1200
           _A					:     Dict     = [floats_list((1, x))[0] for x in range(800				,							1400				,							200)]
           _A					:     Any     = [np.asarray(SCREAMING_SNAKE_CASE) for speech_input in speech_inputs]

           # Test feature size
           _A					:     Optional[Any]     = feature_extractor(audio_target=SCREAMING_SNAKE_CASE				,							padding=SCREAMING_SNAKE_CASE				,							return_tensors='np').input_values
           self.assertTrue(input_values.ndim == 3)
           self.assertTrue(input_values.shape[-1] == feature_extractor.num_mel_bins)

           # Test not batched input
           _A					:     Optional[int]     = feature_extractor(speech_inputs[0]				,							return_tensors='np').input_values
           _A					:     Tuple     = feature_extractor(np_speech_inputs[0]				,							return_tensors='np').input_values
           self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE				,							atol=1e-3))

           # Test batched
           _A					:     Optional[int]     = feature_extractor(SCREAMING_SNAKE_CASE				,							return_tensors='np').input_values
           _A					:     Tuple     = feature_extractor(SCREAMING_SNAKE_CASE				,							return_tensors='np').input_values
           for enc_seq_a, enc_seq_a in zip(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE):
                  self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE				,							atol=1e-3))

           # Test 2-D numpy arrays are batched.
           _A					:     Optional[Any]     = [floats_list((1, x))[0] for x in (800, 800, 800)]
           _A					:     Any     = np.asarray(SCREAMING_SNAKE_CASE)
           _A					:     str     = feature_extractor(SCREAMING_SNAKE_CASE				,							return_tensors='np').input_values
           _A					:     Any     = feature_extractor(SCREAMING_SNAKE_CASE				,							return_tensors='np').input_values
           for enc_seq_a, enc_seq_a in zip(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE):
                  self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE				,							atol=1e-3))


    def        A   (  self      :							Optional[Any]):
           _A					:     Dict     = self.feat_extract_tester.prepare_inputs_for_target()
           _A					:     int     = self.feature_extraction_class(**self.feat_extract_dict)
           _A					:     Optional[int]     = feat_extract.model_input_names[0]

           _A					:     Tuple     = BatchFeature({input_name: speech_inputs})

           self.assertTrue(all(len(SCREAMING_SNAKE_CASE) == len(SCREAMING_SNAKE_CASE) for x, y in zip(SCREAMING_SNAKE_CASE				,							processed_features[input_name])))

           _A					:     List[Any]     = self.feat_extract_tester.prepare_inputs_for_target(equal_length=SCREAMING_SNAKE_CASE)
           _A					:     Union[str, Any]     = BatchFeature({input_name: speech_inputs}				,							tensor_type='np')

           _A					:     Tuple     = processed_features[input_name]

           if len(batch_features_input.shape) < 3:
                  _A					:     Any     = batch_features_input[:, :, None]

           self.assertTrue(
               batch_features_input.shape
               == (self.feat_extract_tester.batch_size, len(speech_inputs[0]), self.feat_extract_tester.num_mel_bins))


    @require_torch
    def        A   (  self      :							Union[str, Any]):
           _A					:     Tuple     = self.feat_extract_tester.prepare_inputs_for_target(equal_length=SCREAMING_SNAKE_CASE)
           _A					:     str     = self.feature_extraction_class(**self.feat_extract_dict)
           _A					:     Dict     = feat_extract.model_input_names[0]

           _A					:     Any     = BatchFeature({input_name: speech_inputs}				,							tensor_type='pt')

           _A					:     List[Any]     = processed_features[input_name]

           if len(batch_features_input.shape) < 3:
                  _A					:     List[Any]     = batch_features_input[:, :, None]

           self.assertTrue(
               batch_features_input.shape
               == (self.feat_extract_tester.batch_size, len(speech_inputs[0]), self.feat_extract_tester.num_mel_bins))


    @require_torch
    def        A   (  self      :							List[Any]):
           _A					:     Optional[int]     = self.feature_extraction_class(**self.feat_extract_dict)
           _A					:     List[str]     = self.feat_extract_tester.prepare_inputs_for_target()
           _A					:     List[Any]     = feat_extract.model_input_names[0]

           _A					:     List[str]     = BatchFeature({input_name: speech_inputs})

           _A					:     int     = feat_extract.num_mel_bins  # hack!

