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from typing import Dict
from .base import GenericTensor, Pipeline
class _A ( __magic_name__):
def UpperCAmelCase ( self , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None , **_SCREAMING_SNAKE_CASE ):
"""simple docstring"""
if tokenize_kwargs is None:
SCREAMING_SNAKE_CASE_ : Dict = {}
if truncation is not None:
if "truncation" in tokenize_kwargs:
raise ValueError(
'truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)' )
SCREAMING_SNAKE_CASE_ : List[Any] = truncation
SCREAMING_SNAKE_CASE_ : int = tokenize_kwargs
SCREAMING_SNAKE_CASE_ : int = {}
if return_tensors is not None:
SCREAMING_SNAKE_CASE_ : Optional[int] = return_tensors
return preprocess_params, {}, postprocess_params
def UpperCAmelCase ( self , _SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Any = self.framework
SCREAMING_SNAKE_CASE_ : str = self.tokenizer(_SCREAMING_SNAKE_CASE , return_tensors=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
return model_inputs
def UpperCAmelCase ( self , _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : int = self.model(**_SCREAMING_SNAKE_CASE )
return model_outputs
def UpperCAmelCase ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=False ):
"""simple docstring"""
if return_tensors:
return model_outputs[0]
if self.framework == "pt":
return model_outputs[0].tolist()
elif self.framework == "tf":
return model_outputs[0].numpy().tolist()
def __call__( self , *_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE ):
"""simple docstring"""
return super().__call__(*_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
| 253
|
import math
def A_ ( a , a = 0 , a = 0 ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[Any] = end or len(a )
for i in range(a , a ):
SCREAMING_SNAKE_CASE_ : List[Any] = i
SCREAMING_SNAKE_CASE_ : Optional[Any] = array[i]
while temp_index != start and temp_index_value < array[temp_index - 1]:
SCREAMING_SNAKE_CASE_ : Tuple = array[temp_index - 1]
temp_index -= 1
SCREAMING_SNAKE_CASE_ : str = temp_index_value
return array
def A_ ( a , a , a ): # Max Heap
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Any = index
SCREAMING_SNAKE_CASE_ : str = 2 * index + 1 # Left Node
SCREAMING_SNAKE_CASE_ : Union[str, Any] = 2 * index + 2 # Right Node
if left_index < heap_size and array[largest] < array[left_index]:
SCREAMING_SNAKE_CASE_ : Dict = left_index
if right_index < heap_size and array[largest] < array[right_index]:
SCREAMING_SNAKE_CASE_ : Any = right_index
if largest != index:
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : Tuple = array[largest], array[index]
heapify(a , a , a )
def A_ ( a ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : str = len(a )
for i in range(n // 2 , -1 , -1 ):
heapify(a , a , a )
for i in range(n - 1 , 0 , -1 ):
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : Tuple = array[0], array[i]
heapify(a , 0 , a )
return array
def A_ ( a , a , a , a ):
"""simple docstring"""
if (array[first_index] > array[middle_index]) != (
array[first_index] > array[last_index]
):
return array[first_index]
elif (array[middle_index] > array[first_index]) != (
array[middle_index] > array[last_index]
):
return array[middle_index]
else:
return array[last_index]
def A_ ( a , a , a , a ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : List[Any] = low
SCREAMING_SNAKE_CASE_ : Tuple = high
while True:
while array[i] < pivot:
i += 1
j -= 1
while pivot < array[j]:
j -= 1
if i >= j:
return i
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : Tuple = array[j], array[i]
i += 1
def A_ ( a ):
"""simple docstring"""
if len(a ) == 0:
return array
SCREAMING_SNAKE_CASE_ : Any = 2 * math.ceil(math.loga(len(a ) ) )
SCREAMING_SNAKE_CASE_ : int = 1_6
return intro_sort(a , 0 , len(a ) , a , a )
def A_ ( a , a , a , a , a ):
"""simple docstring"""
while end - start > size_threshold:
if max_depth == 0:
return heap_sort(a )
max_depth -= 1
SCREAMING_SNAKE_CASE_ : Optional[int] = median_of_a(a , a , start + ((end - start) // 2) + 1 , end - 1 )
SCREAMING_SNAKE_CASE_ : Dict = partition(a , a , a , a )
intro_sort(a , a , a , a , a )
SCREAMING_SNAKE_CASE_ : List[Any] = p
return insertion_sort(a , a , a )
if __name__ == "__main__":
import doctest
doctest.testmod()
lowerCAmelCase : List[str] = input('Enter numbers separated by a comma : ').strip()
lowerCAmelCase : Optional[Any] = [float(item) for item in user_input.split(',')]
print(sort(unsorted))
| 253
| 1
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ..utils import _LazyModule
SCREAMING_SNAKE_CASE__ = {
"config": [
"EXTERNAL_DATA_FORMAT_SIZE_LIMIT",
"OnnxConfig",
"OnnxConfigWithPast",
"OnnxSeq2SeqConfigWithPast",
"PatchingSpec",
],
"convert": ["export", "validate_model_outputs"],
"features": ["FeaturesManager"],
"utils": ["ParameterFormat", "compute_serialized_parameters_size"],
}
if TYPE_CHECKING:
from .config import (
EXTERNAL_DATA_FORMAT_SIZE_LIMIT,
OnnxConfig,
OnnxConfigWithPast,
OnnxSeqaSeqConfigWithPast,
PatchingSpec,
)
from .convert import export, validate_model_outputs
from .features import FeaturesManager
from .utils import ParameterFormat, compute_serialized_parameters_size
else:
import sys
SCREAMING_SNAKE_CASE__ = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 149
|
"""simple docstring"""
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
if is_torch_available():
import torch
if is_datasets_available():
from datasets import load_dataset
SCREAMING_SNAKE_CASE__ = random.Random()
def lowerCAmelCase__ ( _UpperCamelCase : List[Any] , _UpperCamelCase : Optional[Any]=1.0 , _UpperCamelCase : Optional[int]=None , _UpperCamelCase : Optional[int]=None ) -> Optional[int]:
"""simple docstring"""
if rng is None:
snake_case = global_rng
snake_case = []
for batch_idx in range(shape[0] ):
values.append([] )
for _ in range(shape[1] ):
values[-1].append(rng.random() * scale )
return values
class lowerCAmelCase_ ( unittest.TestCase ):
"""simple docstring"""
def __init__( self , lowerCAmelCase , lowerCAmelCase=7 , lowerCAmelCase=4_00 , lowerCAmelCase=20_00 , lowerCAmelCase=20_48 , lowerCAmelCase=1_28 , lowerCAmelCase=1 , lowerCAmelCase=5_12 , lowerCAmelCase=30 , lowerCAmelCase=4_41_00 , ):
"""simple docstring"""
snake_case = parent
snake_case = batch_size
snake_case = min_seq_length
snake_case = max_seq_length
snake_case = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
snake_case = spectrogram_length
snake_case = feature_size
snake_case = num_audio_channels
snake_case = hop_length
snake_case = chunk_length
snake_case = sampling_rate
def snake_case ( self ):
"""simple docstring"""
return {
"spectrogram_length": self.spectrogram_length,
"feature_size": self.feature_size,
"num_audio_channels": self.num_audio_channels,
"hop_length": self.hop_length,
"chunk_length": self.chunk_length,
"sampling_rate": self.sampling_rate,
}
def snake_case ( self , lowerCAmelCase=False , lowerCAmelCase=False ):
"""simple docstring"""
def _flatten(lowerCAmelCase ):
return list(itertools.chain(*lowerCAmelCase ) )
if equal_length:
snake_case = [floats_list((self.max_seq_length, self.feature_size) ) for _ in range(self.batch_size )]
else:
# make sure that inputs increase in size
snake_case = [
floats_list((x, self.feature_size) )
for x in range(self.min_seq_length , self.max_seq_length , self.seq_length_diff )
]
if numpify:
snake_case = [np.asarray(lowerCAmelCase ) for x in speech_inputs]
return speech_inputs
@require_torch
@require_torchaudio
class lowerCAmelCase_ ( lowerCAmelCase , unittest.TestCase ):
"""simple docstring"""
_lowerCAmelCase : Union[str, Any] = TvltFeatureExtractor
def snake_case ( self ):
"""simple docstring"""
snake_case = TvltFeatureExtractionTester(self )
def snake_case ( self ):
"""simple docstring"""
snake_case = self.feature_extraction_class(**self.feat_extract_dict )
self.assertTrue(hasattr(lowerCAmelCase , 'spectrogram_length' ) )
self.assertTrue(hasattr(lowerCAmelCase , 'feature_size' ) )
self.assertTrue(hasattr(lowerCAmelCase , 'num_audio_channels' ) )
self.assertTrue(hasattr(lowerCAmelCase , 'hop_length' ) )
self.assertTrue(hasattr(lowerCAmelCase , 'chunk_length' ) )
self.assertTrue(hasattr(lowerCAmelCase , 'sampling_rate' ) )
def snake_case ( self ):
"""simple docstring"""
snake_case = self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
snake_case = feat_extract_first.save_pretrained(lowerCAmelCase )[0]
check_json_file_has_correct_format(lowerCAmelCase )
snake_case = self.feature_extraction_class.from_pretrained(lowerCAmelCase )
snake_case = feat_extract_first.to_dict()
snake_case = feat_extract_second.to_dict()
snake_case = dict_first.pop('mel_filters' )
snake_case = dict_second.pop('mel_filters' )
self.assertTrue(np.allclose(lowerCAmelCase , lowerCAmelCase ) )
self.assertEqual(lowerCAmelCase , lowerCAmelCase )
def snake_case ( self ):
"""simple docstring"""
snake_case = self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
snake_case = os.path.join(lowerCAmelCase , 'feat_extract.json' )
feat_extract_first.to_json_file(lowerCAmelCase )
snake_case = self.feature_extraction_class.from_json_file(lowerCAmelCase )
snake_case = feat_extract_first.to_dict()
snake_case = feat_extract_second.to_dict()
snake_case = dict_first.pop('mel_filters' )
snake_case = dict_second.pop('mel_filters' )
self.assertTrue(np.allclose(lowerCAmelCase , lowerCAmelCase ) )
self.assertEqual(lowerCAmelCase , lowerCAmelCase )
def snake_case ( self ):
"""simple docstring"""
snake_case = self.feature_extraction_class(**self.feat_extract_dict )
# create three inputs of length 800, 1000, and 1200
snake_case = [floats_list((1, x) )[0] for x in range(8_00 , 14_00 , 2_00 )]
snake_case = [np.asarray(lowerCAmelCase ) for speech_input in speech_inputs]
# Test not batched input
snake_case = feature_extractor(np_speech_inputs[0] , return_tensors='np' , sampling_rate=4_41_00 ).audio_values
self.assertTrue(encoded_audios.ndim == 4 )
self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size )
self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length )
self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels )
# Test batched
snake_case = feature_extractor(lowerCAmelCase , return_tensors='np' , sampling_rate=4_41_00 ).audio_values
self.assertTrue(encoded_audios.ndim == 4 )
self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size )
self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length )
self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels )
# Test audio masking
snake_case = feature_extractor(
lowerCAmelCase , return_tensors='np' , sampling_rate=4_41_00 , mask_audio=lowerCAmelCase ).audio_values
self.assertTrue(encoded_audios.ndim == 4 )
self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size )
self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length )
self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels )
# Test 2-D numpy arrays are batched.
snake_case = [floats_list((1, x) )[0] for x in (8_00, 8_00, 8_00)]
snake_case = np.asarray(lowerCAmelCase )
snake_case = feature_extractor(lowerCAmelCase , return_tensors='np' , sampling_rate=4_41_00 ).audio_values
self.assertTrue(encoded_audios.ndim == 4 )
self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size )
self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length )
self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels )
def snake_case ( self , lowerCAmelCase ):
"""simple docstring"""
snake_case = load_dataset('hf-internal-testing/librispeech_asr_dummy' , 'clean' , split='validation' )
# automatic decoding with librispeech
snake_case = ds.sort('id' ).select(range(lowerCAmelCase ) )[:num_samples]['audio']
return [x["array"] for x in speech_samples]
def snake_case ( self ):
"""simple docstring"""
snake_case = self._load_datasamples(1 )
snake_case = TvltFeatureExtractor()
snake_case = feature_extractor(lowerCAmelCase , return_tensors='pt' ).audio_values
self.assertEquals(audio_values.shape , (1, 1, 1_92, 1_28) )
snake_case = torch.tensor([[-0.30_32, -0.27_08], [-0.44_34, -0.40_07]] )
self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2] , lowerCAmelCase , atol=1E-4 ) )
| 149
| 1
|
'''simple docstring'''
import json
import os
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import ImageGPTImageProcessor
class a__ ( unittest.TestCase ):
"""simple docstring"""
def __init__(self , __lowercase , __lowercase=7 , __lowercase=3 , __lowercase=18 , __lowercase=30 , __lowercase=4_00 , __lowercase=True , __lowercase=None , __lowercase=True , ):
__lowerCAmelCase = size if size is not None else {'''height''': 18, '''width''': 18}
__lowerCAmelCase = parent
__lowerCAmelCase = batch_size
__lowerCAmelCase = num_channels
__lowerCAmelCase = image_size
__lowerCAmelCase = min_resolution
__lowerCAmelCase = max_resolution
__lowerCAmelCase = do_resize
__lowerCAmelCase = size
__lowerCAmelCase = do_normalize
def _snake_case (self ):
return {
# here we create 2 clusters for the sake of simplicity
"clusters": np.asarray(
[
[0.8_8_6_6_4_4_3_6_3_4_0_3_3_2_0_3, 0.6_6_1_8_8_2_9_3_6_9_5_4_4_9_8_3, 0.3_8_9_1_7_4_6_4_0_1_7_8_6_8_0_4],
[-0.6_0_4_2_5_5_9_1_4_6_8_8_1_1_0_4, -0.0_2_2_9_5_0_0_8_8_6_0_5_2_8_4_6_9, 0.5_4_2_3_7_9_7_3_6_9_0_0_3_2_9_6],
] ),
"do_resize": self.do_resize,
"size": self.size,
"do_normalize": self.do_normalize,
}
@require_torch
@require_vision
class a__ ( __A , unittest.TestCase ):
"""simple docstring"""
__UpperCamelCase : Optional[int] = ImageGPTImageProcessor if is_vision_available() else None
def _snake_case (self ):
__lowerCAmelCase = ImageGPTImageProcessingTester(self )
@property
def _snake_case (self ):
return self.image_processor_tester.prepare_image_processor_dict()
def _snake_case (self ):
__lowerCAmelCase = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(__lowercase , '''clusters''' ) )
self.assertTrue(hasattr(__lowercase , '''do_resize''' ) )
self.assertTrue(hasattr(__lowercase , '''size''' ) )
self.assertTrue(hasattr(__lowercase , '''do_normalize''' ) )
def _snake_case (self ):
__lowerCAmelCase = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'''height''': 18, '''width''': 18} )
__lowerCAmelCase = self.image_processing_class.from_dict(self.image_processor_dict , size=42 )
self.assertEqual(image_processor.size , {'''height''': 42, '''width''': 42} )
def _snake_case (self ):
__lowerCAmelCase = self.image_processing_class(**self.image_processor_dict )
__lowerCAmelCase = json.loads(image_processor.to_json_string() )
for key, value in self.image_processor_dict.items():
if key == "clusters":
self.assertTrue(np.array_equal(__lowercase , obj[key] ) )
else:
self.assertEqual(obj[key] , __lowercase )
def _snake_case (self ):
__lowerCAmelCase = self.image_processing_class(**self.image_processor_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
__lowerCAmelCase = os.path.join(__lowercase , '''image_processor.json''' )
image_processor_first.to_json_file(__lowercase )
__lowerCAmelCase = self.image_processing_class.from_json_file(__lowercase ).to_dict()
__lowerCAmelCase = image_processor_first.to_dict()
for key, value in image_processor_first.items():
if key == "clusters":
self.assertTrue(np.array_equal(__lowercase , image_processor_second[key] ) )
else:
self.assertEqual(image_processor_first[key] , __lowercase )
def _snake_case (self ):
__lowerCAmelCase = self.image_processing_class(**self.image_processor_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
image_processor_first.save_pretrained(__lowercase )
__lowerCAmelCase = self.image_processing_class.from_pretrained(__lowercase ).to_dict()
__lowerCAmelCase = image_processor_first.to_dict()
for key, value in image_processor_first.items():
if key == "clusters":
self.assertTrue(np.array_equal(__lowercase , image_processor_second[key] ) )
else:
self.assertEqual(image_processor_first[key] , __lowercase )
@unittest.skip('''ImageGPT requires clusters at initialization''' )
def _snake_case (self ):
pass
def __magic_name__( ):
__lowerCAmelCase = load_dataset('''hf-internal-testing/fixtures_image_utils''', split='''test''')
__lowerCAmelCase = Image.open(dataset[4]['''file'''])
__lowerCAmelCase = Image.open(dataset[5]['''file'''])
__lowerCAmelCase = [imagea, imagea]
return images
@require_vision
@require_torch
class a__ ( unittest.TestCase ):
"""simple docstring"""
@slow
def _snake_case (self ):
__lowerCAmelCase = ImageGPTImageProcessor.from_pretrained('''openai/imagegpt-small''' )
__lowerCAmelCase = prepare_images()
# test non-batched
__lowerCAmelCase = image_processing(images[0] , return_tensors='''pt''' )
self.assertIsInstance(encoding.input_ids , torch.LongTensor )
self.assertEqual(encoding.input_ids.shape , (1, 10_24) )
__lowerCAmelCase = [3_06, 1_91, 1_91]
self.assertEqual(encoding.input_ids[0, :3].tolist() , __lowercase )
# test batched
__lowerCAmelCase = image_processing(__lowercase , return_tensors='''pt''' )
self.assertIsInstance(encoding.input_ids , torch.LongTensor )
self.assertEqual(encoding.input_ids.shape , (2, 10_24) )
__lowerCAmelCase = [3_03, 13, 13]
self.assertEqual(encoding.input_ids[1, -3:].tolist() , __lowercase )
| 174
|
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DiffusionPipeline,
EulerDiscreteScheduler,
StableDiffusionXLImgaImgPipeline,
UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import (
IMAGE_TO_IMAGE_IMAGE_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class a__ ( __A , __A , unittest.TestCase ):
"""simple docstring"""
__UpperCamelCase : Union[str, Any] = StableDiffusionXLImgaImgPipeline
__UpperCamelCase : str = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width'}
__UpperCamelCase : List[str] = PipelineTesterMixin.required_optional_params - {'latents'}
__UpperCamelCase : List[str] = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
__UpperCamelCase : Union[str, Any] = IMAGE_TO_IMAGE_IMAGE_PARAMS
__UpperCamelCase : int = IMAGE_TO_IMAGE_IMAGE_PARAMS
def _snake_case (self ):
torch.manual_seed(0 )
__lowerCAmelCase = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''') , up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''') , attention_head_dim=(2, 4) , use_linear_projection=__lowercase , addition_embed_type='''text_time''' , addition_time_embed_dim=8 , transformer_layers_per_block=(1, 2) , projection_class_embeddings_input_dim=80 , cross_attention_dim=64 , )
__lowerCAmelCase = EulerDiscreteScheduler(
beta_start=0.0_0_0_8_5 , beta_end=0.0_1_2 , steps_offset=1 , beta_schedule='''scaled_linear''' , timestep_spacing='''leading''' , )
torch.manual_seed(0 )
__lowerCAmelCase = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''] , up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''] , latent_channels=4 , sample_size=1_28 , )
torch.manual_seed(0 )
__lowerCAmelCase = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=10_00 , hidden_act='''gelu''' , projection_dim=32 , )
__lowerCAmelCase = CLIPTextModel(__lowercase )
__lowerCAmelCase = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' , local_files_only=__lowercase )
__lowerCAmelCase = CLIPTextModelWithProjection(__lowercase )
__lowerCAmelCase = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' , local_files_only=__lowercase )
__lowerCAmelCase = {
'''unet''': unet,
'''scheduler''': scheduler,
'''vae''': vae,
'''text_encoder''': text_encoder,
'''tokenizer''': tokenizer,
'''text_encoder_2''': text_encoder_a,
'''tokenizer_2''': tokenizer_a,
# "safety_checker": None,
# "feature_extractor": None,
}
return components
def _snake_case (self , __lowercase , __lowercase=0 ):
__lowerCAmelCase = floats_tensor((1, 3, 32, 32) , rng=random.Random(__lowercase ) ).to(__lowercase )
__lowerCAmelCase = image / 2 + 0.5
if str(__lowercase ).startswith('''mps''' ):
__lowerCAmelCase = torch.manual_seed(__lowercase )
else:
__lowerCAmelCase = torch.Generator(device=__lowercase ).manual_seed(__lowercase )
__lowerCAmelCase = {
'''prompt''': '''A painting of a squirrel eating a burger''',
'''image''': image,
'''generator''': generator,
'''num_inference_steps''': 2,
'''guidance_scale''': 5.0,
'''output_type''': '''numpy''',
'''strength''': 0.7_5,
}
return inputs
def _snake_case (self ):
__lowerCAmelCase = '''cpu''' # ensure determinism for the device-dependent torch.Generator
__lowerCAmelCase = self.get_dummy_components()
__lowerCAmelCase = StableDiffusionXLImgaImgPipeline(**__lowercase )
__lowerCAmelCase = sd_pipe.to(__lowercase )
sd_pipe.set_progress_bar_config(disable=__lowercase )
__lowerCAmelCase = self.get_dummy_inputs(__lowercase )
__lowerCAmelCase = sd_pipe(**__lowercase ).images
__lowerCAmelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
__lowerCAmelCase = np.array([0.4_6_5_6, 0.4_8_4_0, 0.4_4_3_9, 0.6_6_9_8, 0.5_5_7_4, 0.4_5_2_4, 0.5_7_9_9, 0.5_9_4_3, 0.5_1_6_5] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def _snake_case (self ):
super().test_attention_slicing_forward_pass(expected_max_diff=3e-3 )
def _snake_case (self ):
super().test_inference_batch_single_identical(expected_max_diff=3e-3 )
def _snake_case (self ):
pass
def _snake_case (self ):
__lowerCAmelCase = self.get_dummy_components()
__lowerCAmelCase = StableDiffusionXLImgaImgPipeline(**__lowercase )
__lowerCAmelCase = sd_pipe.to(__lowercase )
__lowerCAmelCase = sd_pipe.to(__lowercase )
sd_pipe.set_progress_bar_config(disable=__lowercase )
# forward without prompt embeds
__lowerCAmelCase = self.get_dummy_inputs(__lowercase )
__lowerCAmelCase = 3 * ['''this is a negative prompt''']
__lowerCAmelCase = negative_prompt
__lowerCAmelCase = 3 * [inputs['''prompt''']]
__lowerCAmelCase = sd_pipe(**__lowercase )
__lowerCAmelCase = output.images[0, -3:, -3:, -1]
# forward with prompt embeds
__lowerCAmelCase = self.get_dummy_inputs(__lowercase )
__lowerCAmelCase = 3 * ['''this is a negative prompt''']
__lowerCAmelCase = 3 * [inputs.pop('''prompt''' )]
(
(
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) ,
) = sd_pipe.encode_prompt(__lowercase , negative_prompt=__lowercase )
__lowerCAmelCase = sd_pipe(
**__lowercase , prompt_embeds=__lowercase , negative_prompt_embeds=__lowercase , pooled_prompt_embeds=__lowercase , negative_pooled_prompt_embeds=__lowercase , )
__lowerCAmelCase = output.images[0, -3:, -3:, -1]
# make sure that it's equal
assert np.abs(image_slice_a.flatten() - image_slice_a.flatten() ).max() < 1e-4
@slow
@require_torch_gpu
class a__ ( unittest.TestCase ):
"""simple docstring"""
def _snake_case (self ):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _snake_case (self , __lowercase , __lowercase="cpu" , __lowercase=torch.floataa , __lowercase=0 ):
__lowerCAmelCase = torch.Generator(device=__lowercase ).manual_seed(__lowercase )
__lowerCAmelCase = np.random.RandomState(__lowercase ).standard_normal((1, 4, 64, 64) )
__lowerCAmelCase = torch.from_numpy(__lowercase ).to(device=__lowercase , dtype=__lowercase )
__lowerCAmelCase = {
'''prompt''': '''a photograph of an astronaut riding a horse''',
'''latents''': latents,
'''generator''': generator,
'''num_inference_steps''': 3,
'''guidance_scale''': 7.5,
'''output_type''': '''numpy''',
}
return inputs
def _snake_case (self ):
__lowerCAmelCase = DiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-base''' )
pipe.to(__lowercase )
pipe.set_progress_bar_config(disable=__lowercase )
__lowerCAmelCase = self.get_inputs(__lowercase )
__lowerCAmelCase = pipe(**__lowercase ).images
__lowerCAmelCase = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 5_12, 5_12, 3)
__lowerCAmelCase = np.array([0.4_9_4_9_3, 0.4_7_8_9_6, 0.4_0_7_9_8, 0.5_4_2_1_4, 0.5_3_2_1_2, 0.4_8_2_0_2, 0.4_7_6_5_6, 0.4_6_3_2_9, 0.4_8_5_0_6] )
assert np.abs(image_slice - expected_slice ).max() < 7e-3
| 174
| 1
|
import argparse
import gc
import json
import os
import re
import torch
from huggingface_hub import hf_hub_download
from transformers import AutoModelForCausalLM, AutoTokenizer, PreTrainedTokenizerFast, RwkvConfig
from transformers.modeling_utils import WEIGHTS_INDEX_NAME, shard_checkpoint
__A = {
'''169M''': 12,
'''430M''': 24,
'''1B5''': 24,
'''3B''': 32,
'''7B''': 32,
'''14B''': 40,
}
__A = {
'''169M''': 7_68,
'''430M''': 10_24,
'''1B5''': 20_48,
'''3B''': 25_60,
'''7B''': 40_96,
'''14B''': 51_20,
}
def snake_case_(_UpperCamelCase ) -> str:
"""simple docstring"""
_snake_case = list(state_dict.keys() )
for name in state_dict_keys:
_snake_case = state_dict.pop(__a )
# emb -> embedding
if name.startswith('''emb.''' ):
_snake_case = name.replace('''emb.''' , '''embeddings.''' )
# ln_0 -> pre_ln (only present at block 0)
if name.startswith('''blocks.0.ln0''' ):
_snake_case = name.replace('''blocks.0.ln0''' , '''blocks.0.pre_ln''' )
# att -> attention
_snake_case = re.sub(R'''blocks\.(\d+)\.att''' , R'''blocks.\1.attention''' , __a )
# ffn -> feed_forward
_snake_case = re.sub(R'''blocks\.(\d+)\.ffn''' , R'''blocks.\1.feed_forward''' , __a )
# time_mix_k -> time_mix_key and reshape
if name.endswith('''.time_mix_k''' ):
_snake_case = name.replace('''.time_mix_k''' , '''.time_mix_key''' )
# time_mix_v -> time_mix_value and reshape
if name.endswith('''.time_mix_v''' ):
_snake_case = name.replace('''.time_mix_v''' , '''.time_mix_value''' )
# time_mix_r -> time_mix_key and reshape
if name.endswith('''.time_mix_r''' ):
_snake_case = name.replace('''.time_mix_r''' , '''.time_mix_receptance''' )
if name != "head.weight":
_snake_case = 'rwkv.' + name
_snake_case = weight
return state_dict
def snake_case_(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase=None , _UpperCamelCase=None , _UpperCamelCase=False , _UpperCamelCase=None ) -> int:
"""simple docstring"""
if tokenizer_file is None:
print('''No `--tokenizer_file` provided, we will use the default tokenizer.''' )
_snake_case = 50_277
_snake_case = AutoTokenizer.from_pretrained('''EleutherAI/gpt-neox-20b''' )
else:
_snake_case = PreTrainedTokenizerFast(tokenizer_file=__a )
_snake_case = len(__a )
tokenizer.save_pretrained(__a )
# 2. Build the config
_snake_case = list(NUM_HIDDEN_LAYERS_MAPPING.keys() )
if size is None:
# Try to infer size from the checkpoint name
for candidate in possible_sizes:
if candidate in checkpoint_file:
_snake_case = candidate
break
if size is None:
raise ValueError('''Could not infer the size, please provide it with the `--size` argument.''' )
if size not in possible_sizes:
raise ValueError(F"""`size` should be one of {possible_sizes}, got {size}.""" )
_snake_case = RwkvConfig(
vocab_size=__a , num_hidden_layers=NUM_HIDDEN_LAYERS_MAPPING[size] , hidden_size=HIDEN_SIZE_MAPPING[size] , )
config.save_pretrained(__a )
# 3. Download model file then convert state_dict
_snake_case = hf_hub_download(__a , __a )
_snake_case = torch.load(__a , map_location='''cpu''' )
_snake_case = convert_state_dict(__a )
# 4. Split in shards and save
_snake_case = shard_checkpoint(__a )
for shard_file, shard in shards.items():
torch.save(__a , os.path.join(__a , __a ) )
if index is not None:
_snake_case = os.path.join(__a , __a )
# Save the index as well
with open(__a , '''w''' , encoding='''utf-8''' ) as f:
_snake_case = json.dumps(__a , indent=2 , sort_keys=__a ) + '\n'
f.write(__a )
# 5. Clean up shards (for some reason the file PyTorch saves take the same space as the whole state_dict
print(
'''Cleaning up shards. This may error with an OOM error, it this is the case don\'t worry you still have converted the model.''' )
_snake_case = list(shards.keys() )
del state_dict
del shards
gc.collect()
for shard_file in shard_files:
_snake_case = torch.load(os.path.join(__a , __a ) )
torch.save({k: v.cpu().clone() for k, v in state_dict.items()} , os.path.join(__a , __a ) )
del state_dict
gc.collect()
if push_to_hub:
if model_name is None:
raise ValueError('''Please provide a `model_name` to push the model to the Hub.''' )
_snake_case = AutoModelForCausalLM.from_pretrained(__a )
model.push_to_hub(__a , max_shard_size='''2GB''' )
tokenizer.push_to_hub(__a )
if __name__ == "__main__":
__A = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--repo_id''', default=None, type=str, required=True, help='''Repo ID from which to pull the checkpoint.'''
)
parser.add_argument(
'''--checkpoint_file''', default=None, type=str, required=True, help='''Name of the checkpoint file in the repo.'''
)
parser.add_argument(
'''--output_dir''', default=None, type=str, required=True, help='''Where to save the converted model.'''
)
parser.add_argument(
'''--tokenizer_file''',
default=None,
type=str,
help='''Path to the tokenizer file to use (if not provided, only the model is converted).''',
)
parser.add_argument(
'''--size''',
default=None,
type=str,
help='''Size of the model. Will be inferred from the `checkpoint_file` if not passed.''',
)
parser.add_argument(
'''--push_to_hub''',
action='''store_true''',
help='''Push to the Hub the converted model.''',
)
parser.add_argument(
'''--model_name''',
default=None,
type=str,
help='''Name of the pushed model on the Hub, including the username / organization.''',
)
__A = parser.parse_args()
convert_rmkv_checkpoint_to_hf_format(
args.repo_id,
args.checkpoint_file,
args.output_dir,
size=args.size,
tokenizer_file=args.tokenizer_file,
push_to_hub=args.push_to_hub,
model_name=args.model_name,
)
| 355
|
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer
__A = logging.get_logger(__name__)
__A = {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}
__A = {
'''vocab_file''': {
'''allegro/herbert-base-cased''': '''https://huggingface.co/allegro/herbert-base-cased/resolve/main/vocab.json'''
},
'''merges_file''': {
'''allegro/herbert-base-cased''': '''https://huggingface.co/allegro/herbert-base-cased/resolve/main/merges.txt'''
},
}
__A = {'''allegro/herbert-base-cased''': 5_14}
__A = {}
class lowercase_ ( __lowercase ):
UpperCamelCase_ : Any = VOCAB_FILES_NAMES
UpperCamelCase_ : Optional[int] = PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase_ : List[str] = PRETRAINED_INIT_CONFIGURATION
UpperCamelCase_ : Dict = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase_ : Optional[Any] = HerbertTokenizer
def __init__( self : Tuple , A__ : str=None , A__ : Optional[Any]=None , A__ : Union[str, Any]=None , A__ : Optional[int]="<s>" , A__ : Optional[int]="<unk>" , A__ : str="<pad>" , A__ : List[Any]="<mask>" , A__ : Dict="</s>" , **A__ : Optional[int] , ) -> Optional[int]:
super().__init__(
A__ , A__ , tokenizer_file=A__ , cls_token=A__ , unk_token=A__ , pad_token=A__ , mask_token=A__ , sep_token=A__ , **A__ , )
def UpperCamelCase_ ( self : str , A__ : List[int] , A__ : Optional[List[int]] = None ) -> List[int]:
_snake_case = [self.cls_token_id]
_snake_case = [self.sep_token_id]
if token_ids_a is None:
return cls + token_ids_a + sep
return cls + token_ids_a + sep + token_ids_a + sep
def UpperCamelCase_ ( self : Tuple , 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__ )
if token_ids_a is None:
return [1] + ([0] * len(A__ )) + [1]
return [1] + ([0] * len(A__ )) + [1] + ([0] * len(A__ )) + [1]
def UpperCamelCase_ ( self : Any , A__ : List[int] , A__ : Optional[List[int]] = None ) -> List[int]:
_snake_case = [self.sep_token_id]
_snake_case = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def UpperCamelCase_ ( self : Union[str, Any] , A__ : str , A__ : Optional[str] = None ) -> Tuple[str]:
_snake_case = self._tokenizer.model.save(A__ , name=A__ )
return tuple(A__ )
| 278
| 0
|
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_UpperCAmelCase : List[Any] = logging.get_logger(__name__)
_UpperCAmelCase : Tuple = {
"""microsoft/beit-base-patch16-224-pt22k""": (
"""https://huggingface.co/microsoft/beit-base-patch16-224-pt22k/resolve/main/config.json"""
),
# See all BEiT models at https://huggingface.co/models?filter=beit
}
class a__ ( __A ):
"""simple docstring"""
__UpperCamelCase : Tuple = 'beit'
def __init__(self , __lowercase=81_92 , __lowercase=7_68 , __lowercase=12 , __lowercase=12 , __lowercase=30_72 , __lowercase="gelu" , __lowercase=0.0 , __lowercase=0.0 , __lowercase=0.0_2 , __lowercase=1e-12 , __lowercase=2_24 , __lowercase=16 , __lowercase=3 , __lowercase=False , __lowercase=False , __lowercase=False , __lowercase=False , __lowercase=0.1 , __lowercase=0.1 , __lowercase=True , __lowercase=[3, 5, 7, 11] , __lowercase=[1, 2, 3, 6] , __lowercase=True , __lowercase=0.4 , __lowercase=2_56 , __lowercase=1 , __lowercase=False , __lowercase=2_55 , **__lowercase , ):
super().__init__(**__lowercase )
__lowerCAmelCase = vocab_size
__lowerCAmelCase = hidden_size
__lowerCAmelCase = num_hidden_layers
__lowerCAmelCase = num_attention_heads
__lowerCAmelCase = intermediate_size
__lowerCAmelCase = hidden_act
__lowerCAmelCase = hidden_dropout_prob
__lowerCAmelCase = attention_probs_dropout_prob
__lowerCAmelCase = initializer_range
__lowerCAmelCase = layer_norm_eps
__lowerCAmelCase = image_size
__lowerCAmelCase = patch_size
__lowerCAmelCase = num_channels
__lowerCAmelCase = use_mask_token
__lowerCAmelCase = use_absolute_position_embeddings
__lowerCAmelCase = use_relative_position_bias
__lowerCAmelCase = use_shared_relative_position_bias
__lowerCAmelCase = layer_scale_init_value
__lowerCAmelCase = drop_path_rate
__lowerCAmelCase = use_mean_pooling
# decode head attributes (semantic segmentation)
__lowerCAmelCase = out_indices
__lowerCAmelCase = pool_scales
# auxiliary head attributes (semantic segmentation)
__lowerCAmelCase = use_auxiliary_head
__lowerCAmelCase = auxiliary_loss_weight
__lowerCAmelCase = auxiliary_channels
__lowerCAmelCase = auxiliary_num_convs
__lowerCAmelCase = auxiliary_concat_input
__lowerCAmelCase = semantic_loss_ignore_index
class a__ ( __A ):
"""simple docstring"""
__UpperCamelCase : Any = version.parse('1.11' )
@property
def _snake_case (self ):
return OrderedDict(
[
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
] )
@property
def _snake_case (self ):
return 1e-4
| 174
|
'''simple docstring'''
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
if is_torch_available():
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
@require_torch
@require_sentencepiece
@require_tokenizers
class a__ ( unittest.TestCase ):
"""simple docstring"""
@slow
def _snake_case (self ):
__lowerCAmelCase = AutoModelForSeqaSeqLM.from_pretrained('''google/mt5-small''' , return_dict=__lowercase ).to(__lowercase )
__lowerCAmelCase = AutoTokenizer.from_pretrained('''google/mt5-small''' )
__lowerCAmelCase = tokenizer('''Hello there''' , return_tensors='''pt''' ).input_ids
__lowerCAmelCase = tokenizer('''Hi I am''' , return_tensors='''pt''' ).input_ids
__lowerCAmelCase = model(input_ids.to(__lowercase ) , labels=labels.to(__lowercase ) ).loss
__lowerCAmelCase = -(labels.shape[-1] * loss.item())
__lowerCAmelCase = -8_4.9_1_2_7
self.assertTrue(abs(mtf_score - EXPECTED_SCORE ) < 1e-4 )
| 174
| 1
|
"""simple docstring"""
from typing import Callable, List, Optional, Union
import PIL
import torch
from transformers import (
CLIPImageProcessor,
CLIPSegForImageSegmentation,
CLIPSegProcessor,
CLIPTextModel,
CLIPTokenizer,
)
from diffusers import DiffusionPipeline
from diffusers.configuration_utils import FrozenDict
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionInpaintPipeline
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import deprecate, is_accelerate_available, logging
lowerCamelCase__ = logging.get_logger(__name__) # pylint: disable=invalid-name
class A__ ( _lowerCamelCase):
def __init__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , ):
super().__init__()
if hasattr(scheduler.config , 'steps_offset' ) and scheduler.config.steps_offset != 1:
__lowerCAmelCase : Optional[Any] = (
f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
'to update the config accordingly as leaving `steps_offset` might led to incorrect results'
' in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,'
' it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`'
' file'
)
deprecate('steps_offset!=1' , '1.0.0' , _SCREAMING_SNAKE_CASE , standard_warn=_SCREAMING_SNAKE_CASE )
__lowerCAmelCase : Optional[int] = dict(scheduler.config )
__lowerCAmelCase : str = 1
__lowerCAmelCase : Tuple = FrozenDict(_SCREAMING_SNAKE_CASE )
if hasattr(scheduler.config , 'skip_prk_steps' ) and scheduler.config.skip_prk_steps is False:
__lowerCAmelCase : List[Any] = (
f"The configuration file of this scheduler: {scheduler} has not set the configuration"
' `skip_prk_steps`. `skip_prk_steps` should be set to True in the configuration file. Please make'
' sure to update the config accordingly as not setting `skip_prk_steps` in the config might lead to'
' incorrect results in future versions. If you have downloaded this checkpoint from the Hugging Face'
' Hub, it would be very nice if you could open a Pull request for the'
' `scheduler/scheduler_config.json` file'
)
deprecate('skip_prk_steps not set' , '1.0.0' , _SCREAMING_SNAKE_CASE , standard_warn=_SCREAMING_SNAKE_CASE )
__lowerCAmelCase : Optional[Any] = dict(scheduler.config )
__lowerCAmelCase : List[str] = True
__lowerCAmelCase : Union[str, Any] = FrozenDict(_SCREAMING_SNAKE_CASE )
if safety_checker is None:
logger.warning(
f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
' that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered'
' results in services or applications open to the public. Both the diffusers team and Hugging Face'
' strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling'
' it only for use-cases that involve analyzing network behavior or auditing its results. For more'
' information, please have a look at https://github.com/huggingface/diffusers/pull/254 .' )
self.register_modules(
segmentation_model=_SCREAMING_SNAKE_CASE , segmentation_processor=_SCREAMING_SNAKE_CASE , vae=_SCREAMING_SNAKE_CASE , text_encoder=_SCREAMING_SNAKE_CASE , tokenizer=_SCREAMING_SNAKE_CASE , unet=_SCREAMING_SNAKE_CASE , scheduler=_SCREAMING_SNAKE_CASE , safety_checker=_SCREAMING_SNAKE_CASE , feature_extractor=_SCREAMING_SNAKE_CASE , )
def __lowerCamelCase ( self , _SCREAMING_SNAKE_CASE = "auto" ):
if slice_size == "auto":
# half the attention head size is usually a good trade-off between
# speed and memory
__lowerCAmelCase : Any = self.unet.config.attention_head_dim // 2
self.unet.set_attention_slice(_SCREAMING_SNAKE_CASE )
def __lowerCamelCase ( self ):
self.enable_attention_slicing(_SCREAMING_SNAKE_CASE )
def __lowerCamelCase ( self ):
if is_accelerate_available():
from accelerate import cpu_offload
else:
raise ImportError('Please install accelerate via `pip install accelerate`' )
__lowerCAmelCase : Any = torch.device('cuda' )
for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae, self.safety_checker]:
if cpu_offloaded_model is not None:
cpu_offload(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
@property
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
def __lowerCamelCase ( self ):
if self.device != torch.device('meta' ) or not hasattr(self.unet , '_hf_hook' ):
return self.device
for module in self.unet.modules():
if (
hasattr(_SCREAMING_SNAKE_CASE , '_hf_hook' )
and hasattr(module._hf_hook , 'execution_device' )
and module._hf_hook.execution_device is not None
):
return torch.device(module._hf_hook.execution_device )
return self.device
@torch.no_grad()
def __call__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = 5_12 , _SCREAMING_SNAKE_CASE = 5_12 , _SCREAMING_SNAKE_CASE = 50 , _SCREAMING_SNAKE_CASE = 7.5 , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = 1 , _SCREAMING_SNAKE_CASE = 0.0 , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = "pil" , _SCREAMING_SNAKE_CASE = True , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = 1 , **_SCREAMING_SNAKE_CASE , ):
__lowerCAmelCase : List[str] = self.segmentation_processor(
text=[text] , images=[image] , padding='max_length' , return_tensors='pt' ).to(self.device )
__lowerCAmelCase : int = self.segmentation_model(**_SCREAMING_SNAKE_CASE )
__lowerCAmelCase : str = torch.sigmoid(outputs.logits ).cpu().detach().unsqueeze(-1 ).numpy()
__lowerCAmelCase : Tuple = self.numpy_to_pil(_SCREAMING_SNAKE_CASE )[0].resize(image.size )
# Run inpainting pipeline with the generated mask
__lowerCAmelCase : Optional[int] = StableDiffusionInpaintPipeline(
vae=self.vae , text_encoder=self.text_encoder , tokenizer=self.tokenizer , unet=self.unet , scheduler=self.scheduler , safety_checker=self.safety_checker , feature_extractor=self.feature_extractor , )
return inpainting_pipeline(
prompt=_SCREAMING_SNAKE_CASE , image=_SCREAMING_SNAKE_CASE , mask_image=_SCREAMING_SNAKE_CASE , height=_SCREAMING_SNAKE_CASE , width=_SCREAMING_SNAKE_CASE , num_inference_steps=_SCREAMING_SNAKE_CASE , guidance_scale=_SCREAMING_SNAKE_CASE , negative_prompt=_SCREAMING_SNAKE_CASE , num_images_per_prompt=_SCREAMING_SNAKE_CASE , eta=_SCREAMING_SNAKE_CASE , generator=_SCREAMING_SNAKE_CASE , latents=_SCREAMING_SNAKE_CASE , output_type=_SCREAMING_SNAKE_CASE , return_dict=_SCREAMING_SNAKE_CASE , callback=_SCREAMING_SNAKE_CASE , callback_steps=_SCREAMING_SNAKE_CASE , )
| 359
|
"""simple docstring"""
import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rouge, chunks, parse_numeric_n_bool_cl_kwargs, use_task_specific_params
lowerCamelCase__ = getLogger(__name__)
lowerCamelCase__ = """cuda""" if torch.cuda.is_available() else """cpu"""
def __lowerCAmelCase (_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase = 8 , _UpperCamelCase = DEFAULT_DEVICE , _UpperCamelCase=False , _UpperCamelCase="summarization" , _UpperCamelCase=None , **_UpperCamelCase , ):
__lowerCAmelCase : str = Path(_UpperCamelCase ).open('w' , encoding='utf-8' )
__lowerCAmelCase : Union[str, Any] = str(_UpperCamelCase )
__lowerCAmelCase : List[str] = AutoModelForSeqaSeqLM.from_pretrained(_UpperCamelCase ).to(_UpperCamelCase )
if fpaa:
__lowerCAmelCase : Optional[Any] = model.half()
__lowerCAmelCase : List[str] = AutoTokenizer.from_pretrained(_UpperCamelCase )
logger.info(F"Inferred tokenizer type: {tokenizer.__class__}" ) # if this is wrong, check config.model_type.
__lowerCAmelCase : List[Any] = time.time()
# update config with task specific params
use_task_specific_params(_UpperCamelCase , _UpperCamelCase )
if prefix is None:
__lowerCAmelCase : Optional[int] = prefix or getattr(model.config , 'prefix' , '' ) or ''
for examples_chunk in tqdm(list(chunks(_UpperCamelCase , _UpperCamelCase ) ) ):
__lowerCAmelCase : List[str] = [prefix + text for text in examples_chunk]
__lowerCAmelCase : List[str] = tokenizer(_UpperCamelCase , return_tensors='pt' , truncation=_UpperCamelCase , padding='longest' ).to(_UpperCamelCase )
__lowerCAmelCase : str = model.generate(
input_ids=batch.input_ids , attention_mask=batch.attention_mask , **_UpperCamelCase , )
__lowerCAmelCase : str = tokenizer.batch_decode(_UpperCamelCase , skip_special_tokens=_UpperCamelCase , clean_up_tokenization_spaces=_UpperCamelCase )
for hypothesis in dec:
fout.write(hypothesis + '\n' )
fout.flush()
fout.close()
__lowerCAmelCase : Optional[int] = int(time.time() - start_time ) # seconds
__lowerCAmelCase : List[Any] = len(_UpperCamelCase )
return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs , 4 )}
def __lowerCAmelCase ():
return datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S' )
def __lowerCAmelCase (_UpperCamelCase=True ):
__lowerCAmelCase : List[Any] = argparse.ArgumentParser()
parser.add_argument('model_name' , type=_UpperCamelCase , help='like facebook/bart-large-cnn,t5-base, etc.' )
parser.add_argument('input_path' , type=_UpperCamelCase , help='like cnn_dm/test.source' )
parser.add_argument('save_path' , type=_UpperCamelCase , help='where to save summaries' )
parser.add_argument('--reference_path' , type=_UpperCamelCase , required=_UpperCamelCase , help='like cnn_dm/test.target' )
parser.add_argument('--score_path' , type=_UpperCamelCase , required=_UpperCamelCase , default='metrics.json' , help='where to save metrics' )
parser.add_argument('--device' , type=_UpperCamelCase , required=_UpperCamelCase , default=_UpperCamelCase , help='cuda, cuda:1, cpu etc.' )
parser.add_argument(
'--prefix' , type=_UpperCamelCase , required=_UpperCamelCase , default=_UpperCamelCase , help='will be added to the begininng of src examples' )
parser.add_argument('--task' , type=_UpperCamelCase , default='summarization' , help='used for task_specific_params + metrics' )
parser.add_argument('--bs' , type=_UpperCamelCase , default=8 , required=_UpperCamelCase , help='batch size' )
parser.add_argument(
'--n_obs' , type=_UpperCamelCase , default=-1 , required=_UpperCamelCase , help='How many observations. Defaults to all.' )
parser.add_argument('--fp16' , action='store_true' )
parser.add_argument('--dump-args' , action='store_true' , help='print the custom hparams with the results' )
parser.add_argument(
'--info' , nargs='?' , type=_UpperCamelCase , const=datetime_now() , help=(
'use in conjunction w/ --dump-args to print with the results whatever other info you\'d like, e.g.'
' lang=en-ru. If no value is passed, the current datetime string will be used.'
) , )
# Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate
__lowerCAmelCase , __lowerCAmelCase : Optional[int] = parser.parse_known_args()
__lowerCAmelCase : Optional[int] = parse_numeric_n_bool_cl_kwargs(_UpperCamelCase )
if parsed_args and verbose:
print(F"parsed the following generate kwargs: {parsed_args}" )
__lowerCAmelCase : Dict = [' ' + x.rstrip() if 't5' in args.model_name else x.rstrip() for x in open(args.input_path ).readlines()]
if args.n_obs > 0:
__lowerCAmelCase : int = examples[: args.n_obs]
Path(args.save_path ).parent.mkdir(exist_ok=_UpperCamelCase )
if args.reference_path is None and Path(args.score_path ).exists():
warnings.warn(F"score_path {args.score_path} will be overwritten unless you type ctrl-c." )
if args.device == "cpu" and args.fpaa:
# this mix leads to RuntimeError: "threshold_cpu" not implemented for 'Half'
raise ValueError('Can\'t mix --fp16 and --device cpu' )
__lowerCAmelCase : Optional[Any] = generate_summaries_or_translations(
_UpperCamelCase , args.save_path , args.model_name , batch_size=args.bs , device=args.device , fpaa=args.fpaa , task=args.task , prefix=args.prefix , **_UpperCamelCase , )
if args.reference_path is None:
return {}
# Compute scores
__lowerCAmelCase : str = calculate_bleu if 'translation' in args.task else calculate_rouge
__lowerCAmelCase : Dict = [x.rstrip() for x in open(args.save_path ).readlines()]
__lowerCAmelCase : Dict = [x.rstrip() for x in open(args.reference_path ).readlines()][: len(_UpperCamelCase )]
__lowerCAmelCase : dict = score_fn(_UpperCamelCase , _UpperCamelCase )
scores.update(_UpperCamelCase )
if args.dump_args:
scores.update(_UpperCamelCase )
if args.info:
__lowerCAmelCase : Optional[Any] = args.info
if verbose:
print(_UpperCamelCase )
if args.score_path is not None:
json.dump(_UpperCamelCase , open(args.score_path , 'w' ) )
return scores
if __name__ == "__main__":
# Usage for MT:
# python run_eval.py MODEL_NAME $DATA_DIR/test.source $save_dir/test_translations.txt --reference_path $DATA_DIR/test.target --score_path $save_dir/test_bleu.json --task translation $@
run_generate(verbose=True)
| 182
| 0
|
"""simple docstring"""
import os
# Precomputes a list of the 100 first triangular numbers
lowerCamelCase_ : List[str] = [int(0.5 * n * (n + 1)) for n in range(1, 1_01)]
def UpperCAmelCase__ ( ):
"""simple docstring"""
A_ : Union[str, Any] = os.path.dirname(os.path.realpath(_UpperCAmelCase ) )
A_ : Tuple = os.path.join(_UpperCAmelCase , 'words.txt' )
A_ : List[Any] = ''
with open(_UpperCAmelCase ) as f:
A_ : int = f.readline()
A_ : Optional[Any] = [word.strip('"' ) for word in words.strip('\r\n' ).split(',' )]
A_ : Dict = [
word
for word in [sum(ord(_UpperCAmelCase ) - 64 for x in word ) for word in words]
if word in TRIANGULAR_NUMBERS
]
return len(_UpperCAmelCase )
if __name__ == "__main__":
print(solution())
| 286
|
"""simple docstring"""
import os
def UpperCAmelCase__ ( ):
"""simple docstring"""
A_ : Any = os.path.join(os.path.dirname(_UpperCAmelCase ) , 'num.txt' )
with open(_UpperCAmelCase ) as file_hand:
return str(sum(int(_UpperCAmelCase ) for line in file_hand ) )[:10]
if __name__ == "__main__":
print(solution())
| 286
| 1
|
"""simple docstring"""
from __future__ import annotations
from collections.abc import Iterator
class __A :
'''simple docstring'''
def __init__( self : Any , UpperCAmelCase_ : int ) ->None:
"""simple docstring"""
snake_case_ = value
snake_case_ = None
snake_case_ = None
class __A :
'''simple docstring'''
def __init__( self : List[Any] , UpperCAmelCase_ : Node ) ->None:
"""simple docstring"""
snake_case_ = tree
def lowerCAmelCase ( self : List[str] , UpperCAmelCase_ : Node | None ) ->int:
"""simple docstring"""
if node is None:
return 0
return node.value + (
self.depth_first_search(node.left ) + self.depth_first_search(node.right )
)
def __iter__( self : str ) ->Iterator[int]:
"""simple docstring"""
yield self.depth_first_search(self.tree )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 364
|
"""simple docstring"""
import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation
def _a ( _SCREAMING_SNAKE_CASE = 8 ) -> str:
snake_case_ = ascii_letters + digits + punctuation
return "".join(secrets.choice(_SCREAMING_SNAKE_CASE ) for _ in range(_SCREAMING_SNAKE_CASE ) )
def _a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> str:
# Password Generator = full boot with random_number, random_letters, and
# random_character FUNCTIONS
# Put your code here...
i -= len(_SCREAMING_SNAKE_CASE )
snake_case_ = i // 3
snake_case_ = i % 3
# chars = chars_incl + random_letters(ascii_letters, i / 3 + remainder) +
# random_number(digits, i / 3) + random_characters(punctuation, i / 3)
snake_case_ = (
chars_incl
+ random(_SCREAMING_SNAKE_CASE , quotient + remainder )
+ random(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
+ random(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
)
snake_case_ = list(_SCREAMING_SNAKE_CASE )
shuffle(_SCREAMING_SNAKE_CASE )
return "".join(_SCREAMING_SNAKE_CASE )
# random is a generalised function for letters, characters and numbers
def _a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> str:
return "".join(secrets.choice(_SCREAMING_SNAKE_CASE ) for _ in range(_SCREAMING_SNAKE_CASE ) )
def _a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Any:
pass # Put your code here...
def _a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Optional[int]:
pass # Put your code here...
def _a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> List[Any]:
pass # Put your code here...
def _a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = 8 ) -> bool:
if len(_SCREAMING_SNAKE_CASE ) < min_length:
# Your Password must be at least 8 characters long
return False
snake_case_ = any(char in ascii_uppercase for char in password )
snake_case_ = any(char in ascii_lowercase for char in password )
snake_case_ = any(char in digits for char in password )
snake_case_ = any(char in punctuation for char in password )
return upper and lower and num and spec_char
# Passwords should contain UPPERCASE, lowerase
# numbers, and special characters
def _a ( ) -> Union[str, Any]:
snake_case_ = int(input("""Please indicate the max length of your password: """ ).strip() )
snake_case_ = input(
"""Please indicate the characters that must be in your password: """ ).strip()
print("""Password generated:""" , password_generator(_SCREAMING_SNAKE_CASE ) )
print(
"""Alternative Password generated:""" , alternative_password_generator(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) , )
print("""[If you are thinking of using this passsword, You better save it.]""" )
if __name__ == "__main__":
main()
| 233
| 0
|
import math
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : int) -> Any:
'''simple docstring'''
__UpperCamelCase : str = [True] * n
__UpperCamelCase : List[str] = False
__UpperCamelCase : Union[str, Any] = False
__UpperCamelCase : Optional[Any] = True
for i in range(3 , int(n**0.5 + 1) , 2):
__UpperCamelCase : Tuple = i * 2
while index < n:
__UpperCamelCase : List[str] = False
__UpperCamelCase : Any = index + i
__UpperCamelCase : Any = [2]
for i in range(3 , __lowerCAmelCase , 2):
if is_prime[i]:
primes.append(__lowerCAmelCase)
return primes
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : int = 999_966_663_333) -> Tuple:
'''simple docstring'''
__UpperCamelCase : Dict = math.floor(math.sqrt(__lowerCAmelCase)) + 100
__UpperCamelCase : List[str] = prime_sieve(__lowerCAmelCase)
__UpperCamelCase : Dict = 0
__UpperCamelCase : Optional[int] = 0
__UpperCamelCase : Union[str, Any] = primes[prime_index]
while (last_prime**2) <= limit:
__UpperCamelCase : Tuple = primes[prime_index + 1]
__UpperCamelCase : int = last_prime**2
__UpperCamelCase : Tuple = next_prime**2
# Get numbers divisible by lps(current)
__UpperCamelCase : List[str] = lower_bound + last_prime
while upper_bound > current <= limit:
matches_sum += current
current += last_prime
# Reset the upper_bound
while (upper_bound - next_prime) > limit:
upper_bound -= next_prime
# Add the numbers divisible by ups(current)
__UpperCamelCase : Tuple = upper_bound - next_prime
while current > lower_bound:
matches_sum += current
current -= next_prime
# Remove the numbers divisible by both ups and lps
__UpperCamelCase : Optional[Any] = 0
while upper_bound > current <= limit:
if current <= lower_bound:
# Increment the current number
current += last_prime * next_prime
continue
if current > limit:
break
# Remove twice since it was added by both ups and lps
matches_sum -= current * 2
# Increment the current number
current += last_prime * next_prime
# Setup for next pair
__UpperCamelCase : List[Any] = next_prime
prime_index += 1
return matches_sum
if __name__ == "__main__":
print(solution())
| 232
|
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_DEFAULT_MEAN,
IMAGENET_DEFAULT_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
is_batched,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, logging
snake_case : Union[str, Any] = logging.get_logger(__name__)
class snake_case_ (lowerCamelCase_ ):
UpperCAmelCase__ : List[Any] = ['''pixel_values''']
def __init__( self :Optional[int] ,__snake_case :bool = True ,__snake_case :Optional[Dict[str, int]] = None ,__snake_case :PILImageResampling = PILImageResampling.BICUBIC ,__snake_case :bool = True ,__snake_case :bool = True ,__snake_case :Union[int, float] = 1 / 2_55 ,__snake_case :Dict[str, int] = None ,__snake_case :bool = True ,__snake_case :Optional[Union[float, List[float]]] = None ,__snake_case :Optional[Union[float, List[float]]] = None ,**__snake_case :Dict ,) -> None:
super().__init__(**__snake_case )
a__ = size if size is not None else {'height': 2_24, 'width': 2_24}
a__ = get_size_dict(__snake_case )
a__ = crop_size if crop_size is not None else {'height': 2_24, 'width': 2_24}
a__ = get_size_dict(__snake_case ,default_to_square=__snake_case ,param_name='crop_size' )
a__ = do_resize
a__ = do_rescale
a__ = do_normalize
a__ = do_center_crop
a__ = crop_size
a__ = size
a__ = resample
a__ = rescale_factor
a__ = image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
a__ = image_std if image_std is not None else IMAGENET_DEFAULT_STD
def lowerCamelCase__( self :Dict ,__snake_case :np.ndarray ,__snake_case :Dict[str, int] ,__snake_case :PILImageResampling = PILImageResampling.BILINEAR ,__snake_case :Optional[Union[str, ChannelDimension]] = None ,**__snake_case :List[Any] ,) -> np.ndarray:
a__ = get_size_dict(__snake_case )
if "shortest_edge" in size:
a__ = get_resize_output_image_size(__snake_case ,size=size['shortest_edge'] ,default_to_square=__snake_case )
# size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
elif "height" in size and "width" in size:
a__ = (size['height'], size['width'])
else:
raise ValueError(F'Size must contain \'height\' and \'width\' keys or \'shortest_edge\' key. Got {size.keys()}' )
return resize(__snake_case ,size=__snake_case ,resample=__snake_case ,data_format=__snake_case ,**__snake_case )
def lowerCamelCase__( self :Dict ,__snake_case :np.ndarray ,__snake_case :Dict[str, int] ,__snake_case :Optional[Union[str, ChannelDimension]] = None ,**__snake_case :Any ,) -> np.ndarray:
a__ = get_size_dict(__snake_case )
if "height" not in size or "width" not in size:
raise ValueError(F'The `size` parameter must contain the keys (height, width). Got {size.keys()}' )
return center_crop(__snake_case ,size=(size['height'], size['width']) ,data_format=__snake_case ,**__snake_case )
def lowerCamelCase__( self :List[Any] ,__snake_case :np.ndarray ,__snake_case :float ,__snake_case :Optional[Union[str, ChannelDimension]] = None ,**__snake_case :int ) -> np.ndarray:
return rescale(__snake_case ,scale=__snake_case ,data_format=__snake_case ,**__snake_case )
def lowerCamelCase__( self :Tuple ,__snake_case :np.ndarray ,__snake_case :Union[float, List[float]] ,__snake_case :Union[float, List[float]] ,__snake_case :Optional[Union[str, ChannelDimension]] = None ,**__snake_case :Any ,) -> np.ndarray:
return normalize(__snake_case ,mean=__snake_case ,std=__snake_case ,data_format=__snake_case ,**__snake_case )
def lowerCamelCase__( self :Any ,__snake_case :ImageInput ,__snake_case :Optional[bool] = None ,__snake_case :Dict[str, int] = None ,__snake_case :PILImageResampling = None ,__snake_case :bool = None ,__snake_case :int = None ,__snake_case :Optional[bool] = None ,__snake_case :Optional[float] = None ,__snake_case :Optional[bool] = None ,__snake_case :Optional[Union[float, List[float]]] = None ,__snake_case :Optional[Union[float, List[float]]] = None ,__snake_case :Optional[Union[str, TensorType]] = None ,__snake_case :Union[str, ChannelDimension] = ChannelDimension.FIRST ,**__snake_case :Optional[int] ,) -> BatchFeature:
a__ = do_resize if do_resize is not None else self.do_resize
a__ = do_rescale if do_rescale is not None else self.do_rescale
a__ = do_normalize if do_normalize is not None else self.do_normalize
a__ = do_center_crop if do_center_crop is not None else self.do_center_crop
a__ = crop_size if crop_size is not None else self.crop_size
a__ = get_size_dict(__snake_case ,param_name='crop_size' ,default_to_square=__snake_case )
a__ = resample if resample is not None else self.resample
a__ = rescale_factor if rescale_factor is not None else self.rescale_factor
a__ = image_mean if image_mean is not None else self.image_mean
a__ = image_std if image_std is not None else self.image_std
a__ = size if size is not None else self.size
a__ = get_size_dict(__snake_case )
if not is_batched(__snake_case ):
a__ = [images]
if not valid_images(__snake_case ):
raise ValueError(
'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
'torch.Tensor, tf.Tensor or jax.ndarray.' )
if do_resize and size is None:
raise ValueError('Size must be specified if do_resize is True.' )
if do_center_crop and crop_size is None:
raise ValueError('Crop size must be specified if do_center_crop is True.' )
if do_rescale and rescale_factor is None:
raise ValueError('Rescale factor must be specified if do_rescale is True.' )
# All transformations expect numpy arrays.
a__ = [to_numpy_array(__snake_case ) for image in images]
if do_resize:
a__ = [self.resize(image=__snake_case ,size=__snake_case ,resample=__snake_case ) for image in images]
if do_center_crop:
a__ = [self.center_crop(image=__snake_case ,size=__snake_case ) for image in images]
if do_rescale:
a__ = [self.rescale(image=__snake_case ,scale=__snake_case ) for image in images]
if do_normalize:
a__ = [self.normalize(image=__snake_case ,mean=__snake_case ,std=__snake_case ) for image in images]
a__ = [to_channel_dimension_format(__snake_case ,__snake_case ) for image in images]
a__ = {'pixel_values': images}
return BatchFeature(data=__snake_case ,tensor_type=__snake_case )
| 240
| 0
|
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device
if is_torch_available():
from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments
@require_torch
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
"""simple docstring"""
def __lowerCamelCase ( self , __UpperCamelCase ) -> Tuple:
'''simple docstring'''
for model_result in results.values():
for batch_size, sequence_length in zip(model_result["bs"] , model_result["ss"] ):
__UpperCamelCase : Union[str, Any] = model_result["result"][batch_size][sequence_length]
self.assertIsNotNone(__UpperCamelCase )
def __lowerCamelCase ( self ) -> Optional[int]:
'''simple docstring'''
__UpperCamelCase : Tuple = "sshleifer/tiny-gpt2"
__UpperCamelCase : str = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=__UpperCamelCase , inference=__UpperCamelCase , sequence_lengths=[8] , batch_sizes=[1] , multi_process=__UpperCamelCase , )
__UpperCamelCase : int = PyTorchBenchmark(__UpperCamelCase )
__UpperCamelCase : Dict = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def __lowerCamelCase ( self ) -> Dict:
'''simple docstring'''
__UpperCamelCase : List[str] = "sgugger/tiny-distilbert-classification"
__UpperCamelCase : Any = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=__UpperCamelCase , inference=__UpperCamelCase , sequence_lengths=[8] , batch_sizes=[1] , multi_process=__UpperCamelCase , only_pretrain_model=__UpperCamelCase , )
__UpperCamelCase : Dict = PyTorchBenchmark(__UpperCamelCase )
__UpperCamelCase : List[Any] = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def __lowerCamelCase ( self ) -> Optional[int]:
'''simple docstring'''
__UpperCamelCase : Union[str, Any] = "sshleifer/tiny-gpt2"
__UpperCamelCase : List[Any] = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=__UpperCamelCase , inference=__UpperCamelCase , torchscript=__UpperCamelCase , sequence_lengths=[8] , batch_sizes=[1] , multi_process=__UpperCamelCase , )
__UpperCamelCase : int = PyTorchBenchmark(__UpperCamelCase )
__UpperCamelCase : List[Any] = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
@unittest.skipIf(torch_device == "cpu" , "Cant do half precision" )
def __lowerCamelCase ( self ) -> List[Any]:
'''simple docstring'''
__UpperCamelCase : Any = "sshleifer/tiny-gpt2"
__UpperCamelCase : Optional[Any] = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=__UpperCamelCase , inference=__UpperCamelCase , fpaa=__UpperCamelCase , sequence_lengths=[8] , batch_sizes=[1] , multi_process=__UpperCamelCase , )
__UpperCamelCase : Dict = PyTorchBenchmark(__UpperCamelCase )
__UpperCamelCase : str = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def __lowerCamelCase ( self ) -> int:
'''simple docstring'''
__UpperCamelCase : Union[str, Any] = "sshleifer/tiny-gpt2"
__UpperCamelCase : List[Any] = AutoConfig.from_pretrained(__UpperCamelCase )
# set architectures equal to `None`
__UpperCamelCase : Union[str, Any] = None
__UpperCamelCase : Optional[Any] = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=__UpperCamelCase , inference=__UpperCamelCase , sequence_lengths=[8] , batch_sizes=[1] , multi_process=__UpperCamelCase , )
__UpperCamelCase : Union[str, Any] = PyTorchBenchmark(__UpperCamelCase , configs=[config] )
__UpperCamelCase : List[str] = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def __lowerCamelCase ( self ) -> int:
'''simple docstring'''
__UpperCamelCase : Tuple = "sshleifer/tiny-gpt2"
__UpperCamelCase : Tuple = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=__UpperCamelCase , inference=__UpperCamelCase , sequence_lengths=[8] , batch_sizes=[1] , multi_process=__UpperCamelCase , )
__UpperCamelCase : str = PyTorchBenchmark(__UpperCamelCase )
__UpperCamelCase : Union[str, Any] = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result )
self.check_results_dict_not_empty(results.memory_train_result )
@unittest.skipIf(torch_device == "cpu" , "Can't do half precision" )
def __lowerCamelCase ( self ) -> Union[str, Any]:
'''simple docstring'''
__UpperCamelCase : int = "sshleifer/tiny-gpt2"
__UpperCamelCase : Optional[int] = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=__UpperCamelCase , inference=__UpperCamelCase , sequence_lengths=[8] , batch_sizes=[1] , fpaa=__UpperCamelCase , multi_process=__UpperCamelCase , )
__UpperCamelCase : int = PyTorchBenchmark(__UpperCamelCase )
__UpperCamelCase : Union[str, Any] = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result )
self.check_results_dict_not_empty(results.memory_train_result )
def __lowerCamelCase ( self ) -> List[Any]:
'''simple docstring'''
__UpperCamelCase : Dict = "sshleifer/tiny-gpt2"
__UpperCamelCase : Optional[Any] = AutoConfig.from_pretrained(__UpperCamelCase )
__UpperCamelCase : str = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=__UpperCamelCase , inference=__UpperCamelCase , sequence_lengths=[8] , batch_sizes=[1] , multi_process=__UpperCamelCase , )
__UpperCamelCase : Union[str, Any] = PyTorchBenchmark(__UpperCamelCase , configs=[config] )
__UpperCamelCase : Any = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def __lowerCamelCase ( self ) -> Any:
'''simple docstring'''
__UpperCamelCase : List[str] = "sshleifer/tinier_bart"
__UpperCamelCase : str = AutoConfig.from_pretrained(__UpperCamelCase )
__UpperCamelCase : int = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=__UpperCamelCase , inference=__UpperCamelCase , sequence_lengths=[8] , batch_sizes=[1] , multi_process=__UpperCamelCase , )
__UpperCamelCase : Tuple = PyTorchBenchmark(__UpperCamelCase , configs=[config] )
__UpperCamelCase : Union[str, Any] = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def __lowerCamelCase ( self ) -> List[Any]:
'''simple docstring'''
__UpperCamelCase : int = "sshleifer/tiny-gpt2"
__UpperCamelCase : Union[str, Any] = AutoConfig.from_pretrained(__UpperCamelCase )
__UpperCamelCase : str = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=__UpperCamelCase , inference=__UpperCamelCase , sequence_lengths=[8] , batch_sizes=[1] , multi_process=__UpperCamelCase , )
__UpperCamelCase : Optional[int] = PyTorchBenchmark(__UpperCamelCase , configs=[config] )
__UpperCamelCase : List[Any] = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result )
self.check_results_dict_not_empty(results.memory_train_result )
def __lowerCamelCase ( self ) -> Union[str, Any]:
'''simple docstring'''
__UpperCamelCase : Optional[Any] = "sshleifer/tinier_bart"
__UpperCamelCase : Optional[int] = AutoConfig.from_pretrained(__UpperCamelCase )
__UpperCamelCase : Optional[Any] = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=__UpperCamelCase , inference=__UpperCamelCase , sequence_lengths=[8] , batch_sizes=[1] , multi_process=__UpperCamelCase , )
__UpperCamelCase : Optional[Any] = PyTorchBenchmark(__UpperCamelCase , configs=[config] )
__UpperCamelCase : str = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result )
self.check_results_dict_not_empty(results.memory_train_result )
def __lowerCamelCase ( self ) -> Tuple:
'''simple docstring'''
__UpperCamelCase : Union[str, Any] = "sshleifer/tiny-gpt2"
with tempfile.TemporaryDirectory() as tmp_dir:
__UpperCamelCase : Union[str, Any] = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=__UpperCamelCase , inference=__UpperCamelCase , save_to_csv=__UpperCamelCase , sequence_lengths=[8] , batch_sizes=[1] , inference_time_csv_file=os.path.join(__UpperCamelCase , "inf_time.csv" ) , train_memory_csv_file=os.path.join(__UpperCamelCase , "train_mem.csv" ) , inference_memory_csv_file=os.path.join(__UpperCamelCase , "inf_mem.csv" ) , train_time_csv_file=os.path.join(__UpperCamelCase , "train_time.csv" ) , env_info_csv_file=os.path.join(__UpperCamelCase , "env.csv" ) , multi_process=__UpperCamelCase , )
__UpperCamelCase : Any = PyTorchBenchmark(__UpperCamelCase )
benchmark.run()
self.assertTrue(Path(os.path.join(__UpperCamelCase , "inf_time.csv" ) ).exists() )
self.assertTrue(Path(os.path.join(__UpperCamelCase , "train_time.csv" ) ).exists() )
self.assertTrue(Path(os.path.join(__UpperCamelCase , "inf_mem.csv" ) ).exists() )
self.assertTrue(Path(os.path.join(__UpperCamelCase , "train_mem.csv" ) ).exists() )
self.assertTrue(Path(os.path.join(__UpperCamelCase , "env.csv" ) ).exists() )
def __lowerCamelCase ( self ) -> Optional[Any]:
'''simple docstring'''
__UpperCamelCase : Optional[Any] = "sshleifer/tiny-gpt2"
def _check_summary_is_not_empty(__UpperCamelCase ):
self.assertTrue(hasattr(__UpperCamelCase , "sequential" ) )
self.assertTrue(hasattr(__UpperCamelCase , "cumulative" ) )
self.assertTrue(hasattr(__UpperCamelCase , "current" ) )
self.assertTrue(hasattr(__UpperCamelCase , "total" ) )
with tempfile.TemporaryDirectory() as tmp_dir:
__UpperCamelCase : int = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=__UpperCamelCase , inference=__UpperCamelCase , sequence_lengths=[8] , batch_sizes=[1] , log_filename=os.path.join(__UpperCamelCase , "log.txt" ) , log_print=__UpperCamelCase , trace_memory_line_by_line=__UpperCamelCase , multi_process=__UpperCamelCase , )
__UpperCamelCase : Optional[int] = PyTorchBenchmark(__UpperCamelCase )
__UpperCamelCase : str = benchmark.run()
_check_summary_is_not_empty(result.inference_summary )
_check_summary_is_not_empty(result.train_summary )
self.assertTrue(Path(os.path.join(__UpperCamelCase , "log.txt" ) ).exists() )
| 171
|
from .constants import (
MODEL_NAME,
OPTIMIZER_NAME,
RNG_STATE_NAME,
SAFE_WEIGHTS_INDEX_NAME,
SAFE_WEIGHTS_NAME,
SCALER_NAME,
SCHEDULER_NAME,
TORCH_LAUNCH_PARAMS,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
)
from .dataclasses import (
BnbQuantizationConfig,
ComputeEnvironment,
CustomDtype,
DeepSpeedPlugin,
DistributedDataParallelKwargs,
DistributedType,
DynamoBackend,
FPaRecipeKwargs,
FullyShardedDataParallelPlugin,
GradientAccumulationPlugin,
GradScalerKwargs,
InitProcessGroupKwargs,
KwargsHandler,
LoggerType,
MegatronLMPlugin,
PrecisionType,
ProjectConfiguration,
RNGType,
SageMakerDistributedType,
TensorInformation,
TorchDynamoPlugin,
)
from .environment import get_int_from_env, parse_choice_from_env, parse_flag_from_env
from .imports import (
get_ccl_version,
is_abit_bnb_available,
is_abit_bnb_available,
is_aim_available,
is_bfaa_available,
is_bnb_available,
is_botoa_available,
is_ccl_available,
is_comet_ml_available,
is_datasets_available,
is_deepspeed_available,
is_fpa_available,
is_ipex_available,
is_megatron_lm_available,
is_mlflow_available,
is_mps_available,
is_npu_available,
is_rich_available,
is_safetensors_available,
is_sagemaker_available,
is_tensorboard_available,
is_tpu_available,
is_transformers_available,
is_wandb_available,
is_xpu_available,
)
from .modeling import (
check_device_map,
check_tied_parameters_in_config,
check_tied_parameters_on_same_device,
compute_module_sizes,
convert_file_size_to_int,
dtype_byte_size,
find_tied_parameters,
get_balanced_memory,
get_max_layer_size,
get_max_memory,
get_mixed_precision_context_manager,
id_tensor_storage,
infer_auto_device_map,
load_checkpoint_in_model,
load_offloaded_weights,
load_state_dict,
named_module_tensors,
retie_parameters,
set_module_tensor_to_device,
shard_checkpoint,
)
from .offload import (
OffloadedWeightsLoader,
PrefixedDataset,
extract_submodules_state_dict,
load_offloaded_weight,
offload_state_dict,
offload_weight,
save_offload_index,
)
from .operations import (
broadcast,
broadcast_object_list,
concatenate,
convert_outputs_to_fpaa,
convert_to_fpaa,
find_batch_size,
find_device,
gather,
gather_object,
get_data_structure,
honor_type,
initialize_tensors,
is_namedtuple,
is_tensor_information,
is_torch_tensor,
listify,
pad_across_processes,
recursively_apply,
reduce,
send_to_device,
slice_tensors,
)
from .versions import compare_versions, is_torch_version
if is_deepspeed_available():
from .deepspeed import (
DeepSpeedEngineWrapper,
DeepSpeedOptimizerWrapper,
DeepSpeedSchedulerWrapper,
DummyOptim,
DummyScheduler,
HfDeepSpeedConfig,
)
from .bnb import has_abit_bnb_layers, load_and_quantize_model
from .fsdp_utils import load_fsdp_model, load_fsdp_optimizer, save_fsdp_model, save_fsdp_optimizer
from .launch import (
PrepareForLaunch,
_filter_args,
prepare_deepspeed_cmd_env,
prepare_multi_gpu_env,
prepare_sagemager_args_inputs,
prepare_simple_launcher_cmd_env,
prepare_tpu,
)
from .megatron_lm import (
AbstractTrainStep,
BertTrainStep,
GPTTrainStep,
MegatronEngine,
MegatronLMDummyDataLoader,
MegatronLMDummyScheduler,
MegatronLMOptimizerWrapper,
MegatronLMSchedulerWrapper,
TaTrainStep,
avg_losses_across_data_parallel_group,
gather_across_data_parallel_groups,
)
from .megatron_lm import initialize as megatron_lm_initialize
from .megatron_lm import prepare_data_loader as megatron_lm_prepare_data_loader
from .megatron_lm import prepare_model as megatron_lm_prepare_model
from .megatron_lm import prepare_optimizer as megatron_lm_prepare_optimizer
from .megatron_lm import prepare_scheduler as megatron_lm_prepare_scheduler
from .memory import find_executable_batch_size, release_memory
from .other import (
extract_model_from_parallel,
get_pretty_name,
is_port_in_use,
merge_dicts,
patch_environment,
save,
wait_for_everyone,
write_basic_config,
)
from .random import set_seed, synchronize_rng_state, synchronize_rng_states
from .torch_xla import install_xla
from .tqdm import tqdm
from .transformer_engine import convert_model, has_transformer_engine_layers
| 171
| 1
|
import argparse
import os.path as osp
import re
import torch
from safetensors.torch import load_file, save_file
# =================#
# UNet Conversion #
# =================#
lowercase__ : Union[str, Any] = [
# (stable-diffusion, HF Diffusers)
('''time_embed.0.weight''', '''time_embedding.linear_1.weight'''),
('''time_embed.0.bias''', '''time_embedding.linear_1.bias'''),
('''time_embed.2.weight''', '''time_embedding.linear_2.weight'''),
('''time_embed.2.bias''', '''time_embedding.linear_2.bias'''),
('''input_blocks.0.0.weight''', '''conv_in.weight'''),
('''input_blocks.0.0.bias''', '''conv_in.bias'''),
('''out.0.weight''', '''conv_norm_out.weight'''),
('''out.0.bias''', '''conv_norm_out.bias'''),
('''out.2.weight''', '''conv_out.weight'''),
('''out.2.bias''', '''conv_out.bias'''),
]
lowercase__ : int = [
# (stable-diffusion, HF Diffusers)
('''in_layers.0''', '''norm1'''),
('''in_layers.2''', '''conv1'''),
('''out_layers.0''', '''norm2'''),
('''out_layers.3''', '''conv2'''),
('''emb_layers.1''', '''time_emb_proj'''),
('''skip_connection''', '''conv_shortcut'''),
]
lowercase__ : Union[str, Any] = []
# hardcoded number of downblocks and resnets/attentions...
# would need smarter logic for other networks.
for i in range(4):
# loop over downblocks/upblocks
for j in range(2):
# loop over resnets/attentions for downblocks
lowercase__ : Dict = f'''down_blocks.{i}.resnets.{j}.'''
lowercase__ : List[Any] = f'''input_blocks.{3*i + j + 1}.0.'''
unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix))
if i < 3:
# no attention layers in down_blocks.3
lowercase__ : Optional[int] = f'''down_blocks.{i}.attentions.{j}.'''
lowercase__ : str = f'''input_blocks.{3*i + j + 1}.1.'''
unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix))
for j in range(3):
# loop over resnets/attentions for upblocks
lowercase__ : Any = f'''up_blocks.{i}.resnets.{j}.'''
lowercase__ : Dict = f'''output_blocks.{3*i + j}.0.'''
unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix))
if i > 0:
# no attention layers in up_blocks.0
lowercase__ : Union[str, Any] = f'''up_blocks.{i}.attentions.{j}.'''
lowercase__ : Optional[Any] = f'''output_blocks.{3*i + j}.1.'''
unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix))
if i < 3:
# no downsample in down_blocks.3
lowercase__ : str = f'''down_blocks.{i}.downsamplers.0.conv.'''
lowercase__ : int = f'''input_blocks.{3*(i+1)}.0.op.'''
unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix))
# no upsample in up_blocks.3
lowercase__ : Optional[Any] = f'''up_blocks.{i}.upsamplers.0.'''
lowercase__ : Union[str, Any] = f'''output_blocks.{3*i + 2}.{1 if i == 0 else 2}.'''
unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))
lowercase__ : int = '''mid_block.attentions.0.'''
lowercase__ : Any = '''middle_block.1.'''
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))
for j in range(2):
lowercase__ : Tuple = f'''mid_block.resnets.{j}.'''
lowercase__ : Optional[Any] = f'''middle_block.{2*j}.'''
unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))
def lowerCamelCase__ ( _A ):
'''simple docstring'''
snake_case_ = {k: k for k in unet_state_dict.keys()}
for sd_name, hf_name in unet_conversion_map:
snake_case_ = sd_name
for k, v in mapping.items():
if "resnets" in k:
for sd_part, hf_part in unet_conversion_map_resnet:
snake_case_ = v.replace(A_ , A_ )
snake_case_ = v
for k, v in mapping.items():
for sd_part, hf_part in unet_conversion_map_layer:
snake_case_ = v.replace(A_ , A_ )
snake_case_ = v
snake_case_ = {v: unet_state_dict[k] for k, v in mapping.items()}
return new_state_dict
# ================#
# VAE Conversion #
# ================#
lowercase__ : Any = [
# (stable-diffusion, HF Diffusers)
('''nin_shortcut''', '''conv_shortcut'''),
('''norm_out''', '''conv_norm_out'''),
('''mid.attn_1.''', '''mid_block.attentions.0.'''),
]
for i in range(4):
# down_blocks have two resnets
for j in range(2):
lowercase__ : Optional[Any] = f'''encoder.down_blocks.{i}.resnets.{j}.'''
lowercase__ : Tuple = f'''encoder.down.{i}.block.{j}.'''
vae_conversion_map.append((sd_down_prefix, hf_down_prefix))
if i < 3:
lowercase__ : Union[str, Any] = f'''down_blocks.{i}.downsamplers.0.'''
lowercase__ : Any = f'''down.{i}.downsample.'''
vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))
lowercase__ : Dict = f'''up_blocks.{i}.upsamplers.0.'''
lowercase__ : str = f'''up.{3-i}.upsample.'''
vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix))
# up_blocks have three resnets
# also, up blocks in hf are numbered in reverse from sd
for j in range(3):
lowercase__ : List[str] = f'''decoder.up_blocks.{i}.resnets.{j}.'''
lowercase__ : List[str] = f'''decoder.up.{3-i}.block.{j}.'''
vae_conversion_map.append((sd_up_prefix, hf_up_prefix))
# this part accounts for mid blocks in both the encoder and the decoder
for i in range(2):
lowercase__ : List[str] = f'''mid_block.resnets.{i}.'''
lowercase__ : List[Any] = f'''mid.block_{i+1}.'''
vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))
lowercase__ : Optional[int] = [
# (stable-diffusion, HF Diffusers)
('''norm.''', '''group_norm.'''),
('''q.''', '''query.'''),
('''k.''', '''key.'''),
('''v.''', '''value.'''),
('''proj_out.''', '''proj_attn.'''),
]
def lowerCamelCase__ ( _A ):
'''simple docstring'''
return w.reshape(*w.shape , 1 , 1 )
def lowerCamelCase__ ( _A ):
'''simple docstring'''
snake_case_ = {k: k for k in vae_state_dict.keys()}
for k, v in mapping.items():
for sd_part, hf_part in vae_conversion_map:
snake_case_ = v.replace(A_ , A_ )
snake_case_ = v
for k, v in mapping.items():
if "attentions" in k:
for sd_part, hf_part in vae_conversion_map_attn:
snake_case_ = v.replace(A_ , A_ )
snake_case_ = v
snake_case_ = {v: vae_state_dict[k] for k, v in mapping.items()}
snake_case_ = ['''q''', '''k''', '''v''', '''proj_out''']
for k, v in new_state_dict.items():
for weight_name in weights_to_convert:
if f"mid.attn_1.{weight_name}.weight" in k:
print(f"Reshaping {k} for SD format" )
snake_case_ = reshape_weight_for_sd(A_ )
return new_state_dict
# =========================#
# Text Encoder Conversion #
# =========================#
lowercase__ : int = [
# (stable-diffusion, HF Diffusers)
('''resblocks.''', '''text_model.encoder.layers.'''),
('''ln_1''', '''layer_norm1'''),
('''ln_2''', '''layer_norm2'''),
('''.c_fc.''', '''.fc1.'''),
('''.c_proj.''', '''.fc2.'''),
('''.attn''', '''.self_attn'''),
('''ln_final.''', '''transformer.text_model.final_layer_norm.'''),
('''token_embedding.weight''', '''transformer.text_model.embeddings.token_embedding.weight'''),
('''positional_embedding''', '''transformer.text_model.embeddings.position_embedding.weight'''),
]
lowercase__ : List[str] = {re.escape(x[1]): x[0] for x in textenc_conversion_lst}
lowercase__ : int = re.compile("|".join(protected.keys()))
# Ordering is from https://github.com/pytorch/pytorch/blob/master/test/cpp/api/modules.cpp
lowercase__ : Union[str, Any] = {'''q''': 0, '''k''': 1, '''v''': 2}
def lowerCamelCase__ ( _A ):
'''simple docstring'''
snake_case_ = {}
snake_case_ = {}
snake_case_ = {}
for k, v in text_enc_dict.items():
if (
k.endswith(".self_attn.q_proj.weight" )
or k.endswith(".self_attn.k_proj.weight" )
or k.endswith(".self_attn.v_proj.weight" )
):
snake_case_ = k[: -len(".q_proj.weight" )]
snake_case_ = k[-len("q_proj.weight" )]
if k_pre not in capture_qkv_weight:
snake_case_ = [None, None, None]
snake_case_ = v
continue
if (
k.endswith(".self_attn.q_proj.bias" )
or k.endswith(".self_attn.k_proj.bias" )
or k.endswith(".self_attn.v_proj.bias" )
):
snake_case_ = k[: -len(".q_proj.bias" )]
snake_case_ = k[-len("q_proj.bias" )]
if k_pre not in capture_qkv_bias:
snake_case_ = [None, None, None]
snake_case_ = v
continue
snake_case_ = textenc_pattern.sub(lambda _A : protected[re.escape(m.group(0 ) )] , A_ )
snake_case_ = v
for k_pre, tensors in capture_qkv_weight.items():
if None in tensors:
raise Exception("CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing" )
snake_case_ = textenc_pattern.sub(lambda _A : protected[re.escape(m.group(0 ) )] , A_ )
snake_case_ = torch.cat(A_ )
for k_pre, tensors in capture_qkv_bias.items():
if None in tensors:
raise Exception("CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing" )
snake_case_ = textenc_pattern.sub(lambda _A : protected[re.escape(m.group(0 ) )] , A_ )
snake_case_ = torch.cat(A_ )
return new_state_dict
def lowerCamelCase__ ( _A ):
'''simple docstring'''
return text_enc_dict
if __name__ == "__main__":
lowercase__ : List[str] = argparse.ArgumentParser()
parser.add_argument("--model_path", default=None, type=str, required=True, help="Path to the model to convert.")
parser.add_argument("--checkpoint_path", default=None, type=str, required=True, help="Path to the output model.")
parser.add_argument("--half", action="store_true", help="Save weights in half precision.")
parser.add_argument(
"--use_safetensors", action="store_true", help="Save weights use safetensors, default is ckpt."
)
lowercase__ : Dict = parser.parse_args()
assert args.model_path is not None, "Must provide a model path!"
assert args.checkpoint_path is not None, "Must provide a checkpoint path!"
# Path for safetensors
lowercase__ : Optional[int] = osp.join(args.model_path, "unet", "diffusion_pytorch_model.safetensors")
lowercase__ : Dict = osp.join(args.model_path, "vae", "diffusion_pytorch_model.safetensors")
lowercase__ : Optional[int] = osp.join(args.model_path, "text_encoder", "model.safetensors")
# Load models from safetensors if it exists, if it doesn't pytorch
if osp.exists(unet_path):
lowercase__ : str = load_file(unet_path, device="cpu")
else:
lowercase__ : Any = osp.join(args.model_path, "unet", "diffusion_pytorch_model.bin")
lowercase__ : List[Any] = torch.load(unet_path, map_location="cpu")
if osp.exists(vae_path):
lowercase__ : Dict = load_file(vae_path, device="cpu")
else:
lowercase__ : Union[str, Any] = osp.join(args.model_path, "vae", "diffusion_pytorch_model.bin")
lowercase__ : Optional[int] = torch.load(vae_path, map_location="cpu")
if osp.exists(text_enc_path):
lowercase__ : str = load_file(text_enc_path, device="cpu")
else:
lowercase__ : Optional[Any] = osp.join(args.model_path, "text_encoder", "pytorch_model.bin")
lowercase__ : Tuple = torch.load(text_enc_path, map_location="cpu")
# Convert the UNet model
lowercase__ : Optional[Any] = convert_unet_state_dict(unet_state_dict)
lowercase__ : List[str] = {'''model.diffusion_model.''' + k: v for k, v in unet_state_dict.items()}
# Convert the VAE model
lowercase__ : Optional[Any] = convert_vae_state_dict(vae_state_dict)
lowercase__ : str = {'''first_stage_model.''' + k: v for k, v in vae_state_dict.items()}
# Easiest way to identify v2.0 model seems to be that the text encoder (OpenCLIP) is deeper
lowercase__ : Dict = '''text_model.encoder.layers.22.layer_norm2.bias''' in text_enc_dict
if is_vaa_model:
# Need to add the tag 'transformer' in advance so we can knock it out from the final layer-norm
lowercase__ : int = {'''transformer.''' + k: v for k, v in text_enc_dict.items()}
lowercase__ : Any = convert_text_enc_state_dict_vaa(text_enc_dict)
lowercase__ : Optional[Any] = {'''cond_stage_model.model.''' + k: v for k, v in text_enc_dict.items()}
else:
lowercase__ : str = convert_text_enc_state_dict(text_enc_dict)
lowercase__ : Optional[int] = {'''cond_stage_model.transformer.''' + k: v for k, v in text_enc_dict.items()}
# Put together new checkpoint
lowercase__ : Dict = {**unet_state_dict, **vae_state_dict, **text_enc_dict}
if args.half:
lowercase__ : Dict = {k: v.half() for k, v in state_dict.items()}
if args.use_safetensors:
save_file(state_dict, args.checkpoint_path)
else:
lowercase__ : int = {'''state_dict''': state_dict}
torch.save(state_dict, args.checkpoint_path)
| 187
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__UpperCamelCase : Optional[Any] = {
'''configuration_rag''': ['''RagConfig'''],
'''retrieval_rag''': ['''RagRetriever'''],
'''tokenization_rag''': ['''RagTokenizer'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCamelCase : Tuple = [
'''RagModel''',
'''RagPreTrainedModel''',
'''RagSequenceForGeneration''',
'''RagTokenForGeneration''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCamelCase : Union[str, Any] = [
'''TFRagModel''',
'''TFRagPreTrainedModel''',
'''TFRagSequenceForGeneration''',
'''TFRagTokenForGeneration''',
]
if TYPE_CHECKING:
from .configuration_rag import RagConfig
from .retrieval_rag import RagRetriever
from .tokenization_rag import RagTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_rag import (
TFRagModel,
TFRagPreTrainedModel,
TFRagSequenceForGeneration,
TFRagTokenForGeneration,
)
else:
import sys
__UpperCamelCase : Dict = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 106
| 0
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_lowerCamelCase : Dict = {
'''configuration_roc_bert''': ['''ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RoCBertConfig'''],
'''tokenization_roc_bert''': ['''RoCBertTokenizer'''],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
pass
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase : Dict = [
'''ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''RoCBertForCausalLM''',
'''RoCBertForMaskedLM''',
'''RoCBertForMultipleChoice''',
'''RoCBertForPreTraining''',
'''RoCBertForQuestionAnswering''',
'''RoCBertForSequenceClassification''',
'''RoCBertForTokenClassification''',
'''RoCBertLayer''',
'''RoCBertModel''',
'''RoCBertPreTrainedModel''',
'''load_tf_weights_in_roc_bert''',
]
if TYPE_CHECKING:
from .configuration_roc_bert import ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RoCBertConfig
from .tokenization_roc_bert import RoCBertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
raise OptionalDependencyNotAvailable()
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_roc_bert import (
ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
RoCBertForCausalLM,
RoCBertForMaskedLM,
RoCBertForMultipleChoice,
RoCBertForPreTraining,
RoCBertForQuestionAnswering,
RoCBertForSequenceClassification,
RoCBertForTokenClassification,
RoCBertLayer,
RoCBertModel,
RoCBertPreTrainedModel,
load_tf_weights_in_roc_bert,
)
else:
import sys
_lowerCamelCase : List[str] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 206
|
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import ChineseCLIPImageProcessor, ChineseCLIPProcessor
@require_vision
class lowercase ( unittest.TestCase ):
def __snake_case( self : Tuple ) -> Tuple:
'''simple docstring'''
SCREAMING_SNAKE_CASE = tempfile.mkdtemp()
SCREAMING_SNAKE_CASE = [
"[UNK]",
"[CLS]",
"[SEP]",
"[PAD]",
"[MASK]",
"的",
"价",
"格",
"是",
"15",
"便",
"alex",
"##andra",
"，",
"。",
"-",
"t",
"shirt",
]
SCREAMING_SNAKE_CASE = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["vocab_file"] )
with open(self.vocab_file , "w" , encoding="utf-8" ) as vocab_writer:
vocab_writer.write("".join([x + "\n" for x in vocab_tokens] ) )
SCREAMING_SNAKE_CASE = {
"do_resize": True,
"size": {"height": 224, "width": 224},
"do_center_crop": True,
"crop_size": {"height": 18, "width": 18},
"do_normalize": True,
"image_mean": [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3],
"image_std": [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1],
"do_convert_rgb": True,
}
SCREAMING_SNAKE_CASE = os.path.join(self.tmpdirname , _UpperCamelCase )
with open(self.image_processor_file , "w" , encoding="utf-8" ) as fp:
json.dump(_UpperCamelCase , _UpperCamelCase )
def __snake_case( self : Union[str, Any] , **_UpperCamelCase : str ) -> int:
'''simple docstring'''
return BertTokenizer.from_pretrained(self.tmpdirname , **_UpperCamelCase )
def __snake_case( self : List[str] , **_UpperCamelCase : Tuple ) -> str:
'''simple docstring'''
return BertTokenizerFast.from_pretrained(self.tmpdirname , **_UpperCamelCase )
def __snake_case( self : List[Any] , **_UpperCamelCase : Optional[Any] ) -> Any:
'''simple docstring'''
return ChineseCLIPImageProcessor.from_pretrained(self.tmpdirname , **_UpperCamelCase )
def __snake_case( self : Optional[Any] ) -> int:
'''simple docstring'''
shutil.rmtree(self.tmpdirname )
def __snake_case( self : Dict ) -> Optional[int]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
SCREAMING_SNAKE_CASE = [Image.fromarray(np.moveaxis(_UpperCamelCase , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def __snake_case( self : List[str] ) -> Any:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.get_tokenizer()
SCREAMING_SNAKE_CASE = self.get_rust_tokenizer()
SCREAMING_SNAKE_CASE = self.get_image_processor()
SCREAMING_SNAKE_CASE = ChineseCLIPProcessor(tokenizer=_UpperCamelCase , image_processor=_UpperCamelCase )
processor_slow.save_pretrained(self.tmpdirname )
SCREAMING_SNAKE_CASE = ChineseCLIPProcessor.from_pretrained(self.tmpdirname , use_fast=_UpperCamelCase )
SCREAMING_SNAKE_CASE = ChineseCLIPProcessor(tokenizer=_UpperCamelCase , image_processor=_UpperCamelCase )
processor_fast.save_pretrained(self.tmpdirname )
SCREAMING_SNAKE_CASE = ChineseCLIPProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor_slow.tokenizer.get_vocab() , tokenizer_slow.get_vocab() )
self.assertEqual(processor_fast.tokenizer.get_vocab() , tokenizer_fast.get_vocab() )
self.assertEqual(tokenizer_slow.get_vocab() , tokenizer_fast.get_vocab() )
self.assertIsInstance(processor_slow.tokenizer , _UpperCamelCase )
self.assertIsInstance(processor_fast.tokenizer , _UpperCamelCase )
self.assertEqual(processor_slow.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertEqual(processor_fast.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertIsInstance(processor_slow.image_processor , _UpperCamelCase )
self.assertIsInstance(processor_fast.image_processor , _UpperCamelCase )
def __snake_case( self : str ) -> Tuple:
'''simple docstring'''
SCREAMING_SNAKE_CASE = ChineseCLIPProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
SCREAMING_SNAKE_CASE = self.get_tokenizer(cls_token="(CLS)" , sep_token="(SEP)" )
SCREAMING_SNAKE_CASE = self.get_image_processor(do_normalize=_UpperCamelCase )
SCREAMING_SNAKE_CASE = ChineseCLIPProcessor.from_pretrained(
self.tmpdirname , cls_token="(CLS)" , sep_token="(SEP)" , do_normalize=_UpperCamelCase )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , _UpperCamelCase )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , _UpperCamelCase )
def __snake_case( self : List[Any] ) -> Optional[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.get_image_processor()
SCREAMING_SNAKE_CASE = self.get_tokenizer()
SCREAMING_SNAKE_CASE = ChineseCLIPProcessor(tokenizer=_UpperCamelCase , image_processor=_UpperCamelCase )
SCREAMING_SNAKE_CASE = self.prepare_image_inputs()
SCREAMING_SNAKE_CASE = image_processor(_UpperCamelCase , return_tensors="np" )
SCREAMING_SNAKE_CASE = processor(images=_UpperCamelCase , return_tensors="np" )
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1e-2 )
def __snake_case( self : List[Any] ) -> str:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.get_image_processor()
SCREAMING_SNAKE_CASE = self.get_tokenizer()
SCREAMING_SNAKE_CASE = ChineseCLIPProcessor(tokenizer=_UpperCamelCase , image_processor=_UpperCamelCase )
SCREAMING_SNAKE_CASE = "Alexandra，T-shirt的价格是15便士。"
SCREAMING_SNAKE_CASE = processor(text=_UpperCamelCase )
SCREAMING_SNAKE_CASE = tokenizer(_UpperCamelCase )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def __snake_case( self : Optional[Any] ) -> List[str]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.get_image_processor()
SCREAMING_SNAKE_CASE = self.get_tokenizer()
SCREAMING_SNAKE_CASE = ChineseCLIPProcessor(tokenizer=_UpperCamelCase , image_processor=_UpperCamelCase )
SCREAMING_SNAKE_CASE = "Alexandra，T-shirt的价格是15便士。"
SCREAMING_SNAKE_CASE = self.prepare_image_inputs()
SCREAMING_SNAKE_CASE = processor(text=_UpperCamelCase , images=_UpperCamelCase )
self.assertListEqual(list(inputs.keys() ) , ["input_ids", "token_type_ids", "attention_mask", "pixel_values"] )
# test if it raises when no input is passed
with pytest.raises(_UpperCamelCase ):
processor()
def __snake_case( self : Optional[Any] ) -> Dict:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.get_image_processor()
SCREAMING_SNAKE_CASE = self.get_tokenizer()
SCREAMING_SNAKE_CASE = ChineseCLIPProcessor(tokenizer=_UpperCamelCase , image_processor=_UpperCamelCase )
SCREAMING_SNAKE_CASE = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
SCREAMING_SNAKE_CASE = processor.batch_decode(_UpperCamelCase )
SCREAMING_SNAKE_CASE = tokenizer.batch_decode(_UpperCamelCase )
self.assertListEqual(_UpperCamelCase , _UpperCamelCase )
def __snake_case( self : int ) -> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.get_image_processor()
SCREAMING_SNAKE_CASE = self.get_tokenizer()
SCREAMING_SNAKE_CASE = ChineseCLIPProcessor(tokenizer=_UpperCamelCase , image_processor=_UpperCamelCase )
SCREAMING_SNAKE_CASE = "Alexandra，T-shirt的价格是15便士。"
SCREAMING_SNAKE_CASE = self.prepare_image_inputs()
SCREAMING_SNAKE_CASE = processor(text=_UpperCamelCase , images=_UpperCamelCase )
self.assertListEqual(list(inputs.keys() ) , processor.model_input_names )
| 206
| 1
|
from collections import defaultdict
from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst
def __UpperCamelCase ( ) -> int:
"""simple docstring"""
A , A : Optional[int] = 9, 14 # noqa: F841
A : Optional[int] = [
[0, 1, 4],
[0, 7, 8],
[1, 2, 8],
[7, 8, 7],
[7, 6, 1],
[2, 8, 2],
[8, 6, 6],
[2, 3, 7],
[2, 5, 4],
[6, 5, 2],
[3, 5, 14],
[3, 4, 9],
[5, 4, 10],
[1, 7, 11],
]
A : str = defaultdict(_lowerCAmelCase )
for nodea, nodea, cost in edges:
adjancency[nodea].append([nodea, cost] )
adjancency[nodea].append([nodea, cost] )
A : str = mst(_lowerCAmelCase )
A : Optional[Any] = [
[7, 6, 1],
[2, 8, 2],
[6, 5, 2],
[0, 1, 4],
[2, 5, 4],
[2, 3, 7],
[0, 7, 8],
[3, 4, 9],
]
for answer in expected:
A : Tuple = tuple(answer[:2] )
A : int = tuple(edge[::-1] )
assert edge in result or reverse in result
| 116
|
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
SCREAMING_SNAKE_CASE_:List[Any] = """▁"""
SCREAMING_SNAKE_CASE_:int = {"""vocab_file""": """spiece.model"""}
SCREAMING_SNAKE_CASE_:Any = {
"""vocab_file""": {"""google/pegasus-xsum""": """https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"""}
}
SCREAMING_SNAKE_CASE_:Optional[Any] = {
"""google/pegasus-xsum""": 512,
}
SCREAMING_SNAKE_CASE_:Tuple = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
__lowerCamelCase : Union[str, Any] = VOCAB_FILES_NAMES
__lowerCamelCase : List[Any] = VOCAB_FILES_NAMES
__lowerCamelCase : Optional[Any] = PRETRAINED_VOCAB_FILES_MAP
__lowerCamelCase : str = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__lowerCamelCase : str = ["input_ids", "attention_mask"]
def __init__( self, lowerCamelCase__, lowerCamelCase__="<pad>", lowerCamelCase__="</s>", lowerCamelCase__="<unk>", lowerCamelCase__="<mask_2>", lowerCamelCase__="<mask_1>", lowerCamelCase__=None, lowerCamelCase__=103, lowerCamelCase__ = None, **lowerCamelCase__, ):
A : int = offset
if additional_special_tokens is not None:
if not isinstance(lowerCamelCase__, lowerCamelCase__ ):
raise TypeError(
f'''additional_special_tokens should be of type {type(lowerCamelCase__ )}, but is'''
f''' {type(lowerCamelCase__ )}''' )
A : Dict = (
([mask_token_sent] + additional_special_tokens)
if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
else additional_special_tokens
)
# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
additional_special_tokens_extended += [
f'''<unk_{i}>''' for i in range(len(lowerCamelCase__ ), self.offset - 1 )
]
if len(set(lowerCamelCase__ ) ) != len(lowerCamelCase__ ):
raise ValueError(
"""Please make sure that the provided additional_special_tokens do not contain an incorrectly"""
f''' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.''' )
A : int = additional_special_tokens_extended
else:
A : Optional[int] = [mask_token_sent] if mask_token_sent is not None else []
additional_special_tokens += [f'''<unk_{i}>''' for i in range(2, self.offset )]
A : Any = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
eos_token=lowerCamelCase__, unk_token=lowerCamelCase__, mask_token=lowerCamelCase__, pad_token=lowerCamelCase__, mask_token_sent=lowerCamelCase__, offset=lowerCamelCase__, additional_special_tokens=lowerCamelCase__, sp_model_kwargs=self.sp_model_kwargs, **lowerCamelCase__, )
A : Union[str, Any] = mask_token_sent
A : Optional[Any] = vocab_file
A : Optional[int] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(lowerCamelCase__ )
# add special tokens to encoder dict
A : Dict[int, str] = {
0: self.pad_token,
1: self.eos_token,
}
if self.mask_token_sent is not None:
self.encoder.update(
{
2: self.mask_token_sent,
3: self.mask_token,
} )
if self.offset > 0:
# entries 2-104 are only used for pretraining and called <mask_1>, <mask_2>, unk_2, ...unk_102
# mask_token_sent is already added to list -> so start at 1
self.encoder.update({i + 3: additional_special_tokens[i] for i in range(1, self.offset - 1 )} )
A : Dict[str, int] = {v: k for k, v in self.encoder.items()}
@property
def _lowerCAmelCase ( self ):
return len(self.sp_model ) + self.offset
def _lowerCAmelCase ( self ):
A : Optional[int] = {self.convert_ids_to_tokens(lowerCamelCase__ ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self ):
A : List[Any] = self.__dict__.copy()
A : Union[str, Any] = None
return state
def __setstate__( self, lowerCamelCase__ ):
A : List[Any] = d
# for backward compatibility
if not hasattr(self, """sp_model_kwargs""" ):
A : int = {}
A : Tuple = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def _lowerCAmelCase ( self, lowerCamelCase__ ):
return self.sp_model.encode(lowerCamelCase__, out_type=lowerCamelCase__ )
def _lowerCAmelCase ( self, lowerCamelCase__ ):
if token in self.decoder:
return self.decoder[token]
elif token in self.added_tokens_decoder:
return self.added_tokens_decoder[token]
A : List[str] = self.sp_model.piece_to_id(lowerCamelCase__ )
return sp_id + self.offset
def _lowerCAmelCase ( self, lowerCamelCase__ ):
if index in self.encoder:
return self.encoder[index]
elif index in self.added_tokens_encoder:
return self.added_tokens_encoder[index]
else:
A : Dict = self.sp_model.IdToPiece(index - self.offset )
return token
def _lowerCAmelCase ( self, lowerCamelCase__ ):
A : Optional[int] = []
A : Optional[Any] = """"""
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
out_string += self.sp_model.decode(lowerCamelCase__ ) + token
A : int = []
else:
current_sub_tokens.append(lowerCamelCase__ )
out_string += self.sp_model.decode(lowerCamelCase__ )
return out_string.strip()
def _lowerCAmelCase ( self, lowerCamelCase__=False ):
return 1
def _lowerCAmelCase ( self, lowerCamelCase__ ):
A : Optional[int] = set(self.all_special_ids ) # call it once instead of inside list comp
all_special_ids.remove(self.unk_token_id ) # <unk> is only sometimes special
return [1 if x in all_special_ids else 0 for x in seq]
def _lowerCAmelCase ( self, lowerCamelCase__, lowerCamelCase__ = None, lowerCamelCase__ = False ):
if already_has_special_tokens:
return self._special_token_mask(lowerCamelCase__ )
elif token_ids_a is None:
return self._special_token_mask(lowerCamelCase__ ) + [1]
else:
return self._special_token_mask(token_ids_a + token_ids_a ) + [1]
def _lowerCAmelCase ( self, lowerCamelCase__, lowerCamelCase__=None ):
if token_ids_a is None:
return token_ids_a + [self.eos_token_id]
# We don't expect to process pairs, but leave the pair logic for API consistency
return token_ids_a + token_ids_a + [self.eos_token_id]
def _lowerCAmelCase ( self, lowerCamelCase__, lowerCamelCase__ = None ):
if not os.path.isdir(lowerCamelCase__ ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
A : Any = os.path.join(
lowerCamelCase__, (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(lowerCamelCase__ ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file, lowerCamelCase__ )
elif not os.path.isfile(self.vocab_file ):
with open(lowerCamelCase__, """wb""" ) as fi:
A : Dict = self.sp_model.serialized_model_proto()
fi.write(lowerCamelCase__ )
return (out_vocab_file,)
| 116
| 1
|
def lowercase_ ( _lowerCamelCase: List[str] = 1000 ) -> Dict:
'''simple docstring'''
return sum(e for e in range(3 , _lowerCamelCase ) if e % 3 == 0 or e % 5 == 0 )
if __name__ == "__main__":
print(F"""{solution() = }""")
| 354
|
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import (
CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS,
CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class _snake_case ( a__ , unittest.TestCase ):
snake_case__ = DiTPipeline
snake_case__ = CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
snake_case__ = PipelineTesterMixin.required_optional_params - {
"latents",
"num_images_per_prompt",
"callback",
"callback_steps",
}
snake_case__ = CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
snake_case__ = False
def lowerCamelCase__ ( self : Tuple ):
torch.manual_seed(0 )
__lowerCamelCase : Optional[Any] = TransformeraDModel(
sample_size=16 , num_layers=2 , patch_size=4 , attention_head_dim=8 , num_attention_heads=2 , in_channels=4 , out_channels=8 , attention_bias=UpperCAmelCase , activation_fn="gelu-approximate" , num_embeds_ada_norm=1000 , norm_type="ada_norm_zero" , norm_elementwise_affine=UpperCAmelCase , )
__lowerCamelCase : List[str] = AutoencoderKL()
__lowerCamelCase : List[Any] = DDIMScheduler()
__lowerCamelCase : Optional[Any] = {"transformer": transformer.eval(), "vae": vae.eval(), "scheduler": scheduler}
return components
def lowerCamelCase__ ( self : List[str] , UpperCAmelCase : Any , UpperCAmelCase : Union[str, Any]=0 ):
if str(UpperCAmelCase ).startswith("mps" ):
__lowerCamelCase : List[str] = torch.manual_seed(UpperCAmelCase )
else:
__lowerCamelCase : List[str] = torch.Generator(device=UpperCAmelCase ).manual_seed(UpperCAmelCase )
__lowerCamelCase : str = {
"class_labels": [1],
"generator": generator,
"num_inference_steps": 2,
"output_type": "numpy",
}
return inputs
def lowerCamelCase__ ( self : Optional[Any] ):
__lowerCamelCase : Dict = "cpu"
__lowerCamelCase : int = self.get_dummy_components()
__lowerCamelCase : Optional[Any] = self.pipeline_class(**UpperCAmelCase )
pipe.to(UpperCAmelCase )
pipe.set_progress_bar_config(disable=UpperCAmelCase )
__lowerCamelCase : Optional[int] = self.get_dummy_inputs(UpperCAmelCase )
__lowerCamelCase : List[Any] = pipe(**UpperCAmelCase ).images
__lowerCamelCase : Optional[int] = image[0, -3:, -3:, -1]
self.assertEqual(image.shape , (1, 16, 16, 3) )
__lowerCamelCase : Optional[int] = np.array([0.2_9_4_6, 0.6_6_0_1, 0.4_3_2_9, 0.3_2_9_6, 0.4_1_4_4, 0.5_3_1_9, 0.7_2_7_3, 0.5_0_1_3, 0.4_4_5_7] )
__lowerCamelCase : Optional[int] = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(UpperCAmelCase , 1E-3 )
def lowerCamelCase__ ( self : Any ):
self._test_inference_batch_single_identical(relax_max_difference=UpperCAmelCase , expected_max_diff=1E-3 )
@unittest.skipIf(
torch_device != "cuda" or not is_xformers_available() , reason="XFormers attention is only available with CUDA and `xformers` installed" , )
def lowerCamelCase__ ( self : List[str] ):
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3 )
@require_torch_gpu
@slow
class _snake_case ( unittest.TestCase ):
def lowerCamelCase__ ( self : Any ):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowerCamelCase__ ( self : List[str] ):
__lowerCamelCase : Optional[int] = torch.manual_seed(0 )
__lowerCamelCase : str = DiTPipeline.from_pretrained("facebook/DiT-XL-2-256" )
pipe.to("cuda" )
__lowerCamelCase : Tuple = ["vase", "umbrella", "white shark", "white wolf"]
__lowerCamelCase : Optional[int] = pipe.get_label_ids(UpperCAmelCase )
__lowerCamelCase : Optional[Any] = pipe(UpperCAmelCase , generator=UpperCAmelCase , num_inference_steps=40 , output_type="np" ).images
for word, image in zip(UpperCAmelCase , UpperCAmelCase ):
__lowerCamelCase : Dict = load_numpy(
F"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/dit/{word}.npy""" )
assert np.abs((expected_image - image).max() ) < 1E-2
def lowerCamelCase__ ( self : str ):
__lowerCamelCase : Tuple = DiTPipeline.from_pretrained("facebook/DiT-XL-2-512" )
__lowerCamelCase : Any = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
pipe.to("cuda" )
__lowerCamelCase : Union[str, Any] = ["vase", "umbrella"]
__lowerCamelCase : int = pipe.get_label_ids(UpperCAmelCase )
__lowerCamelCase : Dict = torch.manual_seed(0 )
__lowerCamelCase : Dict = pipe(UpperCAmelCase , generator=UpperCAmelCase , num_inference_steps=25 , output_type="np" ).images
for word, image in zip(UpperCAmelCase , UpperCAmelCase ):
__lowerCamelCase : Union[str, Any] = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
F"""/dit/{word}_512.npy""" )
assert np.abs((expected_image - image).max() ) < 1E-1
| 64
| 0
|
"""simple docstring"""
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert_fast import BertTokenizerFast
from .tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer, DPRReaderTokenizer
lowerCAmelCase__ : Optional[int] = logging.get_logger(__name__)
lowerCAmelCase__ : Tuple = {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}
lowerCAmelCase__ : Optional[int] = {
'vocab_file': {
'facebook/dpr-ctx_encoder-single-nq-base': (
'https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/vocab.txt'
),
'facebook/dpr-ctx_encoder-multiset-base': (
'https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/vocab.txt'
),
},
'tokenizer_file': {
'facebook/dpr-ctx_encoder-single-nq-base': (
'https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/tokenizer.json'
),
'facebook/dpr-ctx_encoder-multiset-base': (
'https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/tokenizer.json'
),
},
}
lowerCAmelCase__ : List[Any] = {
'vocab_file': {
'facebook/dpr-question_encoder-single-nq-base': (
'https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/vocab.txt'
),
'facebook/dpr-question_encoder-multiset-base': (
'https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/vocab.txt'
),
},
'tokenizer_file': {
'facebook/dpr-question_encoder-single-nq-base': (
'https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/tokenizer.json'
),
'facebook/dpr-question_encoder-multiset-base': (
'https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/tokenizer.json'
),
},
}
lowerCAmelCase__ : List[Any] = {
'vocab_file': {
'facebook/dpr-reader-single-nq-base': (
'https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/vocab.txt'
),
'facebook/dpr-reader-multiset-base': (
'https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/vocab.txt'
),
},
'tokenizer_file': {
'facebook/dpr-reader-single-nq-base': (
'https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/tokenizer.json'
),
'facebook/dpr-reader-multiset-base': (
'https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/tokenizer.json'
),
},
}
lowerCAmelCase__ : Optional[int] = {
'facebook/dpr-ctx_encoder-single-nq-base': 512,
'facebook/dpr-ctx_encoder-multiset-base': 512,
}
lowerCAmelCase__ : Any = {
'facebook/dpr-question_encoder-single-nq-base': 512,
'facebook/dpr-question_encoder-multiset-base': 512,
}
lowerCAmelCase__ : Union[str, Any] = {
'facebook/dpr-reader-single-nq-base': 512,
'facebook/dpr-reader-multiset-base': 512,
}
lowerCAmelCase__ : Optional[Any] = {
'facebook/dpr-ctx_encoder-single-nq-base': {'do_lower_case': True},
'facebook/dpr-ctx_encoder-multiset-base': {'do_lower_case': True},
}
lowerCAmelCase__ : List[str] = {
'facebook/dpr-question_encoder-single-nq-base': {'do_lower_case': True},
'facebook/dpr-question_encoder-multiset-base': {'do_lower_case': True},
}
lowerCAmelCase__ : str = {
'facebook/dpr-reader-single-nq-base': {'do_lower_case': True},
'facebook/dpr-reader-multiset-base': {'do_lower_case': True},
}
class snake_case ( __UpperCAmelCase ):
"""simple docstring"""
snake_case__ = VOCAB_FILES_NAMES
snake_case__ = CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
snake_case__ = CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
snake_case__ = CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
snake_case__ = DPRContextEncoderTokenizer
class snake_case ( __UpperCAmelCase ):
"""simple docstring"""
snake_case__ = VOCAB_FILES_NAMES
snake_case__ = QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
snake_case__ = QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
snake_case__ = QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
snake_case__ = DPRQuestionEncoderTokenizer
lowerCAmelCase__ : Optional[int] = collections.namedtuple(
'DPRSpanPrediction', ['span_score', 'relevance_score', 'doc_id', 'start_index', 'end_index', 'text']
)
lowerCAmelCase__ : Tuple = collections.namedtuple('DPRReaderOutput', ['start_logits', 'end_logits', 'relevance_logits'])
lowerCAmelCase__ : Tuple = r'\n Return a dictionary with the token ids of the input strings and other information to give to `.decode_best_spans`.\n It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers),\n using the tokenizer and vocabulary. The resulting `input_ids` is a matrix of size `(n_passages, sequence_length)`\n with the format:\n\n [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>\n\n Args:\n questions (`str` or `List[str]`):\n The questions to be encoded. You can specify one question for many passages. In this case, the question\n will be duplicated like `[questions] * n_passages`. Otherwise you have to specify as many questions as in\n `titles` or `texts`.\n titles (`str` or `List[str]`):\n The passages titles to be encoded. This can be a string or a list of strings if there are several passages.\n texts (`str` or `List[str]`):\n The passages texts to be encoded. This can be a string or a list of strings if there are several passages.\n padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):\n Activates and controls padding. Accepts the following values:\n\n - `True` or `\'longest\'`: Pad to the longest sequence in the batch (or no padding if only a single sequence\n if provided).\n - `\'max_length\'`: Pad to a maximum length specified with the argument `max_length` or to the maximum\n acceptable input length for the model if that argument is not provided.\n - `False` or `\'do_not_pad\'` (default): No padding (i.e., can output a batch with sequences of different\n lengths).\n truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):\n Activates and controls truncation. Accepts the following values:\n\n - `True` or `\'longest_first\'`: Truncate to a maximum length specified with the argument `max_length` or to\n the maximum acceptable input length for the model if that argument is not provided. This will truncate\n token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch\n of pairs) is provided.\n - `\'only_first\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n acceptable input length for the model if that argument is not provided. This will only truncate the first\n sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n - `\'only_second\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n acceptable input length for the model if that argument is not provided. This will only truncate the\n second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n - `False` or `\'do_not_truncate\'` (default): No truncation (i.e., can output batch with sequence lengths\n greater than the model maximum admissible input size).\n max_length (`int`, *optional*):\n Controls the maximum length to use by one of the truncation/padding parameters.\n\n If left unset or set to `None`, this will use the predefined model maximum length if a maximum length\n is required by one of the truncation/padding parameters. If the model has no specific maximum input\n length (like XLNet) truncation/padding to a maximum length will be deactivated.\n return_tensors (`str` or [`~utils.TensorType`], *optional*):\n If set, will return tensors instead of list of python integers. Acceptable values are:\n\n - `\'tf\'`: Return TensorFlow `tf.constant` objects.\n - `\'pt\'`: Return PyTorch `torch.Tensor` objects.\n - `\'np\'`: Return Numpy `np.ndarray` objects.\n return_attention_mask (`bool`, *optional*):\n Whether or not to return the attention mask. If not set, will return the attention mask according to the\n specific tokenizer\'s default, defined by the `return_outputs` attribute.\n\n [What are attention masks?](../glossary#attention-mask)\n\n Return:\n `Dict[str, List[List[int]]]`: A dictionary with the following keys:\n\n - `input_ids`: List of token ids to be fed to a model.\n - `attention_mask`: List of indices specifying which tokens should be attended to by the model.\n '
@add_start_docstrings(__UpperCAmelCase )
class snake_case :
"""simple docstring"""
def __call__( self : Optional[int] ,lowerCamelCase__ : Optional[Any] ,lowerCamelCase__ : Optional[str] = None ,lowerCamelCase__ : Optional[str] = None ,lowerCamelCase__ : Union[bool, str] = False ,lowerCamelCase__ : Union[bool, str] = False ,lowerCamelCase__ : Optional[int] = None ,lowerCamelCase__ : Optional[Union[str, TensorType]] = None ,lowerCamelCase__ : Optional[bool] = None ,**lowerCamelCase__ : List[Any] ,):
if titles is None and texts is None:
return super().__call__(
lowerCamelCase__ ,padding=lowerCamelCase__ ,truncation=lowerCamelCase__ ,max_length=lowerCamelCase__ ,return_tensors=lowerCamelCase__ ,return_attention_mask=lowerCamelCase__ ,**lowerCamelCase__ ,)
elif titles is None or texts is None:
UpperCAmelCase__ = titles if texts is None else texts
return super().__call__(
lowerCamelCase__ ,lowerCamelCase__ ,padding=lowerCamelCase__ ,truncation=lowerCamelCase__ ,max_length=lowerCamelCase__ ,return_tensors=lowerCamelCase__ ,return_attention_mask=lowerCamelCase__ ,**lowerCamelCase__ ,)
UpperCAmelCase__ = titles if not isinstance(lowerCamelCase__ ,lowerCamelCase__ ) else [titles]
UpperCAmelCase__ = texts if not isinstance(lowerCamelCase__ ,lowerCamelCase__ ) else [texts]
UpperCAmelCase__ = len(lowerCamelCase__ )
UpperCAmelCase__ = questions if not isinstance(lowerCamelCase__ ,lowerCamelCase__ ) else [questions] * n_passages
assert len(lowerCamelCase__ ) == len(
lowerCamelCase__ ), f'''There should be as many titles than texts but got {len(lowerCamelCase__ )} titles and {len(lowerCamelCase__ )} texts.'''
UpperCAmelCase__ = super().__call__(lowerCamelCase__ ,lowerCamelCase__ ,padding=lowerCamelCase__ ,truncation=lowerCamelCase__ )['input_ids']
UpperCAmelCase__ = super().__call__(lowerCamelCase__ ,add_special_tokens=lowerCamelCase__ ,padding=lowerCamelCase__ ,truncation=lowerCamelCase__ )['input_ids']
UpperCAmelCase__ = {
'input_ids': [
(encoded_question_and_title + encoded_text)[:max_length]
if max_length is not None and truncation
else encoded_question_and_title + encoded_text
for encoded_question_and_title, encoded_text in zip(lowerCamelCase__ ,lowerCamelCase__ )
]
}
if return_attention_mask is not False:
UpperCAmelCase__ = []
for input_ids in encoded_inputs["input_ids"]:
attention_mask.append([int(input_id != self.pad_token_id ) for input_id in input_ids] )
UpperCAmelCase__ = attention_mask
return self.pad(lowerCamelCase__ ,padding=lowerCamelCase__ ,max_length=lowerCamelCase__ ,return_tensors=lowerCamelCase__ )
def __lowerCAmelCase ( self : Optional[Any] ,lowerCamelCase__ : BatchEncoding ,lowerCamelCase__ : DPRReaderOutput ,lowerCamelCase__ : int = 16 ,lowerCamelCase__ : int = 64 ,lowerCamelCase__ : int = 4 ,):
UpperCAmelCase__ = reader_input['input_ids']
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ = reader_output[:3]
UpperCAmelCase__ = len(lowerCamelCase__ )
UpperCAmelCase__ = sorted(range(lowerCamelCase__ ) ,reverse=lowerCamelCase__ ,key=relevance_logits.__getitem__ )
UpperCAmelCase__ = []
for doc_id in sorted_docs:
UpperCAmelCase__ = list(input_ids[doc_id] )
# assuming question & title information is at the beginning of the sequence
UpperCAmelCase__ = sequence_ids.index(self.sep_token_id ,2 ) + 1 # second sep id
if sequence_ids[-1] == self.pad_token_id:
UpperCAmelCase__ = sequence_ids.index(self.pad_token_id )
else:
UpperCAmelCase__ = len(lowerCamelCase__ )
UpperCAmelCase__ = self._get_best_spans(
start_logits=start_logits[doc_id][passage_offset:sequence_len] ,end_logits=end_logits[doc_id][passage_offset:sequence_len] ,max_answer_length=lowerCamelCase__ ,top_spans=lowerCamelCase__ ,)
for start_index, end_index in best_spans:
start_index += passage_offset
end_index += passage_offset
nbest_spans_predictions.append(
DPRSpanPrediction(
span_score=start_logits[doc_id][start_index] + end_logits[doc_id][end_index] ,relevance_score=relevance_logits[doc_id] ,doc_id=lowerCamelCase__ ,start_index=lowerCamelCase__ ,end_index=lowerCamelCase__ ,text=self.decode(sequence_ids[start_index : end_index + 1] ) ,) )
if len(lowerCamelCase__ ) >= num_spans:
break
return nbest_spans_predictions[:num_spans]
def __lowerCAmelCase ( self : Union[str, Any] ,lowerCamelCase__ : List[int] ,lowerCamelCase__ : List[int] ,lowerCamelCase__ : int ,lowerCamelCase__ : int ,):
UpperCAmelCase__ = []
for start_index, start_score in enumerate(lowerCamelCase__ ):
for answer_length, end_score in enumerate(end_logits[start_index : start_index + max_answer_length] ):
scores.append(((start_index, start_index + answer_length), start_score + end_score) )
UpperCAmelCase__ = sorted(lowerCamelCase__ ,key=lambda lowerCamelCase__ : x[1] ,reverse=lowerCamelCase__ )
UpperCAmelCase__ = []
for (start_index, end_index), score in scores:
assert start_index <= end_index, f'''Wrong span indices: [{start_index}:{end_index}]'''
UpperCAmelCase__ = end_index - start_index + 1
assert length <= max_answer_length, f'''Span is too long: {length} > {max_answer_length}'''
if any(
start_index <= prev_start_index <= prev_end_index <= end_index
or prev_start_index <= start_index <= end_index <= prev_end_index
for (prev_start_index, prev_end_index) in chosen_span_intervals ):
continue
chosen_span_intervals.append((start_index, end_index) )
if len(lowerCamelCase__ ) == top_spans:
break
return chosen_span_intervals
@add_end_docstrings(__UpperCAmelCase )
class snake_case ( __UpperCAmelCase , __UpperCAmelCase ):
"""simple docstring"""
snake_case__ = VOCAB_FILES_NAMES
snake_case__ = READER_PRETRAINED_VOCAB_FILES_MAP
snake_case__ = READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
snake_case__ = READER_PRETRAINED_INIT_CONFIGURATION
snake_case__ = ["input_ids", "attention_mask"]
snake_case__ = DPRReaderTokenizer
| 98
|
"""simple docstring"""
import functools
def a_ ( lowerCamelCase , lowerCamelCase ):
UpperCAmelCase__ = len(lowerCamelCase )
UpperCAmelCase__ = len(lowerCamelCase )
@functools.cache
def min_distance(lowerCamelCase , lowerCamelCase ) -> int:
# if first word index is overflow - delete all from the second word
if indexa >= len_worda:
return len_worda - indexa
# if second word index is overflow - delete all from the first word
if indexa >= len_worda:
return len_worda - indexa
UpperCAmelCase__ = int(worda[indexa] != worda[indexa] ) # current letters not identical
return min(
1 + min_distance(indexa + 1 , lowerCamelCase ) , 1 + min_distance(lowerCamelCase , indexa + 1 ) , diff + min_distance(indexa + 1 , indexa + 1 ) , )
return min_distance(0 , 0 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 98
| 1
|
import os
from pathlib import Path
import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader
from transformers import AutoTokenizer
from transformers.models.mbart.modeling_mbart import shift_tokens_right
from transformers.testing_utils import TestCasePlus, slow
from utils import FAIRSEQ_AVAILABLE, DistributedSortishSampler, LegacySeqaSeqDataset, SeqaSeqDataset
__UpperCamelCase : Union[str, Any] = "bert-base-cased"
__UpperCamelCase : Tuple = "google/pegasus-xsum"
__UpperCamelCase : Union[str, Any] = [" Sam ate lunch today.", "Sams lunch ingredients."]
__UpperCamelCase : Union[str, Any] = ["A very interesting story about what I ate for lunch.", "Avocado, celery, turkey, coffee"]
__UpperCamelCase : Any = "patrickvonplaten/t5-tiny-random"
__UpperCamelCase : List[Any] = "sshleifer/bart-tiny-random"
__UpperCamelCase : Any = "sshleifer/tiny-mbart"
__UpperCamelCase : Optional[Any] = "sshleifer/tiny-marian-en-de"
def _a ( SCREAMING_SNAKE_CASE : Path , SCREAMING_SNAKE_CASE : list ):
"""simple docstring"""
UpperCamelCase__ : Optional[Any] = '''\n'''.join(SCREAMING_SNAKE_CASE )
Path(SCREAMING_SNAKE_CASE ).open('''w''' ).writelines(SCREAMING_SNAKE_CASE )
def _a ( SCREAMING_SNAKE_CASE : Dict ):
"""simple docstring"""
for split in ["train", "val", "test"]:
_dump_articles(os.path.join(SCREAMING_SNAKE_CASE , F"{split}.source" ) , SCREAMING_SNAKE_CASE )
_dump_articles(os.path.join(SCREAMING_SNAKE_CASE , F"{split}.target" ) , SCREAMING_SNAKE_CASE )
return tmp_dir
class __magic_name__ ( __lowerCAmelCase):
@parameterized.expand(
[
MBART_TINY,
MARIAN_TINY,
T5_TINY,
BART_TINY,
PEGASUS_XSUM,
] , )
@slow
def UpperCAmelCase__ ( self : Tuple , lowerCamelCase__ : Dict ) -> Optional[Any]:
'''simple docstring'''
UpperCamelCase__ : Dict = AutoTokenizer.from_pretrained(lowerCamelCase__ )
UpperCamelCase__ : Optional[int] = make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() )
UpperCamelCase__ : Tuple = max(len(tokenizer.encode(lowerCamelCase__ ) ) for a in ARTICLES )
UpperCamelCase__ : List[str] = max(len(tokenizer.encode(lowerCamelCase__ ) ) for a in SUMMARIES )
UpperCamelCase__ : int = 4
UpperCamelCase__ : Union[str, Any] = 8
assert max_len_target > max_src_len # Will be truncated
assert max_len_source > max_src_len # Will be truncated
UpperCamelCase__ , UpperCamelCase__ : Union[str, Any] = '''ro_RO''', '''de_DE''' # ignored for all but mbart, but never causes error.
UpperCamelCase__ : List[str] = SeqaSeqDataset(
lowerCamelCase__ , data_dir=lowerCamelCase__ , type_path='''train''' , max_source_length=lowerCamelCase__ , max_target_length=lowerCamelCase__ , src_lang=lowerCamelCase__ , tgt_lang=lowerCamelCase__ , )
UpperCamelCase__ : Dict = DataLoader(lowerCamelCase__ , batch_size=2 , collate_fn=train_dataset.collate_fn )
for batch in dataloader:
assert isinstance(lowerCamelCase__ , lowerCamelCase__ )
assert batch["attention_mask"].shape == batch["input_ids"].shape
# show that articles were trimmed.
assert batch["input_ids"].shape[1] == max_src_len
# show that targets are the same len
assert batch["labels"].shape[1] == max_tgt_len
if tok_name != MBART_TINY:
continue
# check language codes in correct place
UpperCamelCase__ : Dict = shift_tokens_right(batch['''labels'''] , tokenizer.pad_token_id )
assert batch["decoder_input_ids"][0, 0].item() == tokenizer.lang_code_to_id[tgt_lang]
assert batch["decoder_input_ids"][0, -1].item() == tokenizer.eos_token_id
assert batch["input_ids"][0, -2].item() == tokenizer.eos_token_id
assert batch["input_ids"][0, -1].item() == tokenizer.lang_code_to_id[src_lang]
break # No need to test every batch
@parameterized.expand([BART_TINY, BERT_BASE_CASED] )
def UpperCAmelCase__ ( self : int , lowerCamelCase__ : Tuple ) -> List[Any]:
'''simple docstring'''
UpperCamelCase__ : Dict = AutoTokenizer.from_pretrained(lowerCamelCase__ )
UpperCamelCase__ : str = make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() )
UpperCamelCase__ : Union[str, Any] = max(len(tokenizer.encode(lowerCamelCase__ ) ) for a in ARTICLES )
UpperCamelCase__ : str = max(len(tokenizer.encode(lowerCamelCase__ ) ) for a in SUMMARIES )
UpperCamelCase__ : Union[str, Any] = 4
UpperCamelCase__ : Optional[int] = LegacySeqaSeqDataset(
lowerCamelCase__ , data_dir=lowerCamelCase__ , type_path='''train''' , max_source_length=20 , max_target_length=lowerCamelCase__ , )
UpperCamelCase__ : List[str] = DataLoader(lowerCamelCase__ , batch_size=2 , collate_fn=train_dataset.collate_fn )
for batch in dataloader:
assert batch["attention_mask"].shape == batch["input_ids"].shape
# show that articles were trimmed.
assert batch["input_ids"].shape[1] == max_len_source
assert 20 >= batch["input_ids"].shape[1] # trimmed significantly
# show that targets were truncated
assert batch["labels"].shape[1] == trunc_target # Truncated
assert max_len_target > trunc_target # Truncated
break # No need to test every batch
def UpperCAmelCase__ ( self : List[str] ) -> Dict:
'''simple docstring'''
UpperCamelCase__ : Dict = AutoTokenizer.from_pretrained('''facebook/mbart-large-cc25''' )
UpperCamelCase__ : List[str] = Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() ) )
UpperCamelCase__ : int = tmp_dir.joinpath('''train.source''' ).open().readlines()
UpperCamelCase__ : Tuple = Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() ) )
pack_data_dir(lowerCamelCase__ , lowerCamelCase__ , 128 , lowerCamelCase__ )
UpperCamelCase__ : str = {x.name for x in tmp_dir.iterdir()}
UpperCamelCase__ : Optional[int] = {x.name for x in save_dir.iterdir()}
UpperCamelCase__ : Dict = save_dir.joinpath('''train.source''' ).open().readlines()
# orig: [' Sam ate lunch today.\n', 'Sams lunch ingredients.']
# desired_packed: [' Sam ate lunch today.\n Sams lunch ingredients.']
assert len(lowerCamelCase__ ) < len(lowerCamelCase__ )
assert len(lowerCamelCase__ ) == 1
assert len(packed_examples[0] ) == sum(len(lowerCamelCase__ ) for x in orig_examples )
assert orig_paths == new_paths
@pytest.mark.skipif(not FAIRSEQ_AVAILABLE , reason='''This test requires fairseq''' )
def UpperCAmelCase__ ( self : str ) -> Optional[Any]:
'''simple docstring'''
if not FAIRSEQ_AVAILABLE:
return
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ : Tuple = self._get_dataset(max_len=64 )
UpperCamelCase__ : List[str] = 64
UpperCamelCase__ : Optional[int] = ds.make_dynamic_sampler(lowerCamelCase__ , required_batch_size_multiple=lowerCamelCase__ )
UpperCamelCase__ : Union[str, Any] = [len(lowerCamelCase__ ) for x in batch_sampler]
assert len(set(lowerCamelCase__ ) ) > 1 # it's not dynamic batch size if every batch is the same length
assert sum(lowerCamelCase__ ) == len(lowerCamelCase__ ) # no dropped or added examples
UpperCamelCase__ : Any = DataLoader(lowerCamelCase__ , batch_sampler=lowerCamelCase__ , collate_fn=ds.collate_fn , num_workers=2 )
UpperCamelCase__ : int = []
UpperCamelCase__ : Tuple = []
for batch in data_loader:
UpperCamelCase__ : int = batch['''input_ids'''].shape
UpperCamelCase__ : Any = src_shape[0]
assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
UpperCamelCase__ : Tuple = np.product(batch['''input_ids'''].shape )
num_src_per_batch.append(lowerCamelCase__ )
if num_src_tokens > (max_tokens * 1.1):
failures.append(lowerCamelCase__ )
assert num_src_per_batch[0] == max(lowerCamelCase__ )
if failures:
raise AssertionError(F"too many tokens in {len(lowerCamelCase__ )} batches" )
def UpperCAmelCase__ ( self : str ) -> Optional[Any]:
'''simple docstring'''
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ : Tuple = self._get_dataset(max_len=512 )
UpperCamelCase__ : Union[str, Any] = 2
UpperCamelCase__ : Optional[int] = ds.make_sortish_sampler(lowerCamelCase__ , shuffle=lowerCamelCase__ )
UpperCamelCase__ : List[Any] = DataLoader(lowerCamelCase__ , batch_size=lowerCamelCase__ , collate_fn=ds.collate_fn , num_workers=2 )
UpperCamelCase__ : str = DataLoader(lowerCamelCase__ , batch_size=lowerCamelCase__ , collate_fn=ds.collate_fn , num_workers=2 , sampler=lowerCamelCase__ )
UpperCamelCase__ : Optional[Any] = tokenizer.pad_token_id
def count_pad_tokens(lowerCamelCase__ : int , lowerCamelCase__ : List[Any]="input_ids" ):
return [batch[k].eq(lowerCamelCase__ ).sum().item() for batch in data_loader]
assert sum(count_pad_tokens(lowerCamelCase__ , k='''labels''' ) ) < sum(count_pad_tokens(lowerCamelCase__ , k='''labels''' ) )
assert sum(count_pad_tokens(lowerCamelCase__ ) ) < sum(count_pad_tokens(lowerCamelCase__ ) )
assert len(lowerCamelCase__ ) == len(lowerCamelCase__ )
def UpperCAmelCase__ ( self : int , lowerCamelCase__ : List[Any]=1000 , lowerCamelCase__ : Tuple=128 ) -> str:
'''simple docstring'''
if os.getenv('''USE_REAL_DATA''' , lowerCamelCase__ ):
UpperCamelCase__ : List[str] = '''examples/seq2seq/wmt_en_ro'''
UpperCamelCase__ : int = max_len * 2 * 64
if not Path(lowerCamelCase__ ).joinpath('''train.len''' ).exists():
save_len_file(lowerCamelCase__ , lowerCamelCase__ )
else:
UpperCamelCase__ : Optional[Any] = '''examples/seq2seq/test_data/wmt_en_ro'''
UpperCamelCase__ : Optional[Any] = max_len * 4
save_len_file(lowerCamelCase__ , lowerCamelCase__ )
UpperCamelCase__ : List[str] = AutoTokenizer.from_pretrained(lowerCamelCase__ )
UpperCamelCase__ : Any = SeqaSeqDataset(
lowerCamelCase__ , data_dir=lowerCamelCase__ , type_path='''train''' , max_source_length=lowerCamelCase__ , max_target_length=lowerCamelCase__ , n_obs=lowerCamelCase__ , )
return ds, max_tokens, tokenizer
def UpperCAmelCase__ ( self : int ) -> str:
'''simple docstring'''
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ : List[Any] = self._get_dataset()
UpperCamelCase__ : Any = set(DistributedSortishSampler(lowerCamelCase__ , 256 , num_replicas=2 , rank=0 , add_extra_examples=lowerCamelCase__ ) )
UpperCamelCase__ : str = set(DistributedSortishSampler(lowerCamelCase__ , 256 , num_replicas=2 , rank=1 , add_extra_examples=lowerCamelCase__ ) )
assert idsa.intersection(lowerCamelCase__ ) == set()
@parameterized.expand(
[
MBART_TINY,
MARIAN_TINY,
T5_TINY,
BART_TINY,
PEGASUS_XSUM,
] , )
def UpperCAmelCase__ ( self : Any , lowerCamelCase__ : Tuple ) -> str:
'''simple docstring'''
UpperCamelCase__ : Union[str, Any] = AutoTokenizer.from_pretrained(lowerCamelCase__ , use_fast=lowerCamelCase__ )
if tok_name == MBART_TINY:
UpperCamelCase__ : Dict = SeqaSeqDataset(
lowerCamelCase__ , data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() ) , type_path='''train''' , max_source_length=4 , max_target_length=8 , src_lang='''EN''' , tgt_lang='''FR''' , )
UpperCamelCase__ : List[Any] = train_dataset.dataset_kwargs
assert "src_lang" in kwargs and "tgt_lang" in kwargs
else:
UpperCamelCase__ : List[Any] = SeqaSeqDataset(
lowerCamelCase__ , data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() ) , type_path='''train''' , max_source_length=4 , max_target_length=8 , )
UpperCamelCase__ : Optional[Any] = train_dataset.dataset_kwargs
assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
assert len(lowerCamelCase__ ) == 1 if tok_name == BART_TINY else len(lowerCamelCase__ ) == 0
| 51
|
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
flip_channel_order,
get_resize_output_image_size,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, is_vision_available, logging
if is_vision_available():
import PIL
if is_torch_available():
import torch
__UpperCamelCase : int = logging.get_logger(__name__)
class __magic_name__ ( __lowerCAmelCase):
A: str = ["pixel_values"]
def __init__( self : str , lowerCamelCase__ : bool = True , lowerCamelCase__ : Dict[str, int] = None , lowerCamelCase__ : PILImageResampling = PILImageResampling.BILINEAR , lowerCamelCase__ : bool = True , lowerCamelCase__ : Union[int, float] = 1 / 255 , lowerCamelCase__ : bool = True , lowerCamelCase__ : Dict[str, int] = None , lowerCamelCase__ : bool = True , **lowerCamelCase__ : Any , ) -> None:
'''simple docstring'''
super().__init__(**lowerCamelCase__ )
UpperCamelCase__ : Optional[int] = size if size is not None else {'''shortest_edge''': 224}
UpperCamelCase__ : List[str] = get_size_dict(lowerCamelCase__ , default_to_square=lowerCamelCase__ )
UpperCamelCase__ : Union[str, Any] = crop_size if crop_size is not None else {'''height''': 256, '''width''': 256}
UpperCamelCase__ : Dict = get_size_dict(lowerCamelCase__ , param_name='''crop_size''' )
UpperCamelCase__ : Optional[Any] = do_resize
UpperCamelCase__ : List[Any] = size
UpperCamelCase__ : Optional[int] = resample
UpperCamelCase__ : Optional[int] = do_rescale
UpperCamelCase__ : Dict = rescale_factor
UpperCamelCase__ : Optional[Any] = do_center_crop
UpperCamelCase__ : int = crop_size
UpperCamelCase__ : List[str] = do_flip_channel_order
def UpperCAmelCase__ ( self : Tuple , lowerCamelCase__ : np.ndarray , lowerCamelCase__ : Dict[str, int] , lowerCamelCase__ : PILImageResampling = PIL.Image.BILINEAR , lowerCamelCase__ : Optional[Union[str, ChannelDimension]] = None , **lowerCamelCase__ : List[str] , ) -> np.ndarray:
'''simple docstring'''
UpperCamelCase__ : Optional[int] = get_size_dict(lowerCamelCase__ , default_to_square=lowerCamelCase__ )
if "shortest_edge" not in size:
raise ValueError(F"The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}" )
UpperCamelCase__ : int = get_resize_output_image_size(lowerCamelCase__ , size=size['''shortest_edge'''] , default_to_square=lowerCamelCase__ )
return resize(lowerCamelCase__ , size=lowerCamelCase__ , resample=lowerCamelCase__ , data_format=lowerCamelCase__ , **lowerCamelCase__ )
def UpperCAmelCase__ ( self : int , lowerCamelCase__ : np.ndarray , lowerCamelCase__ : Dict[str, int] , lowerCamelCase__ : Optional[Union[str, ChannelDimension]] = None , **lowerCamelCase__ : List[Any] , ) -> np.ndarray:
'''simple docstring'''
UpperCamelCase__ : Optional[int] = get_size_dict(lowerCamelCase__ )
if "height" not in size or "width" not in size:
raise ValueError(F"The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}" )
return center_crop(lowerCamelCase__ , size=(size['''height'''], size['''width''']) , data_format=lowerCamelCase__ , **lowerCamelCase__ )
def UpperCAmelCase__ ( self : Tuple , lowerCamelCase__ : np.ndarray , lowerCamelCase__ : Union[int, float] , lowerCamelCase__ : Optional[Union[str, ChannelDimension]] = None , **lowerCamelCase__ : Tuple , ) -> List[Any]:
'''simple docstring'''
return rescale(lowerCamelCase__ , scale=lowerCamelCase__ , data_format=lowerCamelCase__ , **lowerCamelCase__ )
def UpperCAmelCase__ ( self : List[Any] , lowerCamelCase__ : np.ndarray , lowerCamelCase__ : Optional[Union[str, ChannelDimension]] = None ) -> np.ndarray:
'''simple docstring'''
return flip_channel_order(lowerCamelCase__ , data_format=lowerCamelCase__ )
def UpperCAmelCase__ ( self : Dict , lowerCamelCase__ : ImageInput , lowerCamelCase__ : bool = None , lowerCamelCase__ : Dict[str, int] = None , lowerCamelCase__ : PILImageResampling = None , lowerCamelCase__ : bool = None , lowerCamelCase__ : float = None , lowerCamelCase__ : bool = None , lowerCamelCase__ : Dict[str, int] = None , lowerCamelCase__ : bool = None , lowerCamelCase__ : Optional[Union[str, TensorType]] = None , lowerCamelCase__ : ChannelDimension = ChannelDimension.FIRST , **lowerCamelCase__ : List[Any] , ) -> PIL.Image.Image:
'''simple docstring'''
UpperCamelCase__ : Optional[Any] = do_resize if do_resize is not None else self.do_resize
UpperCamelCase__ : List[Any] = resample if resample is not None else self.resample
UpperCamelCase__ : str = do_rescale if do_rescale is not None else self.do_rescale
UpperCamelCase__ : Optional[Any] = rescale_factor if rescale_factor is not None else self.rescale_factor
UpperCamelCase__ : Any = do_center_crop if do_center_crop is not None else self.do_center_crop
UpperCamelCase__ : List[str] = (
do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
)
UpperCamelCase__ : List[str] = size if size is not None else self.size
UpperCamelCase__ : int = get_size_dict(lowerCamelCase__ , default_to_square=lowerCamelCase__ )
UpperCamelCase__ : Tuple = crop_size if crop_size is not None else self.crop_size
UpperCamelCase__ : int = get_size_dict(lowerCamelCase__ , param_name='''crop_size''' )
UpperCamelCase__ : int = make_list_of_images(lowerCamelCase__ )
if not valid_images(lowerCamelCase__ ):
raise ValueError(
'''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
'''torch.Tensor, tf.Tensor or jax.ndarray.''' )
if do_resize and size is None:
raise ValueError('''Size must be specified if do_resize is True.''' )
if do_rescale and rescale_factor is None:
raise ValueError('''Rescale factor must be specified if do_rescale is True.''' )
if do_center_crop and crop_size is None:
raise ValueError('''Crop size must be specified if do_center_crop is True.''' )
# All transformations expect numpy arrays.
UpperCamelCase__ : Union[str, Any] = [to_numpy_array(lowerCamelCase__ ) for image in images]
if do_resize:
UpperCamelCase__ : Tuple = [self.resize(image=lowerCamelCase__ , size=lowerCamelCase__ , resample=lowerCamelCase__ ) for image in images]
if do_center_crop:
UpperCamelCase__ : Optional[Any] = [self.center_crop(image=lowerCamelCase__ , size=lowerCamelCase__ ) for image in images]
if do_rescale:
UpperCamelCase__ : List[Any] = [self.rescale(image=lowerCamelCase__ , scale=lowerCamelCase__ ) for image in images]
# the pretrained checkpoints assume images are BGR, not RGB
if do_flip_channel_order:
UpperCamelCase__ : List[Any] = [self.flip_channel_order(image=lowerCamelCase__ ) for image in images]
UpperCamelCase__ : Union[str, Any] = [to_channel_dimension_format(lowerCamelCase__ , lowerCamelCase__ ) for image in images]
UpperCamelCase__ : int = {'''pixel_values''': images}
return BatchFeature(data=lowerCamelCase__ , tensor_type=lowerCamelCase__ )
def UpperCAmelCase__ ( self : Any , lowerCamelCase__ : Optional[int] , lowerCamelCase__ : List[Tuple] = None ) -> Tuple:
'''simple docstring'''
UpperCamelCase__ : Tuple = outputs.logits
# Resize logits and compute semantic segmentation maps
if target_sizes is not None:
if len(lowerCamelCase__ ) != len(lowerCamelCase__ ):
raise ValueError(
'''Make sure that you pass in as many target sizes as the batch dimension of the logits''' )
if is_torch_tensor(lowerCamelCase__ ):
UpperCamelCase__ : Tuple = target_sizes.numpy()
UpperCamelCase__ : Any = []
for idx in range(len(lowerCamelCase__ ) ):
UpperCamelCase__ : Optional[Any] = torch.nn.functional.interpolate(
logits[idx].unsqueeze(dim=0 ) , size=target_sizes[idx] , mode='''bilinear''' , align_corners=lowerCamelCase__ )
UpperCamelCase__ : Optional[Any] = resized_logits[0].argmax(dim=0 )
semantic_segmentation.append(lowerCamelCase__ )
else:
UpperCamelCase__ : Dict = logits.argmax(dim=1 )
UpperCamelCase__ : Dict = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0] )]
return semantic_segmentation
| 51
| 1
|
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_vision_available():
import PIL
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
class a ( __lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase :Tuple = ['''pixel_values''']
def __init__( self , lowerCAmelCase_ = True , lowerCAmelCase_ = None , lowerCAmelCase_ = None , lowerCAmelCase_ = PILImageResampling.BILINEAR , lowerCAmelCase_ = True , lowerCAmelCase_ = 1 / 2_55 , lowerCAmelCase_ = True , lowerCAmelCase_ = None , lowerCAmelCase_ = None , **lowerCAmelCase_ , ) -> None:
super().__init__(**lowerCAmelCase_ )
_A = size if size is not None else {"""shortest_edge""": 3_84}
_A = get_size_dict(lowerCAmelCase_ , default_to_square=lowerCAmelCase_ )
_A = do_resize
_A = size
# Default value set here for backwards compatibility where the value in config is None
_A = crop_pct if crop_pct is not None else 2_24 / 2_56
_A = resample
_A = do_rescale
_A = rescale_factor
_A = do_normalize
_A = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
_A = image_std if image_std is not None else IMAGENET_STANDARD_STD
def UpperCAmelCase ( self , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ = PILImageResampling.BICUBIC , lowerCAmelCase_ = None , **lowerCAmelCase_ , ) -> np.ndarray:
_A = get_size_dict(lowerCAmelCase_ , default_to_square=lowerCAmelCase_ )
if "shortest_edge" not in size:
raise ValueError(F'''Size dictionary must contain \'shortest_edge\' key. Got {size.keys()}''' )
_A = size["""shortest_edge"""]
if shortest_edge < 3_84:
# maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
_A = int(shortest_edge / crop_pct )
_A = get_resize_output_image_size(lowerCAmelCase_ , size=lowerCAmelCase_ , default_to_square=lowerCAmelCase_ )
_A = resize(image=lowerCAmelCase_ , size=lowerCAmelCase_ , resample=lowerCAmelCase_ , data_format=lowerCAmelCase_ , **lowerCAmelCase_ )
# then crop to (shortest_edge, shortest_edge)
return center_crop(image=lowerCAmelCase_ , size=(shortest_edge, shortest_edge) , data_format=lowerCAmelCase_ , **lowerCAmelCase_ )
else:
# warping (no cropping) when evaluated at 384 or larger
return resize(
lowerCAmelCase_ , size=(shortest_edge, shortest_edge) , resample=lowerCAmelCase_ , data_format=lowerCAmelCase_ , **lowerCAmelCase_ )
def UpperCAmelCase ( self , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ = None , **lowerCAmelCase_ , ) -> Tuple:
return rescale(lowerCAmelCase_ , scale=lowerCAmelCase_ , data_format=lowerCAmelCase_ , **lowerCAmelCase_ )
def UpperCAmelCase ( self , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ = None , **lowerCAmelCase_ , ) -> np.ndarray:
return normalize(lowerCAmelCase_ , mean=lowerCAmelCase_ , std=lowerCAmelCase_ , data_format=lowerCAmelCase_ , **lowerCAmelCase_ )
def UpperCAmelCase ( self , lowerCAmelCase_ , lowerCAmelCase_ = None , lowerCAmelCase_ = None , lowerCAmelCase_ = None , lowerCAmelCase_ = None , lowerCAmelCase_ = None , lowerCAmelCase_ = None , lowerCAmelCase_ = None , lowerCAmelCase_ = None , lowerCAmelCase_ = None , lowerCAmelCase_ = None , lowerCAmelCase_ = ChannelDimension.FIRST , **lowerCAmelCase_ , ) -> PIL.Image.Image:
_A = do_resize if do_resize is not None else self.do_resize
_A = crop_pct if crop_pct is not None else self.crop_pct
_A = resample if resample is not None else self.resample
_A = do_rescale if do_rescale is not None else self.do_rescale
_A = rescale_factor if rescale_factor is not None else self.rescale_factor
_A = do_normalize if do_normalize is not None else self.do_normalize
_A = image_mean if image_mean is not None else self.image_mean
_A = image_std if image_std is not None else self.image_std
_A = size if size is not None else self.size
_A = get_size_dict(lowerCAmelCase_ , default_to_square=lowerCAmelCase_ )
_A = make_list_of_images(lowerCAmelCase_ )
if not valid_images(lowerCAmelCase_ ):
raise ValueError(
"""Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
"""torch.Tensor, tf.Tensor or jax.ndarray.""" )
if do_resize and size is None or resample is None:
raise ValueError("""Size and resample must be specified if do_resize is True.""" )
if do_resize and size["shortest_edge"] < 3_84 and crop_pct is None:
raise ValueError("""crop_pct must be specified if size < 384.""" )
if do_rescale and rescale_factor is None:
raise ValueError("""Rescale factor must be specified if do_rescale is True.""" )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError("""Image mean and std must be specified if do_normalize is True.""" )
# All transformations expect numpy arrays.
_A = [to_numpy_array(lowerCAmelCase_ ) for image in images]
if do_resize:
_A = [self.resize(image=lowerCAmelCase_ , size=lowerCAmelCase_ , crop_pct=lowerCAmelCase_ , resample=lowerCAmelCase_ ) for image in images]
if do_rescale:
_A = [self.rescale(image=lowerCAmelCase_ , scale=lowerCAmelCase_ ) for image in images]
if do_normalize:
_A = [self.normalize(image=lowerCAmelCase_ , mean=lowerCAmelCase_ , std=lowerCAmelCase_ ) for image in images]
_A = [to_channel_dimension_format(lowerCAmelCase_ , lowerCAmelCase_ ) for image in images]
_A = {"""pixel_values""": images}
return BatchFeature(data=lowerCAmelCase_ , tensor_type=lowerCAmelCase_ )
| 180
|
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bert import BertTokenizer
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE = {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}
_SCREAMING_SNAKE_CASE = {
'vocab_file': {
'bert-base-uncased': 'https://huggingface.co/bert-base-uncased/resolve/main/vocab.txt',
'bert-large-uncased': 'https://huggingface.co/bert-large-uncased/resolve/main/vocab.txt',
'bert-base-cased': 'https://huggingface.co/bert-base-cased/resolve/main/vocab.txt',
'bert-large-cased': 'https://huggingface.co/bert-large-cased/resolve/main/vocab.txt',
'bert-base-multilingual-uncased': (
'https://huggingface.co/bert-base-multilingual-uncased/resolve/main/vocab.txt'
),
'bert-base-multilingual-cased': 'https://huggingface.co/bert-base-multilingual-cased/resolve/main/vocab.txt',
'bert-base-chinese': 'https://huggingface.co/bert-base-chinese/resolve/main/vocab.txt',
'bert-base-german-cased': 'https://huggingface.co/bert-base-german-cased/resolve/main/vocab.txt',
'bert-large-uncased-whole-word-masking': (
'https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/vocab.txt'
),
'bert-large-cased-whole-word-masking': (
'https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/vocab.txt'
),
'bert-large-uncased-whole-word-masking-finetuned-squad': (
'https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/vocab.txt'
),
'bert-large-cased-whole-word-masking-finetuned-squad': (
'https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/vocab.txt'
),
'bert-base-cased-finetuned-mrpc': (
'https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/vocab.txt'
),
'bert-base-german-dbmdz-cased': 'https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/vocab.txt',
'bert-base-german-dbmdz-uncased': (
'https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/vocab.txt'
),
'TurkuNLP/bert-base-finnish-cased-v1': (
'https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/vocab.txt'
),
'TurkuNLP/bert-base-finnish-uncased-v1': (
'https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/vocab.txt'
),
'wietsedv/bert-base-dutch-cased': (
'https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/vocab.txt'
),
},
'tokenizer_file': {
'bert-base-uncased': 'https://huggingface.co/bert-base-uncased/resolve/main/tokenizer.json',
'bert-large-uncased': 'https://huggingface.co/bert-large-uncased/resolve/main/tokenizer.json',
'bert-base-cased': 'https://huggingface.co/bert-base-cased/resolve/main/tokenizer.json',
'bert-large-cased': 'https://huggingface.co/bert-large-cased/resolve/main/tokenizer.json',
'bert-base-multilingual-uncased': (
'https://huggingface.co/bert-base-multilingual-uncased/resolve/main/tokenizer.json'
),
'bert-base-multilingual-cased': (
'https://huggingface.co/bert-base-multilingual-cased/resolve/main/tokenizer.json'
),
'bert-base-chinese': 'https://huggingface.co/bert-base-chinese/resolve/main/tokenizer.json',
'bert-base-german-cased': 'https://huggingface.co/bert-base-german-cased/resolve/main/tokenizer.json',
'bert-large-uncased-whole-word-masking': (
'https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/tokenizer.json'
),
'bert-large-cased-whole-word-masking': (
'https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/tokenizer.json'
),
'bert-large-uncased-whole-word-masking-finetuned-squad': (
'https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/tokenizer.json'
),
'bert-large-cased-whole-word-masking-finetuned-squad': (
'https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/tokenizer.json'
),
'bert-base-cased-finetuned-mrpc': (
'https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/tokenizer.json'
),
'bert-base-german-dbmdz-cased': (
'https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/tokenizer.json'
),
'bert-base-german-dbmdz-uncased': (
'https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/tokenizer.json'
),
'TurkuNLP/bert-base-finnish-cased-v1': (
'https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/tokenizer.json'
),
'TurkuNLP/bert-base-finnish-uncased-v1': (
'https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/tokenizer.json'
),
'wietsedv/bert-base-dutch-cased': (
'https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/tokenizer.json'
),
},
}
_SCREAMING_SNAKE_CASE = {
'bert-base-uncased': 512,
'bert-large-uncased': 512,
'bert-base-cased': 512,
'bert-large-cased': 512,
'bert-base-multilingual-uncased': 512,
'bert-base-multilingual-cased': 512,
'bert-base-chinese': 512,
'bert-base-german-cased': 512,
'bert-large-uncased-whole-word-masking': 512,
'bert-large-cased-whole-word-masking': 512,
'bert-large-uncased-whole-word-masking-finetuned-squad': 512,
'bert-large-cased-whole-word-masking-finetuned-squad': 512,
'bert-base-cased-finetuned-mrpc': 512,
'bert-base-german-dbmdz-cased': 512,
'bert-base-german-dbmdz-uncased': 512,
'TurkuNLP/bert-base-finnish-cased-v1': 512,
'TurkuNLP/bert-base-finnish-uncased-v1': 512,
'wietsedv/bert-base-dutch-cased': 512,
}
_SCREAMING_SNAKE_CASE = {
'bert-base-uncased': {'do_lower_case': True},
'bert-large-uncased': {'do_lower_case': True},
'bert-base-cased': {'do_lower_case': False},
'bert-large-cased': {'do_lower_case': False},
'bert-base-multilingual-uncased': {'do_lower_case': True},
'bert-base-multilingual-cased': {'do_lower_case': False},
'bert-base-chinese': {'do_lower_case': False},
'bert-base-german-cased': {'do_lower_case': False},
'bert-large-uncased-whole-word-masking': {'do_lower_case': True},
'bert-large-cased-whole-word-masking': {'do_lower_case': False},
'bert-large-uncased-whole-word-masking-finetuned-squad': {'do_lower_case': True},
'bert-large-cased-whole-word-masking-finetuned-squad': {'do_lower_case': False},
'bert-base-cased-finetuned-mrpc': {'do_lower_case': False},
'bert-base-german-dbmdz-cased': {'do_lower_case': False},
'bert-base-german-dbmdz-uncased': {'do_lower_case': True},
'TurkuNLP/bert-base-finnish-cased-v1': {'do_lower_case': False},
'TurkuNLP/bert-base-finnish-uncased-v1': {'do_lower_case': True},
'wietsedv/bert-base-dutch-cased': {'do_lower_case': False},
}
class a ( __lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase :Tuple = VOCAB_FILES_NAMES
lowerCamelCase :Union[str, Any] = PRETRAINED_VOCAB_FILES_MAP
lowerCamelCase :Dict = PRETRAINED_INIT_CONFIGURATION
lowerCamelCase :Optional[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCamelCase :Optional[Any] = BertTokenizer
def __init__( self , lowerCAmelCase_=None , lowerCAmelCase_=None , lowerCAmelCase_=True , lowerCAmelCase_="[UNK]" , lowerCAmelCase_="[SEP]" , lowerCAmelCase_="[PAD]" , lowerCAmelCase_="[CLS]" , lowerCAmelCase_="[MASK]" , lowerCAmelCase_=True , lowerCAmelCase_=None , **lowerCAmelCase_ , ) -> List[str]:
super().__init__(
lowerCAmelCase_ , tokenizer_file=lowerCAmelCase_ , do_lower_case=lowerCAmelCase_ , unk_token=lowerCAmelCase_ , sep_token=lowerCAmelCase_ , pad_token=lowerCAmelCase_ , cls_token=lowerCAmelCase_ , mask_token=lowerCAmelCase_ , tokenize_chinese_chars=lowerCAmelCase_ , strip_accents=lowerCAmelCase_ , **lowerCAmelCase_ , )
_A = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get("""lowercase""" , lowerCAmelCase_ ) != do_lower_case
or normalizer_state.get("""strip_accents""" , lowerCAmelCase_ ) != strip_accents
or normalizer_state.get("""handle_chinese_chars""" , lowerCAmelCase_ ) != tokenize_chinese_chars
):
_A = getattr(lowerCAmelCase_ , normalizer_state.pop("""type""" ) )
_A = do_lower_case
_A = strip_accents
_A = tokenize_chinese_chars
_A = normalizer_class(**lowerCAmelCase_ )
_A = do_lower_case
def UpperCAmelCase ( self , lowerCAmelCase_ , lowerCAmelCase_=None ) -> List[str]:
_A = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def UpperCAmelCase ( self , lowerCAmelCase_ , lowerCAmelCase_ = None ) -> List[int]:
_A = [self.sep_token_id]
_A = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def UpperCAmelCase ( self , lowerCAmelCase_ , lowerCAmelCase_ = None ) -> Tuple[str]:
_A = self._tokenizer.model.save(lowerCAmelCase_ , name=lowerCAmelCase_ )
return tuple(lowerCAmelCase_ )
| 180
| 1
|
'''simple docstring'''
import os
import pytest
from transformers.dynamic_module_utils import get_imports
__UpperCamelCase = "\nimport os\n"
__UpperCamelCase = "\ndef foo():\n import os\n return False\n"
__UpperCamelCase = "\ndef foo():\n def bar():\n if True:\n import os\n return False\n return bar()\n"
__UpperCamelCase = "\nimport os\n\ntry:\n import bar\nexcept ImportError:\n raise ValueError()\n"
__UpperCamelCase = "\nimport os\n\ndef foo():\n try:\n import bar\n except ImportError:\n raise ValueError()\n"
__UpperCamelCase = "\nimport os\n\ntry:\n import bar\nexcept (ImportError, AttributeError):\n raise ValueError()\n"
__UpperCamelCase = "\nimport os\n\ntry:\n import bar\nexcept ImportError as e:\n raise ValueError()\n"
__UpperCamelCase = "\nimport os\n\ntry:\n import bar\nexcept:\n raise ValueError()\n"
__UpperCamelCase = "\nimport os\n\ntry:\n import bar\n import baz\nexcept ImportError:\n raise ValueError()\n"
__UpperCamelCase = "\nimport os\n\ntry:\n import bar\n import baz\nexcept ImportError:\n x = 1\n raise ValueError()\n"
__UpperCamelCase = [
TOP_LEVEL_IMPORT,
IMPORT_IN_FUNCTION,
DEEPLY_NESTED_IMPORT,
TOP_LEVEL_TRY_IMPORT,
GENERIC_EXCEPT_IMPORT,
MULTILINE_TRY_IMPORT,
MULTILINE_BOTH_IMPORT,
MULTIPLE_EXCEPTS_IMPORT,
EXCEPT_AS_IMPORT,
TRY_IMPORT_IN_FUNCTION,
]
@pytest.mark.parametrize("""case""" , _lowerCamelCase )
def _a ( _lowerCamelCase , _lowerCamelCase ) -> List[str]:
"""simple docstring"""
__snake_case : Union[str, Any] = os.path.join(_lowerCamelCase , """test_file.py""" )
with open(_lowerCamelCase , """w""" ) as _tmp_file:
_tmp_file.write(_lowerCamelCase )
__snake_case : List[str] = get_imports(_lowerCamelCase )
assert parsed_imports == ["os"]
| 367
|
'''simple docstring'''
from __future__ import annotations
def _a ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , ) -> None:
"""simple docstring"""
__snake_case : int = len(_lowerCamelCase )
# If row is equal to the size of the board it means there are a queen in each row in
# the current board (possible_board)
if row == n:
# We convert the variable possible_board that looks like this: [1, 3, 0, 2] to
# this: ['. Q . . ', '. . . Q ', 'Q . . . ', '. . Q . ']
boards.append([""". """ * i + """Q """ + """. """ * (n - 1 - i) for i in possible_board] )
return
# We iterate each column in the row to find all possible results in each row
for col in range(_lowerCamelCase ):
# We apply that we learned previously. First we check that in the current board
# (possible_board) there are not other same value because if there is it means
# that there are a collision in vertical. Then we apply the two formulas we
# learned before:
#
# 45º: y - x = b or 45: row - col = b
# 135º: y + x = b or row + col = b.
#
# And we verify if the results of this two formulas not exist in their variables
# respectively. (diagonal_right_collisions, diagonal_left_collisions)
#
# If any or these are True it means there is a collision so we continue to the
# next value in the for loop.
if (
col in possible_board
or row - col in diagonal_right_collisions
or row + col in diagonal_left_collisions
):
continue
# If it is False we call dfs function again and we update the inputs
depth_first_search(
[*possible_board, col] , [*diagonal_right_collisions, row - col] , [*diagonal_left_collisions, row + col] , _lowerCamelCase , _lowerCamelCase , )
def _a ( _lowerCamelCase ) -> None:
"""simple docstring"""
__snake_case : list[list[str]] = []
depth_first_search([] , [] , [] , _lowerCamelCase , _lowerCamelCase )
# Print all the boards
for board in boards:
for column in board:
print(_lowerCamelCase )
print("""""" )
print(len(_lowerCamelCase ) , """solutions were found.""" )
if __name__ == "__main__":
import doctest
doctest.testmod()
n_queens_solution(4)
| 13
| 0
|
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
import math
from typing import Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin
class __lowerCAmelCase ( a , a ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = 1
@register_to_config
def __init__( self : Optional[Any] , _lowerCAmelCase : Dict=2_0_0_0 , _lowerCAmelCase : Dict=0.1 , _lowerCAmelCase : List[Any]=2_0 , _lowerCAmelCase : Any=1e-3 ) -> Optional[int]:
"""simple docstring"""
snake_case_ = None
snake_case_ = None
snake_case_ = None
def lowerCAmelCase__ ( self : Any , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : Union[str, torch.device] = None ) -> int:
"""simple docstring"""
snake_case_ = torch.linspace(1 , self.config.sampling_eps , _lowerCAmelCase , device=_lowerCAmelCase )
def lowerCAmelCase__ ( self : int , _lowerCAmelCase : List[Any] , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : int , _lowerCAmelCase : Optional[Any]=None ) -> Any:
"""simple docstring"""
if self.timesteps is None:
raise ValueError(
"`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler" )
# TODO(Patrick) better comments + non-PyTorch
# postprocess model score
snake_case_ = (
-0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
)
snake_case_ = torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff ) )
snake_case_ = std.flatten()
while len(std.shape ) < len(score.shape ):
snake_case_ = std.unsqueeze(-1 )
snake_case_ = -score / std
# compute
snake_case_ = -1.0 / len(self.timesteps )
snake_case_ = self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
snake_case_ = beta_t.flatten()
while len(beta_t.shape ) < len(x.shape ):
snake_case_ = beta_t.unsqueeze(-1 )
snake_case_ = -0.5 * beta_t * x
snake_case_ = torch.sqrt(_lowerCAmelCase )
snake_case_ = drift - diffusion**2 * score
snake_case_ = x + drift * dt
# add noise
snake_case_ = randn_tensor(x.shape , layout=x.layout , generator=_lowerCAmelCase , device=x.device , dtype=x.dtype )
snake_case_ = x_mean + diffusion * math.sqrt(-dt ) * noise
return x, x_mean
def __len__( self : Optional[int] ) -> int:
"""simple docstring"""
return self.config.num_train_timesteps
| 159
|
import unittest
from knapsack import greedy_knapsack as kp
class __lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def lowerCAmelCase__ ( self : Any ) -> str:
"""simple docstring"""
snake_case_ = [1_0, 2_0, 3_0, 4_0, 5_0, 6_0]
snake_case_ = [2, 4, 6, 8, 1_0, 1_2]
snake_case_ = 1_0_0
self.assertEqual(kp.calc_profit(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) , 2_1_0 )
def lowerCAmelCase__ ( self : List[str] ) -> Tuple:
"""simple docstring"""
self.assertRaisesRegex(_lowerCAmelCase , "max_weight must greater than zero." )
def lowerCAmelCase__ ( self : Dict ) -> Optional[Any]:
"""simple docstring"""
self.assertRaisesRegex(_lowerCAmelCase , "Weight can not be negative." )
def lowerCAmelCase__ ( self : Dict ) -> Tuple:
"""simple docstring"""
self.assertRaisesRegex(_lowerCAmelCase , "Profit can not be negative." )
def lowerCAmelCase__ ( self : Optional[int] ) -> str:
"""simple docstring"""
self.assertRaisesRegex(_lowerCAmelCase , "max_weight must greater than zero." )
def lowerCAmelCase__ ( self : Union[str, Any] ) -> str:
"""simple docstring"""
self.assertRaisesRegex(
_lowerCAmelCase , "The length of profit and weight must be same." )
if __name__ == "__main__":
unittest.main()
| 159
| 1
|
import argparse
import json
import os
import torch
from torch import nn
from transformers import NllbMoeConfig, NllbMoeModel
from transformers.modeling_utils import dtype_byte_size
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
def A__ ( SCREAMING_SNAKE_CASE__) -> List[str]:
__snake_case: Optional[int] = [
"""encoder.version""",
"""decoder.version""",
"""model.encoder.version""",
"""model.decoder.version""",
"""decoder.output_projection.weight""",
"""_float_tensor""",
"""encoder.embed_positions._float_tensor""",
"""decoder.embed_positions._float_tensor""",
]
for k in ignore_keys:
state_dict.pop(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__)
def A__ ( SCREAMING_SNAKE_CASE__) -> Dict:
__snake_case , __snake_case: Tuple = emb.weight.shape
__snake_case: str = nn.Linear(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , bias=SCREAMING_SNAKE_CASE__)
__snake_case: Any = emb.weight.data
return lin_layer
def A__ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__=None) -> Optional[int]:
__snake_case: List[str] = {}
for old_key in state_dict.keys():
__snake_case: Optional[int] = old_key
if "moe_layer.experts." in key:
if expert_idx is not None:
__snake_case: List[str] = key.replace("""moe_layer.experts.0""" , F'''ffn.experts.expert_{expert_idx}''')
else:
__snake_case: int = key.replace("""moe_layer.experts.""" , """ffn.experts.expert_""")
if "gate" in key:
__snake_case: Any = key.replace(""".moe_layer.gate.wg""" , """.ffn.router.classifier""")
if "fc2" and "experts" not in key:
__snake_case: Optional[int] = key.replace(""".fc2.""" , """.ffn.fc2.""")
if "fc1" and "experts" not in key:
__snake_case: List[Any] = key.replace(""".fc1.""" , """.ffn.fc1.""")
if ".encoder_attn." in key:
__snake_case: Optional[Any] = key.replace(""".encoder_attn.""" , """.cross_attention.""")
if "encoder_attn_layer_norm" in key:
__snake_case: Optional[Any] = key.replace("""encoder_attn_layer_norm""" , """cross_attention_layer_norm""")
if "final_layer_norm" in key:
__snake_case: str = key.replace("""final_layer_norm""" , """ff_layer_norm""")
__snake_case: Tuple = state_dict[old_key]
return new_dict
def A__ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = WEIGHTS_NAME) -> Optional[Any]:
__snake_case: str = []
__snake_case: Optional[int] = 0
os.makedirs(SCREAMING_SNAKE_CASE__ , exist_ok=SCREAMING_SNAKE_CASE__)
for expert in range(SCREAMING_SNAKE_CASE__):
__snake_case: Optional[int] = switch_checkpoint_path + F'''-rank-{expert}.pt'''
if os.path.isfile(SCREAMING_SNAKE_CASE__):
__snake_case: Optional[Any] = torch.load(SCREAMING_SNAKE_CASE__)["""model"""]
remove_ignore_keys_(SCREAMING_SNAKE_CASE__)
__snake_case: Any = rename_fairseq_keys(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__)
__snake_case: int = os.path.join(
SCREAMING_SNAKE_CASE__ , weights_name.replace(""".bin""" , F'''-{len(SCREAMING_SNAKE_CASE__)+1:05d}-of-???.bin'''))
torch.save(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__)
sharded_state_dicts.append(expert_state.keys())
total_size += sum([value.numel() for key, value in expert_state.items()]) * dtype_byte_size(
expert_state[list(SCREAMING_SNAKE_CASE__)[0]].dtype)
# Add the last block
__snake_case: str = os.path.join(SCREAMING_SNAKE_CASE__ , weights_name.replace(""".bin""" , F'''-{len(SCREAMING_SNAKE_CASE__)+1:05d}-of-???.bin'''))
__snake_case: Optional[int] = torch.load(switch_checkpoint_path + """-shared.pt""")["""model"""]
remove_ignore_keys_(SCREAMING_SNAKE_CASE__)
__snake_case: Optional[Any] = rename_fairseq_keys(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__)
__snake_case: List[str] = shared_weights["""decoder.embed_tokens.weight"""]
sharded_state_dicts.append(shared_weights.keys())
# If we only have the shared weights (dummy model/experts saved on the same file)
if len(SCREAMING_SNAKE_CASE__) == 1:
__snake_case: Tuple = os.path.join(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__)
torch.save(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__)
return {weights_name: sharded_state_dicts[0]}, None
else:
torch.save(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__)
# Otherwise, let's build the index
__snake_case: Any = {}
for idx, shard in enumerate(SCREAMING_SNAKE_CASE__):
__snake_case: Tuple = weights_name.replace(""".bin""" , F'''-{idx+1:05d}-of-{len(SCREAMING_SNAKE_CASE__):05d}.bin''')
__snake_case: Optional[Any] = os.path.join(SCREAMING_SNAKE_CASE__ , weights_name.replace(""".bin""" , F'''-{idx+1:05d}-of-???.bin'''))
os.rename(SCREAMING_SNAKE_CASE__ , os.path.join(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__))
for key in shard:
__snake_case: List[str] = shard_file
# Add the metadata
__snake_case: Dict = {"""total_size""": total_size}
__snake_case: Optional[int] = {"""metadata""": metadata, """weight_map""": weight_map}
with open(os.path.join(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__) , """w""" , encoding="""utf-8""") as f:
__snake_case: Dict = json.dumps(SCREAMING_SNAKE_CASE__ , indent=2 , sort_keys=SCREAMING_SNAKE_CASE__) + """\n"""
f.write(SCREAMING_SNAKE_CASE__)
return metadata, index
if __name__ == "__main__":
__UpperCAmelCase : Dict = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--nllb_moe_checkpoint_path",
default="/home/arthur_huggingface_co/fairseq/weights/checkpoints/model_moe_54b/checkpoint_2_300000",
type=str,
required=False,
help="Path to a directory containing a folder per layer. Follows the original Google format.",
)
parser.add_argument("--dtype", default="float32", type=str, required=False, help="dtype of the saved model")
parser.add_argument(
"--pytorch_dump_folder_path",
default="/home/arthur_huggingface_co/fairseq/weights/checkpoints/hf-converted-moe-54b",
type=str,
required=False,
help="Path to the output pytorch model.",
)
__UpperCAmelCase : Optional[int] = parser.parse_args()
__UpperCAmelCase , __UpperCAmelCase : Optional[Any] = shard_on_the_fly(
args.nllb_moe_checkpoint_path,
args.pytorch_dump_folder_path,
128,
args.dtype,
)
__UpperCAmelCase : Union[str, Any] = NllbMoeConfig.from_pretrained(
"facebook/nllb-200-3.3B", encoder_sparse_step=4, decoder_sparse_step=4, num_experts=128
)
config.save_pretrained(args.pytorch_dump_folder_path)
__UpperCAmelCase : Union[str, Any] = NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
print("Done")
model.save_pretrained(args.pytorch_dump_folder_path)
| 293
|
import unittest
import numpy as np
from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
nightly,
require_onnxruntime,
require_torch_gpu,
)
from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin
if is_onnx_available():
import onnxruntime as ort
class __snake_case ( __lowerCamelCase , unittest.TestCase ):
'''simple docstring'''
pass
@nightly
@require_onnxruntime
@require_torch_gpu
class __snake_case ( unittest.TestCase ):
'''simple docstring'''
@property
def UpperCAmelCase__ ( self : Dict ):
return (
"CUDAExecutionProvider",
{
"gpu_mem_limit": "15000000000", # 15GB
"arena_extend_strategy": "kSameAsRequested",
},
)
@property
def UpperCAmelCase__ ( self : Dict ):
__snake_case: Optional[int] = ort.SessionOptions()
__snake_case: List[Any] = False
return options
def UpperCAmelCase__ ( self : Optional[Any] ):
__snake_case: Optional[Any] = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/in_paint/overture-creations-5sI6fQgYIuo.png""" )
__snake_case: Any = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/in_paint/overture-creations-5sI6fQgYIuo_mask.png""" )
__snake_case: List[str] = OnnxStableDiffusionInpaintPipeline.from_pretrained(
"""runwayml/stable-diffusion-inpainting""" , revision="""onnx""" , safety_checker=A , feature_extractor=A , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=A )
__snake_case: int = """A red cat sitting on a park bench"""
__snake_case: Any = np.random.RandomState(0 )
__snake_case: Optional[Any] = pipe(
prompt=A , image=A , mask_image=A , guidance_scale=7.5 , num_inference_steps=10 , generator=A , output_type="""np""" , )
__snake_case: List[Any] = output.images
__snake_case: str = images[0, 255:258, 255:258, -1]
assert images.shape == (1, 512, 512, 3)
__snake_case: Any = np.array([0.2514, 0.3007, 0.3517, 0.1790, 0.2382, 0.3167, 0.1944, 0.2273, 0.2464] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3
def UpperCAmelCase__ ( self : Optional[Any] ):
__snake_case: Optional[Any] = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/in_paint/overture-creations-5sI6fQgYIuo.png""" )
__snake_case: Optional[int] = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/in_paint/overture-creations-5sI6fQgYIuo_mask.png""" )
__snake_case: Optional[int] = LMSDiscreteScheduler.from_pretrained(
"""runwayml/stable-diffusion-inpainting""" , subfolder="""scheduler""" , revision="""onnx""" )
__snake_case: List[Any] = OnnxStableDiffusionInpaintPipeline.from_pretrained(
"""runwayml/stable-diffusion-inpainting""" , revision="""onnx""" , scheduler=A , safety_checker=A , feature_extractor=A , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=A )
__snake_case: Optional[int] = """A red cat sitting on a park bench"""
__snake_case: Dict = np.random.RandomState(0 )
__snake_case: Optional[Any] = pipe(
prompt=A , image=A , mask_image=A , guidance_scale=7.5 , num_inference_steps=20 , generator=A , output_type="""np""" , )
__snake_case: List[str] = output.images
__snake_case: str = images[0, 255:258, 255:258, -1]
assert images.shape == (1, 512, 512, 3)
__snake_case: Union[str, Any] = np.array([0.0086, 0.0077, 0.0083, 0.0093, 0.0107, 0.0139, 0.0094, 0.0097, 0.0125] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3
| 293
| 1
|
def UpperCamelCase ( snake_case__ : list , snake_case__ : list ) -> float:
_validate_point(lowerCAmelCase__ )
_validate_point(lowerCAmelCase__ )
if len(lowerCAmelCase__ ) != len(lowerCAmelCase__ ):
raise ValueError('Both points must be in the same n-dimensional space' )
return float(sum(abs(a - b ) for a, b in zip(lowerCAmelCase__ , lowerCAmelCase__ ) ) )
def UpperCamelCase ( snake_case__ : list[float] ) -> None:
if point:
if isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
for item in point:
if not isinstance(lowerCAmelCase__ , (int, float) ):
UpperCamelCase : Optional[Any] = (
'Expected a list of numbers as input, found '
F"""{type(lowerCAmelCase__ ).__name__}"""
)
raise TypeError(lowerCAmelCase__ )
else:
UpperCamelCase : Dict = F"""Expected a list of numbers as input, found {type(lowerCAmelCase__ ).__name__}"""
raise TypeError(lowerCAmelCase__ )
else:
raise ValueError('Missing an input' )
def UpperCamelCase ( snake_case__ : list , snake_case__ : list ) -> float:
_validate_point(lowerCAmelCase__ )
_validate_point(lowerCAmelCase__ )
if len(lowerCAmelCase__ ) != len(lowerCAmelCase__ ):
raise ValueError('Both points must be in the same n-dimensional space' )
return float(sum(abs(x - y ) for x, y in zip(lowerCAmelCase__ , lowerCAmelCase__ ) ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 119
|
'''simple docstring'''
import torch
from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class a ( _SCREAMING_SNAKE_CASE ):
_lowerCAmelCase = (KDPMaDiscreteScheduler,)
_lowerCAmelCase = 1_0
def __UpperCAmelCase ( self , **__magic_name__ ) -> int:
_a = {
'num_train_timesteps': 11_00,
'beta_start': 0.0_0_0_1,
'beta_end': 0.0_2,
'beta_schedule': 'linear',
}
config.update(**__magic_name__ )
return config
def __UpperCAmelCase ( self ) -> Union[str, Any]:
for timesteps in [10, 50, 1_00, 10_00]:
self.check_over_configs(num_train_timesteps=__magic_name__ )
def __UpperCAmelCase ( self ) -> Union[str, Any]:
for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1] , [0.0_0_0_2, 0.0_0_2, 0.0_2] ):
self.check_over_configs(beta_start=__magic_name__ , beta_end=__magic_name__ )
def __UpperCAmelCase ( self ) -> str:
for schedule in ["linear", "scaled_linear"]:
self.check_over_configs(beta_schedule=__magic_name__ )
def __UpperCAmelCase ( self ) -> List[Any]:
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=__magic_name__ )
def __UpperCAmelCase ( self ) -> int:
_a = self.scheduler_classes[0]
_a = self.get_scheduler_config(prediction_type='v_prediction' )
_a = scheduler_class(**__magic_name__ )
scheduler.set_timesteps(self.num_inference_steps )
_a = self.dummy_model()
_a = self.dummy_sample_deter * scheduler.init_noise_sigma
_a = sample.to(__magic_name__ )
for i, t in enumerate(scheduler.timesteps ):
_a = scheduler.scale_model_input(__magic_name__ , __magic_name__ )
_a = model(__magic_name__ , __magic_name__ )
_a = scheduler.step(__magic_name__ , __magic_name__ , __magic_name__ )
_a = output.prev_sample
_a = torch.sum(torch.abs(__magic_name__ ) )
_a = torch.mean(torch.abs(__magic_name__ ) )
if torch_device in ["cpu", "mps"]:
assert abs(result_sum.item() - 4.6934e-07 ) < 1e-2
assert abs(result_mean.item() - 6.1112e-10 ) < 1e-3
else:
# CUDA
assert abs(result_sum.item() - 4.693428650170972e-07 ) < 1e-2
assert abs(result_mean.item() - 0.0_0_0_2 ) < 1e-3
def __UpperCAmelCase ( self ) -> Tuple:
if torch_device == "mps":
return
_a = self.scheduler_classes[0]
_a = self.get_scheduler_config()
_a = scheduler_class(**__magic_name__ )
scheduler.set_timesteps(self.num_inference_steps )
_a = self.dummy_model()
_a = self.dummy_sample_deter * scheduler.init_noise_sigma
_a = sample.to(__magic_name__ )
for i, t in enumerate(scheduler.timesteps ):
_a = scheduler.scale_model_input(__magic_name__ , __magic_name__ )
_a = model(__magic_name__ , __magic_name__ )
_a = scheduler.step(__magic_name__ , __magic_name__ , __magic_name__ )
_a = output.prev_sample
_a = torch.sum(torch.abs(__magic_name__ ) )
_a = torch.mean(torch.abs(__magic_name__ ) )
if torch_device in ["cpu", "mps"]:
assert abs(result_sum.item() - 2_0.4_1_2_5 ) < 1e-2
assert abs(result_mean.item() - 0.0_2_6_6 ) < 1e-3
else:
# CUDA
assert abs(result_sum.item() - 2_0.4_1_2_5 ) < 1e-2
assert abs(result_mean.item() - 0.0_2_6_6 ) < 1e-3
def __UpperCAmelCase ( self ) -> List[Any]:
if torch_device == "mps":
return
_a = self.scheduler_classes[0]
_a = self.get_scheduler_config()
_a = scheduler_class(**__magic_name__ )
scheduler.set_timesteps(self.num_inference_steps , device=__magic_name__ )
_a = self.dummy_model()
_a = self.dummy_sample_deter.to(__magic_name__ ) * scheduler.init_noise_sigma
for t in scheduler.timesteps:
_a = scheduler.scale_model_input(__magic_name__ , __magic_name__ )
_a = model(__magic_name__ , __magic_name__ )
_a = scheduler.step(__magic_name__ , __magic_name__ , __magic_name__ )
_a = output.prev_sample
_a = torch.sum(torch.abs(__magic_name__ ) )
_a = torch.mean(torch.abs(__magic_name__ ) )
if str(__magic_name__ ).startswith('cpu' ):
# The following sum varies between 148 and 156 on mps. Why?
assert abs(result_sum.item() - 2_0.4_1_2_5 ) < 1e-2
assert abs(result_mean.item() - 0.0_2_6_6 ) < 1e-3
else:
# CUDA
assert abs(result_sum.item() - 2_0.4_1_2_5 ) < 1e-2
assert abs(result_mean.item() - 0.0_2_6_6 ) < 1e-3
| 168
| 0
|
import argparse
import json
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler
_lowerCamelCase = 16
_lowerCamelCase = 32
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : Accelerator , __UpperCamelCase : int = 16 , __UpperCamelCase : str = "bert-base-cased" ) -> Union[str, Any]:
UpperCAmelCase_ = AutoTokenizer.from_pretrained(__UpperCamelCase )
UpperCAmelCase_ = load_dataset('''glue''' , '''mrpc''' )
def tokenize_function(__UpperCamelCase : str ):
# max_length=None => use the model max length (it's actually the default)
UpperCAmelCase_ = tokenizer(examples['''sentence1'''] , examples['''sentence2'''] , truncation=__UpperCamelCase , max_length=__UpperCamelCase )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
UpperCAmelCase_ = datasets.map(
__UpperCamelCase , batched=__UpperCamelCase , remove_columns=['''idx''', '''sentence1''', '''sentence2'''] , load_from_cache_file=__UpperCamelCase )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
UpperCAmelCase_ = tokenized_datasets.rename_column('''label''' , '''labels''' )
def collate_fn(__UpperCamelCase : Any ):
# On TPU it's best to pad everything to the same length or training will be very slow.
if accelerator.distributed_type == DistributedType.TPU:
return tokenizer.pad(__UpperCamelCase , padding='''max_length''' , max_length=128 , return_tensors='''pt''' )
return tokenizer.pad(__UpperCamelCase , padding='''longest''' , return_tensors='''pt''' )