           _A					:     List[Any]     = feat_extract.pad(SCREAMING_SNAKE_CASE				,							padding='longest'				,							return_tensors='np')[input_name]
           _A					:     Tuple     = feat_extract.pad(SCREAMING_SNAKE_CASE				,							padding='longest'				,							return_tensors='pt')[input_name]

           self.assertTrue(abs(input_np.astype(np.floataa).sum() - input_pt.numpy().astype(np.floataa).sum()) < 1e-2)


    def        A   (  self      :							Tuple):
           _A					:     int     = self.feat_extract_dict
           _A					:     List[str]     = True
           _A					:     Tuple     = self.feature_extraction_class(**SCREAMING_SNAKE_CASE)
           _A					:     str     = self.feat_extract_tester.prepare_inputs_for_target()
           _A					:     Any     = [len(SCREAMING_SNAKE_CASE) for x in speech_inputs]
           _A					:     Union[str, Any]     = feat_extract.model_input_names[0]

           _A					:     List[str]     = BatchFeature({input_name: speech_inputs})

           _A					:     Optional[Any]     = feat_extract.num_mel_bins  # hack!

           _A					:     str     = feat_extract.pad(SCREAMING_SNAKE_CASE				,							padding='longest'				,							return_tensors='np')
           self.assertIn('attention_mask'				,							SCREAMING_SNAKE_CASE)
           self.assertListEqual(list(processed.attention_mask.shape)				,							list(processed[input_name].shape[:2]))
           self.assertListEqual(processed.attention_mask.sum(-1).tolist()				,							SCREAMING_SNAKE_CASE)


    def        A   (  self      :							int):
           _A					:     Optional[int]     = self.feat_extract_dict
           _A					:     Optional[int]     = True
           _A					:     List[str]     = self.feature_extraction_class(**SCREAMING_SNAKE_CASE)
           _A					:     Optional[Any]     = self.feat_extract_tester.prepare_inputs_for_target()
           _A					:     List[Any]     = [len(SCREAMING_SNAKE_CASE) for x in speech_inputs]
           _A					:     Dict     = feat_extract.model_input_names[0]

           _A					:     Optional[int]     = BatchFeature({input_name: speech_inputs})
           _A					:     Any     = min(SCREAMING_SNAKE_CASE)

           _A					:     Tuple     = feat_extract.num_mel_bins  # hack!

           _A					:     Any     = feat_extract.pad(
               SCREAMING_SNAKE_CASE				,							padding='max_length'				,							max_length=SCREAMING_SNAKE_CASE				,							truncation=SCREAMING_SNAKE_CASE				,							return_tensors='np')
           self.assertIn('attention_mask'				,							SCREAMING_SNAKE_CASE)
           self.assertListEqual(
               list(processed_pad.attention_mask.shape)				,							[processed_pad[input_name].shape[0], max_length])
           self.assertListEqual(
               processed_pad.attention_mask[:, :max_length].sum(-1).tolist()				,							[max_length for x in speech_inputs])


    def        A   (  self      :							Dict				,							SCREAMING_SNAKE_CASE      :							int):
           from datasets import load_dataset

           _A					:     int     = load_dataset('hf-internal-testing/librispeech_asr_dummy'				,							'clean'				,							split='validation')
           # automatic decoding with librispeech
           _A					:     Optional[int]     = ds.sort('id').select(range(SCREAMING_SNAKE_CASE))[:num_samples]['audio']

           return [x["array"] for x in speech_samples]


    def        A   (  self      :							List[str]):
           # fmt: off
           _A					:     List[Any]     = torch.tensor(
               [2.38_04e-03, 2.07_52e-03, 1.98_36e-03, 2.10_57e-03, 1.61_74e-03,
                3.05_18e-04, 9.15_53e-05, 3.35_69e-04, 9.76_56e-04, 1.83_11e-03,
                2.01_42e-03, 2.10_57e-03, 1.73_95e-03, 4.57_76e-04, -3.96_73e-04,
                4.57_76e-04, 1.00_71e-03, 9.15_53e-05, 4.88_28e-04, 1.15_97e-03,
                7.32_42e-04, 9.46_04e-04, 1.80_05e-03, 1.83_11e-03, 8.85_01e-04,
                4.27_25e-04, 4.88_28e-04, 7.32_42e-04, 1.09_86e-03, 2.10_57e-03])
           # fmt: on