# Instantiate dataloaders.
UpperCAmelCase_ = DataLoader(
tokenized_datasets['''train'''] , shuffle=__UpperCamelCase , collate_fn=__UpperCamelCase , batch_size=__UpperCamelCase )
UpperCAmelCase_ = DataLoader(
tokenized_datasets['''validation'''] , shuffle=__UpperCamelCase , collate_fn=__UpperCamelCase , batch_size=__UpperCamelCase )
return train_dataloader, eval_dataloader
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : Optional[Any] , __UpperCamelCase : Optional[Any] ) -> Tuple:
# Initialize accelerator
UpperCAmelCase_ = Accelerator()
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
UpperCAmelCase_ = config['''lr''']
UpperCAmelCase_ = int(config['''num_epochs'''] )
UpperCAmelCase_ = int(config['''seed'''] )
UpperCAmelCase_ = int(config['''batch_size'''] )
UpperCAmelCase_ = args.model_name_or_path
set_seed(__UpperCamelCase )
UpperCAmelCase_ , UpperCAmelCase_ = get_dataloaders(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
UpperCAmelCase_ = AutoModelForSequenceClassification.from_pretrained(__UpperCamelCase , return_dict=__UpperCamelCase )
# Instantiate optimizer
UpperCAmelCase_ = (
AdamW
if accelerator.state.deepspeed_plugin is None
or '''optimizer''' not in accelerator.state.deepspeed_plugin.deepspeed_config
else DummyOptim
)
UpperCAmelCase_ = optimizer_cls(params=model.parameters() , lr=__UpperCamelCase )
if accelerator.state.deepspeed_plugin is not None:
UpperCAmelCase_ = accelerator.state.deepspeed_plugin.deepspeed_config[
'''gradient_accumulation_steps'''
]
else:
UpperCAmelCase_ = 1
UpperCAmelCase_ = (len(__UpperCamelCase ) * num_epochs) // gradient_accumulation_steps
# Instantiate scheduler
if (
accelerator.state.deepspeed_plugin is None
or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
):
UpperCAmelCase_ = get_linear_schedule_with_warmup(
optimizer=__UpperCamelCase , num_warmup_steps=0 , num_training_steps=__UpperCamelCase , )
else:
UpperCAmelCase_ = DummyScheduler(__UpperCamelCase , total_num_steps=__UpperCamelCase , warmup_num_steps=0 )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = accelerator.prepare(
__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
# We need to keep track of how many total steps we have iterated over
UpperCAmelCase_ = 0
# We also need to keep track of the stating epoch so files are named properly
UpperCAmelCase_ = 0
# Now we train the model
UpperCAmelCase_ = evaluate.load('''glue''' , '''mrpc''' )
UpperCAmelCase_ = 0
UpperCAmelCase_ = {}
for epoch in range(__UpperCamelCase , __UpperCamelCase ):
model.train()
for step, batch in enumerate(__UpperCamelCase ):
UpperCAmelCase_ = model(**__UpperCamelCase )
UpperCAmelCase_ = outputs.loss
UpperCAmelCase_ = loss / gradient_accumulation_steps
accelerator.backward(__UpperCamelCase )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
overall_step += 1
model.eval()
UpperCAmelCase_ = 0
for step, batch in enumerate(__UpperCamelCase ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
UpperCAmelCase_ = model(**__UpperCamelCase )
UpperCAmelCase_ = outputs.logits.argmax(dim=-1 )
# It is slightly faster to call this once, than multiple times
UpperCAmelCase_ , UpperCAmelCase_ = accelerator.gather(
(predictions, batch['''labels''']) ) # If we are in a multiprocess environment, the last batch has duplicates
if accelerator.use_distributed:
if step == len(__UpperCamelCase ) - 1:
UpperCAmelCase_ = predictions[: len(eval_dataloader.dataset ) - samples_seen]
UpperCAmelCase_ = references[: len(eval_dataloader.dataset ) - samples_seen]
else:
samples_seen += references.shape[0]
metric.add_batch(
predictions=__UpperCamelCase , references=__UpperCamelCase , )
UpperCAmelCase_ = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(f'epoch {epoch}:' , __UpperCamelCase )
UpperCAmelCase_ = eval_metric['''accuracy''']
if best_performance < eval_metric["accuracy"]:
UpperCAmelCase_ = eval_metric['''accuracy''']
if args.performance_lower_bound is not None:
assert (
args.performance_lower_bound <= best_performance
), f'Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}'
accelerator.wait_for_everyone()
if accelerator.is_main_process:
with open(os.path.join(args.output_dir , '''all_results.json''' ) , '''w''' ) as f:
json.dump(__UpperCamelCase , __UpperCamelCase )
def SCREAMING_SNAKE_CASE ( ) -> List[Any]:
UpperCAmelCase_ = argparse.ArgumentParser(description='''Simple example of training script tracking peak GPU memory usage.''' )
parser.add_argument(
'''--model_name_or_path''' , type=__UpperCamelCase , default='''bert-base-cased''' , help='''Path to pretrained model or model identifier from huggingface.co/models.''' , required=__UpperCamelCase , )
parser.add_argument(
'''--output_dir''' , type=__UpperCamelCase , default='''.''' , help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.''' , )
parser.add_argument(
'''--performance_lower_bound''' , type=__UpperCamelCase , default=__UpperCamelCase , help='''Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value.''' , )
parser.add_argument(
'''--num_epochs''' , type=__UpperCamelCase , default=3 , help='''Number of train epochs.''' , )
UpperCAmelCase_ = parser.parse_args()
UpperCAmelCase_ = {'''lr''': 2e-5, '''num_epochs''': args.num_epochs, '''seed''': 42, '''batch_size''': 16}
training_function(__UpperCamelCase , __UpperCamelCase )
if __name__ == "__main__":
main()
| 177
|
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import MobileNetVaImageProcessor
class a ( unittest.TestCase ):
'''simple docstring'''
def __init__( self : Tuple , __snake_case : int , __snake_case : List[Any]=7 , __snake_case : Any=3 , __snake_case : Any=18 , __snake_case : str=30 , __snake_case : Any=4_00 , __snake_case : Optional[int]=True , __snake_case : str=None , __snake_case : Any=True , __snake_case : List[Any]=None , ):
UpperCAmelCase_ = size if size is not None else {'''shortest_edge''': 20}
UpperCAmelCase_ = crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
UpperCAmelCase_ = parent
UpperCAmelCase_ = batch_size
UpperCAmelCase_ = num_channels
UpperCAmelCase_ = image_size
UpperCAmelCase_ = min_resolution
UpperCAmelCase_ = max_resolution
UpperCAmelCase_ = do_resize
UpperCAmelCase_ = size
UpperCAmelCase_ = do_center_crop
UpperCAmelCase_ = crop_size
def lowerCamelCase_ ( self : Any ):
return {
"do_resize": self.do_resize,
"size": self.size,
"do_center_crop": self.do_center_crop,
"crop_size": self.crop_size,
}
@require_torch
@require_vision
class a ( _A , unittest.TestCase ):
'''simple docstring'''
lowerCAmelCase : Union[str, Any] = MobileNetVaImageProcessor if is_vision_available() else None
def lowerCamelCase_ ( self : Any ):
UpperCAmelCase_ = MobileNetVaImageProcessingTester(self )
@property
def lowerCamelCase_ ( self : List[str] ):
return self.image_processor_tester.prepare_image_processor_dict()
def lowerCamelCase_ ( self : Optional[int] ):
UpperCAmelCase_ = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(__snake_case , '''do_resize''' ) )
self.assertTrue(hasattr(__snake_case , '''size''' ) )
self.assertTrue(hasattr(__snake_case , '''do_center_crop''' ) )
self.assertTrue(hasattr(__snake_case , '''crop_size''' ) )
def lowerCamelCase_ ( self : Dict ):
UpperCAmelCase_ = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'''shortest_edge''': 20} )
self.assertEqual(image_processor.crop_size , {'''height''': 18, '''width''': 18} )
UpperCAmelCase_ = self.image_processing_class.from_dict(self.image_processor_dict , size=42 , crop_size=84 )
self.assertEqual(image_processor.size , {'''shortest_edge''': 42} )
self.assertEqual(image_processor.crop_size , {'''height''': 84, '''width''': 84} )
def lowerCamelCase_ ( self : Optional[int] ):
pass
def lowerCamelCase_ ( self : Tuple ):
# Initialize image_processing
UpperCAmelCase_ = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
UpperCAmelCase_ = prepare_image_inputs(self.image_processor_tester , equal_resolution=__snake_case )
for image in image_inputs:
self.assertIsInstance(__snake_case , Image.Image )
# Test not batched input
UpperCAmelCase_ = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
# Test batched
UpperCAmelCase_ = image_processing(__snake_case , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
def lowerCamelCase_ ( self : str ):
# Initialize image_processing
UpperCAmelCase_ = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
UpperCAmelCase_ = prepare_image_inputs(self.image_processor_tester , equal_resolution=__snake_case , numpify=__snake_case )
for image in image_inputs:
self.assertIsInstance(__snake_case , np.ndarray )
# Test not batched input
UpperCAmelCase_ = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
# Test batched
UpperCAmelCase_ = image_processing(__snake_case , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
def lowerCamelCase_ ( self : int ):
# Initialize image_processing
UpperCAmelCase_ = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
UpperCAmelCase_ = prepare_image_inputs(self.image_processor_tester , equal_resolution=__snake_case , torchify=__snake_case )
for image in image_inputs:
self.assertIsInstance(__snake_case , torch.Tensor )
# Test not batched input
UpperCAmelCase_ = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
# Test batched
UpperCAmelCase_ = image_processing(__snake_case , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
| 177
| 1
|
import unittest
from transformers import DonutProcessor
SCREAMING_SNAKE_CASE_ = """naver-clova-ix/donut-base"""
class UpperCamelCase__ ( unittest.TestCase ):
'''simple docstring'''
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ) -> Any:
'''simple docstring'''
SCREAMING_SNAKE_CASE = DonutProcessor.from_pretrained(lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : Dict ) -> Tuple:
'''simple docstring'''
SCREAMING_SNAKE_CASE = {
"""name""": """John Doe""",
"""age""": """99""",
"""city""": """Atlanta""",
"""state""": """GA""",
"""zip""": """30301""",
"""phone""": """123-4567""",
"""nicknames""": [{"""nickname""": """Johnny"""}, {"""nickname""": """JD"""}],
}
SCREAMING_SNAKE_CASE = (
"""<s_name>John Doe</s_name><s_age>99</s_age><s_city>Atlanta</s_city>"""
"""<s_state>GA</s_state><s_zip>30301</s_zip><s_phone>123-4567</s_phone>"""
"""<s_nicknames><s_nickname>Johnny</s_nickname>"""
"""<sep/><s_nickname>JD</s_nickname></s_nicknames>"""
)
SCREAMING_SNAKE_CASE = self.processor.tokenajson(lowerCamelCase__ )
self.assertDictEqual(lowerCamelCase__ ,lowerCamelCase__ )
| 296
|
from argparse import ArgumentParser
from .env import EnvironmentCommand
def __lowercase ( ) -> Dict:
'''simple docstring'''
SCREAMING_SNAKE_CASE = ArgumentParser("""Diffusers CLI tool""" , usage="""diffusers-cli <command> [<args>]""" )
SCREAMING_SNAKE_CASE = parser.add_subparsers(help="""diffusers-cli command helpers""" )
# Register commands
EnvironmentCommand.register_subcommand(_SCREAMING_SNAKE_CASE )
# Let's go
SCREAMING_SNAKE_CASE = parser.parse_args()
if not hasattr(_SCREAMING_SNAKE_CASE , """func""" ):
parser.print_help()
exit(1 )
# Run
SCREAMING_SNAKE_CASE = args.func(_SCREAMING_SNAKE_CASE )
service.run()
if __name__ == "__main__":
main()
| 296
| 1
|
"""simple docstring"""
import unittest
import numpy as np
from transformers import DistilBertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from transformers.models.distilbert.modeling_flax_distilbert import (
FlaxDistilBertForMaskedLM,
FlaxDistilBertForMultipleChoice,
FlaxDistilBertForQuestionAnswering,
FlaxDistilBertForSequenceClassification,
FlaxDistilBertForTokenClassification,
FlaxDistilBertModel,
)
class lowerCAmelCase_ ( unittest.TestCase ):
"""simple docstring"""
def __init__( self , lowerCAmelCase , lowerCAmelCase=13 , lowerCAmelCase=7 , lowerCAmelCase=True , lowerCAmelCase=True , lowerCAmelCase=True , lowerCAmelCase=True , lowerCAmelCase=99 , lowerCAmelCase=32 , lowerCAmelCase=5 , lowerCAmelCase=4 , lowerCAmelCase=37 , lowerCAmelCase="gelu" , lowerCAmelCase=0.1 , lowerCAmelCase=0.1 , lowerCAmelCase=5_12 , lowerCAmelCase=16 , lowerCAmelCase=2 , lowerCAmelCase=0.02 , lowerCAmelCase=4 , ):
"""simple docstring"""
snake_case = parent
snake_case = batch_size
snake_case = seq_length
snake_case = is_training
snake_case = use_attention_mask
snake_case = use_token_type_ids
snake_case = use_labels
snake_case = vocab_size
snake_case = hidden_size
snake_case = num_hidden_layers
snake_case = num_attention_heads
snake_case = intermediate_size
snake_case = hidden_act
snake_case = hidden_dropout_prob
snake_case = attention_probs_dropout_prob
snake_case = max_position_embeddings
snake_case = type_vocab_size
snake_case = type_sequence_label_size
snake_case = initializer_range
snake_case = num_choices
def snake_case ( self ):
"""simple docstring"""
snake_case = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
snake_case = None
if self.use_attention_mask:
snake_case = random_attention_mask([self.batch_size, self.seq_length] )
snake_case = DistilBertConfig(
vocab_size=self.vocab_size , dim=self.hidden_size , n_layers=self.num_hidden_layers , n_heads=self.num_attention_heads , hidden_dim=self.intermediate_size , hidden_act=self.hidden_act , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , tie_weights_=lowerCAmelCase , )
return config, input_ids, attention_mask
def snake_case ( self ):
"""simple docstring"""
snake_case = self.prepare_config_and_inputs()
snake_case ,snake_case ,snake_case = config_and_inputs
snake_case = {'input_ids': input_ids, 'attention_mask': attention_mask}
return config, inputs_dict
@require_flax
class lowerCAmelCase_ ( lowerCAmelCase , unittest.TestCase ):
"""simple docstring"""
_lowerCAmelCase : List[Any] = (
(
FlaxDistilBertModel,
FlaxDistilBertForMaskedLM,
FlaxDistilBertForMultipleChoice,
FlaxDistilBertForQuestionAnswering,
FlaxDistilBertForSequenceClassification,
FlaxDistilBertForTokenClassification,
FlaxDistilBertForQuestionAnswering,
)
if is_flax_available()
else ()
)
def snake_case ( self ):
"""simple docstring"""
snake_case = FlaxDistilBertModelTester(self )
@slow
def snake_case ( self ):
"""simple docstring"""
for model_class_name in self.all_model_classes:
snake_case = model_class_name.from_pretrained('distilbert-base-uncased' )
snake_case = model(np.ones((1, 1) ) )
self.assertIsNotNone(lowerCAmelCase )
@require_flax
class lowerCAmelCase_ ( unittest.TestCase ):
"""simple docstring"""
@slow
def snake_case ( self ):
"""simple docstring"""
snake_case = FlaxDistilBertModel.from_pretrained('distilbert-base-uncased' )
snake_case = np.array([[0, 3_45, 2_32, 3_28, 7_40, 1_40, 16_95, 69, 60_78, 15_88, 2]] )
snake_case = np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
snake_case = model(lowerCAmelCase , attention_mask=lowerCAmelCase )[0]
snake_case = (1, 11, 7_68)
self.assertEqual(output.shape , lowerCAmelCase )
snake_case = np.array([[[-0.16_39, 0.32_99, 0.16_48], [-0.17_46, 0.32_89, 0.17_10], [-0.18_84, 0.33_57, 0.18_10]]] )
self.assertTrue(jnp.allclose(output[:, 1:4, 1:4] , lowerCAmelCase , atol=1E-4 ) )
| 149
|
"""simple docstring"""
from typing import Optional
import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class lowerCAmelCase_ ( lowerCAmelCase , lowerCAmelCase , lowerCAmelCase ):
"""simple docstring"""
_lowerCAmelCase : int = [r"""h\.\d+\.attn\.bias""", r"""h\.\d+\.attn\.masked_bias"""]
@register_to_config
def __init__( self , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase = None , lowerCAmelCase = 5_02_57 , lowerCAmelCase = 10_24 , lowerCAmelCase = 7_68 , lowerCAmelCase = 12 , lowerCAmelCase = 12 , lowerCAmelCase = None , lowerCAmelCase = "gelu_new" , lowerCAmelCase = 0.1 , lowerCAmelCase = 0.1 , lowerCAmelCase = 0.1 , lowerCAmelCase = 1E-5 , lowerCAmelCase = 0.02 , lowerCAmelCase = True , lowerCAmelCase = True , lowerCAmelCase = False , lowerCAmelCase = False , ):
"""simple docstring"""
super().__init__()
snake_case = prefix_length
if prefix_inner_dim != n_embd and prefix_hidden_dim is None:
raise ValueError(
F"""`prefix_hidden_dim` cannot be `None` when `prefix_inner_dim`: {prefix_hidden_dim} and"""
F""" `n_embd`: {n_embd} are not equal.""" )
snake_case = prefix_inner_dim
snake_case = prefix_hidden_dim
snake_case = (
nn.Linear(self.prefix_inner_dim , self.prefix_hidden_dim )
if self.prefix_hidden_dim is not None
else nn.Identity()
)
snake_case = (
nn.Linear(self.prefix_hidden_dim , lowerCAmelCase ) if self.prefix_hidden_dim is not None else nn.Identity()
)
snake_case = GPTaConfig(
vocab_size=lowerCAmelCase , n_positions=lowerCAmelCase , n_embd=lowerCAmelCase , n_layer=lowerCAmelCase , n_head=lowerCAmelCase , n_inner=lowerCAmelCase , activation_function=lowerCAmelCase , resid_pdrop=lowerCAmelCase , embd_pdrop=lowerCAmelCase , attn_pdrop=lowerCAmelCase , layer_norm_epsilon=lowerCAmelCase , initializer_range=lowerCAmelCase , scale_attn_weights=lowerCAmelCase , use_cache=lowerCAmelCase , scale_attn_by_inverse_layer_idx=lowerCAmelCase , reorder_and_upcast_attn=lowerCAmelCase , )
snake_case = GPTaLMHeadModel(lowerCAmelCase )
def snake_case ( self , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase = None , lowerCAmelCase = None , ):
"""simple docstring"""
snake_case = self.transformer.transformer.wte(lowerCAmelCase )
snake_case = self.encode_prefix(lowerCAmelCase )
snake_case = self.decode_prefix(lowerCAmelCase )
snake_case = torch.cat((prefix_embeds, embedding_text) , dim=1 )
if labels is not None:
snake_case = self.get_dummy_token(input_ids.shape[0] , input_ids.device )
snake_case = torch.cat((dummy_token, input_ids) , dim=1 )
snake_case = self.transformer(inputs_embeds=lowerCAmelCase , labels=lowerCAmelCase , attention_mask=lowerCAmelCase )
if self.prefix_hidden_dim is not None:
return out, hidden
else:
return out
def snake_case ( self , lowerCAmelCase , lowerCAmelCase ):
"""simple docstring"""
return torch.zeros(lowerCAmelCase , self.prefix_length , dtype=torch.intaa , device=lowerCAmelCase )
def snake_case ( self , lowerCAmelCase ):
"""simple docstring"""
return self.encode_prefix(lowerCAmelCase )
@torch.no_grad()
def snake_case ( self , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase ):
"""simple docstring"""
snake_case = torch.split(lowerCAmelCase , 1 , dim=0 )
snake_case = []
snake_case = []
for feature in features:
snake_case = self.decode_prefix(feature.to(lowerCAmelCase ) ) # back to the clip feature
# Only support beam search for now
snake_case ,snake_case = self.generate_beam(
input_embeds=lowerCAmelCase , device=lowerCAmelCase , eos_token_id=lowerCAmelCase )
generated_tokens.append(output_tokens[0] )
generated_seq_lengths.append(seq_lengths[0] )
snake_case = torch.stack(lowerCAmelCase )
snake_case = torch.stack(lowerCAmelCase )
return generated_tokens, generated_seq_lengths
@torch.no_grad()
def snake_case ( self , lowerCAmelCase=None , lowerCAmelCase=None , lowerCAmelCase=None , lowerCAmelCase = 5 , lowerCAmelCase = 67 , lowerCAmelCase = 1.0 , lowerCAmelCase = None , ):
"""simple docstring"""
snake_case = eos_token_id
snake_case = None
snake_case = None
snake_case = torch.ones(lowerCAmelCase , device=lowerCAmelCase , dtype=torch.int )
snake_case = torch.zeros(lowerCAmelCase , device=lowerCAmelCase , dtype=torch.bool )
if input_embeds is not None:
snake_case = input_embeds
else:
snake_case = self.transformer.transformer.wte(lowerCAmelCase )
for i in range(lowerCAmelCase ):
snake_case = self.transformer(inputs_embeds=lowerCAmelCase )
snake_case = outputs.logits
snake_case = logits[:, -1, :] / (temperature if temperature > 0 else 1.0)
snake_case = logits.softmax(-1 ).log()
if scores is None:
snake_case ,snake_case = logits.topk(lowerCAmelCase , -1 )
snake_case = generated.expand(lowerCAmelCase , *generated.shape[1:] )
snake_case ,snake_case = next_tokens.permute(1 , 0 ), scores.squeeze(0 )
if tokens is None:
snake_case = next_tokens
else:
snake_case = tokens.expand(lowerCAmelCase , *tokens.shape[1:] )
snake_case = torch.cat((tokens, next_tokens) , dim=1 )
else:
snake_case = -float(np.inf )
snake_case = 0
snake_case = scores[:, None] + logits
seq_lengths[~is_stopped] += 1
snake_case = scores_sum / seq_lengths[:, None]
snake_case ,snake_case = scores_sum_average.view(-1 ).topk(lowerCAmelCase , -1 )
snake_case = next_tokens // scores_sum.shape[1]
snake_case = seq_lengths[next_tokens_source]
snake_case = next_tokens % scores_sum.shape[1]
snake_case = next_tokens.unsqueeze(1 )
snake_case = tokens[next_tokens_source]
snake_case = torch.cat((tokens, next_tokens) , dim=1 )
snake_case = generated[next_tokens_source]
snake_case = scores_sum_average * seq_lengths
snake_case = is_stopped[next_tokens_source]
snake_case = self.transformer.transformer.wte(next_tokens.squeeze() ).view(generated.shape[0] , 1 , -1 )
snake_case = torch.cat((generated, next_token_embed) , dim=1 )
snake_case = is_stopped + next_tokens.eq(lowerCAmelCase ).squeeze()
if is_stopped.all():
break
snake_case = scores / seq_lengths
snake_case = scores.argsort(descending=lowerCAmelCase )
# tokens tensors are already padded to max_seq_length
snake_case = [tokens[i] for i in order]
snake_case = torch.stack(lowerCAmelCase , dim=0 )
snake_case = torch.tensor([seq_lengths[i] for i in order] , dtype=seq_lengths.dtype )
return output_texts, seq_lengths
| 149
| 1
|
"""simple docstring"""
import argparse
import json
import numpy
import torch
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def A ( snake_case :List[str] , snake_case :str ) -> Dict:
# Load checkpoint
__UpperCamelCase = torch.load(snake_case , map_location='cpu' )
__UpperCamelCase = chkpt['model']
# We have the base model one level deeper than the original XLM repository
__UpperCamelCase = {}
for k, v in state_dict.items():
if "pred_layer" in k:
__UpperCamelCase = v
else:
__UpperCamelCase = v
__UpperCamelCase = chkpt['params']
__UpperCamelCase = {n: v for n, v in config.items() if not isinstance(snake_case , (torch.FloatTensor, numpy.ndarray) )}
__UpperCamelCase = chkpt['dico_word2id']
__UpperCamelCase = {s + '</w>' if s.find('@@' ) == -1 and i > 1_3 else s.replace('@@' , '' ): i for s, i in vocab.items()}
# Save pytorch-model
__UpperCamelCase = pytorch_dump_folder_path + '/' + WEIGHTS_NAME
__UpperCamelCase = pytorch_dump_folder_path + '/' + CONFIG_NAME
__UpperCamelCase = pytorch_dump_folder_path + '/' + VOCAB_FILES_NAMES['vocab_file']
print(f'Save PyTorch model to {pytorch_weights_dump_path}' )
torch.save(snake_case , snake_case )
print(f'Save configuration file to {pytorch_config_dump_path}' )
with open(snake_case , 'w' , encoding='utf-8' ) as f:
f.write(json.dumps(snake_case , indent=2 ) + '\n' )
print(f'Save vocab file to {pytorch_config_dump_path}' )
with open(snake_case , 'w' , encoding='utf-8' ) as f:
f.write(json.dumps(snake_case , indent=2 ) + '\n' )
if __name__ == "__main__":
UpperCamelCase : Optional[int] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--xlm_checkpoint_path", default=None, type=str, required=True, help="Path the official PyTorch dump."
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
UpperCamelCase : Optional[Any] = parser.parse_args()
convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)
| 316
|
"""simple docstring"""
UpperCamelCase : dict[str, float] = {
"km/h": 1.0,
"m/s": 3.6,
"mph": 1.60_93_44,
"knot": 1.8_52,
}
UpperCamelCase : dict[str, float] = {
"km/h": 1.0,
"m/s": 0.2_77_77_77_78,
"mph": 0.6_21_37_11_92,
"knot": 0.5_39_95_68_03,
}
def A ( snake_case :float , snake_case :str , snake_case :str ) -> float:
if unit_to not in speed_chart or unit_from not in speed_chart_inverse:
__UpperCamelCase = (
f'Incorrect \'from_type\' or \'to_type\' value: {unit_from!r}, {unit_to!r}\n'
f'Valid values are: {", ".join(snake_case )}'
)
raise ValueError(snake_case )
return round(speed * speed_chart[unit_from] * speed_chart_inverse[unit_to] , 3 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 316
| 1
|
'''simple docstring'''
def __UpperCamelCase ( _UpperCAmelCase ):
__UpperCAmelCase : Union[str, Any] = 0
# if input_string is "aba" than new_input_string become "a|b|a"
__UpperCAmelCase : Any = ""
__UpperCAmelCase : List[Any] = ""
# append each character + "|" in new_string for range(0, length-1)
for i in input_string[: len(_UpperCAmelCase ) - 1]:
new_input_string += i + "|"
# append last character
new_input_string += input_string[-1]
# we will store the starting and ending of previous furthest ending palindromic
# substring
__UpperCAmelCase , __UpperCAmelCase : Union[str, Any] = 0, 0
# length[i] shows the length of palindromic substring with center i
__UpperCAmelCase : Dict = [1 for i in range(len(_UpperCAmelCase ) )]
# for each character in new_string find corresponding palindromic string
__UpperCAmelCase : str = 0
for j in range(len(_UpperCAmelCase ) ):
__UpperCAmelCase : str = 1 if j > r else min(length[l + r - j] // 2, r - j + 1 )
while (
j - k >= 0
and j + k < len(_UpperCAmelCase )
and new_input_string[k + j] == new_input_string[j - k]
):
k += 1
__UpperCAmelCase : Optional[int] = 2 * k - 1
# does this string is ending after the previously explored end (that is r) ?
# if yes the update the new r to the last index of this
if j + k - 1 > r:
__UpperCAmelCase : Tuple = j - k + 1 # noqa: E741
__UpperCAmelCase : int = j + k - 1
# update max_length and start position
if max_length < length[j]:
__UpperCAmelCase : List[str] = length[j]
__UpperCAmelCase : Dict = j
# create that string
__UpperCAmelCase : List[str] = new_input_string[start - max_length // 2 : start + max_length // 2 + 1]
for i in s:
if i != "|":
output_string += i
return output_string
if __name__ == "__main__":
import doctest
doctest.testmod()
| 37
|
'''simple docstring'''
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import os
import platform
import numpy as np
import psutil
import torch
from accelerate import __version__ as version
from accelerate.commands.config import default_config_file, load_config_from_file
from ..utils import is_npu_available, is_xpu_available
def __UpperCamelCase ( _UpperCAmelCase=None ):
if subparsers is not None:
__UpperCAmelCase : Optional[int] = subparsers.add_parser("env" )
else:
__UpperCAmelCase : List[Any] = argparse.ArgumentParser("Accelerate env command" )
parser.add_argument(
"--config_file", default=_UpperCAmelCase, help="The config file to use for the default values in the launching script." )
if subparsers is not None:
parser.set_defaults(func=_UpperCAmelCase )
return parser
def __UpperCamelCase ( _UpperCAmelCase ):
__UpperCAmelCase : Dict = torch.__version__
__UpperCAmelCase : str = torch.cuda.is_available()
__UpperCAmelCase : str = is_xpu_available()
__UpperCAmelCase : List[Any] = is_npu_available()
__UpperCAmelCase : Union[str, Any] = "Not found"
# Get the default from the config file.
if args.config_file is not None or os.path.isfile(_UpperCAmelCase ):
__UpperCAmelCase : Union[str, Any] = load_config_from_file(args.config_file ).to_dict()
__UpperCAmelCase : List[str] = {
"`Accelerate` version": version,
"Platform": platform.platform(),
"Python version": platform.python_version(),
"Numpy version": np.__version__,
"PyTorch version (GPU?)": F"{pt_version} ({pt_cuda_available})",
"PyTorch XPU available": str(_UpperCAmelCase ),
"PyTorch NPU available": str(_UpperCAmelCase ),
"System RAM": F"{psutil.virtual_memory().total / 1024 ** 3:.2f} GB",
}
if pt_cuda_available:
__UpperCAmelCase : int = torch.cuda.get_device_name()
print("\nCopy-and-paste the text below in your GitHub issue\n" )
print("\n".join([F"- {prop}: {val}" for prop, val in info.items()] ) )
print("- `Accelerate` default config:" if args.config_file is None else "- `Accelerate` config passed:" )
__UpperCAmelCase : Tuple = (
"\n".join([F"\t- {prop}: {val}" for prop, val in accelerate_config.items()] )
if isinstance(_UpperCAmelCase, _UpperCAmelCase )
else F"\t{accelerate_config}"
)
print(_UpperCAmelCase )
__UpperCAmelCase : Any = accelerate_config
return info
def __UpperCamelCase ( ):
__UpperCAmelCase : Tuple = env_command_parser()
__UpperCAmelCase : Dict = parser.parse_args()
env_command(_UpperCAmelCase )
return 0
if __name__ == "__main__":
raise SystemExit(main())
| 37
| 1
|
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
def _A ( SCREAMING_SNAKE_CASE : dict ):
"""simple docstring"""
return (data["data"], data["target"])
def _A ( SCREAMING_SNAKE_CASE : np.ndarray , SCREAMING_SNAKE_CASE : np.ndarray ):
"""simple docstring"""
a__ : Tuple =XGBClassifier()
classifier.fit(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
return classifier
def _A ( ):
"""simple docstring"""
a__ : Optional[Any] =load_iris()
a__ , a__ : Union[str, Any] =data_handling(SCREAMING_SNAKE_CASE )
a__ , a__ , a__ , a__ : int =train_test_split(
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , test_size=0.2_5 )
a__ : List[str] =iris["target_names"]
# Create an XGBoost Classifier from the training data
a__ : Union[str, Any] =xgboost(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
# Display the confusion matrix of the classifier with both training and test sets
ConfusionMatrixDisplay.from_estimator(
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , display_labels=SCREAMING_SNAKE_CASE , cmap="Blues" , normalize="true" , )
plt.title("Normalized Confusion Matrix - IRIS Dataset" )
plt.show()
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
main()
| 95
|
from typing import List, Optional, Union
import numpy as np
import PIL.Image
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
PILImageResampling,
get_image_size,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, logging
__lowercase = logging.get_logger(__name__)
class lowerCamelCase_ ( UpperCAmelCase_ ):
'''simple docstring'''
a__ : Optional[Any] = ["""pixel_values"""]
def __init__( self , __lowercase = True , __lowercase = 32 , __lowercase=PILImageResampling.BILINEAR , __lowercase = True , **__lowercase , ) -> None:
__UpperCamelCase :Optional[int] = do_resize
__UpperCamelCase :Any = do_rescale
__UpperCamelCase :str = size_divisor
__UpperCamelCase :Dict = resample
super().__init__(**__lowercase)
def UpperCamelCase__ ( self , __lowercase , __lowercase , __lowercase , __lowercase = None , **__lowercase) -> np.ndarray:
__UpperCamelCase , __UpperCamelCase :int = get_image_size(__lowercase)
# Rounds the height and width down to the closest multiple of size_divisor
__UpperCamelCase :List[Any] = height // size_divisor * size_divisor
__UpperCamelCase :List[str] = width // size_divisor * size_divisor
__UpperCamelCase :str = resize(__lowercase , (new_h, new_w) , resample=__lowercase , data_format=__lowercase , **__lowercase)
return image
def UpperCamelCase__ ( self , __lowercase , __lowercase , __lowercase = None , **__lowercase) -> np.ndarray:
return rescale(image=__lowercase , scale=__lowercase , data_format=__lowercase , **__lowercase)
def UpperCamelCase__ ( self , __lowercase , __lowercase = None , __lowercase = None , __lowercase=None , __lowercase = None , __lowercase = None , __lowercase = ChannelDimension.FIRST , **__lowercase , ) -> BatchFeature:
__UpperCamelCase :Union[str, Any] = do_resize if do_resize is not None else self.do_resize
__UpperCamelCase :Tuple = do_rescale if do_rescale is not None else self.do_rescale
__UpperCamelCase :List[str] = size_divisor if size_divisor is not None else self.size_divisor
__UpperCamelCase :List[Any] = resample if resample is not None else self.resample
if do_resize and size_divisor is None:
raise ValueError('''size_divisor is required for resizing''')
__UpperCamelCase :List[Any] = make_list_of_images(__lowercase)
if not valid_images(__lowercase):
raise ValueError('''Invalid image(s)''')
# All transformations expect numpy arrays.
__UpperCamelCase :Optional[Any] = [to_numpy_array(__lowercase) for img in images]
if do_resize:
__UpperCamelCase :List[str] = [self.resize(__lowercase , size_divisor=__lowercase , resample=__lowercase) for image in images]
if do_rescale:
__UpperCamelCase :Dict = [self.rescale(__lowercase , scale=1 / 255) for image in images]
__UpperCamelCase :str = [to_channel_dimension_format(__lowercase , __lowercase) for image in images]
__UpperCamelCase :int = {'''pixel_values''': images}
return BatchFeature(data=__lowercase , tensor_type=__lowercase)
| 43
| 0
|
"""simple docstring"""
from timeit import timeit
UpperCAmelCase = {
'''MALAYALAM''': True,
'''String''': False,
'''rotor''': True,
'''level''': True,
'''A''': True,
'''BB''': True,
'''ABC''': False,
'''amanaplanacanalpanama''': True, # "a man a plan a canal panama"
}
# Ensure our test data is valid
assert all((key == key[::-1]) is value for key, value in test_data.items())
def lowerCamelCase (a_ :str) -> bool:
lowercase :Tuple = 0
lowercase :str = len(a_) - 1
while start_i < end_i:
if s[start_i] == s[end_i]:
start_i += 1
end_i -= 1
else:
return False
return True
def lowerCamelCase (a_ :str) -> bool:
lowercase :str = len(a_) // 2
lowercase :Optional[Any] = len(a_)
# We need to traverse till half of the length of string
# as we can get access of the i'th last element from
# i'th index.
# eg: [0,1,2,3,4,5] => 4th index can be accessed
# with the help of 1st index (i==n-i-1)
# where n is length of string
return all(s[i] == s[n - i - 1] for i in range(a_))
def lowerCamelCase (a_ :str) -> bool:
if len(a_) <= 2:
return True
if s[0] == s[len(a_) - 1]:
return is_palindrome_recursive(s[1:-1])
else:
return False
def lowerCamelCase (a_ :str) -> bool:
return s == s[::-1]
def lowerCamelCase (a_ :str) -> None:
lowercase :Union[str, Any] = F"""all({name}(key) is value for key, value in test_data.items())"""
lowercase :str = F"""from __main__ import test_data, {name}"""
lowercase :Union[str, Any] = 50_0000
lowercase :List[str] = timeit(stmt=a_ , setup=a_ , number=a_)
print(F"""{name:<35} finished {number:,} runs in {result:.5f} seconds""")
if __name__ == "__main__":
for key, value in test_data.items():
assert is_palindrome(key) is is_palindrome_recursive(key)
assert is_palindrome(key) is is_palindrome_slice(key)
print(F"""{key:21} {value}""")
print('''a man a plan a canal panama''')
# finished 500,000 runs in 0.46793 seconds
benchmark_function('''is_palindrome_slice''')
# finished 500,000 runs in 0.85234 seconds
benchmark_function('''is_palindrome''')
# finished 500,000 runs in 1.32028 seconds
benchmark_function('''is_palindrome_recursive''')
# finished 500,000 runs in 2.08679 seconds
benchmark_function('''is_palindrome_traversal''')
| 361
|
"""simple docstring"""
def lowerCamelCase (a_ :int , a_ :int) -> int:
while a != 0:
lowercase , lowercase :Dict = b % a, a
return b
def lowerCamelCase (a_ :int , a_ :int) -> int:
if gcd(a_ , a_) != 1:
lowercase :List[Any] = F"""mod inverse of {a!r} and {m!r} does not exist"""
raise ValueError(a_)
lowercase , lowercase , lowercase :List[str] = 1, 0, a
lowercase , lowercase , lowercase :int = 0, 1, m
while va != 0:
lowercase :Union[str, Any] = ua // va
lowercase , lowercase , lowercase , lowercase , lowercase , lowercase :Dict = (ua - q * va), (ua - q * va), (ua - q * va), va, va, va
return ua % m
| 172
| 0
|
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_owlvit import OwlViTImageProcessor
_lowerCamelCase : int = logging.get_logger(__name__)
class lowercase ( __UpperCAmelCase):
def __init__( self : str , *_lowerCamelCase : int , **_lowerCamelCase : Tuple ):
"""simple docstring"""
warnings.warn(
'''The class OwlViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
''' use OwlViTImageProcessor instead.''' , _lowerCamelCase , )
super().__init__(*_lowerCamelCase , **_lowerCamelCase )
| 167
|
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
_lowerCamelCase : Union[str, Any] = logging.get_logger(__name__)
_lowerCamelCase : str = {
'facebook/convnextv2-tiny-1k-224': 'https://huggingface.co/facebook/convnextv2-tiny-1k-224/resolve/main/config.json',
}
class lowercase ( __UpperCAmelCase , __UpperCAmelCase):
__lowerCAmelCase : List[Any] = """convnextv2"""
def __init__( self : int , _lowerCamelCase : str=3 , _lowerCamelCase : str=4 , _lowerCamelCase : List[Any]=4 , _lowerCamelCase : Optional[Any]=None , _lowerCamelCase : List[Any]=None , _lowerCamelCase : Optional[int]="gelu" , _lowerCamelCase : Union[str, Any]=0.02 , _lowerCamelCase : List[str]=1E-12 , _lowerCamelCase : Tuple=0.0 , _lowerCamelCase : Optional[int]=2_24 , _lowerCamelCase : Optional[Any]=None , _lowerCamelCase : Optional[Any]=None , **_lowerCamelCase : Optional[Any] , ):
"""simple docstring"""
super().__init__(**_lowerCamelCase )
A_ : str = num_channels
A_ : int = patch_size
A_ : Union[str, Any] = num_stages
A_ : Any = [96, 1_92, 3_84, 7_68] if hidden_sizes is None else hidden_sizes
A_ : Any = [3, 3, 9, 3] if depths is None else depths
A_ : Optional[int] = hidden_act
A_ : Tuple = initializer_range
A_ : int = layer_norm_eps
A_ : List[Any] = drop_path_rate
A_ : Union[str, Any] = image_size
A_ : Any = ['''stem'''] + [F"""stage{idx}""" for idx in range(1 , len(self.depths ) + 1 )]
A_ , A_ : Tuple = get_aligned_output_features_output_indices(
out_features=_lowerCamelCase , out_indices=_lowerCamelCase , stage_names=self.stage_names )
| 167
| 1
|
import json
import os
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import ImageGPTImageProcessor
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
def __init__( self : Optional[int] , snake_case_ : Dict , snake_case_ : Tuple=7 , snake_case_ : Optional[Any]=3 , snake_case_ : Dict=18 , snake_case_ : Dict=30 , snake_case_ : Union[str, Any]=400 , snake_case_ : List[Any]=True , snake_case_ : Any=None , snake_case_ : List[str]=True , ):
UpperCamelCase_: Dict = size if size is not None else {"""height""": 18, """width""": 18}
UpperCamelCase_: Union[str, Any] = parent
UpperCamelCase_: Tuple = batch_size
UpperCamelCase_: List[str] = num_channels
UpperCamelCase_: Optional[int] = image_size
UpperCamelCase_: Dict = min_resolution
UpperCamelCase_: Optional[int] = max_resolution
UpperCamelCase_: str = do_resize
UpperCamelCase_: Tuple = size
UpperCamelCase_: Dict = do_normalize
def lowerCAmelCase__ ( self : str ):
return {
# here we create 2 clusters for the sake of simplicity
"clusters": np.asarray(
[
[0.8866_4436_3403_3203, 0.6618_8293_6954_4983, 0.3891_7464_0178_6804],
[-0.6042_5591_4688_1104, -0.0_2295_0088_6052_8469, 0.5423_7973_6900_3296],
] ),
"do_resize": self.do_resize,
"size": self.size,
"do_normalize": self.do_normalize,
}
@require_torch
@require_vision
class _UpperCamelCase ( _A , unittest.TestCase ):
'''simple docstring'''
__UpperCamelCase : Optional[int] = ImageGPTImageProcessor if is_vision_available() else None
def lowerCAmelCase__ ( self : List[str] ):
UpperCamelCase_: Any = ImageGPTImageProcessingTester(self )
@property
def lowerCAmelCase__ ( self : Optional[int] ):
return self.image_processor_tester.prepare_image_processor_dict()
def lowerCAmelCase__ ( self : Optional[int] ):
UpperCamelCase_: List[Any] = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(snake_case_ , """clusters""" ) )
self.assertTrue(hasattr(snake_case_ , """do_resize""" ) )
self.assertTrue(hasattr(snake_case_ , """size""" ) )
self.assertTrue(hasattr(snake_case_ , """do_normalize""" ) )
def lowerCAmelCase__ ( self : int ):
UpperCamelCase_: List[Any] = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {"""height""": 18, """width""": 18} )
UpperCamelCase_: Tuple = self.image_processing_class.from_dict(self.image_processor_dict , size=42 )
self.assertEqual(image_processor.size , {"""height""": 42, """width""": 42} )
def lowerCAmelCase__ ( self : Optional[Any] ):
UpperCamelCase_: int = self.image_processing_class(**self.image_processor_dict )
UpperCamelCase_: Optional[int] = json.loads(image_processor.to_json_string() )
for key, value in self.image_processor_dict.items():
if key == "clusters":
self.assertTrue(np.array_equal(snake_case_ , obj[key] ) )
else:
self.assertEqual(obj[key] , snake_case_ )
def lowerCAmelCase__ ( self : List[Any] ):
UpperCamelCase_: Dict = self.image_processing_class(**self.image_processor_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
UpperCamelCase_: int = os.path.join(snake_case_ , """image_processor.json""" )
image_processor_first.to_json_file(snake_case_ )
UpperCamelCase_: Any = self.image_processing_class.from_json_file(snake_case_ ).to_dict()
UpperCamelCase_: str = image_processor_first.to_dict()
for key, value in image_processor_first.items():
if key == "clusters":
self.assertTrue(np.array_equal(snake_case_ , image_processor_second[key] ) )
else:
self.assertEqual(image_processor_first[key] , snake_case_ )
def lowerCAmelCase__ ( self : Union[str, Any] ):
UpperCamelCase_: Optional[Any] = self.image_processing_class(**self.image_processor_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
image_processor_first.save_pretrained(snake_case_ )
UpperCamelCase_: Optional[int] = self.image_processing_class.from_pretrained(snake_case_ ).to_dict()
UpperCamelCase_: Union[str, Any] = image_processor_first.to_dict()
for key, value in image_processor_first.items():
if key == "clusters":
self.assertTrue(np.array_equal(snake_case_ , image_processor_second[key] ) )
else:
self.assertEqual(image_processor_first[key] , snake_case_ )
@unittest.skip("""ImageGPT requires clusters at initialization""" )
def lowerCAmelCase__ ( self : List[Any] ):
pass
def A__ ( ) -> Optional[int]:
UpperCamelCase_: Optional[int] = load_dataset("""hf-internal-testing/fixtures_image_utils""" , split="""test""" )
UpperCamelCase_: Tuple = Image.open(dataset[4]["""file"""] )
UpperCamelCase_: Union[str, Any] = Image.open(dataset[5]["""file"""] )
UpperCamelCase_: List[str] = [imagea, imagea]
return images
@require_vision
@require_torch
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
@slow
def lowerCAmelCase__ ( self : List[str] ):
UpperCamelCase_: List[Any] = ImageGPTImageProcessor.from_pretrained("""openai/imagegpt-small""" )
UpperCamelCase_: List[str] = prepare_images()
# test non-batched
UpperCamelCase_: List[str] = image_processing(images[0] , return_tensors="""pt""" )
self.assertIsInstance(encoding.input_ids , torch.LongTensor )
self.assertEqual(encoding.input_ids.shape , (1, 1024) )
UpperCamelCase_: Union[str, Any] = [306, 191, 191]
self.assertEqual(encoding.input_ids[0, :3].tolist() , snake_case_ )
# test batched
UpperCamelCase_: Optional[int] = image_processing(snake_case_ , return_tensors="""pt""" )
self.assertIsInstance(encoding.input_ids , torch.LongTensor )
self.assertEqual(encoding.input_ids.shape , (2, 1024) )
UpperCamelCase_: str = [303, 13, 13]
self.assertEqual(encoding.input_ids[1, -3:].tolist() , snake_case_ )
| 223
|
def A__ ( lowerCamelCase , lowerCamelCase ) -> list:
UpperCamelCase_: Optional[int] = word.split()
def justify(lowerCamelCase , lowerCamelCase , lowerCamelCase ) -> str:
UpperCamelCase_: Tuple = max_width - width
UpperCamelCase_: Optional[Any] = len(lowerCamelCase )
if len(lowerCamelCase ) == 1:
# if there is only word in line
# just insert overall_spaces_count for the remainder of line
return line[0] + " " * overall_spaces_count
else:
UpperCamelCase_: List[Any] = words_count - 1
# num_spaces_between_words_list[i] : tells you to insert
# num_spaces_between_words_list[i] spaces
# after word on line[i]
UpperCamelCase_: Optional[Any] = spaces_to_insert_between_words * [
overall_spaces_count // spaces_to_insert_between_words
]
UpperCamelCase_: List[str] = (
overall_spaces_count % spaces_to_insert_between_words
)
# distribute spaces via round robin to the left words
for i in range(lowerCamelCase ):
num_spaces_between_words_list[i] += 1
UpperCamelCase_: Dict = []
for i in range(lowerCamelCase ):
# add the word
aligned_words_list.append(line[i] )
# add the spaces to insert
aligned_words_list.append(num_spaces_between_words_list[i] * """ """ )
# just add the last word to the sentence
aligned_words_list.append(line[-1] )
# join the aligned words list to form a justified line
return "".join(lowerCamelCase )
UpperCamelCase_: Optional[int] = []
UpperCamelCase_: list[str] = []
UpperCamelCase_: List[str] = 0
for word in words:
if width + len(lowerCamelCase ) + len(lowerCamelCase ) <= max_width:
# keep adding words until we can fill out max_width
# width = sum of length of all words (without overall_spaces_count)
# len(word) = length of current word
# len(line) = number of overall_spaces_count to insert between words
line.append(lowerCamelCase )
width += len(lowerCamelCase )
else:
# justify the line and add it to result
answer.append(justify(lowerCamelCase , lowerCamelCase , lowerCamelCase ) )
# reset new line and new width
UpperCamelCase_, UpperCamelCase_: List[str] = [word], len(lowerCamelCase )
UpperCamelCase_: List[str] = max_width - width - len(lowerCamelCase )
answer.append(""" """.join(lowerCamelCase ) + (remaining_spaces + 1) * """ """ )
return answer
if __name__ == "__main__":
from doctest import testmod
testmod()
| 223
| 1
|
import os
import posixpath
import uuid
from dataclasses import dataclass
from typing import TYPE_CHECKING, Iterable, List, Optional, Tuple, Union
import numpy as np
import pyarrow as pa
import datasets
from datasets.arrow_writer import ArrowWriter, ParquetWriter
from datasets.config import MAX_SHARD_SIZE
from datasets.filesystems import (
is_remote_filesystem,
rename,
)
from datasets.iterable_dataset import _BaseExamplesIterable
from datasets.utils.py_utils import convert_file_size_to_int
_snake_case = datasets.utils.logging.get_logger(__name__)
if TYPE_CHECKING:
import pyspark
@dataclass
class lowercase ( datasets.BuilderConfig ):
_a = None
def lowerCAmelCase_ ( snake_case_,snake_case_,):
import pyspark
def generate_fn():
_A : List[Any] = df.select("""*""",pyspark.sql.functions.spark_partition_id().alias("""part_id""" ) )
for partition_id in partition_order:
_A : Optional[Any] = df_with_partition_id.select("""*""" ).where(f'''part_id = {partition_id}''' ).drop("""part_id""" )
_A : int = partition_df.collect()
_A : Optional[int] = 0
for row in rows:
yield f'''{partition_id}_{row_id}''', row.asDict()
row_id += 1
return generate_fn
class lowercase ( _BaseExamplesIterable ):
def __init__( self , _a , _a=None , ) -> Any:
_A : Optional[Any] = df
_A : Tuple = partition_order or range(self.df.rdd.getNumPartitions() )
_A : Tuple = _generate_iterable_examples(self.df , self.partition_order )
def __iter__( self ) -> Optional[int]:
yield from self.generate_examples_fn()
def a__ ( self , _a ) -> List[str]:
_A : List[str] = list(range(self.df.rdd.getNumPartitions() ) )
generator.shuffle(lowerCAmelCase__ )
return SparkExamplesIterable(self.df , partition_order=lowerCAmelCase__ )
def a__ ( self , _a , _a ) -> Optional[Any]:
_A : str = self.split_shard_indices_by_worker(lowerCAmelCase__ , lowerCAmelCase__ )
return SparkExamplesIterable(self.df , partition_order=lowerCAmelCase__ )
@property
def a__ ( self ) -> Dict:
return len(self.partition_order )
class lowercase ( datasets.DatasetBuilder ):
_a = SparkConfig
def __init__( self , _a , _a = None , _a = None , **_a , ) -> Any:
import pyspark
_A : str = pyspark.sql.SparkSession.builder.getOrCreate()
_A : List[str] = df
_A : Optional[Any] = working_dir
super().__init__(
cache_dir=lowerCAmelCase__ , config_name=str(self.df.semanticHash() ) , **lowerCAmelCase__ , )
def a__ ( self ) -> str:
# Returns the path of the created file.
def create_cache_and_write_probe(_a ):
# makedirs with exist_ok will recursively create the directory. It will not throw an error if directories
# already exist.
os.makedirs(self._cache_dir , exist_ok=lowerCAmelCase__ )
_A : Union[str, Any] = os.path.join(self._cache_dir , """fs_test""" + uuid.uuida().hex )
# Opening the file in append mode will create a new file unless it already exists, in which case it will not
# change the file contents.
open(lowerCAmelCase__ , """a""" )
return [probe_file]
if self._spark.conf.get("""spark.master""" , """""" ).startswith("""local""" ):
return
# If the cluster is multi-node, make sure that the user provided a cache_dir and that it is on an NFS
# accessible to the driver.
# TODO: Stream batches to the driver using ArrowCollectSerializer instead of throwing an error.
if self._cache_dir:
_A : Dict = (
self._spark.sparkContext.parallelize(range(1 ) , 1 ).mapPartitions(lowerCAmelCase__ ).collect()
)
if os.path.isfile(probe[0] ):
return
raise ValueError(
"""When using Dataset.from_spark on a multi-node cluster, the driver and all workers should be able to access cache_dir""" )
def a__ ( self ) -> Tuple:
return datasets.DatasetInfo(features=self.config.features )
def a__ ( self , _a ) -> Dict:
return [datasets.SplitGenerator(name=datasets.Split.TRAIN )]
def a__ ( self , _a ) -> Optional[Any]:
import pyspark
def get_arrow_batch_size(_a ):
for batch in it:
yield pa.RecordBatch.from_pydict({"""batch_bytes""": [batch.nbytes]} )
_A : Optional[Any] = self.df.count()
_A : Tuple = df_num_rows if df_num_rows <= 100 else 100
# Approximate the size of each row (in Arrow format) by averaging over a max-100-row sample.
_A : Optional[int] = (
self.df.limit(lowerCAmelCase__ )
.repartition(1 )
.mapInArrow(lowerCAmelCase__ , """batch_bytes: long""" )
.agg(pyspark.sql.functions.sum("""batch_bytes""" ).alias("""sample_bytes""" ) )
.collect()[0]
.sample_bytes
/ sample_num_rows
)
_A : Union[str, Any] = approx_bytes_per_row * df_num_rows
if approx_total_size > max_shard_size:
# Make sure there is at least one row per partition.
_A : Any = min(lowerCAmelCase__ , int(approx_total_size / max_shard_size ) )
_A : Any = self.df.repartition(lowerCAmelCase__ )
def a__ ( self , _a , _a , _a , ) -> str:
import pyspark
_A : Dict = ParquetWriter if file_format == "parquet" else ArrowWriter
_A : Tuple = os.path.join(self._working_dir , os.path.basename(lowerCAmelCase__ ) ) if self._working_dir else fpath
_A : str = file_format == "parquet"
# Define these so that we don't reference self in write_arrow, which will result in a pickling error due to
# pickling the SparkContext.
_A : List[Any] = self.config.features
_A : List[Any] = self._writer_batch_size
_A : Optional[Any] = self._fs.storage_options
def write_arrow(_a ):
# Within the same SparkContext, no two task attempts will share the same attempt ID.
_A : Tuple = pyspark.TaskContext().taskAttemptId()
_A : Optional[Any] = next(lowerCAmelCase__ , lowerCAmelCase__ )
if first_batch is None:
# Some partitions might not receive any data.
return pa.RecordBatch.from_arrays(
[[task_id], [0], [0]] , names=["""task_id""", """num_examples""", """num_bytes"""] , )
_A : Dict = 0
_A : Dict = writer_class(
features=lowerCAmelCase__ , path=working_fpath.replace("""SSSSS""" , F'''{shard_id:05d}''' ).replace("""TTTTT""" , F'''{task_id:05d}''' ) , writer_batch_size=lowerCAmelCase__ , storage_options=lowerCAmelCase__ , embed_local_files=lowerCAmelCase__ , )
_A : List[str] = pa.Table.from_batches([first_batch] )
writer.write_table(lowerCAmelCase__ )
for batch in it:
if max_shard_size is not None and writer._num_bytes >= max_shard_size:
_A : Optional[int] = writer.finalize()
writer.close()
yield pa.RecordBatch.from_arrays(
[[task_id], [num_examples], [num_bytes]] , names=["""task_id""", """num_examples""", """num_bytes"""] , )
shard_id += 1
_A : Optional[int] = writer_class(
features=writer._features , path=working_fpath.replace("""SSSSS""" , F'''{shard_id:05d}''' ).replace("""TTTTT""" , F'''{task_id:05d}''' ) , writer_batch_size=lowerCAmelCase__ , storage_options=lowerCAmelCase__ , embed_local_files=lowerCAmelCase__ , )
_A : Tuple = pa.Table.from_batches([batch] )
writer.write_table(lowerCAmelCase__ )
if writer._num_bytes > 0:
_A : List[Any] = writer.finalize()
writer.close()
yield pa.RecordBatch.from_arrays(
[[task_id], [num_examples], [num_bytes]] , names=["""task_id""", """num_examples""", """num_bytes"""] , )
if working_fpath != fpath:
for file in os.listdir(os.path.dirname(lowerCAmelCase__ ) ):
_A : List[Any] = os.path.join(os.path.dirname(lowerCAmelCase__ ) , os.path.basename(lowerCAmelCase__ ) )
shutil.move(lowerCAmelCase__ , lowerCAmelCase__ )
_A : Optional[Any] = (
self.df.mapInArrow(lowerCAmelCase__ , """task_id: long, num_examples: long, num_bytes: long""" )
.groupBy("""task_id""" )
.agg(
pyspark.sql.functions.sum("""num_examples""" ).alias("""total_num_examples""" ) , pyspark.sql.functions.sum("""num_bytes""" ).alias("""total_num_bytes""" ) , pyspark.sql.functions.count("""num_bytes""" ).alias("""num_shards""" ) , pyspark.sql.functions.collect_list("""num_examples""" ).alias("""shard_lengths""" ) , )
.collect()
)
for row in stats:
yield row.task_id, (row.total_num_examples, row.total_num_bytes, row.num_shards, row.shard_lengths)
def a__ ( self , _a , _a = "arrow" , _a = None , _a = None , **_a , ) -> Tuple:
self._validate_cache_dir()
_A : Dict = convert_file_size_to_int(max_shard_size or MAX_SHARD_SIZE )
self._repartition_df_if_needed(lowerCAmelCase__ )
_A : Union[str, Any] = not is_remote_filesystem(self._fs )
_A : List[str] = os.path.join if is_local else posixpath.join
_A : List[Any] = "-TTTTT-SSSSS-of-NNNNN"
_A : int = F'''{self.name}-{split_generator.name}{SUFFIX}.{file_format}'''
_A : Any = path_join(self._output_dir , lowerCAmelCase__ )
_A : List[Any] = 0
_A : Any = 0
_A : Union[str, Any] = 0
_A : Optional[Any] = []
_A : Tuple = []
for task_id, content in self._prepare_split_single(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ):
(
_A
) : List[str] = content
if num_bytes > 0:
total_num_examples += num_examples
total_num_bytes += num_bytes
total_shards += num_shards
task_id_and_num_shards.append((task_id, num_shards) )
all_shard_lengths.extend(lowerCAmelCase__ )
_A : Any = total_num_examples
_A : Any = total_num_bytes
# should rename everything at the end
logger.debug(F'''Renaming {total_shards} shards.''' )
if total_shards > 1:
_A : int = all_shard_lengths
# Define fs outside of _rename_shard so that we don't reference self in the function, which will result in a
# pickling error due to pickling the SparkContext.
_A : Tuple = self._fs
# use the -SSSSS-of-NNNNN pattern
def _rename_shard(
_a , _a , _a , ):
rename(
lowerCAmelCase__ , fpath.replace("""SSSSS""" , F'''{shard_id:05d}''' ).replace("""TTTTT""" , F'''{task_id:05d}''' ) , fpath.replace("""TTTTT-SSSSS""" , F'''{global_shard_id:05d}''' ).replace("""NNNNN""" , F'''{total_shards:05d}''' ) , )
_A : Tuple = []
_A : Tuple = 0
for i in range(len(lowerCAmelCase__ ) ):
_A : int = task_id_and_num_shards[i]
for shard_id in range(lowerCAmelCase__ ):
args.append([task_id, shard_id, global_shard_id] )
global_shard_id += 1
self._spark.sparkContext.parallelize(lowerCAmelCase__ , len(lowerCAmelCase__ ) ).map(lambda _a : _rename_shard(*lowerCAmelCase__ ) ).collect()
else:
# don't use any pattern
_A : Optional[int] = 0
_A : Optional[Any] = task_id_and_num_shards[0][0]
self._rename(
fpath.replace("""SSSSS""" , F'''{shard_id:05d}''' ).replace("""TTTTT""" , F'''{task_id:05d}''' ) , fpath.replace(lowerCAmelCase__ , """""" ) , )
def a__ ( self , _a , ) -> Dict:
return SparkExamplesIterable(self.df )
| 26
|
def A_ ( _UpperCAmelCase ):
SCREAMING_SNAKE_CASE_: List[str] = [0] * len(_UpperCAmelCase )
SCREAMING_SNAKE_CASE_: List[Any] = []
SCREAMING_SNAKE_CASE_: str = []
SCREAMING_SNAKE_CASE_: List[str] = 0
for values in graph.values():
for i in values:
indegree[i] += 1
for i in range(len(_UpperCAmelCase ) ):
if indegree[i] == 0:
queue.append(_UpperCAmelCase )
while queue:
SCREAMING_SNAKE_CASE_: Optional[int] = queue.pop(0 )
cnt += 1
topo.append(_UpperCAmelCase )
for x in graph[vertex]:
indegree[x] -= 1
if indegree[x] == 0:
queue.append(_UpperCAmelCase )
if cnt != len(_UpperCAmelCase ):
print("Cycle exists" )
else:
print(_UpperCAmelCase )
# Adjacency List of Graph
lowerCAmelCase : Any = {0: [1, 2], 1: [3], 2: [3], 3: [4, 5], 4: [], 5: []}
topological_sort(graph)
| 13
| 0
|
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor
@require_tokenizers
@require_vision
class _snake_case ( unittest.TestCase ):
def lowerCAmelCase__ ( self ) -> Optional[int]:
'''simple docstring'''
snake_case_ = tempfile.mkdtemp()
# fmt: off
snake_case_ = ["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "want", "##want", "##ed", "wa", "un", "runn", "##ing", ",", "low", "lowest"]
# fmt: on
snake_case_ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["vocab_file"] )
with open(self.vocab_file , "w" , encoding="utf-8" ) as vocab_writer:
vocab_writer.write("".join([x + "\n" for x in vocab_tokens] ) )
snake_case_ = {
"do_resize": True,
"size": {"height": 18, "width": 18},
"do_normalize": True,
"image_mean": [0.5, 0.5, 0.5],
"image_std": [0.5, 0.5, 0.5],
}
snake_case_ = os.path.join(self.tmpdirname , a__ )
with open(self.image_processor_file , "w" , encoding="utf-8" ) as fp:
json.dump(a__ , a__ )
def lowerCAmelCase__ ( self , **a__ ) -> Any:
'''simple docstring'''
return BertTokenizer.from_pretrained(self.tmpdirname , **a__ )
def lowerCAmelCase__ ( self , **a__ ) -> List[str]:
'''simple docstring'''
return ViTImageProcessor.from_pretrained(self.tmpdirname , **a__ )
def lowerCAmelCase__ ( self ) -> List[str]:
'''simple docstring'''
shutil.rmtree(self.tmpdirname )
def lowerCAmelCase__ ( self ) -> Optional[Any]:
'''simple docstring'''
snake_case_ = [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
snake_case_ = [Image.fromarray(np.moveaxis(a__ , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def lowerCAmelCase__ ( self ) -> Any:
'''simple docstring'''
snake_case_ = self.get_tokenizer()
snake_case_ = self.get_image_processor()
snake_case_ = VisionTextDualEncoderProcessor(tokenizer=a__ , image_processor=a__ )
processor.save_pretrained(self.tmpdirname )
snake_case_ = VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer.get_vocab() )
self.assertIsInstance(processor.tokenizer , (BertTokenizer, BertTokenizerFast) )
self.assertEqual(processor.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertIsInstance(processor.image_processor , a__ )
def lowerCAmelCase__ ( self ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ = VisionTextDualEncoderProcessor(
tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
snake_case_ = self.get_tokenizer(bos_token="(BOS)" , eos_token="(EOS)" )
snake_case_ = self.get_image_processor(do_normalize=a__ , padding_value=1.0 )
snake_case_ = VisionTextDualEncoderProcessor.from_pretrained(
self.tmpdirname , bos_token="(BOS)" , eos_token="(EOS)" , do_normalize=a__ , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , (BertTokenizer, BertTokenizerFast) )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , a__ )
def lowerCAmelCase__ ( self ) -> str:
'''simple docstring'''
snake_case_ = self.get_image_processor()
snake_case_ = self.get_tokenizer()
snake_case_ = VisionTextDualEncoderProcessor(tokenizer=a__ , image_processor=a__ )
snake_case_ = self.prepare_image_inputs()
snake_case_ = image_processor(a__ , return_tensors="np" )
snake_case_ = processor(images=a__ , return_tensors="np" )
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1e-2 )
def lowerCAmelCase__ ( self ) -> Optional[int]:
'''simple docstring'''
snake_case_ = self.get_image_processor()
snake_case_ = self.get_tokenizer()
snake_case_ = VisionTextDualEncoderProcessor(tokenizer=a__ , image_processor=a__ )
snake_case_ = "lower newer"
snake_case_ = processor(text=a__ )
snake_case_ = tokenizer(a__ )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def lowerCAmelCase__ ( self ) -> Tuple:
'''simple docstring'''
snake_case_ = self.get_image_processor()
snake_case_ = self.get_tokenizer()
snake_case_ = VisionTextDualEncoderProcessor(tokenizer=a__ , image_processor=a__ )
snake_case_ = "lower newer"
snake_case_ = self.prepare_image_inputs()
snake_case_ = processor(text=a__ , images=a__ )
self.assertListEqual(list(inputs.keys() ) , ["input_ids", "token_type_ids", "attention_mask", "pixel_values"] )
# test if it raises when no input is passed
with self.assertRaises(a__ ):
processor()
def lowerCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
snake_case_ = self.get_image_processor()
snake_case_ = self.get_tokenizer()
snake_case_ = VisionTextDualEncoderProcessor(tokenizer=a__ , image_processor=a__ )
snake_case_ = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
snake_case_ = processor.batch_decode(a__ )
snake_case_ = tokenizer.batch_decode(a__ )
self.assertListEqual(a__ , a__ )
def lowerCAmelCase__ ( self ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ = self.get_image_processor()
snake_case_ = self.get_tokenizer()
snake_case_ = VisionTextDualEncoderProcessor(tokenizer=a__ , image_processor=a__ )
snake_case_ = "lower newer"
snake_case_ = self.prepare_image_inputs()
snake_case_ = processor(text=a__ , images=a__ )
self.assertListEqual(list(inputs.keys() ) , processor.model_input_names )
| 92
|
'''simple docstring'''
import logging
import os
from .state import PartialState
class _snake_case ( logging.LoggerAdapter ):
@staticmethod
def lowerCAmelCase__ ( a__ ) -> Optional[Any]:
'''simple docstring'''
snake_case_ = PartialState()
return not main_process_only or (main_process_only and state.is_main_process)
def lowerCAmelCase__ ( self , a__ , a__ , *a__ , **a__ ) -> List[Any]:
'''simple docstring'''
if PartialState._shared_state == {}:
raise RuntimeError(
"You must initialize the accelerate state by calling either `PartialState()` or `Accelerator()` before using the logging utility." )
snake_case_ = kwargs.pop("main_process_only" , a__ )
snake_case_ = kwargs.pop("in_order" , a__ )
if self.isEnabledFor(a__ ):
if self._should_log(a__ ):
snake_case_ , snake_case_ = self.process(a__ , a__ )
self.logger.log(a__ , a__ , *a__ , **a__ )
elif in_order:
snake_case_ = PartialState()
for i in range(state.num_processes ):
if i == state.process_index:
snake_case_ , snake_case_ = self.process(a__ , a__ )
self.logger.log(a__ , a__ , *a__ , **a__ )
state.wait_for_everyone()
def UpperCamelCase_( snake_case : str , snake_case : str = None ):
'''simple docstring'''
if log_level is None:
snake_case_ = os.environ.get("ACCELERATE_LOG_LEVEL" , snake_case )
snake_case_ = logging.getLogger(snake_case )
if log_level is not None:
logger.setLevel(log_level.upper() )
logger.root.setLevel(log_level.upper() )
return MultiProcessAdapter(snake_case , {} )
| 92
| 1
|
'''simple docstring'''
import flax.linen as nn
import jax
import jax.numpy as jnp
class lowerCAmelCase__ ( nn.Module ):
lowerCAmelCase : int
lowerCAmelCase : jnp.dtype = jnp.floataa
def lowerCAmelCase__ ( self : List[str] ) ->List[str]:
'''simple docstring'''
_UpperCAmelCase : Any = nn.Conv(
self.out_channels , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
def __call__( self : List[str] , lowerCamelCase__ : Dict ) ->Optional[int]:
'''simple docstring'''
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase : Dict = hidden_states.shape
_UpperCAmelCase : Optional[int] = jax.image.resize(
lowerCamelCase__ , shape=(batch, height * 2, width * 2, channels) , method="nearest" , )
_UpperCAmelCase : Optional[Any] = self.conv(lowerCamelCase__ )
return hidden_states
class lowerCAmelCase__ ( nn.Module ):
lowerCAmelCase : int
lowerCAmelCase : jnp.dtype = jnp.floataa
def lowerCAmelCase__ ( self : Union[str, Any] ) ->Optional[int]:
'''simple docstring'''
_UpperCAmelCase : Dict = nn.Conv(
self.out_channels , kernel_size=(3, 3) , strides=(2, 2) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
def __call__( self : Optional[int] , lowerCamelCase__ : Optional[Any] ) ->Dict:
'''simple docstring'''
_UpperCAmelCase : int = self.conv(lowerCamelCase__ )
return hidden_states
class lowerCAmelCase__ ( nn.Module ):
lowerCAmelCase : int
lowerCAmelCase : int = None
lowerCAmelCase : float = 0.0
lowerCAmelCase : bool = None
lowerCAmelCase : jnp.dtype = jnp.floataa
def lowerCAmelCase__ ( self : int ) ->Any:
'''simple docstring'''
_UpperCAmelCase : Optional[Any] = self.in_channels if self.out_channels is None else self.out_channels
_UpperCAmelCase : Dict = nn.GroupNorm(num_groups=32 , epsilon=1E-5 )
_UpperCAmelCase : List[str] = nn.Conv(
lowerCamelCase__ , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
_UpperCAmelCase : Any = nn.Dense(lowerCamelCase__ , dtype=self.dtype )
_UpperCAmelCase : int = nn.GroupNorm(num_groups=32 , epsilon=1E-5 )
_UpperCAmelCase : List[str] = nn.Dropout(self.dropout_prob )
_UpperCAmelCase : Optional[Any] = nn.Conv(
lowerCamelCase__ , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
_UpperCAmelCase : Tuple = self.in_channels != out_channels if self.use_nin_shortcut is None else self.use_nin_shortcut
_UpperCAmelCase : Dict = None
if use_nin_shortcut:
_UpperCAmelCase : str = nn.Conv(
lowerCamelCase__ , kernel_size=(1, 1) , strides=(1, 1) , padding="VALID" , dtype=self.dtype , )
def __call__( self : Optional[Any] , lowerCamelCase__ : Union[str, Any] , lowerCamelCase__ : List[str] , lowerCamelCase__ : str=True ) ->Optional[int]:
'''simple docstring'''
_UpperCAmelCase : str = hidden_states
_UpperCAmelCase : Union[str, Any] = self.norma(lowerCamelCase__ )
_UpperCAmelCase : List[str] = nn.swish(lowerCamelCase__ )
_UpperCAmelCase : Tuple = self.conva(lowerCamelCase__ )
_UpperCAmelCase : List[str] = self.time_emb_proj(nn.swish(lowerCamelCase__ ) )
_UpperCAmelCase : List[Any] = jnp.expand_dims(jnp.expand_dims(lowerCamelCase__ , 1 ) , 1 )
_UpperCAmelCase : int = hidden_states + temb
_UpperCAmelCase : Dict = self.norma(lowerCamelCase__ )
_UpperCAmelCase : Tuple = nn.swish(lowerCamelCase__ )
_UpperCAmelCase : str = self.dropout(lowerCamelCase__ , lowerCamelCase__ )
_UpperCAmelCase : Dict = self.conva(lowerCamelCase__ )
if self.conv_shortcut is not None:
_UpperCAmelCase : int = self.conv_shortcut(lowerCamelCase__ )
return hidden_states + residual
| 234
|
'''simple docstring'''
def __lowerCAmelCase (__lowerCAmelCase = 50 ):
_UpperCAmelCase : Tuple = [[0] * 3 for _ in range(length + 1 )]
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_start in range(row_length - tile_length + 1 ):
different_colour_ways_number[row_length][tile_length - 2] += (
different_colour_ways_number[row_length - tile_start - tile_length][
tile_length - 2
]
+ 1
)
return sum(different_colour_ways_number[length] )
if __name__ == "__main__":
print(F'''{solution() = }''')
| 234
| 1
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case__ : Optional[Any] = logging.get_logger(__name__)
snake_case__ : Optional[Any] = {'openai-gpt': 'https://huggingface.co/openai-gpt/resolve/main/config.json'}
class A_ ( _lowerCamelCase ):
lowerCAmelCase__ = """openai-gpt"""
lowerCAmelCase__ = {
"""max_position_embeddings""": """n_positions""",
"""hidden_size""": """n_embd""",
"""num_attention_heads""": """n_head""",
"""num_hidden_layers""": """n_layer""",
}
def __init__(self :Tuple , _UpperCamelCase :Any=4_0478 , _UpperCamelCase :int=512 , _UpperCamelCase :str=768 , _UpperCamelCase :List[Any]=12 , _UpperCamelCase :Any=12 , _UpperCamelCase :str="gelu" , _UpperCamelCase :Tuple=0.1 , _UpperCamelCase :Dict=0.1 , _UpperCamelCase :Tuple=0.1 , _UpperCamelCase :int=1e-5 , _UpperCamelCase :Union[str, Any]=0.0_2 , _UpperCamelCase :str="cls_index" , _UpperCamelCase :List[Any]=True , _UpperCamelCase :Dict=None , _UpperCamelCase :Union[str, Any]=True , _UpperCamelCase :Union[str, Any]=0.1 , **_UpperCamelCase :List[str] , )-> Tuple:
__A = vocab_size
__A = n_positions
__A = n_embd
__A = n_layer
__A = n_head
__A = afn
__A = resid_pdrop
__A = embd_pdrop
__A = attn_pdrop
__A = layer_norm_epsilon
__A = initializer_range
__A = summary_type
__A = summary_use_proj
__A = summary_activation
__A = summary_first_dropout
__A = summary_proj_to_labels
super().__init__(**_UpperCamelCase )
| 250
|
from __future__ import annotations
snake_case__ : Dict = [True] * 1000001
snake_case__ : int = 2
while i * i <= 1000000:
if seive[i]:
for j in range(i * i, 1000001, i):
snake_case__ : str = False
i += 1
def _a ( lowerCamelCase: int ) -> bool:
'''simple docstring'''
return seive[n]
def _a ( lowerCamelCase: int ) -> bool:
'''simple docstring'''
return any(digit in '''02468''' for digit in str(lowerCamelCase ) )
def _a ( lowerCamelCase: int = 1_00_00_00 ) -> list[int]:
'''simple docstring'''
__A = [2] # result already includes the number 2.
for num in range(3 , limit + 1 , 2 ):
if is_prime(lowerCamelCase ) and not contains_an_even_digit(lowerCamelCase ):
__A = str(lowerCamelCase )
__A = [int(str_num[j:] + str_num[:j] ) for j in range(len(lowerCamelCase ) )]
if all(is_prime(lowerCamelCase ) for i in list_nums ):
result.append(lowerCamelCase )
return result
def _a ( ) -> int:
'''simple docstring'''
return len(find_circular_primes() )
if __name__ == "__main__":
print(f'{len(find_circular_primes()) = }')
| 250
| 1
|
"""simple docstring"""
import numpy as np
from transformers import Pipeline
def UpperCAmelCase__ ( SCREAMING_SNAKE_CASE : List[Any] ):
'''simple docstring'''
lowerCAmelCase = np.max(SCREAMING_SNAKE_CASE , axis=-1 , keepdims=SCREAMING_SNAKE_CASE )
lowerCAmelCase = np.exp(outputs - maxes )
return shifted_exp / shifted_exp.sum(axis=-1 , keepdims=SCREAMING_SNAKE_CASE )
class lowercase ( _UpperCAmelCase ):
def _snake_case ( self , **lowercase ) -> Optional[int]:
lowerCAmelCase = {}
if "second_text" in kwargs:
lowerCAmelCase = kwargs["""second_text"""]
return preprocess_kwargs, {}, {}
def _snake_case ( self , lowercase , lowercase=None ) -> List[Any]:
return self.tokenizer(lowercase , text_pair=lowercase , return_tensors=self.framework )
def _snake_case ( self , lowercase ) -> Optional[int]:
return self.model(**lowercase )
def _snake_case ( self , lowercase ) -> str:
lowerCAmelCase = model_outputs.logits[0].numpy()
lowerCAmelCase = softmax(lowercase )
lowerCAmelCase = np.argmax(lowercase )
lowerCAmelCase = self.model.config.idalabel[best_class]
lowerCAmelCase = probabilities[best_class].item()
lowerCAmelCase = logits.tolist()
return {"label": label, "score": score, "logits": logits}
| 46
|
'''simple docstring'''
import inspect
import unittest
from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import ViTHybridForImageClassification, ViTHybridImageProcessor, ViTHybridModel
from transformers.models.vit_hybrid.modeling_vit_hybrid import VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
class a :
def __init__( self , __magic_name__ , __magic_name__=13 , __magic_name__=64 , __magic_name__=2 , __magic_name__=3 , __magic_name__=True , __magic_name__=True , __magic_name__=32 , __magic_name__=5 , __magic_name__=4 , __magic_name__=37 , __magic_name__="gelu" , __magic_name__=0.1 , __magic_name__=0.1 , __magic_name__=10 , __magic_name__=0.0_2 , __magic_name__=[1, 16, 4, 4] , __magic_name__=None , ) -> str:
_a = parent
_a = batch_size
_a = image_size
_a = patch_size
_a = num_channels
_a = is_training
_a = use_labels
_a = hidden_size
_a = num_hidden_layers
_a = num_attention_heads
_a = intermediate_size
_a = hidden_act
_a = hidden_dropout_prob
_a = attention_probs_dropout_prob
_a = type_sequence_label_size
_a = initializer_range
_a = scope
_a = backbone_featmap_shape
# in ViT hybrid, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
# the number of patches is based on the feature map of the backbone, which by default uses an output stride
# of 32, which means that the feature map has a spatial resolution of 1/32 of the input image size
_a = (self.image_size // 32) ** 2
_a = num_patches + 1
def __UpperCAmelCase ( self ) -> str:
_a = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
_a = None
if self.use_labels:
_a = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_a = self.get_config()
return config, pixel_values, labels
def __UpperCAmelCase ( self ) -> Dict:
_a = {
'global_padding': 'same',
'layer_type': 'bottleneck',
'depths': [3, 4, 9],
'out_features': ['stage1', 'stage2', 'stage3'],
'embedding_dynamic_padding': True,
'hidden_sizes': [4, 8, 16, 32],
'num_groups': 2,
}
return ViTHybridConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=__magic_name__ , initializer_range=self.initializer_range , backbone_featmap_shape=self.backbone_featmap_shape , backbone_config=__magic_name__ , )
def __UpperCAmelCase ( self , __magic_name__ , __magic_name__ , __magic_name__ ) -> int:
_a = ViTHybridModel(config=__magic_name__ )
model.to(__magic_name__ )
model.eval()
_a = model(__magic_name__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __UpperCAmelCase ( self , __magic_name__ , __magic_name__ , __magic_name__ ) -> Union[str, Any]:
_a = self.type_sequence_label_size
_a = ViTHybridForImageClassification(__magic_name__ )
model.to(__magic_name__ )
model.eval()
_a = model(__magic_name__ , labels=__magic_name__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def __UpperCAmelCase ( self ) -> int:
_a = self.prepare_config_and_inputs()
_a , _a , _a = config_and_inputs
_a = {'pixel_values': pixel_values}
return config, inputs_dict
@require_torch
class a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , unittest.TestCase ):
_lowerCAmelCase = (ViTHybridModel, ViTHybridForImageClassification) if is_torch_available() else ()
_lowerCAmelCase = (
{"""feature-extraction""": ViTHybridModel, """image-classification""": ViTHybridForImageClassification}
if is_torch_available()
else {}
)
_lowerCAmelCase = False
_lowerCAmelCase = False
_lowerCAmelCase = False
def __UpperCAmelCase ( self ) -> int:
_a = ViTHybridModelTester(self )
_a = ConfigTester(self , config_class=__magic_name__ , has_text_modality=__magic_name__ , hidden_size=37 )
def __UpperCAmelCase ( self ) -> Optional[Any]:
self.config_tester.run_common_tests()
@unittest.skip(reason='ViT does not use inputs_embeds' )
def __UpperCAmelCase ( self ) -> str:
pass
def __UpperCAmelCase ( self ) -> List[str]:
_a , _a = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_a = model_class(__magic_name__ )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
_a = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(__magic_name__ , nn.Linear ) )
def __UpperCAmelCase ( self ) -> Optional[int]:
_a , _a = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_a = model_class(__magic_name__ )
_a = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_a = [*signature.parameters.keys()]
_a = ['pixel_values']
self.assertListEqual(arg_names[:1] , __magic_name__ )
def __UpperCAmelCase ( self ) -> str:
_a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__magic_name__ )
def __UpperCAmelCase ( self ) -> Optional[int]:
_a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__magic_name__ )
def __UpperCAmelCase ( self ) -> Optional[int]:
_a , _a = self.model_tester.prepare_config_and_inputs_for_common()
_a = _config_zero_init(__magic_name__ )
for model_class in self.all_model_classes:
_a = model_class(config=__magic_name__ )
# Skip the check for the backbone
for name, module in model.named_modules():
if module.__class__.__name__ == "ViTHybridPatchEmbeddings":
_a = [f'{name}.{key}' for key in module.state_dict().keys()]
break
for name, param in model.named_parameters():
if param.requires_grad:
if name in backbone_params:
continue
self.assertIn(
((param.data.mean() * 1e9).round() / 1e9).item() , [0.0, 1.0] , msg=f'Parameter {name} of model {model_class} seems not properly initialized' , )
@slow
def __UpperCAmelCase ( self ) -> Tuple:
for model_name in VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_a = ViTHybridModel.from_pretrained(__magic_name__ )
self.assertIsNotNone(__magic_name__ )
def _A () -> int:
'''simple docstring'''
_a = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_torch
@require_vision
class a ( unittest.TestCase ):
@cached_property
def __UpperCAmelCase ( self ) -> Union[str, Any]:
return (
ViTHybridImageProcessor.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
if is_vision_available()
else None
)
@slow
def __UpperCAmelCase ( self ) -> str:
_a = ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0] ).to(
__magic_name__ )
_a = self.default_image_processor
_a = prepare_img()
_a = image_processor(images=__magic_name__ , return_tensors='pt' ).to(__magic_name__ )
# forward pass
with torch.no_grad():
_a = model(**__magic_name__ )
# verify the logits
_a = torch.Size((1, 10_00) )
self.assertEqual(outputs.logits.shape , __magic_name__ )
_a = torch.tensor([-1.9_0_9_0, -0.4_9_9_3, -0.2_3_8_9] ).to(__magic_name__ )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , __magic_name__ , atol=1e-4 ) )
@slow
@require_accelerate
def __UpperCAmelCase ( self ) -> Optional[Any]:
_a = ViTHybridImageProcessor.from_pretrained('google/vit-hybrid-base-bit-384' )
_a = ViTHybridForImageClassification.from_pretrained('google/vit-hybrid-base-bit-384' , device_map='auto' )
_a = prepare_img()
_a = image_processor(images=__magic_name__ , return_tensors='pt' )
_a = model(**__magic_name__ )
_a = outputs.logits
# model predicts one of the 1000 ImageNet classes
_a = logits.argmax(-1 ).item()
self.assertTrue(model.config.idalabel[predicted_class_idx] , 'tabby, tabby cat' )
| 168
| 0
|
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings
UpperCAmelCase__ = r"\n [`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and\n can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information.\n\n Args:\n title_sep (`str`, *optional*, defaults to `\" / \"`):\n Separator inserted between the title and the text of the retrieved document when calling [`RagRetriever`].\n doc_sep (`str`, *optional*, defaults to `\" // \"`):\n Separator inserted between the text of the retrieved document and the original input when calling\n [`RagRetriever`].\n n_docs (`int`, *optional*, defaults to 5):\n Number of documents to retrieve.\n max_combined_length (`int`, *optional*, defaults to 300):\n Max length of contextualized input returned by [`~RagRetriever.__call__`].\n retrieval_vector_size (`int`, *optional*, defaults to 768):\n Dimensionality of the document embeddings indexed by [`RagRetriever`].\n retrieval_batch_size (`int`, *optional*, defaults to 8):\n Retrieval batch size, defined as the number of queries issues concurrently to the faiss index encapsulated\n [`RagRetriever`].\n dataset (`str`, *optional*, defaults to `\"wiki_dpr\"`):\n A dataset identifier of the indexed dataset in HuggingFace Datasets (list all available datasets and ids\n using `datasets.list_datasets()`).\n dataset_split (`str`, *optional*, defaults to `\"train\"`)\n Which split of the `dataset` to load.\n index_name (`str`, *optional*, defaults to `\"compressed\"`)\n The index name of the index associated with the `dataset`. One can choose between `\"legacy\"`, `\"exact\"` and\n `\"compressed\"`.\n index_path (`str`, *optional*)\n The path to the serialized faiss index on disk.\n passages_path (`str`, *optional*):\n A path to text passages compatible with the faiss index. Required if using\n [`~models.rag.retrieval_rag.LegacyIndex`]\n use_dummy_dataset (`bool`, *optional*, defaults to `False`)\n Whether to load a \"dummy\" variant of the dataset specified by `dataset`.\n label_smoothing (`float`, *optional*, defaults to 0.0):\n Only relevant if `return_loss` is set to `True`. Controls the `epsilon` parameter value for label smoothing\n in the loss calculation. If set to 0, no label smoothing is performed.\n do_marginalize (`bool`, *optional*, defaults to `False`):\n If `True`, the logits are marginalized over all documents by making use of\n `torch.nn.functional.log_softmax`.\n reduce_loss (`bool`, *optional*, defaults to `False`):\n Whether or not to reduce the NLL loss using the `torch.Tensor.sum` operation.\n do_deduplication (`bool`, *optional*, defaults to `True`):\n Whether or not to deduplicate the generations from different context documents for a given input. Has to be\n set to `False` if used while training with distributed backend.\n exclude_bos_score (`bool`, *optional*, defaults to `False`):\n Whether or not to disregard the BOS token when computing the loss.\n output_retrieved(`bool`, *optional*, defaults to `False`):\n If set to `True`, `retrieved_doc_embeds`, `retrieved_doc_ids`, `context_input_ids` and\n `context_attention_mask` are returned. See returned tensors for more detail.\n use_cache (`bool`, *optional*, defaults to `True`):\n Whether or not the model should return the last key/values attentions (not used by all models).\n forced_eos_token_id (`int`, *optional*):\n The id of the token to force as the last generated token when `max_length` is reached. Usually set to\n `eos_token_id`.\n"
@add_start_docstrings(A )
class __lowerCAmelCase ( A ):
UpperCamelCase = '''rag'''
UpperCamelCase = True
def __init__( self : str , A : int=None , A : Dict=True , A : int=None , A : Any=None , A : Any=None , A : Any=None , A : int=None , A : str=" / " , A : int=" // " , A : Any=5 , A : Optional[Any]=3_00 , A : Optional[Any]=7_68 , A : Optional[int]=8 , A : Union[str, Any]="wiki_dpr" , A : int="train" , A : Tuple="compressed" , A : str=None , A : Any=None , A : Tuple=False , A : List[str]=False , A : Tuple=0.0 , A : Any=True , A : int=False , A : Dict=False , A : List[str]=False , A : Optional[Any]=True , A : Tuple=None , **A : Optional[Any] , ) -> Optional[Any]:
"""simple docstring"""
super().__init__(
bos_token_id=A , pad_token_id=A , eos_token_id=A , decoder_start_token_id=A , forced_eos_token_id=A , is_encoder_decoder=A , prefix=A , vocab_size=A , **A , )
assert (
"question_encoder" in kwargs and "generator" in kwargs
), "Config has to be initialized with question_encoder and generator config"
_UpperCAmelCase = kwargs.pop('question_encoder')
_UpperCAmelCase = question_encoder_config.pop('model_type')
_UpperCAmelCase = kwargs.pop('generator')
_UpperCAmelCase = decoder_config.pop('model_type')
from ..auto.configuration_auto import AutoConfig
_UpperCAmelCase = AutoConfig.for_model(A , **A)
_UpperCAmelCase = AutoConfig.for_model(A , **A)
_UpperCAmelCase = reduce_loss
_UpperCAmelCase = label_smoothing
_UpperCAmelCase = exclude_bos_score
_UpperCAmelCase = do_marginalize
_UpperCAmelCase = title_sep
_UpperCAmelCase = doc_sep
_UpperCAmelCase = n_docs
_UpperCAmelCase = max_combined_length
_UpperCAmelCase = dataset
_UpperCAmelCase = dataset_split
_UpperCAmelCase = index_name
_UpperCAmelCase = retrieval_vector_size
_UpperCAmelCase = retrieval_batch_size
_UpperCAmelCase = passages_path
_UpperCAmelCase = index_path
_UpperCAmelCase = use_dummy_dataset
_UpperCAmelCase = output_retrieved
_UpperCAmelCase = do_deduplication
_UpperCAmelCase = use_cache
if self.forced_eos_token_id is None:
_UpperCAmelCase = getattr(self.generator , 'forced_eos_token_id' , A)
@classmethod
def _lowerCamelCase ( cls : Union[str, Any] , A : PretrainedConfig , A : PretrainedConfig , **A : Optional[Any]) -> PretrainedConfig:
"""simple docstring"""
return cls(question_encoder=question_encoder_config.to_dict() , generator=generator_config.to_dict() , **A)
def _lowerCamelCase ( self : Dict) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase = copy.deepcopy(self.__dict__)
_UpperCAmelCase = self.question_encoder.to_dict()
_UpperCAmelCase = self.generator.to_dict()
_UpperCAmelCase = self.__class__.model_type
return output
| 290
|
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def A ( ) -> tuple[list[int], int]:
'''simple docstring'''
_UpperCAmelCase = [randint(-1_000 , 1_000 ) for i in range(10 )]
_UpperCAmelCase = randint(-5_000 , 5_000 )
return (arr, r)
UpperCAmelCase__ = make_dataset()
def A ( _UpperCAmelCase : list[int] , _UpperCAmelCase : int ) -> tuple[int, ...]:
'''simple docstring'''
for triplet in permutations(_UpperCAmelCase , 3 ):
if sum(_UpperCAmelCase ) == target:
return tuple(sorted(_UpperCAmelCase ) )
return (0, 0, 0)
def A ( _UpperCAmelCase : list[int] , _UpperCAmelCase : int ) -> tuple[int, int, int]:
'''simple docstring'''
arr.sort()
_UpperCAmelCase = len(_UpperCAmelCase )
for i in range(n - 1 ):
_UpperCAmelCase , _UpperCAmelCase = i + 1, n - 1
while left < right:
if arr[i] + arr[left] + arr[right] == target:
return (arr[i], arr[left], arr[right])
elif arr[i] + arr[left] + arr[right] < target:
left += 1
elif arr[i] + arr[left] + arr[right] > target:
right -= 1
return (0, 0, 0)
def A ( ) -> tuple[float, float]:
'''simple docstring'''
_UpperCAmelCase = '\nfrom __main__ import dataset, triplet_sum1, triplet_sum2\n'
_UpperCAmelCase = '\ntriplet_sum1(*dataset)\n'
_UpperCAmelCase = '\ntriplet_sum2(*dataset)\n'
_UpperCAmelCase = repeat(setup=_UpperCAmelCase , stmt=_UpperCAmelCase , repeat=5 , number=10_000 )
_UpperCAmelCase = repeat(setup=_UpperCAmelCase , stmt=_UpperCAmelCase , repeat=5 , number=10_000 )
return (min(_UpperCAmelCase ), min(_UpperCAmelCase ))
if __name__ == "__main__":
from doctest import testmod
testmod()
UpperCAmelCase__ = solution_times()
print(f"""The time for naive implementation is {times[0]}.""")
print(f"""The time for optimized implementation is {times[1]}.""")
| 290
| 1
|
import unittest
from transformers import DebertaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
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 (
DebertaForMaskedLM,
DebertaForQuestionAnswering,
DebertaForSequenceClassification,
DebertaForTokenClassification,
DebertaModel,
)
from transformers.models.deberta.modeling_deberta import DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST
class _A ( __UpperCAmelCase ):
def __init__( self : List[str] , __SCREAMING_SNAKE_CASE : Union[str, Any] , __SCREAMING_SNAKE_CASE : Optional[Any]=13 , __SCREAMING_SNAKE_CASE : Tuple=7 , __SCREAMING_SNAKE_CASE : List[str]=True , __SCREAMING_SNAKE_CASE : List[Any]=True , __SCREAMING_SNAKE_CASE : Dict=True , __SCREAMING_SNAKE_CASE : List[Any]=True , __SCREAMING_SNAKE_CASE : str=99 , __SCREAMING_SNAKE_CASE : List[str]=32 , __SCREAMING_SNAKE_CASE : Tuple=5 , __SCREAMING_SNAKE_CASE : List[Any]=4 , __SCREAMING_SNAKE_CASE : int=37 , __SCREAMING_SNAKE_CASE : List[Any]="gelu" , __SCREAMING_SNAKE_CASE : List[Any]=0.1 , __SCREAMING_SNAKE_CASE : List[str]=0.1 , __SCREAMING_SNAKE_CASE : Optional[int]=512 , __SCREAMING_SNAKE_CASE : str=16 , __SCREAMING_SNAKE_CASE : List[Any]=2 , __SCREAMING_SNAKE_CASE : Optional[int]=0.02 , __SCREAMING_SNAKE_CASE : str=False , __SCREAMING_SNAKE_CASE : str=True , __SCREAMING_SNAKE_CASE : Tuple="None" , __SCREAMING_SNAKE_CASE : Union[str, Any]=3 , __SCREAMING_SNAKE_CASE : Any=4 , __SCREAMING_SNAKE_CASE : Optional[Any]=None , ):
'''simple docstring'''
__a = parent
__a = batch_size
__a = seq_length
__a = is_training
__a = use_input_mask
__a = use_token_type_ids
__a = use_labels
__a = vocab_size
__a = hidden_size
__a = num_hidden_layers
__a = num_attention_heads
__a = intermediate_size
__a = hidden_act
__a = hidden_dropout_prob
__a = attention_probs_dropout_prob
__a = max_position_embeddings
__a = type_vocab_size
__a = type_sequence_label_size
__a = initializer_range
__a = num_labels
__a = num_choices
__a = relative_attention
__a = position_biased_input
__a = pos_att_type
__a = scope
def _lowerCamelCase ( self : List[str]):
'''simple docstring'''
__a = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size)
__a = None
if self.use_input_mask:
__a = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2)
__a = None
if self.use_token_type_ids:
__a = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size)
__a = None
__a = None
__a = None
if self.use_labels:
__a = ids_tensor([self.batch_size] , self.type_sequence_label_size)
__a = ids_tensor([self.batch_size, self.seq_length] , self.num_labels)
__a = ids_tensor([self.batch_size] , self.num_choices)
__a = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def _lowerCamelCase ( self : Union[str, Any]):
'''simple docstring'''
return DebertaConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , relative_attention=self.relative_attention , position_biased_input=self.position_biased_input , pos_att_type=self.pos_att_type , )
def _lowerCamelCase ( self : Union[str, Any]):
'''simple docstring'''
__a = self.get_config()
__a = 300
return config
def _lowerCamelCase ( self : Union[str, Any] , __SCREAMING_SNAKE_CASE : Tuple):
'''simple docstring'''
self.parent.assertListEqual(list(result.loss.size()) , [])
def _lowerCamelCase ( self : Optional[int] , __SCREAMING_SNAKE_CASE : Tuple , __SCREAMING_SNAKE_CASE : List[Any] , __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : Union[str, Any] , __SCREAMING_SNAKE_CASE : Optional[Any] , __SCREAMING_SNAKE_CASE : List[str] , __SCREAMING_SNAKE_CASE : Union[str, Any]):
'''simple docstring'''
__a = DebertaModel(config=__SCREAMING_SNAKE_CASE)
model.to(__SCREAMING_SNAKE_CASE)
model.eval()
__a = model(__SCREAMING_SNAKE_CASE , attention_mask=__SCREAMING_SNAKE_CASE , token_type_ids=__SCREAMING_SNAKE_CASE)[0]
__a = model(__SCREAMING_SNAKE_CASE , token_type_ids=__SCREAMING_SNAKE_CASE)[0]
__a = model(__SCREAMING_SNAKE_CASE)[0]
self.parent.assertListEqual(list(sequence_output.size()) , [self.batch_size, self.seq_length, self.hidden_size])
def _lowerCamelCase ( self : Optional[int] , __SCREAMING_SNAKE_CASE : Dict , __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : Tuple , __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : Tuple , __SCREAMING_SNAKE_CASE : Any , __SCREAMING_SNAKE_CASE : List[Any]):
'''simple docstring'''
__a = DebertaForMaskedLM(config=__SCREAMING_SNAKE_CASE)
model.to(__SCREAMING_SNAKE_CASE)
model.eval()
__a = model(__SCREAMING_SNAKE_CASE , attention_mask=__SCREAMING_SNAKE_CASE , token_type_ids=__SCREAMING_SNAKE_CASE , labels=__SCREAMING_SNAKE_CASE)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size))
def _lowerCamelCase ( self : Any , __SCREAMING_SNAKE_CASE : Optional[Any] , __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : Optional[int] , __SCREAMING_SNAKE_CASE : Optional[int] , __SCREAMING_SNAKE_CASE : List[Any] , __SCREAMING_SNAKE_CASE : Optional[Any] , __SCREAMING_SNAKE_CASE : Any):
'''simple docstring'''
__a = self.num_labels
__a = DebertaForSequenceClassification(__SCREAMING_SNAKE_CASE)
model.to(__SCREAMING_SNAKE_CASE)
model.eval()
__a = model(__SCREAMING_SNAKE_CASE , attention_mask=__SCREAMING_SNAKE_CASE , token_type_ids=__SCREAMING_SNAKE_CASE , labels=__SCREAMING_SNAKE_CASE)
self.parent.assertListEqual(list(result.logits.size()) , [self.batch_size, self.num_labels])
self.check_loss_output(__SCREAMING_SNAKE_CASE)
def _lowerCamelCase ( self : Optional[int] , __SCREAMING_SNAKE_CASE : Dict , __SCREAMING_SNAKE_CASE : Any , __SCREAMING_SNAKE_CASE : Optional[int] , __SCREAMING_SNAKE_CASE : List[Any] , __SCREAMING_SNAKE_CASE : Union[str, Any] , __SCREAMING_SNAKE_CASE : List[str] , __SCREAMING_SNAKE_CASE : Optional[int]):
'''simple docstring'''
__a = self.num_labels
__a = DebertaForTokenClassification(config=__SCREAMING_SNAKE_CASE)
model.to(__SCREAMING_SNAKE_CASE)
model.eval()
__a = model(__SCREAMING_SNAKE_CASE , attention_mask=__SCREAMING_SNAKE_CASE , token_type_ids=__SCREAMING_SNAKE_CASE , labels=__SCREAMING_SNAKE_CASE)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels))
def _lowerCamelCase ( self : str , __SCREAMING_SNAKE_CASE : Dict , __SCREAMING_SNAKE_CASE : List[str] , __SCREAMING_SNAKE_CASE : Optional[int] , __SCREAMING_SNAKE_CASE : Any , __SCREAMING_SNAKE_CASE : Optional[int] , __SCREAMING_SNAKE_CASE : Dict , __SCREAMING_SNAKE_CASE : Optional[int]):
'''simple docstring'''
__a = DebertaForQuestionAnswering(config=__SCREAMING_SNAKE_CASE)
model.to(__SCREAMING_SNAKE_CASE)
model.eval()
__a = model(
__SCREAMING_SNAKE_CASE , attention_mask=__SCREAMING_SNAKE_CASE , token_type_ids=__SCREAMING_SNAKE_CASE , start_positions=__SCREAMING_SNAKE_CASE , end_positions=__SCREAMING_SNAKE_CASE , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length))
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length))
def _lowerCamelCase ( self : Tuple):
'''simple docstring'''
__a = self.prepare_config_and_inputs()
(
(
__a
) , (
__a
) , (
__a
) , (
__a
) , (
__a
) , (
__a
) , (
__a
) ,
) = config_and_inputs
__a = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_torch
class _A ( __UpperCAmelCase ,__UpperCAmelCase ,unittest.TestCase ):
UpperCamelCase__ : int = (
(
DebertaModel,
DebertaForMaskedLM,
DebertaForSequenceClassification,
DebertaForTokenClassification,
DebertaForQuestionAnswering,
)
if is_torch_available()
else ()
)
UpperCamelCase__ : List[str] = (
{
'''feature-extraction''': DebertaModel,
'''fill-mask''': DebertaForMaskedLM,
'''question-answering''': DebertaForQuestionAnswering,
'''text-classification''': DebertaForSequenceClassification,
'''token-classification''': DebertaForTokenClassification,
'''zero-shot''': DebertaForSequenceClassification,
}
if is_torch_available()
else {}
)
UpperCamelCase__ : Union[str, Any] = True
UpperCamelCase__ : Any = False
UpperCamelCase__ : Any = False
UpperCamelCase__ : Dict = False
UpperCamelCase__ : Tuple = False
def _lowerCamelCase ( self : Tuple):
'''simple docstring'''
__a = DebertaModelTester(self)
__a = ConfigTester(self , config_class=__SCREAMING_SNAKE_CASE , hidden_size=37)
def _lowerCamelCase ( self : List[str]):
'''simple docstring'''
self.config_tester.run_common_tests()
def _lowerCamelCase ( self : Tuple):
'''simple docstring'''
__a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_model(*__SCREAMING_SNAKE_CASE)
def _lowerCamelCase ( self : Tuple):
'''simple docstring'''
__a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_sequence_classification(*__SCREAMING_SNAKE_CASE)
def _lowerCamelCase ( self : Any):
'''simple docstring'''
__a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_masked_lm(*__SCREAMING_SNAKE_CASE)
def _lowerCamelCase ( self : Dict):
'''simple docstring'''
__a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_question_answering(*__SCREAMING_SNAKE_CASE)
def _lowerCamelCase ( self : Any):
'''simple docstring'''
__a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_token_classification(*__SCREAMING_SNAKE_CASE)
@slow
def _lowerCamelCase ( self : Tuple):
'''simple docstring'''
for model_name in DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__a = DebertaModel.from_pretrained(__SCREAMING_SNAKE_CASE)
self.assertIsNotNone(__SCREAMING_SNAKE_CASE)
@require_torch
@require_sentencepiece
@require_tokenizers
class _A ( unittest.TestCase ):
@unittest.skip(reason='''Model not available yet''')
def _lowerCamelCase ( self : int):
'''simple docstring'''
pass
@slow
def _lowerCamelCase ( self : int):
'''simple docstring'''
__a = DebertaModel.from_pretrained('''microsoft/deberta-base''')
__a = torch.tensor([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]])
__a = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])
with torch.no_grad():
__a = model(__SCREAMING_SNAKE_CASE , attention_mask=__SCREAMING_SNAKE_CASE)[0]
# compare the actual values for a slice.
__a = torch.tensor(
[[[-0.59_86, -0.80_55, -0.84_62], [1.44_84, -0.93_48, -0.80_59], [0.31_23, 0.00_32, -1.41_31]]])
self.assertTrue(torch.allclose(output[:, 1:4, 1:4] , __SCREAMING_SNAKE_CASE , atol=1E-4) , F'{output[:, 1:4, 1:4]}')
| 49
|
import argparse
import torch
from transformers import LxmertConfig, LxmertForPreTraining, load_tf_weights_in_lxmert
from transformers.utils import logging
logging.set_verbosity_info()
def __snake_case ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
# Initialise PyTorch model
__a = LxmertConfig.from_json_file(_UpperCAmelCase )
print(f'Building PyTorch model from configuration: {config}' )
__a = LxmertForPreTraining(_UpperCAmelCase )
# Load weights from tf checkpoint
load_tf_weights_in_lxmert(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
# Save pytorch-model
print(f'Save PyTorch model to {pytorch_dump_path}' )
torch.save(model.state_dict() , _UpperCAmelCase )
if __name__ == "__main__":
__snake_case :List[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
)
parser.add_argument(
'''--config_file''',
default=None,
type=str,
required=True,
help='''The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.''',
)
parser.add_argument(
'''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
__snake_case :Optional[Any] = parser.parse_args()
convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)
| 49
| 1
|
"""simple docstring"""
from copy import deepcopy
import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import DistributedType, is_torch_version, set_seed
def A_ ( _lowerCAmelCase : Optional[Any], _lowerCAmelCase : int, _lowerCAmelCase : int, _lowerCAmelCase : str ):
"""simple docstring"""
for param, grad_param in zip(model_a.parameters(), model_b.parameters() ):
if not param.requires_grad:
continue
if not did_step:
# Grads should not be in sync
assert (
torch.allclose(param.grad, grad_param.grad ) is False
), f'Gradients in sync when they should not be at iteration {iteration}:\nmodel_a grad ({param.grad}) == model_b grad ({grad_param.grad})'
else:
# Grads should be in sync
assert (
torch.allclose(param.grad, grad_param.grad ) is True
), f'Gradients not in sync when they should be at iteration {iteration}:\nmodel_a grad ({param.grad}) != model_b grad ({grad_param.grad})'
def A_ ( _lowerCAmelCase : List[Any], _lowerCAmelCase : List[str], _lowerCAmelCase : Any, _lowerCAmelCase : Optional[int], _lowerCAmelCase : str=True ):
"""simple docstring"""
model.train()
_a = model(a__ )
_a = F.mse_loss(a__, target.to(output.device ) )
if not do_backward:
loss /= accelerator.gradient_accumulation_steps
loss.backward()
else:
accelerator.backward(a__ )
def A_ ( _lowerCAmelCase : Optional[int], _lowerCAmelCase : Any=False ):
"""simple docstring"""
set_seed(42 )
_a = RegressionModel()
_a = deepcopy(a__ )
_a = RegressionDataset(length=80 )
_a = DataLoader(a__, batch_size=16 )
model.to(accelerator.device )
if sched:
_a = AdamW(params=model.parameters(), lr=1e-3 )
_a = AdamW(params=ddp_model.parameters(), lr=1e-3 )
_a = LambdaLR(a__, lr_lambda=lambda _lowerCAmelCase : epoch**0.6_5 )
_a = LambdaLR(a__, lr_lambda=lambda _lowerCAmelCase : epoch**0.6_5 )
# Make a copy of `model`
if sched:
_a , _a , _a , _a = accelerator.prepare(a__, a__, a__, a__ )
else:
_a , _a = accelerator.prepare(a__, a__ )
if sched:
return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
return model, ddp_model, dataloader
def A_ ( _lowerCAmelCase : Tuple ):
"""simple docstring"""
_a , _a , _a = get_training_setup(a__ )
# Use a single batch
_a , _a = next(iter(a__ ) ).values()
for iteration in range(3 ):
# Gather the distributed inputs and targs for the base model
_a , _a = accelerator.gather((ddp_input, ddp_target) )
_a , _a = input.to(accelerator.device ), target.to(accelerator.device )
# Perform our initial ground truth step in non "DDP"
step_model(a__, a__, a__, a__ )
# Do "gradient accumulation" (noop)
if iteration % 2 == 0:
# Accumulate grads locally
with accelerator.no_sync(a__ ):
step_model(a__, a__, a__, a__ )
else:
# Sync grads
step_model(a__, a__, a__, a__ )
# Since `no_sync` is a noop, `ddp_model` and `model` grads should always be in sync
check_model_parameters(a__, a__, a__, a__ )
for param, ddp_param in zip(model.parameters(), ddp_model.parameters() ):
if not param.requires_grad:
continue
assert torch.allclose(
param.grad, ddp_param.grad ), f'Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})'
# Shuffle ddp_input on each iteration
torch.manual_seed(13_37 + iteration )
_a = ddp_input[torch.randperm(len(a__ ) )]
def A_ ( _lowerCAmelCase : Dict ):
"""simple docstring"""
_a , _a , _a = get_training_setup(a__ )
# Use a single batch
_a , _a = next(iter(a__ ) ).values()
for iteration in range(3 ):
# Gather the distributed inputs and targs for the base model
_a , _a = accelerator.gather((ddp_input, ddp_target) )
_a , _a = input.to(accelerator.device ), target.to(accelerator.device )
# Perform our initial ground truth step in non "DDP"
step_model(a__, a__, a__, a__ )
# Do "gradient accumulation" (noop)
if iteration % 2 == 0:
# Accumulate grads locally
with accelerator.no_sync(a__ ):
step_model(a__, a__, a__, a__ )
else:
# Sync grads
step_model(a__, a__, a__, a__ )
# DDP model and model should only be in sync when not (iteration % 2 == 0)
for param, ddp_param in zip(model.parameters(), ddp_model.parameters() ):
if not param.requires_grad:
continue
if iteration % 2 == 0:
# Grads should not be in sync
assert (
torch.allclose(param.grad, ddp_param.grad ) is False
), f'Gradients in sync when they should not be:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})'
else:
# Grads should be in sync
assert (
torch.allclose(param.grad, ddp_param.grad ) is True
), f'Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})'
# Shuffle ddp_input on each iteration
torch.manual_seed(13_37 + iteration )
_a = ddp_input[torch.randperm(len(a__ ) )]
def A_ ( _lowerCAmelCase : Any=False, _lowerCAmelCase : Optional[int]=False ):
"""simple docstring"""
_a = Accelerator(
split_batches=a__, dispatch_batches=a__, gradient_accumulation_steps=2 )
# Test that context manager behaves properly
_a , _a , _a = get_training_setup(a__ )
for iteration, batch in enumerate(a__ ):
_a , _a = batch.values()
# Gather the distributed inputs and targs for the base model
_a , _a = accelerator.gather((ddp_input, ddp_target) )
_a , _a = input.to(accelerator.device ), target.to(accelerator.device )
# Perform our initial ground truth step in non "DDP"
step_model(a__, a__, a__, a__, a__ )
# Do "gradient accumulation" (noop)
with accelerator.accumulate(a__ ):
step_model(a__, a__, a__, a__ )
# DDP model and model should only be in sync when not (iteration % 2 == 0)
for param, ddp_param in zip(model.parameters(), ddp_model.parameters() ):
if not param.requires_grad:
continue
if ((iteration + 1) % 2 == 0) or (iteration == len(a__ ) - 1):
# Grads should be in sync
assert (
torch.allclose(param.grad, ddp_param.grad ) is True
), f'Gradients not in sync when they should be at iteration {iteration}:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})'
else:
# Grads should not be in sync
assert (
torch.allclose(param.grad, ddp_param.grad ) is False
), f'Gradients in sync when they should not be at iteration {iteration}:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})'
# Shuffle ddp_input on each iteration
torch.manual_seed(13_37 + iteration )
_a = ddp_input[torch.randperm(len(a__ ) )]
GradientState._reset_state()
def A_ ( _lowerCAmelCase : Union[str, Any]=False, _lowerCAmelCase : Any=False ):
"""simple docstring"""
_a = Accelerator(
split_batches=a__, dispatch_batches=a__, gradient_accumulation_steps=2 )
# Test that context manager behaves properly
_a , _a , _a , _a , _a , _a , _a = get_training_setup(a__, a__ )
for iteration, batch in enumerate(a__ ):
_a , _a = batch.values()
# Gather the distributed inputs and targs for the base model
_a , _a = accelerator.gather((ddp_input, ddp_target) )
_a , _a = input.to(accelerator.device ), target.to(accelerator.device )
# Perform our initial ground truth step in non "DDP"
model.train()
ddp_model.train()
step_model(a__, a__, a__, a__, a__ )
opt.step()
if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(a__ )):
if split_batches:
sched.step()
else:
for _ in range(accelerator.num_processes ):
sched.step()
opt.zero_grad()
# Perform gradient accumulation under wrapper
with accelerator.accumulate(a__ ):
step_model(a__, a__, a__, a__ )
ddp_opt.step()
ddp_sched.step()
ddp_opt.zero_grad()
# Learning rates should be the same
assert (
opt.param_groups[0]["lr"] == ddp_opt.param_groups[0]["lr"]
), f'Learning rates found in each optimizer did not align\nopt: {opt.param_groups[0]["lr"]}\nDDP opt: {ddp_opt.param_groups[0]["lr"]}\n'
_a = (((iteration + 1) % 2) == 0) or ((iteration + 1) == len(a__ ))
if accelerator.num_processes > 1:
check_model_parameters(a__, a__, a__, a__ )
# Shuffle ddp_input on each iteration
torch.manual_seed(13_37 + iteration )
GradientState._reset_state()
def A_ ( ):
"""simple docstring"""
_a = Accelerator()
_a = RegressionDataset(length=80 )
_a = DataLoader(a__, batch_size=16 )
_a = RegressionDataset(length=96 )
_a = DataLoader(a__, batch_size=16 )
_a , _a = accelerator.prepare(a__, a__ )
assert accelerator.gradient_state.active_dataloader is None
for iteration, _ in enumerate(a__ ):
assert id(accelerator.gradient_state.active_dataloader ) == id(a__ )
if iteration < len(a__ ) - 1:
assert not accelerator.gradient_state.end_of_dataloader
if iteration == 1:
for batch_num, _ in enumerate(a__ ):
assert id(accelerator.gradient_state.active_dataloader ) == id(a__ )
if batch_num < len(a__ ) - 1:
assert not accelerator.gradient_state.end_of_dataloader
else:
assert accelerator.gradient_state.end_of_dataloader
else:
assert accelerator.gradient_state.end_of_dataloader
assert accelerator.gradient_state.active_dataloader is None
def A_ ( ):
"""simple docstring"""
_a = Accelerator()
_a = accelerator.state
if state.local_process_index == 0:
print('''**Test `accumulate` gradient accumulation with dataloader break**''' )
test_dataloader_break()
if state.distributed_type == DistributedType.NO:
if state.local_process_index == 0:
print('''**Test NOOP `no_sync` context manager**''' )
test_noop_sync(a__ )
if state.distributed_type in (DistributedType.MULTI_GPU, DistributedType.MULTI_CPU):
if state.local_process_index == 0:
print('''**Test Distributed `no_sync` context manager**''' )
test_distributed_sync(a__ )
if state.distributed_type == DistributedType.MULTI_GPU:
for split_batch in [True, False]:
for dispatch_batches in [True, False]:
if state.local_process_index == 0:
print(
'''**Test `accumulate` gradient accumulation, ''', f'`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**', )
test_gradient_accumulation(a__, a__ )
# Currently will break on torch 2.0 +, need to investigate why
if is_torch_version('''<''', '''2.0''' ) or state.distributed_type == DistributedType.NO:
if state.local_process_index == 0:
print(
'''**Test `accumulate` gradient accumulation with optimizer and scheduler, ''', '''`split_batches=False`, `dispatch_batches=False`**''', )
test_gradient_accumulation_with_opt_and_scheduler()
if state.distributed_type == DistributedType.MULTI_GPU:
for split_batch in [True, False]:
for dispatch_batches in [True, False]:
if not split_batch and not dispatch_batches:
continue
if state.local_process_index == 0:
print(
'''**Test `accumulate` gradient accumulation with optimizer and scheduler, ''', f'`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**', )
test_gradient_accumulation_with_opt_and_scheduler(a__, a__ )
def A_ ( _lowerCAmelCase : Optional[int] ):
"""simple docstring"""
main()
if __name__ == "__main__":
main()
| 369
|
"""simple docstring"""
import argparse
import torch
from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging
logging.set_verbosity_info()
def A_ ( _lowerCAmelCase : Dict, _lowerCAmelCase : List[str], _lowerCAmelCase : List[str] ):
"""simple docstring"""
_a = BertConfig.from_json_file(_lowerCAmelCase )
print(f'Building PyTorch model from configuration: {config}' )
_a = BertForPreTraining(_lowerCAmelCase )
# Load weights from tf checkpoint
load_tf_weights_in_bert(_lowerCAmelCase, _lowerCAmelCase, _lowerCAmelCase )
# Save pytorch-model
print(f'Save PyTorch model to {pytorch_dump_path}' )
torch.save(model.state_dict(), _lowerCAmelCase )
if __name__ == "__main__":
__snake_case = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
)
parser.add_argument(
'''--bert_config_file''',
default=None,
type=str,
required=True,
help=(
'''The config json file corresponding to the pre-trained BERT model. \n'''
'''This specifies the model architecture.'''
),
)
parser.add_argument(
'''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
__snake_case = parser.parse_args()
convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)
| 153
| 0
|
'''simple docstring'''
_lowerCAmelCase = {
'''Pillow''': '''Pillow<10.0.0''',
'''accelerate''': '''accelerate>=0.20.3''',
'''av''': '''av==9.2.0''',
'''beautifulsoup4''': '''beautifulsoup4''',
'''black''': '''black~=23.1''',
'''codecarbon''': '''codecarbon==1.2.0''',
'''cookiecutter''': '''cookiecutter==1.7.3''',
'''dataclasses''': '''dataclasses''',
'''datasets''': '''datasets!=2.5.0''',
'''decord''': '''decord==0.6.0''',
'''deepspeed''': '''deepspeed>=0.9.3''',
'''diffusers''': '''diffusers''',
'''dill''': '''dill<0.3.5''',
'''evaluate''': '''evaluate>=0.2.0''',
'''fairscale''': '''fairscale>0.3''',
'''faiss-cpu''': '''faiss-cpu''',
'''fastapi''': '''fastapi''',
'''filelock''': '''filelock''',
'''flax''': '''flax>=0.4.1,<=0.7.0''',
'''ftfy''': '''ftfy''',
'''fugashi''': '''fugashi>=1.0''',
'''GitPython''': '''GitPython<3.1.19''',
'''hf-doc-builder''': '''hf-doc-builder>=0.3.0''',
'''huggingface-hub''': '''huggingface-hub>=0.14.1,<1.0''',
'''importlib_metadata''': '''importlib_metadata''',
'''ipadic''': '''ipadic>=1.0.0,<2.0''',
'''isort''': '''isort>=5.5.4''',
'''jax''': '''jax>=0.2.8,!=0.3.2,<=0.4.13''',
'''jaxlib''': '''jaxlib>=0.1.65,<=0.4.13''',
'''jieba''': '''jieba''',
'''kenlm''': '''kenlm''',
'''keras-nlp''': '''keras-nlp>=0.3.1''',
'''librosa''': '''librosa''',
'''nltk''': '''nltk''',
'''natten''': '''natten>=0.14.6''',
'''numpy''': '''numpy>=1.17''',
'''onnxconverter-common''': '''onnxconverter-common''',
'''onnxruntime-tools''': '''onnxruntime-tools>=1.4.2''',
'''onnxruntime''': '''onnxruntime>=1.4.0''',
'''opencv-python''': '''opencv-python''',
'''optuna''': '''optuna''',
'''optax''': '''optax>=0.0.8,<=0.1.4''',
'''packaging''': '''packaging>=20.0''',
'''parameterized''': '''parameterized''',
'''phonemizer''': '''phonemizer''',
'''protobuf''': '''protobuf''',
'''psutil''': '''psutil''',
'''pyyaml''': '''pyyaml>=5.1''',
'''pydantic''': '''pydantic<2''',
'''pytest''': '''pytest>=7.2.0''',
'''pytest-timeout''': '''pytest-timeout''',
'''pytest-xdist''': '''pytest-xdist''',
'''python''': '''python>=3.8.0''',
'''ray[tune]''': '''ray[tune]''',
'''regex''': '''regex!=2019.12.17''',
'''requests''': '''requests''',
'''rhoknp''': '''rhoknp>=1.1.0,<1.3.1''',
'''rjieba''': '''rjieba''',
'''rouge-score''': '''rouge-score!=0.0.7,!=0.0.8,!=0.1,!=0.1.1''',
'''ruff''': '''ruff>=0.0.241,<=0.0.259''',
'''sacrebleu''': '''sacrebleu>=1.4.12,<2.0.0''',
'''sacremoses''': '''sacremoses''',
'''safetensors''': '''safetensors>=0.3.1''',
'''sagemaker''': '''sagemaker>=2.31.0''',
'''scikit-learn''': '''scikit-learn''',
'''sentencepiece''': '''sentencepiece>=0.1.91,!=0.1.92''',
'''sigopt''': '''sigopt''',
'''starlette''': '''starlette''',
'''sudachipy''': '''sudachipy>=0.6.6''',
'''sudachidict_core''': '''sudachidict_core>=20220729''',
'''tensorflow-cpu''': '''tensorflow-cpu>=2.6,<2.14''',
'''tensorflow''': '''tensorflow>=2.6,<2.14''',
'''tensorflow-text''': '''tensorflow-text<2.14''',
'''tf2onnx''': '''tf2onnx''',
'''timeout-decorator''': '''timeout-decorator''',
'''timm''': '''timm''',
'''tokenizers''': '''tokenizers>=0.11.1,!=0.11.3,<0.14''',
'''torch''': '''torch>=1.9,!=1.12.0''',
'''torchaudio''': '''torchaudio''',
'''torchvision''': '''torchvision''',
'''pyctcdecode''': '''pyctcdecode>=0.4.0''',
'''tqdm''': '''tqdm>=4.27''',
'''unidic''': '''unidic>=1.0.2''',
'''unidic_lite''': '''unidic_lite>=1.0.7''',
'''urllib3''': '''urllib3<2.0.0''',
'''uvicorn''': '''uvicorn''',
}
| 37
|
'''simple docstring'''
import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging
if is_torch_available():
import torch
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
_lowerCAmelCase = logging.get_logger(__name__)
_lowerCAmelCase = {'''vocab_file''': '''spiece.model'''}
_lowerCAmelCase = {
'''vocab_file''': {
'''AI-Sweden/gpt-sw3-126m''': '''https://huggingface.co/AI-Sweden/gpt-sw3-126m/resolve/main/spiece.model''',
'''AI-Sweden/gpt-sw3-350m''': '''https://huggingface.co/AI-Sweden/gpt-sw3-350m/resolve/main/spiece.model''',
'''AI-Sweden/gpt-sw3-1.6b''': '''https://huggingface.co/AI-Sweden/gpt-sw3-1.6b/resolve/main/spiece.model''',
'''AI-Sweden/gpt-sw3-6.7b''': '''https://huggingface.co/AI-Sweden/gpt-sw3-6.7b/resolve/main/spiece.model''',
'''AI-Sweden/gpt-sw3-20b''': '''https://huggingface.co/AI-Sweden/gpt-sw3-20b/resolve/main/spiece.model''',
}
}
_lowerCAmelCase = {
'''AI-Sweden/gpt-sw3-126m''': 2048,
'''AI-Sweden/gpt-sw3-350m''': 2048,
'''AI-Sweden/gpt-sw3-1.6b''': 2048,
'''AI-Sweden/gpt-sw3-6.7b''': 2048,
'''AI-Sweden/gpt-sw3-20b''': 2048,
}
class lowerCAmelCase_( SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
__lowercase : Dict = VOCAB_FILES_NAMES
__lowercase : str = PRETRAINED_VOCAB_FILES_MAP
__lowercase : Dict = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__lowercase : Optional[int] = ['''input_ids''', '''attention_mask''']
def __init__( self ,__UpperCAmelCase ,__UpperCAmelCase=False ,__UpperCAmelCase=False ,__UpperCAmelCase=False ,__UpperCAmelCase=None ,__UpperCAmelCase=None ,__UpperCAmelCase=None ,__UpperCAmelCase=None ,__UpperCAmelCase = None ,**__UpperCAmelCase ,) -> None:
lowerCAmelCase__ : Optional[int] = {} if sp_model_kwargs is None else sp_model_kwargs
lowerCAmelCase__ : Dict = kwargs.get("""name_or_path""" )
if name_or_path is None:
logger.warning(
"""name_or_path not provided, will work for all GPTSw3 models except gpt-sw3-7b,"""
""" you are testing the model, this can safely be ignored""" )
lowerCAmelCase__ : Tuple = """None"""
# Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
lowerCAmelCase__ : Union[str, Any] = """<|endoftext|>""" if eos_token is None else eos_token
lowerCAmelCase__ : Dict = """<unk>""" if unk_token is None else unk_token
if "gpt-sw3-7b" in name_or_path:
lowerCAmelCase__ : Any = unk_token if pad_token is None else pad_token
lowerCAmelCase__ : Dict = eos_token if bos_token is None else bos_token
else:
lowerCAmelCase__ : List[str] = """<pad>""" if pad_token is None else pad_token
lowerCAmelCase__ : Optional[int] = """<s>""" if bos_token is None else bos_token
super().__init__(
do_lower_case=__UpperCAmelCase ,remove_space=__UpperCAmelCase ,keep_accents=__UpperCAmelCase ,bos_token=__UpperCAmelCase ,eos_token=__UpperCAmelCase ,unk_token=__UpperCAmelCase ,pad_token=__UpperCAmelCase ,sp_model_kwargs=self.sp_model_kwargs ,**__UpperCAmelCase ,)
lowerCAmelCase__ : Optional[int] = do_lower_case
lowerCAmelCase__ : Dict = remove_space
lowerCAmelCase__ : Optional[Any] = keep_accents
lowerCAmelCase__ : int = vocab_file
lowerCAmelCase__ : str = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(__UpperCAmelCase )
# Used for whitespace normalization in input texts
# fmt : off
lowerCAmelCase__ : int = {""" """, """ """, """ """, """ """, """ """, """ """, """ """, """ """, """ """, """ """, """""", """"""}
# fmt : on
# Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
lowerCAmelCase__ : List[str] = re.compile(
F"""[{''.join(map(__UpperCAmelCase ,list(range(0 ,9 ) ) + list(range(11 ,32 ) ) + list(range(127 ,160 ) ) + [160, 173, 8203] ) )}]""" )
def __getstate__( self ) -> Any:
lowerCAmelCase__ : int = self.__dict__.copy()
lowerCAmelCase__ : Optional[int] = None
return state
def __setstate__( self ,__UpperCAmelCase ) -> List[str]:
lowerCAmelCase__ : List[str] = d
# for backward compatibility
if not hasattr(self ,"""sp_model_kwargs""" ):
lowerCAmelCase__ : Tuple = {}
lowerCAmelCase__ : Optional[int] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
@property
# Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.vocab_size
def UpperCAmelCase_ ( self ) -> int:
return len(self.sp_model )
def UpperCAmelCase_ ( self ,__UpperCAmelCase ) -> str:
lowerCAmelCase__ : Tuple = self.non_printing_characters_re.sub("""""" ,__UpperCAmelCase )
# Normalize whitespaces
lowerCAmelCase__ : List[Any] = """""".join([char if char not in self.whitespaces else """ """ for char in text] )
# NFC Unicode normalization
lowerCAmelCase__ : List[Any] = unicodedata.normalize("""NFC""" ,__UpperCAmelCase )
return text
def UpperCAmelCase_ ( self ,__UpperCAmelCase ,**__UpperCAmelCase ) -> List[str]:
lowerCAmelCase__ : List[Any] = self.preprocess_text(__UpperCAmelCase )
return self.sp_model.encode(__UpperCAmelCase ,out_type=__UpperCAmelCase )
def UpperCAmelCase_ ( self ,__UpperCAmelCase ) -> int:
return self.sp_model.PieceToId(__UpperCAmelCase )
def UpperCAmelCase_ ( self ,__UpperCAmelCase ) -> str:
return self.sp_model.IdToPiece(__UpperCAmelCase )
@staticmethod
def UpperCAmelCase_ ( __UpperCAmelCase ) -> str:
return out_string
def UpperCAmelCase_ ( self ,__UpperCAmelCase ) -> str:
lowerCAmelCase__ : int = []
lowerCAmelCase__ : Optional[int] = """"""
lowerCAmelCase__ : Tuple = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
# TODO: Check if this is needed, as it ensures that decode(encode(doc)) != doc by adding extra whitespace in the decoded document
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(__UpperCAmelCase ) + token
lowerCAmelCase__ : Union[str, Any] = True
lowerCAmelCase__ : Optional[Any] = []
else:
current_sub_tokens.append(__UpperCAmelCase )
lowerCAmelCase__ : Any = False
out_string += self.sp_model.decode(__UpperCAmelCase )
return out_string
def UpperCAmelCase_ ( self ) -> Dict[str, int]:
lowerCAmelCase__ : Optional[int] = {self.convert_ids_to_tokens(__UpperCAmelCase ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def UpperCAmelCase_ ( self ,__UpperCAmelCase ,__UpperCAmelCase = None ) -> Tuple[str]:
if not os.path.isdir(__UpperCAmelCase ):
logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
return
lowerCAmelCase__ : Optional[int] = os.path.join(
__UpperCAmelCase ,(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(__UpperCAmelCase ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file ,__UpperCAmelCase )
elif not os.path.isfile(self.vocab_file ):
with open(__UpperCAmelCase ,"""wb""" ) as fi:
lowerCAmelCase__ : str = self.sp_model.serialized_model_proto()
fi.write(__UpperCAmelCase )
return (out_vocab_file,)
def UpperCAmelCase_ ( self ,__UpperCAmelCase ,__UpperCAmelCase = False ) -> Union[List[int], List[List[int]], "torch.Tensor"]:
if isinstance(__UpperCAmelCase ,__UpperCAmelCase ):
lowerCAmelCase__ : Tuple = self.preprocess_text(__UpperCAmelCase )
lowerCAmelCase__ : int = self.sp_model.encode(__UpperCAmelCase )
else:
lowerCAmelCase__ : int = [self.preprocess_text(__UpperCAmelCase ) for t in text]
lowerCAmelCase__ : Any = self.sp_model.encode(__UpperCAmelCase )
if return_tensors is True or return_tensors == "pt":
lowerCAmelCase__ : Tuple = torch.tensor(__UpperCAmelCase )
return token_ids
def UpperCAmelCase_ ( self ,__UpperCAmelCase ) -> str:
return self.sp_model.decode(__UpperCAmelCase )
def UpperCAmelCase_ ( self ,__UpperCAmelCase ) -> List[int]:
lowerCAmelCase__ : List[Any] = [F"""User: {text}""" if is_user else F"""Bot: {text}""" for is_user, text in conversation.iter_texts()]
lowerCAmelCase__ : Any = (
F"""{self.eos_token}{self.bos_token}""" + F"""{self.bos_token}""".join(__UpperCAmelCase ) + F"""{self.bos_token}Bot:"""
)
return self.encode(text=__UpperCAmelCase )
| 37
| 1
|
'''simple docstring'''
import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
_UpperCAmelCase : Union[str, Any] = logging.get_logger(__name__)
_UpperCAmelCase : int = {
"""vocab_file""": """vocab.json""",
"""merges_file""": """merges.txt""",
"""tokenizer_config_file""": """tokenizer_config.json""",
}
_UpperCAmelCase : Dict = {
"""vocab_file""": {"""facebook/blenderbot-3B""": """https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json"""},
"""merges_file""": {"""facebook/blenderbot-3B""": """https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt"""},
"""tokenizer_config_file""": {
"""facebook/blenderbot-3B""": """https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json"""
},
}
_UpperCAmelCase : Optional[Any] = {"""facebook/blenderbot-3B""": 1_2_8}
@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def __magic_name__( ):
__lowerCAmelCase = (
list(range(ord('''!'''), ord('''~''') + 1)) + list(range(ord('''¡'''), ord('''¬''') + 1)) + list(range(ord('''®'''), ord('''ÿ''') + 1))
)
__lowerCAmelCase = bs[:]
__lowerCAmelCase = 0
for b in range(2**8):
if b not in bs:
bs.append(lowerCamelCase)
cs.append(2**8 + n)
n += 1
__lowerCAmelCase = [chr(lowerCamelCase) for n in cs]
return dict(zip(lowerCamelCase, lowerCamelCase))
def __magic_name__( lowerCamelCase):
__lowerCAmelCase = set()
__lowerCAmelCase = word[0]
for char in word[1:]:
pairs.add((prev_char, char))
__lowerCAmelCase = char
return pairs
class a__ ( __A ):
"""simple docstring"""
__UpperCamelCase : Optional[int] = VOCAB_FILES_NAMES
__UpperCamelCase : Optional[Any] = PRETRAINED_VOCAB_FILES_MAP
__UpperCamelCase : str = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__UpperCamelCase : str = ['input_ids', 'attention_mask']
def __init__(self , __lowercase , __lowercase , __lowercase="replace" , __lowercase="<s>" , __lowercase="</s>" , __lowercase="</s>" , __lowercase="<s>" , __lowercase="<unk>" , __lowercase="<pad>" , __lowercase="<mask>" , __lowercase=False , **__lowercase , ):
__lowerCAmelCase = AddedToken(__lowercase , lstrip=__lowercase , rstrip=__lowercase ) if isinstance(__lowercase , __lowercase ) else bos_token
__lowerCAmelCase = AddedToken(__lowercase , lstrip=__lowercase , rstrip=__lowercase ) if isinstance(__lowercase , __lowercase ) else eos_token
__lowerCAmelCase = AddedToken(__lowercase , lstrip=__lowercase , rstrip=__lowercase ) if isinstance(__lowercase , __lowercase ) else sep_token
__lowerCAmelCase = AddedToken(__lowercase , lstrip=__lowercase , rstrip=__lowercase ) if isinstance(__lowercase , __lowercase ) else cls_token
__lowerCAmelCase = AddedToken(__lowercase , lstrip=__lowercase , rstrip=__lowercase ) if isinstance(__lowercase , __lowercase ) else unk_token
__lowerCAmelCase = AddedToken(__lowercase , lstrip=__lowercase , rstrip=__lowercase ) if isinstance(__lowercase , __lowercase ) else pad_token
# Mask token behave like a normal word, i.e. include the space before it
__lowerCAmelCase = AddedToken(__lowercase , lstrip=__lowercase , rstrip=__lowercase ) if isinstance(__lowercase , __lowercase ) else mask_token
super().__init__(
errors=__lowercase , bos_token=__lowercase , eos_token=__lowercase , unk_token=__lowercase , sep_token=__lowercase , cls_token=__lowercase , pad_token=__lowercase , mask_token=__lowercase , add_prefix_space=__lowercase , **__lowercase , )
with open(__lowercase , encoding='''utf-8''' ) as vocab_handle:
__lowerCAmelCase = json.load(__lowercase )
__lowerCAmelCase = {v: k for k, v in self.encoder.items()}
__lowerCAmelCase = errors # how to handle errors in decoding
__lowerCAmelCase = bytes_to_unicode()
__lowerCAmelCase = {v: k for k, v in self.byte_encoder.items()}
with open(__lowercase , encoding='''utf-8''' ) as merges_handle:
__lowerCAmelCase = merges_handle.read().split('''\n''' )[1:-1]
__lowerCAmelCase = [tuple(merge.split() ) for merge in bpe_merges]
__lowerCAmelCase = dict(zip(__lowercase , range(len(__lowercase ) ) ) )
__lowerCAmelCase = {}
__lowerCAmelCase = add_prefix_space
# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
__lowerCAmelCase = re.compile(R'''\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+''' )
@property
# Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.vocab_size with Roberta->Blenderbot, RoBERTa->Blenderbot
def _snake_case (self ):
return len(self.encoder )
def _snake_case (self ):
return dict(self.encoder , **self.added_tokens_encoder )
def _snake_case (self , __lowercase ):
if token in self.cache:
return self.cache[token]
__lowerCAmelCase = tuple(__lowercase )
__lowerCAmelCase = get_pairs(__lowercase )
if not pairs:
return token
while True:
__lowerCAmelCase = min(__lowercase , key=lambda __lowercase : self.bpe_ranks.get(__lowercase , float('''inf''' ) ) )
if bigram not in self.bpe_ranks:
break
__lowerCAmelCase , __lowerCAmelCase = bigram
__lowerCAmelCase = []
__lowerCAmelCase = 0
while i < len(__lowercase ):
try:
__lowerCAmelCase = word.index(__lowercase , __lowercase )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
__lowerCAmelCase = j
if word[i] == first and i < len(__lowercase ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
__lowerCAmelCase = tuple(__lowercase )
__lowerCAmelCase = new_word
if len(__lowercase ) == 1:
break
else:
__lowerCAmelCase = get_pairs(__lowercase )
__lowerCAmelCase = ''' '''.join(__lowercase )
__lowerCAmelCase = word
return word
def _snake_case (self , __lowercase ):
__lowerCAmelCase = []
for token in re.findall(self.pat , __lowercase ):
__lowerCAmelCase = ''''''.join(
self.byte_encoder[b] for b in token.encode('''utf-8''' ) ) # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
bpe_tokens.extend(bpe_token for bpe_token in self.bpe(__lowercase ).split(''' ''' ) )
return bpe_tokens
def _snake_case (self , __lowercase ):
return self.encoder.get(__lowercase , self.encoder.get(self.unk_token ) )
def _snake_case (self , __lowercase ):
return self.decoder.get(__lowercase )
def _snake_case (self , __lowercase ):
__lowerCAmelCase = ''''''.join(__lowercase )
__lowerCAmelCase = bytearray([self.byte_decoder[c] for c in text] ).decode('''utf-8''' , errors=self.errors )
return text
def _snake_case (self , __lowercase , __lowercase = None ):
if not os.path.isdir(__lowercase ):
logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
return
__lowerCAmelCase = os.path.join(
__lowercase , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
__lowerCAmelCase = os.path.join(
__lowercase , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file'''] )
with open(__lowercase , '''w''' , encoding='''utf-8''' ) as f:
f.write(json.dumps(self.encoder , indent=2 , sort_keys=__lowercase , ensure_ascii=__lowercase ) + '''\n''' )
__lowerCAmelCase = 0
with open(__lowercase , '''w''' , encoding='''utf-8''' ) as writer:
writer.write('''#version: 0.2\n''' )
for bpe_tokens, token_index in sorted(self.bpe_ranks.items() , key=lambda __lowercase : kv[1] ):
if index != token_index:
logger.warning(
F"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
''' Please check that the tokenizer is not corrupted!''' )
__lowerCAmelCase = token_index
writer.write(''' '''.join(__lowercase ) + '''\n''' )
index += 1
return vocab_file, merge_file
def _snake_case (self , __lowercase , __lowercase = None , __lowercase = False ):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=__lowercase , token_ids_a=__lowercase , already_has_special_tokens=__lowercase )
if token_ids_a is None:
return [1] + ([0] * len(__lowercase )) + [1]
return [1] + ([0] * len(__lowercase )) + [1, 1] + ([0] * len(__lowercase )) + [1]
def _snake_case (self , __lowercase , __lowercase = None ):
__lowerCAmelCase = [self.sep_token_id]
__lowerCAmelCase = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def _snake_case (self , __lowercase , __lowercase=False , **__lowercase ):
__lowerCAmelCase = kwargs.pop('''add_prefix_space''' , self.add_prefix_space )
if (is_split_into_words or add_prefix_space) and (len(__lowercase ) > 0 and not text[0].isspace()):
__lowerCAmelCase = ''' ''' + text
return (text, kwargs)
def _snake_case (self , __lowercase , __lowercase = None ):
return token_ids_a + [self.eos_token_id]
def _snake_case (self , __lowercase ):
__lowerCAmelCase = []
for is_user, text in conversation.iter_texts():
if is_user:
# We need to space prefix as it's being done within blenderbot
inputs.append(''' ''' + text )
else:
# Generated responses should contain them already.
inputs.append(__lowercase )
__lowerCAmelCase = ''' '''.join(__lowercase )
__lowerCAmelCase = self.encode(__lowercase )
if len(__lowercase ) > self.model_max_length:
__lowerCAmelCase = input_ids[-self.model_max_length :]
logger.warning(F"""Trimmed input from conversation as it was longer than {self.model_max_length} tokens.""" )
return input_ids
| 9
|
'''simple docstring'''
from typing import Callable, Dict, Optional, Tuple
import torch
from torch import nn
from torch.distributions import (
AffineTransform,
Distribution,
Independent,
NegativeBinomial,
Normal,
StudentT,
TransformedDistribution,
)
class a__ ( __A ):
"""simple docstring"""
def __init__(self , __lowercase , __lowercase=None , __lowercase=None , __lowercase=0 ):
__lowerCAmelCase = 1.0 if scale is None else scale
__lowerCAmelCase = 0.0 if loc is None else loc
super().__init__(__lowercase , [AffineTransform(loc=self.loc , scale=self.scale , event_dim=__lowercase )] )
@property
def _snake_case (self ):
return self.base_dist.mean * self.scale + self.loc
@property
def _snake_case (self ):
return self.base_dist.variance * self.scale**2
@property
def _snake_case (self ):
return self.variance.sqrt()
class a__ ( nn.Module ):
"""simple docstring"""
def __init__(self , __lowercase , __lowercase , __lowercase , **__lowercase ):
super().__init__(**__lowercase )
__lowerCAmelCase = args_dim
__lowerCAmelCase = nn.ModuleList([nn.Linear(__lowercase , __lowercase ) for dim in args_dim.values()] )
__lowerCAmelCase = domain_map
def _snake_case (self , __lowercase ):
__lowerCAmelCase = [proj(__lowercase ) for proj in self.proj]
return self.domain_map(*__lowercase )
class a__ ( nn.Module ):
"""simple docstring"""
def __init__(self , __lowercase ):
super().__init__()
__lowerCAmelCase = function
def _snake_case (self , __lowercase , *__lowercase ):
return self.function(__lowercase , *__lowercase )
class a__ :
"""simple docstring"""
__UpperCamelCase : type
__UpperCamelCase : int
__UpperCamelCase : Dict[str, int]
def __init__(self , __lowercase = 1 ):
__lowerCAmelCase = dim
__lowerCAmelCase = {k: dim * self.args_dim[k] for k in self.args_dim}
def _snake_case (self , __lowercase ):
if self.dim == 1:
return self.distribution_class(*__lowercase )
else:
return Independent(self.distribution_class(*__lowercase ) , 1 )
def _snake_case (self , __lowercase , __lowercase = None , __lowercase = None , ):
__lowerCAmelCase = self._base_distribution(__lowercase )
if loc is None and scale is None:
return distr
else:
return AffineTransformed(__lowercase , loc=__lowercase , scale=__lowercase , event_dim=self.event_dim )
@property
def _snake_case (self ):
return () if self.dim == 1 else (self.dim,)
@property
def _snake_case (self ):
return len(self.event_shape )
@property
def _snake_case (self ):
return 0.0
def _snake_case (self , __lowercase ):
return ParameterProjection(
in_features=__lowercase , args_dim=self.args_dim , domain_map=LambdaLayer(self.domain_map ) , )
def _snake_case (self , *__lowercase ):
raise NotImplementedError()
@staticmethod
def _snake_case (__lowercase ):
return (x + torch.sqrt(torch.square(__lowercase ) + 4.0 )) / 2.0
class a__ ( __A ):
"""simple docstring"""
__UpperCamelCase : Dict[str, int] = {"df": 1, "loc": 1, "scale": 1}
__UpperCamelCase : type = StudentT
@classmethod
def _snake_case (cls , __lowercase , __lowercase , __lowercase ):
__lowerCAmelCase = cls.squareplus(__lowercase ).clamp_min(torch.finfo(scale.dtype ).eps )
__lowerCAmelCase = 2.0 + cls.squareplus(__lowercase )
return df.squeeze(-1 ), loc.squeeze(-1 ), scale.squeeze(-1 )
class a__ ( __A ):
"""simple docstring"""
__UpperCamelCase : Dict[str, int] = {"loc": 1, "scale": 1}
__UpperCamelCase : type = Normal
@classmethod
def _snake_case (cls , __lowercase , __lowercase ):
__lowerCAmelCase = cls.squareplus(__lowercase ).clamp_min(torch.finfo(scale.dtype ).eps )
return loc.squeeze(-1 ), scale.squeeze(-1 )
class a__ ( __A ):
"""simple docstring"""
__UpperCamelCase : Dict[str, int] = {"total_count": 1, "logits": 1}
__UpperCamelCase : type = NegativeBinomial
@classmethod
def _snake_case (cls , __lowercase , __lowercase ):
__lowerCAmelCase = cls.squareplus(__lowercase )
return total_count.squeeze(-1 ), logits.squeeze(-1 )
def _snake_case (self , __lowercase ):
__lowerCAmelCase , __lowerCAmelCase = distr_args
if self.dim == 1:
return self.distribution_class(total_count=__lowercase , logits=__lowercase )
else:
return Independent(self.distribution_class(total_count=__lowercase , logits=__lowercase ) , 1 )
def _snake_case (self , __lowercase , __lowercase = None , __lowercase = None ):
__lowerCAmelCase , __lowerCAmelCase = distr_args
if scale is not None:
# See scaling property of Gamma.
logits += scale.log()
return self._base_distribution((total_count, logits) )
| 9
| 1
|
import argparse
import logging
import sys
from unittest.mock import patch
import run_glue_deebert
from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow
logging.basicConfig(level=logging.DEBUG)
a : Any = logging.getLogger()
def lowerCamelCase__ ( ):
__UpperCAmelCase : Optional[Any] = argparse.ArgumentParser()
parser.add_argument("""-f""" )
__UpperCAmelCase : List[Any] = parser.parse_args()
return args.f
class a ( lowercase__ ):
"""simple docstring"""
def UpperCAmelCase ( self : Optional[int] ) -> None:
__UpperCAmelCase : Tuple = logging.StreamHandler(sys.stdout )
logger.addHandler(__lowercase )
def UpperCAmelCase ( self : Optional[Any] , __lowercase : Optional[int] ) -> Tuple:
__UpperCAmelCase : Optional[Any] = get_gpu_count()
if n_gpu > 1:
pass
# XXX: doesn't quite work with n_gpu > 1 https://github.com/huggingface/transformers/issues/10560
# script = f"{self.examples_dir_str}/research_projects/deebert/run_glue_deebert.py"
# distributed_args = f"-m torch.distributed.launch --nproc_per_node={n_gpu} {script}".split()
# cmd = [sys.executable] + distributed_args + args
# execute_subprocess_async(cmd, env=self.get_env())
# XXX: test the results - need to save them first into .json file
else:
args.insert(0 , """run_glue_deebert.py""" )
with patch.object(__lowercase , """argv""" , __lowercase ):
__UpperCAmelCase : List[str] = run_glue_deebert.main()
for value in result.values():
self.assertGreaterEqual(__lowercase , 0.666 )
@slow
@require_torch_non_multi_gpu
def UpperCAmelCase ( self : Any ) -> List[str]:
__UpperCAmelCase : List[str] = """\n --model_type roberta\n --model_name_or_path roberta-base\n --task_name MRPC\n --do_train\n --do_eval\n --do_lower_case\n --data_dir ./tests/fixtures/tests_samples/MRPC/\n --max_seq_length 128\n --per_gpu_eval_batch_size=1\n --per_gpu_train_batch_size=8\n --learning_rate 2e-4\n --num_train_epochs 3\n --overwrite_output_dir\n --seed 42\n --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n --plot_data_dir ./examples/deebert/results/\n --save_steps 0\n --overwrite_cache\n --eval_after_first_stage\n """.split()
self.run_and_check(__lowercase )
__UpperCAmelCase : Union[str, Any] = """\n --model_type roberta\n --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n --task_name MRPC\n --do_eval\n --do_lower_case\n --data_dir ./tests/fixtures/tests_samples/MRPC/\n --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n --plot_data_dir ./examples/deebert/results/\n --max_seq_length 128\n --eval_each_highway\n --eval_highway\n --overwrite_cache\n --per_gpu_eval_batch_size=1\n """.split()
self.run_and_check(__lowercase )
__UpperCAmelCase : Tuple = """\n --model_type roberta\n --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n --task_name MRPC\n --do_eval\n --do_lower_case\n --data_dir ./tests/fixtures/tests_samples/MRPC/\n --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n --plot_data_dir ./examples/deebert/results/\n --max_seq_length 128\n --early_exit_entropy 0.1\n --eval_highway\n --overwrite_cache\n --per_gpu_eval_batch_size=1\n """.split()
self.run_and_check(__lowercase )
| 114
|
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import logging
_lowerCAmelCase : Optional[Any] = logging.get_logger(__name__) # pylint: disable=invalid-name
class _UpperCamelCase ( lowerCAmelCase ):
def __init__( self :int , lowerCamelCase :AutoencoderKL , lowerCamelCase :CLIPTextModel , lowerCamelCase :CLIPTokenizer , lowerCamelCase :UNetaDConditionModel , lowerCamelCase :Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler] , lowerCamelCase :StableDiffusionSafetyChecker , lowerCamelCase :CLIPImageProcessor , ) -> Optional[int]:
super().__init__()
self.register_modules(
vae=lowerCamelCase , text_encoder=lowerCamelCase , tokenizer=lowerCamelCase , unet=lowerCamelCase , scheduler=lowerCamelCase , safety_checker=lowerCamelCase , feature_extractor=lowerCamelCase , )
def UpperCAmelCase_ ( self :Tuple , lowerCamelCase :Optional[Union[str, int]] = "auto" ) -> int:
if slice_size == "auto":
# half the attention head size is usually a good trade-off between
# speed and memory
UpperCAmelCase__ = self.unet.config.attention_head_dim // 2
self.unet.set_attention_slice(lowerCamelCase )
def UpperCAmelCase_ ( self :Optional[int] ) -> Union[str, Any]:
self.enable_attention_slicing(lowerCamelCase )
@torch.no_grad()
def __call__( self :int , lowerCamelCase :Union[str, List[str]] , lowerCamelCase :int = 512 , lowerCamelCase :int = 512 , lowerCamelCase :int = 50 , lowerCamelCase :float = 7.5 , lowerCamelCase :Optional[Union[str, List[str]]] = None , lowerCamelCase :Optional[int] = 1 , lowerCamelCase :float = 0.0 , lowerCamelCase :Optional[torch.Generator] = None , lowerCamelCase :Optional[torch.FloatTensor] = None , lowerCamelCase :Optional[str] = "pil" , lowerCamelCase :bool = True , lowerCamelCase :Optional[Callable[[int, int, torch.FloatTensor], None]] = None , lowerCamelCase :int = 1 , lowerCamelCase :Optional[torch.FloatTensor] = None , **lowerCamelCase :List[str] , ) -> str:
if isinstance(lowerCamelCase , lowerCamelCase ):
UpperCAmelCase__ = 1
elif isinstance(lowerCamelCase , lowerCamelCase ):
UpperCAmelCase__ = len(lowerCamelCase )
else:
raise ValueError(f'''`prompt` has to be of type `str` or `list` but is {type(lowerCamelCase )}''' )
if height % 8 != 0 or width % 8 != 0:
raise ValueError(f'''`height` and `width` have to be divisible by 8 but are {height} and {width}.''' )
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(lowerCamelCase , lowerCamelCase ) or callback_steps <= 0)
):
raise ValueError(
f'''`callback_steps` has to be a positive integer but is {callback_steps} of type'''
f''' {type(lowerCamelCase )}.''' )
# get prompt text embeddings
UpperCAmelCase__ = self.tokenizer(
lowerCamelCase , padding="max_length" , max_length=self.tokenizer.model_max_length , return_tensors="pt" , )
UpperCAmelCase__ = text_inputs.input_ids
if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
UpperCAmelCase__ = self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :] )
logger.warning(
"The following part of your input was truncated because CLIP can only handle sequences up to"
f''' {self.tokenizer.model_max_length} tokens: {removed_text}''' )
UpperCAmelCase__ = text_input_ids[:, : self.tokenizer.model_max_length]
if text_embeddings is None:
UpperCAmelCase__ = self.text_encoder(text_input_ids.to(self.device ) )[0]
# duplicate text embeddings for each generation per prompt, using mps friendly method
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ = text_embeddings.shape
UpperCAmelCase__ = text_embeddings.repeat(1 , lowerCamelCase , 1 )
UpperCAmelCase__ = text_embeddings.view(bs_embed * num_images_per_prompt , lowerCamelCase , -1 )
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
UpperCAmelCase__ = guidance_scale > 1.0
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance:
UpperCAmelCase__ = 42
if negative_prompt is None:
UpperCAmelCase__ = [""]
elif type(lowerCamelCase ) is not type(lowerCamelCase ):
raise TypeError(
f'''`negative_prompt` should be the same type to `prompt`, but got {type(lowerCamelCase )} !='''
f''' {type(lowerCamelCase )}.''' )
elif isinstance(lowerCamelCase , lowerCamelCase ):
UpperCAmelCase__ = [negative_prompt]
elif batch_size != len(lowerCamelCase ):
raise ValueError(
f'''`negative_prompt`: {negative_prompt} has batch size {len(lowerCamelCase )}, but `prompt`:'''
f''' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'''
" the batch size of `prompt`." )
else:
UpperCAmelCase__ = negative_prompt
UpperCAmelCase__ = text_input_ids.shape[-1]
UpperCAmelCase__ = self.tokenizer(
lowerCamelCase , padding="max_length" , max_length=lowerCamelCase , truncation=lowerCamelCase , return_tensors="pt" , )
UpperCAmelCase__ = self.text_encoder(uncond_input.input_ids.to(self.device ) )[0]
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
UpperCAmelCase__ = uncond_embeddings.shape[1]
UpperCAmelCase__ = uncond_embeddings.repeat(lowerCamelCase , lowerCamelCase , 1 )
UpperCAmelCase__ = uncond_embeddings.view(batch_size * num_images_per_prompt , lowerCamelCase , -1 )
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
UpperCAmelCase__ = torch.cat([uncond_embeddings, text_embeddings] )
# get the initial random noise unless the user supplied it
# Unlike in other pipelines, latents need to be generated in the target device
# for 1-to-1 results reproducibility with the CompVis implementation.
# However this currently doesn't work in `mps`.
UpperCAmelCase__ = (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
UpperCAmelCase__ = (batch_size * num_images_per_prompt, self.unet.config.in_channels, 64, 64)
UpperCAmelCase__ = text_embeddings.dtype
if latents is None:
if self.device.type == "mps":
# randn does not exist on mps
UpperCAmelCase__ = torch.randn(
lowerCamelCase , generator=lowerCamelCase , device="cpu" , dtype=lowerCamelCase ).to(self.device )
UpperCAmelCase__ = torch.randn(lowerCamelCase , generator=lowerCamelCase , device="cpu" , dtype=lowerCamelCase ).to(
self.device )
else:
UpperCAmelCase__ = torch.randn(
lowerCamelCase , generator=lowerCamelCase , device=self.device , dtype=lowerCamelCase )
UpperCAmelCase__ = torch.randn(lowerCamelCase , generator=lowerCamelCase , device=self.device , dtype=lowerCamelCase )
else:
if latents_reference.shape != latents_shape:
raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}''' )
UpperCAmelCase__ = latents_reference.to(self.device )
UpperCAmelCase__ = latents.to(self.device )
# This is the key part of the pipeline where we
# try to ensure that the generated images w/ the same seed
# but different sizes actually result in similar images
UpperCAmelCase__ = (latents_shape[3] - latents_shape_reference[3]) // 2
UpperCAmelCase__ = (latents_shape[2] - latents_shape_reference[2]) // 2
UpperCAmelCase__ = latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
UpperCAmelCase__ = latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
UpperCAmelCase__ = 0 if dx < 0 else dx
UpperCAmelCase__ = 0 if dy < 0 else dy
UpperCAmelCase__ = max(-dx , 0 )
UpperCAmelCase__ = max(-dy , 0 )
# import pdb
# pdb.set_trace()
UpperCAmelCase__ = latents_reference[:, :, dy : dy + h, dx : dx + w]
# set timesteps
self.scheduler.set_timesteps(lowerCamelCase )
# Some schedulers like PNDM have timesteps as arrays
# It's more optimized to move all timesteps to correct device beforehand
UpperCAmelCase__ = self.scheduler.timesteps.to(self.device )
# scale the initial noise by the standard deviation required by the scheduler
UpperCAmelCase__ = latents * self.scheduler.init_noise_sigma
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
UpperCAmelCase__ = "eta" in set(inspect.signature(self.scheduler.step ).parameters.keys() )
UpperCAmelCase__ = {}
if accepts_eta:
UpperCAmelCase__ = eta
for i, t in enumerate(self.progress_bar(lowerCamelCase ) ):
# expand the latents if we are doing classifier free guidance
UpperCAmelCase__ = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
UpperCAmelCase__ = self.scheduler.scale_model_input(lowerCamelCase , lowerCamelCase )
# predict the noise residual
UpperCAmelCase__ = self.unet(lowerCamelCase , lowerCamelCase , encoder_hidden_states=lowerCamelCase ).sample
# perform guidance
if do_classifier_free_guidance:
UpperCAmelCase__ , UpperCAmelCase__ = noise_pred.chunk(2 )
UpperCAmelCase__ = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t-1
UpperCAmelCase__ = self.scheduler.step(lowerCamelCase , lowerCamelCase , lowerCamelCase , **lowerCamelCase ).prev_sample
# call the callback, if provided
if callback is not None and i % callback_steps == 0:
callback(lowerCamelCase , lowerCamelCase , lowerCamelCase )
UpperCAmelCase__ = 1 / 0.1_82_15 * latents
UpperCAmelCase__ = self.vae.decode(lowerCamelCase ).sample
UpperCAmelCase__ = (image / 2 + 0.5).clamp(0 , 1 )
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
UpperCAmelCase__ = image.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
if self.safety_checker is not None:
UpperCAmelCase__ = self.feature_extractor(self.numpy_to_pil(lowerCamelCase ) , return_tensors="pt" ).to(
self.device )
UpperCAmelCase__ , UpperCAmelCase__ = self.safety_checker(
images=lowerCamelCase , clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype ) )
else:
UpperCAmelCase__ = None
if output_type == "pil":
UpperCAmelCase__ = self.numpy_to_pil(lowerCamelCase )
if not return_dict:
return (image, has_nsfw_concept)
return StableDiffusionPipelineOutput(images=lowerCamelCase , nsfw_content_detected=lowerCamelCase )
| 169
| 0
|
'''simple docstring'''
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase : str = logging.get_logger(__name__)
_lowercase : Tuple = {
'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json',
}
class lowerCAmelCase__ ( lowerCAmelCase__ ):
lowerCAmelCase_ = "mvp"
lowerCAmelCase_ = ["past_key_values"]
lowerCAmelCase_ = {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}
def __init__( self , __SCREAMING_SNAKE_CASE=5_02_67 , __SCREAMING_SNAKE_CASE=10_24 , __SCREAMING_SNAKE_CASE=12 , __SCREAMING_SNAKE_CASE=40_96 , __SCREAMING_SNAKE_CASE=16 , __SCREAMING_SNAKE_CASE=12 , __SCREAMING_SNAKE_CASE=40_96 , __SCREAMING_SNAKE_CASE=16 , __SCREAMING_SNAKE_CASE=0.0 , __SCREAMING_SNAKE_CASE=0.0 , __SCREAMING_SNAKE_CASE="gelu" , __SCREAMING_SNAKE_CASE=10_24 , __SCREAMING_SNAKE_CASE=0.1 , __SCREAMING_SNAKE_CASE=0.0 , __SCREAMING_SNAKE_CASE=0.0 , __SCREAMING_SNAKE_CASE=0.02 , __SCREAMING_SNAKE_CASE=0.0 , __SCREAMING_SNAKE_CASE=False , __SCREAMING_SNAKE_CASE=True , __SCREAMING_SNAKE_CASE=1 , __SCREAMING_SNAKE_CASE=0 , __SCREAMING_SNAKE_CASE=2 , __SCREAMING_SNAKE_CASE=True , __SCREAMING_SNAKE_CASE=2 , __SCREAMING_SNAKE_CASE=2 , __SCREAMING_SNAKE_CASE=False , __SCREAMING_SNAKE_CASE=1_00 , __SCREAMING_SNAKE_CASE=8_00 , **__SCREAMING_SNAKE_CASE , ):
"""simple docstring"""
lowercase_ : Optional[Any] = vocab_size
lowercase_ : List[Any] = max_position_embeddings
lowercase_ : int = d_model
lowercase_ : Optional[Any] = encoder_ffn_dim
lowercase_ : List[Any] = encoder_layers
lowercase_ : Any = encoder_attention_heads
lowercase_ : Optional[int] = decoder_ffn_dim
lowercase_ : Optional[int] = decoder_layers
lowercase_ : List[Any] = decoder_attention_heads
lowercase_ : str = dropout
lowercase_ : List[Any] = attention_dropout
lowercase_ : Optional[Any] = activation_dropout
lowercase_ : List[Any] = activation_function
lowercase_ : Optional[int] = init_std
lowercase_ : Optional[Any] = encoder_layerdrop
lowercase_ : List[Any] = decoder_layerdrop
lowercase_ : Optional[Any] = classifier_dropout
lowercase_ : str = use_cache
lowercase_ : Any = encoder_layers
lowercase_ : List[str] = scale_embedding # scale factor will be sqrt(d_model) if True
lowercase_ : Optional[Any] = use_prompt
lowercase_ : Optional[Any] = prompt_length
lowercase_ : List[str] = prompt_mid_dim
super().__init__(
pad_token_id=_SCREAMING_SNAKE_CASE , bos_token_id=_SCREAMING_SNAKE_CASE , eos_token_id=_SCREAMING_SNAKE_CASE , is_encoder_decoder=_SCREAMING_SNAKE_CASE , decoder_start_token_id=_SCREAMING_SNAKE_CASE , forced_eos_token_id=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE , )
if self.forced_bos_token_id is None and kwargs.get('''force_bos_token_to_be_generated''' , _SCREAMING_SNAKE_CASE ):
lowercase_ : List[str] = self.bos_token_id
warnings.warn(
F'''Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. '''
'''The config can simply be saved and uploaded again to be fixed.''' )
| 355
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowercase : Tuple = {"configuration_vit_msn": ["VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViTMSNConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase : str = [
"VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST",
"ViTMSNModel",
"ViTMSNForImageClassification",
"ViTMSNPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_vit_msn import VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMSNConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_vit_msn import (
VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST,
ViTMSNForImageClassification,
ViTMSNModel,
ViTMSNPreTrainedModel,
)
else:
import sys
_lowercase : int = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 264
| 0
|
'''simple docstring'''
from ...processing_utils import ProcessorMixin
class lowerCAmelCase_ ( lowercase__ ):
__lowerCamelCase : Union[str, Any] = """SpeechT5FeatureExtractor"""
__lowerCamelCase : Tuple = """SpeechT5Tokenizer"""
def __init__( self , _lowerCAmelCase , _lowerCAmelCase ) -> int:
super().__init__(_A , _A )
def __call__( self , *_lowerCAmelCase , **_lowerCAmelCase ) -> Optional[Any]:
_lowerCAmelCase = kwargs.pop("audio" , _A )
_lowerCAmelCase = kwargs.pop("text" , _A )
_lowerCAmelCase = kwargs.pop("text_target" , _A )
_lowerCAmelCase = kwargs.pop("audio_target" , _A )
_lowerCAmelCase = kwargs.pop("sampling_rate" , _A )
if audio is not None and text is not None:
raise ValueError(
"Cannot process both `audio` and `text` inputs. Did you mean `audio_target` or `text_target`?" )
if audio_target is not None and text_target is not None:
raise ValueError(
"Cannot process both `audio_target` and `text_target` inputs. Did you mean `audio` or `text`?" )
if audio is None and audio_target is None and text is None and text_target is None:
raise ValueError(
"You need to specify either an `audio`, `audio_target`, `text`, or `text_target` input to process." )
if audio is not None:
_lowerCAmelCase = self.feature_extractor(_A , *_A , sampling_rate=_A , **_A )
elif text is not None:
_lowerCAmelCase = self.tokenizer(_A , **_A )
else:
_lowerCAmelCase = None
if audio_target is not None:
_lowerCAmelCase = self.feature_extractor(audio_target=_A , *_A , sampling_rate=_A , **_A )
_lowerCAmelCase = targets['input_values']
elif text_target is not None:
_lowerCAmelCase = self.tokenizer(_A , **_A )
_lowerCAmelCase = targets['input_ids']
else:
_lowerCAmelCase = None
if inputs is None:
return targets
if targets is not None:
_lowerCAmelCase = labels
_lowerCAmelCase = targets.get("attention_mask" )
if decoder_attention_mask is not None:
_lowerCAmelCase = decoder_attention_mask
return inputs
def _snake_case ( self , *_lowerCAmelCase , **_lowerCAmelCase ) -> Optional[Any]:
_lowerCAmelCase = kwargs.pop("input_values" , _A )
_lowerCAmelCase = kwargs.pop("input_ids" , _A )
_lowerCAmelCase = kwargs.pop("labels" , _A )
if input_values is not None and input_ids is not None:
raise ValueError("Cannot process both `input_values` and `input_ids` inputs." )
if input_values is None and input_ids is None and labels is None:
raise ValueError(
"You need to specify either an `input_values`, `input_ids`, or `labels` input to be padded." )
if input_values is not None:
_lowerCAmelCase = self.feature_extractor.pad(_A , *_A , **_A )
elif input_ids is not None:
_lowerCAmelCase = self.tokenizer.pad(_A , **_A )
else:
_lowerCAmelCase = None
if labels is not None:
if "input_ids" in labels or (isinstance(_A , _A ) and "input_ids" in labels[0]):
_lowerCAmelCase = self.tokenizer.pad(_A , **_A )
_lowerCAmelCase = targets['input_ids']
else:
_lowerCAmelCase = self.feature_extractor.feature_size
_lowerCAmelCase = self.feature_extractor.num_mel_bins
_lowerCAmelCase = self.feature_extractor.pad(_A , *_A , **_A )
_lowerCAmelCase = feature_size_hack
_lowerCAmelCase = targets['input_values']
else:
_lowerCAmelCase = None
if inputs is None:
return targets
if targets is not None:
_lowerCAmelCase = labels
_lowerCAmelCase = targets.get("attention_mask" )
if decoder_attention_mask is not None:
_lowerCAmelCase = decoder_attention_mask
return inputs
def _snake_case ( self , *_lowerCAmelCase , **_lowerCAmelCase ) -> Any:
return self.tokenizer.batch_decode(*_A , **_A )
def _snake_case ( self , *_lowerCAmelCase , **_lowerCAmelCase ) -> Tuple:
return self.tokenizer.decode(*_A , **_A )
| 158
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase :str = {'''configuration_xglm''': ['''XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XGLMConfig''']}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase :Optional[Any] = ['''XGLMTokenizer''']
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase :Dict = ['''XGLMTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase :Tuple = [
'''XGLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''XGLMForCausalLM''',
'''XGLMModel''',
'''XGLMPreTrainedModel''',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase :int = [
'''FlaxXGLMForCausalLM''',
'''FlaxXGLMModel''',
'''FlaxXGLMPreTrainedModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase :Any = [
'''TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFXGLMForCausalLM''',
'''TFXGLMModel''',
'''TFXGLMPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_xglm import XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XGLMConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_xglm import XGLMTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_xglm_fast import XGLMTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_xglm import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_xglm import (
TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFXGLMForCausalLM,
TFXGLMModel,
TFXGLMPreTrainedModel,
)
else:
import sys
lowerCAmelCase :int = _LazyModule(__name__, globals()['''__file__'''], _import_structure)
| 331
| 0
|
"""simple docstring"""
def __lowercase ( _a ):
if not numbers:
return 0
if not isinstance(_a , (list, tuple) ) or not all(
isinstance(_a , _a ) for number in numbers ):
raise ValueError('''numbers must be an iterable of integers''' )
snake_case_ : List[str] = numbers[0]
for i in range(1 , len(_a ) ):
# update the maximum and minimum subarray products
snake_case_ : Tuple = numbers[i]
if number < 0:
snake_case_, snake_case_ : List[Any] = min_till_now, max_till_now
snake_case_ : Optional[Any] = max(_a , max_till_now * number )
snake_case_ : Any = min(_a , min_till_now * number )
# update the maximum product found till now
snake_case_ : Optional[Any] = max(_a , _a )
return max_prod
| 155
|
"""simple docstring"""
import math
import sys
def __lowercase ( _a ):
if number != int(_a ):
raise ValueError('''the value of input must be a natural number''' )
if number < 0:
raise ValueError('''the value of input must not be a negative number''' )
if number == 0:
return 1
snake_case_ : int = [-1] * (number + 1)
snake_case_ : int = 0
for i in range(1 , number + 1 ):
snake_case_ : Tuple = sys.maxsize
snake_case_ : List[Any] = int(math.sqrt(_a ) )
for j in range(1 , root + 1 ):
snake_case_ : Dict = 1 + answers[i - (j**2)]
snake_case_ : int = min(_a , _a )
snake_case_ : Any = answer
return answers[number]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 155
| 1
|
import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging
a_ = logging.get_logger(__name__)
class lowercase__ ( _UpperCAmelCase ):
def __init__( self , __UpperCAmelCase )-> List[Any]:
'''simple docstring'''
super().__init__()
lowerCAmelCase__ = nn.ModuleList(__UpperCAmelCase )
def UpperCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = False , __UpperCAmelCase = True , )-> Union[ControlNetOutput, Tuple]:
'''simple docstring'''
for i, (image, scale, controlnet) in enumerate(zip(__UpperCAmelCase , __UpperCAmelCase , self.nets ) ):
lowerCAmelCase__ , lowerCAmelCase__ = controlnet(
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , )
# merge samples
if i == 0:
lowerCAmelCase__ , lowerCAmelCase__ = down_samples, mid_sample
else:
lowerCAmelCase__ = [
samples_prev + samples_curr
for samples_prev, samples_curr in zip(__UpperCAmelCase , __UpperCAmelCase )
]
mid_block_res_sample += mid_sample
return down_block_res_samples, mid_block_res_sample
def UpperCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase = True , __UpperCAmelCase = None , __UpperCAmelCase = False , __UpperCAmelCase = None , )-> Any:
'''simple docstring'''
lowerCAmelCase__ = 0
lowerCAmelCase__ = save_directory
for controlnet in self.nets:
controlnet.save_pretrained(
__UpperCAmelCase , is_main_process=__UpperCAmelCase , save_function=__UpperCAmelCase , safe_serialization=__UpperCAmelCase , variant=__UpperCAmelCase , )
idx += 1
lowerCAmelCase__ = model_path_to_save + F"_{idx}"
@classmethod
def UpperCAmelCase ( cls , __UpperCAmelCase , **__UpperCAmelCase )-> List[Any]:
'''simple docstring'''
lowerCAmelCase__ = 0
lowerCAmelCase__ = []
# load controlnet and append to list until no controlnet directory exists anymore
# first controlnet has to be saved under `./mydirectory/controlnet` to be compliant with `DiffusionPipeline.from_prertained`
# second, third, ... controlnets have to be saved under `./mydirectory/controlnet_1`, `./mydirectory/controlnet_2`, ...
lowerCAmelCase__ = pretrained_model_path
while os.path.isdir(__UpperCAmelCase ):
lowerCAmelCase__ = ControlNetModel.from_pretrained(__UpperCAmelCase , **__UpperCAmelCase )
controlnets.append(__UpperCAmelCase )
idx += 1
lowerCAmelCase__ = pretrained_model_path + F"_{idx}"
logger.info(F"{len(__UpperCAmelCase )} controlnets loaded from {pretrained_model_path}." )
if len(__UpperCAmelCase ) == 0:
raise ValueError(
F"No ControlNets found under {os.path.dirname(__UpperCAmelCase )}. Expected at least {pretrained_model_path + '_0'}." )
return cls(__UpperCAmelCase )
| 340
|
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
'''xlnet-base-cased''': '''https://huggingface.co/xlnet-base-cased/resolve/main/config.json''',
'''xlnet-large-cased''': '''https://huggingface.co/xlnet-large-cased/resolve/main/config.json''',
}
class lowercase__ ( _UpperCAmelCase ):
a_ ="""xlnet"""
a_ =["""mems"""]
a_ ={
"""n_token""": """vocab_size""", # Backward compatibility
"""hidden_size""": """d_model""",
"""num_attention_heads""": """n_head""",
"""num_hidden_layers""": """n_layer""",
}
def __init__( self , __UpperCAmelCase=32000 , __UpperCAmelCase=1024 , __UpperCAmelCase=24 , __UpperCAmelCase=16 , __UpperCAmelCase=4096 , __UpperCAmelCase="gelu" , __UpperCAmelCase=True , __UpperCAmelCase="bi" , __UpperCAmelCase=0.02 , __UpperCAmelCase=1E-1_2 , __UpperCAmelCase=0.1 , __UpperCAmelCase=512 , __UpperCAmelCase=None , __UpperCAmelCase=True , __UpperCAmelCase=False , __UpperCAmelCase=False , __UpperCAmelCase=-1 , __UpperCAmelCase=False , __UpperCAmelCase="last" , __UpperCAmelCase=True , __UpperCAmelCase="tanh" , __UpperCAmelCase=0.1 , __UpperCAmelCase=5 , __UpperCAmelCase=5 , __UpperCAmelCase=5 , __UpperCAmelCase=1 , __UpperCAmelCase=2 , **__UpperCAmelCase , )-> int:
'''simple docstring'''
lowerCAmelCase__ = vocab_size
lowerCAmelCase__ = d_model
lowerCAmelCase__ = n_layer
lowerCAmelCase__ = n_head
if d_model % n_head != 0:
raise ValueError(F"'d_model % n_head' ({d_model % n_head}) should be equal to 0" )
if "d_head" in kwargs:
if kwargs["d_head"] != d_model // n_head:
raise ValueError(
F"`d_head` ({kwargs['d_head']}) should be equal to `d_model // n_head` ({d_model // n_head})" )
lowerCAmelCase__ = d_model // n_head
lowerCAmelCase__ = ff_activation
lowerCAmelCase__ = d_inner
lowerCAmelCase__ = untie_r
lowerCAmelCase__ = attn_type
lowerCAmelCase__ = initializer_range
lowerCAmelCase__ = layer_norm_eps
lowerCAmelCase__ = dropout
lowerCAmelCase__ = mem_len
lowerCAmelCase__ = reuse_len
lowerCAmelCase__ = bi_data
lowerCAmelCase__ = clamp_len
lowerCAmelCase__ = same_length
lowerCAmelCase__ = summary_type
lowerCAmelCase__ = summary_use_proj
lowerCAmelCase__ = summary_activation
lowerCAmelCase__ = summary_last_dropout
lowerCAmelCase__ = start_n_top
lowerCAmelCase__ = end_n_top
lowerCAmelCase__ = bos_token_id
lowerCAmelCase__ = pad_token_id
lowerCAmelCase__ = eos_token_id
if "use_cache" in kwargs:
warnings.warn(
"The `use_cache` argument is deprecated and will be removed in a future version, use `use_mems_eval`"
" instead." , __UpperCAmelCase , )
lowerCAmelCase__ = kwargs["use_cache"]
lowerCAmelCase__ = use_mems_eval
lowerCAmelCase__ = use_mems_train
super().__init__(pad_token_id=__UpperCAmelCase , bos_token_id=__UpperCAmelCase , eos_token_id=__UpperCAmelCase , **__UpperCAmelCase )
@property
def UpperCAmelCase ( self )-> Dict:
'''simple docstring'''
logger.info(F"The model {self.model_type} is one of the few models that has no sequence length limit." )
return -1
@max_position_embeddings.setter
def UpperCAmelCase ( self , __UpperCAmelCase )-> Union[str, Any]:
'''simple docstring'''
raise NotImplementedError(
F"The model {self.model_type} is one of the few models that has no sequence length limit." )
| 340
| 1
|
"""simple docstring"""
import argparse
import math
import traceback
import dateutil.parser as date_parser
import requests
def lowerCAmelCase__ ( UpperCamelCase__ ):
'''simple docstring'''
_a : List[str] = {}
_a : List[str] = job["""started_at"""]
_a : List[str] = job["""completed_at"""]
_a : List[Any] = date_parser.parse(UpperCamelCase__ )
_a : Union[str, Any] = date_parser.parse(UpperCamelCase__ )
_a : Tuple = round((end_datetime - start_datetime).total_seconds() / 60.0 )
_a : List[Any] = start
_a : Dict = end
_a : Any = duration_in_min
return job_info
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__=None ):
'''simple docstring'''
_a : Optional[int] = None
if token is not None:
_a : Dict = {"""Accept""": """application/vnd.github+json""", """Authorization""": F"""Bearer {token}"""}
_a : List[Any] = F"""https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100"""
_a : List[str] = requests.get(UpperCamelCase__ , headers=UpperCamelCase__ ).json()
_a : int = {}
try:
job_time.update({job["""name"""]: extract_time_from_single_job(UpperCamelCase__ ) for job in result["""jobs"""]} )
_a : str = math.ceil((result["""total_count"""] - 1_0_0) / 1_0_0 )
for i in range(UpperCamelCase__ ):
_a : Optional[int] = requests.get(url + F"""&page={i + 2}""" , headers=UpperCamelCase__ ).json()
job_time.update({job["""name"""]: extract_time_from_single_job(UpperCamelCase__ ) for job in result["""jobs"""]} )
return job_time
except Exception:
print(F"""Unknown error, could not fetch links:\n{traceback.format_exc()}""" )
return {}
if __name__ == "__main__":
_snake_case = argparse.ArgumentParser()
# Required parameters
parser.add_argument('--workflow_run_id', type=str, required=True, help='A GitHub Actions workflow run id.')
_snake_case = parser.parse_args()
_snake_case = get_job_time(args.workflow_run_id)
_snake_case = dict(sorted(job_time.items(), key=lambda item: item[1]["duration"], reverse=True))
for k, v in job_time.items():
print(F'''{k}: {v['duration']}''')
| 324
|
"""simple docstring"""
import unittest
from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available
from ...test_tokenization_common import TokenizerTesterMixin
_snake_case = get_tests_dir('fixtures/test_sentencepiece.model')
_snake_case = get_tests_dir('fixtures/test_sentencepiece_bpe.model')
_snake_case = 'pt' if is_torch_available() else 'tf'
@require_sentencepiece
@require_tokenizers
class UpperCamelCase ( snake_case_ , unittest.TestCase ):
UpperCamelCase : str = CamembertTokenizer
UpperCamelCase : List[Any] = CamembertTokenizerFast
UpperCamelCase : Optional[int] = True
UpperCamelCase : Union[str, Any] = True
def _lowercase ( self : List[Any] ) -> Union[str, Any]:
super().setUp()
# We have a SentencePiece fixture for testing
_a : List[Any] = CamembertTokenizer(UpperCAmelCase__ )
tokenizer.save_pretrained(self.tmpdirname )
def _lowercase ( self : List[str] ) -> Tuple:
_a : Optional[Any] = """<pad>"""
_a : Tuple = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(UpperCAmelCase__ ) , UpperCAmelCase__ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(UpperCAmelCase__ ) , UpperCAmelCase__ )
def _lowercase ( self : Union[str, Any] ) -> str:
_a : List[str] = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , """<s>NOTUSED""" )
self.assertEqual(vocab_keys[1] , """<pad>""" )
self.assertEqual(vocab_keys[-1] , """<mask>""" )
self.assertEqual(len(UpperCAmelCase__ ) , 1004 )
def _lowercase ( self : List[str] ) -> List[Any]:
self.assertEqual(self.get_tokenizer().vocab_size , 1005 )
def _lowercase ( self : Union[str, Any] ) -> str:
_a : Tuple = CamembertTokenizer(UpperCAmelCase__ )
tokenizer.save_pretrained(self.tmpdirname )
_a : List[Any] = CamembertTokenizerFast.from_pretrained(self.tmpdirname )
_a : Any = """I was born in 92000, and this is falsé."""
_a : Union[str, Any] = tokenizer.encode(UpperCAmelCase__ )
_a : Dict = rust_tokenizer.encode(UpperCAmelCase__ )
self.assertListEqual(UpperCAmelCase__ , UpperCAmelCase__ )
_a : Tuple = tokenizer.encode(UpperCAmelCase__ , add_special_tokens=UpperCAmelCase__ )
_a : List[Any] = rust_tokenizer.encode(UpperCAmelCase__ , add_special_tokens=UpperCAmelCase__ )
self.assertListEqual(UpperCAmelCase__ , UpperCAmelCase__ )
# <unk> tokens are not the same for `rust` than for `slow`.
# Because spm gives back raw token instead of `unk` in EncodeAsPieces
# tokens = tokenizer.tokenize(sequence)
_a : List[str] = tokenizer.convert_ids_to_tokens(UpperCAmelCase__ )
_a : int = rust_tokenizer.tokenize(UpperCAmelCase__ )
self.assertListEqual(UpperCAmelCase__ , UpperCAmelCase__ )
def _lowercase ( self : Dict ) -> List[str]:
if not self.test_rust_tokenizer:
return
_a : Optional[int] = self.get_tokenizer()
_a : Tuple = self.get_rust_tokenizer()
_a : List[Any] = """I was born in 92000, and this is falsé."""
_a : List[str] = tokenizer.tokenize(UpperCAmelCase__ )
_a : Union[str, Any] = rust_tokenizer.tokenize(UpperCAmelCase__ )
self.assertListEqual(UpperCAmelCase__ , UpperCAmelCase__ )
_a : int = tokenizer.encode(UpperCAmelCase__ , add_special_tokens=UpperCAmelCase__ )
_a : Optional[int] = rust_tokenizer.encode(UpperCAmelCase__ , add_special_tokens=UpperCAmelCase__ )
self.assertListEqual(UpperCAmelCase__ , UpperCAmelCase__ )
_a : int = self.get_rust_tokenizer()
_a : Optional[Any] = tokenizer.encode(UpperCAmelCase__ )
_a : Dict = rust_tokenizer.encode(UpperCAmelCase__ )
self.assertListEqual(UpperCAmelCase__ , UpperCAmelCase__ )
@slow
def _lowercase ( self : Tuple ) -> List[Any]:
# fmt: off
_a : Dict = {"""input_ids""": [[5, 54, 7196, 297, 30, 23, 776, 18, 11, 3215, 3705, 8252, 22, 3164, 1181, 2116, 29, 16, 813, 25, 791, 3314, 20, 3446, 38, 27575, 120, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 468, 17, 11, 9088, 20, 1517, 8, 22804, 18818, 10, 38, 629, 607, 607, 142, 19, 7196, 867, 56, 10326, 24, 2267, 20, 416, 5072, 15612, 233, 734, 7, 2399, 27, 16, 3015, 1649, 7, 24, 20, 4338, 2399, 27, 13, 3400, 14, 13, 6189, 8, 930, 9, 6]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]} # noqa: E501
# fmt: on
# camembert is a french model. So we also use french texts.
_a : Union[str, Any] = [
"""Le transformeur est un modèle d'apprentissage profond introduit en 2017, """
"""utilisé principalement dans le domaine du traitement automatique des langues (TAL).""",
"""À l'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus """
"""pour gérer des données séquentielles, telles que le langage naturel, pour des tâches """
"""telles que la traduction et la synthèse de texte.""",
]
self.tokenizer_integration_test_util(
expected_encoding=UpperCAmelCase__ , model_name="""camembert-base""" , revision="""3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf""" , sequences=UpperCAmelCase__ , )
| 324
| 1
|
class lowercase__ :
'''simple docstring'''
def __init__( self, __magic_name__ ) -> None:
"""simple docstring"""
UpperCamelCase__ : int = size
UpperCamelCase__ : Union[str, Any] = [0] * size
UpperCamelCase__ : int = [0] * size
@staticmethod
def UpperCamelCase__ ( __magic_name__ ) -> int:
"""simple docstring"""
return index | (index + 1)
@staticmethod
def UpperCamelCase__ ( __magic_name__ ) -> int:
"""simple docstring"""
return (index & (index + 1)) - 1
def UpperCamelCase__ ( self, __magic_name__, __magic_name__ ) -> None:
"""simple docstring"""
UpperCamelCase__ : List[str] = value
while index < self.size:
UpperCamelCase__ : Any = self.get_prev(__magic_name__ ) + 1
if current_left_border == index:
UpperCamelCase__ : Union[str, Any] = value
else:
UpperCamelCase__ : List[str] = max(__magic_name__, __magic_name__, __magic_name__ )
UpperCamelCase__ : Union[str, Any] = self.get_next(__magic_name__ )
def UpperCamelCase__ ( self, __magic_name__, __magic_name__ ) -> int:
"""simple docstring"""
right -= 1 # Because of right is exclusive
UpperCamelCase__ : Union[str, Any] = 0
while left <= right:
UpperCamelCase__ : Dict = self.get_prev(__magic_name__ )
if left <= current_left:
UpperCamelCase__ : Dict = max(__magic_name__, self.tree[right] )
UpperCamelCase__ : Tuple = current_left
else:
UpperCamelCase__ : List[str] = max(__magic_name__, self.arr[right] )
right -= 1
return result
if __name__ == "__main__":
import doctest
doctest.testmod()
| 201
|
def lowerCAmelCase_ ( __UpperCAmelCase: int ) -> int:
if not isinstance(__UpperCAmelCase , __UpperCAmelCase ) or number < 0:
raise ValueError('''Input must be a non-negative integer''' )
UpperCamelCase__ : Optional[Any] = 0
while number:
# This way we arrive at next set bit (next 1) instead of looping
# through each bit and checking for 1s hence the
# loop won't run 32 times it will only run the number of `1` times
number &= number - 1
count += 1
return count
if __name__ == "__main__":
import doctest
doctest.testmod()
| 201
| 1
|
'''simple docstring'''
from __future__ import annotations
from math import pi, sqrt
def UpperCAmelCase_ ( __lowerCamelCase : float ,__lowerCamelCase : float ):
if inductance <= 0:
raise ValueError("Inductance cannot be 0 or negative" )
elif capacitance <= 0:
raise ValueError("Capacitance cannot be 0 or negative" )
else:
return (
"Resonant frequency",
float(1 / (2 * pi * (sqrt(inductance * capacitance ))) ),
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 370
|
'''simple docstring'''
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class a_ ( _lowerCAmelCase ):
__A = ["image_processor", "tokenizer"]
__A = "LayoutLMv3ImageProcessor"
__A = ("LayoutLMv3Tokenizer", "LayoutLMv3TokenizerFast")
def __init__( self : int , lowercase : Optional[Any]=None , lowercase : List[str]=None , **lowercase : Optional[int] ):
"""simple docstring"""
lowercase_ :int = None
if "feature_extractor" in kwargs:
warnings.warn(
"The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
" instead." , lowercase , )
lowercase_ :Optional[int] = kwargs.pop("feature_extractor" )
lowercase_ :Union[str, Any] = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError("You need to specify an `image_processor`." )
if tokenizer is None:
raise ValueError("You need to specify a `tokenizer`." )
super().__init__(lowercase , lowercase )
def __call__( self : Optional[Any] , lowercase : List[str] , lowercase : Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None , lowercase : Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None , lowercase : Union[List[List[int]], List[List[List[int]]]] = None , lowercase : Optional[Union[List[int], List[List[int]]]] = None , lowercase : bool = True , lowercase : Union[bool, str, PaddingStrategy] = False , lowercase : Union[bool, str, TruncationStrategy] = None , lowercase : Optional[int] = None , lowercase : int = 0 , lowercase : Optional[int] = None , lowercase : Optional[bool] = None , lowercase : Optional[bool] = None , lowercase : bool = False , lowercase : bool = False , lowercase : bool = False , lowercase : bool = False , lowercase : bool = True , lowercase : Optional[Union[str, TensorType]] = None , **lowercase : List[Any] , ):
"""simple docstring"""
if self.image_processor.apply_ocr and (boxes is not None):
raise ValueError(
"You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True." )
if self.image_processor.apply_ocr and (word_labels is not None):
raise ValueError(
"You cannot provide word labels if you initialized the image processor with apply_ocr set to True." )
# first, apply the image processor
lowercase_ :Dict = self.image_processor(images=lowercase , return_tensors=lowercase )
# second, apply the tokenizer
if text is not None and self.image_processor.apply_ocr and text_pair is None:
if isinstance(lowercase , lowercase ):
lowercase_ :str = [text] # add batch dimension (as the image processor always adds a batch dimension)
lowercase_ :Union[str, Any] = features["words"]
lowercase_ :Optional[Any] = self.tokenizer(
text=text if text is not None else features["words"] , text_pair=text_pair if text_pair is not None else None , boxes=boxes if boxes is not None else features["boxes"] , word_labels=lowercase , add_special_tokens=lowercase , padding=lowercase , truncation=lowercase , max_length=lowercase , stride=lowercase , pad_to_multiple_of=lowercase , return_token_type_ids=lowercase , return_attention_mask=lowercase , return_overflowing_tokens=lowercase , return_special_tokens_mask=lowercase , return_offsets_mapping=lowercase , return_length=lowercase , verbose=lowercase , return_tensors=lowercase , **lowercase , )
# add pixel values
lowercase_ :Any = features.pop("pixel_values" )
if return_overflowing_tokens is True:
lowercase_ :Any = self.get_overflowing_images(lowercase , encoded_inputs["overflow_to_sample_mapping"] )
lowercase_ :Any = images
return encoded_inputs
def lowercase__ ( self : List[Any] , lowercase : Any , lowercase : Optional[Any] ):
"""simple docstring"""
lowercase_ :Union[str, Any] = []
for sample_idx in overflow_to_sample_mapping:
images_with_overflow.append(images[sample_idx] )
if len(lowercase ) != len(lowercase ):
raise ValueError(
"Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"
F' {len(lowercase )} and {len(lowercase )}' )
return images_with_overflow
def lowercase__ ( self : Union[str, Any] , *lowercase : List[Any] , **lowercase : Optional[Any] ):
"""simple docstring"""
return self.tokenizer.batch_decode(*lowercase , **lowercase )
def lowercase__ ( self : List[Any] , *lowercase : Any , **lowercase : str ):
"""simple docstring"""
return self.tokenizer.decode(*lowercase , **lowercase )
@property
def lowercase__ ( self : Optional[Any] ):
"""simple docstring"""
return ["input_ids", "bbox", "attention_mask", "pixel_values"]
@property
def lowercase__ ( self : str ):
"""simple docstring"""
warnings.warn(
"`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead." , lowercase , )
return self.image_processor_class
@property
def lowercase__ ( self : Tuple ):
"""simple docstring"""
warnings.warn(
"`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead." , lowercase , )
return self.image_processor
| 147
| 0
|
from typing import TYPE_CHECKING
from ...utils import _LazyModule
lowercase = {"processing_wav2vec2_with_lm": ["Wav2Vec2ProcessorWithLM"]}
if TYPE_CHECKING:
from .processing_wavaveca_with_lm import WavaVecaProcessorWithLM
else:
import sys
lowercase = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 178
|
import collections
import importlib.util
import os
import re
from pathlib import Path
lowercase = "src/transformers"
# Matches is_xxx_available()
lowercase = re.compile(r"is\_([a-z_]*)_available()")
# Catches a one-line _import_struct = {xxx}
lowercase = re.compile(r"^_import_structure\s+=\s+\{([^\}]+)\}")
# Catches a line with a key-values pattern: "bla": ["foo", "bar"]
lowercase = re.compile(r"\s+\"\S*\":\s+\[([^\]]*)\]")
# Catches a line if not is_foo_available
lowercase = re.compile(r"^\s*if\s+not\s+is\_[a-z_]*\_available\(\)")
# Catches a line _import_struct["bla"].append("foo")
lowercase = re.compile(r"^\s*_import_structure\[\"\S*\"\]\.append\(\"(\S*)\"\)")
# Catches a line _import_struct["bla"].extend(["foo", "bar"]) or _import_struct["bla"] = ["foo", "bar"]
lowercase = re.compile(r"^\s*_import_structure\[\S*\](?:\.extend\(|\s*=\s+)\[([^\]]*)\]")
# Catches a line with an object between quotes and a comma: "MyModel",
lowercase = re.compile("^\s+\"([^\"]+)\",")
# Catches a line with objects between brackets only: ["foo", "bar"],
lowercase = re.compile("^\s+\[([^\]]+)\]")
# Catches a line with from foo import bar, bla, boo
lowercase = re.compile(r"\s+from\s+\S*\s+import\s+([^\(\s].*)\n")
# Catches a line with try:
lowercase = re.compile(r"^\s*try:")
# Catches a line with else:
lowercase = re.compile(r"^\s*else:")
def __UpperCAmelCase ( a_):
if _re_test_backend.search(a_) is None:
return None
snake_case_ = [b[0] for b in _re_backend.findall(a_)]
backends.sort()
return "_and_".join(a_)
def __UpperCAmelCase ( a_):
with open(a_ , 'r' , encoding='utf-8' , newline='\n') as f:
snake_case_ = f.readlines()
snake_case_ = 0
while line_index < len(a_) and not lines[line_index].startswith('_import_structure = {'):
line_index += 1
# If this is a traditional init, just return.
if line_index >= len(a_):
return None
# First grab the objects without a specific backend in _import_structure
snake_case_ = []
while not lines[line_index].startswith('if TYPE_CHECKING') and find_backend(lines[line_index]) is None:
snake_case_ = lines[line_index]
# If we have everything on a single line, let's deal with it.
if _re_one_line_import_struct.search(a_):
snake_case_ = _re_one_line_import_struct.search(a_).groups()[0]
snake_case_ = re.findall('\[([^\]]+)\]' , a_)
for imp in imports:
objects.extend([obj[1:-1] for obj in imp.split(', ')])
line_index += 1
continue
snake_case_ = _re_import_struct_key_value.search(a_)
if single_line_import_search is not None:
snake_case_ = [obj[1:-1] for obj in single_line_import_search.groups()[0].split(', ') if len(a_) > 0]
objects.extend(a_)
elif line.startswith(' ' * 8 + '"'):
objects.append(line[9:-3])
line_index += 1
snake_case_ = {'none': objects}
# Let's continue with backend-specific objects in _import_structure
while not lines[line_index].startswith('if TYPE_CHECKING'):
# If the line is an if not is_backend_available, we grab all objects associated.
snake_case_ = find_backend(lines[line_index])
# Check if the backend declaration is inside a try block:
if _re_try.search(lines[line_index - 1]) is None:
snake_case_ = None
if backend is not None:
line_index += 1
# Scroll until we hit the else block of try-except-else
while _re_else.search(lines[line_index]) is None:
line_index += 1
line_index += 1
snake_case_ = []
# Until we unindent, add backend objects to the list
while len(lines[line_index]) <= 1 or lines[line_index].startswith(' ' * 4):
snake_case_ = lines[line_index]
if _re_import_struct_add_one.search(a_) is not None:
objects.append(_re_import_struct_add_one.search(a_).groups()[0])
elif _re_import_struct_add_many.search(a_) is not None:
snake_case_ = _re_import_struct_add_many.search(a_).groups()[0].split(', ')
snake_case_ = [obj[1:-1] for obj in imports if len(a_) > 0]
objects.extend(a_)
elif _re_between_brackets.search(a_) is not None:
snake_case_ = _re_between_brackets.search(a_).groups()[0].split(', ')
snake_case_ = [obj[1:-1] for obj in imports if len(a_) > 0]
objects.extend(a_)
elif _re_quote_object.search(a_) is not None:
objects.append(_re_quote_object.search(a_).groups()[0])
elif line.startswith(' ' * 8 + '"'):
objects.append(line[9:-3])
elif line.startswith(' ' * 12 + '"'):
objects.append(line[13:-3])
line_index += 1
snake_case_ = objects
else:
line_index += 1
# At this stage we are in the TYPE_CHECKING part, first grab the objects without a specific backend
snake_case_ = []
while (
line_index < len(a_)
and find_backend(lines[line_index]) is None
and not lines[line_index].startswith('else')
):
snake_case_ = lines[line_index]
snake_case_ = _re_import.search(a_)
if single_line_import_search is not None:
objects.extend(single_line_import_search.groups()[0].split(', '))
elif line.startswith(' ' * 8):
objects.append(line[8:-2])
line_index += 1
snake_case_ = {'none': objects}
# Let's continue with backend-specific objects
while line_index < len(a_):
# If the line is an if is_backend_available, we grab all objects associated.
snake_case_ = find_backend(lines[line_index])
# Check if the backend declaration is inside a try block:
if _re_try.search(lines[line_index - 1]) is None:
snake_case_ = None
if backend is not None:
line_index += 1
# Scroll until we hit the else block of try-except-else
while _re_else.search(lines[line_index]) is None:
line_index += 1
line_index += 1
snake_case_ = []
# Until we unindent, add backend objects to the list
while len(lines[line_index]) <= 1 or lines[line_index].startswith(' ' * 8):
snake_case_ = lines[line_index]
snake_case_ = _re_import.search(a_)
if single_line_import_search is not None:
objects.extend(single_line_import_search.groups()[0].split(', '))
elif line.startswith(' ' * 12):
objects.append(line[12:-2])
line_index += 1
snake_case_ = objects
else:
line_index += 1
return import_dict_objects, type_hint_objects
def __UpperCAmelCase ( a_ , a_):
def find_duplicates(a_):
return [k for k, v in collections.Counter(a_).items() if v > 1]
if list(import_dict_objects.keys()) != list(type_hint_objects.keys()):
return ["Both sides of the init do not have the same backends!"]
snake_case_ = []
for key in import_dict_objects.keys():
snake_case_ = find_duplicates(import_dict_objects[key])
if duplicate_imports:
errors.append(f'''Duplicate _import_structure definitions for: {duplicate_imports}''')
snake_case_ = find_duplicates(type_hint_objects[key])
if duplicate_type_hints:
errors.append(f'''Duplicate TYPE_CHECKING objects for: {duplicate_type_hints}''')
if sorted(set(import_dict_objects[key])) != sorted(set(type_hint_objects[key])):
snake_case_ = 'base imports' if key == 'none' else f'''{key} backend'''
errors.append(f'''Differences for {name}:''')
for a in type_hint_objects[key]:
if a not in import_dict_objects[key]:
errors.append(f''' {a} in TYPE_HINT but not in _import_structure.''')
for a in import_dict_objects[key]:
if a not in type_hint_objects[key]:
errors.append(f''' {a} in _import_structure but not in TYPE_HINT.''')
return errors
def __UpperCAmelCase ( ):
snake_case_ = []
for root, _, files in os.walk(a_):
if "__init__.py" in files:
snake_case_ = os.path.join(a_ , '__init__.py')
snake_case_ = parse_init(a_)
if objects is not None:
snake_case_ = analyze_results(*a_)
if len(a_) > 0:
snake_case_ = f'''Problem in {fname}, both halves do not define the same objects.\n{errors[0]}'''
failures.append('\n'.join(a_))
if len(a_) > 0:
raise ValueError('\n\n'.join(a_))
def __UpperCAmelCase ( ):
snake_case_ = []
for path, directories, files in os.walk(a_):
for folder in directories:
# Ignore private modules
if folder.startswith('_'):
directories.remove(a_)
continue
# Ignore leftovers from branches (empty folders apart from pycache)
if len(list((Path(a_) / folder).glob('*.py'))) == 0:
continue
snake_case_ = str((Path(a_) / folder).relative_to(a_))
snake_case_ = short_path.replace(os.path.sep , '.')
submodules.append(a_)
for fname in files:
if fname == "__init__.py":
continue
snake_case_ = str((Path(a_) / fname).relative_to(a_))
snake_case_ = short_path.replace('.py' , '').replace(os.path.sep , '.')
if len(submodule.split('.')) == 1:
submodules.append(a_)
return submodules
lowercase = [
"convert_pytorch_checkpoint_to_tf2",
"modeling_flax_pytorch_utils",
]
def __UpperCAmelCase ( ):
# This is to make sure the transformers module imported is the one in the repo.
snake_case_ = importlib.util.spec_from_file_location(
'transformers' , os.path.join(a_ , '__init__.py') , submodule_search_locations=[PATH_TO_TRANSFORMERS] , )
snake_case_ = spec.loader.load_module()
snake_case_ = [
module
for module in get_transformers_submodules()
if module not in IGNORE_SUBMODULES and module not in transformers._import_structure.keys()
]
if len(a_) > 0:
snake_case_ = '\n'.join(f'''- {module}''' for module in module_not_registered)
raise ValueError(
'The following submodules are not properly registered in the main init of Transformers:\n'
f'''{list_of_modules}\n'''
'Make sure they appear somewhere in the keys of `_import_structure` with an empty list as value.')
if __name__ == "__main__":
check_all_inits()
check_submodules()
| 178
| 1
|
import numpy as np
import skfuzzy as fuzz
if __name__ == "__main__":
# Create universe of discourse in Python using linspace ()
lowerCAmelCase__ : Optional[int] =np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)
# Create two fuzzy sets by defining any membership function
# (trapmf(), gbellmf(), gaussmf(), etc).
lowerCAmelCase__ : Optional[int] =[0, 25, 50]
lowerCAmelCase__ : str =[25, 50, 75]
lowerCAmelCase__ : List[Any] =fuzz.membership.trimf(X, abca)
lowerCAmelCase__ : int =fuzz.membership.trimf(X, abca)
# Compute the different operations using inbuilt functions.
lowerCAmelCase__ : str =np.ones(75)
lowerCAmelCase__ : List[Any] =np.zeros((75,))
# 1. Union = max(µA(x), µB(x))
lowerCAmelCase__ : Any =fuzz.fuzzy_or(X, young, X, middle_aged)[1]
# 2. Intersection = min(µA(x), µB(x))
lowerCAmelCase__ : Any =fuzz.fuzzy_and(X, young, X, middle_aged)[1]
# 3. Complement (A) = (1- min(µA(x))
lowerCAmelCase__ : Any =fuzz.fuzzy_not(young)
# 4. Difference (A/B) = min(µA(x),(1- µB(x)))
lowerCAmelCase__ : str =fuzz.fuzzy_and(X, young, X, fuzz.fuzzy_not(middle_aged)[1])[1]
# 5. Algebraic Sum = [µA(x) + µB(x) – (µA(x) * µB(x))]
lowerCAmelCase__ : Any =young + middle_aged - (young * middle_aged)
# 6. Algebraic Product = (µA(x) * µB(x))
lowerCAmelCase__ : Any =young * middle_aged
# 7. Bounded Sum = min[1,(µA(x), µB(x))]
lowerCAmelCase__ : Union[str, Any] =fuzz.fuzzy_and(X, one, X, young + middle_aged)[1]
# 8. Bounded difference = min[0,(µA(x), µB(x))]
lowerCAmelCase__ : Any =fuzz.fuzzy_or(X, zero, X, young - middle_aged)[1]
# max-min composition
# max-product composition
# Plot each set A, set B and each operation result using plot() and subplot().
from matplotlib import pyplot as plt
plt.figure()
plt.subplot(4, 3, 1)
plt.plot(X, young)
plt.title('''Young''')
plt.grid(True)
plt.subplot(4, 3, 2)
plt.plot(X, middle_aged)
plt.title('''Middle aged''')
plt.grid(True)
plt.subplot(4, 3, 3)
plt.plot(X, union)
plt.title('''union''')
plt.grid(True)
plt.subplot(4, 3, 4)
plt.plot(X, intersection)
plt.title('''intersection''')
plt.grid(True)
plt.subplot(4, 3, 5)
plt.plot(X, complement_a)
plt.title('''complement_a''')
plt.grid(True)
plt.subplot(4, 3, 6)
plt.plot(X, difference)
plt.title('''difference a/b''')
plt.grid(True)
plt.subplot(4, 3, 7)
plt.plot(X, alg_sum)
plt.title('''alg_sum''')
plt.grid(True)
plt.subplot(4, 3, 8)
plt.plot(X, alg_product)
plt.title('''alg_product''')
plt.grid(True)
plt.subplot(4, 3, 9)
plt.plot(X, bdd_sum)
plt.title('''bdd_sum''')
plt.grid(True)
plt.subplot(4, 3, 10)
plt.plot(X, bdd_difference)
plt.title('''bdd_difference''')
plt.grid(True)
plt.subplots_adjust(hspace=0.5)
plt.show()
| 118
|
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ : Dict =logging.get_logger(__name__)
lowerCAmelCase__ : Optional[int] ={
'''asapp/sew-tiny-100k''': '''https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json''',
# See all SEW models at https://huggingface.co/models?filter=sew
}
class UpperCAmelCase_ ( UpperCamelCase_ ):
'''simple docstring'''
UpperCamelCase__ : Optional[Any] = '''sew'''
def __init__( self , _A=32 , _A=768 , _A=12 , _A=12 , _A=3_072 , _A=2 , _A="gelu" , _A=0.1 , _A=0.1 , _A=0.1 , _A=0.0 , _A=0.1 , _A=0.1 , _A=0.0_2 , _A=1e-5 , _A="group" , _A="gelu" , _A=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512) , _A=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1) , _A=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1) , _A=False , _A=128 , _A=16 , _A=True , _A=0.0_5 , _A=10 , _A=2 , _A=0.0 , _A=10 , _A=0 , _A="mean" , _A=False , _A=False , _A=256 , _A=0 , _A=1 , _A=2 , **_A , ):
'''simple docstring'''
super().__init__(**_A , pad_token_id=_A , bos_token_id=_A , eos_token_id=_A )
__SCREAMING_SNAKE_CASE = hidden_size
__SCREAMING_SNAKE_CASE = feat_extract_norm
__SCREAMING_SNAKE_CASE = feat_extract_activation
__SCREAMING_SNAKE_CASE = list(_A )
__SCREAMING_SNAKE_CASE = list(_A )
__SCREAMING_SNAKE_CASE = list(_A )
__SCREAMING_SNAKE_CASE = conv_bias
__SCREAMING_SNAKE_CASE = num_conv_pos_embeddings
__SCREAMING_SNAKE_CASE = num_conv_pos_embedding_groups
__SCREAMING_SNAKE_CASE = len(self.conv_dim )
__SCREAMING_SNAKE_CASE = num_hidden_layers
__SCREAMING_SNAKE_CASE = intermediate_size
__SCREAMING_SNAKE_CASE = squeeze_factor
__SCREAMING_SNAKE_CASE = hidden_act
__SCREAMING_SNAKE_CASE = num_attention_heads
__SCREAMING_SNAKE_CASE = hidden_dropout
__SCREAMING_SNAKE_CASE = attention_dropout
__SCREAMING_SNAKE_CASE = activation_dropout
__SCREAMING_SNAKE_CASE = feat_proj_dropout
__SCREAMING_SNAKE_CASE = final_dropout
__SCREAMING_SNAKE_CASE = layerdrop
__SCREAMING_SNAKE_CASE = layer_norm_eps
__SCREAMING_SNAKE_CASE = initializer_range
__SCREAMING_SNAKE_CASE = vocab_size
if (
(len(self.conv_stride ) != self.num_feat_extract_layers)
or (len(self.conv_kernel ) != self.num_feat_extract_layers)
or (len(self.conv_dim ) != self.num_feat_extract_layers)
):
raise ValueError(
'Configuration for convolutional layers is incorrect.'