           _A					:     Any     = self._load_datasamples(1)
           _A					:     Tuple     = SpeechTaFeatureExtractor()
           _A					:     List[str]     = feature_extractor(SCREAMING_SNAKE_CASE				,							return_tensors='pt').input_values
           self.assertEquals(input_values.shape				,							(1, 93680))
           self.assertTrue(torch.allclose(input_values[0, :30]				,							SCREAMING_SNAKE_CASE				,							atol=1e-6))


    def        A   (  self      :							List[Any]):
           # fmt: off
           _A					:     List[str]     = torch.tensor(
               [-2.6870, -3.0104, -3.1356, -3.5352, -3.0044, -3.0353, -3.4719, -3.6777,
                -3.1520, -2.9435, -2.6553, -2.8795, -2.9944, -2.5921, -3.0279, -3.0386,
                -3.0864, -3.1291, -3.2353, -2.7444, -2.6831, -2.7287, -3.1761, -3.1571,
                -3.2726, -3.0582, -3.1007, -3.4533, -3.4695, -3.0998])
           # fmt: on

           _A					:     str     = self._load_datasamples(1)
           _A					:     List[str]     = SpeechTaFeatureExtractor()
           _A					:     List[str]     = feature_extractor(audio_target=SCREAMING_SNAKE_CASE				,							return_tensors='pt').input_values
           self.assertEquals(input_values.shape				,							(1, 366, 80))
           self.assertTrue(torch.allclose(input_values[0, 0, :30]				,							SCREAMING_SNAKE_CASE				,							atol=1e-4))










'''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__	(    lowerCamelCase					:      Optional[int]       ):
       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	):
       """simple docstring"""


       def __init__(  self      :							int				,							SCREAMING_SNAKE_CASE      :							nn.Module				,							SCREAMING_SNAKE_CASE      :							int):
              super().__init__()
              _A					:     Tuple     = module
              _A					:     str     = 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					:     Any     = (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        A   (  self      :							Any				,							SCREAMING_SNAKE_CASE      :							Any				,							*SCREAMING_SNAKE_CASE      :							List[str]				,							**SCREAMING_SNAKE_CASE      :							int):
              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 			__lowerCamelCase			(	unittest.TestCase	):
    """simple docstring"""
    # 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
    a													=   "bigscience/bloom-1b7"

    # Constant values
    a													=   2.109_6595_5269_2574

    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													=   10


    def        A   (  self      :							List[Any]):
           # Models and tokenizer
           _A					:     List[Any]     = AutoTokenizer.from_pretrained(self.model_name)




class 			__lowerCamelCase			(	a_	):
    """simple docstring"""


    def        A   (  self      :							List[str]):
           super().setUp()

           # Models and tokenizer
           _A					:     Tuple     = AutoModelForCausalLM.from_pretrained(
               self.model_name				,							torch_dtype=torch.floataa				,							device_map='auto')
           _A					:     str     = AutoModelForCausalLM.from_pretrained(self.model_name				,							load_in_abit=SCREAMING_SNAKE_CASE				,							device_map='auto')


    def        A   (  self      :							List[Any]):
           del self.model_fpaa
           del self.model_abit

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


    def        A   (  self      :							str):
           _A					:     Any     = self.model_abit.config

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

           _A					:     str     = config.to_dict()
           _A					:     Dict     = config.to_diff_dict()

           _A					:     int     = config.to_json_string()


    def        A   (  self      :							List[str]):
           from bitsandbytes.nn import Paramsabit

           _A					:     List[str]     = self.model_fpaa.get_memory_footprint()
           _A					:     List[Any]     = self.model_abit.get_memory_footprint()

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


    def        A   (  self      :							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(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        A   (  self      :							Any):
           _A					:     List[str]     = self.tokenizer(self.input_text				,							return_tensors='pt')
           _A					:     Any     = 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        A   (  self      :							List[str]):
           _A					:     List[Any]     = BitsAndBytesConfig()
           _A					:     Optional[int]     = True