'It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,'
f"""but is `len(config.conv_dim) = {len(self.conv_dim )}`, `len(config.conv_stride)"""
f"""= {len(self.conv_stride )}`, `len(config.conv_kernel) = {len(self.conv_kernel )}`.""" )
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
__SCREAMING_SNAKE_CASE = apply_spec_augment
__SCREAMING_SNAKE_CASE = mask_time_prob
__SCREAMING_SNAKE_CASE = mask_time_length
__SCREAMING_SNAKE_CASE = mask_time_min_masks
__SCREAMING_SNAKE_CASE = mask_feature_prob
__SCREAMING_SNAKE_CASE = mask_feature_length
__SCREAMING_SNAKE_CASE = mask_feature_min_masks
# ctc loss
__SCREAMING_SNAKE_CASE = ctc_loss_reduction
__SCREAMING_SNAKE_CASE = ctc_zero_infinity
# sequence classification
__SCREAMING_SNAKE_CASE = use_weighted_layer_sum
__SCREAMING_SNAKE_CASE = classifier_proj_size
@property
def _A ( self ):
'''simple docstring'''
return functools.reduce(operator.mul , self.conv_stride , 1 )
| 118
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"""simple docstring"""
import argparse
from collections import defaultdict
import yaml
_UpperCamelCase : str = 'docs/source/en/_toctree.yml'
def _SCREAMING_SNAKE_CASE ( __snake_case : List[Any] ):
'''simple docstring'''
lowercase = defaultdict(__snake_case )
lowercase = []
lowercase = []
for doc in doc_list:
if "local" in doc:
counts[doc["local"]] += 1
if doc["title"].lower() == "overview":
overview_doc.append({'local': doc['local'], 'title': doc['title']} )
else:
new_doc_list.append(__snake_case )
lowercase = new_doc_list
lowercase = [key for key, value in counts.items() if value > 1]
lowercase = []
for duplicate_key in duplicates:
lowercase = list({doc['title'] for doc in doc_list if doc['local'] == duplicate_key} )
if len(__snake_case ) > 1:
raise ValueError(
f'{duplicate_key} is present several times in the documentation table of content at '
'`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the '
'others.' )
# Only add this once
new_doc.append({'local': duplicate_key, 'title': titles[0]} )
# Add none duplicate-keys
new_doc.extend([doc for doc in doc_list if 'local' not in counts or counts[doc['local']] == 1] )
lowercase = sorted(__snake_case , key=lambda __snake_case : s["title"].lower() )
# "overview" gets special treatment and is always first
if len(__snake_case ) > 1:
raise ValueError('{doc_list} has two \'overview\' docs which is not allowed.' )
overview_doc.extend(__snake_case )
# Sort
return overview_doc
def _SCREAMING_SNAKE_CASE ( __snake_case : Optional[int]=False ):
'''simple docstring'''
with open(__snake_case , encoding='utf-8' ) as f:
lowercase = yaml.safe_load(f.read() )
# Get to the API doc
lowercase = 0
while content[api_idx]["title"] != "API":
api_idx += 1
lowercase = content[api_idx]['sections']
# Then to the model doc
lowercase = 0
while api_doc[scheduler_idx]["title"] != "Schedulers":
scheduler_idx += 1
lowercase = api_doc[scheduler_idx]['sections']
lowercase = clean_doc_toc(__snake_case )
lowercase = False
if new_scheduler_doc != scheduler_doc:
lowercase = True
if overwrite:
lowercase = new_scheduler_doc
if diff:
if overwrite:
lowercase = api_doc
with open(__snake_case , 'w' , encoding='utf-8' ) as f:
f.write(yaml.dump(__snake_case , allow_unicode=__snake_case ) )
else:
raise ValueError(
'The model doc part of the table of content is not properly sorted, run `make style` to fix this.' )
def _SCREAMING_SNAKE_CASE ( __snake_case : int=False ):
'''simple docstring'''
with open(__snake_case , encoding='utf-8' ) as f:
lowercase = yaml.safe_load(f.read() )
# Get to the API doc
lowercase = 0
while content[api_idx]["title"] != "API":
api_idx += 1
lowercase = content[api_idx]['sections']
# Then to the model doc
lowercase = 0
while api_doc[pipeline_idx]["title"] != "Pipelines":
pipeline_idx += 1
lowercase = False
lowercase = api_doc[pipeline_idx]['sections']
lowercase = []
# sort sub pipeline docs
for pipeline_doc in pipeline_docs:
if "section" in pipeline_doc:
lowercase = pipeline_doc['section']
lowercase = clean_doc_toc(__snake_case )
if overwrite:
lowercase = new_sub_pipeline_doc
new_pipeline_docs.append(__snake_case )
# sort overall pipeline doc
lowercase = clean_doc_toc(__snake_case )
if new_pipeline_docs != pipeline_docs:
lowercase = True
if overwrite:
lowercase = new_pipeline_docs
if diff:
if overwrite:
lowercase = api_doc
with open(__snake_case , 'w' , encoding='utf-8' ) as f:
f.write(yaml.dump(__snake_case , allow_unicode=__snake_case ) )
else:
raise ValueError(
'The model doc part of the table of content is not properly sorted, run `make style` to fix this.' )
if __name__ == "__main__":
_UpperCamelCase : Optional[Any] = argparse.ArgumentParser()
parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
_UpperCamelCase : Any = parser.parse_args()
check_scheduler_doc(args.fix_and_overwrite)
check_pipeline_doc(args.fix_and_overwrite)
| 220
|
"""simple docstring"""
import argparse
import json
from tqdm import tqdm
def _SCREAMING_SNAKE_CASE ( ):
'''simple docstring'''
lowercase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--src_path' , type=__snake_case , default='biencoder-nq-dev.json' , help='Path to raw DPR training data' , )
parser.add_argument(
'--evaluation_set' , type=__snake_case , help='where to store parsed evaluation_set file' , )
parser.add_argument(
'--gold_data_path' , type=__snake_case , help='where to store parsed gold_data_path file' , )
lowercase = parser.parse_args()
with open(args.src_path , 'r' ) as src_file, open(args.evaluation_set , 'w' ) as eval_file, open(
args.gold_data_path , 'w' ) as gold_file:
lowercase = json.load(__snake_case )
for dpr_record in tqdm(__snake_case ):
lowercase = dpr_record['question']
lowercase = [context['title'] for context in dpr_record['positive_ctxs']]
eval_file.write(question + '\n' )
gold_file.write('\t'.join(__snake_case ) + '\n' )
if __name__ == "__main__":
main()
| 220
| 1
|
"""simple docstring"""
from typing import List
import datasets
from datasets.tasks import AudioClassification
from ..folder_based_builder import folder_based_builder
_a : str = datasets.utils.logging.get_logger(__name__)
class __A ( folder_based_builder.FolderBasedBuilderConfig ):
_UpperCamelCase : bool = None
_UpperCamelCase : bool = None
class __A ( folder_based_builder.FolderBasedBuilder ):
_UpperCamelCase : str = datasets.Audio()
_UpperCamelCase : Optional[int] = "audio"
_UpperCamelCase : int = AudioFolderConfig
_UpperCamelCase : List[str] # definition at the bottom of the script
_UpperCamelCase : Optional[int] = AudioClassification(audio_column="audio" , label_column="label" )
_a : Any = [
'.aiff',
'.au',
'.avr',
'.caf',
'.flac',
'.htk',
'.svx',
'.mat4',
'.mat5',
'.mpc2k',
'.ogg',
'.paf',
'.pvf',
'.raw',
'.rf64',
'.sd2',
'.sds',
'.ircam',
'.voc',
'.w64',
'.wav',
'.nist',
'.wavex',
'.wve',
'.xi',
'.mp3',
'.opus',
]
_a : Optional[int] = AUDIO_EXTENSIONS
| 126
|
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_a : Any = logging.get_logger(__name__)
_a : Any = {
'uclanlp/visualbert-vqa': 'https://huggingface.co/uclanlp/visualbert-vqa/resolve/main/config.json',
'uclanlp/visualbert-vqa-pre': 'https://huggingface.co/uclanlp/visualbert-vqa-pre/resolve/main/config.json',
'uclanlp/visualbert-vqa-coco-pre': (
'https://huggingface.co/uclanlp/visualbert-vqa-coco-pre/resolve/main/config.json'
),
'uclanlp/visualbert-vcr': 'https://huggingface.co/uclanlp/visualbert-vcr/resolve/main/config.json',
'uclanlp/visualbert-vcr-pre': 'https://huggingface.co/uclanlp/visualbert-vcr-pre/resolve/main/config.json',
'uclanlp/visualbert-vcr-coco-pre': (
'https://huggingface.co/uclanlp/visualbert-vcr-coco-pre/resolve/main/config.json'
),
'uclanlp/visualbert-nlvr2': 'https://huggingface.co/uclanlp/visualbert-nlvr2/resolve/main/config.json',
'uclanlp/visualbert-nlvr2-pre': 'https://huggingface.co/uclanlp/visualbert-nlvr2-pre/resolve/main/config.json',
'uclanlp/visualbert-nlvr2-coco-pre': (
'https://huggingface.co/uclanlp/visualbert-nlvr2-coco-pre/resolve/main/config.json'
)
# See all VisualBERT models at https://huggingface.co/models?filter=visual_bert
}
class __A ( SCREAMING_SNAKE_CASE_ ):
_UpperCamelCase : List[Any] = "visual_bert"
def __init__( self , a__=30522 , a__=768 , a__=512 , a__=12 , a__=12 , a__=3072 , a__="gelu" , a__=0.1 , a__=0.1 , a__=512 , a__=2 , a__=0.0_2 , a__=1e-12 , a__=False , a__=True , a__=1 , a__=0 , a__=2 , **a__ , ):
super().__init__(pad_token_id=a__ , bos_token_id=a__ , eos_token_id=a__ , **a__ )
_lowerCAmelCase : Optional[Any] = vocab_size
_lowerCAmelCase : int = max_position_embeddings
_lowerCAmelCase : Optional[Any] = hidden_size
_lowerCAmelCase : Optional[int] = visual_embedding_dim
_lowerCAmelCase : Optional[int] = num_hidden_layers
_lowerCAmelCase : List[Any] = num_attention_heads
_lowerCAmelCase : Any = intermediate_size
_lowerCAmelCase : List[Any] = hidden_act
_lowerCAmelCase : int = hidden_dropout_prob
_lowerCAmelCase : Optional[Any] = attention_probs_dropout_prob
_lowerCAmelCase : Any = initializer_range
_lowerCAmelCase : List[Any] = type_vocab_size
_lowerCAmelCase : Union[str, Any] = layer_norm_eps
_lowerCAmelCase : Optional[Any] = bypass_transformer
_lowerCAmelCase : List[Any] = special_visual_initialize
| 126
| 1
|
import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
__lowerCAmelCase : Tuple =logging.get_logger(__name__)
__lowerCAmelCase : List[Any] ={
'vocab_file': 'vocab.json',
'merges_file': 'merges.txt',
'tokenizer_config_file': 'tokenizer_config.json',
}
__lowerCAmelCase : Dict ={
'vocab_file': {'facebook/blenderbot-3B': 'https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json'},
'merges_file': {'facebook/blenderbot-3B': 'https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt'},
'tokenizer_config_file': {
'facebook/blenderbot-3B': 'https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json'
},
}
__lowerCAmelCase : Optional[int] ={'facebook/blenderbot-3B': 1_2_8}
@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def _UpperCamelCase ( ):
__SCREAMING_SNAKE_CASE : int = (
list(range(ord('''!''' ) , ord('''~''' ) + 1 ) ) + list(range(ord('''¡''' ) , ord('''¬''' ) + 1 ) ) + list(range(ord('''®''' ) , ord('''ÿ''' ) + 1 ) )
)
__SCREAMING_SNAKE_CASE : List[Any] = bs[:]
__SCREAMING_SNAKE_CASE : str = 0
for b in range(2**8 ):
if b not in bs:
bs.append(lowercase__ )
cs.append(2**8 + n )
n += 1
__SCREAMING_SNAKE_CASE : str = [chr(lowercase__ ) for n in cs]
return dict(zip(lowercase__ , lowercase__ ) )
def _UpperCamelCase ( lowercase__ ):
__SCREAMING_SNAKE_CASE : int = set()
__SCREAMING_SNAKE_CASE : int = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
__SCREAMING_SNAKE_CASE : str = char
return pairs
class _lowercase ( A__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[Any] = VOCAB_FILES_NAMES
SCREAMING_SNAKE_CASE__ : Any = PRETRAINED_VOCAB_FILES_MAP
SCREAMING_SNAKE_CASE__ : Optional[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
SCREAMING_SNAKE_CASE__ : List[str] = ['''input_ids''', '''attention_mask''']
def __init__( self :List[Any] , lowerCAmelCase__ :str , lowerCAmelCase__ :str , lowerCAmelCase__ :Optional[Any]="replace" , lowerCAmelCase__ :List[str]="<s>" , lowerCAmelCase__ :Union[str, Any]="</s>" , lowerCAmelCase__ :Optional[Any]="</s>" , lowerCAmelCase__ :Optional[Any]="<s>" , lowerCAmelCase__ :str="<unk>" , lowerCAmelCase__ :int="<pad>" , lowerCAmelCase__ :Tuple="<mask>" , lowerCAmelCase__ :int=False , **lowerCAmelCase__ :int , ) -> Dict:
__SCREAMING_SNAKE_CASE : Optional[Any] = AddedToken(lowerCAmelCase__ , lstrip=lowerCAmelCase__ , rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ , lowerCAmelCase__ ) else bos_token
__SCREAMING_SNAKE_CASE : List[str] = AddedToken(lowerCAmelCase__ , lstrip=lowerCAmelCase__ , rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ , lowerCAmelCase__ ) else eos_token
__SCREAMING_SNAKE_CASE : Tuple = AddedToken(lowerCAmelCase__ , lstrip=lowerCAmelCase__ , rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ , lowerCAmelCase__ ) else sep_token
__SCREAMING_SNAKE_CASE : Dict = AddedToken(lowerCAmelCase__ , lstrip=lowerCAmelCase__ , rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ , lowerCAmelCase__ ) else cls_token
__SCREAMING_SNAKE_CASE : Any = AddedToken(lowerCAmelCase__ , lstrip=lowerCAmelCase__ , rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ , lowerCAmelCase__ ) else unk_token
__SCREAMING_SNAKE_CASE : Union[str, Any] = AddedToken(lowerCAmelCase__ , lstrip=lowerCAmelCase__ , rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ , lowerCAmelCase__ ) else pad_token
# Mask token behave like a normal word, i.e. include the space before it
__SCREAMING_SNAKE_CASE : Tuple = AddedToken(lowerCAmelCase__ , lstrip=lowerCAmelCase__ , rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ , lowerCAmelCase__ ) else mask_token
super().__init__(
errors=lowerCAmelCase__ , bos_token=lowerCAmelCase__ , eos_token=lowerCAmelCase__ , unk_token=lowerCAmelCase__ , sep_token=lowerCAmelCase__ , cls_token=lowerCAmelCase__ , pad_token=lowerCAmelCase__ , mask_token=lowerCAmelCase__ , add_prefix_space=lowerCAmelCase__ , **lowerCAmelCase__ , )
with open(lowerCAmelCase__ , encoding='''utf-8''' ) as vocab_handle:
__SCREAMING_SNAKE_CASE : str = json.load(lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : Any = {v: k for k, v in self.encoder.items()}
__SCREAMING_SNAKE_CASE : Dict = errors # how to handle errors in decoding
__SCREAMING_SNAKE_CASE : int = bytes_to_unicode()
__SCREAMING_SNAKE_CASE : int = {v: k for k, v in self.byte_encoder.items()}
with open(lowerCAmelCase__ , encoding='''utf-8''' ) as merges_handle:
__SCREAMING_SNAKE_CASE : Tuple = merges_handle.read().split('''\n''' )[1:-1]
__SCREAMING_SNAKE_CASE : List[str] = [tuple(merge.split() ) for merge in bpe_merges]
__SCREAMING_SNAKE_CASE : Tuple = dict(zip(lowerCAmelCase__ , range(len(lowerCAmelCase__ ) ) ) )
__SCREAMING_SNAKE_CASE : Any = {}
__SCREAMING_SNAKE_CASE : Tuple = add_prefix_space
# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
__SCREAMING_SNAKE_CASE : Any = re.compile(r'''\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+''' )
@property
# Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.vocab_size with Roberta->Blenderbot, RoBERTa->Blenderbot
def __magic_name__( self :Optional[int] ) -> Tuple:
return len(self.encoder )
def __magic_name__( self :Optional[Any] ) -> Tuple:
return dict(self.encoder , **self.added_tokens_encoder )
def __magic_name__( self :Dict , lowerCAmelCase__ :Optional[Any] ) -> Dict:
if token in self.cache:
return self.cache[token]
__SCREAMING_SNAKE_CASE : Union[str, Any] = tuple(lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : Optional[Any] = get_pairs(lowerCAmelCase__ )
if not pairs:
return token
while True:
__SCREAMING_SNAKE_CASE : List[Any] = min(lowerCAmelCase__ , key=lambda lowerCAmelCase__ : self.bpe_ranks.get(lowerCAmelCase__ , float('''inf''' ) ) )
if bigram not in self.bpe_ranks:
break
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Tuple = bigram
__SCREAMING_SNAKE_CASE : Optional[Any] = []
__SCREAMING_SNAKE_CASE : Any = 0
while i < len(lowerCAmelCase__ ):
try:
__SCREAMING_SNAKE_CASE : Tuple = word.index(lowerCAmelCase__ , lowerCAmelCase__ )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
__SCREAMING_SNAKE_CASE : Optional[int] = j
if word[i] == first and i < len(lowerCAmelCase__ ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
__SCREAMING_SNAKE_CASE : Any = tuple(lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : List[str] = new_word
if len(lowerCAmelCase__ ) == 1:
break
else:
__SCREAMING_SNAKE_CASE : Tuple = get_pairs(lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : Tuple = ''' '''.join(lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : Dict = word
return word
def __magic_name__( self :Any , lowerCAmelCase__ :Union[str, Any] ) -> List[Any]:
__SCREAMING_SNAKE_CASE : List[str] = []
for token in re.findall(self.pat , lowerCAmelCase__ ):
__SCREAMING_SNAKE_CASE : int = ''''''.join(
self.byte_encoder[b] for b in token.encode('''utf-8''' ) ) # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
bpe_tokens.extend(bpe_token for bpe_token in self.bpe(lowerCAmelCase__ ).split(''' ''' ) )
return bpe_tokens
def __magic_name__( self :str , lowerCAmelCase__ :List[str] ) -> int:
return self.encoder.get(lowerCAmelCase__ , self.encoder.get(self.unk_token ) )
def __magic_name__( self :Any , lowerCAmelCase__ :Optional[int] ) -> List[Any]:
return self.decoder.get(lowerCAmelCase__ )
def __magic_name__( self :Union[str, Any] , lowerCAmelCase__ :List[Any] ) -> Dict:
__SCREAMING_SNAKE_CASE : Optional[int] = ''''''.join(lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : Tuple = bytearray([self.byte_decoder[c] for c in text] ).decode('''utf-8''' , errors=self.errors )
return text
def __magic_name__( self :Union[str, Any] , lowerCAmelCase__ :str , lowerCAmelCase__ :Optional[str] = None ) -> Tuple[str]:
if not os.path.isdir(lowerCAmelCase__ ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
__SCREAMING_SNAKE_CASE : List[str] = os.path.join(
lowerCAmelCase__ , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
__SCREAMING_SNAKE_CASE : List[Any] = os.path.join(
lowerCAmelCase__ , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file'''] )
with open(lowerCAmelCase__ , '''w''' , encoding='''utf-8''' ) as f:
f.write(json.dumps(self.encoder , indent=2 , sort_keys=lowerCAmelCase__ , ensure_ascii=lowerCAmelCase__ ) + '''\n''' )
__SCREAMING_SNAKE_CASE : Dict = 0
with open(lowerCAmelCase__ , '''w''' , encoding='''utf-8''' ) as writer:
writer.write('''#version: 0.2\n''' )
for bpe_tokens, token_index in sorted(self.bpe_ranks.items() , key=lambda lowerCAmelCase__ : kv[1] ):
if index != token_index:
logger.warning(
f'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
''' Please check that the tokenizer is not corrupted!''' )
__SCREAMING_SNAKE_CASE : Any = token_index
writer.write(''' '''.join(lowerCAmelCase__ ) + '''\n''' )
index += 1
return vocab_file, merge_file
def __magic_name__( self :Optional[int] , lowerCAmelCase__ :List[int] , lowerCAmelCase__ :Optional[List[int]] = None , lowerCAmelCase__ :bool = False ) -> List[int]:
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=lowerCAmelCase__ , token_ids_a=lowerCAmelCase__ , already_has_special_tokens=lowerCAmelCase__ )
if token_ids_a is None:
return [1] + ([0] * len(lowerCAmelCase__ )) + [1]
return [1] + ([0] * len(lowerCAmelCase__ )) + [1, 1] + ([0] * len(lowerCAmelCase__ )) + [1]
def __magic_name__( self :Any , lowerCAmelCase__ :List[int] , lowerCAmelCase__ :Optional[List[int]] = None ) -> List[int]:
__SCREAMING_SNAKE_CASE : Tuple = [self.sep_token_id]
__SCREAMING_SNAKE_CASE : Optional[Any] = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def __magic_name__( self :Optional[Any] , lowerCAmelCase__ :Union[str, Any] , lowerCAmelCase__ :Tuple=False , **lowerCAmelCase__ :int ) -> Union[str, Any]:
__SCREAMING_SNAKE_CASE : List[str] = kwargs.pop('''add_prefix_space''' , self.add_prefix_space )
if (is_split_into_words or add_prefix_space) and (len(lowerCAmelCase__ ) > 0 and not text[0].isspace()):
__SCREAMING_SNAKE_CASE : List[Any] = ''' ''' + text
return (text, kwargs)
def __magic_name__( self :List[str] , lowerCAmelCase__ :List[int] , lowerCAmelCase__ :Optional[List[int]] = None ) -> Tuple:
return token_ids_a + [self.eos_token_id]
def __magic_name__( self :Any , lowerCAmelCase__ :"Conversation" ) -> List[int]:
__SCREAMING_SNAKE_CASE : Optional[int] = []
for is_user, text in conversation.iter_texts():
if is_user:
# We need to space prefix as it's being done within blenderbot
inputs.append(''' ''' + text )
else:
# Generated responses should contain them already.
inputs.append(lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : Any = ''' '''.join(lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : Optional[Any] = self.encode(lowerCAmelCase__ )
if len(lowerCAmelCase__ ) > self.model_max_length:
__SCREAMING_SNAKE_CASE : Optional[int] = input_ids[-self.model_max_length :]
logger.warning(f'''Trimmed input from conversation as it was longer than {self.model_max_length} tokens.''' )
return input_ids
| 9
|
def _UpperCamelCase ( lowercase__ , lowercase__ , lowercase__=False ):
if isinstance(lowercase__ , lowercase__ ) and isinstance(lowercase__ , lowercase__ ):
__SCREAMING_SNAKE_CASE : List[str] = len(set_a.intersection(lowercase__ ) )
if alternative_union:
__SCREAMING_SNAKE_CASE : int = len(lowercase__ ) + len(lowercase__ )
else:
__SCREAMING_SNAKE_CASE : int = len(set_a.union(lowercase__ ) )
return intersection / union
if isinstance(lowercase__ , (list, tuple) ) and isinstance(lowercase__ , (list, tuple) ):
__SCREAMING_SNAKE_CASE : Dict = [element for element in set_a if element in set_b]
if alternative_union:
__SCREAMING_SNAKE_CASE : Optional[int] = len(lowercase__ ) + len(lowercase__ )
return len(lowercase__ ) / union
else:
__SCREAMING_SNAKE_CASE : Tuple = set_a + [element for element in set_b if element not in set_a]
return len(lowercase__ ) / len(lowercase__ )
return len(lowercase__ ) / len(lowercase__ )
return None
if __name__ == "__main__":
__lowerCAmelCase : List[Any] ={'a', 'b', 'c', 'd', 'e'}
__lowerCAmelCase : Optional[Any] ={'c', 'd', 'e', 'f', 'h', 'i'}
print(jaccard_similarity(set_a, set_b))
| 9
| 1
|
'''simple docstring'''
def __a ( _UpperCamelCase: int , _UpperCamelCase: int ) -> str:
"""simple docstring"""
return "\n".join(
F"""{number} * {i} = {number * i}""" for i in range(1 , number_of_terms + 1 ) )
if __name__ == "__main__":
print(multiplication_table(number=5, number_of_terms=10))
| 350
|
'''simple docstring'''
from __future__ import annotations
from typing import Any
def __a ( _UpperCamelCase: list[Any] ) -> None:
"""simple docstring"""
create_state_space_tree(_UpperCamelCase , [] , 0 )
def __a ( _UpperCamelCase: list[Any] , _UpperCamelCase: list[Any] , _UpperCamelCase: int ) -> None:
"""simple docstring"""
if index == len(_UpperCamelCase ):
print(_UpperCamelCase )
return
create_state_space_tree(_UpperCamelCase , _UpperCamelCase , index + 1 )
current_subsequence.append(sequence[index] )
create_state_space_tree(_UpperCamelCase , _UpperCamelCase , index + 1 )
current_subsequence.pop()
if __name__ == "__main__":
UpperCamelCase_ : list[Any] = [3, 1, 2, 4]
generate_all_subsequences(seq)
seq.clear()
seq.extend(['''A''', '''B''', '''C'''])
generate_all_subsequences(seq)
| 142
| 0
|
"""simple docstring"""
from __future__ import annotations
from random import choice
def __lowercase ( _a ):
return choice(_lowerCAmelCase )
def __lowercase ( _a , _a ):
snake_case_ : Any = random_pivot(_lowerCAmelCase )
# partition based on pivot
# linear time
snake_case_ : Any = [e for e in lst if e < pivot]
snake_case_ : str = [e for e in lst if e > pivot]
# if we get lucky, pivot might be the element we want.
# we can easily see this:
# small (elements smaller than k)
# + pivot (kth element)
# + big (elements larger than k)
if len(_lowerCAmelCase ) == k - 1:
return pivot
# pivot is in elements bigger than k
elif len(_lowerCAmelCase ) < k - 1:
return kth_number(_lowerCAmelCase , k - len(_lowerCAmelCase ) - 1 )
# pivot is in elements smaller than k
else:
return kth_number(_lowerCAmelCase , _lowerCAmelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 264
|
def _lowerCAmelCase (_lowerCAmelCase):
UpperCamelCase_ = len(_lowerCAmelCase)
UpperCamelCase_ = len(matrix[0])
UpperCamelCase_ = min(_lowerCAmelCase , _lowerCAmelCase)
for row in range(_lowerCAmelCase):
# Check if diagonal element is not zero
if matrix[row][row] != 0:
# Eliminate all the elements below the diagonal
for col in range(row + 1 , _lowerCAmelCase):
UpperCamelCase_ = matrix[col][row] / matrix[row][row]
for i in range(_lowerCAmelCase , _lowerCAmelCase):
matrix[col][i] -= multiplier * matrix[row][i]
else:
# Find a non-zero diagonal element to swap rows
UpperCamelCase_ = True
for i in range(row + 1 , _lowerCAmelCase):
if matrix[i][row] != 0:
UpperCamelCase_ , UpperCamelCase_ = matrix[i], matrix[row]
UpperCamelCase_ = False
break
if reduce:
rank -= 1
for i in range(_lowerCAmelCase):
UpperCamelCase_ = matrix[i][rank]
# Reduce the row pointer by one to stay on the same row
row -= 1
return rank
if __name__ == "__main__":
import doctest
doctest.testmod()
| 128
| 0
|
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
enable_full_determinism()
class __snake_case ( __lowerCamelCase , unittest.TestCase ):
'''simple docstring'''
lowerCAmelCase__ = KandinskyInpaintPipeline
lowerCAmelCase__ = ['prompt', 'image_embeds', 'negative_image_embeds', 'image', 'mask_image']
lowerCAmelCase__ = [
'prompt',
'negative_prompt',
'image_embeds',
'negative_image_embeds',
'image',
'mask_image',
]
lowerCAmelCase__ = [
'generator',
'height',
'width',
'latents',
'guidance_scale',
'negative_prompt',
'num_inference_steps',
'return_dict',
'guidance_scale',
'num_images_per_prompt',
'output_type',
'return_dict',
]
lowerCAmelCase__ = False
@property
def UpperCAmelCase__ ( self : Dict ):
return 32
@property
def UpperCAmelCase__ ( self : Optional[int] ):
return 32
@property
def UpperCAmelCase__ ( self : str ):
return self.time_input_dim
@property
def UpperCAmelCase__ ( self : List[str] ):
return self.time_input_dim * 4
@property
def UpperCAmelCase__ ( self : List[str] ):
return 100
@property
def UpperCAmelCase__ ( self : Union[str, Any] ):
__snake_case: List[str] = XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base""" )
return tokenizer
@property
def UpperCAmelCase__ ( self : List[Any] ):
torch.manual_seed(0 )
__snake_case: Optional[Any] = MCLIPConfig(
numDims=self.cross_attention_dim , transformerDimensions=self.text_embedder_hidden_size , hidden_size=self.text_embedder_hidden_size , intermediate_size=37 , num_attention_heads=4 , num_hidden_layers=5 , vocab_size=1_005 , )
__snake_case: Optional[Any] = MultilingualCLIP(UpperCamelCase_ )
__snake_case: Tuple = text_encoder.eval()
return text_encoder
@property
def UpperCAmelCase__ ( self : Optional[Any] ):
torch.manual_seed(0 )
__snake_case: Tuple = {
"""in_channels""": 9,
# Out channels is double in channels because predicts mean and variance
"""out_channels""": 8,
"""addition_embed_type""": """text_image""",
"""down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
"""up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
"""mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
"""block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
"""layers_per_block""": 1,
"""encoder_hid_dim""": self.text_embedder_hidden_size,
"""encoder_hid_dim_type""": """text_image_proj""",
"""cross_attention_dim""": self.cross_attention_dim,
"""attention_head_dim""": 4,
"""resnet_time_scale_shift""": """scale_shift""",
"""class_embed_type""": None,
}
__snake_case: Any = UNetaDConditionModel(**UpperCamelCase_ )
return model
@property
def UpperCAmelCase__ ( self : Dict ):
return {
"block_out_channels": [32, 64],
"down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
"in_channels": 3,
"latent_channels": 4,
"layers_per_block": 1,
"norm_num_groups": 8,
"norm_type": "spatial",
"num_vq_embeddings": 12,
"out_channels": 3,
"up_block_types": [
"AttnUpDecoderBlock2D",
"UpDecoderBlock2D",
],
"vq_embed_dim": 4,
}
@property
def UpperCAmelCase__ ( self : List[Any] ):
torch.manual_seed(0 )
__snake_case: Tuple = VQModel(**self.dummy_movq_kwargs )
return model
def UpperCAmelCase__ ( self : Dict ):
__snake_case: Union[str, Any] = self.dummy_text_encoder
__snake_case: List[str] = self.dummy_tokenizer
__snake_case: str = self.dummy_unet
__snake_case: List[Any] = self.dummy_movq
__snake_case: int = DDIMScheduler(
num_train_timesteps=1_000 , beta_schedule="""linear""" , beta_start=0.0_0085 , beta_end=0.012 , clip_sample=UpperCamelCase_ , set_alpha_to_one=UpperCamelCase_ , steps_offset=1 , prediction_type="""epsilon""" , thresholding=UpperCamelCase_ , )
__snake_case: Union[str, Any] = {
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
"""unet""": unet,
"""scheduler""": scheduler,
"""movq""": movq,
}
return components
def UpperCAmelCase__ ( self : Union[str, Any] , A : Optional[Any] , A : Optional[Any]=0 ):
__snake_case: List[str] = floats_tensor((1, self.cross_attention_dim) , rng=random.Random(UpperCamelCase_ ) ).to(UpperCamelCase_ )
__snake_case: Tuple = floats_tensor((1, self.cross_attention_dim) , rng=random.Random(seed + 1 ) ).to(UpperCamelCase_ )
# create init_image
__snake_case: Union[str, Any] = floats_tensor((1, 3, 64, 64) , rng=random.Random(UpperCamelCase_ ) ).to(UpperCamelCase_ )
__snake_case: Tuple = image.cpu().permute(0 , 2 , 3 , 1 )[0]
__snake_case: Optional[int] = Image.fromarray(np.uinta(UpperCamelCase_ ) ).convert("""RGB""" ).resize((256, 256) )
# create mask
__snake_case: int = np.ones((64, 64) , dtype=np.floataa )
__snake_case: Tuple = 0
if str(UpperCamelCase_ ).startswith("""mps""" ):
__snake_case: List[str] = torch.manual_seed(UpperCamelCase_ )
else:
__snake_case: List[str] = torch.Generator(device=UpperCamelCase_ ).manual_seed(UpperCamelCase_ )
__snake_case: int = {
"""prompt""": """horse""",
"""image""": init_image,
"""mask_image""": mask,
"""image_embeds""": image_embeds,
"""negative_image_embeds""": negative_image_embeds,
"""generator""": generator,
"""height""": 64,
"""width""": 64,
"""num_inference_steps""": 2,
"""guidance_scale""": 4.0,
"""output_type""": """np""",
}
return inputs
def UpperCAmelCase__ ( self : List[Any] ):
__snake_case: Tuple = """cpu"""
__snake_case: int = self.get_dummy_components()
__snake_case: str = self.pipeline_class(**UpperCamelCase_ )
__snake_case: Dict = pipe.to(UpperCamelCase_ )
pipe.set_progress_bar_config(disable=UpperCamelCase_ )
__snake_case: List[str] = pipe(**self.get_dummy_inputs(UpperCamelCase_ ) )
__snake_case: List[Any] = output.images
__snake_case: str = pipe(
**self.get_dummy_inputs(UpperCamelCase_ ) , return_dict=UpperCamelCase_ , )[0]
__snake_case: Tuple = image[0, -3:, -3:, -1]
__snake_case: Dict = image_from_tuple[0, -3:, -3:, -1]
print(f'''image.shape {image.shape}''' )
assert image.shape == (1, 64, 64, 3)
__snake_case: Optional[int] = np.array(
[0.832_6919, 0.7379_0467, 0.2091_8581, 0.930_9612, 0.551_1791, 0.4371_3328, 0.551_3321, 0.4992_2934, 0.5949_7786] )
assert (
np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
), f''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''
assert (
np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
), f''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''
def UpperCAmelCase__ ( self : Optional[Any] ):
super().test_inference_batch_single_identical(expected_max_diff=3E-3 )
@slow
@require_torch_gpu
class __snake_case ( unittest.TestCase ):
'''simple docstring'''
def UpperCAmelCase__ ( self : Optional[int] ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCAmelCase__ ( self : Dict ):
__snake_case: List[Any] = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy""" )
__snake_case: List[str] = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png""" )
__snake_case: Union[str, Any] = np.ones((768, 768) , dtype=np.floataa )
__snake_case: Union[str, Any] = 0
__snake_case: Tuple = """a hat"""
__snake_case: Optional[int] = KandinskyPriorPipeline.from_pretrained(
"""kandinsky-community/kandinsky-2-1-prior""" , torch_dtype=torch.floataa )
pipe_prior.to(UpperCamelCase_ )
__snake_case: str = KandinskyInpaintPipeline.from_pretrained(
"""kandinsky-community/kandinsky-2-1-inpaint""" , torch_dtype=torch.floataa )
__snake_case: Any = pipeline.to(UpperCamelCase_ )
pipeline.set_progress_bar_config(disable=UpperCamelCase_ )
__snake_case: Union[str, Any] = torch.Generator(device="""cpu""" ).manual_seed(0 )
__snake_case , __snake_case: List[Any] = pipe_prior(
UpperCamelCase_ , generator=UpperCamelCase_ , num_inference_steps=5 , negative_prompt="""""" , ).to_tuple()
__snake_case: Tuple = pipeline(
UpperCamelCase_ , image=UpperCamelCase_ , mask_image=UpperCamelCase_ , image_embeds=UpperCamelCase_ , negative_image_embeds=UpperCamelCase_ , generator=UpperCamelCase_ , num_inference_steps=100 , height=768 , width=768 , output_type="""np""" , )
__snake_case: Union[str, Any] = output.images[0]
assert image.shape == (768, 768, 3)
assert_mean_pixel_difference(UpperCamelCase_ , UpperCamelCase_ )
| 357
|
import warnings
from diffusers import StableDiffusionImgaImgPipeline # noqa F401
warnings.warn(
"The `image_to_image.py` script is outdated. Please use directly `from diffusers import"
" StableDiffusionImg2ImgPipeline` instead."
)
| 293
| 0
|
def snake_case_ ( snake_case , snake_case ) -> int:
return x if y == 0 else greatest_common_divisor(snake_case , x % y )
def snake_case_ ( snake_case , snake_case ) -> int:
return (x * y) // greatest_common_divisor(snake_case , snake_case )
def snake_case_ ( snake_case = 20 ) -> int:
lowercase__: Union[str, Any] = 1
for i in range(1 , n + 1 ):
lowercase__: List[str] = lcm(snake_case , snake_case )
return g
if __name__ == "__main__":
print(F'''{solution() = }''')
| 196
|
import numpy as np
import torch
from torch.utils.data import Dataset
from utils import logger
class __a ( __UpperCamelCase ):
def __init__( self , lowerCAmelCase__ , lowerCAmelCase__ ) -> Any:
'''simple docstring'''
lowercase__: Any = params
lowercase__: List[Any] = np.array(lowerCAmelCase__ )
lowercase__: Optional[Any] = np.array([len(lowerCAmelCase__ ) for t in data] )
self.check()
self.remove_long_sequences()
self.remove_empty_sequences()
self.remove_unknown_sequences()
self.check()
self.print_statistics()
def __getitem__( self , lowerCAmelCase__ ) -> List[Any]:
'''simple docstring'''
return (self.token_ids[index], self.lengths[index])
def __len__( self ) -> List[Any]:
'''simple docstring'''
return len(self.lengths )
def SCREAMING_SNAKE_CASE__ ( self ) -> Tuple:
'''simple docstring'''
assert len(self.token_ids ) == len(self.lengths )
assert all(self.lengths[i] == len(self.token_ids[i] ) for i in range(len(self.lengths ) ) )
def SCREAMING_SNAKE_CASE__ ( self ) -> Union[str, Any]:
'''simple docstring'''
lowercase__: Any = self.params.max_model_input_size
lowercase__: Dict = self.lengths > max_len
logger.info(F'Splitting {sum(lowerCAmelCase__ )} too long sequences.' )
def divide_chunks(lowerCAmelCase__ , lowerCAmelCase__ ):
return [l[i : i + n] for i in range(0 , len(lowerCAmelCase__ ) , lowerCAmelCase__ )]
lowercase__: str = []
lowercase__: List[str] = []
if self.params.mlm:
lowercase__ , lowercase__: str = self.params.special_tok_ids['cls_token'], self.params.special_tok_ids['sep_token']
else:
lowercase__ , lowercase__: int = self.params.special_tok_ids['bos_token'], self.params.special_tok_ids['eos_token']
for seq_, len_ in zip(self.token_ids , self.lengths ):
assert (seq_[0] == cls_id) and (seq_[-1] == sep_id), seq_
if len_ <= max_len:
new_tok_ids.append(seq_ )
new_lengths.append(len_ )
else:
lowercase__: Optional[Any] = []
for sub_s in divide_chunks(seq_ , max_len - 2 ):
if sub_s[0] != cls_id:
lowercase__: int = np.insert(lowerCAmelCase__ , 0 , lowerCAmelCase__ )
if sub_s[-1] != sep_id:
lowercase__: Union[str, Any] = np.insert(lowerCAmelCase__ , len(lowerCAmelCase__ ) , lowerCAmelCase__ )
assert len(lowerCAmelCase__ ) <= max_len
assert (sub_s[0] == cls_id) and (sub_s[-1] == sep_id), sub_s
sub_seqs.append(lowerCAmelCase__ )
new_tok_ids.extend(lowerCAmelCase__ )
new_lengths.extend([len(lowerCAmelCase__ ) for l in sub_seqs] )
lowercase__: Union[str, Any] = np.array(lowerCAmelCase__ )
lowercase__: Union[str, Any] = np.array(lowerCAmelCase__ )
def SCREAMING_SNAKE_CASE__ ( self ) -> Tuple:
'''simple docstring'''
lowercase__: Dict = len(self )
lowercase__: Union[str, Any] = self.lengths > 11
lowercase__: List[Any] = self.token_ids[indices]
lowercase__: Optional[Any] = self.lengths[indices]
lowercase__: List[str] = len(self )
logger.info(F'Remove {init_size - new_size} too short (<=11 tokens) sequences.' )
def SCREAMING_SNAKE_CASE__ ( self ) -> int:
'''simple docstring'''
if "unk_token" not in self.params.special_tok_ids:
return
else:
lowercase__: List[str] = self.params.special_tok_ids['unk_token']
lowercase__: str = len(self )
lowercase__: Tuple = np.array([np.count_nonzero(a == unk_token_id ) for a in self.token_ids] )
lowercase__: Any = (unk_occs / self.lengths) < 0.5
lowercase__: Tuple = self.token_ids[indices]
lowercase__: str = self.lengths[indices]
lowercase__: Dict = len(self )
logger.info(F'Remove {init_size - new_size} sequences with a high level of unknown tokens (50%).' )
def SCREAMING_SNAKE_CASE__ ( self ) -> str:
'''simple docstring'''
if not self.params.is_master:
return
logger.info(F'{len(self )} sequences' )
# data_len = sum(self.lengths)
# nb_unique_tokens = len(Counter(list(chain(*self.token_ids))))
# logger.info(f'{data_len} tokens ({nb_unique_tokens} unique)')
# unk_idx = self.params.special_tok_ids['unk_token']
# nb_unknown = sum([(t==unk_idx).sum() for t in self.token_ids])
# logger.info(f'{nb_unknown} unknown tokens (covering {100*nb_unknown/data_len:.2f}% of the data)')
def SCREAMING_SNAKE_CASE__ ( self , lowerCAmelCase__ ) -> int:
'''simple docstring'''
lowercase__: Union[str, Any] = [t[0] for t in batch]
lowercase__: Dict = [t[1] for t in batch]
assert len(lowerCAmelCase__ ) == len(lowerCAmelCase__ )
# Max for paddings
lowercase__: List[Any] = max(lowerCAmelCase__ )
# Pad token ids
if self.params.mlm:
lowercase__: Dict = self.params.special_tok_ids['pad_token']
else:
lowercase__: Optional[Any] = self.params.special_tok_ids['unk_token']
lowercase__: int = [list(t.astype(lowerCAmelCase__ ) ) + [pad_idx] * (max_seq_len_ - len(lowerCAmelCase__ )) for t in token_ids]
assert len(tk_ ) == len(lowerCAmelCase__ )
assert all(len(lowerCAmelCase__ ) == max_seq_len_ for t in tk_ )
lowercase__: Tuple = torch.tensor(tk_ ) # (bs, max_seq_len_)
lowercase__: Optional[Any] = torch.tensor(lowerCAmelCase__ ) # (bs)
return tk_t, lg_t
| 196
| 1
|
from __future__ import annotations
from math import pi
def UpperCAmelCase__ ( lowerCamelCase, lowerCamelCase, lowerCamelCase ):
if (inductance, frequency, reactance).count(0 ) != 1:
raise ValueError("One and only one argument must be 0" )
if inductance < 0:
raise ValueError("Inductance cannot be negative" )
if frequency < 0:
raise ValueError("Frequency cannot be negative" )
if reactance < 0:
raise ValueError("Inductive reactance cannot be negative" )
if inductance == 0:
return {"inductance": reactance / (2 * pi * frequency)}
elif frequency == 0:
return {"frequency": reactance / (2 * pi * inductance)}
elif reactance == 0:
return {"reactance": 2 * pi * frequency * inductance}
else:
raise ValueError("Exactly one argument must be 0" )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 158
|
from . import __version__
# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
CLOUDFRONT_DISTRIB_PREFIX,
CONFIG_NAME,
DISABLE_TELEMETRY,
DUMMY_INPUTS,
DUMMY_MASK,
ENV_VARS_TRUE_AND_AUTO_VALUES,
ENV_VARS_TRUE_VALUES,
FEATURE_EXTRACTOR_NAME,
FLAX_WEIGHTS_NAME,
HF_MODULES_CACHE,
HUGGINGFACE_CO_PREFIX,
HUGGINGFACE_CO_RESOLVE_ENDPOINT,
MODEL_CARD_NAME,
MULTIPLE_CHOICE_DUMMY_INPUTS,
PYTORCH_PRETRAINED_BERT_CACHE,
PYTORCH_TRANSFORMERS_CACHE,
S3_BUCKET_PREFIX,
SENTENCEPIECE_UNDERLINE,
SPIECE_UNDERLINE,
TF2_WEIGHTS_NAME,
TF_WEIGHTS_NAME,
TORCH_FX_REQUIRED_VERSION,
TRANSFORMERS_CACHE,
TRANSFORMERS_DYNAMIC_MODULE_NAME,
USE_JAX,
USE_TF,
USE_TORCH,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
ContextManagers,
DummyObject,
EntryNotFoundError,
ExplicitEnum,
ModelOutput,
PaddingStrategy,
PushToHubMixin,
RepositoryNotFoundError,
RevisionNotFoundError,
TensorType,
_LazyModule,
add_code_sample_docstrings,
add_end_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
cached_property,
copy_func,
default_cache_path,
define_sagemaker_information,
get_cached_models,
get_file_from_repo,
get_full_repo_name,
get_torch_version,
has_file,
http_user_agent,
is_apex_available,
is_bsa_available,
is_coloredlogs_available,
is_datasets_available,
is_detectrona_available,
is_faiss_available,
is_flax_available,
is_ftfy_available,
is_in_notebook,
is_ipex_available,
is_librosa_available,
is_offline_mode,
is_onnx_available,
is_pandas_available,
is_phonemizer_available,
is_protobuf_available,
is_psutil_available,
is_pyanvml_available,
is_pyctcdecode_available,
is_pytesseract_available,
is_pytorch_quantization_available,
is_rjieba_available,
is_sagemaker_dp_enabled,
is_sagemaker_mp_enabled,
is_scipy_available,
is_sentencepiece_available,
is_seqio_available,
is_sklearn_available,
is_soundfile_availble,
is_spacy_available,
is_speech_available,
is_tensor,
is_tensorflow_probability_available,
is_tfaonnx_available,
is_tf_available,
is_timm_available,
is_tokenizers_available,
is_torch_available,
is_torch_bfaa_available,
is_torch_cuda_available,
is_torch_fx_available,
is_torch_fx_proxy,
is_torch_mps_available,
is_torch_tfaa_available,
is_torch_tpu_available,
is_torchaudio_available,
is_training_run_on_sagemaker,
is_vision_available,
replace_return_docstrings,
requires_backends,
to_numpy,
to_py_obj,
torch_only_method,
)
| 158
| 1
|
'''simple docstring'''
from collections.abc import Sequence
def UpperCAmelCase ( a_ , a_ ) -> float:
"""simple docstring"""
return sum(c * (x**i) for i, c in enumerate(a_ ) )
def UpperCAmelCase ( a_ , a_ ) -> float:
"""simple docstring"""
A_ : List[str] = 0.0
for coeff in reversed(a_ ):
A_ : List[Any] = result * x + coeff
return result
if __name__ == "__main__":
UpperCamelCase__ : List[str] = (0.0, 0.0, 5.0, 9.3, 7.0)
UpperCamelCase__ : List[str] = 1_0.0
print(evaluate_poly(poly, x))
print(horner(poly, x))
| 344
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
UpperCamelCase__ : Any = {
'configuration_data2vec_audio': ['DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Data2VecAudioConfig'],
'configuration_data2vec_text': [
'DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP',
'Data2VecTextConfig',
'Data2VecTextOnnxConfig',
],
'configuration_data2vec_vision': [
'DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP',
'Data2VecVisionConfig',
'Data2VecVisionOnnxConfig',
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase__ : Optional[Any] = [
'DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST',
'Data2VecAudioForAudioFrameClassification',
'Data2VecAudioForCTC',
'Data2VecAudioForSequenceClassification',
'Data2VecAudioForXVector',
'Data2VecAudioModel',
'Data2VecAudioPreTrainedModel',
]
UpperCamelCase__ : List[str] = [
'DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST',
'Data2VecTextForCausalLM',
'Data2VecTextForMaskedLM',
'Data2VecTextForMultipleChoice',
'Data2VecTextForQuestionAnswering',
'Data2VecTextForSequenceClassification',
'Data2VecTextForTokenClassification',
'Data2VecTextModel',
'Data2VecTextPreTrainedModel',
]
UpperCamelCase__ : str = [
'DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST',
'Data2VecVisionForImageClassification',
'Data2VecVisionForMaskedImageModeling',
'Data2VecVisionForSemanticSegmentation',
'Data2VecVisionModel',
'Data2VecVisionPreTrainedModel',
]
if is_tf_available():
UpperCamelCase__ : List[str] = [
'TFData2VecVisionForImageClassification',
'TFData2VecVisionForSemanticSegmentation',
'TFData2VecVisionModel',
'TFData2VecVisionPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_dataavec_audio import DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP, DataaVecAudioConfig
from .configuration_dataavec_text import (
DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP,
DataaVecTextConfig,
DataaVecTextOnnxConfig,
)
from .configuration_dataavec_vision import (
DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP,
DataaVecVisionConfig,
DataaVecVisionOnnxConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_dataavec_audio import (
DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST,
DataaVecAudioForAudioFrameClassification,
DataaVecAudioForCTC,
DataaVecAudioForSequenceClassification,
DataaVecAudioForXVector,
DataaVecAudioModel,
DataaVecAudioPreTrainedModel,
)
from .modeling_dataavec_text import (
DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
DataaVecTextForCausalLM,
DataaVecTextForMaskedLM,
DataaVecTextForMultipleChoice,
DataaVecTextForQuestionAnswering,
DataaVecTextForSequenceClassification,
DataaVecTextForTokenClassification,
DataaVecTextModel,
DataaVecTextPreTrainedModel,
)
from .modeling_dataavec_vision import (
DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST,
DataaVecVisionForImageClassification,
DataaVecVisionForMaskedImageModeling,
DataaVecVisionForSemanticSegmentation,
DataaVecVisionModel,
DataaVecVisionPreTrainedModel,
)
if is_tf_available():
from .modeling_tf_dataavec_vision import (
TFDataaVecVisionForImageClassification,
TFDataaVecVisionForSemanticSegmentation,
TFDataaVecVisionModel,
TFDataaVecVisionPreTrainedModel,
)
else:
import sys
UpperCamelCase__ : int = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 344
| 1
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE :Optional[int] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE :Union[str, Any] = {
'weiweishi/roc-bert-base-zh': 'https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json',
}
class UpperCAmelCase ( _lowerCamelCase ):
'''simple docstring'''
snake_case_ = "roc_bert"
def __init__( self : Union[str, Any] ,A : str=3_05_22 ,A : int=7_68 ,A : Dict=12 ,A : int=12 ,A : Dict=30_72 ,A : Dict="gelu" ,A : Optional[Any]=0.1 ,A : int=0.1 ,A : Dict=5_12 ,A : int=2 ,A : Union[str, Any]=0.02 ,A : str=1E-12 ,A : Any=True ,A : Any=0 ,A : Optional[int]="absolute" ,A : Dict=None ,A : Tuple=True ,A : Tuple=True ,A : Tuple=7_68 ,A : str=9_10 ,A : Tuple=5_12 ,A : Tuple=2_48_58 ,A : List[str]=True ,**A : int ,):
__A = vocab_size
__A = max_position_embeddings
__A = hidden_size
__A = num_hidden_layers
__A = num_attention_heads
__A = intermediate_size
__A = hidden_act
__A = hidden_dropout_prob
__A = attention_probs_dropout_prob
__A = initializer_range
__A = type_vocab_size
__A = layer_norm_eps
__A = use_cache
__A = enable_pronunciation
__A = enable_shape
__A = pronunciation_embed_dim
__A = pronunciation_vocab_size
__A = shape_embed_dim
__A = shape_vocab_size
__A = concat_input
__A = position_embedding_type
__A = classifier_dropout
super().__init__(pad_token_id=A ,**A )
| 353
|
import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
SCREAMING_SNAKE_CASE :Any = logging.get_logger(__name__)
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__( self : int ,*A : Dict ,**A : Dict ):
warnings.warn(
"The class CLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
" use CLIPImageProcessor instead." ,A ,)
super().__init__(*A ,**A )
| 124
| 0
|
UpperCAmelCase_ = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ'
def lowerCamelCase__ ( ):
'''simple docstring'''
__lowerCamelCase = input("""Enter message: """ )
__lowerCamelCase = input("""Enter key [alphanumeric]: """ )
__lowerCamelCase = input("""Encrypt/Decrypt [e/d]: """ )
if mode.lower().startswith("""e""" ):
__lowerCamelCase = """encrypt"""
__lowerCamelCase = encrypt_message(A__ , A__ )
elif mode.lower().startswith("""d""" ):
__lowerCamelCase = """decrypt"""
__lowerCamelCase = decrypt_message(A__ , A__ )
print(f'\n{mode.title()}ed message:' )
print(A__ )
def lowerCamelCase__ ( A__ : str , A__ : str ):
'''simple docstring'''
return translate_message(A__ , A__ , """encrypt""" )
def lowerCamelCase__ ( A__ : str , A__ : str ):
'''simple docstring'''
return translate_message(A__ , A__ , """decrypt""" )
def lowerCamelCase__ ( A__ : str , A__ : str , A__ : str ):
'''simple docstring'''
__lowerCamelCase = []
__lowerCamelCase = 0
__lowerCamelCase = key.upper()
for symbol in message:
__lowerCamelCase = LETTERS.find(symbol.upper() )
if num != -1:
if mode == "encrypt":
num += LETTERS.find(key[key_index] )
elif mode == "decrypt":
num -= LETTERS.find(key[key_index] )
num %= len(A__ )
if symbol.isupper():
translated.append(LETTERS[num] )
elif symbol.islower():
translated.append(LETTERS[num].lower() )
key_index += 1
if key_index == len(A__ ):
__lowerCamelCase = 0
else:
translated.append(A__ )
return "".join(A__ )
if __name__ == "__main__":
main()
| 12
|
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional
import numpy as np
import torch
import torch.nn as nn
from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_up_block
@dataclass
class __UpperCAmelCase ( _lowerCamelCase ):
__lowercase = 42
class __UpperCAmelCase ( nn.Module ):
def __init__( self , lowerCAmelCase_=3 , lowerCAmelCase_=3 , lowerCAmelCase_=("DownEncoderBlock2D",) , lowerCAmelCase_=(64,) , lowerCAmelCase_=2 , lowerCAmelCase_=32 , lowerCAmelCase_="silu" , lowerCAmelCase_=True , ):
"""simple docstring"""
super().__init__()
_snake_case = layers_per_block
_snake_case = torch.nn.Convad(
lowerCAmelCase_ , block_out_channels[0] , kernel_size=3 , stride=1 , padding=1 , )
_snake_case = None
_snake_case = nn.ModuleList([] )
# down
_snake_case = block_out_channels[0]
for i, down_block_type in enumerate(lowerCAmelCase_ ):
_snake_case = output_channel
_snake_case = block_out_channels[i]
_snake_case = i == len(lowerCAmelCase_ ) - 1
_snake_case = get_down_block(
lowerCAmelCase_ , num_layers=self.layers_per_block , in_channels=lowerCAmelCase_ , out_channels=lowerCAmelCase_ , add_downsample=not is_final_block , resnet_eps=1E-6 , downsample_padding=0 , resnet_act_fn=lowerCAmelCase_ , resnet_groups=lowerCAmelCase_ , attention_head_dim=lowerCAmelCase_ , temb_channels=lowerCAmelCase_ , )
self.down_blocks.append(lowerCAmelCase_ )
# mid
_snake_case = UNetMidBlockaD(
in_channels=block_out_channels[-1] , resnet_eps=1E-6 , resnet_act_fn=lowerCAmelCase_ , output_scale_factor=1 , resnet_time_scale_shift='default' , attention_head_dim=block_out_channels[-1] , resnet_groups=lowerCAmelCase_ , temb_channels=lowerCAmelCase_ , )
# out
_snake_case = nn.GroupNorm(num_channels=block_out_channels[-1] , num_groups=lowerCAmelCase_ , eps=1E-6 )
_snake_case = nn.SiLU()
_snake_case = 2 * out_channels if double_z else out_channels
_snake_case = nn.Convad(block_out_channels[-1] , lowerCAmelCase_ , 3 , padding=1 )
_snake_case = False
def lowerCamelCase ( self , lowerCAmelCase_ ):
"""simple docstring"""
_snake_case = x
_snake_case = self.conv_in(lowerCAmelCase_ )
if self.training and self.gradient_checkpointing:
def create_custom_forward(lowerCAmelCase_ ):
def custom_forward(*lowerCAmelCase_ ):
return module(*lowerCAmelCase_ )
return custom_forward
# down
if is_torch_version('>=' , '1.11.0' ):
for down_block in self.down_blocks:
_snake_case = torch.utils.checkpoint.checkpoint(
create_custom_forward(lowerCAmelCase_ ) , lowerCAmelCase_ , use_reentrant=lowerCAmelCase_ )
# middle
_snake_case = torch.utils.checkpoint.checkpoint(
create_custom_forward(self.mid_block ) , lowerCAmelCase_ , use_reentrant=lowerCAmelCase_ )
else:
for down_block in self.down_blocks:
_snake_case = torch.utils.checkpoint.checkpoint(create_custom_forward(lowerCAmelCase_ ) , lowerCAmelCase_ )
# middle
_snake_case = torch.utils.checkpoint.checkpoint(create_custom_forward(self.mid_block ) , lowerCAmelCase_ )
else:
# down
for down_block in self.down_blocks:
_snake_case = down_block(lowerCAmelCase_ )
# middle
_snake_case = self.mid_block(lowerCAmelCase_ )
# post-process
_snake_case = self.conv_norm_out(lowerCAmelCase_ )
_snake_case = self.conv_act(lowerCAmelCase_ )
_snake_case = self.conv_out(lowerCAmelCase_ )
return sample
class __UpperCAmelCase ( nn.Module ):
def __init__( self , lowerCAmelCase_=3 , lowerCAmelCase_=3 , lowerCAmelCase_=("UpDecoderBlock2D",) , lowerCAmelCase_=(64,) , lowerCAmelCase_=2 , lowerCAmelCase_=32 , lowerCAmelCase_="silu" , lowerCAmelCase_="group" , ):
"""simple docstring"""
super().__init__()
_snake_case = layers_per_block
_snake_case = nn.Convad(
lowerCAmelCase_ , block_out_channels[-1] , kernel_size=3 , stride=1 , padding=1 , )
_snake_case = None
_snake_case = nn.ModuleList([] )
_snake_case = in_channels if norm_type == 'spatial' else None
# mid
_snake_case = UNetMidBlockaD(
in_channels=block_out_channels[-1] , resnet_eps=1E-6 , resnet_act_fn=lowerCAmelCase_ , output_scale_factor=1 , resnet_time_scale_shift='default' if norm_type == 'group' else norm_type , attention_head_dim=block_out_channels[-1] , resnet_groups=lowerCAmelCase_ , temb_channels=lowerCAmelCase_ , )
# up
_snake_case = list(reversed(lowerCAmelCase_ ) )
_snake_case = reversed_block_out_channels[0]
for i, up_block_type in enumerate(lowerCAmelCase_ ):
_snake_case = output_channel
_snake_case = reversed_block_out_channels[i]
_snake_case = i == len(lowerCAmelCase_ ) - 1
_snake_case = get_up_block(
lowerCAmelCase_ , num_layers=self.layers_per_block + 1 , in_channels=lowerCAmelCase_ , out_channels=lowerCAmelCase_ , prev_output_channel=lowerCAmelCase_ , add_upsample=not is_final_block , resnet_eps=1E-6 , resnet_act_fn=lowerCAmelCase_ , resnet_groups=lowerCAmelCase_ , attention_head_dim=lowerCAmelCase_ , temb_channels=lowerCAmelCase_ , resnet_time_scale_shift=lowerCAmelCase_ , )
self.up_blocks.append(lowerCAmelCase_ )
_snake_case = output_channel
# out
if norm_type == "spatial":
_snake_case = SpatialNorm(block_out_channels[0] , lowerCAmelCase_ )
else:
_snake_case = nn.GroupNorm(num_channels=block_out_channels[0] , num_groups=lowerCAmelCase_ , eps=1E-6 )
_snake_case = nn.SiLU()
_snake_case = nn.Convad(block_out_channels[0] , lowerCAmelCase_ , 3 , padding=1 )
_snake_case = False
def lowerCamelCase ( self , lowerCAmelCase_ , lowerCAmelCase_=None ):
"""simple docstring"""
_snake_case = z
_snake_case = self.conv_in(lowerCAmelCase_ )
_snake_case = next(iter(self.up_blocks.parameters() ) ).dtype
if self.training and self.gradient_checkpointing:
def create_custom_forward(lowerCAmelCase_ ):
def custom_forward(*lowerCAmelCase_ ):
return module(*lowerCAmelCase_ )
return custom_forward
if is_torch_version('>=' , '1.11.0' ):
# middle
_snake_case = torch.utils.checkpoint.checkpoint(
create_custom_forward(self.mid_block ) , lowerCAmelCase_ , lowerCAmelCase_ , use_reentrant=lowerCAmelCase_ )
_snake_case = sample.to(lowerCAmelCase_ )
# up
for up_block in self.up_blocks:
_snake_case = torch.utils.checkpoint.checkpoint(
create_custom_forward(lowerCAmelCase_ ) , lowerCAmelCase_ , lowerCAmelCase_ , use_reentrant=lowerCAmelCase_ )
else:
# middle
_snake_case = torch.utils.checkpoint.checkpoint(
create_custom_forward(self.mid_block ) , lowerCAmelCase_ , lowerCAmelCase_ )
_snake_case = sample.to(lowerCAmelCase_ )
# up
for up_block in self.up_blocks:
_snake_case = torch.utils.checkpoint.checkpoint(create_custom_forward(lowerCAmelCase_ ) , lowerCAmelCase_ , lowerCAmelCase_ )
else:
# middle
_snake_case = self.mid_block(lowerCAmelCase_ , lowerCAmelCase_ )
_snake_case = sample.to(lowerCAmelCase_ )
# up
for up_block in self.up_blocks:
_snake_case = up_block(lowerCAmelCase_ , lowerCAmelCase_ )
# post-process
if latent_embeds is None:
_snake_case = self.conv_norm_out(lowerCAmelCase_ )
else:
_snake_case = self.conv_norm_out(lowerCAmelCase_ , lowerCAmelCase_ )
_snake_case = self.conv_act(lowerCAmelCase_ )
_snake_case = self.conv_out(lowerCAmelCase_ )
return sample
class __UpperCAmelCase ( nn.Module ):
def __init__( self , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_=None , lowerCAmelCase_="random" , lowerCAmelCase_=False , lowerCAmelCase_=True ):
"""simple docstring"""
super().__init__()
_snake_case = n_e
_snake_case = vq_embed_dim
_snake_case = beta
_snake_case = legacy
_snake_case = nn.Embedding(self.n_e , self.vq_embed_dim )
self.embedding.weight.data.uniform_(-1.0 / self.n_e , 1.0 / self.n_e )
_snake_case = remap
if self.remap is not None:
self.register_buffer('used' , torch.tensor(np.load(self.remap ) ) )
_snake_case = self.used.shape[0]
_snake_case = unknown_index # "random" or "extra" or integer
if self.unknown_index == "extra":
_snake_case = self.re_embed
_snake_case = self.re_embed + 1
print(
F'Remapping {self.n_e} indices to {self.re_embed} indices. '
F'Using {self.unknown_index} for unknown indices.' )
else:
_snake_case = n_e
_snake_case = sane_index_shape
def lowerCamelCase ( self , lowerCAmelCase_ ):
"""simple docstring"""
_snake_case = inds.shape
assert len(lowerCAmelCase_ ) > 1
_snake_case = inds.reshape(ishape[0] , -1 )
_snake_case = self.used.to(lowerCAmelCase_ )
_snake_case = (inds[:, :, None] == used[None, None, ...]).long()
_snake_case = match.argmax(-1 )
_snake_case = match.sum(2 ) < 1
if self.unknown_index == "random":
_snake_case = torch.randint(0 , self.re_embed , size=new[unknown].shape ).to(device=new.device )
else:
_snake_case = self.unknown_index
return new.reshape(lowerCAmelCase_ )
def lowerCamelCase ( self , lowerCAmelCase_ ):
"""simple docstring"""
_snake_case = inds.shape
assert len(lowerCAmelCase_ ) > 1
_snake_case = inds.reshape(ishape[0] , -1 )
_snake_case = self.used.to(lowerCAmelCase_ )
if self.re_embed > self.used.shape[0]: # extra token
_snake_case = 0 # simply set to zero
_snake_case = torch.gather(used[None, :][inds.shape[0] * [0], :] , 1 , lowerCAmelCase_ )
return back.reshape(lowerCAmelCase_ )
def lowerCamelCase ( self , lowerCAmelCase_ ):
"""simple docstring"""
_snake_case = z.permute(0 , 2 , 3 , 1 ).contiguous()
_snake_case = z.view(-1 , self.vq_embed_dim )
# distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
_snake_case = torch.argmin(torch.cdist(lowerCAmelCase_ , self.embedding.weight ) , dim=1 )
_snake_case = self.embedding(lowerCAmelCase_ ).view(z.shape )
_snake_case = None
_snake_case = None
# compute loss for embedding
if not self.legacy:
_snake_case = self.beta * torch.mean((z_q.detach() - z) ** 2 ) + torch.mean((z_q - z.detach()) ** 2 )
else:
_snake_case = torch.mean((z_q.detach() - z) ** 2 ) + self.beta * torch.mean((z_q - z.detach()) ** 2 )
# preserve gradients
_snake_case = z + (z_q - z).detach()
# reshape back to match original input shape
_snake_case = z_q.permute(0 , 3 , 1 , 2 ).contiguous()
if self.remap is not None:
_snake_case = min_encoding_indices.reshape(z.shape[0] , -1 ) # add batch axis
_snake_case = self.remap_to_used(lowerCAmelCase_ )
_snake_case = min_encoding_indices.reshape(-1 , 1 ) # flatten
if self.sane_index_shape:
_snake_case = min_encoding_indices.reshape(z_q.shape[0] , z_q.shape[2] , z_q.shape[3] )
return z_q, loss, (perplexity, min_encodings, min_encoding_indices)
def lowerCamelCase ( self , lowerCAmelCase_ , lowerCAmelCase_ ):
"""simple docstring"""
if self.remap is not None:
_snake_case = indices.reshape(shape[0] , -1 ) # add batch axis
_snake_case = self.unmap_to_all(lowerCAmelCase_ )
_snake_case = indices.reshape(-1 ) # flatten again
# get quantized latent vectors
_snake_case = self.embedding(lowerCAmelCase_ )
if shape is not None:
_snake_case = z_q.view(lowerCAmelCase_ )
# reshape back to match original input shape
_snake_case = z_q.permute(0 , 3 , 1 , 2 ).contiguous()
return z_q
class __UpperCAmelCase ( _lowerCamelCase ):
def __init__( self , lowerCAmelCase_ , lowerCAmelCase_=False ):
"""simple docstring"""
_snake_case = parameters
_snake_case , _snake_case = torch.chunk(lowerCAmelCase_ , 2 , dim=1 )
_snake_case = torch.clamp(self.logvar , -30.0 , 20.0 )
_snake_case = deterministic
_snake_case = torch.exp(0.5 * self.logvar )
_snake_case = torch.exp(self.logvar )
if self.deterministic:
_snake_case = _snake_case = torch.zeros_like(
self.mean , device=self.parameters.device , dtype=self.parameters.dtype )
def lowerCamelCase ( self , lowerCAmelCase_ = None ):
"""simple docstring"""
_snake_case = randn_tensor(
self.mean.shape , generator=lowerCAmelCase_ , device=self.parameters.device , dtype=self.parameters.dtype )
_snake_case = self.mean + self.std * sample
return x
def lowerCamelCase ( self , lowerCAmelCase_=None ):
"""simple docstring"""
if self.deterministic:
return torch.Tensor([0.0] )
else:
if other is None:
return 0.5 * torch.sum(torch.pow(self.mean , 2 ) + self.var - 1.0 - self.logvar , dim=[1, 2, 3] )
else:
return 0.5 * torch.sum(
torch.pow(self.mean - other.mean , 2 ) / other.var
+ self.var / other.var
- 1.0
- self.logvar
+ other.logvar , dim=[1, 2, 3] , )
def lowerCamelCase ( self , lowerCAmelCase_ , lowerCAmelCase_=[1, 2, 3] ):
"""simple docstring"""
if self.deterministic:
return torch.Tensor([0.0] )
_snake_case = np.log(2.0 * np.pi )
return 0.5 * torch.sum(logtwopi + self.logvar + torch.pow(sample - self.mean , 2 ) / self.var , dim=lowerCAmelCase_ )
def lowerCamelCase ( self ):
"""simple docstring"""
return self.mean
| 42
| 0
|
'''simple docstring'''
import os
from itertools import chain
from random import randrange, shuffle
import pytest
from .sola import PokerHand
A : List[str] = (
'''4S 3H 2C 7S 5H''',
'''9D 8H 2C 6S 7H''',
'''2D 6D 9D TH 7D''',
'''TC 8C 2S JH 6C''',
'''JH 8S TH AH QH''',
'''TS KS 5S 9S AC''',
'''KD 6S 9D TH AD''',
'''KS 8D 4D 9S 4S''', # pair
'''8C 4S KH JS 4D''', # pair
'''QH 8H KD JH 8S''', # pair
'''KC 4H KS 2H 8D''', # pair
'''KD 4S KC 3H 8S''', # pair
'''AH 8S AS KC JH''', # pair
'''3H 4C 4H 3S 2H''', # 2 pairs
'''5S 5D 2C KH KH''', # 2 pairs
'''3C KH 5D 5S KH''', # 2 pairs
'''AS 3C KH AD KH''', # 2 pairs
'''7C 7S 3S 7H 5S''', # 3 of a kind
'''7C 7S KH 2H 7H''', # 3 of a kind
'''AC KH QH AH AS''', # 3 of a kind
'''2H 4D 3C AS 5S''', # straight (low ace)
'''3C 5C 4C 2C 6H''', # straight
'''6S 8S 7S 5H 9H''', # straight
'''JS QS 9H TS KH''', # straight
'''QC KH TS JS AH''', # straight (high ace)
'''8C 9C 5C 3C TC''', # flush
'''3S 8S 9S 5S KS''', # flush
'''4C 5C 9C 8C KC''', # flush
'''JH 8H AH KH QH''', # flush
'''3D 2H 3H 2C 2D''', # full house
'''2H 2C 3S 3H 3D''', # full house
'''KH KC 3S 3H 3D''', # full house
'''JC 6H JS JD JH''', # 4 of a kind
'''JC 7H JS JD JH''', # 4 of a kind
'''JC KH JS JD JH''', # 4 of a kind
'''2S AS 4S 5S 3S''', # straight flush (low ace)
'''2D 6D 3D 4D 5D''', # straight flush
'''5C 6C 3C 7C 4C''', # straight flush
'''JH 9H TH KH QH''', # straight flush
'''JH AH TH KH QH''', # royal flush (high ace straight flush)
)
A : Any = (
('''2H 3H 4H 5H 6H''', '''KS AS TS QS JS''', '''Loss'''),
('''2H 3H 4H 5H 6H''', '''AS AD AC AH JD''', '''Win'''),
('''AS AH 2H AD AC''', '''JS JD JC JH 3D''', '''Win'''),
('''2S AH 2H AS AC''', '''JS JD JC JH AD''', '''Loss'''),
('''2S AH 2H AS AC''', '''2H 3H 5H 6H 7H''', '''Win'''),
('''AS 3S 4S 8S 2S''', '''2H 3H 5H 6H 7H''', '''Win'''),
('''2H 3H 5H 6H 7H''', '''2S 3H 4H 5S 6C''', '''Win'''),
('''2S 3H 4H 5S 6C''', '''3D 4C 5H 6H 2S''', '''Tie'''),
('''2S 3H 4H 5S 6C''', '''AH AC 5H 6H AS''', '''Win'''),
('''2S 2H 4H 5S 4C''', '''AH AC 5H 6H AS''', '''Loss'''),
('''2S 2H 4H 5S 4C''', '''AH AC 5H 6H 7S''', '''Win'''),
('''6S AD 7H 4S AS''', '''AH AC 5H 6H 7S''', '''Loss'''),
('''2S AH 4H 5S KC''', '''AH AC 5H 6H 7S''', '''Loss'''),
('''2S 3H 6H 7S 9C''', '''7H 3C TH 6H 9S''', '''Loss'''),
('''4S 5H 6H TS AC''', '''3S 5H 6H TS AC''', '''Win'''),
('''2S AH 4H 5S 6C''', '''AD 4C 5H 6H 2C''', '''Tie'''),
('''AS AH 3H AD AC''', '''AS AH 2H AD AC''', '''Win'''),
('''AH AC 5H 5C QS''', '''AH AC 5H 5C KS''', '''Loss'''),
('''AH AC 5H 5C QS''', '''KH KC 5H 5C QS''', '''Win'''),
('''7C 7S KH 2H 7H''', '''3C 3S AH 2H 3H''', '''Win'''),
('''3C 3S AH 2H 3H''', '''7C 7S KH 2H 7H''', '''Loss'''),
('''6H 5H 4H 3H 2H''', '''5H 4H 3H 2H AH''', '''Win'''),
('''5H 4H 3H 2H AH''', '''5H 4H 3H 2H AH''', '''Tie'''),
('''5H 4H 3H 2H AH''', '''6H 5H 4H 3H 2H''', '''Loss'''),
('''AH AD KS KC AC''', '''AH KD KH AC KC''', '''Win'''),
('''2H 4D 3C AS 5S''', '''2H 4D 3C 6S 5S''', '''Loss'''),
('''2H 3S 3C 3H 2S''', '''3S 3C 2S 2H 2D''', '''Win'''),
('''4D 6D 5D 2D JH''', '''3S 8S 3H TC KH''', '''Loss'''),
('''4S 6C 8S 3S 7S''', '''AD KS 2D 7D 7C''', '''Loss'''),
('''6S 4C 7H 8C 3H''', '''5H JC AH 9D 9C''', '''Loss'''),
('''9D 9H JH TC QH''', '''3C 2S JS 5C 7H''', '''Win'''),
('''2H TC 8S AD 9S''', '''4H TS 7H 2C 5C''', '''Win'''),
('''9D 3S 2C 7S 7C''', '''JC TD 3C TC 9H''', '''Loss'''),
)
A : Optional[Any] = (
('''2H 3H 4H 5H 6H''', True),
('''AS AH 2H AD AC''', False),
('''2H 3H 5H 6H 7H''', True),
('''KS AS TS QS JS''', True),
('''8H 9H QS JS TH''', False),
('''AS 3S 4S 8S 2S''', True),
)
A : List[Any] = (
('''2H 3H 4H 5H 6H''', True),
('''AS AH 2H AD AC''', False),
('''2H 3H 5H 6H 7H''', False),
('''KS AS TS QS JS''', True),
('''8H 9H QS JS TH''', True),
)
A : List[str] = (
('''2H 4D 3C AS 5S''', True, [5, 4, 3, 2, 14]),
('''2H 5D 3C AS 5S''', False, [14, 5, 5, 3, 2]),
('''JH QD KC AS TS''', False, [14, 13, 12, 11, 10]),
('''9D 3S 2C 7S 7C''', False, [9, 7, 7, 3, 2]),
)
A : Optional[Any] = (
('''JH AH TH KH QH''', 0),
('''JH 9H TH KH QH''', 0),
('''JC KH JS JD JH''', 7),
('''KH KC 3S 3H 3D''', 6),
('''8C 9C 5C 3C TC''', 0),
('''JS QS 9H TS KH''', 0),
('''7C 7S KH 2H 7H''', 3),
('''3C KH 5D 5S KH''', 2),
('''QH 8H KD JH 8S''', 1),
('''2D 6D 9D TH 7D''', 0),
)
A : str = (
('''JH AH TH KH QH''', 23),
('''JH 9H TH KH QH''', 22),
('''JC KH JS JD JH''', 21),
('''KH KC 3S 3H 3D''', 20),
('''8C 9C 5C 3C TC''', 19),
('''JS QS 9H TS KH''', 18),
('''7C 7S KH 2H 7H''', 17),
('''3C KH 5D 5S KH''', 16),
('''QH 8H KD JH 8S''', 15),
('''2D 6D 9D TH 7D''', 14),
)
def lowerCAmelCase__ ( ):
_A , _A : Dict = randrange(len(lowerCamelCase ) ), randrange(len(lowerCamelCase ) )
_A : Dict = ['Loss', 'Tie', 'Win'][(play >= oppo) + (play > oppo)]
_A , _A : Any = SORTED_HANDS[play], SORTED_HANDS[oppo]
return hand, other, expected
def lowerCAmelCase__ ( lowerCamelCase : int = 100 ):
return (generate_random_hand() for _ in range(lowerCamelCase ))
@pytest.mark.parametrize('hand, expected' ,lowerCamelCase )
def lowerCAmelCase__ ( lowerCamelCase : Dict ,lowerCamelCase : Dict ):
assert PokerHand(lowerCamelCase )._is_flush() == expected
@pytest.mark.parametrize('hand, expected' ,lowerCamelCase )
def lowerCAmelCase__ ( lowerCamelCase : Dict ,lowerCamelCase : Dict ):
assert PokerHand(lowerCamelCase )._is_straight() == expected
@pytest.mark.parametrize('hand, expected, card_values' ,lowerCamelCase )
def lowerCAmelCase__ ( lowerCamelCase : Dict ,lowerCamelCase : Any ,lowerCamelCase : List[Any] ):
_A : List[Any] = PokerHand(lowerCamelCase )
assert player._is_five_high_straight() == expected
assert player._card_values == card_values
@pytest.mark.parametrize('hand, expected' ,lowerCamelCase )
def lowerCAmelCase__ ( lowerCamelCase : int ,lowerCamelCase : Optional[int] ):
assert PokerHand(lowerCamelCase )._is_same_kind() == expected
@pytest.mark.parametrize('hand, expected' ,lowerCamelCase )
def lowerCAmelCase__ ( lowerCamelCase : Dict ,lowerCamelCase : str ):
assert PokerHand(lowerCamelCase )._hand_type == expected
@pytest.mark.parametrize('hand, other, expected' ,lowerCamelCase )
def lowerCAmelCase__ ( lowerCamelCase : Union[str, Any] ,lowerCamelCase : str ,lowerCamelCase : List[Any] ):
assert PokerHand(lowerCamelCase ).compare_with(PokerHand(lowerCamelCase ) ) == expected
@pytest.mark.parametrize('hand, other, expected' ,generate_random_hands() )
def lowerCAmelCase__ ( lowerCamelCase : Union[str, Any] ,lowerCamelCase : Optional[Any] ,lowerCamelCase : Dict ):
assert PokerHand(lowerCamelCase ).compare_with(PokerHand(lowerCamelCase ) ) == expected
def lowerCAmelCase__ ( ):
_A : int = [PokerHand(lowerCamelCase ) for hand in SORTED_HANDS]
_A : str = poker_hands.copy()
shuffle(lowerCamelCase )
_A : List[str] = chain(sorted(lowerCamelCase ) )
for index, hand in enumerate(lowerCamelCase ):
assert hand == poker_hands[index]
def lowerCAmelCase__ ( ):
# Test that five high straights are compared correctly.
_A : Tuple = [PokerHand('2D AC 3H 4H 5S' ), PokerHand('2S 3H 4H 5S 6C' )]
pokerhands.sort(reverse=lowerCamelCase )
assert pokerhands[0].__str__() == "2S 3H 4H 5S 6C"
def lowerCAmelCase__ ( ):
# Multiple calls to five_high_straight function should still return True
# and shouldn't mutate the list in every call other than the first.
_A : List[Any] = PokerHand('2C 4S AS 3D 5C' )
_A : List[Any] = True
_A : int = [5, 4, 3, 2, 14]
for _ in range(10 ):
assert pokerhand._is_five_high_straight() == expected
assert pokerhand._card_values == expected_card_values
def lowerCAmelCase__ ( ):
# Problem number 54 from Project Euler
# Testing from poker_hands.txt file
_A : Union[str, Any] = 0
_A : List[Any] = os.path.abspath(os.path.dirname(lowerCamelCase ) )
_A : Tuple = os.path.join(lowerCamelCase ,'poker_hands.txt' )
with open(lowerCamelCase ) as file_hand:
for line in file_hand:
_A : str = line[:14].strip()
_A : Tuple = line[15:].strip()
_A , _A : Union[str, Any] = PokerHand(lowerCamelCase ), PokerHand(lowerCamelCase )
_A : Optional[int] = player.compare_with(lowerCamelCase )
if output == "Win":
answer += 1
assert answer == 376
| 227
|
'''simple docstring'''
class __lowerCamelCase : # Public class to implement a graph
"""simple docstring"""
def __init__( self : Optional[Any] , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : list[list[bool]]):
_A : List[Any] = row
_A : Union[str, Any] = col
_A : List[str] = graph
def A ( self : Any , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : list[list[bool]]):
return (
0 <= i < self.ROW
and 0 <= j < self.COL
and not visited[i][j]
and self.graph[i][j]
)
def A ( self : Tuple , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : list[list[bool]]):
# Checking all 8 elements surrounding nth element
_A : Tuple = [-1, -1, -1, 0, 0, 1, 1, 1] # Coordinate order
_A : Dict = [-1, 0, 1, -1, 1, -1, 0, 1]
_A : List[Any] = True # Make those cells visited
for k in range(8):
if self.is_safe(i + row_nbr[k] , j + col_nbr[k] , SCREAMING_SNAKE_CASE):
self.diffs(i + row_nbr[k] , j + col_nbr[k] , SCREAMING_SNAKE_CASE)
def A ( self : int): # And finally, count all islands.
_A : Dict = [[False for j in range(self.COL)] for i in range(self.ROW)]
_A : Union[str, Any] = 0
for i in range(self.ROW):
for j in range(self.COL):
if visited[i][j] is False and self.graph[i][j] == 1:
self.diffs(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE)
count += 1
return count
| 227
| 1
|
"""simple docstring"""
import inspect
import unittest
from transformers import BitConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import BitBackbone, BitForImageClassification, BitImageProcessor, BitModel
from transformers.models.bit.modeling_bit import BIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
class A_ :
"""simple docstring"""
def __init__( self :Tuple , lowercase_ :str , lowercase_ :Optional[Any]=3 , lowercase_ :List[str]=32 , lowercase_ :str=3 , lowercase_ :Optional[int]=10 , lowercase_ :Union[str, Any]=[8, 16, 32, 64] , lowercase_ :Any=[1, 1, 2, 1] , lowercase_ :Dict=True , lowercase_ :Optional[int]=True , lowercase_ :List[Any]="relu" , lowercase_ :List[str]=3 , lowercase_ :Optional[Any]=None , lowercase_ :Dict=["stage2", "stage3", "stage4"] , lowercase_ :str=[2, 3, 4] , lowercase_ :Union[str, Any]=1 , ) -> Union[str, Any]:
UpperCAmelCase = parent
UpperCAmelCase = batch_size
UpperCAmelCase = image_size
UpperCAmelCase = num_channels
UpperCAmelCase = embeddings_size
UpperCAmelCase = hidden_sizes
UpperCAmelCase = depths
UpperCAmelCase = is_training
UpperCAmelCase = use_labels
UpperCAmelCase = hidden_act
UpperCAmelCase = num_labels
UpperCAmelCase = scope
UpperCAmelCase = len(lowercase_ )
UpperCAmelCase = out_features
UpperCAmelCase = out_indices
UpperCAmelCase = num_groups
def UpperCAmelCase__ ( self :Optional[int] ) -> List[str]:
UpperCAmelCase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
UpperCAmelCase = None
if self.use_labels:
UpperCAmelCase = ids_tensor([self.batch_size] , self.num_labels )
UpperCAmelCase = self.get_config()
return config, pixel_values, labels
def UpperCAmelCase__ ( self :Union[str, Any] ) -> List[str]:
return BitConfig(
num_channels=self.num_channels , embeddings_size=self.embeddings_size , hidden_sizes=self.hidden_sizes , depths=self.depths , hidden_act=self.hidden_act , num_labels=self.num_labels , out_features=self.out_features , out_indices=self.out_indices , num_groups=self.num_groups , )
def UpperCAmelCase__ ( self :List[str] , lowercase_ :str , lowercase_ :List[str] , lowercase_ :Dict ) -> List[Any]:
UpperCAmelCase = BitModel(config=lowercase_ )
model.to(lowercase_ )
model.eval()
UpperCAmelCase = model(lowercase_ )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32) , )
def UpperCAmelCase__ ( self :Tuple , lowercase_ :List[str] , lowercase_ :Optional[int] , lowercase_ :Tuple ) -> List[str]:
UpperCAmelCase = self.num_labels
UpperCAmelCase = BitForImageClassification(lowercase_ )
model.to(lowercase_ )
model.eval()
UpperCAmelCase = model(lowercase_ , labels=lowercase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def UpperCAmelCase__ ( self :Union[str, Any] , lowercase_ :Optional[int] , lowercase_ :Tuple , lowercase_ :Optional[Any] ) -> List[str]:
UpperCAmelCase = BitBackbone(config=lowercase_ )
model.to(lowercase_ )
model.eval()
UpperCAmelCase = model(lowercase_ )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ) , len(config.out_features ) )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [self.batch_size, self.hidden_sizes[1], 4, 4] )
# verify channels
self.parent.assertEqual(len(model.channels ) , len(config.out_features ) )
self.parent.assertListEqual(model.channels , config.hidden_sizes[1:] )
# verify backbone works with out_features=None
UpperCAmelCase = None
UpperCAmelCase = BitBackbone(config=lowercase_ )
model.to(lowercase_ )
model.eval()
UpperCAmelCase = model(lowercase_ )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ) , 1 )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [self.batch_size, self.hidden_sizes[-1], 1, 1] )
# verify channels
self.parent.assertEqual(len(model.channels ) , 1 )
self.parent.assertListEqual(model.channels , [config.hidden_sizes[-1]] )
def UpperCAmelCase__ ( self :int ) -> int:
UpperCAmelCase = self.prepare_config_and_inputs()
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = config_and_inputs
UpperCAmelCase = {'pixel_values': pixel_values}
return config, inputs_dict
@require_torch
class A_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , unittest.TestCase ):
"""simple docstring"""
__UpperCamelCase = (BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
__UpperCamelCase = (
{"""feature-extraction""": BitModel, """image-classification""": BitForImageClassification}
if is_torch_available()
else {}
)
__UpperCamelCase = False
__UpperCamelCase = False
__UpperCamelCase = False
__UpperCamelCase = False
__UpperCamelCase = False
def UpperCAmelCase__ ( self :Tuple ) -> Union[str, Any]:
UpperCAmelCase = BitModelTester(self )
UpperCAmelCase = ConfigTester(self , config_class=lowercase_ , has_text_modality=lowercase_ )
def UpperCAmelCase__ ( self :Optional[int] ) -> str:
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def UpperCAmelCase__ ( self :int ) -> Tuple:
return
@unittest.skip(reason='Bit does not output attentions' )
def UpperCAmelCase__ ( self :Dict ) -> int:
pass
@unittest.skip(reason='Bit does not use inputs_embeds' )
def UpperCAmelCase__ ( self :str ) -> Dict:
pass
@unittest.skip(reason='Bit does not support input and output embeddings' )
def UpperCAmelCase__ ( self :str ) -> Optional[Any]:
pass
def UpperCAmelCase__ ( self :List[Any] ) -> Union[str, Any]:
UpperCAmelCase , UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase = model_class(lowercase_ )
UpperCAmelCase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
UpperCAmelCase = [*signature.parameters.keys()]
UpperCAmelCase = ['pixel_values']
self.assertListEqual(arg_names[:1] , lowercase_ )
def UpperCAmelCase__ ( self :int ) -> List[Any]:
UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowercase_ )
def UpperCAmelCase__ ( self :int ) -> List[Any]:
UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_backbone(*lowercase_ )
def UpperCAmelCase__ ( self :int ) -> Optional[Any]:
UpperCAmelCase , UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase = model_class(config=lowercase_ )
for name, module in model.named_modules():
if isinstance(lowercase_ , (nn.BatchNormad, nn.GroupNorm) ):
self.assertTrue(
torch.all(module.weight == 1 ) , msg=f"""Parameter {name} of model {model_class} seems not properly initialized""" , )
self.assertTrue(
torch.all(module.bias == 0 ) , msg=f"""Parameter {name} of model {model_class} seems not properly initialized""" , )
def UpperCAmelCase__ ( self :List[Any] ) -> str:
def check_hidden_states_output(lowercase_ :int , lowercase_ :Optional[Any] , lowercase_ :List[str] ):
UpperCAmelCase = model_class(lowercase_ )
model.to(lowercase_ )
model.eval()
with torch.no_grad():
UpperCAmelCase = model(**self._prepare_for_class(lowercase_ , lowercase_ ) )
UpperCAmelCase = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
UpperCAmelCase = self.model_tester.num_stages
self.assertEqual(len(lowercase_ ) , expected_num_stages + 1 )
# Bit's feature maps are of shape (batch_size, num_channels, height, width)
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [self.model_tester.image_size // 4, self.model_tester.image_size // 4] , )
UpperCAmelCase , UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
UpperCAmelCase = ['preactivation', 'bottleneck']
for model_class in self.all_model_classes:
for layer_type in layers_type:
UpperCAmelCase = layer_type
UpperCAmelCase = True
check_hidden_states_output(lowercase_ , lowercase_ , lowercase_ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
UpperCAmelCase = True
check_hidden_states_output(lowercase_ , lowercase_ , lowercase_ )
@unittest.skip(reason='Bit does not use feedforward chunking' )
def UpperCAmelCase__ ( self :Optional[int] ) -> List[Any]:
pass
def UpperCAmelCase__ ( self :Union[str, Any] ) -> Union[str, Any]:
UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*lowercase_ )
@slow
def UpperCAmelCase__ ( self :Optional[int] ) -> Union[str, Any]:
for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCAmelCase = BitModel.from_pretrained(lowercase_ )
self.assertIsNotNone(lowercase_ )
def _lowerCAmelCase ( ):
UpperCAmelCase = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_torch
@require_vision
class A_ ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def UpperCAmelCase__ ( self :Tuple ) -> Tuple:
return (
BitImageProcessor.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0] ) if is_vision_available() else None
)
@slow
def UpperCAmelCase__ ( self :List[str] ) -> List[Any]:
UpperCAmelCase = BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0] ).to(lowercase_ )
UpperCAmelCase = self.default_image_processor
UpperCAmelCase = prepare_img()
UpperCAmelCase = image_processor(images=lowercase_ , return_tensors='pt' ).to(lowercase_ )
# forward pass
with torch.no_grad():
UpperCAmelCase = model(**lowercase_ )
# verify the logits
UpperCAmelCase = torch.Size((1, 10_00) )
self.assertEqual(outputs.logits.shape , lowercase_ )
UpperCAmelCase = torch.tensor([[-0.6526, -0.5263, -1.4398]] ).to(lowercase_ )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , lowercase_ , atol=1E-4 ) )
@require_torch
class A_ ( SCREAMING_SNAKE_CASE_ , unittest.TestCase ):
"""simple docstring"""
__UpperCamelCase = (BitBackbone,) if is_torch_available() else ()
__UpperCamelCase = BitConfig
__UpperCamelCase = False
def UpperCAmelCase__ ( self :Optional[int] ) -> List[str]:
UpperCAmelCase = BitModelTester(self )
| 78
|
"""simple docstring"""
import os
from typing import Dict, List, Tuple, TypeVar, Union
UpperCAmelCase : Union[str, Any] = TypeVar("T")
UpperCAmelCase : Dict = Union[List[T], Tuple[T, ...]]
UpperCAmelCase : int = Union[T, List[T], Dict[str, T]]
UpperCAmelCase : Tuple = Union[str, bytes, os.PathLike]
| 136
| 0
|
def SCREAMING_SNAKE_CASE_ (UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> int:
def update_area_of_max_square(UpperCamelCase , UpperCamelCase ) -> int:
# BASE CASE
if row >= rows or col >= cols:
return 0
lowerCamelCase__ : Union[str, Any] = update_area_of_max_square(UpperCamelCase , col + 1 )
lowerCamelCase__ : Tuple = update_area_of_max_square(row + 1 , col + 1 )
lowerCamelCase__ : str = update_area_of_max_square(row + 1 , UpperCamelCase )
if mat[row][col]:
lowerCamelCase__ : Optional[Any] = 1 + min([right, diagonal, down] )
lowerCamelCase__ : Optional[Any] = max(largest_square_area[0] , UpperCamelCase )
return sub_problem_sol
else:
return 0
lowerCamelCase__ : Optional[int] = [0]
update_area_of_max_square(0 , 0 )
return largest_square_area[0]
def SCREAMING_SNAKE_CASE_ (UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> int:
def update_area_of_max_square_using_dp_array(
UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> int:
if row >= rows or col >= cols:
return 0
if dp_array[row][col] != -1:
return dp_array[row][col]
lowerCamelCase__ : Union[str, Any] = update_area_of_max_square_using_dp_array(UpperCamelCase , col + 1 , UpperCamelCase )
lowerCamelCase__ : List[Any] = update_area_of_max_square_using_dp_array(row + 1 , col + 1 , UpperCamelCase )
lowerCamelCase__ : List[Any] = update_area_of_max_square_using_dp_array(row + 1 , UpperCamelCase , UpperCamelCase )
if mat[row][col]:
lowerCamelCase__ : str = 1 + min([right, diagonal, down] )
lowerCamelCase__ : int = max(largest_square_area[0] , UpperCamelCase )
lowerCamelCase__ : Tuple = sub_problem_sol
return sub_problem_sol
else:
return 0
lowerCamelCase__ : Tuple = [0]
lowerCamelCase__ : Optional[int] = [[-1] * cols for _ in range(UpperCamelCase )]
update_area_of_max_square_using_dp_array(0 , 0 , UpperCamelCase )
return largest_square_area[0]
def SCREAMING_SNAKE_CASE_ (UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> int:
lowerCamelCase__ : str = [[0] * (cols + 1) for _ in range(rows + 1 )]
lowerCamelCase__ : int = 0
for row in range(rows - 1 , -1 , -1 ):
for col in range(cols - 1 , -1 , -1 ):
lowerCamelCase__ : Tuple = dp_array[row][col + 1]
lowerCamelCase__ : Optional[Any] = dp_array[row + 1][col + 1]
lowerCamelCase__ : int = dp_array[row + 1][col]
if mat[row][col] == 1:
lowerCamelCase__ : List[Any] = 1 + min(UpperCamelCase , UpperCamelCase , UpperCamelCase )
lowerCamelCase__ : int = max(dp_array[row][col] , UpperCamelCase )
else:
lowerCamelCase__ : Union[str, Any] = 0
return largest_square_area
def SCREAMING_SNAKE_CASE_ (UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> int:
lowerCamelCase__ : List[str] = [0] * (cols + 1)
lowerCamelCase__ : Optional[int] = [0] * (cols + 1)
lowerCamelCase__ : Dict = 0
for row in range(rows - 1 , -1 , -1 ):
for col in range(cols - 1 , -1 , -1 ):
lowerCamelCase__ : List[str] = current_row[col + 1]
lowerCamelCase__ : Tuple = next_row[col + 1]
lowerCamelCase__ : Any = next_row[col]
if mat[row][col] == 1:
lowerCamelCase__ : Dict = 1 + min(UpperCamelCase , UpperCamelCase , UpperCamelCase )
lowerCamelCase__ : str = max(current_row[col] , UpperCamelCase )
else:
lowerCamelCase__ : List[str] = 0
lowerCamelCase__ : Optional[Any] = current_row
return largest_square_area
if __name__ == "__main__":
import doctest
doctest.testmod()
print(largest_square_area_in_matrix_bottom_up(2, 2, [[1, 1], [1, 1]]))
| 367
|
'''simple docstring'''
import argparse
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline
if __name__ == "__main__":
_A : List[Any] =argparse.ArgumentParser()
parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')
parser.add_argument(
'''--txt2img_unclip''',
default='''kakaobrain/karlo-v1-alpha''',
type=str,
required=False,
help='''The pretrained txt2img unclip.''',
)
_A : Dict =parser.parse_args()
_A : List[str] =UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)
_A : Any =CLIPImageProcessor()
_A : Union[str, Any] =CLIPVisionModelWithProjection.from_pretrained('''openai/clip-vit-large-patch14''')
_A : Union[str, Any] =UnCLIPImageVariationPipeline(
decoder=txtaimg.decoder,
text_encoder=txtaimg.text_encoder,
tokenizer=txtaimg.tokenizer,
text_proj=txtaimg.text_proj,
feature_extractor=feature_extractor,
image_encoder=image_encoder,
super_res_first=txtaimg.super_res_first,
super_res_last=txtaimg.super_res_last,
decoder_scheduler=txtaimg.decoder_scheduler,
super_res_scheduler=txtaimg.super_res_scheduler,
)
imgaimg.save_pretrained(args.dump_path)
| 129
| 0
|
'''simple docstring'''
from __future__ import annotations
def lowerCamelCase__ ( _A ):
a : List[str] = 0.00
a : List[Any] = 0
for resistor in resistors:
if resistor <= 0:
a : int = f"""Resistor at index {index} has a negative or zero value!"""
raise ValueError(SCREAMING_SNAKE_CASE_ )
first_sum += 1 / float(SCREAMING_SNAKE_CASE_ )
index += 1
return 1 / first_sum
def lowerCamelCase__ ( _A ):
a : Optional[Any] = 0.00
a : Any = 0
for resistor in resistors:
sum_r += resistor
if resistor < 0:
a : Dict = f"""Resistor at index {index} has a negative value!"""
raise ValueError(SCREAMING_SNAKE_CASE_ )
index += 1
return sum_r
if __name__ == "__main__":
import doctest
doctest.testmod()
| 297
|
import argparse
import os
import re
import packaging.version
UpperCamelCase__ = """examples/"""
UpperCamelCase__ = {
"""examples""": (re.compile(R"""^check_min_version\(\"[^\"]+\"\)\s*$""", re.MULTILINE), """check_min_version(\"VERSION\")\n"""),
"""init""": (re.compile(R"""^__version__\s+=\s+\"([^\"]+)\"\s*$""", re.MULTILINE), """__version__ = \"VERSION\"\n"""),
"""setup""": (re.compile(R"""^(\s*)version\s*=\s*\"[^\"]+\",""", re.MULTILINE), R"""\1version=\"VERSION\","""),
"""doc""": (re.compile(R"""^(\s*)release\s*=\s*\"[^\"]+\"$""", re.MULTILINE), """release = \"VERSION\"\n"""),
}
UpperCamelCase__ = {
"""init""": """src/transformers/__init__.py""",
"""setup""": """setup.py""",
}
UpperCamelCase__ = """README.md"""
def _a ( SCREAMING_SNAKE_CASE_ : Optional[int] , SCREAMING_SNAKE_CASE_ : Any , SCREAMING_SNAKE_CASE_ : List[str] ):
with open(SCREAMING_SNAKE_CASE_ , "r" , encoding="utf-8" , newline="\n" ) as f:
__lowerCAmelCase = f.read()
__lowerCAmelCase , __lowerCAmelCase = REPLACE_PATTERNS[pattern]
__lowerCAmelCase = replace.replace("VERSION" , SCREAMING_SNAKE_CASE_ )
__lowerCAmelCase = re_pattern.sub(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
with open(SCREAMING_SNAKE_CASE_ , "w" , encoding="utf-8" , newline="\n" ) as f:
f.write(SCREAMING_SNAKE_CASE_ )
def _a ( SCREAMING_SNAKE_CASE_ : List[Any] ):
for folder, directories, fnames in os.walk(SCREAMING_SNAKE_CASE_ ):
# Removing some of the folders with non-actively maintained examples from the walk
if "research_projects" in directories:
directories.remove("research_projects" )
if "legacy" in directories:
directories.remove("legacy" )
for fname in fnames:
if fname.endswith(".py" ):
update_version_in_file(os.path.join(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) , SCREAMING_SNAKE_CASE_ , pattern="examples" )
def _a ( SCREAMING_SNAKE_CASE_ : Tuple , SCREAMING_SNAKE_CASE_ : Optional[int]=False ):
for pattern, fname in REPLACE_FILES.items():
update_version_in_file(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
if not patch:
update_version_in_examples(SCREAMING_SNAKE_CASE_ )
def _a ( ):
__lowerCAmelCase = "🤗 Transformers currently provides the following architectures"
__lowerCAmelCase = "1. Want to contribute a new model?"
with open(SCREAMING_SNAKE_CASE_ , "r" , encoding="utf-8" , newline="\n" ) as f:
__lowerCAmelCase = f.readlines()
# Find the start of the list.
__lowerCAmelCase = 0
while not lines[start_index].startswith(_start_prompt ):
start_index += 1
start_index += 1
__lowerCAmelCase = start_index
# Update the lines in the model list.
while not lines[index].startswith(_end_prompt ):
if lines[index].startswith("1." ):
__lowerCAmelCase = lines[index].replace(
"https://huggingface.co/docs/transformers/main/model_doc" , "https://huggingface.co/docs/transformers/model_doc" , )
index += 1
with open(SCREAMING_SNAKE_CASE_ , "w" , encoding="utf-8" , newline="\n" ) as f:
f.writelines(SCREAMING_SNAKE_CASE_ )
def _a ( ):
with open(REPLACE_FILES["init"] , "r" ) as f:
__lowerCAmelCase = f.read()
__lowerCAmelCase = REPLACE_PATTERNS["init"][0].search(SCREAMING_SNAKE_CASE_ ).groups()[0]
return packaging.version.parse(SCREAMING_SNAKE_CASE_ )
def _a ( SCREAMING_SNAKE_CASE_ : List[Any]=False ):
__lowerCAmelCase = get_version()
if patch and default_version.is_devrelease:
raise ValueError("Can't create a patch version from the dev branch, checkout a released version!" )
if default_version.is_devrelease:
__lowerCAmelCase = default_version.base_version
elif patch:
__lowerCAmelCase = F"""{default_version.major}.{default_version.minor}.{default_version.micro + 1}"""
else:
__lowerCAmelCase = F"""{default_version.major}.{default_version.minor + 1}.0"""
# Now let's ask nicely if that's the right one.
__lowerCAmelCase = input(F"""Which version are you releasing? [{default_version}]""" )
if len(SCREAMING_SNAKE_CASE_ ) == 0:
__lowerCAmelCase = default_version
print(F"""Updating version to {version}.""" )
global_version_update(SCREAMING_SNAKE_CASE_ , patch=SCREAMING_SNAKE_CASE_ )
if not patch:
print("Cleaning main README, don't forget to run `make fix-copies`." )
clean_main_ref_in_model_list()
def _a ( ):
__lowerCAmelCase = get_version()
__lowerCAmelCase = F"""{current_version.major}.{current_version.minor + 1}.0.dev0"""
__lowerCAmelCase = current_version.base_version
# Check with the user we got that right.
__lowerCAmelCase = input(F"""Which version are we developing now? [{dev_version}]""" )
if len(SCREAMING_SNAKE_CASE_ ) == 0:
__lowerCAmelCase = dev_version
print(F"""Updating version to {version}.""" )
global_version_update(SCREAMING_SNAKE_CASE_ )
print("Cleaning main README, don't forget to run `make fix-copies`." )
clean_main_ref_in_model_list()
if __name__ == "__main__":
UpperCamelCase__ = argparse.ArgumentParser()
parser.add_argument("""--post_release""", action="""store_true""", help="""Whether this is pre or post release.""")
parser.add_argument("""--patch""", action="""store_true""", help="""Whether or not this is a patch release.""")
UpperCamelCase__ = parser.parse_args()
if not args.post_release:
pre_release_work(patch=args.patch)
elif args.patch:
print("""Nothing to do after a patch :-)""")
else:
post_release_work()
| 92
| 0
|
import os
import string
import sys
__UpperCamelCase : Any = 1 << 8
__UpperCamelCase : Optional[Any] = {
"tab": ord("\t"),
"newline": ord("\r"),
"esc": 27,
"up": 65 + ARROW_KEY_FLAG,
"down": 66 + ARROW_KEY_FLAG,
"right": 67 + ARROW_KEY_FLAG,
"left": 68 + ARROW_KEY_FLAG,
"mod_int": 91,
"undefined": sys.maxsize,
"interrupt": 3,
"insert": 50,
"delete": 51,
"pg_up": 53,
"pg_down": 54,
}
__UpperCamelCase : Union[str, Any] = KEYMAP["up"]
__UpperCamelCase : int = KEYMAP["left"]
if sys.platform == "win32":
__UpperCamelCase : Tuple = []
__UpperCamelCase : List[Any] = {
b"\xe0H": KEYMAP["up"] - ARROW_KEY_FLAG,
b"\x00H": KEYMAP["up"] - ARROW_KEY_FLAG,
b"\xe0P": KEYMAP["down"] - ARROW_KEY_FLAG,
b"\x00P": KEYMAP["down"] - ARROW_KEY_FLAG,
b"\xe0M": KEYMAP["right"] - ARROW_KEY_FLAG,
b"\x00M": KEYMAP["right"] - ARROW_KEY_FLAG,
b"\xe0K": KEYMAP["left"] - ARROW_KEY_FLAG,
b"\x00K": KEYMAP["left"] - ARROW_KEY_FLAG,
}
for i in range(10):
__UpperCamelCase : Any = ord(str(i))
def __A ( ) -> List[str]:
if os.name == "nt":
import msvcrt
a = """mbcs"""
# Flush the keyboard buffer
while msvcrt.kbhit():
msvcrt.getch()
if len(UpperCAmelCase__ ) == 0:
# Read the keystroke
a = msvcrt.getch()
# If it is a prefix char, get second part
if ch in (b"\x00", b"\xe0"):
a = ch + msvcrt.getch()
# Translate actual Win chars to bullet char types
try:
a = chr(WIN_KEYMAP[cha] )
WIN_CH_BUFFER.append(chr(KEYMAP["""mod_int"""] ) )
WIN_CH_BUFFER.append(UpperCAmelCase__ )
if ord(UpperCAmelCase__ ) in (
KEYMAP["insert"] - 1 << 9,
KEYMAP["delete"] - 1 << 9,
KEYMAP["pg_up"] - 1 << 9,
KEYMAP["pg_down"] - 1 << 9,
):
WIN_CH_BUFFER.append(chr(126 ) )
a = chr(KEYMAP["""esc"""] )
except KeyError:
a = cha[1]
else:
a = ch.decode(UpperCAmelCase__ )
else:
a = WIN_CH_BUFFER.pop(0 )
elif os.name == "posix":
import termios
import tty
a = sys.stdin.fileno()
a = termios.tcgetattr(UpperCAmelCase__ )
try:
tty.setraw(UpperCAmelCase__ )
a = sys.stdin.read(1 )
finally:
termios.tcsetattr(UpperCAmelCase__ , termios.TCSADRAIN , UpperCAmelCase__ )
return ch
def __A ( ) -> Union[str, Any]:
a = get_raw_chars()
if ord(UpperCAmelCase__ ) in [KEYMAP["interrupt"], KEYMAP["newline"]]:
return char
elif ord(UpperCAmelCase__ ) == KEYMAP["esc"]:
a = get_raw_chars()
if ord(UpperCAmelCase__ ) == KEYMAP["mod_int"]:
a = get_raw_chars()
if ord(UpperCAmelCase__ ) >= KEYMAP["arrow_begin"] - ARROW_KEY_FLAG and ord(UpperCAmelCase__ ) <= KEYMAP["arrow_end"] - ARROW_KEY_FLAG:
return chr(ord(UpperCAmelCase__ ) + ARROW_KEY_FLAG )
else:
return KEYMAP["undefined"]
else:
return get_raw_chars()
else:
if char in string.printable:
return char
else:
return KEYMAP["undefined"]
| 371
|
import unittest
import numpy as np
from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
from transformers.models.roberta.modeling_flax_roberta import (
FlaxRobertaForCausalLM,
FlaxRobertaForMaskedLM,
FlaxRobertaForMultipleChoice,
FlaxRobertaForQuestionAnswering,
FlaxRobertaForSequenceClassification,
FlaxRobertaForTokenClassification,
FlaxRobertaModel,
)
class __lowerCAmelCase ( unittest.TestCase ):
def __init__( self :Optional[int] , __magic_name__ :Tuple , __magic_name__ :Tuple=13 , __magic_name__ :List[Any]=7 , __magic_name__ :Optional[Any]=True , __magic_name__ :List[Any]=True , __magic_name__ :Union[str, Any]=True , __magic_name__ :List[str]=True , __magic_name__ :str=99 , __magic_name__ :Optional[Any]=32 , __magic_name__ :Union[str, Any]=5 , __magic_name__ :Any=4 , __magic_name__ :int=37 , __magic_name__ :Tuple="gelu" , __magic_name__ :List[str]=0.1 , __magic_name__ :Dict=0.1 , __magic_name__ :Tuple=512 , __magic_name__ :Dict=16 , __magic_name__ :Optional[int]=2 , __magic_name__ :Optional[int]=0.02 , __magic_name__ :Optional[Any]=4 , ):
'''simple docstring'''
a = parent
a = batch_size
a = seq_length
a = is_training
a = use_attention_mask
a = use_token_type_ids
a = use_labels
a = vocab_size
a = hidden_size
a = num_hidden_layers
a = num_attention_heads
a = intermediate_size
a = hidden_act
a = hidden_dropout_prob
a = attention_probs_dropout_prob
a = max_position_embeddings
a = type_vocab_size
a = type_sequence_label_size
a = initializer_range
a = num_choices
def lowerCamelCase__ ( self :int ):
'''simple docstring'''
a = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
a = None
if self.use_attention_mask:
a = random_attention_mask([self.batch_size, self.seq_length] )
a = None
if self.use_token_type_ids:
a = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
a = RobertaConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=__magic_name__ , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def lowerCamelCase__ ( self :List[Any] ):
'''simple docstring'''
a = self.prepare_config_and_inputs()
a , a , a , a = config_and_inputs
a = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
return config, inputs_dict
def lowerCamelCase__ ( self :Dict ):
'''simple docstring'''
a = self.prepare_config_and_inputs()
a , a , a , a = config_and_inputs
a = True
a = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
a = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
return (
config,
input_ids,
token_type_ids,
encoder_hidden_states,
encoder_attention_mask,
)
@require_flax
class __lowerCAmelCase ( __magic_name__ , unittest.TestCase ):
UpperCamelCase__ = True
UpperCamelCase__ = (
(
FlaxRobertaModel,
FlaxRobertaForCausalLM,
FlaxRobertaForMaskedLM,
FlaxRobertaForSequenceClassification,
FlaxRobertaForTokenClassification,
FlaxRobertaForMultipleChoice,
FlaxRobertaForQuestionAnswering,
)
if is_flax_available()
else ()
)
def lowerCamelCase__ ( self :Dict ):
'''simple docstring'''
a = FlaxRobertaModelTester(self )
@slow
def lowerCamelCase__ ( self :Optional[Any] ):
'''simple docstring'''
for model_class_name in self.all_model_classes:
a = model_class_name.from_pretrained("""roberta-base""" , from_pt=__magic_name__ )
a = model(np.ones((1, 1) ) )
self.assertIsNotNone(__magic_name__ )
| 347
| 0
|
import gc
import inspect
import unittest
import torch
from parameterized import parameterized
from diffusers import PriorTransformer
from diffusers.utils import floats_tensor, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMixin
enable_full_determinism()
class __snake_case ( a , unittest.TestCase ):
UpperCAmelCase__ : Tuple = PriorTransformer
UpperCAmelCase__ : Tuple = '''hidden_states'''
@property
def lowerCamelCase ( self : Any):
"""simple docstring"""
UpperCAmelCase_ = 4
UpperCAmelCase_ = 8
UpperCAmelCase_ = 7
UpperCAmelCase_ = floats_tensor((batch_size, embedding_dim)).to(_snake_case)
UpperCAmelCase_ = floats_tensor((batch_size, embedding_dim)).to(_snake_case)
UpperCAmelCase_ = floats_tensor((batch_size, num_embeddings, embedding_dim)).to(_snake_case)
return {
"hidden_states": hidden_states,
"timestep": 2,
"proj_embedding": proj_embedding,
"encoder_hidden_states": encoder_hidden_states,
}
def lowerCamelCase ( self : int , _snake_case : List[str]=0):
"""simple docstring"""
torch.manual_seed(_snake_case)
UpperCAmelCase_ = 4
UpperCAmelCase_ = 8
UpperCAmelCase_ = 7
UpperCAmelCase_ = torch.randn((batch_size, embedding_dim)).to(_snake_case)
UpperCAmelCase_ = torch.randn((batch_size, embedding_dim)).to(_snake_case)
UpperCAmelCase_ = torch.randn((batch_size, num_embeddings, embedding_dim)).to(_snake_case)
return {
"hidden_states": hidden_states,
"timestep": 2,
"proj_embedding": proj_embedding,
"encoder_hidden_states": encoder_hidden_states,
}
@property
def lowerCamelCase ( self : List[Any]):
"""simple docstring"""
return (4, 8)
@property
def lowerCamelCase ( self : str):
"""simple docstring"""
return (4, 8)
def lowerCamelCase ( self : List[Any]):
"""simple docstring"""
UpperCAmelCase_ = {
'''num_attention_heads''': 2,
'''attention_head_dim''': 4,
'''num_layers''': 2,
'''embedding_dim''': 8,
'''num_embeddings''': 7,
'''additional_embeddings''': 4,
}
UpperCAmelCase_ = self.dummy_input
return init_dict, inputs_dict
def lowerCamelCase ( self : int):
"""simple docstring"""
UpperCAmelCase_ , UpperCAmelCase_ = PriorTransformer.from_pretrained(
'''hf-internal-testing/prior-dummy''' , output_loading_info=_snake_case)
self.assertIsNotNone(_snake_case)
self.assertEqual(len(loading_info['''missing_keys''']) , 0)
model.to(_snake_case)
UpperCAmelCase_ = model(**self.dummy_input)[0]
assert hidden_states is not None, "Make sure output is not None"
def lowerCamelCase ( self : List[str]):
"""simple docstring"""
UpperCAmelCase_ , UpperCAmelCase_ = self.prepare_init_args_and_inputs_for_common()
UpperCAmelCase_ = self.model_class(**_snake_case)
UpperCAmelCase_ = inspect.signature(model.forward)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
UpperCAmelCase_ = [*signature.parameters.keys()]
UpperCAmelCase_ = ['''hidden_states''', '''timestep''']
self.assertListEqual(arg_names[:2] , _snake_case)
def lowerCamelCase ( self : str):
"""simple docstring"""
UpperCAmelCase_ = PriorTransformer.from_pretrained('''hf-internal-testing/prior-dummy''')
UpperCAmelCase_ = model.to(_snake_case)
if hasattr(_snake_case , '''set_default_attn_processor'''):
model.set_default_attn_processor()
UpperCAmelCase_ = self.get_dummy_seed_input()
with torch.no_grad():
UpperCAmelCase_ = model(**_snake_case)[0]
UpperCAmelCase_ = output[0, :5].flatten().cpu()
print(_snake_case)
# Since the VAE Gaussian prior's generator is seeded on the appropriate device,
# the expected output slices are not the same for CPU and GPU.
UpperCAmelCase_ = torch.tensor([-1.3_4_3_6, -0.2_8_7_0, 0.7_5_3_8, 0.4_3_6_8, -0.0_2_3_9])
self.assertTrue(torch_all_close(_snake_case , _snake_case , rtol=1e-2))
@slow
class __snake_case ( unittest.TestCase ):
def lowerCamelCase ( self : Union[str, Any] , _snake_case : Optional[Any]=1 , _snake_case : Any=768 , _snake_case : Optional[Any]=77 , _snake_case : Optional[int]=0):
"""simple docstring"""
torch.manual_seed(_snake_case)
UpperCAmelCase_ = batch_size
UpperCAmelCase_ = embedding_dim
UpperCAmelCase_ = num_embeddings
UpperCAmelCase_ = torch.randn((batch_size, embedding_dim)).to(_snake_case)
UpperCAmelCase_ = torch.randn((batch_size, embedding_dim)).to(_snake_case)
UpperCAmelCase_ = torch.randn((batch_size, num_embeddings, embedding_dim)).to(_snake_case)
return {
"hidden_states": hidden_states,
"timestep": 2,
"proj_embedding": proj_embedding,
"encoder_hidden_states": encoder_hidden_states,
}
def lowerCamelCase ( self : str):
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@parameterized.expand(
[
# fmt: off
[13, [-0.5_8_6_1, 0.1_2_8_3, -0.0_9_3_1, 0.0_8_8_2, 0.4_4_7_6, 0.1_3_2_9, -0.0_4_9_8, 0.0_6_4_0]],
[37, [-0.4_9_1_3, 0.0_1_1_0, -0.0_4_8_3, 0.0_5_4_1, 0.4_9_5_4, -0.0_1_7_0, 0.0_3_5_4, 0.1_6_5_1]],
# fmt: on
])
def lowerCamelCase ( self : List[str] , _snake_case : List[Any] , _snake_case : List[Any]):
"""simple docstring"""
UpperCAmelCase_ = PriorTransformer.from_pretrained('''kandinsky-community/kandinsky-2-1-prior''' , subfolder='''prior''')
model.to(_snake_case)
UpperCAmelCase_ = self.get_dummy_seed_input(seed=_snake_case)
with torch.no_grad():
UpperCAmelCase_ = model(**_snake_case)[0]
assert list(sample.shape) == [1, 768]
UpperCAmelCase_ = sample[0, :8].flatten().cpu()
print(_snake_case)
UpperCAmelCase_ = torch.tensor(_snake_case)
assert torch_all_close(_snake_case , _snake_case , atol=1e-3)
| 51
|
from __future__ import annotations
import numpy as np
def __lowerCamelCase ( UpperCamelCase__ ):
'''simple docstring'''
snake_case_ , snake_case_ = np.shape(UpperCamelCase__ )
if rows != columns:
snake_case_ = (
'\'table\' has to be of square shaped array but got a '
F'''{rows}x{columns} array:\n{table}'''
)
raise ValueError(UpperCamelCase__ )
snake_case_ = np.zeros((rows, columns) )
snake_case_ = np.zeros((rows, columns) )
for i in range(UpperCamelCase__ ):
for j in range(UpperCamelCase__ ):
snake_case_ = sum(lower[i][k] * upper[k][j] for k in range(UpperCamelCase__ ) )
if upper[j][j] == 0:
raise ArithmeticError('No LU decomposition exists' )
snake_case_ = (table[i][j] - total) / upper[j][j]
snake_case_ = 1
for j in range(UpperCamelCase__ , UpperCamelCase__ ):
snake_case_ = sum(lower[i][k] * upper[k][j] for k in range(UpperCamelCase__ ) )
snake_case_ = table[i][j] - total
return lower, upper
if __name__ == "__main__":
import doctest
doctest.testmod()
| 285
| 0
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ : List[Any] =logging.get_logger(__name__)
lowerCAmelCase__ : Optional[int] ={
'facebook/timesformer': 'https://huggingface.co/facebook/timesformer/resolve/main/config.json',
}
class __lowercase (__SCREAMING_SNAKE_CASE ):
"""simple docstring"""
_UpperCAmelCase = """timesformer"""
def __init__( self , lowerCAmelCase__=2_2_4 , lowerCAmelCase__=1_6 , lowerCAmelCase__=3 , lowerCAmelCase__=8 , lowerCAmelCase__=7_6_8 , lowerCAmelCase__=1_2 , lowerCAmelCase__=1_2 , lowerCAmelCase__=3_0_7_2 , lowerCAmelCase__="gelu" , lowerCAmelCase__=0.0 , lowerCAmelCase__=0.0 , lowerCAmelCase__=0.02 , lowerCAmelCase__=1E-6 , lowerCAmelCase__=True , lowerCAmelCase__="divided_space_time" , lowerCAmelCase__=0 , **lowerCAmelCase__ , ):
"""simple docstring"""
super().__init__(**lowerCAmelCase__ )
SCREAMING_SNAKE_CASE_ : Tuple = image_size
SCREAMING_SNAKE_CASE_ : int = patch_size
SCREAMING_SNAKE_CASE_ : Tuple = num_channels
SCREAMING_SNAKE_CASE_ : Optional[Any] = num_frames
SCREAMING_SNAKE_CASE_ : int = hidden_size
SCREAMING_SNAKE_CASE_ : Union[str, Any] = num_hidden_layers
SCREAMING_SNAKE_CASE_ : str = num_attention_heads
SCREAMING_SNAKE_CASE_ : List[str] = intermediate_size
SCREAMING_SNAKE_CASE_ : Optional[int] = hidden_act
SCREAMING_SNAKE_CASE_ : Tuple = hidden_dropout_prob
SCREAMING_SNAKE_CASE_ : Tuple = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE_ : Optional[Any] = initializer_range
SCREAMING_SNAKE_CASE_ : Optional[int] = layer_norm_eps
SCREAMING_SNAKE_CASE_ : int = qkv_bias
SCREAMING_SNAKE_CASE_ : Any = attention_type
SCREAMING_SNAKE_CASE_ : Tuple = drop_path_rate
| 162
|
import unittest
import torch
from torch import nn
from accelerate.test_utils import require_cuda
from accelerate.utils.memory import find_executable_batch_size, release_memory
def a__ ( ):
raise RuntimeError('CUDA out of memory.' )
class __lowercase (nn.Module ):
"""simple docstring"""
def __init__( self ):
"""simple docstring"""
super().__init__()
SCREAMING_SNAKE_CASE_ : int = nn.Linear(3 , 4 )
SCREAMING_SNAKE_CASE_ : Tuple = nn.BatchNormad(4 )
SCREAMING_SNAKE_CASE_ : str = nn.Linear(4 , 5 )
def UpperCamelCase__ ( self , lowerCAmelCase__ ):
"""simple docstring"""
return self.lineara(self.batchnorm(self.lineara(lowerCAmelCase__ ) ) )
class __lowercase (unittest.TestCase ):
"""simple docstring"""
def UpperCamelCase__ ( self ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : List[Any] = []
@find_executable_batch_size(starting_batch_size=1_2_8 )
def mock_training_loop_function(lowerCAmelCase__ ):
nonlocal batch_sizes
batch_sizes.append(lowerCAmelCase__ )
if batch_size != 8:
raise_fake_out_of_memory()
mock_training_loop_function()
self.assertListEqual(lowerCAmelCase__ , [1_2_8, 6_4, 3_2, 1_6, 8] )
def UpperCamelCase__ ( self ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Tuple = []
@find_executable_batch_size(starting_batch_size=1_2_8 )
def mock_training_loop_function(lowerCAmelCase__ , lowerCAmelCase__ ):
nonlocal batch_sizes
batch_sizes.append(lowerCAmelCase__ )
if batch_size != 8:
raise_fake_out_of_memory()
return batch_size, arga
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : Dict = mock_training_loop_function('hello' )
self.assertListEqual(lowerCAmelCase__ , [1_2_8, 6_4, 3_2, 1_6, 8] )
self.assertListEqual([bs, arga] , [8, 'hello'] )
def UpperCamelCase__ ( self ):
"""simple docstring"""
@find_executable_batch_size(starting_batch_size=0 )
def mock_training_loop_function(lowerCAmelCase__ ):
pass
with self.assertRaises(lowerCAmelCase__ ) as cm:
mock_training_loop_function()
self.assertIn('No executable batch size found, reached zero.' , cm.exception.args[0] )
def UpperCamelCase__ ( self ):
"""simple docstring"""
@find_executable_batch_size(starting_batch_size=1_6 )
def mock_training_loop_function(lowerCAmelCase__ ):
if batch_size > 0:
raise_fake_out_of_memory()
pass
with self.assertRaises(lowerCAmelCase__ ) as cm:
mock_training_loop_function()
self.assertIn('No executable batch size found, reached zero.' , cm.exception.args[0] )
def UpperCamelCase__ ( self ):
"""simple docstring"""
@find_executable_batch_size(starting_batch_size=1_2_8 )
def mock_training_loop_function(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ):
if batch_size != 8:
raise raise_fake_out_of_memory()
with self.assertRaises(lowerCAmelCase__ ) as cm:
mock_training_loop_function(1_2_8 , 'hello' , 'world' )
self.assertIn('Batch size was passed into `f`' , cm.exception.args[0] )
self.assertIn('`f(arg1=\'hello\', arg2=\'world\')' , cm.exception.args[0] )
def UpperCamelCase__ ( self ):
"""simple docstring"""
@find_executable_batch_size(starting_batch_size=1_6 )
def mock_training_loop_function(lowerCAmelCase__ ):
raise ValueError('Oops, we had an error!' )
with self.assertRaises(lowerCAmelCase__ ) as cm:
mock_training_loop_function()
self.assertIn('Oops, we had an error!' , cm.exception.args[0] )
@require_cuda
def UpperCamelCase__ ( self ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Union[str, Any] = torch.cuda.memory_allocated()
SCREAMING_SNAKE_CASE_ : Optional[int] = ModelForTest()
model.cuda()
self.assertGreater(torch.cuda.memory_allocated() , lowerCAmelCase__ )
SCREAMING_SNAKE_CASE_ : List[Any] = release_memory(lowerCAmelCase__ )
self.assertEqual(torch.cuda.memory_allocated() , lowerCAmelCase__ )
| 162
| 1
|
"""simple docstring"""
import math
def UpperCAmelCase__ ( ):
"""simple docstring"""
A_ : List[str] = input('Enter message: ' )
A_ : str = int(input(f"""Enter key [2-{len(_UpperCAmelCase ) - 1}]: """ ) )
A_ : Optional[int] = input('Encryption/Decryption [e/d]: ' )
if mode.lower().startswith('e' ):
A_ : Any = encrypt_message(_UpperCAmelCase , _UpperCAmelCase )
elif mode.lower().startswith('d' ):
A_ : int = decrypt_message(_UpperCAmelCase , _UpperCAmelCase )
# Append pipe symbol (vertical bar) to identify spaces at the end.
print(f"""Output:\n{text + "|"}""" )
def UpperCAmelCase__ ( _UpperCAmelCase , _UpperCAmelCase ):
"""simple docstring"""
A_ : List[str] = [''] * key
for col in range(_UpperCAmelCase ):
A_ : Optional[Any] = col
while pointer < len(_UpperCAmelCase ):
cipher_text[col] += message[pointer]
pointer += key
return "".join(_UpperCAmelCase )
def UpperCAmelCase__ ( _UpperCAmelCase , _UpperCAmelCase ):
"""simple docstring"""
A_ : str = math.ceil(len(_UpperCAmelCase ) / key )
A_ : Optional[Any] = key
A_ : Any = (num_cols * num_rows) - len(_UpperCAmelCase )
A_ : Any = [''] * num_cols
A_ : Dict = 0
A_ : str = 0
for symbol in message:
plain_text[col] += symbol
col += 1
if (
(col == num_cols)
or (col == num_cols - 1)
and (row >= num_rows - num_shaded_boxes)
):
A_ : str = 0
row += 1
return "".join(_UpperCAmelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 286
|
"""simple docstring"""
from transformers import BertTokenizerFast
from .custom_tokenization import CustomTokenizer
class _UpperCAmelCase ( UpperCAmelCase__ ):
'''simple docstring'''
lowercase_ : List[str] = CustomTokenizer
pass
| 286
| 1
|
"""simple docstring"""
import unittest
from .lib import (
Matrix,
Vector,
axpy,
square_zero_matrix,
unit_basis_vector,
zero_vector,
)
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
"""simple docstring"""
def lowercase__ ( self ):
"""simple docstring"""
lowerCAmelCase : Dict = Vector([1, 2, 3] )
self.assertEqual(x.component(0 ) , 1 )
self.assertEqual(x.component(2 ) , 3 )
lowerCAmelCase : Dict = Vector()
def lowercase__ ( self ):
"""simple docstring"""
lowerCAmelCase : List[Any] = Vector([0, 0, 0, 0, 0, 1] )
self.assertEqual(str(snake_case__ ) , "(0,0,0,0,0,1)" )
def lowercase__ ( self ):
"""simple docstring"""
lowerCAmelCase : List[Any] = Vector([1, 2, 3, 4] )
self.assertEqual(len(snake_case__ ) , 4 )
def lowercase__ ( self ):
"""simple docstring"""
lowerCAmelCase : List[str] = Vector([1, 2] )
lowerCAmelCase : str = Vector([1, 2, 3, 4, 5] )
lowerCAmelCase : Any = Vector([0, 0, 0, 0, 0, 0, 0, 0, 0, 0] )
lowerCAmelCase : Tuple = Vector([1, -1, 1, -1, 2, -3, 4, -5] )
self.assertAlmostEqual(x.euclidean_length() , 2.236 , 3 )
self.assertAlmostEqual(y.euclidean_length() , 7.416 , 3 )
self.assertEqual(z.euclidean_length() , 0 )
self.assertAlmostEqual(w.euclidean_length() , 7.616 , 3 )
def lowercase__ ( self ):
"""simple docstring"""
lowerCAmelCase : List[str] = Vector([1, 2, 3] )
lowerCAmelCase : str = Vector([1, 1, 1] )
self.assertEqual((x + y).component(0 ) , 2 )
self.assertEqual((x + y).component(1 ) , 3 )
self.assertEqual((x + y).component(2 ) , 4 )
def lowercase__ ( self ):
"""simple docstring"""
lowerCAmelCase : Optional[int] = Vector([1, 2, 3] )
lowerCAmelCase : Tuple = Vector([1, 1, 1] )
self.assertEqual((x - y).component(0 ) , 0 )
self.assertEqual((x - y).component(1 ) , 1 )
self.assertEqual((x - y).component(2 ) , 2 )
def lowercase__ ( self ):
"""simple docstring"""
lowerCAmelCase : List[Any] = Vector([1, 2, 3] )
lowerCAmelCase : Optional[int] = Vector([2, -1, 4] ) # for test of dot product
lowerCAmelCase : Any = Vector([1, -2, -1] )
self.assertEqual(str(x * 3.0 ) , "(3.0,6.0,9.0)" )
self.assertEqual((a * b) , 0 )
def lowercase__ ( self ):
"""simple docstring"""
self.assertEqual(str(zero_vector(10 ) ).count("0" ) , 10 )
def lowercase__ ( self ):
"""simple docstring"""
self.assertEqual(str(unit_basis_vector(3 , 1 ) ) , "(0,1,0)" )
def lowercase__ ( self ):
"""simple docstring"""
lowerCAmelCase : Optional[Any] = Vector([1, 2, 3] )
lowerCAmelCase : Union[str, Any] = Vector([1, 0, 1] )
self.assertEqual(str(axpy(2 , snake_case__ , snake_case__ ) ) , "(3,4,7)" )
def lowercase__ ( self ):
"""simple docstring"""
lowerCAmelCase : Tuple = Vector([1, 0, 0, 0, 0, 0] )
lowerCAmelCase : Union[str, Any] = x.copy()
self.assertEqual(str(snake_case__ ) , str(snake_case__ ) )
def lowercase__ ( self ):
"""simple docstring"""
lowerCAmelCase : Optional[Any] = Vector([1, 0, 0] )
x.change_component(0 , 0 )
x.change_component(1 , 1 )
self.assertEqual(str(snake_case__ ) , "(0,1,0)" )
def lowercase__ ( self ):
"""simple docstring"""
lowerCAmelCase : Union[str, Any] = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
self.assertEqual("|1,2,3|\n|2,4,5|\n|6,7,8|\n" , str(snake_case__ ) )
def lowercase__ ( self ):
"""simple docstring"""
lowerCAmelCase : Any = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
lowerCAmelCase : str = [[-3, -14, -10], [-5, -10, -5], [-2, -1, 0]]
for x in range(a.height() ):
for y in range(a.width() ):
self.assertEqual(minors[x][y] , a.minor(snake_case__ , snake_case__ ) )
def lowercase__ ( self ):
"""simple docstring"""
lowerCAmelCase : List[str] = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
lowerCAmelCase : List[Any] = [[-3, 14, -10], [5, -10, 5], [-2, 1, 0]]
for x in range(a.height() ):
for y in range(a.width() ):
self.assertEqual(cofactors[x][y] , a.cofactor(snake_case__ , snake_case__ ) )
def lowercase__ ( self ):
"""simple docstring"""
lowerCAmelCase : Dict = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
self.assertEqual(-5 , a.determinant() )
def lowercase__ ( self ):
"""simple docstring"""
lowerCAmelCase : Dict = Matrix([[1, 2, 3], [4, 5, 6], [7, 8, 9]] , 3 , 3 )
lowerCAmelCase : int = Vector([1, 2, 3] )
self.assertEqual("(14,32,50)" , str(a * x ) )
self.assertEqual("|2,4,6|\n|8,10,12|\n|14,16,18|\n" , str(a * 2 ) )
def lowercase__ ( self ):
"""simple docstring"""
lowerCAmelCase : Any = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
a.change_component(0 , 2 , 5 )
self.assertEqual("|1,2,5|\n|2,4,5|\n|6,7,8|\n" , str(snake_case__ ) )
def lowercase__ ( self ):
"""simple docstring"""
lowerCAmelCase : List[str] = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
self.assertEqual(7 , a.component(2 , 1 ) , 0.01 )
def lowercase__ ( self ):
"""simple docstring"""
lowerCAmelCase : Any = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
lowerCAmelCase : int = Matrix([[1, 2, 7], [2, 4, 5], [6, 7, 10]] , 3 , 3 )
self.assertEqual("|2,4,10|\n|4,8,10|\n|12,14,18|\n" , str(a + b ) )
def lowercase__ ( self ):
"""simple docstring"""
lowerCAmelCase : Any = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
lowerCAmelCase : Dict = Matrix([[1, 2, 7], [2, 4, 5], [6, 7, 10]] , 3 , 3 )
self.assertEqual("|0,0,-4|\n|0,0,0|\n|0,0,-2|\n" , str(a - b ) )
def lowercase__ ( self ):
"""simple docstring"""
self.assertEqual(
"|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n" , str(square_zero_matrix(5 ) ) , )
if __name__ == "__main__":
unittest.main()
| 368
|
"""simple docstring"""
import argparse
import glob
import importlib.util
import os
import re
import black
from doc_builder.style_doc import style_docstrings_in_code
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
lowerCAmelCase__ = '''src/diffusers'''
lowerCAmelCase__ = '''.'''
# This is to make sure the diffusers module imported is the one in the repo.
lowerCAmelCase__ = importlib.util.spec_from_file_location(
'''diffusers''',
os.path.join(DIFFUSERS_PATH, '''__init__.py'''),
submodule_search_locations=[DIFFUSERS_PATH],
)
lowerCAmelCase__ = spec.loader.load_module()
def a__ ( SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
return line.startswith(SCREAMING_SNAKE_CASE ) or len(SCREAMING_SNAKE_CASE ) <= 1 or re.search(r"^\s*\)(\s*->.*:|:)\s*$" , SCREAMING_SNAKE_CASE ) is not None
def a__ ( SCREAMING_SNAKE_CASE : Tuple ):
'''simple docstring'''
lowerCAmelCase : Dict = object_name.split("." )
lowerCAmelCase : Optional[int] = 0
# First let's find the module where our object lives.
lowerCAmelCase : Any = parts[i]
while i < len(SCREAMING_SNAKE_CASE ) and not os.path.isfile(os.path.join(SCREAMING_SNAKE_CASE , f"""{module}.py""" ) ):
i += 1
if i < len(SCREAMING_SNAKE_CASE ):
lowerCAmelCase : Optional[Any] = os.path.join(SCREAMING_SNAKE_CASE , parts[i] )
if i >= len(SCREAMING_SNAKE_CASE ):
raise ValueError(f"""`object_name` should begin with the name of a module of diffusers but got {object_name}.""" )
with open(os.path.join(SCREAMING_SNAKE_CASE , f"""{module}.py""" ) , "r" , encoding="utf-8" , newline="\n" ) as f:
lowerCAmelCase : List[Any] = f.readlines()
# Now let's find the class / func in the code!
lowerCAmelCase : List[str] = ""
lowerCAmelCase : int = 0
for name in parts[i + 1 :]:
while (
line_index < len(SCREAMING_SNAKE_CASE ) and re.search(rf"""^{indent}(class|def)\s+{name}(\(|\:)""" , lines[line_index] ) is None
):
line_index += 1
indent += " "
line_index += 1
if line_index >= len(SCREAMING_SNAKE_CASE ):
raise ValueError(f""" {object_name} does not match any function or class in {module}.""" )
# We found the beginning of the class / func, now let's find the end (when the indent diminishes).
lowerCAmelCase : List[str] = line_index
while line_index < len(SCREAMING_SNAKE_CASE ) and _should_continue(lines[line_index] , SCREAMING_SNAKE_CASE ):
line_index += 1
# Clean up empty lines at the end (if any).
while len(lines[line_index - 1] ) <= 1:
line_index -= 1
lowerCAmelCase : List[Any] = lines[start_index:line_index]
return "".join(SCREAMING_SNAKE_CASE )
lowerCAmelCase__ = re.compile(r'''^(\s*)#\s*Copied from\s+diffusers\.(\S+\.\S+)\s*($|\S.*$)''')
lowerCAmelCase__ = re.compile(r'''^\s*(\S+)->(\S+)(\s+.*|$)''')
lowerCAmelCase__ = re.compile(r'''<FILL\s+[^>]*>''')
def a__ ( SCREAMING_SNAKE_CASE : str ):
'''simple docstring'''
lowerCAmelCase : int = code.split("\n" )
lowerCAmelCase : List[str] = 0
while idx < len(SCREAMING_SNAKE_CASE ) and len(lines[idx] ) == 0:
idx += 1
if idx < len(SCREAMING_SNAKE_CASE ):
return re.search(r"^(\s*)\S" , lines[idx] ).groups()[0]
return ""
def a__ ( SCREAMING_SNAKE_CASE : Optional[int] ):
'''simple docstring'''
lowerCAmelCase : List[Any] = len(get_indent(SCREAMING_SNAKE_CASE ) ) > 0
if has_indent:
lowerCAmelCase : Tuple = f"""class Bla:\n{code}"""
lowerCAmelCase : Optional[Any] = black.Mode(target_versions={black.TargetVersion.PYaa} , line_length=1_1_9 , preview=SCREAMING_SNAKE_CASE )
lowerCAmelCase : Dict = black.format_str(SCREAMING_SNAKE_CASE , mode=SCREAMING_SNAKE_CASE )
lowerCAmelCase , lowerCAmelCase : List[Any] = style_docstrings_in_code(SCREAMING_SNAKE_CASE )
return result[len("class Bla:\n" ) :] if has_indent else result
def a__ ( SCREAMING_SNAKE_CASE : Optional[Any] , SCREAMING_SNAKE_CASE : int=False ):
'''simple docstring'''
with open(SCREAMING_SNAKE_CASE , "r" , encoding="utf-8" , newline="\n" ) as f:
lowerCAmelCase : int = f.readlines()
lowerCAmelCase : List[str] = []
lowerCAmelCase : str = 0
# Not a for loop cause `lines` is going to change (if `overwrite=True`).
while line_index < len(SCREAMING_SNAKE_CASE ):
lowerCAmelCase : List[Any] = _re_copy_warning.search(lines[line_index] )
if search is None:
line_index += 1
continue
# There is some copied code here, let's retrieve the original.
lowerCAmelCase , lowerCAmelCase , lowerCAmelCase : List[Any] = search.groups()
lowerCAmelCase : List[str] = find_code_in_diffusers(SCREAMING_SNAKE_CASE )
lowerCAmelCase : Optional[Any] = get_indent(SCREAMING_SNAKE_CASE )
lowerCAmelCase : Dict = line_index + 1 if indent == theoretical_indent else line_index + 2
lowerCAmelCase : Optional[int] = theoretical_indent
lowerCAmelCase : List[str] = start_index
# Loop to check the observed code, stop when indentation diminishes or if we see a End copy comment.
lowerCAmelCase : str = True
while line_index < len(SCREAMING_SNAKE_CASE ) and should_continue:
line_index += 1
if line_index >= len(SCREAMING_SNAKE_CASE ):
break
lowerCAmelCase : Tuple = lines[line_index]
lowerCAmelCase : str = _should_continue(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) and re.search(f"""^{indent}# End copy""" , SCREAMING_SNAKE_CASE ) is None
# Clean up empty lines at the end (if any).
while len(lines[line_index - 1] ) <= 1:
line_index -= 1
lowerCAmelCase : Tuple = lines[start_index:line_index]
lowerCAmelCase : List[str] = "".join(SCREAMING_SNAKE_CASE )
# Remove any nested `Copied from` comments to avoid circular copies
lowerCAmelCase : List[str] = [line for line in theoretical_code.split("\n" ) if _re_copy_warning.search(SCREAMING_SNAKE_CASE ) is None]
lowerCAmelCase : Union[str, Any] = "\n".join(SCREAMING_SNAKE_CASE )
# Before comparing, use the `replace_pattern` on the original code.
if len(SCREAMING_SNAKE_CASE ) > 0:
lowerCAmelCase : str = replace_pattern.replace("with" , "" ).split("," )
lowerCAmelCase : List[str] = [_re_replace_pattern.search(SCREAMING_SNAKE_CASE ) for p in patterns]
for pattern in patterns:
if pattern is None:
continue
lowerCAmelCase , lowerCAmelCase , lowerCAmelCase : List[str] = pattern.groups()
lowerCAmelCase : List[Any] = re.sub(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
if option.strip() == "all-casing":
lowerCAmelCase : Optional[Any] = re.sub(obja.lower() , obja.lower() , SCREAMING_SNAKE_CASE )
lowerCAmelCase : Dict = re.sub(obja.upper() , obja.upper() , SCREAMING_SNAKE_CASE )
# Blackify after replacement. To be able to do that, we need the header (class or function definition)
# from the previous line
lowerCAmelCase : Union[str, Any] = blackify(lines[start_index - 1] + theoretical_code )
lowerCAmelCase : List[str] = theoretical_code[len(lines[start_index - 1] ) :]
# Test for a diff and act accordingly.
if observed_code != theoretical_code:
diffs.append([object_name, start_index] )
if overwrite:
lowerCAmelCase : Tuple = lines[:start_index] + [theoretical_code] + lines[line_index:]
lowerCAmelCase : int = start_index + 1
if overwrite and len(SCREAMING_SNAKE_CASE ) > 0:
# Warn the user a file has been modified.
print(f"""Detected changes, rewriting {filename}.""" )
with open(SCREAMING_SNAKE_CASE , "w" , encoding="utf-8" , newline="\n" ) as f:
f.writelines(SCREAMING_SNAKE_CASE )
return diffs
def a__ ( SCREAMING_SNAKE_CASE : bool = False ):
'''simple docstring'''
lowerCAmelCase : List[Any] = glob.glob(os.path.join(SCREAMING_SNAKE_CASE , "**/*.py" ) , recursive=SCREAMING_SNAKE_CASE )
lowerCAmelCase : str = []
for filename in all_files:
lowerCAmelCase : List[Any] = is_copy_consistent(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
diffs += [f"""- {filename}: copy does not match {d[0]} at line {d[1]}""" for d in new_diffs]
if not overwrite and len(SCREAMING_SNAKE_CASE ) > 0:
lowerCAmelCase : List[Any] = "\n".join(SCREAMING_SNAKE_CASE )
raise Exception(
"Found the following copy inconsistencies:\n"
+ diff
+ "\nRun `make fix-copies` or `python utils/check_copies.py --fix_and_overwrite` to fix them." )
if __name__ == "__main__":
lowerCAmelCase__ = argparse.ArgumentParser()
parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
lowerCAmelCase__ = parser.parse_args()
check_copies(args.fix_and_overwrite)
| 133
| 0
|
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import SPIECE_UNDERLINE, is_sentencepiece_available
from transformers.models.speech_to_text import SpeechaTextTokenizer
from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FILES_NAMES, save_json
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
UpperCAmelCase : Any =get_tests_dir("""fixtures/test_sentencepiece.model""")
if is_sentencepiece_available():
import sentencepiece as sp
UpperCAmelCase : List[str] =5
UpperCAmelCase : Tuple =10
@require_sentencepiece
@require_tokenizers
class _lowercase (a_ , unittest.TestCase ):
'''simple docstring'''
lowercase__ = SpeechaTextTokenizer
lowercase__ = False
lowercase__ = True
def _lowerCamelCase ( self ):
'''simple docstring'''
super().setUp()
UpperCamelCase_ = sp.SentencePieceProcessor()
spm_model.Load(snake_case__ )
UpperCamelCase_ = ["<s>", "<pad>", "</s>", "<unk>"]
vocab += [spm_model.IdToPiece(id_ ) for id_ in range(len(snake_case__ ) )]
UpperCamelCase_ = dict(zip(snake_case__ , range(len(snake_case__ ) ) ) )
UpperCamelCase_ = Path(self.tmpdirname )
save_json(snake_case__ , save_dir / VOCAB_FILES_NAMES["vocab_file"] )
if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
copyfile(snake_case__ , save_dir / VOCAB_FILES_NAMES["spm_file"] )
UpperCamelCase_ = SpeechaTextTokenizer.from_pretrained(self.tmpdirname )
tokenizer.save_pretrained(self.tmpdirname )
def _lowerCamelCase ( self ):
'''simple docstring'''
UpperCamelCase_ = "<pad>"
UpperCamelCase_ = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(snake_case__ ) , snake_case__ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(snake_case__ ) , snake_case__ )
def _lowerCamelCase ( self ):
'''simple docstring'''
UpperCamelCase_ = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , "<s>" )
self.assertEqual(vocab_keys[1] , "<pad>" )
self.assertEqual(vocab_keys[-1] , "j" )
self.assertEqual(len(snake_case__ ) , 1001 )
def _lowerCamelCase ( self ):
'''simple docstring'''
self.assertEqual(self.get_tokenizer().vocab_size , 1001 )
def _lowerCamelCase ( self ):
'''simple docstring'''
UpperCamelCase_ = SpeechaTextTokenizer.from_pretrained(self.tmpdirname )
UpperCamelCase_ = tokenizer.tokenize("This is a test" )
self.assertListEqual(snake_case__ , ["▁This", "▁is", "▁a", "▁t", "est"] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(snake_case__ ) , [289, 50, 14, 174, 386] , )
UpperCamelCase_ = tokenizer.tokenize("I was born in 92000, and this is falsé." )
self.assertListEqual(
snake_case__ , [SPIECE_UNDERLINE + "I", SPIECE_UNDERLINE + "was", SPIECE_UNDERLINE + "b", "or", "n", SPIECE_UNDERLINE + "in", SPIECE_UNDERLINE + "", "9", "2", "0", "0", "0", ",", SPIECE_UNDERLINE + "and", SPIECE_UNDERLINE + "this", SPIECE_UNDERLINE + "is", SPIECE_UNDERLINE + "f", "al", "s", "é", "."] , )
UpperCamelCase_ = tokenizer.convert_tokens_to_ids(snake_case__ )
self.assertListEqual(snake_case__ , [12, 25, 88, 59, 28, 23, 11, 4, 606, 351, 351, 351, 7, 16, 70, 50, 76, 84, 10, 4, 8] )
UpperCamelCase_ = tokenizer.convert_ids_to_tokens(snake_case__ )
self.assertListEqual(
snake_case__ , [SPIECE_UNDERLINE + "I", SPIECE_UNDERLINE + "was", SPIECE_UNDERLINE + "b", "or", "n", SPIECE_UNDERLINE + "in", SPIECE_UNDERLINE + "", "<unk>", "2", "0", "0", "0", ",", SPIECE_UNDERLINE + "and", SPIECE_UNDERLINE + "this", SPIECE_UNDERLINE + "is", SPIECE_UNDERLINE + "f", "al", "s", "<unk>", "."] , )
@slow
def _lowerCamelCase ( self ):
'''simple docstring'''
UpperCamelCase_ = {"input_ids": [[3791, 797, 31, 11, 64, 797, 31, 2429, 433, 12, 1176, 12, 20, 786, 915, 142, 2413, 240, 37, 3238, 797, 31, 11, 35, 93, 915, 142, 2413, 240, 37, 5540, 567, 1276, 93, 37, 610, 40, 62, 455, 657, 1042, 123, 780, 177, 37, 309, 241, 1298, 514, 20, 292, 2737, 114, 2469, 241, 85, 64, 302, 548, 528, 423, 4, 509, 406, 423, 37, 601, 4, 777, 302, 548, 528, 423, 284, 4, 3388, 511, 459, 4, 3555, 40, 321, 302, 705, 4, 3388, 511, 583, 326, 5, 5, 5, 62, 3310, 560, 177, 2680, 217, 1508, 32, 31, 853, 418, 64, 583, 511, 1605, 62, 35, 93, 560, 177, 2680, 217, 1508, 1521, 64, 583, 511, 519, 62, 20, 1515, 764, 20, 149, 261, 5625, 7972, 20, 5540, 567, 1276, 93, 3925, 1675, 11, 15, 802, 7972, 576, 217, 1508, 11, 35, 93, 1253, 2441, 15, 289, 652, 31, 416, 321, 3842, 115, 40, 911, 8, 476, 619, 4, 380, 142, 423, 335, 240, 35, 93, 264, 8, 11, 335, 569, 420, 163, 5, 2], [260, 548, 528, 423, 20, 451, 20, 2681, 1153, 3434, 20, 5540, 37, 567, 126, 1253, 2441, 3376, 449, 210, 431, 1563, 177, 767, 5540, 11, 1203, 472, 11, 2953, 685, 285, 364, 706, 1153, 20, 6799, 20, 2869, 20, 4464, 126, 40, 2429, 20, 1040, 866, 2664, 418, 20, 318, 20, 1726, 186, 20, 265, 522, 35, 93, 2191, 4634, 20, 1040, 12, 6799, 15, 228, 2356, 142, 31, 11, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [2575, 2666, 684, 1582, 1176, 12, 627, 149, 619, 20, 4902, 563, 11, 20, 149, 261, 3420, 2356, 174, 142, 4714, 131, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=snake_case__ , model_name="facebook/s2t-small-mustc-en-de-st" , revision="a14f04cf0776c02f62a8cb800cf7909e15ea23ad" , )
@require_sentencepiece
class _lowercase (unittest.TestCase ):
'''simple docstring'''
lowercase__ = """valhalla/s2t_mustc_multilinguial_medium"""
lowercase__ = """C'est trop cool"""
lowercase__ = """Esto es genial"""
@classmethod
def _lowerCamelCase ( cls ):
'''simple docstring'''
UpperCamelCase_ = SpeechaTextTokenizer.from_pretrained(cls.checkpoint_name )
return cls
def _lowerCamelCase ( self ):
'''simple docstring'''
self.assertEqual(self.tokenizer.lang_code_to_id["pt"] , 4 )
self.assertEqual(self.tokenizer.lang_code_to_id["ru"] , 6 )
self.assertEqual(self.tokenizer.lang_code_to_id["it"] , 9 )
self.assertEqual(self.tokenizer.lang_code_to_id["de"] , 11 )
def _lowerCamelCase ( self ):
'''simple docstring'''
self.assertEqual(self.tokenizer.vocab_size , 1_0000 )
def _lowerCamelCase ( self ):
'''simple docstring'''
self.assertIn(snake_case__ , self.tokenizer.all_special_ids )
UpperCamelCase_ = [ES_CODE, 4, 1601, 47, 7647, 2]
UpperCamelCase_ = self.tokenizer.decode(snake_case__ , skip_special_tokens=snake_case__ )
UpperCamelCase_ = self.tokenizer.decode(generated_ids[1:] , skip_special_tokens=snake_case__ )
self.assertEqual(snake_case__ , snake_case__ )
self.assertNotIn(self.tokenizer.eos_token , snake_case__ )
def _lowerCamelCase ( self ):
'''simple docstring'''
UpperCamelCase_ = "fr"
UpperCamelCase_ = self.tokenizer(self.french_text ).input_ids
self.assertEqual(encoded[0] , snake_case__ )
self.assertEqual(encoded[-1] , self.tokenizer.eos_token_id )
def _lowerCamelCase ( self ):
'''simple docstring'''
UpperCamelCase_ = "fr"
self.assertListEqual(self.tokenizer.prefix_tokens , [FR_CODE] )
UpperCamelCase_ = "es"
self.assertListEqual(self.tokenizer.prefix_tokens , [ES_CODE] )
| 128
|
import tempfile
import torch
from diffusers import IPNDMScheduler
from .test_schedulers import SchedulerCommonTest
class _lowercase (a_ ):
'''simple docstring'''
lowercase__ = (IPNDMScheduler,)
lowercase__ = (("""num_inference_steps""", 50),)
def _lowerCamelCase ( self , **snake_case__ ):
'''simple docstring'''
UpperCamelCase_ = {"num_train_timesteps": 1000}
config.update(**snake_case__ )
return config
def _lowerCamelCase ( self , snake_case__=0 , **snake_case__ ):
'''simple docstring'''
UpperCamelCase_ = dict(self.forward_default_kwargs )
UpperCamelCase_ = kwargs.pop("num_inference_steps" , snake_case__ )
UpperCamelCase_ = self.dummy_sample
UpperCamelCase_ = 0.1 * sample
UpperCamelCase_ = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
for scheduler_class in self.scheduler_classes:
UpperCamelCase_ = self.get_scheduler_config(**snake_case__ )
UpperCamelCase_ = scheduler_class(**snake_case__ )
scheduler.set_timesteps(snake_case__ )
# copy over dummy past residuals
UpperCamelCase_ = dummy_past_residuals[:]
if time_step is None:
UpperCamelCase_ = scheduler.timesteps[len(scheduler.timesteps ) // 2]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(snake_case__ )
UpperCamelCase_ = scheduler_class.from_pretrained(snake_case__ )
new_scheduler.set_timesteps(snake_case__ )
# copy over dummy past residuals
UpperCamelCase_ = dummy_past_residuals[:]
UpperCamelCase_ = scheduler.step(snake_case__ , snake_case__ , snake_case__ , **snake_case__ ).prev_sample
UpperCamelCase_ = new_scheduler.step(snake_case__ , snake_case__ , snake_case__ , **snake_case__ ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"
UpperCamelCase_ = scheduler.step(snake_case__ , snake_case__ , snake_case__ , **snake_case__ ).prev_sample
UpperCamelCase_ = new_scheduler.step(snake_case__ , snake_case__ , snake_case__ , **snake_case__ ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"
def _lowerCamelCase ( self ):
'''simple docstring'''
pass
def _lowerCamelCase ( self , snake_case__=0 , **snake_case__ ):
'''simple docstring'''
UpperCamelCase_ = dict(self.forward_default_kwargs )
UpperCamelCase_ = kwargs.pop("num_inference_steps" , snake_case__ )
UpperCamelCase_ = self.dummy_sample
UpperCamelCase_ = 0.1 * sample
UpperCamelCase_ = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
for scheduler_class in self.scheduler_classes:
UpperCamelCase_ = self.get_scheduler_config()
UpperCamelCase_ = scheduler_class(**snake_case__ )
scheduler.set_timesteps(snake_case__ )
# copy over dummy past residuals (must be after setting timesteps)
UpperCamelCase_ = dummy_past_residuals[:]
if time_step is None:
UpperCamelCase_ = scheduler.timesteps[len(scheduler.timesteps ) // 2]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(snake_case__ )
UpperCamelCase_ = scheduler_class.from_pretrained(snake_case__ )
# copy over dummy past residuals
new_scheduler.set_timesteps(snake_case__ )
# copy over dummy past residual (must be after setting timesteps)
UpperCamelCase_ = dummy_past_residuals[:]
UpperCamelCase_ = scheduler.step(snake_case__ , snake_case__ , snake_case__ , **snake_case__ ).prev_sample
UpperCamelCase_ = new_scheduler.step(snake_case__ , snake_case__ , snake_case__ , **snake_case__ ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"
UpperCamelCase_ = scheduler.step(snake_case__ , snake_case__ , snake_case__ , **snake_case__ ).prev_sample
UpperCamelCase_ = new_scheduler.step(snake_case__ , snake_case__ , snake_case__ , **snake_case__ ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"
def _lowerCamelCase ( self , **snake_case__ ):
'''simple docstring'''
UpperCamelCase_ = self.scheduler_classes[0]
UpperCamelCase_ = self.get_scheduler_config(**snake_case__ )
UpperCamelCase_ = scheduler_class(**snake_case__ )
UpperCamelCase_ = 10
UpperCamelCase_ = self.dummy_model()
UpperCamelCase_ = self.dummy_sample_deter
scheduler.set_timesteps(snake_case__ )
for i, t in enumerate(scheduler.timesteps ):
UpperCamelCase_ = model(snake_case__ , snake_case__ )
UpperCamelCase_ = scheduler.step(snake_case__ , snake_case__ , snake_case__ ).prev_sample
for i, t in enumerate(scheduler.timesteps ):
UpperCamelCase_ = model(snake_case__ , snake_case__ )
UpperCamelCase_ = scheduler.step(snake_case__ , snake_case__ , snake_case__ ).prev_sample
return sample
def _lowerCamelCase ( self ):
'''simple docstring'''
UpperCamelCase_ = dict(self.forward_default_kwargs )
UpperCamelCase_ = kwargs.pop("num_inference_steps" , snake_case__ )
for scheduler_class in self.scheduler_classes:
UpperCamelCase_ = self.get_scheduler_config()
UpperCamelCase_ = scheduler_class(**snake_case__ )
UpperCamelCase_ = self.dummy_sample
UpperCamelCase_ = 0.1 * sample
if num_inference_steps is not None and hasattr(snake_case__ , "set_timesteps" ):
scheduler.set_timesteps(snake_case__ )
elif num_inference_steps is not None and not hasattr(snake_case__ , "set_timesteps" ):
UpperCamelCase_ = num_inference_steps
# copy over dummy past residuals (must be done after set_timesteps)
UpperCamelCase_ = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
UpperCamelCase_ = dummy_past_residuals[:]
UpperCamelCase_ = scheduler.timesteps[5]
UpperCamelCase_ = scheduler.timesteps[6]
UpperCamelCase_ = scheduler.step(snake_case__ , snake_case__ , snake_case__ , **snake_case__ ).prev_sample
UpperCamelCase_ = scheduler.step(snake_case__ , snake_case__ , snake_case__ , **snake_case__ ).prev_sample
self.assertEqual(output_a.shape , sample.shape )
self.assertEqual(output_a.shape , output_a.shape )
UpperCamelCase_ = scheduler.step(snake_case__ , snake_case__ , snake_case__ , **snake_case__ ).prev_sample
UpperCamelCase_ = scheduler.step(snake_case__ , snake_case__ , snake_case__ , **snake_case__ ).prev_sample
self.assertEqual(output_a.shape , sample.shape )
self.assertEqual(output_a.shape , output_a.shape )
def _lowerCamelCase ( self ):
'''simple docstring'''
for timesteps in [100, 1000]:
self.check_over_configs(num_train_timesteps=snake_case__ , time_step=snake_case__ )
def _lowerCamelCase ( self ):
'''simple docstring'''
for t, num_inference_steps in zip([1, 5, 10] , [10, 50, 100] ):
self.check_over_forward(num_inference_steps=snake_case__ , time_step=snake_case__ )
def _lowerCamelCase ( self ):
'''simple docstring'''
UpperCamelCase_ = self.full_loop()
UpperCamelCase_ = torch.mean(torch.abs(snake_case__ ) )
assert abs(result_mean.item() - 254_0529 ) < 10
| 128
| 1
|
from math import isqrt
def snake_case (UpperCAmelCase__ ) -> bool:
return all(number % divisor != 0 for divisor in range(2 , isqrt(UpperCAmelCase__ ) + 1 ) )
def snake_case (UpperCAmelCase__ = 1_0**6 ) -> int:
UpperCamelCase_: str = 0
UpperCamelCase_: Union[str, Any] = 1
UpperCamelCase_: Dict = 7
while prime_candidate < max_prime:
primes_count += is_prime(UpperCAmelCase__ )
cube_index += 1
prime_candidate += 6 * cube_index
return primes_count
if __name__ == "__main__":
print(F'''{solution() = }''')
| 292
|
def snake_case (UpperCAmelCase__ ) -> int:
assert isinstance(UpperCAmelCase__ , UpperCAmelCase__ ), F'''The input value of [n={number}] is not an integer'''
if number == 1:
return 2
elif number < 1:
UpperCamelCase_: List[Any] = F'''The input value of [n={number}] has to be > 0'''
raise ValueError(UpperCAmelCase__ )
else:
UpperCamelCase_: str = sylvester(number - 1 )
UpperCamelCase_: str = num - 1
UpperCamelCase_: Any = num
return lower * upper + 1
if __name__ == "__main__":
print(F'''The 8th number in Sylvester\'s sequence: {sylvester(8)}''')
| 292
| 1
|
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import MobileViTImageProcessor
class __snake_case ( unittest.TestCase ):
def __init__( self : int , _lowercase : str , _lowercase : Optional[Any]=7 , _lowercase : Tuple=3 , _lowercase : Union[str, Any]=18 , _lowercase : Optional[int]=30 , _lowercase : Tuple=4_00 , _lowercase : Optional[Any]=True , _lowercase : Optional[Any]=None , _lowercase : List[Any]=True , _lowercase : Optional[int]=None , _lowercase : str=True , ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = size if size is not None else {"shortest_edge": 20}
SCREAMING_SNAKE_CASE__ = crop_size if crop_size is not None else {"height": 18, "width": 18}
SCREAMING_SNAKE_CASE__ = parent
SCREAMING_SNAKE_CASE__ = batch_size
SCREAMING_SNAKE_CASE__ = num_channels
SCREAMING_SNAKE_CASE__ = image_size
SCREAMING_SNAKE_CASE__ = min_resolution
SCREAMING_SNAKE_CASE__ = max_resolution
SCREAMING_SNAKE_CASE__ = do_resize
SCREAMING_SNAKE_CASE__ = size
SCREAMING_SNAKE_CASE__ = do_center_crop
SCREAMING_SNAKE_CASE__ = crop_size
SCREAMING_SNAKE_CASE__ = do_flip_channel_order
def __a ( self : Optional[Any] ):
"""simple docstring"""
return {
"do_resize": self.do_resize,
"size": self.size,
"do_center_crop": self.do_center_crop,
"crop_size": self.crop_size,
"do_flip_channel_order": self.do_flip_channel_order,
}
@require_torch
@require_vision
class __snake_case ( lowerCamelCase_ , unittest.TestCase ):
lowerCAmelCase_ = MobileViTImageProcessor if is_vision_available() else None
def __a ( self : int ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = MobileViTImageProcessingTester(self )
@property
def __a ( self : Optional[int] ):
"""simple docstring"""
return self.image_processor_tester.prepare_image_processor_dict()
def __a ( self : Union[str, Any] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(_a , """do_resize""" ) )
self.assertTrue(hasattr(_a , """size""" ) )
self.assertTrue(hasattr(_a , """do_center_crop""" ) )
self.assertTrue(hasattr(_a , """center_crop""" ) )
self.assertTrue(hasattr(_a , """do_flip_channel_order""" ) )
def __a ( self : Optional[int] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {"""shortest_edge""": 20} )
self.assertEqual(image_processor.crop_size , {"""height""": 18, """width""": 18} )
SCREAMING_SNAKE_CASE__ = self.image_processing_class.from_dict(self.image_processor_dict , size=42 , crop_size=84 )
self.assertEqual(image_processor.size , {"""shortest_edge""": 42} )
self.assertEqual(image_processor.crop_size , {"""height""": 84, """width""": 84} )
def __a ( self : str ):
"""simple docstring"""
pass
def __a ( self : Any ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
SCREAMING_SNAKE_CASE__ = prepare_image_inputs(self.image_processor_tester , equal_resolution=_a )
for image in image_inputs:
self.assertIsInstance(_a , Image.Image )
# Test not batched input
SCREAMING_SNAKE_CASE__ = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["""height"""],
self.image_processor_tester.crop_size["""width"""],
) , )
# Test batched
SCREAMING_SNAKE_CASE__ = image_processing(_a , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["""height"""],
self.image_processor_tester.crop_size["""width"""],
) , )
def __a ( self : Optional[Any] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
SCREAMING_SNAKE_CASE__ = prepare_image_inputs(self.image_processor_tester , equal_resolution=_a , numpify=_a )
for image in image_inputs:
self.assertIsInstance(_a , np.ndarray )
# Test not batched input
SCREAMING_SNAKE_CASE__ = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["""height"""],
self.image_processor_tester.crop_size["""width"""],
) , )
# Test batched
SCREAMING_SNAKE_CASE__ = image_processing(_a , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["""height"""],
self.image_processor_tester.crop_size["""width"""],
) , )
def __a ( self : int ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
SCREAMING_SNAKE_CASE__ = prepare_image_inputs(self.image_processor_tester , equal_resolution=_a , torchify=_a )
for image in image_inputs:
self.assertIsInstance(_a , torch.Tensor )
# Test not batched input
SCREAMING_SNAKE_CASE__ = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["""height"""],
self.image_processor_tester.crop_size["""width"""],
) , )
# Test batched
SCREAMING_SNAKE_CASE__ = image_processing(_a , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["""height"""],
self.image_processor_tester.crop_size["""width"""],
) , )
| 219
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from __future__ import annotations
class _snake_case :
def __init__( self , _a ):
__magic_name__ : Optional[Any] = data
__magic_name__ : Node | None = None
__magic_name__ : Node | None = None
def lowerCAmelCase_ ( _snake_case : Node | None ) -> None: # In Order traversal of the tree
'''simple docstring'''
if tree:
display(tree.left )
print(tree.data )
display(tree.right )
def lowerCAmelCase_ ( _snake_case : Node | None ) -> int:
'''simple docstring'''
return 1 + max(depth_of_tree(tree.left ) , depth_of_tree(tree.right ) ) if tree else 0
def lowerCAmelCase_ ( _snake_case : Node ) -> bool:
'''simple docstring'''
if not tree:
return True
if tree.left and tree.right:
return is_full_binary_tree(tree.left ) and is_full_binary_tree(tree.right )
else:
return not tree.left and not tree.right
def lowerCAmelCase_ ( ) -> None: # Main function for testing.
'''simple docstring'''
__magic_name__ : int = Node(1 )
__magic_name__ : Union[str, Any] = Node(2 )
__magic_name__ : Tuple = Node(3 )
__magic_name__ : Optional[Any] = Node(4 )
__magic_name__ : Union[str, Any] = Node(5 )
__magic_name__ : Any = Node(6 )
__magic_name__ : int = Node(7 )
__magic_name__ : List[str] = Node(8 )
__magic_name__ : Union[str, Any] = Node(9 )
print(is_full_binary_tree(_snake_case ) )
print(depth_of_tree(_snake_case ) )
print("Tree is: " )
display(_snake_case )
if __name__ == "__main__":
main()
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"""simple docstring"""
A = {'''a''': ['''c''', '''b'''], '''b''': ['''d''', '''e'''], '''c''': [], '''d''': [], '''e''': []}
A = ['''a''', '''b''', '''c''', '''d''', '''e''']
def __A ( a_ :Union[str, Any] , a_ :Tuple , a_ :Union[str, Any]) -> int:
__a : Dict = start
# add current to visited
visited.append(a_)
__a : Any = edges[current]
for neighbor in neighbors:
# if neighbor not in visited, visit
if neighbor not in visited:
__a : str = topological_sort(a_ , a_ , a_)
# if all neighbors visited add current to sort
sort.append(a_)
# if all vertices haven't been visited select a new one to visit
if len(a_) != len(a_):
for vertice in vertices:
if vertice not in visited:
__a : int = topological_sort(a_ , a_ , a_)
# return sort
return sort
if __name__ == "__main__":
A = topological_sort('''a''', [], [])
print(sort)
| 188
|
"""simple docstring"""
import hashlib
import unittest
from typing import Dict
import numpy as np
from transformers import (
MODEL_FOR_MASK_GENERATION_MAPPING,
TF_MODEL_FOR_MASK_GENERATION_MAPPING,
is_vision_available,
pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
if is_vision_available():
from PIL import Image
else:
class __lowercase :
'''simple docstring'''
@staticmethod
def _lowerCamelCase ( *_UpperCAmelCase , **_UpperCAmelCase ):
pass
def __A ( a_ :Image) -> str:
__a : List[str] = hashlib.mda(image.tobytes())
return m.hexdigest()[:10]
def __A ( a_ :Image) -> Dict:
__a : Any = np.array(a_)
__a : Tuple = npimg.shape
return {"hash": hashimage(a_), "shape": shape}
@is_pipeline_test
@require_vision
@require_torch
class __lowercase ( unittest.TestCase ):
'''simple docstring'''
__lowerCAmelCase = dict(
(list(MODEL_FOR_MASK_GENERATION_MAPPING.items() ) if MODEL_FOR_MASK_GENERATION_MAPPING else []) )
__lowerCAmelCase = dict(
(list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items() ) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else []) )
def _lowerCamelCase ( self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
__a : List[str] = MaskGenerationPipeline(model=_UpperCAmelCase , image_processor=_UpperCAmelCase )
return image_segmenter, [
"./tests/fixtures/tests_samples/COCO/000000039769.png",
"./tests/fixtures/tests_samples/COCO/000000039769.png",
]
def _lowerCamelCase ( self , _UpperCAmelCase , _UpperCAmelCase ):
pass
@require_tf
@unittest.skip('''Image segmentation not implemented in TF''' )
def _lowerCamelCase ( self ):
pass
@slow
@require_torch
def _lowerCamelCase ( self ):
__a : Dict = pipeline('''mask-generation''' , model='''facebook/sam-vit-huge''' )
__a : Optional[Any] = image_segmenter('''http://images.cocodataset.org/val2017/000000039769.jpg''' , points_per_batch=256 )
# Shortening by hashing
__a : Optional[int] = []
for i, o in enumerate(outputs['''masks'''] ):
new_outupt += [{"mask": mask_to_test_readable(_UpperCAmelCase ), "scores": outputs["scores"][i]}]
# fmt: off
self.assertEqual(
nested_simplify(_UpperCAmelCase , decimals=4 ) , [
{'''mask''': {'''hash''': '''115ad19f5f''', '''shape''': (480, 640)}, '''scores''': 1.0_4_4_4},
{'''mask''': {'''hash''': '''6affa964c6''', '''shape''': (480, 640)}, '''scores''': 1.0_2_1},
{'''mask''': {'''hash''': '''dfe28a0388''', '''shape''': (480, 640)}, '''scores''': 1.0_1_6_7},
{'''mask''': {'''hash''': '''c0a5f4a318''', '''shape''': (480, 640)}, '''scores''': 1.0_1_3_2},
{'''mask''': {'''hash''': '''fe8065c197''', '''shape''': (480, 640)}, '''scores''': 1.0_0_5_3},
{'''mask''': {'''hash''': '''e2d0b7a0b7''', '''shape''': (480, 640)}, '''scores''': 0.9_9_6_7},
{'''mask''': {'''hash''': '''453c7844bd''', '''shape''': (480, 640)}, '''scores''': 0.9_9_3},
{'''mask''': {'''hash''': '''3d44f2926d''', '''shape''': (480, 640)}, '''scores''': 0.9_9_0_9},
{'''mask''': {'''hash''': '''64033ddc3f''', '''shape''': (480, 640)}, '''scores''': 0.9_8_7_9},
{'''mask''': {'''hash''': '''801064ff79''', '''shape''': (480, 640)}, '''scores''': 0.9_8_3_4},
{'''mask''': {'''hash''': '''6172f276ef''', '''shape''': (480, 640)}, '''scores''': 0.9_7_1_6},
{'''mask''': {'''hash''': '''b49e60e084''', '''shape''': (480, 640)}, '''scores''': 0.9_6_1_2},
{'''mask''': {'''hash''': '''a811e775fd''', '''shape''': (480, 640)}, '''scores''': 0.9_5_9_9},
{'''mask''': {'''hash''': '''a6a8ebcf4b''', '''shape''': (480, 640)}, '''scores''': 0.9_5_5_2},
{'''mask''': {'''hash''': '''9d8257e080''', '''shape''': (480, 640)}, '''scores''': 0.9_5_3_2},
{'''mask''': {'''hash''': '''32de6454a8''', '''shape''': (480, 640)}, '''scores''': 0.9_5_1_6},
{'''mask''': {'''hash''': '''af3d4af2c8''', '''shape''': (480, 640)}, '''scores''': 0.9_4_9_9},
{'''mask''': {'''hash''': '''3c6db475fb''', '''shape''': (480, 640)}, '''scores''': 0.9_4_8_3},
{'''mask''': {'''hash''': '''c290813fb9''', '''shape''': (480, 640)}, '''scores''': 0.9_4_6_4},
{'''mask''': {'''hash''': '''b6f0b8f606''', '''shape''': (480, 640)}, '''scores''': 0.9_4_3},
{'''mask''': {'''hash''': '''92ce16bfdf''', '''shape''': (480, 640)}, '''scores''': 0.9_4_3},
{'''mask''': {'''hash''': '''c749b25868''', '''shape''': (480, 640)}, '''scores''': 0.9_4_0_8},
{'''mask''': {'''hash''': '''efb6cab859''', '''shape''': (480, 640)}, '''scores''': 0.9_3_3_5},
{'''mask''': {'''hash''': '''1ff2eafb30''', '''shape''': (480, 640)}, '''scores''': 0.9_3_2_6},
{'''mask''': {'''hash''': '''788b798e24''', '''shape''': (480, 640)}, '''scores''': 0.9_2_6_2},
{'''mask''': {'''hash''': '''abea804f0e''', '''shape''': (480, 640)}, '''scores''': 0.8_9_9_9},
{'''mask''': {'''hash''': '''7b9e8ddb73''', '''shape''': (480, 640)}, '''scores''': 0.8_9_8_6},
{'''mask''': {'''hash''': '''cd24047c8a''', '''shape''': (480, 640)}, '''scores''': 0.8_9_8_4},
{'''mask''': {'''hash''': '''6943e6bcbd''', '''shape''': (480, 640)}, '''scores''': 0.8_8_7_3},
{'''mask''': {'''hash''': '''b5f47c9191''', '''shape''': (480, 640)}, '''scores''': 0.8_8_7_1}
] , )
# fmt: on
@require_torch
@slow
def _lowerCamelCase ( self ):
__a : Dict = '''facebook/sam-vit-huge'''
__a : Tuple = pipeline('''mask-generation''' , model=_UpperCAmelCase )
__a : List[Any] = image_segmenter(
'''http://images.cocodataset.org/val2017/000000039769.jpg''' , pred_iou_thresh=1 , points_per_batch=256 )
# Shortening by hashing
__a : Optional[int] = []
for i, o in enumerate(outputs['''masks'''] ):
new_outupt += [{"mask": mask_to_test_readable(_UpperCAmelCase ), "scores": outputs["scores"][i]}]
self.assertEqual(
nested_simplify(_UpperCAmelCase , decimals=4 ) , [
{'''mask''': {'''hash''': '''115ad19f5f''', '''shape''': (480, 640)}, '''scores''': 1.0_4_4_4},
{'''mask''': {'''hash''': '''6affa964c6''', '''shape''': (480, 640)}, '''scores''': 1.0_2_1_0},
{'''mask''': {'''hash''': '''dfe28a0388''', '''shape''': (480, 640)}, '''scores''': 1.0_1_6_7},
{'''mask''': {'''hash''': '''c0a5f4a318''', '''shape''': (480, 640)}, '''scores''': 1.0_1_3_2},
{'''mask''': {'''hash''': '''fe8065c197''', '''shape''': (480, 640)}, '''scores''': 1.0_0_5_3},
] , )
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"""simple docstring"""
def a_ ( lowerCamelCase , lowerCamelCase ):