           _A					:     Union[str, Any]     = AutoModelForCausalLM.from_pretrained(
               self.model_name				,							quantization_config=SCREAMING_SNAKE_CASE				,							device_map='auto')

           _A					:     int     = self.tokenizer(self.input_text				,							return_tensors='pt')
           _A					:     Any     = 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        A   (  self      :							Union[str, Any]):
           with self.assertRaises(SCREAMING_SNAKE_CASE), tempfile.TemporaryDirectory() as tmpdirname:
                  self.model_abit.save_pretrained(SCREAMING_SNAKE_CASE)


    def        A   (  self      :							int):
           _A					:     Any     = BitsAndBytesConfig()

           with self.assertRaises(SCREAMING_SNAKE_CASE):
                  _A					:     Optional[Any]     = 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        A   (  self      :							Optional[Any]):
           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					:     str     = self.tokenizer(self.input_text				,							return_tensors='pt')

           _A					:     Optional[Any]     = self.model_fpaa.to(torch.floataa)
           _A					:     Optional[int]     = self.model_fpaa.generate(input_ids=encoded_input['input_ids'].to(0)				,							max_new_tokens=10)

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

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

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


    def        A   (  self      :							Dict):
           _A					:     Any     = 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 			__lowerCamelCase			(	unittest.TestCase	):
    """simple docstring"""


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


    def        A   (  self      :							Any):
           gc.collect()
           torch.cuda.empty_cache()


    def        A   (  self      :							List[str]):
           from transformers import TaForConditionalGeneration

           _A					:     Union[str, Any]     = TaForConditionalGeneration._keep_in_fpaa_modules
           _A					:     List[str]     = None

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

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


    def        A   (  self      :							Dict):
           import bitsandbytes as bnb

           from transformers import TaForConditionalGeneration

           # test with `t5-small`
           _A					:     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					:     int     = self.tokenizer(self.input_text				,							return_tensors='pt').to(0)
           _A					:     Union[str, Any]     = model.generate(**SCREAMING_SNAKE_CASE)

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




class 			__lowerCamelCase			(	a_	):
    """simple docstring"""


    def        A   (  self      :							Union[str, Any]):
           super().setUp()
           # model_name
           _A					:     Dict     = 'bigscience/bloom-560m'
           _A					:     List[str]     = 't5-small'

           # Different types of model

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


    def        A   (  self      :							str):
           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      :							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 			__lowerCamelCase			(	a_	):
    """simple docstring"""


    def        A   (  self      :							Optional[Any]):
           super().setUp()


    def        A   (  self      :							Any):
           del self.pipe

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


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




@require_torch_multi_gpu
class 			__lowerCamelCase			(	a_	):
    """simple docstring"""


    def        A   (  self      :							List[Any]):
           super().setUp()


    def        A   (  self      :							Any):
           _A					:     int     = 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					:     Tuple     = self.tokenizer(self.input_text				,							return_tensors='pt')

           # Second real batch
           _A					:     Union[str, 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 			__lowerCamelCase			(	a_	):
    """simple docstring"""


    def        A   (  self      :							Optional[Any]):
           _A					:     Any     = 'facebook/opt-350m'
           super().setUp()


    def        A   (  self      :							List[Any]):
           if version.parse(importlib.metadata.version('bitsandbytes')) < version.parse('0.37.0'):
                  return

           # Step 1: freeze all parameters
           _A					:     Any     = 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[Any]     = 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					:     List[Any]     = LoRALayer(module.q_proj				,							rank=16)
                         _A					:     Union[str, Any]     = LoRALayer(module.k_proj				,							rank=16)
                         _A					:     str     = LoRALayer(module.v_proj				,							rank=16)

        # Step 3: dummy batch
           _A					:     Union[str, Any]     = self.tokenizer('Test batch '				,							return_tensors='pt').to(0)

           # Step 4: Check if the gradient is not None
           with torch.cuda.amp.autocast():
                  _A					:     Union[str, Any]     = 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 			__lowerCamelCase			(	a_	):
    """simple docstring"""
    a													=   "gpt2-xl"
    a													=   3.3191_8548_5415_2187