UpperCAmelCase__ = len(lowerCamelCase ) + 1
UpperCAmelCase__ = len(lowerCamelCase ) + 1
# dp is a 2d matrix where dp[i][j] denotes whether prefix string of
# length i of input_string matches with prefix string of length j of
# given pattern.
# "dp" stands for dynamic programming.
UpperCAmelCase__ = [[0 for i in range(lowerCamelCase )] for j in range(lowerCamelCase )]
# since string of zero length match pattern of zero length
UpperCAmelCase__ = 1
# since pattern of zero length will never match with string of non-zero length
for i in range(1 , lowerCamelCase ):
UpperCAmelCase__ = 0
# since string of zero length will match with pattern where there
# is at least one * alternatively
for j in range(1 , lowerCamelCase ):
UpperCAmelCase__ = dp[0][j - 2] if pattern[j - 1] == '*' else 0
# now using bottom-up approach to find for all remaining lengths
for i in range(1 , lowerCamelCase ):
for j in range(1 , lowerCamelCase ):
if input_string[i - 1] == pattern[j - 1] or pattern[j - 1] == ".":
UpperCAmelCase__ = dp[i - 1][j - 1]
elif pattern[j - 1] == "*":
if dp[i][j - 2] == 1:
UpperCAmelCase__ = 1
elif pattern[j - 2] in (input_string[i - 1], "."):
UpperCAmelCase__ = dp[i - 1][j]
else:
UpperCAmelCase__ = 0
else:
UpperCAmelCase__ = 0
return bool(dp[-1][-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
# inputing the strings
# input_string = input("input a string :")
# pattern = input("input a pattern :")
lowerCAmelCase__ : List[Any] = 'aab'
lowerCAmelCase__ : List[Any] = 'c*a*b'
# using function to check whether given string matches the given pattern
if match_pattern(input_string, pattern):
print(F"""{input_string} matches the given pattern {pattern}""")
else:
print(F"""{input_string} does not match with the given pattern {pattern}""")
| 98
|
"""simple docstring"""
import functools
def a_ ( lowerCamelCase , lowerCamelCase ):
UpperCAmelCase__ = len(lowerCamelCase )
UpperCAmelCase__ = len(lowerCamelCase )
@functools.cache
def min_distance(lowerCamelCase , lowerCamelCase ) -> int:
# if first word index is overflow - delete all from the second word
if indexa >= len_worda:
return len_worda - indexa
# if second word index is overflow - delete all from the first word
if indexa >= len_worda:
return len_worda - indexa
UpperCAmelCase__ = int(worda[indexa] != worda[indexa] ) # current letters not identical
return min(
1 + min_distance(indexa + 1 , lowerCamelCase ) , 1 + min_distance(lowerCamelCase , indexa + 1 ) , diff + min_distance(indexa + 1 , indexa + 1 ) , )
return min_distance(0 , 0 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 98
| 1
|
import gc
import unittest
from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.training.common_utils import shard
@slow
@require_flax
class a ( unittest.TestCase ):
def _UpperCAmelCase ( self ):
'''simple docstring'''
super().tearDown()
gc.collect()
def _UpperCAmelCase ( self ):
'''simple docstring'''
_UpperCAmelCase , _UpperCAmelCase : Any = FlaxControlNetModel.from_pretrained(
"lllyasviel/sd-controlnet-canny" , from_pt=A_ , dtype=jnp.bfloataa )
_UpperCAmelCase , _UpperCAmelCase : Tuple = FlaxStableDiffusionControlNetPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5" , controlnet=A_ , from_pt=A_ , dtype=jnp.bfloataa )
_UpperCAmelCase : Optional[int] = controlnet_params
_UpperCAmelCase : List[Any] = "bird"
_UpperCAmelCase : List[Any] = jax.device_count()
_UpperCAmelCase : str = pipe.prepare_text_inputs([prompts] * num_samples )
_UpperCAmelCase : Optional[int] = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png" )
_UpperCAmelCase : Tuple = pipe.prepare_image_inputs([canny_image] * num_samples )
_UpperCAmelCase : str = jax.random.PRNGKey(0 )
_UpperCAmelCase : Optional[int] = jax.random.split(A_ , jax.device_count() )
_UpperCAmelCase : str = replicate(A_ )
_UpperCAmelCase : Tuple = shard(A_ )
_UpperCAmelCase : Any = shard(A_ )
_UpperCAmelCase : List[Any] = pipe(
prompt_ids=A_ , image=A_ , params=A_ , prng_seed=A_ , num_inference_steps=50 , jit=A_ , ).images
assert images.shape == (jax.device_count(), 1, 768, 512, 3)
_UpperCAmelCase : Optional[int] = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:] )
_UpperCAmelCase : Optional[int] = images[0, 253:256, 253:256, -1]
_UpperCAmelCase : Dict = jnp.asarray(jax.device_get(image_slice.flatten() ) )
_UpperCAmelCase : str = jnp.array(
[0.16_79_69, 0.11_66_99, 0.08_15_43, 0.15_42_97, 0.13_28_12, 0.10_88_87, 0.16_99_22, 0.16_99_22, 0.20_50_78] )
print(f'output_slice: {output_slice}' )
assert jnp.abs(output_slice - expected_slice ).max() < 1e-2
def _UpperCAmelCase ( self ):
'''simple docstring'''
_UpperCAmelCase , _UpperCAmelCase : Optional[Any] = FlaxControlNetModel.from_pretrained(
"lllyasviel/sd-controlnet-openpose" , from_pt=A_ , dtype=jnp.bfloataa )
_UpperCAmelCase , _UpperCAmelCase : Dict = FlaxStableDiffusionControlNetPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5" , controlnet=A_ , from_pt=A_ , dtype=jnp.bfloataa )
_UpperCAmelCase : str = controlnet_params
_UpperCAmelCase : Dict = "Chef in the kitchen"
_UpperCAmelCase : Union[str, Any] = jax.device_count()
_UpperCAmelCase : List[Any] = pipe.prepare_text_inputs([prompts] * num_samples )
_UpperCAmelCase : Dict = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png" )
_UpperCAmelCase : List[Any] = pipe.prepare_image_inputs([pose_image] * num_samples )
_UpperCAmelCase : Optional[int] = jax.random.PRNGKey(0 )
_UpperCAmelCase : str = jax.random.split(A_ , jax.device_count() )
_UpperCAmelCase : Tuple = replicate(A_ )
_UpperCAmelCase : List[str] = shard(A_ )
_UpperCAmelCase : Tuple = shard(A_ )
_UpperCAmelCase : Union[str, Any] = pipe(
prompt_ids=A_ , image=A_ , params=A_ , prng_seed=A_ , num_inference_steps=50 , jit=A_ , ).images
assert images.shape == (jax.device_count(), 1, 768, 512, 3)
_UpperCAmelCase : str = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:] )
_UpperCAmelCase : Dict = images[0, 253:256, 253:256, -1]
_UpperCAmelCase : Optional[int] = jnp.asarray(jax.device_get(image_slice.flatten() ) )
_UpperCAmelCase : Tuple = jnp.array(
[[0.27_14_84, 0.26_17_19, 0.27_53_91, 0.27_73_44, 0.27_92_97, 0.29_10_16, 0.29_49_22, 0.30_27_34, 0.30_27_34]] )
print(f'output_slice: {output_slice}' )
assert jnp.abs(output_slice - expected_slice ).max() < 1e-2
| 189
|
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import (
BackboneOutput,
BaseModelOutputWithNoAttention,
BaseModelOutputWithPoolingAndNoAttention,
ImageClassifierOutputWithNoAttention,
)
from ...modeling_utils import PreTrainedModel
from ...utils import (
add_code_sample_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
logging,
replace_return_docstrings,
)
from ...utils.backbone_utils import BackboneMixin
from .configuration_resnet import ResNetConfig
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
# General docstring
SCREAMING_SNAKE_CASE_ = 'ResNetConfig'
# Base docstring
SCREAMING_SNAKE_CASE_ = 'microsoft/resnet-50'
SCREAMING_SNAKE_CASE_ = [1, 2048, 7, 7]
# Image classification docstring
SCREAMING_SNAKE_CASE_ = 'microsoft/resnet-50'
SCREAMING_SNAKE_CASE_ = 'tiger cat'
SCREAMING_SNAKE_CASE_ = [
'microsoft/resnet-50',
# See all resnet models at https://huggingface.co/models?filter=resnet
]
class a ( nn.Module ):
def __init__( self , A_ , A_ , A_ = 3 , A_ = 1 , A_ = "relu" ):
'''simple docstring'''
super().__init__()
_UpperCAmelCase : Union[str, Any] = nn.Convad(
A_ , A_ , kernel_size=A_ , stride=A_ , padding=kernel_size // 2 , bias=A_ )
_UpperCAmelCase : List[Any] = nn.BatchNormad(A_ )
_UpperCAmelCase : Union[str, Any] = ACTaFN[activation] if activation is not None else nn.Identity()
def _UpperCAmelCase ( self , A_ ):
'''simple docstring'''
_UpperCAmelCase : List[Any] = self.convolution(A_ )
_UpperCAmelCase : Optional[int] = self.normalization(A_ )
_UpperCAmelCase : Optional[Any] = self.activation(A_ )
return hidden_state
class a ( nn.Module ):
def __init__( self , A_ ):
'''simple docstring'''
super().__init__()
_UpperCAmelCase : Any = ResNetConvLayer(
config.num_channels , config.embedding_size , kernel_size=7 , stride=2 , activation=config.hidden_act )
_UpperCAmelCase : List[str] = nn.MaxPoolad(kernel_size=3 , stride=2 , padding=1 )
_UpperCAmelCase : List[Any] = config.num_channels
def _UpperCAmelCase ( self , A_ ):
'''simple docstring'''
_UpperCAmelCase : int = pixel_values.shape[1]
if num_channels != self.num_channels:
raise ValueError(
"Make sure that the channel dimension of the pixel values match with the one set in the configuration." )
_UpperCAmelCase : int = self.embedder(A_ )
_UpperCAmelCase : int = self.pooler(A_ )
return embedding
class a ( nn.Module ):
def __init__( self , A_ , A_ , A_ = 2 ):
'''simple docstring'''
super().__init__()
_UpperCAmelCase : Union[str, Any] = nn.Convad(A_ , A_ , kernel_size=1 , stride=A_ , bias=A_ )
_UpperCAmelCase : Optional[int] = nn.BatchNormad(A_ )
def _UpperCAmelCase ( self , A_ ):
'''simple docstring'''
_UpperCAmelCase : str = self.convolution(A_ )
_UpperCAmelCase : List[str] = self.normalization(A_ )
return hidden_state
class a ( nn.Module ):
def __init__( self , A_ , A_ , A_ = 1 , A_ = "relu" ):
'''simple docstring'''
super().__init__()
_UpperCAmelCase : Optional[int] = in_channels != out_channels or stride != 1
_UpperCAmelCase : Dict = (
ResNetShortCut(A_ , A_ , stride=A_ ) if should_apply_shortcut else nn.Identity()
)
_UpperCAmelCase : int = nn.Sequential(
ResNetConvLayer(A_ , A_ , stride=A_ ) , ResNetConvLayer(A_ , A_ , activation=A_ ) , )
_UpperCAmelCase : Dict = ACTaFN[activation]
def _UpperCAmelCase ( self , A_ ):
'''simple docstring'''
_UpperCAmelCase : Optional[Any] = hidden_state
_UpperCAmelCase : Any = self.layer(A_ )
_UpperCAmelCase : Optional[int] = self.shortcut(A_ )
hidden_state += residual
_UpperCAmelCase : Optional[int] = self.activation(A_ )
return hidden_state
class a ( nn.Module ):
def __init__( self , A_ , A_ , A_ = 1 , A_ = "relu" , A_ = 4 ):
'''simple docstring'''
super().__init__()
_UpperCAmelCase : Optional[Any] = in_channels != out_channels or stride != 1
_UpperCAmelCase : Optional[int] = out_channels // reduction
_UpperCAmelCase : List[str] = (
ResNetShortCut(A_ , A_ , stride=A_ ) if should_apply_shortcut else nn.Identity()
)
_UpperCAmelCase : Dict = nn.Sequential(
ResNetConvLayer(A_ , A_ , kernel_size=1 ) , ResNetConvLayer(A_ , A_ , stride=A_ ) , ResNetConvLayer(A_ , A_ , kernel_size=1 , activation=A_ ) , )
_UpperCAmelCase : List[str] = ACTaFN[activation]
def _UpperCAmelCase ( self , A_ ):
'''simple docstring'''
_UpperCAmelCase : List[Any] = hidden_state
_UpperCAmelCase : List[str] = self.layer(A_ )
_UpperCAmelCase : List[str] = self.shortcut(A_ )
hidden_state += residual
_UpperCAmelCase : Dict = self.activation(A_ )
return hidden_state
class a ( nn.Module ):
def __init__( self , A_ , A_ , A_ , A_ = 2 , A_ = 2 , ):
'''simple docstring'''
super().__init__()
_UpperCAmelCase : Any = ResNetBottleNeckLayer if config.layer_type == "bottleneck" else ResNetBasicLayer
_UpperCAmelCase : Optional[Any] = nn.Sequential(
# downsampling is done in the first layer with stride of 2
layer(A_ , A_ , stride=A_ , activation=config.hidden_act ) , *[layer(A_ , A_ , activation=config.hidden_act ) for _ in range(depth - 1 )] , )
def _UpperCAmelCase ( self , A_ ):
'''simple docstring'''
_UpperCAmelCase : List[Any] = input
for layer in self.layers:
_UpperCAmelCase : Optional[Any] = layer(A_ )
return hidden_state
class a ( nn.Module ):
def __init__( self , A_ ):
'''simple docstring'''
super().__init__()
_UpperCAmelCase : Any = nn.ModuleList([] )
# based on `downsample_in_first_stage` the first layer of the first stage may or may not downsample the input
self.stages.append(
ResNetStage(
A_ , config.embedding_size , config.hidden_sizes[0] , stride=2 if config.downsample_in_first_stage else 1 , depth=config.depths[0] , ) )
_UpperCAmelCase : Union[str, Any] = zip(config.hidden_sizes , config.hidden_sizes[1:] )
for (in_channels, out_channels), depth in zip(A_ , config.depths[1:] ):
self.stages.append(ResNetStage(A_ , A_ , A_ , depth=A_ ) )
def _UpperCAmelCase ( self , A_ , A_ = False , A_ = True ):
'''simple docstring'''
_UpperCAmelCase : List[Any] = () if output_hidden_states else None
for stage_module in self.stages:
if output_hidden_states:
_UpperCAmelCase : Dict = hidden_states + (hidden_state,)
_UpperCAmelCase : str = stage_module(A_ )
if output_hidden_states:
_UpperCAmelCase : int = hidden_states + (hidden_state,)
if not return_dict:
return tuple(v for v in [hidden_state, hidden_states] if v is not None )
return BaseModelOutputWithNoAttention(
last_hidden_state=A_ , hidden_states=A_ , )
class a ( UpperCAmelCase ):
_lowercase = ResNetConfig
_lowercase = "resnet"
_lowercase = "pixel_values"
_lowercase = True
def _UpperCAmelCase ( self , A_ ):
'''simple docstring'''
if isinstance(A_ , nn.Convad ):
nn.init.kaiming_normal_(module.weight , mode="fan_out" , nonlinearity="relu" )
elif isinstance(A_ , (nn.BatchNormad, nn.GroupNorm) ):
nn.init.constant_(module.weight , 1 )
nn.init.constant_(module.bias , 0 )
def _UpperCAmelCase ( self , A_ , A_=False ):
'''simple docstring'''
if isinstance(A_ , A_ ):
_UpperCAmelCase : Optional[Any] = value
SCREAMING_SNAKE_CASE_ = R'\n This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it\n as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and\n behavior.\n\n Parameters:\n config ([`ResNetConfig`]): Model configuration class with all the parameters of the model.\n Initializing with a config file does not load the weights associated with the model, only the\n configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n'
SCREAMING_SNAKE_CASE_ = R'\n Args:\n pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):\n Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n [`ConvNextImageProcessor.__call__`] for details.\n\n output_hidden_states (`bool`, *optional*):\n Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n more detail.\n return_dict (`bool`, *optional*):\n Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.\n'
@add_start_docstrings(
"The bare ResNet model outputting raw features without any specific head on top." , UpperCAmelCase , )
class a ( UpperCAmelCase ):
def __init__( self , A_ ):
'''simple docstring'''
super().__init__(A_ )
_UpperCAmelCase : List[str] = config
_UpperCAmelCase : Any = ResNetEmbeddings(A_ )
_UpperCAmelCase : str = ResNetEncoder(A_ )
_UpperCAmelCase : Any = nn.AdaptiveAvgPoolad((1, 1) )
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(A_ )
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC , output_type=A_ , config_class=_CONFIG_FOR_DOC , modality="vision" , expected_output=_EXPECTED_OUTPUT_SHAPE , )
def _UpperCAmelCase ( self , A_ , A_ = None , A_ = None ):
'''simple docstring'''
_UpperCAmelCase : Optional[int] = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
_UpperCAmelCase : List[str] = return_dict if return_dict is not None else self.config.use_return_dict
_UpperCAmelCase : List[Any] = self.embedder(A_ )
_UpperCAmelCase : str = self.encoder(
A_ , output_hidden_states=A_ , return_dict=A_ )
_UpperCAmelCase : List[Any] = encoder_outputs[0]
_UpperCAmelCase : int = self.pooler(A_ )
if not return_dict:
return (last_hidden_state, pooled_output) + encoder_outputs[1:]
return BaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=A_ , pooler_output=A_ , hidden_states=encoder_outputs.hidden_states , )
@add_start_docstrings(
"\n ResNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n ImageNet.\n " , UpperCAmelCase , )
class a ( UpperCAmelCase ):
def __init__( self , A_ ):
'''simple docstring'''
super().__init__(A_ )
_UpperCAmelCase : Optional[int] = config.num_labels
_UpperCAmelCase : str = ResNetModel(A_ )
# classification head
_UpperCAmelCase : int = nn.Sequential(
nn.Flatten() , nn.Linear(config.hidden_sizes[-1] , config.num_labels ) if config.num_labels > 0 else nn.Identity() , )
# initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(A_ )
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT , output_type=A_ , config_class=_CONFIG_FOR_DOC , expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT , )
def _UpperCAmelCase ( self , A_ = None , A_ = None , A_ = None , A_ = None , ):
'''simple docstring'''
_UpperCAmelCase : Optional[int] = return_dict if return_dict is not None else self.config.use_return_dict
_UpperCAmelCase : Tuple = self.resnet(A_ , output_hidden_states=A_ , return_dict=A_ )
_UpperCAmelCase : Optional[int] = outputs.pooler_output if return_dict else outputs[1]
_UpperCAmelCase : int = self.classifier(A_ )
_UpperCAmelCase : Dict = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
_UpperCAmelCase : Optional[Any] = "regression"
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
_UpperCAmelCase : Optional[Any] = "single_label_classification"
else:
_UpperCAmelCase : Any = "multi_label_classification"
if self.config.problem_type == "regression":
_UpperCAmelCase : str = MSELoss()
if self.num_labels == 1:
_UpperCAmelCase : Any = loss_fct(logits.squeeze() , labels.squeeze() )
else:
_UpperCAmelCase : Optional[int] = loss_fct(A_ , A_ )
elif self.config.problem_type == "single_label_classification":
_UpperCAmelCase : Any = CrossEntropyLoss()
_UpperCAmelCase : Any = loss_fct(logits.view(-1 , self.num_labels ) , labels.view(-1 ) )
elif self.config.problem_type == "multi_label_classification":
_UpperCAmelCase : Any = BCEWithLogitsLoss()
_UpperCAmelCase : Tuple = loss_fct(A_ , A_ )
if not return_dict:
_UpperCAmelCase : Any = (logits,) + outputs[2:]
return (loss,) + output if loss is not None else output
return ImageClassifierOutputWithNoAttention(loss=A_ , logits=A_ , hidden_states=outputs.hidden_states )
@add_start_docstrings(
"\n ResNet backbone, to be used with frameworks like DETR and MaskFormer.\n " , UpperCAmelCase , )
class a ( UpperCAmelCase , UpperCAmelCase ):
def __init__( self , A_ ):
'''simple docstring'''
super().__init__(A_ )
super()._init_backbone(A_ )
_UpperCAmelCase : Optional[int] = [config.embedding_size] + config.hidden_sizes
_UpperCAmelCase : str = ResNetEmbeddings(A_ )
_UpperCAmelCase : List[Any] = ResNetEncoder(A_ )
# initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(A_ )
@replace_return_docstrings(output_type=A_ , config_class=_CONFIG_FOR_DOC )
def _UpperCAmelCase ( self , A_ , A_ = None , A_ = None ):
'''simple docstring'''
_UpperCAmelCase : Tuple = return_dict if return_dict is not None else self.config.use_return_dict
_UpperCAmelCase : Tuple = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
_UpperCAmelCase : Tuple = self.embedder(A_ )
_UpperCAmelCase : Optional[int] = self.encoder(A_ , output_hidden_states=A_ , return_dict=A_ )
_UpperCAmelCase : Optional[int] = outputs.hidden_states
_UpperCAmelCase : Any = ()
for idx, stage in enumerate(self.stage_names ):
if stage in self.out_features:
feature_maps += (hidden_states[idx],)
if not return_dict:
_UpperCAmelCase : Union[str, Any] = (feature_maps,)
if output_hidden_states:
output += (outputs.hidden_states,)
return output
return BackboneOutput(
feature_maps=A_ , hidden_states=outputs.hidden_states if output_hidden_states else None , attentions=A_ , )
| 189
| 1
|
"""simple docstring"""
import os
import unittest
from transformers import FunnelTokenizer, FunnelTokenizerFast
from transformers.models.funnel.tokenization_funnel import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class UpperCAmelCase_ ( _lowercase , unittest.TestCase):
snake_case__ = FunnelTokenizer
snake_case__ = FunnelTokenizerFast
snake_case__ = True
snake_case__ = True
def _UpperCamelCase ( self : int ) -> Union[str, Any]:
super().setUp()
_UpperCamelCase = [
'''<unk>''',
'''<cls>''',
'''<sep>''',
'''want''',
'''##want''',
'''##ed''',
'''wa''',
'''un''',
'''runn''',
'''##ing''',
''',''',
'''low''',
'''lowest''',
]
_UpperCamelCase = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as vocab_writer:
vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens] ) )
def _UpperCamelCase ( self : Union[str, Any] , **__UpperCamelCase : Tuple ) -> Tuple:
return FunnelTokenizer.from_pretrained(self.tmpdirname , **__UpperCamelCase )
def _UpperCamelCase ( self : str , **__UpperCamelCase : str ) -> int:
return FunnelTokenizerFast.from_pretrained(self.tmpdirname , **__UpperCamelCase )
def _UpperCamelCase ( self : Tuple , __UpperCamelCase : Optional[int] ) -> List[str]:
_UpperCamelCase = '''UNwant\u00E9d,running'''
_UpperCamelCase = '''unwanted, running'''
return input_text, output_text
def _UpperCamelCase ( self : List[Any] ) -> Tuple:
_UpperCamelCase = self.tokenizer_class(self.vocab_file )
_UpperCamelCase = tokenizer.tokenize('''UNwant\u00E9d,running''' )
self.assertListEqual(__UpperCamelCase , ['''un''', '''##want''', '''##ed''', ''',''', '''runn''', '''##ing'''] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCamelCase ) , [7, 4, 5, 10, 8, 9] )
def _UpperCamelCase ( self : Optional[Any] ) -> Any:
_UpperCamelCase = self.get_tokenizers(do_lower_case=__UpperCamelCase )
for tokenizer in tokenizers:
_UpperCamelCase = tokenizer('''UNwant\u00E9d,running''' )
_UpperCamelCase = len(inputs['''input_ids'''] ) - 1
self.assertListEqual(inputs['''token_type_ids'''] , [2] + [0] * sentence_len )
_UpperCamelCase = tokenizer('''UNwant\u00E9d,running''' , '''UNwant\u00E9d,running''' )
self.assertListEqual(inputs['''token_type_ids'''] , [2] + [0] * sentence_len + [1] * sentence_len )
| 256
|
"""simple docstring"""
from queue import Queue
from typing import TYPE_CHECKING, Optional
if TYPE_CHECKING:
from ..models.auto import AutoTokenizer
class UpperCAmelCase_ :
def _UpperCamelCase ( self : List[str] , __UpperCamelCase : Any ) -> Tuple:
raise NotImplementedError()
def _UpperCamelCase ( self : List[Any] ) -> Dict:
raise NotImplementedError()
class UpperCAmelCase_ ( _lowercase):
def __init__( self : Dict , __UpperCamelCase : "AutoTokenizer" , __UpperCamelCase : bool = False , **__UpperCamelCase : Tuple ) -> str:
_UpperCamelCase = tokenizer
_UpperCamelCase = skip_prompt
_UpperCamelCase = decode_kwargs
# variables used in the streaming process
_UpperCamelCase = []
_UpperCamelCase = 0
_UpperCamelCase = True
def _UpperCamelCase ( self : List[str] , __UpperCamelCase : Dict ) -> Optional[Any]:
if len(value.shape ) > 1 and value.shape[0] > 1:
raise ValueError('''TextStreamer only supports batch size 1''' )
elif len(value.shape ) > 1:
_UpperCamelCase = value[0]
if self.skip_prompt and self.next_tokens_are_prompt:
_UpperCamelCase = False
return
# Add the new token to the cache and decodes the entire thing.
self.token_cache.extend(value.tolist() )
_UpperCamelCase = self.tokenizer.decode(self.token_cache , **self.decode_kwargs )
# After the symbol for a new line, we flush the cache.
if text.endswith('''\n''' ):
_UpperCamelCase = text[self.print_len :]
_UpperCamelCase = []
_UpperCamelCase = 0
# If the last token is a CJK character, we print the characters.
elif len(__UpperCamelCase ) > 0 and self._is_chinese_char(ord(text[-1] ) ):
_UpperCamelCase = text[self.print_len :]
self.print_len += len(__UpperCamelCase )
# Otherwise, prints until the last space char (simple heuristic to avoid printing incomplete words,
# which may change with the subsequent token -- there are probably smarter ways to do this!)
else:
_UpperCamelCase = text[self.print_len : text.rfind(''' ''' ) + 1]
self.print_len += len(__UpperCamelCase )
self.on_finalized_text(__UpperCamelCase )
def _UpperCamelCase ( self : List[Any] ) -> Optional[Any]:
# Flush the cache, if it exists
if len(self.token_cache ) > 0:
_UpperCamelCase = self.tokenizer.decode(self.token_cache , **self.decode_kwargs )
_UpperCamelCase = text[self.print_len :]
_UpperCamelCase = []
_UpperCamelCase = 0
else:
_UpperCamelCase = ''''''
_UpperCamelCase = True
self.on_finalized_text(__UpperCamelCase , stream_end=__UpperCamelCase )
def _UpperCamelCase ( self : int , __UpperCamelCase : str , __UpperCamelCase : bool = False ) -> Tuple:
print(__UpperCamelCase , flush=__UpperCamelCase , end='''''' if not stream_end else None )
def _UpperCamelCase ( self : Tuple , __UpperCamelCase : Dict ) -> str:
# This defines a "chinese character" as anything in the CJK Unicode block:
# https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
#
# Note that the CJK Unicode block is NOT all Japanese and Korean characters,
# despite its name. The modern Korean Hangul alphabet is a different block,
# as is Japanese Hiragana and Katakana. Those alphabets are used to write
# space-separated words, so they are not treated specially and handled
# like the all of the other languages.
if (
(cp >= 0x4e00 and cp <= 0x9fff)
or (cp >= 0x3400 and cp <= 0x4dbf) #
or (cp >= 0x2_0000 and cp <= 0x2_a6df) #
or (cp >= 0x2_a700 and cp <= 0x2_b73f) #
or (cp >= 0x2_b740 and cp <= 0x2_b81f) #
or (cp >= 0x2_b820 and cp <= 0x2_ceaf) #
or (cp >= 0xf900 and cp <= 0xfaff)
or (cp >= 0x2_f800 and cp <= 0x2_fa1f) #
): #
return True
return False
class UpperCAmelCase_ ( _lowercase):
def __init__( self : Union[str, Any] , __UpperCamelCase : "AutoTokenizer" , __UpperCamelCase : bool = False , __UpperCamelCase : Optional[float] = None , **__UpperCamelCase : Optional[int] ) -> Optional[Any]:
super().__init__(__UpperCamelCase , __UpperCamelCase , **__UpperCamelCase )
_UpperCamelCase = Queue()
_UpperCamelCase = None
_UpperCamelCase = timeout
def _UpperCamelCase ( self : Dict , __UpperCamelCase : str , __UpperCamelCase : bool = False ) -> Any:
self.text_queue.put(__UpperCamelCase , timeout=self.timeout )
if stream_end:
self.text_queue.put(self.stop_signal , timeout=self.timeout )
def __iter__( self : Optional[Any] ) -> List[str]:
return self
def _UpperCamelCase ( self : int ) -> Dict:
_UpperCamelCase = self.text_queue.get(timeout=self.timeout )
if value == self.stop_signal:
raise StopIteration()
else:
return value
| 256
| 1
|
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConfig,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaForCTC,
WavaVecaForPreTraining,
WavaVecaProcessor,
logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification
logging.set_verbosity_info()
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
'''post_extract_proj''': '''feature_projection.projection''',
'''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
'''self_attn.k_proj''': '''encoder.layers.*.attention.k_proj''',
'''self_attn.v_proj''': '''encoder.layers.*.attention.v_proj''',
'''self_attn.q_proj''': '''encoder.layers.*.attention.q_proj''',
'''self_attn.out_proj''': '''encoder.layers.*.attention.out_proj''',
'''self_attn_layer_norm''': '''encoder.layers.*.layer_norm''',
'''fc1''': '''encoder.layers.*.feed_forward.intermediate_dense''',
'''fc2''': '''encoder.layers.*.feed_forward.output_dense''',
'''final_layer_norm''': '''encoder.layers.*.final_layer_norm''',
'''encoder.layer_norm''': '''encoder.layer_norm''',
'''adapter_layer''': '''encoder.layers.*.adapter_layer''',
'''w2v_model.layer_norm''': '''feature_projection.layer_norm''',
'''quantizer.weight_proj''': '''quantizer.weight_proj''',
'''quantizer.vars''': '''quantizer.codevectors''',
'''project_q''': '''project_q''',
'''final_proj''': '''project_hid''',
'''w2v_encoder.proj''': '''lm_head''',
'''mask_emb''': '''masked_spec_embed''',
'''pooling_layer.linear''': '''projector''',
'''pooling_layer.projection''': '''classifier''',
}
__UpperCAmelCase = [
'''lm_head''',
'''quantizer.weight_proj''',
'''quantizer.codevectors''',
'''project_q''',
'''project_hid''',
'''projector''',
'''classifier''',
]
def _snake_case ( A ) -> int:
lowerCAmelCase__ = {}
with open(A , '''r''' ) as file:
for line_number, line in enumerate(A ):
lowerCAmelCase__ = line.strip()
if line:
lowerCAmelCase__ = line.split()
lowerCAmelCase__ = line_number
lowerCAmelCase__ = words[0]
lowerCAmelCase__ = value
return result
def _snake_case ( A , A , A , A , A ) -> Dict:
for attribute in key.split('''.''' ):
lowerCAmelCase__ = getattr(A , A )
lowerCAmelCase__ = None
for param_key in PARAM_MAPPING.keys():
if full_name.endswith(A ):
lowerCAmelCase__ = PARAM_MAPPING[full_name.split('''.''' )[-1]]
lowerCAmelCase__ = '''param'''
if weight_type is not None and weight_type != "param":
lowerCAmelCase__ = getattr(A , A ).shape
elif weight_type is not None and weight_type == "param":
lowerCAmelCase__ = hf_pointer
for attribute in hf_param_name.split('''.''' ):
lowerCAmelCase__ = getattr(A , A )
lowerCAmelCase__ = shape_pointer.shape
# let's reduce dimension
lowerCAmelCase__ = value[0]
else:
lowerCAmelCase__ = hf_pointer.shape
if hf_shape != value.shape:
raise ValueError(
F"""Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be"""
F""" {value.shape} for {full_name}""" )
if weight_type == "weight":
lowerCAmelCase__ = value
elif weight_type == "weight_g":
lowerCAmelCase__ = value
elif weight_type == "weight_v":
lowerCAmelCase__ = value
elif weight_type == "bias":
lowerCAmelCase__ = value
elif weight_type == "param":
for attribute in hf_param_name.split('''.''' ):
lowerCAmelCase__ = getattr(A , A )
lowerCAmelCase__ = value
else:
lowerCAmelCase__ = value
logger.info(F"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.""" )
def _snake_case ( A , A , A , A , A ) -> int:
lowerCAmelCase__ = None
for param_key in PARAM_MAPPING.keys():
if full_name.endswith(A ):
lowerCAmelCase__ = PARAM_MAPPING[full_name.split('''.''' )[-1]]
lowerCAmelCase__ = '''param'''
if weight_type is not None and weight_type != "param":
lowerCAmelCase__ = '''.'''.join([key, weight_type] )
elif weight_type is not None and weight_type == "param":
lowerCAmelCase__ = '''.'''.join([key, hf_param_name] )
else:
lowerCAmelCase__ = key
lowerCAmelCase__ = value if '''lm_head''' in full_key else value[0]
__UpperCAmelCase = {
'''W_a''': '''linear_1.weight''',
'''W_b''': '''linear_2.weight''',
'''b_a''': '''linear_1.bias''',
'''b_b''': '''linear_2.bias''',
'''ln_W''': '''norm.weight''',
'''ln_b''': '''norm.bias''',
}
def _snake_case ( A , A , A=None , A=None ) -> str:
lowerCAmelCase__ = False
for key, mapped_key in MAPPING.items():
lowerCAmelCase__ = '''wav2vec2.''' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
if key in name or key.split('''w2v_model.''' )[-1] == name.split('''.''' )[0]:
lowerCAmelCase__ = True
if "*" in mapped_key:
lowerCAmelCase__ = name.split(A )[0].split('''.''' )[-2]
lowerCAmelCase__ = mapped_key.replace('''*''' , A )
if "weight_g" in name:
lowerCAmelCase__ = '''weight_g'''
elif "weight_v" in name:
lowerCAmelCase__ = '''weight_v'''
elif "bias" in name:
lowerCAmelCase__ = '''bias'''
elif "weight" in name:
# TODO: don't match quantizer.weight_proj
lowerCAmelCase__ = '''weight'''
else:
lowerCAmelCase__ = None
if hf_dict is not None:
rename_dict(A , A , A , A , A )
else:
set_recursively(A , A , A , A , A )
return is_used
return is_used
def _snake_case ( A , A , A ) -> Union[str, Any]:
lowerCAmelCase__ = []
lowerCAmelCase__ = fairseq_model.state_dict()
lowerCAmelCase__ = hf_model.wavaveca.feature_extractor
for name, value in fairseq_dict.items():
lowerCAmelCase__ = False
if "conv_layers" in name:
load_conv_layer(
A , A , A , A , hf_model.config.feat_extract_norm == '''group''' , )
lowerCAmelCase__ = True
else:
lowerCAmelCase__ = load_wavaveca_layer(A , A , A )
if not is_used:
unused_weights.append(A )
logger.warning(F"""Unused weights: {unused_weights}""" )
def _snake_case ( A , A , A , A , A ) -> List[Any]:
lowerCAmelCase__ = full_name.split('''conv_layers.''' )[-1]
lowerCAmelCase__ = name.split('''.''' )
lowerCAmelCase__ = int(items[0] )
lowerCAmelCase__ = int(items[1] )
if type_id == 0:
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
raise ValueError(
F"""{full_name} has size {value.shape}, but"""
F""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.""" )
lowerCAmelCase__ = value
logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
raise ValueError(
F"""{full_name} has size {value.shape}, but"""
F""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.""" )
lowerCAmelCase__ = value
logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
raise ValueError(
F"""{full_name} has size {value.shape}, but"""
F""" {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.""" )
lowerCAmelCase__ = value
logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
raise ValueError(
F"""{full_name} has size {value.shape}, but"""
F""" {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.""" )
lowerCAmelCase__ = value
logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
else:
unused_weights.append(A )
@torch.no_grad()
def _snake_case ( A , A , A=None , A=None , A=True , A=False ) -> List[Any]:
if config_path is not None:
lowerCAmelCase__ = WavaVecaConfig.from_pretrained(A )
else:
lowerCAmelCase__ = WavaVecaConfig()
if is_seq_class:
lowerCAmelCase__ = read_txt_into_dict(A )
lowerCAmelCase__ = idalabel
lowerCAmelCase__ = WavaVecaForSequenceClassification(A )
lowerCAmelCase__ = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=16000 , padding_value=0 , do_normalize=A , return_attention_mask=A , )
feature_extractor.save_pretrained(A )
elif is_finetuned:
if dict_path:
lowerCAmelCase__ = Dictionary.load(A )
# important change bos & pad token id since CTC symbol is <pad> and
# not <s> as in fairseq
lowerCAmelCase__ = target_dict.pad_index
lowerCAmelCase__ = target_dict.bos_index
lowerCAmelCase__ = target_dict.eos_index
lowerCAmelCase__ = len(target_dict.symbols )
lowerCAmelCase__ = os.path.join(A , '''vocab.json''' )
if not os.path.isdir(A ):
logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(A ) )
return
os.makedirs(A , exist_ok=A )
lowerCAmelCase__ = target_dict.indices
# fairseq has the <pad> and <s> switched
lowerCAmelCase__ = 0
lowerCAmelCase__ = 1
with open(A , '''w''' , encoding='''utf-8''' ) as vocab_handle:
json.dump(A , A )
lowerCAmelCase__ = WavaVecaCTCTokenizer(
A , unk_token=target_dict.unk_word , pad_token=target_dict.pad_word , bos_token=target_dict.bos_word , eos_token=target_dict.eos_word , word_delimiter_token='''|''' , do_lower_case=A , )
lowerCAmelCase__ = True if config.feat_extract_norm == '''layer''' else False
lowerCAmelCase__ = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=16000 , padding_value=0 , do_normalize=A , return_attention_mask=A , )
lowerCAmelCase__ = WavaVecaProcessor(feature_extractor=A , tokenizer=A )
processor.save_pretrained(A )
lowerCAmelCase__ = WavaVecaForCTC(A )
else:
lowerCAmelCase__ = WavaVecaForPreTraining(A )
if is_finetuned or is_seq_class:
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={'''data''': '''/'''.join(dict_path.split('''/''' )[:-1] )} )
else:
lowerCAmelCase__ = argparse.Namespace(task='''audio_pretraining''' )
lowerCAmelCase__ = fairseq.tasks.setup_task(A )
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] , task=A )
lowerCAmelCase__ = model[0].eval()
recursively_load_weights(A , A , not is_finetuned )
hf_wavavec.save_pretrained(A )
if __name__ == "__main__":
__UpperCAmelCase = argparse.ArgumentParser()
parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to fairseq checkpoint''')
parser.add_argument('''--dict_path''', default=None, type=str, help='''Path to dict of fine-tuned model''')
parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
parser.add_argument(
'''--not_finetuned''', action='''store_true''', help='''Whether the model to convert is a fine-tuned model or not'''
)
parser.add_argument(
'''--is_seq_class''',
action='''store_true''',
help='''Whether the model to convert is a fine-tuned sequence classification model or not''',
)
__UpperCAmelCase = parser.parse_args()
__UpperCAmelCase = not args.not_finetuned and not args.is_seq_class
convert_wavaveca_checkpoint(
args.checkpoint_path,
args.pytorch_dump_folder_path,
args.config_path,
args.dict_path,
is_finetuned,
args.is_seq_class,
)
| 228
|
'''simple docstring'''
from __future__ import annotations
class a__ :
'''simple docstring'''
def __init__( self , lowerCamelCase_ ) -> None:
lowerCAmelCase__ = order
# a_{0} ... a_{k}
lowerCAmelCase__ = [1.0] + [0.0] * order
# b_{0} ... b_{k}
lowerCAmelCase__ = [1.0] + [0.0] * order
# x[n-1] ... x[n-k]
lowerCAmelCase__ = [0.0] * self.order
# y[n-1] ... y[n-k]
lowerCAmelCase__ = [0.0] * self.order
def __SCREAMING_SNAKE_CASE ( self , lowerCamelCase_ , lowerCamelCase_ ) -> None:
if len(lowerCamelCase_ ) < self.order:
lowerCAmelCase__ = [1.0, *a_coeffs]
if len(lowerCamelCase_ ) != self.order + 1:
lowerCAmelCase__ = (
F"""Expected a_coeffs to have {self.order + 1} elements """
F"""for {self.order}-order filter, got {len(lowerCamelCase_ )}"""
)
raise ValueError(lowerCamelCase_ )
if len(lowerCamelCase_ ) != self.order + 1:
lowerCAmelCase__ = (
F"""Expected b_coeffs to have {self.order + 1} elements """
F"""for {self.order}-order filter, got {len(lowerCamelCase_ )}"""
)
raise ValueError(lowerCamelCase_ )
lowerCAmelCase__ = a_coeffs
lowerCAmelCase__ = b_coeffs
def __SCREAMING_SNAKE_CASE ( self , lowerCamelCase_ ) -> float:
lowerCAmelCase__ = 0.0
# Start at index 1 and do index 0 at the end.
for i in range(1 , self.order + 1 ):
result += (
self.b_coeffs[i] * self.input_history[i - 1]
- self.a_coeffs[i] * self.output_history[i - 1]
)
lowerCAmelCase__ = (result + self.b_coeffs[0] * sample) / self.a_coeffs[0]
lowerCAmelCase__ = self.input_history[:-1]
lowerCAmelCase__ = self.output_history[:-1]
lowerCAmelCase__ = sample
lowerCAmelCase__ = result
return result
| 228
| 1
|
import math
class UpperCAmelCase :
'''simple docstring'''
def __init__( self : Optional[int] ,A : Dict=0 ): # a graph with Node 0,1,...,N-1
__A = n
__A = [
[math.inf for j in range(0 ,A )] for i in range(0 ,A )
] # adjacency matrix for weight
__A = [
[math.inf for j in range(0 ,A )] for i in range(0 ,A )
] # dp[i][j] stores minimum distance from i to j
def UpperCamelCase_ ( self : List[str] ,A : Tuple ,A : Union[str, Any] ,A : Optional[int] ):
__A = w
def UpperCamelCase_ ( self : int ):
for k in range(0 ,self.n ):
for i in range(0 ,self.n ):
for j in range(0 ,self.n ):
__A = min(self.dp[i][j] ,self.dp[i][k] + self.dp[k][j] )
def UpperCamelCase_ ( self : Optional[int] ,A : str ,A : Union[str, Any] ):
return self.dp[u][v]
if __name__ == "__main__":
SCREAMING_SNAKE_CASE :Optional[int] = Graph(5)
graph.add_edge(0, 2, 9)
graph.add_edge(0, 4, 10)
graph.add_edge(1, 3, 5)
graph.add_edge(2, 3, 7)
graph.add_edge(3, 0, 10)
graph.add_edge(3, 1, 2)
graph.add_edge(3, 2, 1)
graph.add_edge(3, 4, 6)
graph.add_edge(4, 1, 3)
graph.add_edge(4, 2, 4)
graph.add_edge(4, 3, 9)
graph.floyd_warshall()
graph.show_min(1, 4)
graph.show_min(0, 3)
| 15
|
# This code is adapted from OpenAI's release
# https://github.com/openai/human-eval/blob/master/human_eval/execution.py
import contextlib
import faulthandler
import io
import multiprocessing
import os
import platform
import signal
import tempfile
def __lowerCamelCase ( __a :List[str] , __a :List[Any] , __a :Union[str, Any] , __a :List[Any] ) -> Dict:
"""simple docstring"""
A__ = multiprocessing.Manager()
A__ = manager.list()
A__ = multiprocessing.Process(target=__a , args=(check_program, result, timeout) )
p.start()
p.join(timeout=timeout + 1 )
if p.is_alive():
p.kill()
if not result:
result.append("""timed out""" )
return {
"task_id": task_id,
"passed": result[0] == "passed",
"result": result[0],
"completion_id": completion_id,
}
def __lowerCamelCase ( __a :Optional[Any] , __a :Any , __a :List[Any] ) -> Union[str, Any]:
"""simple docstring"""
with create_tempdir():
# These system calls are needed when cleaning up tempdir.
import os
import shutil
A__ = shutil.rmtree
A__ = os.rmdir
A__ = os.chdir
# Disable functionalities that can make destructive changes to the test.
reliability_guard()
# Run program.
try:
A__ = {}
with swallow_io():
with time_limit(__a ):
exec(__a , __a )
result.append("""passed""" )
except TimeoutException:
result.append("""timed out""" )
except BaseException as e:
result.append(F'failed: {e}' )
# Needed for cleaning up.
A__ = rmtree
A__ = rmdir
A__ = chdir
@contextlib.contextmanager
def __lowerCamelCase ( __a :List[str] ) -> Dict:
"""simple docstring"""
def signal_handler(__a :List[Any] , __a :Optional[Any] ):
raise TimeoutException("""Timed out!""" )
signal.setitimer(signal.ITIMER_REAL , __a )
signal.signal(signal.SIGALRM , __a )
try:
yield
finally:
signal.setitimer(signal.ITIMER_REAL , 0 )
@contextlib.contextmanager
def __lowerCamelCase ( ) -> Union[str, Any]:
"""simple docstring"""
A__ = WriteOnlyStringIO()
with contextlib.redirect_stdout(__a ):
with contextlib.redirect_stderr(__a ):
with redirect_stdin(__a ):
yield
@contextlib.contextmanager
def __lowerCamelCase ( ) -> Dict:
"""simple docstring"""
with tempfile.TemporaryDirectory() as dirname:
with chdir(__a ):
yield dirname
class A (SCREAMING_SNAKE_CASE ):
'''simple docstring'''
pass
class A (io.StringIO ):
'''simple docstring'''
def a_ ( self : Any , *__lowerCAmelCase : List[str] , **__lowerCAmelCase : str ) -> Dict:
"""simple docstring"""
raise OSError
def a_ ( self : Optional[Any] , *__lowerCAmelCase : Any , **__lowerCAmelCase : Optional[int] ) -> str:
"""simple docstring"""
raise OSError
def a_ ( self : Optional[Any] , *__lowerCAmelCase : Any , **__lowerCAmelCase : Any ) -> int:
"""simple docstring"""
raise OSError
def a_ ( self : str , *__lowerCAmelCase : Any , **__lowerCAmelCase : Union[str, Any] ) -> int:
"""simple docstring"""
return False
class A (contextlib._RedirectStream ): # type: ignore
'''simple docstring'''
__lowerCamelCase : Union[str, Any] = '''stdin'''
@contextlib.contextmanager
def __lowerCamelCase ( __a :Union[str, Any] ) -> List[str]:
"""simple docstring"""
if root == ".":
yield
return
A__ = os.getcwd()
os.chdir(__a )
try:
yield
except BaseException as exc:
raise exc
finally:
os.chdir(__a )
def __lowerCamelCase ( __a :Union[str, Any]=None ) -> Dict:
"""simple docstring"""
if maximum_memory_bytes is not None:
import resource
resource.setrlimit(resource.RLIMIT_AS , (maximum_memory_bytes, maximum_memory_bytes) )
resource.setrlimit(resource.RLIMIT_DATA , (maximum_memory_bytes, maximum_memory_bytes) )
if not platform.uname().system == "Darwin":
resource.setrlimit(resource.RLIMIT_STACK , (maximum_memory_bytes, maximum_memory_bytes) )
faulthandler.disable()
import builtins
A__ = None
A__ = None
import os
A__ = """1"""
A__ = None
A__ = None
A__ = None
A__ = None
A__ = None
A__ = None
A__ = None
A__ = None
A__ = None
A__ = None
A__ = None
A__ = None
A__ = None
A__ = None
A__ = None
A__ = None
A__ = None
A__ = None
A__ = None
A__ = None
A__ = None
A__ = None
A__ = None
A__ = None
A__ = None
A__ = None
A__ = None
import shutil
A__ = None
A__ = None
A__ = None
import subprocess
A__ = None # type: ignore
A__ = None
import sys
A__ = None
A__ = None
A__ = None
A__ = None
A__ = None
| 274
| 0
|
import mpmath # for roots of unity
import numpy as np
class __magic_name__ :
def __init__( self , _lowercase=None , _lowercase=None )-> Optional[int]:
# Input as list
UpperCamelCase_ = list(poly_a or [0] )[:]
UpperCamelCase_ = list(poly_b or [0] )[:]
# Remove leading zero coefficients
while self.polyA[-1] == 0:
self.polyA.pop()
UpperCamelCase_ = len(self.polyA )
while self.polyB[-1] == 0:
self.polyB.pop()
UpperCamelCase_ = len(self.polyB )
# Add 0 to make lengths equal a power of 2
UpperCamelCase_ = int(
2 ** np.ceil(np.loga(len(self.polyA ) + len(self.polyB ) - 1 ) ) )
while len(self.polyA ) < self.c_max_length:
self.polyA.append(0 )
while len(self.polyB ) < self.c_max_length:
self.polyB.append(0 )
# A complex root used for the fourier transform
UpperCamelCase_ = complex(mpmath.root(x=1 , n=self.c_max_length , k=1 ) )
# The product
UpperCamelCase_ = self.__multiply()
def UpperCAmelCase_ ( self , _lowercase )-> str:
UpperCamelCase_ = [[x] for x in self.polyA] if which == "A" else [[x] for x in self.polyB]
# Corner case
if len(_lowercase ) <= 1:
return dft[0]
#
UpperCamelCase_ = self.c_max_length // 2
while next_ncol > 0:
UpperCamelCase_ = [[] for i in range(_lowercase )]
UpperCamelCase_ = self.root**next_ncol
# First half of next step
UpperCamelCase_ = 1
for j in range(self.c_max_length // (next_ncol * 2) ):
for i in range(_lowercase ):
new_dft[i].append(dft[i][j] + current_root * dft[i + next_ncol][j] )
current_root *= root
# Second half of next step
UpperCamelCase_ = 1
for j in range(self.c_max_length // (next_ncol * 2) ):
for i in range(_lowercase ):
new_dft[i].append(dft[i][j] - current_root * dft[i + next_ncol][j] )
current_root *= root
# Update
UpperCamelCase_ = new_dft
UpperCamelCase_ = next_ncol // 2
return dft[0]
def UpperCAmelCase_ ( self )-> Any:
UpperCamelCase_ = self.__dft("A" )
UpperCamelCase_ = self.__dft("B" )
UpperCamelCase_ = [[dft_a[i] * dft_b[i] for i in range(self.c_max_length )]]
del dft_a
del dft_b
# Corner Case
if len(inverce_c[0] ) <= 1:
return inverce_c[0]
# Inverse DFT
UpperCamelCase_ = 2
while next_ncol <= self.c_max_length:
UpperCamelCase_ = [[] for i in range(_lowercase )]
UpperCamelCase_ = self.root ** (next_ncol // 2)
UpperCamelCase_ = 1
# First half of next step
for j in range(self.c_max_length // next_ncol ):
for i in range(next_ncol // 2 ):
# Even positions
new_inverse_c[i].append(
(
inverce_c[i][j]
+ inverce_c[i][j + self.c_max_length // next_ncol]
)
/ 2 )
# Odd positions
new_inverse_c[i + next_ncol // 2].append(
(
inverce_c[i][j]
- inverce_c[i][j + self.c_max_length // next_ncol]
)
/ (2 * current_root) )
current_root *= root
# Update
UpperCamelCase_ = new_inverse_c
next_ncol *= 2
# Unpack
UpperCamelCase_ = [round(x[0].real , 8 ) + round(x[0].imag , 8 ) * 1j for x in inverce_c]
# Remove leading 0's
while inverce_c[-1] == 0:
inverce_c.pop()
return inverce_c
def __str__( self )-> List[str]:
UpperCamelCase_ = "A = " + " + ".join(
F"{coef}*x^{i}" for coef, i in enumerate(self.polyA[: self.len_A] ) )
UpperCamelCase_ = "B = " + " + ".join(
F"{coef}*x^{i}" for coef, i in enumerate(self.polyB[: self.len_B] ) )
UpperCamelCase_ = "A*B = " + " + ".join(
F"{coef}*x^{i}" for coef, i in enumerate(self.product ) )
return F"{a}\n{b}\n{c}"
# Unit tests
if __name__ == "__main__":
import doctest
doctest.testmod()
| 351
|
import unittest
from transformers import is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_available():
from PIL import Image
else:
class __magic_name__ :
@staticmethod
def UpperCAmelCase_ ( *_lowercase , **_lowercase )-> Optional[int]:
pass
@is_pipeline_test
@require_vision
class __magic_name__ ( unittest.TestCase ):
@require_torch
def UpperCAmelCase_ ( self )-> int:
UpperCamelCase_ = pipeline(
model="hf-internal-testing/tiny-random-clip-zero-shot-image-classification" , )
UpperCamelCase_ = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
UpperCamelCase_ = image_classifier(_lowercase , candidate_labels=["a", "b", "c"] )
# The floating scores are so close, we enter floating error approximation and the order is not guaranteed across
# python and torch versions.
self.assertIn(
nested_simplify(_lowercase ) , [
[{"score": 0.333, "label": "a"}, {"score": 0.333, "label": "b"}, {"score": 0.333, "label": "c"}],
[{"score": 0.333, "label": "a"}, {"score": 0.333, "label": "c"}, {"score": 0.333, "label": "b"}],
] , )
UpperCamelCase_ = image_classifier([image] * 5 , candidate_labels=["A", "B", "C"] , batch_size=2 )
self.assertEqual(
nested_simplify(_lowercase ) , [
[
{"score": 0.333, "label": ANY(_lowercase )},
{"score": 0.333, "label": ANY(_lowercase )},
{"score": 0.333, "label": ANY(_lowercase )},
],
[
{"score": 0.333, "label": ANY(_lowercase )},
{"score": 0.333, "label": ANY(_lowercase )},
{"score": 0.333, "label": ANY(_lowercase )},
],
[
{"score": 0.333, "label": ANY(_lowercase )},
{"score": 0.333, "label": ANY(_lowercase )},
{"score": 0.333, "label": ANY(_lowercase )},
],
[
{"score": 0.333, "label": ANY(_lowercase )},
{"score": 0.333, "label": ANY(_lowercase )},
{"score": 0.333, "label": ANY(_lowercase )},
],
[
{"score": 0.333, "label": ANY(_lowercase )},
{"score": 0.333, "label": ANY(_lowercase )},
{"score": 0.333, "label": ANY(_lowercase )},
],
] , )
@require_tf
def UpperCAmelCase_ ( self )-> Optional[Any]:
UpperCamelCase_ = pipeline(
model="hf-internal-testing/tiny-random-clip-zero-shot-image-classification" , framework="tf" )
UpperCamelCase_ = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
UpperCamelCase_ = image_classifier(_lowercase , candidate_labels=["a", "b", "c"] )
self.assertEqual(
nested_simplify(_lowercase ) , [{"score": 0.333, "label": "a"}, {"score": 0.333, "label": "b"}, {"score": 0.333, "label": "c"}] , )
UpperCamelCase_ = image_classifier([image] * 5 , candidate_labels=["A", "B", "C"] , batch_size=2 )
self.assertEqual(
nested_simplify(_lowercase ) , [
[
{"score": 0.333, "label": ANY(_lowercase )},
{"score": 0.333, "label": ANY(_lowercase )},
{"score": 0.333, "label": ANY(_lowercase )},
],
[
{"score": 0.333, "label": ANY(_lowercase )},
{"score": 0.333, "label": ANY(_lowercase )},
{"score": 0.333, "label": ANY(_lowercase )},
],
[
{"score": 0.333, "label": ANY(_lowercase )},
{"score": 0.333, "label": ANY(_lowercase )},
{"score": 0.333, "label": ANY(_lowercase )},
],
[
{"score": 0.333, "label": ANY(_lowercase )},
{"score": 0.333, "label": ANY(_lowercase )},
{"score": 0.333, "label": ANY(_lowercase )},
],
[
{"score": 0.333, "label": ANY(_lowercase )},
{"score": 0.333, "label": ANY(_lowercase )},
{"score": 0.333, "label": ANY(_lowercase )},
],
] , )
@slow
@require_torch
def UpperCAmelCase_ ( self )-> Optional[Any]:
UpperCamelCase_ = pipeline(
task="zero-shot-image-classification" , model="openai/clip-vit-base-patch32" , )
# This is an image of 2 cats with remotes and no planes
UpperCamelCase_ = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
UpperCamelCase_ = image_classifier(_lowercase , candidate_labels=["cat", "plane", "remote"] )
self.assertEqual(
nested_simplify(_lowercase ) , [
{"score": 0.511, "label": "remote"},
{"score": 0.485, "label": "cat"},
{"score": 0.004, "label": "plane"},
] , )
UpperCamelCase_ = image_classifier([image] * 5 , candidate_labels=["cat", "plane", "remote"] , batch_size=2 )
self.assertEqual(
nested_simplify(_lowercase ) , [
[
{"score": 0.511, "label": "remote"},
{"score": 0.485, "label": "cat"},
{"score": 0.004, "label": "plane"},
],
]
* 5 , )
@slow
@require_tf
def UpperCAmelCase_ ( self )-> List[str]:
UpperCamelCase_ = pipeline(
task="zero-shot-image-classification" , model="openai/clip-vit-base-patch32" , framework="tf" )
# This is an image of 2 cats with remotes and no planes
UpperCamelCase_ = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
UpperCamelCase_ = image_classifier(_lowercase , candidate_labels=["cat", "plane", "remote"] )
self.assertEqual(
nested_simplify(_lowercase ) , [
{"score": 0.511, "label": "remote"},
{"score": 0.485, "label": "cat"},
{"score": 0.004, "label": "plane"},
] , )
UpperCamelCase_ = image_classifier([image] * 5 , candidate_labels=["cat", "plane", "remote"] , batch_size=2 )
self.assertEqual(
nested_simplify(_lowercase ) , [
[
{"score": 0.511, "label": "remote"},
{"score": 0.485, "label": "cat"},
{"score": 0.004, "label": "plane"},
],
]
* 5 , )
| 60
| 0
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__UpperCamelCase = {'''configuration_unispeech''': ['''UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''UniSpeechConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCamelCase = [
'''UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''UniSpeechForCTC''',
'''UniSpeechForPreTraining''',
'''UniSpeechForSequenceClassification''',
'''UniSpeechModel''',
'''UniSpeechPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_unispeech import UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_unispeech import (
UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST,
UniSpeechForCTC,
UniSpeechForPreTraining,
UniSpeechForSequenceClassification,
UniSpeechModel,
UniSpeechPreTrainedModel,
)
else:
import sys
__UpperCamelCase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 113
|
"""simple docstring"""
import argparse
import json
from pathlib import Path
import torch
import torchaudio
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from transformers import ASTConfig, ASTFeatureExtractor, ASTForAudioClassification
from transformers.utils import logging
logging.set_verbosity_info()
__UpperCamelCase = logging.get_logger(__name__)
def lowercase (SCREAMING_SNAKE_CASE_ : int ) -> List[str]:
SCREAMING_SNAKE_CASE = ASTConfig()
if "10-10" in model_name:
pass
elif "speech-commands" in model_name:
SCREAMING_SNAKE_CASE = 1_28
elif "12-12" in model_name:
SCREAMING_SNAKE_CASE = 12
SCREAMING_SNAKE_CASE = 12
elif "14-14" in model_name:
SCREAMING_SNAKE_CASE = 14
SCREAMING_SNAKE_CASE = 14
elif "16-16" in model_name:
SCREAMING_SNAKE_CASE = 16
SCREAMING_SNAKE_CASE = 16
else:
raise ValueError('Model not supported' )
SCREAMING_SNAKE_CASE = 'huggingface/label-files'
if "speech-commands" in model_name:
SCREAMING_SNAKE_CASE = 35
SCREAMING_SNAKE_CASE = 'speech-commands-v2-id2label.json'
else:
SCREAMING_SNAKE_CASE = 5_27
SCREAMING_SNAKE_CASE = 'audioset-id2label.json'
SCREAMING_SNAKE_CASE = json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , repo_type='dataset' ) , 'r' ) )
SCREAMING_SNAKE_CASE = {int(SCREAMING_SNAKE_CASE_ ): v for k, v in idalabel.items()}
SCREAMING_SNAKE_CASE = idalabel
SCREAMING_SNAKE_CASE = {v: k for k, v in idalabel.items()}
return config
def lowercase (SCREAMING_SNAKE_CASE_ : str ) -> List[Any]:
if "module.v" in name:
SCREAMING_SNAKE_CASE = name.replace('module.v' , 'audio_spectrogram_transformer' )
if "cls_token" in name:
SCREAMING_SNAKE_CASE = name.replace('cls_token' , 'embeddings.cls_token' )
if "dist_token" in name:
SCREAMING_SNAKE_CASE = name.replace('dist_token' , 'embeddings.distillation_token' )
if "pos_embed" in name:
SCREAMING_SNAKE_CASE = name.replace('pos_embed' , 'embeddings.position_embeddings' )
if "patch_embed.proj" in name:
SCREAMING_SNAKE_CASE = name.replace('patch_embed.proj' , 'embeddings.patch_embeddings.projection' )
# transformer blocks
if "blocks" in name:
SCREAMING_SNAKE_CASE = name.replace('blocks' , 'encoder.layer' )
if "attn.proj" in name:
SCREAMING_SNAKE_CASE = name.replace('attn.proj' , 'attention.output.dense' )
if "attn" in name:
SCREAMING_SNAKE_CASE = name.replace('attn' , 'attention.self' )
if "norm1" in name:
SCREAMING_SNAKE_CASE = name.replace('norm1' , 'layernorm_before' )
if "norm2" in name:
SCREAMING_SNAKE_CASE = name.replace('norm2' , 'layernorm_after' )
if "mlp.fc1" in name:
SCREAMING_SNAKE_CASE = name.replace('mlp.fc1' , 'intermediate.dense' )
if "mlp.fc2" in name:
SCREAMING_SNAKE_CASE = name.replace('mlp.fc2' , 'output.dense' )
# final layernorm
if "audio_spectrogram_transformer.norm" in name:
SCREAMING_SNAKE_CASE = name.replace('audio_spectrogram_transformer.norm' , 'audio_spectrogram_transformer.layernorm' )
# classifier head
if "module.mlp_head.0" in name:
SCREAMING_SNAKE_CASE = name.replace('module.mlp_head.0' , 'classifier.layernorm' )
if "module.mlp_head.1" in name:
SCREAMING_SNAKE_CASE = name.replace('module.mlp_head.1' , 'classifier.dense' )
return name
def lowercase (SCREAMING_SNAKE_CASE_ : Any , SCREAMING_SNAKE_CASE_ : Dict ) -> Dict:
for key in orig_state_dict.copy().keys():
SCREAMING_SNAKE_CASE = orig_state_dict.pop(SCREAMING_SNAKE_CASE_ )
if "qkv" in key:
SCREAMING_SNAKE_CASE = key.split('.' )
SCREAMING_SNAKE_CASE = int(key_split[3] )
SCREAMING_SNAKE_CASE = config.hidden_size
if "weight" in key:
SCREAMING_SNAKE_CASE = val[:dim, :]
SCREAMING_SNAKE_CASE = val[dim : dim * 2, :]
SCREAMING_SNAKE_CASE = val[-dim:, :]
else:
SCREAMING_SNAKE_CASE = val[:dim]
SCREAMING_SNAKE_CASE = val[dim : dim * 2]
SCREAMING_SNAKE_CASE = val[-dim:]
else:
SCREAMING_SNAKE_CASE = val
return orig_state_dict
def lowercase (SCREAMING_SNAKE_CASE_ : int ) -> List[Any]:
SCREAMING_SNAKE_CASE = [
'module.v.head.weight',
'module.v.head.bias',
'module.v.head_dist.weight',
'module.v.head_dist.bias',
]
for k in ignore_keys:
state_dict.pop(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
@torch.no_grad()
def lowercase (SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : List[Any] , SCREAMING_SNAKE_CASE_ : Union[str, Any]=False ) -> Optional[int]:
SCREAMING_SNAKE_CASE = get_audio_spectrogram_transformer_config(SCREAMING_SNAKE_CASE_ )
SCREAMING_SNAKE_CASE = {
'ast-finetuned-audioset-10-10-0.4593': (
'https://www.dropbox.com/s/ca0b1v2nlxzyeb4/audioset_10_10_0.4593.pth?dl=1'
),
'ast-finetuned-audioset-10-10-0.450': (
'https://www.dropbox.com/s/1tv0hovue1bxupk/audioset_10_10_0.4495.pth?dl=1'
),
'ast-finetuned-audioset-10-10-0.448': (
'https://www.dropbox.com/s/6u5sikl4b9wo4u5/audioset_10_10_0.4483.pth?dl=1'
),
'ast-finetuned-audioset-10-10-0.448-v2': (
'https://www.dropbox.com/s/kt6i0v9fvfm1mbq/audioset_10_10_0.4475.pth?dl=1'
),
'ast-finetuned-audioset-12-12-0.447': (
'https://www.dropbox.com/s/snfhx3tizr4nuc8/audioset_12_12_0.4467.pth?dl=1'
),
'ast-finetuned-audioset-14-14-0.443': (
'https://www.dropbox.com/s/z18s6pemtnxm4k7/audioset_14_14_0.4431.pth?dl=1'
),
'ast-finetuned-audioset-16-16-0.442': (
'https://www.dropbox.com/s/mdsa4t1xmcimia6/audioset_16_16_0.4422.pth?dl=1'
),
'ast-finetuned-speech-commands-v2': (
'https://www.dropbox.com/s/q0tbqpwv44pquwy/speechcommands_10_10_0.9812.pth?dl=1'
),
}
# load original state_dict
SCREAMING_SNAKE_CASE = model_name_to_url[model_name]
SCREAMING_SNAKE_CASE = torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE_ , map_location='cpu' )
# remove some keys
remove_keys(SCREAMING_SNAKE_CASE_ )
# rename some keys
SCREAMING_SNAKE_CASE = convert_state_dict(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# load 🤗 model
SCREAMING_SNAKE_CASE = ASTForAudioClassification(SCREAMING_SNAKE_CASE_ )
model.eval()
model.load_state_dict(SCREAMING_SNAKE_CASE_ )
# verify outputs on dummy input
# source: https://github.com/YuanGongND/ast/blob/79e873b8a54d0a3b330dd522584ff2b9926cd581/src/run.py#L62
SCREAMING_SNAKE_CASE = -4.2_67_73_93 if 'speech-commands' not in model_name else -6.84_59_78
SCREAMING_SNAKE_CASE = 4.5_68_99_74 if 'speech-commands' not in model_name else 5.5_65_45_26
SCREAMING_SNAKE_CASE = 10_24 if 'speech-commands' not in model_name else 1_28
SCREAMING_SNAKE_CASE = ASTFeatureExtractor(mean=SCREAMING_SNAKE_CASE_ , std=SCREAMING_SNAKE_CASE_ , max_length=SCREAMING_SNAKE_CASE_ )
if "speech-commands" in model_name:
SCREAMING_SNAKE_CASE = load_dataset('speech_commands' , 'v0.02' , split='validation' )
SCREAMING_SNAKE_CASE = dataset[0]['audio']['array']
else:
SCREAMING_SNAKE_CASE = hf_hub_download(
repo_id='nielsr/audio-spectogram-transformer-checkpoint' , filename='sample_audio.flac' , repo_type='dataset' , )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = torchaudio.load(SCREAMING_SNAKE_CASE_ )
SCREAMING_SNAKE_CASE = waveform.squeeze().numpy()
SCREAMING_SNAKE_CASE = feature_extractor(SCREAMING_SNAKE_CASE_ , sampling_rate=1_60_00 , return_tensors='pt' )
# forward pass
SCREAMING_SNAKE_CASE = model(**SCREAMING_SNAKE_CASE_ )
SCREAMING_SNAKE_CASE = outputs.logits
if model_name == "ast-finetuned-audioset-10-10-0.4593":
SCREAMING_SNAKE_CASE = torch.tensor([-0.87_60, -7.00_42, -8.66_02] )
elif model_name == "ast-finetuned-audioset-10-10-0.450":
SCREAMING_SNAKE_CASE = torch.tensor([-1.19_86, -7.09_03, -8.27_18] )
elif model_name == "ast-finetuned-audioset-10-10-0.448":
SCREAMING_SNAKE_CASE = torch.tensor([-2.61_28, -8.00_80, -9.43_44] )
elif model_name == "ast-finetuned-audioset-10-10-0.448-v2":
SCREAMING_SNAKE_CASE = torch.tensor([-1.50_80, -7.45_34, -8.89_17] )
elif model_name == "ast-finetuned-audioset-12-12-0.447":
SCREAMING_SNAKE_CASE = torch.tensor([-0.50_50, -6.58_33, -8.08_43] )
elif model_name == "ast-finetuned-audioset-14-14-0.443":
SCREAMING_SNAKE_CASE = torch.tensor([-0.38_26, -7.03_36, -8.24_13] )
elif model_name == "ast-finetuned-audioset-16-16-0.442":
SCREAMING_SNAKE_CASE = torch.tensor([-1.21_13, -6.91_01, -8.34_70] )
elif model_name == "ast-finetuned-speech-commands-v2":
SCREAMING_SNAKE_CASE = torch.tensor([6.15_89, -8.05_66, -8.79_84] )
else:
raise ValueError('Unknown model name' )
if not torch.allclose(logits[0, :3] , SCREAMING_SNAKE_CASE_ , atol=1E-4 ):
raise ValueError('Logits don\'t match' )
print('Looks ok!' )
if pytorch_dump_folder_path is not None:
Path(SCREAMING_SNAKE_CASE_ ).mkdir(exist_ok=SCREAMING_SNAKE_CASE_ )
print(F'Saving model {model_name} to {pytorch_dump_folder_path}' )
model.save_pretrained(SCREAMING_SNAKE_CASE_ )
print(F'Saving feature extractor to {pytorch_dump_folder_path}' )
feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE_ )
if push_to_hub:
print('Pushing model and feature extractor to the hub...' )
model.push_to_hub(F'MIT/{model_name}' )
feature_extractor.push_to_hub(F'MIT/{model_name}' )
if __name__ == "__main__":
__UpperCamelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--model_name''',
default='''ast-finetuned-audioset-10-10-0.4593''',
type=str,
help='''Name of the Audio Spectrogram Transformer model you\'d like to convert.''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
)
parser.add_argument(
'''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
)
__UpperCamelCase = parser.parse_args()
convert_audio_spectrogram_transformer_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 113
| 1
|
import os
from math import logaa
def SCREAMING_SNAKE_CASE__ ( lowercase = "base_exp.txt" ) -> int:
snake_case : float = 0
snake_case : Optional[Any] = 0
for i, line in enumerate(open(os.path.join(os.path.dirname(lowercase ) ,lowercase ) ) ):
snake_case : str = list(map(lowercase ,line.split(""",""" ) ) )
if x * logaa(lowercase ) > largest:
snake_case : Union[str, Any] = x * logaa(lowercase )
snake_case : Any = i + 1
return result
if __name__ == "__main__":
print(solution())
| 371
|
def SCREAMING_SNAKE_CASE__ ( lowercase ,lowercase ) -> int:
return int((input_a, input_a).count(1 ) != 0 )
def SCREAMING_SNAKE_CASE__ ( ) -> None:
assert or_gate(0 ,0 ) == 0
assert or_gate(0 ,1 ) == 1
assert or_gate(1 ,0 ) == 1
assert or_gate(1 ,1 ) == 1
if __name__ == "__main__":
print(or_gate(0, 1))
print(or_gate(1, 0))
print(or_gate(0, 0))
print(or_gate(1, 1))
| 176
| 0
|
'''simple docstring'''
from __future__ import annotations
from statistics import mean
def __lowercase ( __lowercase , __lowercase , __lowercase ) -> list[int]:
'''simple docstring'''
_A = [0] * no_of_processes
_A = [0] * no_of_processes
# Initialize remaining_time to waiting_time.
for i in range(__lowercase ):
_A = burst_time[i]
_A = []
_A = 0
_A = 0
# When processes are not completed,
# A process whose arrival time has passed \
# and has remaining execution time is put into the ready_process.
# The shortest process in the ready_process, target_process is executed.
while completed != no_of_processes:
_A = []
_A = -1
for i in range(__lowercase ):
if (arrival_time[i] <= total_time) and (remaining_time[i] > 0):
ready_process.append(__lowercase )
if len(__lowercase ) > 0:
_A = ready_process[0]
for i in ready_process:
if remaining_time[i] < remaining_time[target_process]:
_A = i
total_time += burst_time[target_process]
completed += 1
_A = 0
_A = (
total_time - arrival_time[target_process] - burst_time[target_process]
)
else:
total_time += 1
return waiting_time
def __lowercase ( __lowercase , __lowercase , __lowercase ) -> list[int]:
'''simple docstring'''
_A = [0] * no_of_processes
for i in range(__lowercase ):
_A = burst_time[i] + waiting_time[i]
return turn_around_time
if __name__ == "__main__":
print('''[TEST CASE 01]''')
lowerCamelCase_ = 4
lowerCamelCase_ = [2, 5, 3, 7]
lowerCamelCase_ = [0, 0, 0, 0]
lowerCamelCase_ = calculate_waitingtime(arrival_time, burst_time, no_of_processes)
lowerCamelCase_ = calculate_turnaroundtime(
burst_time, no_of_processes, waiting_time
)
# Printing the Result
print('''PID\tBurst Time\tArrival Time\tWaiting Time\tTurnaround Time''')
for i, process_id in enumerate(list(range(1, 5))):
print(
F"""{process_id}\t{burst_time[i]}\t\t\t{arrival_time[i]}\t\t\t\t"""
F"""{waiting_time[i]}\t\t\t\t{turn_around_time[i]}"""
)
print(F"""\nAverage waiting time = {mean(waiting_time):.5f}""")
print(F"""Average turnaround time = {mean(turn_around_time):.5f}""")
| 79
|
'''simple docstring'''
from collections.abc import Sequence
def __UpperCamelCase ( lowercase__ : Sequence[float], lowercase__ : float ):
'''simple docstring'''
return sum(c * (x**i) for i, c in enumerate(lowercase__ ) )
def __UpperCamelCase ( lowercase__ : Sequence[float], lowercase__ : float ):
'''simple docstring'''
__lowercase =0.0
for coeff in reversed(lowercase__ ):
__lowercase =result * x + coeff
return result
if __name__ == "__main__":
UpperCAmelCase = (0.0, 0.0, 5.0, 9.3, 7.0)
UpperCAmelCase = 10.0
print(evaluate_poly(poly, x))
print(horner(poly, x))
| 141
| 0
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase : List[Any] = logging.get_logger(__name__)
_lowerCamelCase : Any = {
'''google/vivit-b-16x2-kinetics400''': (
'''https://huggingface.co/google/vivit-b-16x2-kinetics400/resolve/main/config.json'''
),
# See all Vivit models at https://huggingface.co/models?filter=vivit
}
class lowerCamelCase (__lowerCamelCase ):
"""simple docstring"""
UpperCAmelCase_ = "vivit"
def __init__( self : List[str], _UpperCAmelCase : Optional[int]=2_2_4, _UpperCAmelCase : Any=3_2, _UpperCAmelCase : Dict=[2, 1_6, 1_6], _UpperCAmelCase : int=3, _UpperCAmelCase : List[Any]=7_6_8, _UpperCAmelCase : int=1_2, _UpperCAmelCase : Tuple=1_2, _UpperCAmelCase : Any=3_0_7_2, _UpperCAmelCase : Tuple="gelu_fast", _UpperCAmelCase : Dict=0.0, _UpperCAmelCase : List[str]=0.0, _UpperCAmelCase : Tuple=0.02, _UpperCAmelCase : List[Any]=1E-06, _UpperCAmelCase : Dict=True, **_UpperCAmelCase : Optional[Any], ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = hidden_size
SCREAMING_SNAKE_CASE__ : Optional[Any] = num_hidden_layers
SCREAMING_SNAKE_CASE__ : Dict = num_attention_heads
SCREAMING_SNAKE_CASE__ : Any = intermediate_size
SCREAMING_SNAKE_CASE__ : Dict = hidden_act
SCREAMING_SNAKE_CASE__ : Dict = hidden_dropout_prob
SCREAMING_SNAKE_CASE__ : Union[str, Any] = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE__ : List[str] = initializer_range
SCREAMING_SNAKE_CASE__ : Tuple = layer_norm_eps
SCREAMING_SNAKE_CASE__ : Optional[int] = image_size
SCREAMING_SNAKE_CASE__ : str = num_frames
SCREAMING_SNAKE_CASE__ : Union[str, Any] = tubelet_size
SCREAMING_SNAKE_CASE__ : str = num_channels
SCREAMING_SNAKE_CASE__ : int = qkv_bias
super().__init__(**_UpperCAmelCase )
| 191
|
from queue import PriorityQueue
from typing import Any
import numpy as np
def _a ( SCREAMING_SNAKE_CASE__ : dict , SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : set , SCREAMING_SNAKE_CASE__ : set , SCREAMING_SNAKE_CASE__ : dict , SCREAMING_SNAKE_CASE__ : dict , SCREAMING_SNAKE_CASE__ : PriorityQueue , SCREAMING_SNAKE_CASE__ : dict , SCREAMING_SNAKE_CASE__ : float | int , ) -> float | int:
'''simple docstring'''
for nxt, d in graph[v]:
if nxt in visited_forward:
continue
SCREAMING_SNAKE_CASE__ : Union[str, Any] = cst_fwd.get(SCREAMING_SNAKE_CASE__ , np.inf )
SCREAMING_SNAKE_CASE__ : Optional[int] = cst_fwd[v] + d
if new_cost_f < old_cost_f:
queue.put((new_cost_f, nxt) )
SCREAMING_SNAKE_CASE__ : List[Any] = new_cost_f
SCREAMING_SNAKE_CASE__ : Union[str, Any] = v
if nxt in visited_backward:
if cst_fwd[v] + d + cst_bwd[nxt] < shortest_distance:
SCREAMING_SNAKE_CASE__ : Optional[int] = cst_fwd[v] + d + cst_bwd[nxt]
return shortest_distance
def _a ( SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : dict , SCREAMING_SNAKE_CASE__ : dict ) -> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Union[str, Any] = -1
SCREAMING_SNAKE_CASE__ : List[str] = set()
SCREAMING_SNAKE_CASE__ : List[Any] = set()
SCREAMING_SNAKE_CASE__ : int = {source: 0}
SCREAMING_SNAKE_CASE__ : Union[str, Any] = {destination: 0}
SCREAMING_SNAKE_CASE__ : List[Any] = {source: None}
SCREAMING_SNAKE_CASE__ : Union[str, Any] = {destination: None}
SCREAMING_SNAKE_CASE__ : PriorityQueue[Any] = PriorityQueue()
SCREAMING_SNAKE_CASE__ : PriorityQueue[Any] = PriorityQueue()
SCREAMING_SNAKE_CASE__ : Dict = np.inf
queue_forward.put((0, source) )
queue_backward.put((0, destination) )
if source == destination:
return 0
while not queue_forward.empty() and not queue_backward.empty():
SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ : List[str] = queue_forward.get()
visited_forward.add(SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ : List[Any] = queue_backward.get()
visited_backward.add(SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE__ : Any = pass_and_relaxation(
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , )
SCREAMING_SNAKE_CASE__ : List[Any] = pass_and_relaxation(
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , )
if cst_fwd[v_fwd] + cst_bwd[v_bwd] >= shortest_distance:
break
if shortest_distance != np.inf:
SCREAMING_SNAKE_CASE__ : int = shortest_distance
return shortest_path_distance
_lowerCamelCase : Optional[Any] = {
'''B''': [['''C''', 1]],
'''C''': [['''D''', 1]],
'''D''': [['''F''', 1]],
'''E''': [['''B''', 1], ['''G''', 2]],
'''F''': [],
'''G''': [['''F''', 1]],
}
_lowerCamelCase : Tuple = {
'''B''': [['''E''', 1]],
'''C''': [['''B''', 1]],
'''D''': [['''C''', 1]],
'''F''': [['''D''', 1], ['''G''', 1]],
'''E''': [[None, np.inf]],
'''G''': [['''E''', 2]],
}
if __name__ == "__main__":
import doctest
doctest.testmod()
| 191
| 1
|
"""simple docstring"""
import itertools
import json
import os
import unittest
from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class __magic_name__ ( UpperCAmelCase__ , unittest.TestCase ):
'''simple docstring'''
__UpperCamelCase = RobertaTokenizer
__UpperCamelCase = RobertaTokenizerFast
__UpperCamelCase = True
__UpperCamelCase = {"cls_token": "<s>"}
def _lowerCAmelCase ( self ):
"""simple docstring"""
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
lowerCamelCase = [
"""l""",
"""o""",
"""w""",
"""e""",
"""r""",
"""s""",
"""t""",
"""i""",
"""d""",
"""n""",
"""\u0120""",
"""\u0120l""",
"""\u0120n""",
"""\u0120lo""",
"""\u0120low""",
"""er""",
"""\u0120lowest""",
"""\u0120newer""",
"""\u0120wider""",
"""<unk>""",
]
lowerCamelCase = dict(zip(_a , range(len(_a ) ) ) )
lowerCamelCase = ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
lowerCamelCase = {"""unk_token""": """<unk>"""}
lowerCamelCase = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] )
lowerCamelCase = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""merges_file"""] )
with open(self.vocab_file , """w""" , encoding="""utf-8""" ) as fp:
fp.write(json.dumps(_a ) + """\n""" )
with open(self.merges_file , """w""" , encoding="""utf-8""" ) as fp:
fp.write("""\n""".join(_a ) )
def _lowerCAmelCase ( self , **_a ):
"""simple docstring"""
kwargs.update(self.special_tokens_map )
return self.tokenizer_class.from_pretrained(self.tmpdirname , **_a )
def _lowerCAmelCase ( self , **_a ):
"""simple docstring"""
kwargs.update(self.special_tokens_map )
return RobertaTokenizerFast.from_pretrained(self.tmpdirname , **_a )
def _lowerCAmelCase ( self , _a ):
"""simple docstring"""
lowerCamelCase = """lower newer"""
lowerCamelCase = """lower newer"""
return input_text, output_text
def _lowerCAmelCase ( self ):
"""simple docstring"""
lowerCamelCase = self.tokenizer_class(self.vocab_file , self.merges_file , **self.special_tokens_map )
lowerCamelCase = """lower newer"""
lowerCamelCase = ["""l""", """o""", """w""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
lowerCamelCase = tokenizer.tokenize(_a ) # , add_prefix_space=True)
self.assertListEqual(_a , _a )
lowerCamelCase = tokens + [tokenizer.unk_token]
lowerCamelCase = [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(tokenizer.convert_tokens_to_ids(_a ) , _a )
def _lowerCAmelCase ( self ):
"""simple docstring"""
lowerCamelCase = self.get_tokenizer()
self.assertListEqual(tokenizer.encode("""Hello world!""" , add_special_tokens=_a ) , [0, 31_414, 232, 328, 2] )
self.assertListEqual(
tokenizer.encode("""Hello world! cécé herlolip 418""" , add_special_tokens=_a ) , [0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2] , )
@slow
def _lowerCAmelCase ( self ):
"""simple docstring"""
lowerCamelCase = self.tokenizer_class.from_pretrained("""roberta-base""" )
lowerCamelCase = tokenizer.encode("""sequence builders""" , add_special_tokens=_a )
lowerCamelCase = tokenizer.encode("""multi-sequence build""" , add_special_tokens=_a )
lowerCamelCase = tokenizer.encode(
"""sequence builders""" , add_special_tokens=_a , add_prefix_space=_a )
lowerCamelCase = tokenizer.encode(
"""sequence builders""" , """multi-sequence build""" , add_special_tokens=_a , add_prefix_space=_a )
lowerCamelCase = tokenizer.build_inputs_with_special_tokens(_a )
lowerCamelCase = tokenizer.build_inputs_with_special_tokens(_a , _a )
assert encoded_sentence == encoded_text_from_decode
assert encoded_pair == encoded_pair_from_decode
def _lowerCAmelCase ( self ):
"""simple docstring"""
lowerCamelCase = self.get_tokenizer()
lowerCamelCase = """Encode this sequence."""
lowerCamelCase = tokenizer.byte_encoder[""" """.encode("""utf-8""" )[0]]
# Testing encoder arguments
lowerCamelCase = tokenizer.encode(_a , add_special_tokens=_a , add_prefix_space=_a )
lowerCamelCase = tokenizer.convert_ids_to_tokens(encoded[0] )[0]
self.assertNotEqual(_a , _a )
lowerCamelCase = tokenizer.encode(_a , add_special_tokens=_a , add_prefix_space=_a )
lowerCamelCase = tokenizer.convert_ids_to_tokens(encoded[0] )[0]
self.assertEqual(_a , _a )
tokenizer.add_special_tokens({"""bos_token""": """<s>"""} )
lowerCamelCase = tokenizer.encode(_a , add_special_tokens=_a )
lowerCamelCase = tokenizer.convert_ids_to_tokens(encoded[1] )[0]
self.assertNotEqual(_a , _a )
# Testing spaces after special tokens
lowerCamelCase = """<mask>"""
tokenizer.add_special_tokens(
{"""mask_token""": AddedToken(_a , lstrip=_a , rstrip=_a )} ) # mask token has a left space
lowerCamelCase = tokenizer.convert_tokens_to_ids(_a )
lowerCamelCase = """Encode <mask> sequence"""
lowerCamelCase = """Encode <mask>sequence"""
lowerCamelCase = tokenizer.encode(_a )
lowerCamelCase = encoded.index(_a )
lowerCamelCase = tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1] )[0]
self.assertEqual(_a , _a )
lowerCamelCase = tokenizer.encode(_a )
lowerCamelCase = encoded.index(_a )
lowerCamelCase = tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1] )[0]
self.assertNotEqual(_a , _a )
def _lowerCAmelCase ( self ):
"""simple docstring"""
pass
def _lowerCAmelCase ( self ):
"""simple docstring"""
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})' ):
lowerCamelCase = self.rust_tokenizer_class.from_pretrained(_a , **_a )
lowerCamelCase = self.tokenizer_class.from_pretrained(_a , **_a )
lowerCamelCase = """A, <mask> AllenNLP sentence."""
lowerCamelCase = tokenizer_r.encode_plus(_a , add_special_tokens=_a , return_token_type_ids=_a )
lowerCamelCase = tokenizer_p.encode_plus(_a , add_special_tokens=_a , return_token_type_ids=_a )
# token_type_ids should put 0 everywhere
self.assertEqual(sum(tokens_r["""token_type_ids"""] ) , sum(tokens_p["""token_type_ids"""] ) )
# attention_mask should put 1 everywhere, so sum over length should be 1
self.assertEqual(
sum(tokens_r["""attention_mask"""] ) / len(tokens_r["""attention_mask"""] ) , sum(tokens_p["""attention_mask"""] ) / len(tokens_p["""attention_mask"""] ) , )
lowerCamelCase = tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""] )
lowerCamelCase = tokenizer_p.convert_ids_to_tokens(tokens_p["""input_ids"""] )
# Rust correctly handles the space before the mask while python doesnt
self.assertSequenceEqual(tokens_p["""input_ids"""] , [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2] )
self.assertSequenceEqual(tokens_r["""input_ids"""] , [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2] )
self.assertSequenceEqual(
_a , ["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""] )
self.assertSequenceEqual(
_a , ["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""] )
def _lowerCAmelCase ( self ):
"""simple docstring"""
for trim_offsets, add_prefix_space in itertools.product([True, False] , repeat=2 ):
lowerCamelCase = self.rust_tokenizer_class.from_pretrained(
self.tmpdirname , use_fast=_a , add_prefix_space=_a , trim_offsets=_a )
lowerCamelCase = json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__() )
lowerCamelCase = json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__() )
self.assertEqual(pre_tokenizer_state["""add_prefix_space"""] , _a )
self.assertEqual(post_processor_state["""add_prefix_space"""] , _a )
self.assertEqual(post_processor_state["""trim_offsets"""] , _a )
def _lowerCAmelCase ( self ):
"""simple docstring"""
# Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space` and
# `trim_offsets`
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})' ):
lowerCamelCase = """hello""" # `hello` is a token in the vocabulary of `pretrained_name`
lowerCamelCase = f'{text_of_1_token} {text_of_1_token}'
lowerCamelCase = self.rust_tokenizer_class.from_pretrained(
_a , use_fast=_a , add_prefix_space=_a , trim_offsets=_a )
lowerCamelCase = tokenizer_r(_a , return_offsets_mapping=_a , add_special_tokens=_a )
self.assertEqual(encoding.offset_mapping[0] , (0, len(_a )) )
self.assertEqual(
encoding.offset_mapping[1] , (len(_a ) + 1, len(_a ) + 1 + len(_a )) , )
lowerCamelCase = self.rust_tokenizer_class.from_pretrained(
_a , use_fast=_a , add_prefix_space=_a , trim_offsets=_a )
lowerCamelCase = tokenizer_r(_a , return_offsets_mapping=_a , add_special_tokens=_a )
self.assertEqual(encoding.offset_mapping[0] , (0, len(_a )) )
self.assertEqual(
encoding.offset_mapping[1] , (len(_a ) + 1, len(_a ) + 1 + len(_a )) , )
lowerCamelCase = self.rust_tokenizer_class.from_pretrained(
_a , use_fast=_a , add_prefix_space=_a , trim_offsets=_a )
lowerCamelCase = tokenizer_r(_a , return_offsets_mapping=_a , add_special_tokens=_a )
self.assertEqual(encoding.offset_mapping[0] , (0, len(_a )) )
self.assertEqual(
encoding.offset_mapping[1] , (len(_a ), len(_a ) + 1 + len(_a )) , )
lowerCamelCase = self.rust_tokenizer_class.from_pretrained(
_a , use_fast=_a , add_prefix_space=_a , trim_offsets=_a )
lowerCamelCase = tokenizer_r(_a , return_offsets_mapping=_a , add_special_tokens=_a )
self.assertEqual(encoding.offset_mapping[0] , (0, len(_a )) )
self.assertEqual(
encoding.offset_mapping[1] , (len(_a ), len(_a ) + 1 + len(_a )) , )
lowerCamelCase = f' {text}'
# tokenizer_r = self.rust_tokenizer_class.from_pretrained(
# pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
# )
# encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
# self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
# self.assertEqual(
# encoding.offset_mapping[1],
# (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
# )
lowerCamelCase = self.rust_tokenizer_class.from_pretrained(
_a , use_fast=_a , add_prefix_space=_a , trim_offsets=_a )
lowerCamelCase = tokenizer_r(_a , return_offsets_mapping=_a , add_special_tokens=_a )
self.assertEqual(encoding.offset_mapping[0] , (1, 1 + len(_a )) )
self.assertEqual(
encoding.offset_mapping[1] , (1 + len(_a ) + 1, 1 + len(_a ) + 1 + len(_a )) , )
lowerCamelCase = self.rust_tokenizer_class.from_pretrained(
_a , use_fast=_a , add_prefix_space=_a , trim_offsets=_a )
lowerCamelCase = tokenizer_r(_a , return_offsets_mapping=_a , add_special_tokens=_a )
self.assertEqual(encoding.offset_mapping[0] , (0, 1 + len(_a )) )
self.assertEqual(
encoding.offset_mapping[1] , (1 + len(_a ), 1 + len(_a ) + 1 + len(_a )) , )
lowerCamelCase = self.rust_tokenizer_class.from_pretrained(
_a , use_fast=_a , add_prefix_space=_a , trim_offsets=_a )
lowerCamelCase = tokenizer_r(_a , return_offsets_mapping=_a , add_special_tokens=_a )
self.assertEqual(encoding.offset_mapping[0] , (0, 1 + len(_a )) )
self.assertEqual(
encoding.offset_mapping[1] , (1 + len(_a ), 1 + len(_a ) + 1 + len(_a )) , )
| 291
|
"""simple docstring"""
from __future__ import annotations
def a__ ( snake_case__ , snake_case__ ) -> bool:
if len(snake_case__ ) == 0:
return False
lowerCamelCase = len(snake_case__ ) // 2
if a_list[midpoint] == item:
return True
if item < a_list[midpoint]:
return binary_search(a_list[:midpoint] , snake_case__ )
else:
return binary_search(a_list[midpoint + 1 :] , snake_case__ )
if __name__ == "__main__":
lowerCAmelCase : List[Any] = input("""Enter numbers separated by comma:\n""").strip()
lowerCAmelCase : Optional[Any] = [int(item.strip()) for item in user_input.split(""",""")]
lowerCAmelCase : Optional[int] = int(input("""Enter the number to be found in the list:\n""").strip())
lowerCAmelCase : Union[str, Any] = """""" if binary_search(sequence, target) else """not """
print(F"""{target} was {not_str}found in {sequence}""")
| 291
| 1
|
'''simple docstring'''
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging
a_ : Tuple = logging.get_logger(__name__)
class a ( _SCREAMING_SNAKE_CASE ):
_lowerCAmelCase = ["""input_features"""]
def __init__( self , __magic_name__=80 , __magic_name__=1_60_00 , __magic_name__=1_60 , __magic_name__=30 , __magic_name__=4_00 , __magic_name__=0.0 , __magic_name__=False , **__magic_name__ , ) -> Optional[int]:
super().__init__(
feature_size=__magic_name__ , sampling_rate=__magic_name__ , padding_value=__magic_name__ , return_attention_mask=__magic_name__ , **__magic_name__ , )
_a = n_fft
_a = hop_length
_a = chunk_length
_a = chunk_length * sampling_rate
_a = self.n_samples // hop_length
_a = sampling_rate
_a = mel_filter_bank(
num_frequency_bins=1 + n_fft // 2 , num_mel_filters=__magic_name__ , min_frequency=0.0 , max_frequency=80_00.0 , sampling_rate=__magic_name__ , norm='slaney' , mel_scale='slaney' , )
def __UpperCAmelCase ( self , __magic_name__ ) -> np.ndarray:
_a = spectrogram(
__magic_name__ , window_function(self.n_fft , 'hann' ) , frame_length=self.n_fft , hop_length=self.hop_length , power=2.0 , mel_filters=self.mel_filters , log_mel='log10' , )
_a = log_spec[:, :-1]
_a = np.maximum(__magic_name__ , log_spec.max() - 8.0 )
_a = (log_spec + 4.0) / 4.0
return log_spec
@staticmethod
# Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
def __UpperCAmelCase ( __magic_name__ , __magic_name__ , __magic_name__ = 0.0 ) -> List[np.ndarray]:
if attention_mask is not None:
_a = np.array(__magic_name__ , np.intaa )
_a = []
for vector, length in zip(__magic_name__ , attention_mask.sum(-1 ) ):
_a = (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7 )
if length < normed_slice.shape[0]:
_a = padding_value
normed_input_values.append(__magic_name__ )
else:
_a = [(x - x.mean()) / np.sqrt(x.var() + 1e-7 ) for x in input_values]
return normed_input_values
def __call__( self , __magic_name__ , __magic_name__ = True , __magic_name__ = None , __magic_name__ = None , __magic_name__ = None , __magic_name__ = "max_length" , __magic_name__ = None , __magic_name__ = None , __magic_name__ = None , **__magic_name__ , ) -> BatchFeature:
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
f'The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a'
f' sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input'
f' was sampled with {self.sampling_rate} and not {sampling_rate}.' )
else:
logger.warning(
'It is strongly recommended to pass the `sampling_rate` argument to this function. '
'Failing to do so can result in silent errors that might be hard to debug.' )
_a = isinstance(__magic_name__ , np.ndarray ) and len(raw_speech.shape ) > 1
if is_batched_numpy and len(raw_speech.shape ) > 2:
raise ValueError(f'Only mono-channel audio is supported for input to {self}' )
_a = is_batched_numpy or (
isinstance(__magic_name__ , (list, tuple) ) and (isinstance(raw_speech[0] , (np.ndarray, tuple, list) ))
)
if is_batched:
_a = [np.asarray([speech] , dtype=np.floataa ).T for speech in raw_speech]
elif not is_batched and not isinstance(__magic_name__ , np.ndarray ):
_a = np.asarray(__magic_name__ , dtype=np.floataa )
elif isinstance(__magic_name__ , np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
_a = raw_speech.astype(np.floataa )
# always return batch
if not is_batched:
_a = [np.asarray([raw_speech] ).T]
_a = BatchFeature({'input_features': raw_speech} )
# convert into correct format for padding
_a = self.pad(
__magic_name__ , padding=__magic_name__ , max_length=max_length if max_length else self.n_samples , truncation=__magic_name__ , pad_to_multiple_of=__magic_name__ , return_attention_mask=return_attention_mask or do_normalize , )
# zero-mean and unit-variance normalization
if do_normalize:
_a = self.zero_mean_unit_var_norm(
padded_inputs['input_features'] , attention_mask=padded_inputs['attention_mask'] , padding_value=self.padding_value , )
_a = np.stack(padded_inputs['input_features'] , axis=0 )
# make sure list is in array format
_a = padded_inputs.get('input_features' ).transpose(2 , 0 , 1 )
_a = [self._np_extract_fbank_features(__magic_name__ ) for waveform in input_features[0]]
if isinstance(input_features[0] , __magic_name__ ):
_a = [np.asarray(__magic_name__ , dtype=np.floataa ) for feature in input_features]
else:
_a = input_features
if return_attention_mask:
# rescale from sample (48000) to feature (3000)
_a = padded_inputs['attention_mask'][:, :: self.hop_length]
if return_tensors is not None:
_a = padded_inputs.convert_to_tensors(__magic_name__ )
return padded_inputs
def __UpperCAmelCase ( self ) -> Dict[str, Any]:
_a = copy.deepcopy(self.__dict__ )
_a = self.__class__.__name__
if "mel_filters" in output:
del output["mel_filters"]
return output
| 365
|
'''simple docstring'''
import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def _A () -> Optional[Any]:
'''simple docstring'''
with offline(OfflineSimulationMode.CONNECTION_TIMES_OUT ):
with pytest.raises(lowerCAmelCase__ ):
requests.request('GET' , 'https://huggingface.co' )
with pytest.raises(requests.exceptions.ConnectTimeout ):
requests.request('GET' , 'https://huggingface.co' , timeout=1.0 )
@pytest.mark.integration
def _A () -> Any:
'''simple docstring'''
with offline(OfflineSimulationMode.CONNECTION_FAILS ):
with pytest.raises(requests.exceptions.ConnectionError ):
requests.request('GET' , 'https://huggingface.co' )
def _A () -> Dict:
'''simple docstring'''
with offline(OfflineSimulationMode.HF_DATASETS_OFFLINE_SET_TO_1 ):
with pytest.raises(lowerCAmelCase__ ):
http_head('https://huggingface.co' )
| 104
| 0
|
import functools
import logging
import os
import sys
import threading
from logging import (
CRITICAL, # NOQA
DEBUG, # NOQA
ERROR, # NOQA
FATAL, # NOQA
INFO, # NOQA
NOTSET, # NOQA
WARN, # NOQA
WARNING, # NOQA
)
from typing import Optional
import huggingface_hub.utils as hf_hub_utils
from tqdm import auto as tqdm_lib
lowerCamelCase = threading.Lock()
lowerCamelCase = None
lowerCamelCase = {
'''debug''': logging.DEBUG,
'''info''': logging.INFO,
'''warning''': logging.WARNING,
'''error''': logging.ERROR,
'''critical''': logging.CRITICAL,
}
lowerCamelCase = logging.WARNING
lowerCamelCase = True
def UpperCAmelCase__ ( ):
'''simple docstring'''
a__ =os.getenv('''TRANSFORMERS_VERBOSITY''' , _A )
if env_level_str:
if env_level_str in log_levels:
return log_levels[env_level_str]
else:
logging.getLogger().warning(
F"""Unknown option TRANSFORMERS_VERBOSITY={env_level_str}, """
F"""has to be one of: { ", ".join(log_levels.keys() ) }""" )
return _default_log_level
def UpperCAmelCase__ ( ):
'''simple docstring'''
return __name__.split('''.''' )[0]
def UpperCAmelCase__ ( ):
'''simple docstring'''
return logging.getLogger(_get_library_name() )
def UpperCAmelCase__ ( ):
'''simple docstring'''
global _default_handler
with _lock:
if _default_handler:
# This library has already configured the library root logger.
return
a__ =logging.StreamHandler() # Set sys.stderr as stream.
a__ =sys.stderr.flush
# Apply our default configuration to the library root logger.
a__ =_get_library_root_logger()
library_root_logger.addHandler(_default_handler )
library_root_logger.setLevel(_get_default_logging_level() )
a__ =False
def UpperCAmelCase__ ( ):
'''simple docstring'''
global _default_handler
with _lock:
if not _default_handler:
return
a__ =_get_library_root_logger()
library_root_logger.removeHandler(_default_handler )
library_root_logger.setLevel(logging.NOTSET )
a__ =None
def UpperCAmelCase__ ( ):
'''simple docstring'''
return log_levels
def UpperCAmelCase__ ( _A : Optional[str] = None ):
'''simple docstring'''
if name is None:
a__ =_get_library_name()
_configure_library_root_logger()
return logging.getLogger(_A )
def UpperCAmelCase__ ( ):
'''simple docstring'''
_configure_library_root_logger()
return _get_library_root_logger().getEffectiveLevel()
def UpperCAmelCase__ ( _A : int ):
'''simple docstring'''
_configure_library_root_logger()
_get_library_root_logger().setLevel(_A )
def UpperCAmelCase__ ( ):
'''simple docstring'''
return set_verbosity(_A )
def UpperCAmelCase__ ( ):
'''simple docstring'''
return set_verbosity(_A )
def UpperCAmelCase__ ( ):
'''simple docstring'''
return set_verbosity(_A )
def UpperCAmelCase__ ( ):
'''simple docstring'''
return set_verbosity(_A )
def UpperCAmelCase__ ( ):
'''simple docstring'''
_configure_library_root_logger()
assert _default_handler is not None
_get_library_root_logger().removeHandler(_default_handler )
def UpperCAmelCase__ ( ):
'''simple docstring'''
_configure_library_root_logger()
assert _default_handler is not None
_get_library_root_logger().addHandler(_default_handler )
def UpperCAmelCase__ ( _A : logging.Handler ):
'''simple docstring'''
_configure_library_root_logger()
assert handler is not None
_get_library_root_logger().addHandler(_A )
def UpperCAmelCase__ ( _A : logging.Handler ):
'''simple docstring'''
_configure_library_root_logger()
assert handler is not None and handler not in _get_library_root_logger().handlers
_get_library_root_logger().removeHandler(_A )
def UpperCAmelCase__ ( ):
'''simple docstring'''
_configure_library_root_logger()
a__ =False
def UpperCAmelCase__ ( ):
'''simple docstring'''
_configure_library_root_logger()
a__ =True
def UpperCAmelCase__ ( ):
'''simple docstring'''
a__ =_get_library_root_logger().handlers
for handler in handlers:
a__ =logging.Formatter('''[%(levelname)s|%(filename)s:%(lineno)s] %(asctime)s >> %(message)s''' )
handler.setFormatter(_A )
def UpperCAmelCase__ ( ):
'''simple docstring'''
a__ =_get_library_root_logger().handlers
for handler in handlers:
handler.setFormatter(_A )
def UpperCAmelCase__ ( self : List[Any] , *_A : Optional[Any] , **_A : Union[str, Any] ):
'''simple docstring'''
a__ =os.getenv('''TRANSFORMERS_NO_ADVISORY_WARNINGS''' , _A )
if no_advisory_warnings:
return
self.warning(*_A , **_A )
lowerCamelCase = warning_advice
@functools.lru_cache(_A )
def UpperCAmelCase__ ( self : List[Any] , *_A : str , **_A : Any ):
'''simple docstring'''
self.warning(*_A , **_A )
lowerCamelCase = warning_once
class __magic_name__ :
'''simple docstring'''
def __init__( self, *lowercase_, **lowercase_ ) -> Union[str, Any]: # pylint: disable=unused-argument
"""simple docstring"""
a__ =args[0] if args else None
def __iter__( self ) -> int:
"""simple docstring"""
return iter(self._iterator )
def __getattr__( self, lowercase_ ) -> Union[str, Any]:
"""simple docstring"""
def empty_fn(*lowercase_, **lowercase_ ): # pylint: disable=unused-argument
return
return empty_fn
def __enter__( self ) -> int:
"""simple docstring"""
return self
def __exit__( self, lowercase_, lowercase_, lowercase_ ) -> Optional[int]:
"""simple docstring"""
return
class __magic_name__ :
'''simple docstring'''
def __call__( self, *lowercase_, **lowercase_ ) -> Tuple:
"""simple docstring"""
if _tqdm_active:
return tqdm_lib.tqdm(*lowercase_, **lowercase_ )
else:
return EmptyTqdm(*lowercase_, **lowercase_ )
def _UpperCAmelCase ( self, *lowercase_, **lowercase_ ) -> str:
"""simple docstring"""
a__ =None
if _tqdm_active:
return tqdm_lib.tqdm.set_lock(*lowercase_, **lowercase_ )
def _UpperCAmelCase ( self ) -> Optional[Any]:
"""simple docstring"""
if _tqdm_active:
return tqdm_lib.tqdm.get_lock()
lowerCamelCase = _tqdm_cls()
def UpperCAmelCase__ ( ):
'''simple docstring'''
global _tqdm_active
return bool(_tqdm_active )
def UpperCAmelCase__ ( ):
'''simple docstring'''
global _tqdm_active
a__ =True
hf_hub_utils.enable_progress_bars()
def UpperCAmelCase__ ( ):
'''simple docstring'''
global _tqdm_active
a__ =False
hf_hub_utils.disable_progress_bars()
| 188
|
import os
import tempfile
import unittest
from transformers import FlaubertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
FlaubertForMultipleChoice,
FlaubertForQuestionAnswering,
FlaubertForQuestionAnsweringSimple,
FlaubertForSequenceClassification,
FlaubertForTokenClassification,
FlaubertModel,
FlaubertWithLMHeadModel,
)
from transformers.models.flaubert.modeling_flaubert import FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST
class __magic_name__ ( lowerCamelCase__ ):
'''simple docstring'''
def __init__( self, lowercase_, lowercase_=13, lowercase_=7, lowercase_=True, lowercase_=True, lowercase_=True, lowercase_=True, lowercase_=True, lowercase_=False, lowercase_=False, lowercase_=False, lowercase_=2, lowercase_=99, lowercase_=0, lowercase_=32, lowercase_=5, lowercase_=4, lowercase_=0.1, lowercase_=0.1, lowercase_=512, lowercase_=12, lowercase_=2, lowercase_=0.02, lowercase_=3, lowercase_=4, lowercase_="last", lowercase_=None, lowercase_=None, ) -> List[Any]:
"""simple docstring"""
a__ =parent
a__ =batch_size
a__ =seq_length
a__ =is_training
a__ =use_input_lengths
a__ =use_token_type_ids
a__ =use_labels
a__ =gelu_activation
a__ =sinusoidal_embeddings
a__ =causal
a__ =asm
a__ =n_langs
a__ =vocab_size
a__ =n_special
a__ =hidden_size
a__ =num_hidden_layers
a__ =num_attention_heads
a__ =hidden_dropout_prob
a__ =attention_probs_dropout_prob
a__ =max_position_embeddings
a__ =type_vocab_size
a__ =type_sequence_label_size
a__ =initializer_range
a__ =num_labels
a__ =num_choices
a__ =summary_type
a__ =use_proj
a__ =scope
def _UpperCAmelCase ( self ) -> Any:
"""simple docstring"""
a__ =ids_tensor([self.batch_size, self.seq_length], self.vocab_size )
a__ =random_attention_mask([self.batch_size, self.seq_length] )
a__ =None
if self.use_input_lengths:
a__ =(
ids_tensor([self.batch_size], vocab_size=2 ) + self.seq_length - 2
) # small variation of seq_length
a__ =None
if self.use_token_type_ids:
a__ =ids_tensor([self.batch_size, self.seq_length], self.n_langs )
a__ =None
a__ =None
a__ =None
if self.use_labels:
a__ =ids_tensor([self.batch_size], self.type_sequence_label_size )
a__ =ids_tensor([self.batch_size, self.seq_length], self.num_labels )
a__ =ids_tensor([self.batch_size], 2 ).float()
a__ =ids_tensor([self.batch_size], self.num_choices )
a__ =self.get_config()
return (
config,
input_ids,
token_type_ids,
input_lengths,
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
)
def _UpperCAmelCase ( self ) -> Any:
"""simple docstring"""
return FlaubertConfig(
vocab_size=self.vocab_size, n_special=self.n_special, emb_dim=self.hidden_size, n_layers=self.num_hidden_layers, n_heads=self.num_attention_heads, dropout=self.hidden_dropout_prob, attention_dropout=self.attention_probs_dropout_prob, gelu_activation=self.gelu_activation, sinusoidal_embeddings=self.sinusoidal_embeddings, asm=self.asm, causal=self.causal, n_langs=self.n_langs, max_position_embeddings=self.max_position_embeddings, initializer_range=self.initializer_range, summary_type=self.summary_type, use_proj=self.use_proj, )
def _UpperCAmelCase ( self, lowercase_, lowercase_, lowercase_, lowercase_, lowercase_, lowercase_, lowercase_, lowercase_, lowercase_, ) -> Dict:
"""simple docstring"""
a__ =FlaubertModel(config=lowercase_ )
model.to(lowercase_ )
model.eval()
a__ =model(lowercase_, lengths=lowercase_, langs=lowercase_ )
a__ =model(lowercase_, langs=lowercase_ )
a__ =model(lowercase_ )
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size) )
def _UpperCAmelCase ( self, lowercase_, lowercase_, lowercase_, lowercase_, lowercase_, lowercase_, lowercase_, lowercase_, lowercase_, ) -> str:
"""simple docstring"""
a__ =FlaubertWithLMHeadModel(lowercase_ )
model.to(lowercase_ )
model.eval()
a__ =model(lowercase_, token_type_ids=lowercase_, labels=lowercase_ )
self.parent.assertEqual(result.loss.shape, () )
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size) )
def _UpperCAmelCase ( self, lowercase_, lowercase_, lowercase_, lowercase_, lowercase_, lowercase_, lowercase_, lowercase_, lowercase_, ) -> Dict:
"""simple docstring"""
a__ =FlaubertForQuestionAnsweringSimple(lowercase_ )
model.to(lowercase_ )
model.eval()
a__ =model(lowercase_ )
a__ =model(lowercase_, start_positions=lowercase_, end_positions=lowercase_ )
self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length) )
def _UpperCAmelCase ( self, lowercase_, lowercase_, lowercase_, lowercase_, lowercase_, lowercase_, lowercase_, lowercase_, lowercase_, ) -> Optional[Any]:
"""simple docstring"""
a__ =FlaubertForQuestionAnswering(lowercase_ )
model.to(lowercase_ )
model.eval()
a__ =model(lowercase_ )
a__ =model(
lowercase_, start_positions=lowercase_, end_positions=lowercase_, cls_index=lowercase_, is_impossible=lowercase_, p_mask=lowercase_, )
a__ =model(
lowercase_, start_positions=lowercase_, end_positions=lowercase_, cls_index=lowercase_, is_impossible=lowercase_, )
((a__), ) =result_with_labels.to_tuple()
a__ =model(lowercase_, start_positions=lowercase_, end_positions=lowercase_ )
((a__), ) =result_with_labels.to_tuple()
self.parent.assertEqual(result_with_labels.loss.shape, () )
self.parent.assertEqual(result.start_top_log_probs.shape, (self.batch_size, model.config.start_n_top) )
self.parent.assertEqual(result.start_top_index.shape, (self.batch_size, model.config.start_n_top) )
self.parent.assertEqual(
result.end_top_log_probs.shape, (self.batch_size, model.config.start_n_top * model.config.end_n_top) )
self.parent.assertEqual(
result.end_top_index.shape, (self.batch_size, model.config.start_n_top * model.config.end_n_top) )
self.parent.assertEqual(result.cls_logits.shape, (self.batch_size,) )
def _UpperCAmelCase ( self, lowercase_, lowercase_, lowercase_, lowercase_, lowercase_, lowercase_, lowercase_, lowercase_, lowercase_, ) -> Optional[Any]:
"""simple docstring"""
a__ =FlaubertForSequenceClassification(lowercase_ )
model.to(lowercase_ )
model.eval()
a__ =model(lowercase_ )
a__ =model(lowercase_, labels=lowercase_ )
self.parent.assertEqual(result.loss.shape, () )
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.type_sequence_label_size) )
def _UpperCAmelCase ( self, lowercase_, lowercase_, lowercase_, lowercase_, lowercase_, lowercase_, lowercase_, lowercase_, lowercase_, ) -> Optional[int]:
"""simple docstring"""
a__ =self.num_labels
a__ =FlaubertForTokenClassification(lowercase_ )
model.to(lowercase_ )
model.eval()
a__ =model(lowercase_, attention_mask=lowercase_, labels=lowercase_ )
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels) )
def _UpperCAmelCase ( self, lowercase_, lowercase_, lowercase_, lowercase_, lowercase_, lowercase_, lowercase_, lowercase_, lowercase_, ) -> Dict:
"""simple docstring"""
a__ =self.num_choices
a__ =FlaubertForMultipleChoice(config=lowercase_ )
model.to(lowercase_ )
model.eval()
a__ =input_ids.unsqueeze(1 ).expand(-1, self.num_choices, -1 ).contiguous()
a__ =token_type_ids.unsqueeze(1 ).expand(-1, self.num_choices, -1 ).contiguous()
a__ =input_mask.unsqueeze(1 ).expand(-1, self.num_choices, -1 ).contiguous()
a__ =model(
lowercase_, attention_mask=lowercase_, token_type_ids=lowercase_, labels=lowercase_, )
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_choices) )
def _UpperCAmelCase ( self ) -> Dict:
"""simple docstring"""
a__ =self.prepare_config_and_inputs()
(
(
a__
), (
a__
), (
a__
), (
a__
), (
a__
), (
a__
), (
a__
), (
a__
), (
a__
),
) =config_and_inputs
a__ ={
'''input_ids''': input_ids,
'''token_type_ids''': token_type_ids,
'''lengths''': input_lengths,
'''attention_mask''': input_mask,
}
return config, inputs_dict
@require_torch
class __magic_name__ ( lowerCamelCase__ , lowerCamelCase__ , unittest.TestCase ):
'''simple docstring'''
lowerCamelCase__ : str = (
(
FlaubertModel,
FlaubertWithLMHeadModel,
FlaubertForQuestionAnswering,
FlaubertForQuestionAnsweringSimple,
FlaubertForSequenceClassification,
FlaubertForTokenClassification,
FlaubertForMultipleChoice,
)
if is_torch_available()
else ()
)
lowerCamelCase__ : Dict = (
{
'feature-extraction': FlaubertModel,
'fill-mask': FlaubertWithLMHeadModel,
'question-answering': FlaubertForQuestionAnsweringSimple,
'text-classification': FlaubertForSequenceClassification,
'token-classification': FlaubertForTokenClassification,
'zero-shot': FlaubertForSequenceClassification,
}
if is_torch_available()
else {}
)
def _UpperCAmelCase ( self, lowercase_, lowercase_, lowercase_, lowercase_, lowercase_ ) -> str:
"""simple docstring"""
if (
pipeline_test_casse_name == "QAPipelineTests"
and tokenizer_name is not None
and not tokenizer_name.endswith('''Fast''' )
):
# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
return True
return False
def _UpperCAmelCase ( self, lowercase_, lowercase_, lowercase_=False ) -> str:
"""simple docstring"""
a__ =super()._prepare_for_class(lowercase_, lowercase_, return_labels=lowercase_ )
if return_labels:
if model_class.__name__ == "FlaubertForQuestionAnswering":
a__ =torch.zeros(
self.model_tester.batch_size, dtype=torch.long, device=lowercase_ )
a__ =torch.zeros(
self.model_tester.batch_size, dtype=torch.long, device=lowercase_ )
return inputs_dict
def _UpperCAmelCase ( self ) -> Optional[int]:
"""simple docstring"""
a__ =FlaubertModelTester(self )
a__ =ConfigTester(self, config_class=lowercase_, emb_dim=37 )
def _UpperCAmelCase ( self ) -> Optional[Any]:
"""simple docstring"""
self.config_tester.run_common_tests()
def _UpperCAmelCase ( self ) -> Any:
"""simple docstring"""
a__ =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_model(*lowercase_ )
def _UpperCAmelCase ( self ) -> str:
"""simple docstring"""
a__ =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_lm_head(*lowercase_ )
def _UpperCAmelCase ( self ) -> Dict:
"""simple docstring"""
a__ =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_simple_qa(*lowercase_ )
def _UpperCAmelCase ( self ) -> Dict:
"""simple docstring"""
a__ =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_qa(*lowercase_ )
def _UpperCAmelCase ( self ) -> Any:
"""simple docstring"""
a__ =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_sequence_classif(*lowercase_ )
def _UpperCAmelCase ( self ) -> Any:
"""simple docstring"""
a__ =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_token_classif(*lowercase_ )
def _UpperCAmelCase ( self ) -> Tuple:
"""simple docstring"""
a__ =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_multiple_choice(*lowercase_ )
@slow
def _UpperCAmelCase ( self ) -> Tuple:
"""simple docstring"""
for model_name in FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
a__ =FlaubertModel.from_pretrained(lowercase_ )
self.assertIsNotNone(lowercase_ )
@slow
@require_torch_gpu
def _UpperCAmelCase ( self ) -> int:
"""simple docstring"""
a__, a__ =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
# FlauBertForMultipleChoice behaves incorrectly in JIT environments.
if model_class == FlaubertForMultipleChoice:
return
a__ =True
a__ =model_class(config=lowercase_ )
a__ =self._prepare_for_class(lowercase_, lowercase_ )
a__ =torch.jit.trace(
lowercase_, (inputs_dict['''input_ids'''].to('''cpu''' ), inputs_dict['''attention_mask'''].to('''cpu''' )) )
with tempfile.TemporaryDirectory() as tmp:
torch.jit.save(lowercase_, os.path.join(lowercase_, '''traced_model.pt''' ) )
a__ =torch.jit.load(os.path.join(lowercase_, '''traced_model.pt''' ), map_location=lowercase_ )
loaded(inputs_dict['''input_ids'''].to(lowercase_ ), inputs_dict['''attention_mask'''].to(lowercase_ ) )
@require_torch
class __magic_name__ ( unittest.TestCase ):
'''simple docstring'''
@slow
def _UpperCAmelCase ( self ) -> List[str]:
"""simple docstring"""
a__ =FlaubertModel.from_pretrained('''flaubert/flaubert_base_cased''' )
a__ =torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]] )
with torch.no_grad():
a__ =model(lowercase_ )[0]
a__ =torch.Size((1, 11, 768) )
self.assertEqual(output.shape, lowercase_ )
a__ =torch.tensor(
[[[-2.6251, -1.4298, -0.0227], [-2.8510, -1.6387, 0.2258], [-2.8114, -1.1832, -0.3066]]] )
self.assertTrue(torch.allclose(output[:, :3, :3], lowercase_, atol=1E-4 ) )
| 188
| 1
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowerCAmelCase__ = {'''configuration_swin''': ['''SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SwinConfig''', '''SwinOnnxConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ = [
'''SWIN_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''SwinForImageClassification''',
'''SwinForMaskedImageModeling''',
'''SwinModel''',
'''SwinPreTrainedModel''',
'''SwinBackbone''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ = [
'''TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFSwinForImageClassification''',
'''TFSwinForMaskedImageModeling''',
'''TFSwinModel''',
'''TFSwinPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_swin import SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinConfig, SwinOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_swin import (
SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
SwinBackbone,
SwinForImageClassification,
SwinForMaskedImageModeling,
SwinModel,
SwinPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_swin import (
TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
TFSwinForImageClassification,
TFSwinForMaskedImageModeling,
TFSwinModel,
TFSwinPreTrainedModel,
)
else:
import sys
lowerCAmelCase__ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 352
|
import inspect
import unittest
import numpy as np
from transformers import BeitConfig
from transformers.testing_utils import require_flax, require_vision, slow
from transformers.utils import cached_property, is_flax_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor
if is_flax_available():
import jax
from transformers import FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling, FlaxBeitModel
if is_vision_available():
from PIL import Image
from transformers import BeitImageProcessor
class snake_case__(unittest.TestCase ):
"""simple docstring"""
def __init__( self : List[Any] , SCREAMING_SNAKE_CASE : Tuple , SCREAMING_SNAKE_CASE : Optional[int]=100 , SCREAMING_SNAKE_CASE : Tuple=13 , SCREAMING_SNAKE_CASE : str=30 , SCREAMING_SNAKE_CASE : Union[str, Any]=2 , SCREAMING_SNAKE_CASE : Tuple=3 , SCREAMING_SNAKE_CASE : List[Any]=True , SCREAMING_SNAKE_CASE : int=True , SCREAMING_SNAKE_CASE : Dict=32 , SCREAMING_SNAKE_CASE : str=5 , SCREAMING_SNAKE_CASE : Any=4 , SCREAMING_SNAKE_CASE : str=37 , SCREAMING_SNAKE_CASE : Any="gelu" , SCREAMING_SNAKE_CASE : Tuple=0.1 , SCREAMING_SNAKE_CASE : Optional[Any]=0.1 , SCREAMING_SNAKE_CASE : Union[str, Any]=10 , SCREAMING_SNAKE_CASE : Dict=0.02 , SCREAMING_SNAKE_CASE : Any=3 , ):
lowercase__ : Optional[int] = parent
lowercase__ : Optional[int] = vocab_size
lowercase__ : Dict = batch_size
lowercase__ : List[Any] = image_size
lowercase__ : List[Any] = patch_size
lowercase__ : Tuple = num_channels
lowercase__ : Any = is_training
lowercase__ : str = use_labels
lowercase__ : List[Any] = hidden_size
lowercase__ : Optional[int] = num_hidden_layers
lowercase__ : Dict = num_attention_heads
lowercase__ : Optional[int] = intermediate_size
lowercase__ : int = hidden_act
lowercase__ : str = hidden_dropout_prob
lowercase__ : Union[str, Any] = attention_probs_dropout_prob
lowercase__ : int = type_sequence_label_size
lowercase__ : Optional[int] = initializer_range
# in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
lowercase__ : str = (image_size // patch_size) ** 2
lowercase__ : List[str] = num_patches + 1
def snake_case ( self : Tuple ):
lowercase__ : List[Any] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowercase__ : Union[str, Any] = None
if self.use_labels:
lowercase__ : List[Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowercase__ : int = BeitConfig(
vocab_size=self.vocab_size , image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=SCREAMING_SNAKE_CASE , initializer_range=self.initializer_range , )
return config, pixel_values, labels
def snake_case ( self : Optional[Any] , SCREAMING_SNAKE_CASE : List[str] , SCREAMING_SNAKE_CASE : Dict , SCREAMING_SNAKE_CASE : List[Any] ):
lowercase__ : Optional[Any] = FlaxBeitModel(config=SCREAMING_SNAKE_CASE )
lowercase__ : Optional[int] = model(SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def snake_case ( self : Union[str, Any] , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : Tuple , SCREAMING_SNAKE_CASE : List[str] ):
lowercase__ : int = FlaxBeitForMaskedImageModeling(config=SCREAMING_SNAKE_CASE )
lowercase__ : Union[str, Any] = model(SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length - 1, self.vocab_size) )
def snake_case ( self : Optional[int] , SCREAMING_SNAKE_CASE : str , SCREAMING_SNAKE_CASE : List[Any] , SCREAMING_SNAKE_CASE : Any ):
lowercase__ : Tuple = self.type_sequence_label_size
lowercase__ : Optional[int] = FlaxBeitForImageClassification(config=SCREAMING_SNAKE_CASE )
lowercase__ : str = model(SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
lowercase__ : int = 1
lowercase__ : List[str] = FlaxBeitForImageClassification(SCREAMING_SNAKE_CASE )
lowercase__ : int = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
lowercase__ : List[str] = model(SCREAMING_SNAKE_CASE )
def snake_case ( self : Any ):
lowercase__ : Optional[Any] = self.prepare_config_and_inputs()
(
(
lowercase__
) , (
lowercase__
) , (
lowercase__
) ,
) : str = config_and_inputs
lowercase__ : Dict = {"pixel_values": pixel_values}
return config, inputs_dict
@require_flax
class snake_case__(_UpperCamelCase , unittest.TestCase ):
"""simple docstring"""
lowercase_ = (
(FlaxBeitModel, FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling) if is_flax_available() else ()
)
def snake_case ( self : Any ):
lowercase__ : List[Any] = FlaxBeitModelTester(self )
lowercase__ : str = ConfigTester(self , config_class=SCREAMING_SNAKE_CASE , has_text_modality=SCREAMING_SNAKE_CASE , hidden_size=37 )
def snake_case ( self : int ):
self.config_tester.run_common_tests()
def snake_case ( self : int ):
lowercase__ , lowercase__ : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase__ : List[str] = model_class(SCREAMING_SNAKE_CASE )
lowercase__ : str = inspect.signature(model.__call__ )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowercase__ : Optional[int] = [*signature.parameters.keys()]
lowercase__ : str = ["pixel_values"]
self.assertListEqual(arg_names[:1] , SCREAMING_SNAKE_CASE )
def snake_case ( self : List[Any] ):
lowercase__ , lowercase__ : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
lowercase__ : Optional[int] = self._prepare_for_class(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
lowercase__ : List[str] = model_class(SCREAMING_SNAKE_CASE )
@jax.jit
def model_jitted(SCREAMING_SNAKE_CASE : Optional[Any] , **SCREAMING_SNAKE_CASE : List[Any] ):
return model(pixel_values=SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE )
with self.subTest("JIT Enabled" ):
lowercase__ : Union[str, Any] = model_jitted(**SCREAMING_SNAKE_CASE ).to_tuple()
with self.subTest("JIT Disabled" ):
with jax.disable_jit():
lowercase__ : Optional[int] = model_jitted(**SCREAMING_SNAKE_CASE ).to_tuple()
self.assertEqual(len(SCREAMING_SNAKE_CASE ) , len(SCREAMING_SNAKE_CASE ) )
for jitted_output, output in zip(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
self.assertEqual(jitted_output.shape , output.shape )
def snake_case ( self : Optional[int] ):
lowercase__ : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE )
def snake_case ( self : int ):
lowercase__ : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*SCREAMING_SNAKE_CASE )
def snake_case ( self : Optional[Any] ):
lowercase__ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE )
@slow
def snake_case ( self : Optional[int] ):
for model_class_name in self.all_model_classes:
lowercase__ : Any = model_class_name.from_pretrained("microsoft/beit-base-patch16-224" )
lowercase__ : Optional[int] = model(np.ones((1, 3, 224, 224) ) )
self.assertIsNotNone(SCREAMING_SNAKE_CASE )
def __lowerCamelCase ( ):
"""simple docstring"""
lowercase__ : Dict = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_vision
@require_flax
class snake_case__(unittest.TestCase ):
"""simple docstring"""
@cached_property
def snake_case ( self : int ):
return BeitImageProcessor.from_pretrained("microsoft/beit-base-patch16-224" ) if is_vision_available() else None
@slow
def snake_case ( self : Union[str, Any] ):
lowercase__ : Tuple = FlaxBeitForMaskedImageModeling.from_pretrained("microsoft/beit-base-patch16-224-pt22k" )
lowercase__ : int = self.default_image_processor
lowercase__ : Union[str, Any] = prepare_img()
lowercase__ : str = image_processor(images=SCREAMING_SNAKE_CASE , return_tensors="np" ).pixel_values
# prepare bool_masked_pos
lowercase__ : Optional[Any] = np.ones((1, 196) , dtype=SCREAMING_SNAKE_CASE )
# forward pass
lowercase__ : Any = model(pixel_values=SCREAMING_SNAKE_CASE , bool_masked_pos=SCREAMING_SNAKE_CASE )
lowercase__ : Tuple = outputs.logits
# verify the logits
lowercase__ : List[str] = (1, 196, 8_192)
self.assertEqual(logits.shape , SCREAMING_SNAKE_CASE )
lowercase__ : Dict = np.array(
[[-3.2_437, 0.5_072, -13.9_174], [-3.2_456, 0.4_948, -13.9_401], [-3.2_033, 0.5_121, -13.8_550]] )
self.assertTrue(np.allclose(logits[bool_masked_pos][:3, :3] , SCREAMING_SNAKE_CASE , atol=1E-2 ) )
@slow
def snake_case ( self : Any ):
lowercase__ : Union[str, Any] = FlaxBeitForImageClassification.from_pretrained("microsoft/beit-base-patch16-224" )
lowercase__ : Tuple = self.default_image_processor
lowercase__ : List[Any] = prepare_img()
lowercase__ : Optional[Any] = image_processor(images=SCREAMING_SNAKE_CASE , return_tensors="np" )
# forward pass
lowercase__ : str = model(**SCREAMING_SNAKE_CASE )
lowercase__ : Dict = outputs.logits
# verify the logits
lowercase__ : List[str] = (1, 1_000)
self.assertEqual(logits.shape , SCREAMING_SNAKE_CASE )
lowercase__ : Tuple = np.array([-1.2_385, -1.0_987, -1.0_108] )
self.assertTrue(np.allclose(logits[0, :3] , SCREAMING_SNAKE_CASE , atol=1E-4 ) )
lowercase__ : str = 281
self.assertEqual(logits.argmax(-1 ).item() , SCREAMING_SNAKE_CASE )
@slow
def snake_case ( self : str ):
lowercase__ : List[Any] = FlaxBeitForImageClassification.from_pretrained("microsoft/beit-large-patch16-224-pt22k-ft22k" )
lowercase__ : Dict = self.default_image_processor
lowercase__ : Dict = prepare_img()
lowercase__ : List[Any] = image_processor(images=SCREAMING_SNAKE_CASE , return_tensors="np" )
# forward pass
lowercase__ : Union[str, Any] = model(**SCREAMING_SNAKE_CASE )
lowercase__ : Optional[Any] = outputs.logits
# verify the logits
lowercase__ : int = (1, 21_841)
self.assertEqual(logits.shape , SCREAMING_SNAKE_CASE )
lowercase__ : str = np.array([1.6_881, -0.2_787, 0.5_901] )
self.assertTrue(np.allclose(logits[0, :3] , SCREAMING_SNAKE_CASE , atol=1E-4 ) )
lowercase__ : Union[str, Any] = 2_396
self.assertEqual(logits.argmax(-1 ).item() , SCREAMING_SNAKE_CASE )
| 121
| 0
|
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
UpperCAmelCase : List[str] = {
"""albert-base-v1""": """https://huggingface.co/albert-base-v1/resolve/main/config.json""",
"""albert-large-v1""": """https://huggingface.co/albert-large-v1/resolve/main/config.json""",
"""albert-xlarge-v1""": """https://huggingface.co/albert-xlarge-v1/resolve/main/config.json""",
"""albert-xxlarge-v1""": """https://huggingface.co/albert-xxlarge-v1/resolve/main/config.json""",
"""albert-base-v2""": """https://huggingface.co/albert-base-v2/resolve/main/config.json""",
"""albert-large-v2""": """https://huggingface.co/albert-large-v2/resolve/main/config.json""",
"""albert-xlarge-v2""": """https://huggingface.co/albert-xlarge-v2/resolve/main/config.json""",
"""albert-xxlarge-v2""": """https://huggingface.co/albert-xxlarge-v2/resolve/main/config.json""",
}
class __lowerCAmelCase ( UpperCamelCase__):
_lowercase : List[str] = """albert"""
def __init__( self , lowerCAmelCase__=3_0_0_0_0 , lowerCAmelCase__=1_2_8 , lowerCAmelCase__=4_0_9_6 , lowerCAmelCase__=1_2 , lowerCAmelCase__=1 , lowerCAmelCase__=6_4 , lowerCAmelCase__=1_6_3_8_4 , lowerCAmelCase__=1 , lowerCAmelCase__="gelu_new" , lowerCAmelCase__=0 , lowerCAmelCase__=0 , lowerCAmelCase__=5_1_2 , lowerCAmelCase__=2 , lowerCAmelCase__=0.02 , lowerCAmelCase__=1E-12 , lowerCAmelCase__=0.1 , lowerCAmelCase__="absolute" , lowerCAmelCase__=0 , lowerCAmelCase__=2 , lowerCAmelCase__=3 , **lowerCAmelCase__ , ) -> int:
'''simple docstring'''
super().__init__(pad_token_id=lowerCAmelCase__ , bos_token_id=lowerCAmelCase__ , eos_token_id=lowerCAmelCase__ , **lowerCAmelCase__ )
a__ : str =vocab_size
a__ : Optional[int] =embedding_size
a__ : Optional[Any] =hidden_size
a__ : List[str] =num_hidden_layers
a__ : Any =num_hidden_groups
a__ : int =num_attention_heads
a__ : int =inner_group_num
a__ : List[str] =hidden_act
a__ : Tuple =intermediate_size
a__ : List[Any] =hidden_dropout_prob
a__ : List[str] =attention_probs_dropout_prob
a__ : str =max_position_embeddings
a__ : str =type_vocab_size
a__ : List[str] =initializer_range
a__ : List[Any] =layer_norm_eps
a__ : Optional[Any] =classifier_dropout_prob
a__ : Tuple =position_embedding_type
class __lowerCAmelCase ( UpperCamelCase__):
@property
def _lowercase ( self ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
if self.task == "multiple-choice":
a__ : Any ={0: "batch", 1: "choice", 2: "sequence"}
else:
a__ : Dict ={0: "batch", 1: "sequence"}
return OrderedDict(
[
("input_ids", dynamic_axis),
("attention_mask", dynamic_axis),
("token_type_ids", dynamic_axis),
] )
| 95
|
"""simple docstring"""
from math import ceil
from typing import List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import BatchFeature, SequenceFeatureExtractor
from ...utils import TensorType, logging
lowerCAmelCase = logging.get_logger(__name__)
class A_ ( A__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ = ["""audio_values""", """audio_mask"""]
def __init__( self :List[str] , lowerCamelCase_ :List[str]=2_048 , lowerCamelCase_ :Dict=1 , lowerCamelCase_ :int=[16, 16] , lowerCamelCase_ :str=128 , lowerCamelCase_ :Union[str, Any]=44_100 , lowerCamelCase_ :Optional[Any]=86 , lowerCamelCase_ :Dict=2_048 , lowerCamelCase_ :Union[str, Any]=0.0 , **lowerCamelCase_ :Tuple , ):
"""simple docstring"""
super().__init__(
feature_size=lowerCamelCase_ , sampling_rate=lowerCamelCase_ , padding_value=lowerCamelCase_ , **lowerCamelCase_ , )
lowerCamelCase__ : List[str] =spectrogram_length
lowerCamelCase__ : Dict =num_channels
lowerCamelCase__ : List[Any] =patch_size
lowerCamelCase__ : Union[str, Any] =feature_size // self.patch_size[1]
lowerCamelCase__ : int =n_fft
lowerCamelCase__ : List[str] =sampling_rate // hop_length_to_sampling_rate
lowerCamelCase__ : str =sampling_rate
lowerCamelCase__ : int =padding_value
lowerCamelCase__ : Dict =mel_filter_bank(
num_frequency_bins=1 + n_fft // 2 , num_mel_filters=lowerCamelCase_ , min_frequency=0.0 , max_frequency=2_20_50.0 , sampling_rate=lowerCamelCase_ , norm='slaney' , mel_scale='slaney' , ).T
def UpperCAmelCase__ ( self :Dict , lowerCamelCase_ :np.array ):
"""simple docstring"""
lowerCamelCase__ : List[Any] =spectrogram(
lowerCamelCase_ , window_function(self.n_fft , 'hann' ) , frame_length=self.n_fft , hop_length=self.hop_length , power=2.0 , mel_filters=self.mel_filters.T , log_mel='dB' , db_range=80.0 , )
lowerCamelCase__ : Any =log_spec[:, :-1]
lowerCamelCase__ : Tuple =log_spec - 20.0
lowerCamelCase__ : List[str] =np.clip(log_spec / 40.0 , -2.0 , 0.0 ) + 1.0
return log_spec
def __call__( self :Optional[Any] , lowerCamelCase_ :Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] , lowerCamelCase_ :Optional[Union[str, TensorType]] = None , lowerCamelCase_ :Optional[bool] = True , lowerCamelCase_ :Optional[int] = None , lowerCamelCase_ :bool = False , lowerCamelCase_ :bool = False , **lowerCamelCase_ :Tuple , ):
"""simple docstring"""
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
'This feature extractor is set to support sampling rate'
f""" of {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled"""
f""" with {self.sampling_rate} and not {sampling_rate}.""" )
else:
logger.warning(
'It is strongly recommended to pass the `sampling_rate` argument to this function. '
'Failing to do so can result in silent errors that might be hard to debug.' )
lowerCamelCase__ : Dict =isinstance(lowerCamelCase_ , np.ndarray ) and len(raw_speech.shape ) > 1
if is_batched_numpy and len(raw_speech.shape ) > 2:
raise ValueError(f"""Only mono-channel audio is supported for input to {self}""" )
lowerCamelCase__ : Union[str, Any] =is_batched_numpy or (
isinstance(lowerCamelCase_ , (list, tuple) ) and (isinstance(raw_speech[0] , (np.ndarray, tuple, list) ))
)
if is_batched:
lowerCamelCase__ : Optional[Any] =[np.asarray([speech] , dtype=np.floataa ).T for speech in raw_speech]
elif not is_batched and not isinstance(lowerCamelCase_ , np.ndarray ):
lowerCamelCase__ : Optional[Any] =np.asarray(lowerCamelCase_ , dtype=np.floataa )
elif isinstance(lowerCamelCase_ , np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
lowerCamelCase__ : Union[str, Any] =raw_speech.astype(np.floataa )
# always return batch
if not is_batched:
lowerCamelCase__ : List[str] =[np.asarray([raw_speech] ).T]
# Convert audio signals to log mel spectrograms, truncate by time axis
lowerCamelCase__ : Any =[
self._np_extract_fbank_features(waveform.squeeze() ).T[: self.spectrogram_length] for waveform in raw_speech
]
if isinstance(audio_features[0] , lowerCamelCase_ ):
lowerCamelCase__ : Dict =[np.asarray(lowerCamelCase_ , dtype=np.floataa ) for feature in audio_features]
# Create audio attention mask
lowerCamelCase__ : Optional[Any] =max(
[ceil(feature.shape[0] / self.patch_size[0] ) * self.freq_len for feature in audio_features] ) # The maximum number of audio patches in a batch
if return_attention_mask:
lowerCamelCase__ : Any =[
(ceil(feature.shape[0] / self.patch_size[0] ) * self.freq_len) * [1]
+ (max_patch_len - ceil(feature.shape[0] / self.patch_size[0] ) * self.freq_len) * [0]
for feature in audio_features
]
lowerCamelCase__ : Union[str, Any] =np.array(lowerCamelCase_ ).astype(np.floataa )
# convert into correct format for padding
lowerCamelCase__ : Tuple =max_patch_len // self.freq_len * self.patch_size[0] # The maximum audio size in a batch
lowerCamelCase__ : str =np.ones([len(lowerCamelCase_ ), 1, max_time_len, self.feature_size] ).astype(np.floataa )
lowerCamelCase__ : Dict =padded_audio_features * self.padding_value
for i in range(len(lowerCamelCase_ ) ):
lowerCamelCase__ : Union[str, Any] =audio_features[i]
lowerCamelCase__ : Union[str, Any] =feature
# return as BatchFeature
if return_attention_mask:
lowerCamelCase__ : int ={'audio_values': padded_audio_features, 'audio_mask': audio_mask}
else:
lowerCamelCase__ : Tuple ={'audio_values': padded_audio_features}
lowerCamelCase__ : Union[str, Any] =BatchFeature(data=lowerCamelCase_ , tensor_type=lowerCamelCase_ )
return encoded_inputs
| 126
| 0
|
"""simple docstring"""
import operator as op
A = '''scaler.pt'''
A = '''pytorch_model'''
A = '''random_states'''
A = '''optimizer'''
A = '''scheduler'''
A = '''pytorch_model.bin'''
A = '''pytorch_model.bin.index.json'''
A = '''model.safetensors'''
A = '''model.safetensors.index.json'''
A = '''1.10.2'''
A = '''py38'''
A = '''4.17.0'''
A = ['''ml.p3.16xlarge''', '''ml.p3dn.24xlarge''', '''ml.p4dn.24xlarge''']
A = ['''FULL_SHARD''', '''SHARD_GRAD_OP''', '''NO_SHARD''', '''HYBRID_SHARD''', '''HYBRID_SHARD_ZERO2''']
A = ['''TRANSFORMER_BASED_WRAP''', '''SIZE_BASED_WRAP''', '''NO_WRAP''']
A = ['''BACKWARD_PRE''', '''BACKWARD_POST''', '''NO_PREFETCH''']
A = ['''FULL_STATE_DICT''', '''LOCAL_STATE_DICT''', '''SHARDED_STATE_DICT''']
A = '''2.0.1'''
A = ['''pdsh''', '''standard''', '''openmpi''', '''mvapich''']
A = ['''default''', '''reduce-overhead''', '''max-autotune''']
A = {'''>''': op.gt, '''>=''': op.ge, '''==''': op.eq, '''!=''': op.ne, '''<=''': op.le, '''<''': op.lt}
# These are the args for `torch.distributed.launch` for pytorch < 1.9
A = [
'''nnodes''',
'''nproc_per_node''',
'''rdzv_backend''',
'''rdzv_endpoint''',
'''rdzv_id''',
'''rdzv_conf''',
'''standalone''',
'''max_restarts''',
'''monitor_interval''',
'''start_method''',
'''role''',
'''module''',
'''m''',
'''no_python''',
'''run_path''',
'''log_dir''',
'''r''',
'''redirects''',
'''t''',
'''tee''',
'''node_rank''',
'''master_addr''',
'''master_port''',
]
A = ['''DEEPSPEED''', '''MULTI_GPU''', '''FSDP''', '''MEGATRON_LM''']
A = ['''DEEPSPEED''', '''MULTI_XPU''', '''FSDP''']
| 359
|
"""simple docstring"""
def __A ( a_ :int) -> Union[str, Any]:
__a : int = []
__a : Dict = []
__a : str = {
'''^''': 3,
'''*''': 2,
'''/''': 2,
'''%''': 2,
'''+''': 1,
'''-''': 1,
} # Priority of each operator
__a : Tuple = len(a_) if (len(a_) > 7) else 7
# Print table header for output
print(
'''Symbol'''.center(8) , '''Stack'''.center(a_) , '''Postfix'''.center(a_) , sep=''' | ''' , )
print('''-''' * (print_width * 3 + 7))
for x in infix:
if x.isalpha() or x.isdigit():
post_fix.append(a_) # if x is Alphabet / Digit, add it to Postfix
elif x == "(":
stack.append(a_) # if x is "(" push to Stack
elif x == ")": # if x is ")" pop stack until "(" is encountered
while stack[-1] != "(":
post_fix.append(stack.pop()) # Pop stack & add the content to Postfix
stack.pop()
else:
if len(a_) == 0:
stack.append(a_) # If stack is empty, push x to stack
else: # while priority of x is not > priority of element in the stack
while len(a_) > 0 and priority[x] <= priority[stack[-1]]:
post_fix.append(stack.pop()) # pop stack & add to Postfix
stack.append(a_) # push x to stack
print(
x.center(8) , (''''''.join(a_)).ljust(a_) , (''''''.join(a_)).ljust(a_) , sep=''' | ''' , ) # Output in tabular format
while len(a_) > 0: # while stack is not empty
post_fix.append(stack.pop()) # pop stack & add to Postfix
print(
''' '''.center(8) , (''''''.join(a_)).ljust(a_) , (''''''.join(a_)).ljust(a_) , sep=''' | ''' , ) # Output in tabular format
return "".join(a_) # return Postfix as str
def __A ( a_ :int) -> List[Any]:
__a : Dict = list(infix[::-1]) # reverse the infix equation
for i in range(len(a_)):
if infix[i] == "(":
__a : Union[str, Any] = ''')''' # change "(" to ")"
elif infix[i] == ")":
__a : List[str] = '''(''' # change ")" to "("
return (infix_2_postfix(''''''.join(a_)))[
::-1
] # call infix_2_postfix on Infix, return reverse of Postfix
if __name__ == "__main__":
A = input('''\nEnter an Infix Equation = ''') # Input an Infix equation
A = ''''''.join(Infix.split()) # Remove spaces from the input
print('''\n\t''', Infix, '''(Infix) -> ''', infix_2_prefix(Infix), '''(Prefix)''')
| 188
| 0
|
"""simple docstring"""
# flake8: noqa
# Lint as: python3
_UpperCAmelCase = [
"""VerificationMode""",
"""Version""",
"""disable_progress_bar""",
"""enable_progress_bar""",
"""is_progress_bar_enabled""",
"""experimental""",
]
from .info_utils import VerificationMode
from .logging import disable_progress_bar, enable_progress_bar, is_progress_bar_enabled
from .version import Version
from .experimental import experimental
| 173
|
"""simple docstring"""
from __future__ import annotations
import numpy as np
def __magic_name__ ( lowercase ):
return np.maximum(0 , lowercase )
if __name__ == "__main__":
print(np.array(relu([-1, 0, 5]))) # --> [0, 0, 5]
| 173
| 1
|
from sympy import diff, lambdify, symbols
from sympy.functions import * # noqa: F403
def __lowerCamelCase ( lowerCamelCase__ : Optional[int] , lowerCamelCase__ : List[Any] , lowerCamelCase__ : List[str] = "x" , lowerCamelCase__ : str = 10**-10 , lowerCamelCase__ : str = 1 , ):
'''simple docstring'''
lowerCamelCase = symbols(lowerCamelCase__ )
lowerCamelCase = lambdify(lowerCamelCase__ , lowerCamelCase__ )
lowerCamelCase = lambdify(lowerCamelCase__ , diff(lowerCamelCase__ , lowerCamelCase__ ) )
lowerCamelCase = starting_point
while True:
if diff_function(lowerCamelCase__ ) != 0:
lowerCamelCase = prev_guess - multiplicity * func(lowerCamelCase__ ) / diff_function(
lowerCamelCase__ )
else:
raise ZeroDivisionError("""Could not find root""" ) from None
# Precision is checked by comparing the difference of consecutive guesses
if abs(next_guess - prev_guess ) < precision:
return next_guess
lowerCamelCase = next_guess
# Let's Execute
if __name__ == "__main__":
# Find root of trigonometric function
# Find value of pi
print(f"""The root of sin(x) = 0 is {newton_raphson("sin(x)", 2)}""")
# Find root of polynomial
# Find fourth Root of 5
print(f"""The root of x**4 - 5 = 0 is {newton_raphson("x**4 -5", 0.4 +5J)}""")
# Find value of e
print(
"The root of log(y) - 1 = 0 is ",
f"""{newton_raphson("log(y) - 1", 2, variable="y")}""",
)
# Exponential Roots
print(
"The root of exp(x) - 1 = 0 is",
f"""{newton_raphson("exp(x) - 1", 10, precision=0.0_0_5)}""",
)
# Find root of cos(x)
print(f"""The root of cos(x) = 0 is {newton_raphson("cos(x)", 0)}""")
| 359
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase : Dict = logging.get_logger(__name__)
UpperCAmelCase : Any = {
"google/switch-base-8": "https://huggingface.co/google/switch-base-8/blob/main/config.json",
}
class __lowercase ( a_ ):
"""simple docstring"""
UpperCamelCase : Tuple = "switch_transformers"
UpperCamelCase : Tuple = ["past_key_values"]
UpperCamelCase : Any = {"hidden_size": "d_model", "num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}
def __init__( self , A=3_21_28 , A=7_68 , A=64 , A=20_48 , A=64 , A=12 , A=3 , A=12 , A=3 , A=12 , A=8 , A=False , A=0.01 , A="float32" , A=False , A=32 , A=1_28 , A=0.1 , A=1e-6 , A=0.001 , A=0.001 , A=1.0 , A="relu" , A=True , A=False , A=True , A=0 , A=1 , **A , ) -> str:
'''simple docstring'''
lowerCamelCase = vocab_size
lowerCamelCase = d_model
lowerCamelCase = d_kv
lowerCamelCase = d_ff
lowerCamelCase = num_sparse_encoder_layers
lowerCamelCase = num_layers
lowerCamelCase = (
num_decoder_layers if num_decoder_layers is not None else self.num_layers
) # default = symmetry
lowerCamelCase = num_sparse_decoder_layers
# This tells us, each how many encoder layer we'll have to set a sparse layer.
if self.num_sparse_encoder_layers > 0:
lowerCamelCase = self.num_layers // self.num_sparse_encoder_layers
else:
lowerCamelCase = self.num_layers # HACK: this will create 0 sparse layers
# This tells us, each how many encoder layer we'll have to set a sparse layer.
if self.num_sparse_decoder_layers > 0:
lowerCamelCase = self.num_decoder_layers // self.num_sparse_decoder_layers
else:
lowerCamelCase = self.num_decoder_layers # HACK: this will create 0 sparse layers
lowerCamelCase = num_heads
lowerCamelCase = num_experts
lowerCamelCase = expert_capacity
lowerCamelCase = router_bias
lowerCamelCase = router_jitter_noise
if router_dtype not in ["float32", "float16", "bfloat16"]:
raise ValueError(F'`router_dtype` must be one of \'float32\', \'float16\' or \'bfloat16\', got {router_dtype}' )
lowerCamelCase = router_dtype
lowerCamelCase = router_ignore_padding_tokens
lowerCamelCase = relative_attention_num_buckets
lowerCamelCase = relative_attention_max_distance
lowerCamelCase = dropout_rate
lowerCamelCase = layer_norm_epsilon
lowerCamelCase = initializer_factor
lowerCamelCase = feed_forward_proj
lowerCamelCase = use_cache
lowerCamelCase = add_router_probs
lowerCamelCase = router_z_loss_coef
lowerCamelCase = router_aux_loss_coef
lowerCamelCase = self.feed_forward_proj.split("""-""" )
lowerCamelCase = act_info[-1]
lowerCamelCase = act_info[0] == """gated"""
if len(A ) > 1 and act_info[0] != "gated" or len(A ) > 2:
raise ValueError(
F'`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'
"""Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. """
"""'gated-gelu' or 'relu'""" )
# for backwards compatibility
if feed_forward_proj == "gated-gelu":
lowerCamelCase = """gelu_new"""
super().__init__(
pad_token_id=A , eos_token_id=A , is_encoder_decoder=A , **A , )
| 66
| 0
|
"""simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowercase = logging.get_logger(__name__)
__lowercase = {
"""microsoft/wavlm-base""": """https://huggingface.co/microsoft/wavlm-base/resolve/main/config.json""",
# See all WavLM models at https://huggingface.co/models?filter=wavlm
}
class _A ( _a ):
"""simple docstring"""
UpperCAmelCase : Optional[int] = "wavlm"
def __init__( self : Any , __UpperCAmelCase : Tuple=32 , __UpperCAmelCase : Dict=768 , __UpperCAmelCase : Tuple=12 , __UpperCAmelCase : Dict=12 , __UpperCAmelCase : List[Any]=3072 , __UpperCAmelCase : Any="gelu" , __UpperCAmelCase : Optional[Any]=0.1 , __UpperCAmelCase : Union[str, Any]=0.1 , __UpperCAmelCase : Any=0.1 , __UpperCAmelCase : Any=0.0 , __UpperCAmelCase : Any=0.1 , __UpperCAmelCase : Any=0.1 , __UpperCAmelCase : Dict=0.02 , __UpperCAmelCase : List[str]=1e-5 , __UpperCAmelCase : Any="group" , __UpperCAmelCase : Dict="gelu" , __UpperCAmelCase : Optional[int]=(512, 512, 512, 512, 512, 512, 512) , __UpperCAmelCase : int=(5, 2, 2, 2, 2, 2, 2) , __UpperCAmelCase : Dict=(10, 3, 3, 3, 3, 2, 2) , __UpperCAmelCase : Union[str, Any]=False , __UpperCAmelCase : int=128 , __UpperCAmelCase : Union[str, Any]=16 , __UpperCAmelCase : int=320 , __UpperCAmelCase : int=800 , __UpperCAmelCase : Dict=False , __UpperCAmelCase : Tuple=True , __UpperCAmelCase : Optional[int]=0.05 , __UpperCAmelCase : Tuple=10 , __UpperCAmelCase : Any=2 , __UpperCAmelCase : Any=0.0 , __UpperCAmelCase : Optional[Any]=10 , __UpperCAmelCase : Any=320 , __UpperCAmelCase : Tuple=2 , __UpperCAmelCase : Tuple=0.1 , __UpperCAmelCase : List[str]=100 , __UpperCAmelCase : Tuple=256 , __UpperCAmelCase : Optional[Any]=256 , __UpperCAmelCase : str=0.1 , __UpperCAmelCase : int="mean" , __UpperCAmelCase : Tuple=False , __UpperCAmelCase : Union[str, Any]=False , __UpperCAmelCase : int=256 , __UpperCAmelCase : List[Any]=(512, 512, 512, 512, 1500) , __UpperCAmelCase : Any=(5, 3, 3, 1, 1) , __UpperCAmelCase : str=(1, 2, 3, 1, 1) , __UpperCAmelCase : Optional[int]=512 , __UpperCAmelCase : int=80 , __UpperCAmelCase : List[Any]=0 , __UpperCAmelCase : Optional[Any]=1 , __UpperCAmelCase : Dict=2 , __UpperCAmelCase : List[Any]=False , __UpperCAmelCase : Union[str, Any]=3 , __UpperCAmelCase : Union[str, Any]=2 , __UpperCAmelCase : Tuple=3 , __UpperCAmelCase : str=None , **__UpperCAmelCase : int , ):
super().__init__(**_lowerCAmelCase , pad_token_id=_lowerCAmelCase , bos_token_id=_lowerCAmelCase , eos_token_id=_lowerCAmelCase)
a : Tuple = hidden_size
a : List[str] = feat_extract_norm
a : str = feat_extract_activation
a : str = list(_lowerCAmelCase)
a : Dict = list(_lowerCAmelCase)
a : Dict = list(_lowerCAmelCase)
a : List[Any] = conv_bias
a : Union[str, Any] = num_buckets
a : Union[str, Any] = max_bucket_distance
a : Optional[Any] = num_conv_pos_embeddings
a : Dict = num_conv_pos_embedding_groups
a : Dict = len(self.conv_dim)
a : Dict = num_hidden_layers
a : List[Any] = intermediate_size
a : Union[str, Any] = hidden_act
a : Dict = num_attention_heads
a : List[Any] = hidden_dropout
a : Optional[int] = attention_dropout
a : Optional[int] = activation_dropout
a : Union[str, Any] = feat_proj_dropout
a : List[Any] = final_dropout
a : int = layerdrop
a : Optional[int] = layer_norm_eps
a : Optional[int] = initializer_range
a : Optional[Any] = num_ctc_classes
a : int = vocab_size
a : Dict = do_stable_layer_norm
a : Union[str, Any] = use_weighted_layer_sum
a : str = classifier_proj_size
if (
(len(self.conv_stride) != self.num_feat_extract_layers)
or (len(self.conv_kernel) != self.num_feat_extract_layers)
or (len(self.conv_dim) != self.num_feat_extract_layers)
):
raise ValueError(
"Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =="
" `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ="
f''' {len(self.conv_dim)}`, `len(config.conv_stride) = {len(self.conv_stride)}`,'''
f''' `len(config.conv_kernel) = {len(self.conv_kernel)}`.''')
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
a : str = apply_spec_augment
a : int = mask_time_prob
a : Optional[int] = mask_time_length
a : Optional[int] = mask_time_min_masks
a : str = mask_feature_prob
a : Union[str, Any] = mask_feature_length
# parameters for pretraining with codevector quantized representations
a : Union[str, Any] = num_codevectors_per_group
a : str = num_codevector_groups
a : str = contrastive_logits_temperature
a : Any = num_negatives
a : Union[str, Any] = codevector_dim
a : Union[str, Any] = proj_codevector_dim
a : Tuple = diversity_loss_weight
# ctc loss
a : List[Any] = ctc_loss_reduction
a : Union[str, Any] = ctc_zero_infinity
# adapter
a : Any = add_adapter
a : Tuple = adapter_kernel_size
a : Dict = adapter_stride
a : int = num_adapter_layers
a : Optional[Any] = output_hidden_size or hidden_size
# SequenceClassification-specific parameter. Feel free to ignore for other classes.
a : Union[str, Any] = classifier_proj_size
# XVector-specific parameters. Feel free to ignore for other classes.
a : Union[str, Any] = list(_lowerCAmelCase)
a : Dict = list(_lowerCAmelCase)
a : List[str] = list(_lowerCAmelCase)
a : Optional[Any] = xvector_output_dim
@property
def __snake_case ( self : str):
return functools.reduce(operator.mul , self.conv_stride , 1)
| 40
|
'''simple docstring'''
from __future__ import annotations
def __a(SCREAMING_SNAKE_CASE_ : float , SCREAMING_SNAKE_CASE_ : float , SCREAMING_SNAKE_CASE_ : float ):
'''simple docstring'''
if days_between_payments <= 0:
raise ValueError("days_between_payments must be > 0" )
if daily_interest_rate < 0:
raise ValueError("daily_interest_rate must be >= 0" )
if principal <= 0:
raise ValueError("principal must be > 0" )
return principal * daily_interest_rate * days_between_payments
def __a(SCREAMING_SNAKE_CASE_ : float , SCREAMING_SNAKE_CASE_ : float , SCREAMING_SNAKE_CASE_ : float , ):
'''simple docstring'''
if number_of_compounding_periods <= 0:
raise ValueError("number_of_compounding_periods must be > 0" )
if nominal_annual_interest_rate_percentage < 0:
raise ValueError("nominal_annual_interest_rate_percentage must be >= 0" )
if principal <= 0:
raise ValueError("principal must be > 0" )
return principal * (
(1 + nominal_annual_interest_rate_percentage) ** number_of_compounding_periods
- 1
)
def __a(SCREAMING_SNAKE_CASE_ : float , SCREAMING_SNAKE_CASE_ : float , SCREAMING_SNAKE_CASE_ : float , ):
'''simple docstring'''
if number_of_years <= 0:
raise ValueError("number_of_years must be > 0" )
if nominal_annual_percentage_rate < 0:
raise ValueError("nominal_annual_percentage_rate must be >= 0" )
if principal <= 0:
raise ValueError("principal must be > 0" )
return compound_interest(
SCREAMING_SNAKE_CASE_ , nominal_annual_percentage_rate / 365 , number_of_years * 365 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 158
| 0
|
'''simple docstring'''
def __a ( UpperCAmelCase ) ->bool:
"""simple docstring"""
return str(UpperCAmelCase ) == str(UpperCAmelCase )[::-1]
def __a ( UpperCAmelCase ) ->int:
"""simple docstring"""
return int(UpperCAmelCase ) + int(str(UpperCAmelCase )[::-1] )
def __a ( UpperCAmelCase = 10000 ) ->int:
"""simple docstring"""
A = []
for num in range(1 , UpperCAmelCase ):
A = 0
A = num
while iterations < 50:
A = sum_reverse(UpperCAmelCase )
iterations += 1
if is_palindrome(UpperCAmelCase ):
break
else:
lychrel_nums.append(UpperCAmelCase )
return len(UpperCAmelCase )
if __name__ == "__main__":
print(f"{solution() = }")
| 365
|
'''simple docstring'''
import heapq as hq
import math
from collections.abc import Iterator
class __UpperCAmelCase :
'''simple docstring'''
def __init__(self : Any , _lowerCAmelCase : List[Any] ):
A = str(id_ )
A = None
A = None
A = []
A = {} # {vertex:distance}
def __lt__(self : List[Any] , _lowerCAmelCase : Tuple ):
return self.key < other.key
def __repr__(self : str ):
return self.id
def A (self : Union[str, Any] , _lowerCAmelCase : List[str] ):
self.neighbors.append(_lowerCAmelCase )
def A (self : str , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : Union[str, Any] ):
A = weight
def __a ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) ->List[Any]:
"""simple docstring"""
graph[a - 1].add_neighbor(graph[b - 1] )
graph[b - 1].add_neighbor(graph[a - 1] )
# add the edges:
graph[a - 1].add_edge(graph[b - 1] , UpperCAmelCase )
graph[b - 1].add_edge(graph[a - 1] , UpperCAmelCase )
def __a ( UpperCAmelCase , UpperCAmelCase ) ->list:
"""simple docstring"""
A = []
for u in graph:
A = math.inf
A = None
A = 0
A = graph[:]
while q:
A = min(UpperCAmelCase )
q.remove(UpperCAmelCase )
for v in u.neighbors:
if (v in q) and (u.edges[v.id] < v.key):
A = u
A = u.edges[v.id]
for i in range(1 , len(UpperCAmelCase ) ):
a.append((int(graph[i].id ) + 1, int(graph[i].pi.id ) + 1) )
return a
def __a ( UpperCAmelCase , UpperCAmelCase ) ->Iterator[tuple]:
"""simple docstring"""
for u in graph:
A = math.inf
A = None
A = 0
A = list(UpperCAmelCase )
hq.heapify(UpperCAmelCase )
while h:
A = hq.heappop(UpperCAmelCase )
for v in u.neighbors:
if (v in h) and (u.edges[v.id] < v.key):
A = u
A = u.edges[v.id]
hq.heapify(UpperCAmelCase )
for i in range(1 , len(UpperCAmelCase ) ):
yield (int(graph[i].id ) + 1, int(graph[i].pi.id ) + 1)
def __a ( ) ->None:
"""simple docstring"""
if __name__ == "__main__":
import doctest
doctest.testmod()
| 337
| 0
|
"""simple docstring"""
import cva
import numpy as np
class UpperCAmelCase :
"""simple docstring"""
def __init__( self , _UpperCAmelCase , _UpperCAmelCase ):
if k in (0.04, 0.06):
lowercase__: Tuple = k
lowercase__: Tuple = window_size
else:
raise ValueError('''invalid k value''' )
def __str__( self ):
return str(self.k )
def _snake_case ( self , _UpperCAmelCase ):
lowercase__: List[str] = cva.imread(_UpperCAmelCase , 0 )
lowercase__, lowercase__: List[str] = img.shape
lowercase__: list[list[int]] = []
lowercase__: Union[str, Any] = img.copy()
lowercase__: List[str] = cva.cvtColor(_UpperCAmelCase , cva.COLOR_GRAY2RGB )
lowercase__, lowercase__: List[str] = np.gradient(_UpperCAmelCase )
lowercase__: str = dx**2
lowercase__: List[Any] = dy**2
lowercase__: List[str] = dx * dy
lowercase__: int = 0.04
lowercase__: List[Any] = self.window_size // 2
for y in range(_UpperCAmelCase , h - offset ):
for x in range(_UpperCAmelCase , w - offset ):
lowercase__: int = ixx[
y - offset : y + offset + 1, x - offset : x + offset + 1
].sum()
lowercase__: int = iyy[
y - offset : y + offset + 1, x - offset : x + offset + 1
].sum()
lowercase__: str = ixy[
y - offset : y + offset + 1, x - offset : x + offset + 1
].sum()
lowercase__: List[Any] = (wxx * wyy) - (wxy**2)
lowercase__: int = wxx + wyy
lowercase__: List[str] = det - k * (trace**2)
# Can change the value
if r > 0.5:
corner_list.append([x, y, r] )
color_img.itemset((y, x, 0) , 0 )
color_img.itemset((y, x, 1) , 0 )
color_img.itemset((y, x, 2) , 255 )
return color_img, corner_list
if __name__ == "__main__":
__A = HarrisCorner(0.04, 3)
__A ,__A = edge_detect.detect("path_to_image")
cva.imwrite("detect.png", color_img)
| 177
|
"""simple docstring"""
from __future__ import annotations
from math import pi, sqrt
def SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase , __UpperCAmelCase ) -> tuple:
if inductance <= 0:
raise ValueError('''Inductance cannot be 0 or negative''' )
elif capacitance <= 0:
raise ValueError('''Capacitance cannot be 0 or negative''' )
else:
return (
"Resonant frequency",
float(1 / (2 * pi * (sqrt(inductance * capacitance ))) ),
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 177
| 1
|
import torch
from diffusers import DDIMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class A( UpperCamelCase ):
'''simple docstring'''
UpperCamelCase = (DDIMParallelScheduler,)
UpperCamelCase = (('''eta''', 0.0), ('''num_inference_steps''', 50))
def a__ ( self : Union[str, Any] , **A_ : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
lowerCamelCase_ = {
'num_train_timesteps': 1000,
'beta_start': 0.0001,
'beta_end': 0.02,
'beta_schedule': 'linear',
'clip_sample': True,
}
config.update(**A_ )
return config
def a__ ( self : List[Any] , **A_ : Dict ) -> List[str]:
"""simple docstring"""
lowerCamelCase_ = self.scheduler_classes[0]
lowerCamelCase_ = self.get_scheduler_config(**A_ )
lowerCamelCase_ = scheduler_class(**A_ )
lowerCamelCase_ , lowerCamelCase_ = 10, 0.0
lowerCamelCase_ = self.dummy_model()
lowerCamelCase_ = self.dummy_sample_deter
scheduler.set_timesteps(A_ )
for t in scheduler.timesteps:
lowerCamelCase_ = model(A_ , A_ )
lowerCamelCase_ = scheduler.step(A_ , A_ , A_ , A_ ).prev_sample
return sample
def a__ ( self : Optional[int] ) -> Optional[Any]:
"""simple docstring"""
for timesteps in [100, 500, 1000]:
self.check_over_configs(num_train_timesteps=A_ )
def a__ ( self : int ) -> List[str]:
"""simple docstring"""
for steps_offset in [0, 1]:
self.check_over_configs(steps_offset=A_ )
lowerCamelCase_ = self.scheduler_classes[0]
lowerCamelCase_ = self.get_scheduler_config(steps_offset=1 )
lowerCamelCase_ = scheduler_class(**A_ )
scheduler.set_timesteps(5 )
assert torch.equal(scheduler.timesteps , torch.LongTensor([801, 601, 401, 201, 1] ) )
def a__ ( self : List[str] ) -> Union[str, Any]:
"""simple docstring"""
for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1] , [0.002, 0.02, 0.2, 2] ):
self.check_over_configs(beta_start=A_ , beta_end=A_ )
def a__ ( self : Union[str, Any] ) -> Optional[Any]:
"""simple docstring"""
for schedule in ["linear", "squaredcos_cap_v2"]:
self.check_over_configs(beta_schedule=A_ )
def a__ ( self : int ) -> Optional[int]:
"""simple docstring"""
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=A_ )
def a__ ( self : str ) -> List[Any]:
"""simple docstring"""
for clip_sample in [True, False]:
self.check_over_configs(clip_sample=A_ )
def a__ ( self : Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
for timestep_spacing in ["trailing", "leading"]:
self.check_over_configs(timestep_spacing=A_ )
def a__ ( self : str ) -> List[str]:
"""simple docstring"""
for rescale_betas_zero_snr in [True, False]:
self.check_over_configs(rescale_betas_zero_snr=A_ )
def a__ ( self : Any ) -> Optional[Any]:
"""simple docstring"""
self.check_over_configs(thresholding=A_ )
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(
thresholding=A_ , prediction_type=A_ , sample_max_value=A_ , )
def a__ ( self : Union[str, Any] ) -> Tuple:
"""simple docstring"""
for t in [1, 10, 49]:
self.check_over_forward(time_step=A_ )
def a__ ( self : List[str] ) -> str:
"""simple docstring"""
for t, num_inference_steps in zip([1, 10, 50] , [10, 50, 500] ):
self.check_over_forward(time_step=A_ , num_inference_steps=A_ )
def a__ ( self : Optional[int] ) -> Optional[Any]:
"""simple docstring"""
for t, eta in zip([1, 10, 49] , [0.0, 0.5, 1.0] ):
self.check_over_forward(time_step=A_ , eta=A_ )
def a__ ( self : Optional[int] ) -> Optional[int]:
"""simple docstring"""
lowerCamelCase_ = self.scheduler_classes[0]
lowerCamelCase_ = self.get_scheduler_config()
lowerCamelCase_ = scheduler_class(**A_ )
assert torch.sum(torch.abs(scheduler._get_variance(0 , 0 ) - 0.0 ) ) < 1E-5
assert torch.sum(torch.abs(scheduler._get_variance(420 , 400 ) - 0.14771 ) ) < 1E-5
assert torch.sum(torch.abs(scheduler._get_variance(980 , 960 ) - 0.32460 ) ) < 1E-5
assert torch.sum(torch.abs(scheduler._get_variance(0 , 0 ) - 0.0 ) ) < 1E-5
assert torch.sum(torch.abs(scheduler._get_variance(487 , 486 ) - 0.00979 ) ) < 1E-5
assert torch.sum(torch.abs(scheduler._get_variance(999 , 998 ) - 0.02 ) ) < 1E-5
def a__ ( self : Optional[int] ) -> List[Any]:
"""simple docstring"""
lowerCamelCase_ = self.scheduler_classes[0]
lowerCamelCase_ = self.get_scheduler_config()
lowerCamelCase_ = scheduler_class(**A_ )
lowerCamelCase_ , lowerCamelCase_ = 10, 0.0
scheduler.set_timesteps(A_ )
lowerCamelCase_ = self.dummy_model()
lowerCamelCase_ = self.dummy_sample_deter
lowerCamelCase_ = self.dummy_sample_deter + 0.1
lowerCamelCase_ = self.dummy_sample_deter - 0.1
lowerCamelCase_ = samplea.shape[0]
lowerCamelCase_ = torch.stack([samplea, samplea, samplea] , dim=0 )
lowerCamelCase_ = torch.arange(A_ )[0:3, None].repeat(1 , A_ )
lowerCamelCase_ = model(samples.flatten(0 , 1 ) , timesteps.flatten(0 , 1 ) )
lowerCamelCase_ = scheduler.batch_step_no_noise(A_ , timesteps.flatten(0 , 1 ) , samples.flatten(0 , 1 ) , A_ )
lowerCamelCase_ = torch.sum(torch.abs(A_ ) )
lowerCamelCase_ = torch.mean(torch.abs(A_ ) )
assert abs(result_sum.item() - 1147.7904 ) < 1E-2
assert abs(result_mean.item() - 0.4982 ) < 1E-3
def a__ ( self : Union[str, Any] ) -> Optional[Any]:
"""simple docstring"""
lowerCamelCase_ = self.full_loop()
lowerCamelCase_ = torch.sum(torch.abs(A_ ) )
lowerCamelCase_ = torch.mean(torch.abs(A_ ) )
assert abs(result_sum.item() - 172.0067 ) < 1E-2
assert abs(result_mean.item() - 0.223967 ) < 1E-3
def a__ ( self : List[Any] ) -> Optional[int]:
"""simple docstring"""
lowerCamelCase_ = self.full_loop(prediction_type='v_prediction' )
lowerCamelCase_ = torch.sum(torch.abs(A_ ) )
lowerCamelCase_ = torch.mean(torch.abs(A_ ) )
assert abs(result_sum.item() - 52.5302 ) < 1E-2
assert abs(result_mean.item() - 0.0684 ) < 1E-3
def a__ ( self : Tuple ) -> Optional[Any]:
"""simple docstring"""
lowerCamelCase_ = self.full_loop(set_alpha_to_one=A_ , beta_start=0.01 )
lowerCamelCase_ = torch.sum(torch.abs(A_ ) )
lowerCamelCase_ = torch.mean(torch.abs(A_ ) )
assert abs(result_sum.item() - 149.8295 ) < 1E-2
assert abs(result_mean.item() - 0.1951 ) < 1E-3
def a__ ( self : Any ) -> Dict:
"""simple docstring"""
lowerCamelCase_ = self.full_loop(set_alpha_to_one=A_ , beta_start=0.01 )
lowerCamelCase_ = torch.sum(torch.abs(A_ ) )
lowerCamelCase_ = torch.mean(torch.abs(A_ ) )
assert abs(result_sum.item() - 149.0784 ) < 1E-2
assert abs(result_mean.item() - 0.1941 ) < 1E-3
| 208
|
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings
lowerCamelCase : Any = r"\n [`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and\n can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information.\n\n Args:\n title_sep (`str`, *optional*, defaults to `\" / \"`):\n Separator inserted between the title and the text of the retrieved document when calling [`RagRetriever`].\n doc_sep (`str`, *optional*, defaults to `\" // \"`):\n Separator inserted between the text of the retrieved document and the original input when calling\n [`RagRetriever`].\n n_docs (`int`, *optional*, defaults to 5):\n Number of documents to retrieve.\n max_combined_length (`int`, *optional*, defaults to 300):\n Max length of contextualized input returned by [`~RagRetriever.__call__`].\n retrieval_vector_size (`int`, *optional*, defaults to 768):\n Dimensionality of the document embeddings indexed by [`RagRetriever`].\n retrieval_batch_size (`int`, *optional*, defaults to 8):\n Retrieval batch size, defined as the number of queries issues concurrently to the faiss index encapsulated\n [`RagRetriever`].\n dataset (`str`, *optional*, defaults to `\"wiki_dpr\"`):\n A dataset identifier of the indexed dataset in HuggingFace Datasets (list all available datasets and ids\n using `datasets.list_datasets()`).\n dataset_split (`str`, *optional*, defaults to `\"train\"`)\n Which split of the `dataset` to load.\n index_name (`str`, *optional*, defaults to `\"compressed\"`)\n The index name of the index associated with the `dataset`. One can choose between `\"legacy\"`, `\"exact\"` and\n `\"compressed\"`.\n index_path (`str`, *optional*)\n The path to the serialized faiss index on disk.\n passages_path (`str`, *optional*):\n A path to text passages compatible with the faiss index. Required if using\n [`~models.rag.retrieval_rag.LegacyIndex`]\n use_dummy_dataset (`bool`, *optional*, defaults to `False`)\n Whether to load a \"dummy\" variant of the dataset specified by `dataset`.\n label_smoothing (`float`, *optional*, defaults to 0.0):\n Only relevant if `return_loss` is set to `True`. Controls the `epsilon` parameter value for label smoothing\n in the loss calculation. If set to 0, no label smoothing is performed.\n do_marginalize (`bool`, *optional*, defaults to `False`):\n If `True`, the logits are marginalized over all documents by making use of\n `torch.nn.functional.log_softmax`.\n reduce_loss (`bool`, *optional*, defaults to `False`):\n Whether or not to reduce the NLL loss using the `torch.Tensor.sum` operation.\n do_deduplication (`bool`, *optional*, defaults to `True`):\n Whether or not to deduplicate the generations from different context documents for a given input. Has to be\n set to `False` if used while training with distributed backend.\n exclude_bos_score (`bool`, *optional*, defaults to `False`):\n Whether or not to disregard the BOS token when computing the loss.\n output_retrieved(`bool`, *optional*, defaults to `False`):\n If set to `True`, `retrieved_doc_embeds`, `retrieved_doc_ids`, `context_input_ids` and\n `context_attention_mask` are returned. See returned tensors for more detail.\n use_cache (`bool`, *optional*, defaults to `True`):\n Whether or not the model should return the last key/values attentions (not used by all models).\n forced_eos_token_id (`int`, *optional*):\n The id of the token to force as the last generated token when `max_length` is reached. Usually set to\n `eos_token_id`.\n"
@add_start_docstrings(UpperCamelCase )
class A( UpperCamelCase ):
'''simple docstring'''
UpperCamelCase = '''rag'''
UpperCamelCase = True
def __init__( self : Optional[Any] , A_ : Optional[Any]=None , A_ : Any=True , A_ : Dict=None , A_ : Optional[int]=None , A_ : str=None , A_ : int=None , A_ : List[Any]=None , A_ : List[str]=" / " , A_ : Tuple=" // " , A_ : Union[str, Any]=5 , A_ : Optional[Any]=300 , A_ : int=768 , A_ : Dict=8 , A_ : int="wiki_dpr" , A_ : int="train" , A_ : List[str]="compressed" , A_ : Tuple=None , A_ : Optional[Any]=None , A_ : Optional[int]=False , A_ : str=False , A_ : Optional[Any]=0.0 , A_ : Union[str, Any]=True , A_ : List[Any]=False , A_ : Union[str, Any]=False , A_ : Dict=False , A_ : str=True , A_ : List[str]=None , **A_ : Optional[Any] , ) -> str:
"""simple docstring"""
super().__init__(
bos_token_id=A_ , pad_token_id=A_ , eos_token_id=A_ , decoder_start_token_id=A_ , forced_eos_token_id=A_ , is_encoder_decoder=A_ , prefix=A_ , vocab_size=A_ , **A_ , )
assert (
"question_encoder" in kwargs and "generator" in kwargs
), "Config has to be initialized with question_encoder and generator config"
lowerCamelCase_ = kwargs.pop('question_encoder' )
lowerCamelCase_ = question_encoder_config.pop('model_type' )
lowerCamelCase_ = kwargs.pop('generator' )
lowerCamelCase_ = decoder_config.pop('model_type' )
from ..auto.configuration_auto import AutoConfig
lowerCamelCase_ = AutoConfig.for_model(A_ , **A_ )
lowerCamelCase_ = AutoConfig.for_model(A_ , **A_ )
lowerCamelCase_ = reduce_loss
lowerCamelCase_ = label_smoothing
lowerCamelCase_ = exclude_bos_score
lowerCamelCase_ = do_marginalize
lowerCamelCase_ = title_sep
lowerCamelCase_ = doc_sep
lowerCamelCase_ = n_docs
lowerCamelCase_ = max_combined_length
lowerCamelCase_ = dataset
lowerCamelCase_ = dataset_split
lowerCamelCase_ = index_name
lowerCamelCase_ = retrieval_vector_size
lowerCamelCase_ = retrieval_batch_size
lowerCamelCase_ = passages_path
lowerCamelCase_ = index_path
lowerCamelCase_ = use_dummy_dataset
lowerCamelCase_ = output_retrieved
lowerCamelCase_ = do_deduplication
lowerCamelCase_ = use_cache
if self.forced_eos_token_id is None:
lowerCamelCase_ = getattr(self.generator , 'forced_eos_token_id' , A_ )
@classmethod
def a__ ( cls : str , A_ : PretrainedConfig , A_ : PretrainedConfig , **A_ : List[str] ) -> PretrainedConfig:
"""simple docstring"""
return cls(question_encoder=question_encoder_config.to_dict() , generator=generator_config.to_dict() , **A_ )
def a__ ( self : Tuple ) -> int:
"""simple docstring"""
lowerCamelCase_ = copy.deepcopy(self.__dict__ )
lowerCamelCase_ = self.question_encoder.to_dict()
lowerCamelCase_ = self.generator.to_dict()
lowerCamelCase_ = self.__class__.model_type
return output
| 208
| 1
|
def _A ( SCREAMING_SNAKE_CASE : str ):
"""simple docstring"""
return credit_card_number.startswith(("34", "35", "37", "4", "5", "6") )
def _A ( SCREAMING_SNAKE_CASE : str ):
"""simple docstring"""
a__ : Tuple =credit_card_number
a__ : Union[str, Any] =0
a__ : List[str] =len(SCREAMING_SNAKE_CASE ) - 2
for i in range(SCREAMING_SNAKE_CASE , -1 , -2 ):
# double the value of every second digit
a__ : List[Any] =int(cc_number[i] )
digit *= 2
# If doubling of a number results in a two digit number
# i.e greater than 9(e.g., 6 × 2 = 12),
# then add the digits of the product (e.g., 12: 1 + 2 = 3, 15: 1 + 5 = 6),
# to get a single digit number.
if digit > 9:
digit %= 10
digit += 1
a__ : List[str] =cc_number[:i] + str(SCREAMING_SNAKE_CASE ) + cc_number[i + 1 :]
total += digit
# Sum up the remaining digits
for i in range(len(SCREAMING_SNAKE_CASE ) - 1 , -1 , -2 ):
total += int(cc_number[i] )
return total % 10 == 0
def _A ( SCREAMING_SNAKE_CASE : str ):
"""simple docstring"""
a__ : List[Any] =f'''{credit_card_number} is an invalid credit card number because'''
if not credit_card_number.isdigit():
print(f'''{error_message} it has nonnumerical characters.''' )
return False
if not 13 <= len(SCREAMING_SNAKE_CASE ) <= 16:
print(f'''{error_message} of its length.''' )
return False
if not validate_initial_digits(SCREAMING_SNAKE_CASE ):
print(f'''{error_message} of its first two digits.''' )
return False
if not luhn_validation(SCREAMING_SNAKE_CASE ):
print(f'''{error_message} it fails the Luhn check.''' )
return False
print(f'''{credit_card_number} is a valid credit card number.''' )
return True
if __name__ == "__main__":
import doctest
doctest.testmod()
validate_credit_card_number("""4111111111111111""")
validate_credit_card_number("""32323""")
| 95
|
"""simple docstring"""
def __A (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->bool:
"""simple docstring"""
return numa ^ numa < 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 293
| 0
|
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_beit import BeitImageProcessor
A: Any = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE__ ( UpperCAmelCase__ ):
def __init__( self , *_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE ) -> None:
'''simple docstring'''
warnings.warn(
"""The class BeitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
""" use BeitImageProcessor instead.""" , _SCREAMING_SNAKE_CASE , )
super().__init__(*_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
| 76
|
"""simple docstring"""
from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available
if is_datasets_available():
import datasets
class SCREAMING_SNAKE_CASE__ ( UpperCAmelCase__ ):
@slow
@require_torch
def SCREAMING_SNAKE_CASE ( self ) -> Tuple:
'''simple docstring'''
UpperCAmelCase : Any = EncoderDecoderModel.from_encoder_decoder_pretrained("""prajjwal1/bert-tiny""" , """prajjwal1/bert-tiny""" )
UpperCAmelCase : Optional[int] = BertTokenizer.from_pretrained("""bert-base-uncased""" )
UpperCAmelCase : Tuple = bertabert.config.encoder.vocab_size
UpperCAmelCase : int = tokenizer.sep_token_id
UpperCAmelCase : Dict = tokenizer.cls_token_id
UpperCAmelCase : int = 128
UpperCAmelCase : List[str] = datasets.load_dataset("""cnn_dailymail""" , """3.0.0""" , split="""train[:1%]""" )
UpperCAmelCase : Union[str, Any] = datasets.load_dataset("""cnn_dailymail""" , """3.0.0""" , split="""validation[:1%]""" )
UpperCAmelCase : Optional[int] = train_dataset.select(range(32 ) )
UpperCAmelCase : int = val_dataset.select(range(16 ) )
UpperCAmelCase : List[str] = 4
def _map_to_encoder_decoder_inputs(_SCREAMING_SNAKE_CASE ):
# Tokenizer will automatically set [BOS] <text> [EOS]
UpperCAmelCase : str = tokenizer(batch["""article"""] , padding="""max_length""" , truncation=_SCREAMING_SNAKE_CASE , max_length=512 )
UpperCAmelCase : str = tokenizer(batch["""highlights"""] , padding="""max_length""" , truncation=_SCREAMING_SNAKE_CASE , max_length=128 )
UpperCAmelCase : Optional[Any] = inputs.input_ids
UpperCAmelCase : Union[str, Any] = inputs.attention_mask
UpperCAmelCase : Union[str, Any] = outputs.input_ids
UpperCAmelCase : Any = outputs.input_ids.copy()
UpperCAmelCase : Tuple = [
[-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch["""labels"""]
]
UpperCAmelCase : List[Any] = outputs.attention_mask
assert all(len(_SCREAMING_SNAKE_CASE ) == 512 for x in inputs.input_ids )
assert all(len(_SCREAMING_SNAKE_CASE ) == 128 for x in outputs.input_ids )
return batch
def _compute_metrics(_SCREAMING_SNAKE_CASE ):
UpperCAmelCase : Optional[Any] = pred.label_ids
UpperCAmelCase : Tuple = pred.predictions
# all unnecessary tokens are removed
UpperCAmelCase : Union[str, Any] = tokenizer.batch_decode(_SCREAMING_SNAKE_CASE , skip_special_tokens=_SCREAMING_SNAKE_CASE )
UpperCAmelCase : str = tokenizer.batch_decode(_SCREAMING_SNAKE_CASE , skip_special_tokens=_SCREAMING_SNAKE_CASE )
UpperCAmelCase : Optional[Any] = sum([int(pred_str[i] == label_str[i] ) for i in range(len(_SCREAMING_SNAKE_CASE ) )] ) / len(_SCREAMING_SNAKE_CASE )
return {"accuracy": accuracy}
# map train dataset
UpperCAmelCase : List[Any] = train_dataset.map(
_map_to_encoder_decoder_inputs , batched=_SCREAMING_SNAKE_CASE , batch_size=_SCREAMING_SNAKE_CASE , remove_columns=["""article""", """highlights"""] , )
train_dataset.set_format(
type="""torch""" , columns=["""input_ids""", """attention_mask""", """decoder_input_ids""", """decoder_attention_mask""", """labels"""] , )
# same for validation dataset
UpperCAmelCase : List[str] = val_dataset.map(
_map_to_encoder_decoder_inputs , batched=_SCREAMING_SNAKE_CASE , batch_size=_SCREAMING_SNAKE_CASE , remove_columns=["""article""", """highlights"""] , )
val_dataset.set_format(
type="""torch""" , columns=["""input_ids""", """attention_mask""", """decoder_input_ids""", """decoder_attention_mask""", """labels"""] , )
UpperCAmelCase : Dict = self.get_auto_remove_tmp_dir()
UpperCAmelCase : Dict = SeqaSeqTrainingArguments(
output_dir=_SCREAMING_SNAKE_CASE , per_device_train_batch_size=_SCREAMING_SNAKE_CASE , per_device_eval_batch_size=_SCREAMING_SNAKE_CASE , predict_with_generate=_SCREAMING_SNAKE_CASE , evaluation_strategy="""steps""" , do_train=_SCREAMING_SNAKE_CASE , do_eval=_SCREAMING_SNAKE_CASE , warmup_steps=0 , eval_steps=2 , logging_steps=2 , )
# instantiate trainer
UpperCAmelCase : List[str] = SeqaSeqTrainer(
model=_SCREAMING_SNAKE_CASE , args=_SCREAMING_SNAKE_CASE , compute_metrics=_compute_metrics , train_dataset=_SCREAMING_SNAKE_CASE , eval_dataset=_SCREAMING_SNAKE_CASE , tokenizer=_SCREAMING_SNAKE_CASE , )
# start training
trainer.train()
| 76
| 1
|
import math
from typing import Callable, List, Optional, Union
import numpy as np
import PIL
import torch
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale import StableDiffusionUpscalePipeline
from diffusers.schedulers import DDIMScheduler, DDPMScheduler, LMSDiscreteScheduler, PNDMScheduler
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase=[] ) -> Union[str, Any]:
"""simple docstring"""
snake_case_ : str = size[0] - overlap_pixels * 2
snake_case_ : Optional[Any] = size[1] - overlap_pixels * 2
for letter in ["l", "r"]:
if letter in remove_borders:
size_x += overlap_pixels
for letter in ["t", "b"]:
if letter in remove_borders:
size_y += overlap_pixels
snake_case_ : List[Any] = np.ones((size_y, size_x) , dtype=np.uinta ) * 255
snake_case_ : List[Any] = np.pad(lowercase__ , mode='''linear_ramp''' , pad_width=lowercase__ , end_values=0 )
if "l" in remove_borders:
snake_case_ : List[Any] = mask[:, overlap_pixels : mask.shape[1]]
if "r" in remove_borders:
snake_case_ : List[Any] = mask[:, 0 : mask.shape[1] - overlap_pixels]
if "t" in remove_borders:
snake_case_ : Union[str, Any] = mask[overlap_pixels : mask.shape[0], :]
if "b" in remove_borders:
snake_case_ : Dict = mask[0 : mask.shape[0] - overlap_pixels, :]
return mask
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> Optional[Any]:
"""simple docstring"""
return max(lowercase__ , min(lowercase__ , lowercase__ ) )
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> Union[str, Any]:
"""simple docstring"""
return (
clamp(rect[0] , min[0] , max[0] ),
clamp(rect[1] , min[1] , max[1] ),
clamp(rect[2] , min[0] , max[0] ),
clamp(rect[3] , min[1] , max[1] ),
)
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> Any:
"""simple docstring"""
snake_case_ : Any = list(lowercase__ )
rect[0] -= overlap
rect[1] -= overlap
rect[2] += overlap
rect[3] += overlap
snake_case_ : List[str] = clamp_rect(lowercase__ , [0, 0] , [image_size[0], image_size[1]] )
return rect
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> List[str]:
"""simple docstring"""
snake_case_ : List[str] = Image.new('''RGB''' , (tile.size[0] + original_slice, tile.size[1]) )
result.paste(
original_image.resize((tile.size[0], tile.size[1]) , Image.BICUBIC ).crop(
(slice_x, 0, slice_x + original_slice, tile.size[1]) ) , (0, 0) , )
result.paste(lowercase__ , (original_slice, 0) )
return result
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase ) -> Optional[int]:
"""simple docstring"""
snake_case_ : Optional[Any] = (original_image_slice * 4, 0, tile.size[0], tile.size[1])
snake_case_ : Tuple = tile.crop(lowercase__ )
return tile
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase ) -> Dict:
"""simple docstring"""
snake_case_ : str = n % d
return n - divisor
class __lowerCAmelCase ( _a ):
def __init__(self , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ = 350 , ) -> List[str]:
'''simple docstring'''
super().__init__(
vae=__lowercase , text_encoder=__lowercase , tokenizer=__lowercase , unet=__lowercase , low_res_scheduler=__lowercase , scheduler=__lowercase , max_noise_level=__lowercase , )
def lowerCamelCase (self , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , **__magic_name__ ) -> int:
'''simple docstring'''
torch.manual_seed(0 )
snake_case_ : Tuple = (
min(image.size[0] - (tile_size + original_image_slice) , x * tile_size ),
min(image.size[1] - (tile_size + original_image_slice) , y * tile_size ),
min(image.size[0] , (x + 1) * tile_size ),
min(image.size[1] , (y + 1) * tile_size ),
)
snake_case_ : List[str] = add_overlap_rect(__lowercase , __lowercase , image.size )
snake_case_ : int = image.crop(__lowercase )
snake_case_ : List[str] = ((crop_rect[0] + ((crop_rect[2] - crop_rect[0]) / 2)) / image.size[0]) * tile.size[0]
snake_case_ : str = translated_slice_x - (original_image_slice / 2)
snake_case_ : int = max(0 , __lowercase )
snake_case_ : Union[str, Any] = squeeze_tile(__lowercase , __lowercase , __lowercase , __lowercase )
snake_case_ : str = to_input.size
snake_case_ : str = to_input.resize((tile_size, tile_size) , Image.BICUBIC )
snake_case_ : List[Any] = super(__lowercase , self ).__call__(image=__lowercase , **__lowercase ).images[0]
snake_case_ : str = upscaled_tile.resize((orig_input_size[0] * 4, orig_input_size[1] * 4) , Image.BICUBIC )
snake_case_ : int = unsqueeze_tile(__lowercase , __lowercase )
snake_case_ : Tuple = upscaled_tile.resize((tile.size[0] * 4, tile.size[1] * 4) , Image.BICUBIC )
snake_case_ : Dict = []
if x == 0:
remove_borders.append('''l''' )
elif crop_rect[2] == image.size[0]:
remove_borders.append('''r''' )
if y == 0:
remove_borders.append('''t''' )
elif crop_rect[3] == image.size[1]:
remove_borders.append('''b''' )
snake_case_ : List[Any] = Image.fromarray(
make_transparency_mask(
(upscaled_tile.size[0], upscaled_tile.size[1]) , tile_border * 4 , remove_borders=__lowercase ) , mode='''L''' , )
final_image.paste(
__lowercase , (crop_rect_with_overlap[0] * 4, crop_rect_with_overlap[1] * 4) , __lowercase )
@torch.no_grad()
def __call__(self , __magic_name__ , __magic_name__ , __magic_name__ = 75 , __magic_name__ = 9.0 , __magic_name__ = 50 , __magic_name__ = None , __magic_name__ = 1 , __magic_name__ = 0.0 , __magic_name__ = None , __magic_name__ = None , __magic_name__ = None , __magic_name__ = 1 , __magic_name__ = 128 , __magic_name__ = 32 , __magic_name__ = 32 , ) -> Any:
'''simple docstring'''
snake_case_ : List[str] = Image.new('''RGB''' , (image.size[0] * 4, image.size[1] * 4) )
snake_case_ : Union[str, Any] = math.ceil(image.size[0] / tile_size )
snake_case_ : Any = math.ceil(image.size[1] / tile_size )
snake_case_ : Optional[int] = tcx * tcy
snake_case_ : List[Any] = 0
for y in range(__lowercase ):
for x in range(__lowercase ):
self._process_tile(
__lowercase , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , prompt=__lowercase , num_inference_steps=__lowercase , guidance_scale=__lowercase , noise_level=__lowercase , negative_prompt=__lowercase , num_images_per_prompt=__lowercase , eta=__lowercase , generator=__lowercase , latents=__lowercase , )
current_count += 1
if callback is not None:
callback({'''progress''': current_count / total_tile_count, '''image''': final_image} )
return final_image
def lowerCamelCase_ ( ) -> Optional[int]:
"""simple docstring"""
snake_case_ : Tuple = '''stabilityai/stable-diffusion-x4-upscaler'''
snake_case_ : Any = StableDiffusionTiledUpscalePipeline.from_pretrained(lowercase__ , revision='''fp16''' , torch_dtype=torch.floataa )
snake_case_ : str = pipe.to('''cuda''' )
snake_case_ : Any = Image.open('''../../docs/source/imgs/diffusers_library.jpg''' )
def callback(_UpperCamelCase ):
print(f'''progress: {obj["progress"]:.4f}''' )
obj["image"].save('''diffusers_library_progress.jpg''' )
snake_case_ : str = pipe(image=lowercase__ , prompt='''Black font, white background, vector''' , noise_level=40 , callback=lowercase__ )
final_image.save('''diffusers_library.jpg''' )
if __name__ == "__main__":
main()
| 279
|
'''simple docstring'''
def __UpperCamelCase ( lowercase__ : Union[str, Any]=2_81_23 ):
'''simple docstring'''
__lowercase =[1] * (limit + 1)
for i in range(2, int(limit**0.5 ) + 1 ):
sum_divs[i * i] += i
for k in range(i + 1, limit // i + 1 ):
sum_divs[k * i] += k + i
__lowercase =set()
__lowercase =0
for n in range(1, limit + 1 ):
if sum_divs[n] > n:
abundants.add(lowercase__ )
if not any((n - a in abundants) for a in abundants ):
res += n
return res
if __name__ == "__main__":
print(solution())
| 141
| 0
|
from typing import List, Union
import numpy as np
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
import torch
from ..models.auto.modeling_auto import MODEL_FOR_DEPTH_ESTIMATION_MAPPING
_snake_case : Dict = logging.get_logger(__name__)
@add_end_docstrings(a__ )
class A ( a__ ):
def __init__( self : List[str] , *lowerCAmelCase_ : Tuple , **lowerCAmelCase_ : Optional[Any] ) -> Dict:
"""simple docstring"""
super().__init__(*lowerCAmelCase__ , **lowerCAmelCase__ )
requires_backends(self , '''vision''' )
self.check_model_type(lowerCAmelCase__ )
def __call__( self : Any , lowerCAmelCase_ : int , **lowerCAmelCase_ : Tuple ) -> str:
"""simple docstring"""
return super().__call__(lowerCAmelCase__ , **lowerCAmelCase__ )
def __lowerCAmelCase ( self : List[str] , **lowerCAmelCase_ : str ) -> Any:
"""simple docstring"""
return {}, {}, {}
def __lowerCAmelCase ( self : str , lowerCAmelCase_ : Optional[Any] ) -> Tuple:
"""simple docstring"""
_a = load_image(lowerCAmelCase__ )
_a = image.size
_a = self.image_processor(images=lowerCAmelCase__ , return_tensors=self.framework )
return model_inputs
def __lowerCAmelCase ( self : Dict , lowerCAmelCase_ : Dict ) -> Dict:
"""simple docstring"""
_a = self.model(**lowerCAmelCase__ )
return model_outputs
def __lowerCAmelCase ( self : List[Any] , lowerCAmelCase_ : List[str] ) -> Optional[Any]:
"""simple docstring"""
_a = model_outputs.predicted_depth
_a = torch.nn.functional.interpolate(
predicted_depth.unsqueeze(1 ) , size=self.image_size[::-1] , mode='''bicubic''' , align_corners=lowerCAmelCase__ )
_a = prediction.squeeze().cpu().numpy()
_a = (output * 2_55 / np.max(lowerCAmelCase__ )).astype('''uint8''' )
_a = Image.fromarray(lowerCAmelCase__ )
_a = {}
_a = predicted_depth
_a = depth
return output_dict
| 363
|
'''simple docstring'''
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import VideoMAEConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import (
MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING,
VideoMAEForPreTraining,
VideoMAEForVideoClassification,
VideoMAEModel,
)
from transformers.models.videomae.modeling_videomae import VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from transformers import VideoMAEImageProcessor
class A :
def __init__( self : Any , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : List[str]=13 , lowerCAmelCase_ : Optional[Any]=10 , lowerCAmelCase_ : Optional[int]=3 , lowerCAmelCase_ : Union[str, Any]=2 , lowerCAmelCase_ : Tuple=2 , lowerCAmelCase_ : Dict=2 , lowerCAmelCase_ : str=True , lowerCAmelCase_ : Optional[Any]=True , lowerCAmelCase_ : Dict=32 , lowerCAmelCase_ : Tuple=5 , lowerCAmelCase_ : Optional[int]=4 , lowerCAmelCase_ : int=37 , lowerCAmelCase_ : int="gelu" , lowerCAmelCase_ : List[str]=0.1 , lowerCAmelCase_ : str=0.1 , lowerCAmelCase_ : Dict=10 , lowerCAmelCase_ : int=0.0_2 , lowerCAmelCase_ : Union[str, Any]=0.9 , lowerCAmelCase_ : str=None , ) -> int:
"""simple docstring"""
_a = parent
_a = batch_size
_a = image_size
_a = num_channels
_a = patch_size
_a = tubelet_size
_a = num_frames
_a = is_training
_a = use_labels
_a = hidden_size
_a = num_hidden_layers
_a = num_attention_heads
_a = intermediate_size
_a = hidden_act
_a = hidden_dropout_prob
_a = attention_probs_dropout_prob
_a = type_sequence_label_size
_a = initializer_range
_a = mask_ratio
_a = scope
# in VideoMAE, the number of tokens equals num_frames/tubelet_size * num_patches per frame
_a = (image_size // patch_size) ** 2
_a = (num_frames // tubelet_size) * self.num_patches_per_frame
# use this variable to define bool_masked_pos
_a = int(mask_ratio * self.seq_length )
def __lowerCAmelCase ( self : Any ) -> Optional[int]:
"""simple docstring"""
_a = floats_tensor(
[self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size] )
_a = None
if self.use_labels:
_a = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_a = self.get_config()
return config, pixel_values, labels
def __lowerCAmelCase ( self : List[str] ) -> Any:
"""simple docstring"""
return VideoMAEConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , num_frames=self.num_frames , tubelet_size=self.tubelet_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=lowerCAmelCase_ , initializer_range=self.initializer_range , )
def __lowerCAmelCase ( self : List[str] , lowerCAmelCase_ : Optional[Any] , lowerCAmelCase_ : Optional[Any] , lowerCAmelCase_ : Union[str, Any] ) -> Optional[int]:
"""simple docstring"""
_a = VideoMAEModel(config=lowerCAmelCase_ )
model.to(lowerCAmelCase_ )
model.eval()
_a = model(lowerCAmelCase_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __lowerCAmelCase ( self : Any , lowerCAmelCase_ : Optional[int] , lowerCAmelCase_ : Dict , lowerCAmelCase_ : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
_a = VideoMAEForPreTraining(lowerCAmelCase_ )
model.to(lowerCAmelCase_ )
model.eval()
# important: each video needs to have the same number of masked patches
# hence we define a single mask, which we then repeat for each example in the batch
_a = torch.ones((self.num_masks,) )
_a = torch.cat([mask, torch.zeros(self.seq_length - mask.size(0 ) )] )
_a = mask.expand(self.batch_size , -1 ).bool()
_a = model(lowerCAmelCase_ , lowerCAmelCase_ )
# model only returns predictions for masked patches
_a = mask.sum().item()
_a = 3 * self.tubelet_size * self.patch_size**2
self.parent.assertEqual(result.logits.shape , (self.batch_size, num_masked_patches, decoder_num_labels) )
def __lowerCAmelCase ( self : Optional[Any] ) -> Optional[int]:
"""simple docstring"""
_a = self.prepare_config_and_inputs()
_a , _a , _a = config_and_inputs
_a = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class A ( _a ,_a ,unittest.TestCase ):
lowercase_ = (
(VideoMAEModel, VideoMAEForPreTraining, VideoMAEForVideoClassification) if is_torch_available() else ()
)
lowercase_ = (
{'feature-extraction': VideoMAEModel, 'video-classification': VideoMAEForVideoClassification}
if is_torch_available()
else {}
)
lowercase_ = False
lowercase_ = False
lowercase_ = False
lowercase_ = False
def __lowerCAmelCase ( self : Dict ) -> int:
"""simple docstring"""
_a = VideoMAEModelTester(self )
_a = ConfigTester(self , config_class=lowerCAmelCase_ , has_text_modality=lowerCAmelCase_ , hidden_size=37 )
def __lowerCAmelCase ( self : Optional[int] , lowerCAmelCase_ : Any , lowerCAmelCase_ : int , lowerCAmelCase_ : List[Any]=False ) -> Tuple:
"""simple docstring"""
_a = copy.deepcopy(lowerCAmelCase_ )
if model_class == VideoMAEForPreTraining:
# important: each video needs to have the same number of masked patches
# hence we define a single mask, which we then repeat for each example in the batch
_a = torch.ones((self.model_tester.num_masks,) )
_a = torch.cat([mask, torch.zeros(self.model_tester.seq_length - mask.size(0 ) )] )
_a = mask.expand(self.model_tester.batch_size , -1 ).bool()
_a = bool_masked_pos.to(lowerCAmelCase_ )
if return_labels:
if model_class in [
*get_values(lowerCAmelCase_ ),
]:
_a = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=lowerCAmelCase_ )
return inputs_dict
def __lowerCAmelCase ( self : List[Any] ) -> Optional[int]:
"""simple docstring"""
self.config_tester.run_common_tests()
@unittest.skip(reason='''VideoMAE does not use inputs_embeds''' )
def __lowerCAmelCase ( self : Any ) -> Any:
"""simple docstring"""
pass
def __lowerCAmelCase ( self : Union[str, Any] ) -> Union[str, Any]:
"""simple docstring"""
_a , _a = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_a = model_class(lowerCAmelCase_ )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
_a = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(lowerCAmelCase_ , nn.Linear ) )
def __lowerCAmelCase ( self : Any ) -> Any:
"""simple docstring"""
_a , _a = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_a = model_class(lowerCAmelCase_ )
_a = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_a = [*signature.parameters.keys()]
_a = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , lowerCAmelCase_ )
def __lowerCAmelCase ( self : int ) -> Any:
"""simple docstring"""
_a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowerCAmelCase_ )
def __lowerCAmelCase ( self : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
_a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*lowerCAmelCase_ )
@slow
def __lowerCAmelCase ( self : Dict ) -> Optional[int]:
"""simple docstring"""
for model_name in VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_a = VideoMAEModel.from_pretrained(lowerCAmelCase_ )
self.assertIsNotNone(lowerCAmelCase_ )
def __lowerCAmelCase ( self : int ) -> Any:
"""simple docstring"""
if not self.has_attentions:
pass
else:
_a , _a = self.model_tester.prepare_config_and_inputs_for_common()
_a = True
for model_class in self.all_model_classes:
_a = self.model_tester.seq_length - self.model_tester.num_masks
_a = (
num_visible_patches if model_class == VideoMAEForPreTraining else self.model_tester.seq_length
)
_a = True
_a = False
_a = True
_a = model_class(lowerCAmelCase_ )
model.to(lowerCAmelCase_ )
model.eval()
with torch.no_grad():
_a = model(**self._prepare_for_class(lowerCAmelCase_ , lowerCAmelCase_ ) )
_a = outputs.attentions
self.assertEqual(len(lowerCAmelCase_ ) , self.model_tester.num_hidden_layers )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
_a = True
_a = model_class(lowerCAmelCase_ )
model.to(lowerCAmelCase_ )
model.eval()
with torch.no_grad():
_a = model(**self._prepare_for_class(lowerCAmelCase_ , lowerCAmelCase_ ) )
_a = outputs.attentions
self.assertEqual(len(lowerCAmelCase_ ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_len, seq_len] , )
_a = len(lowerCAmelCase_ )
# Check attention is always last and order is fine
_a = True
_a = True
_a = model_class(lowerCAmelCase_ )
model.to(lowerCAmelCase_ )
model.eval()
with torch.no_grad():
_a = model(**self._prepare_for_class(lowerCAmelCase_ , lowerCAmelCase_ ) )
self.assertEqual(out_len + 1 , len(lowerCAmelCase_ ) )
_a = outputs.attentions
self.assertEqual(len(lowerCAmelCase_ ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(self_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_len, seq_len] , )
def __lowerCAmelCase ( self : Tuple ) -> Tuple:
"""simple docstring"""
def check_hidden_states_output(lowerCAmelCase_ : str , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : Tuple ):
_a = model_class(lowerCAmelCase_ )
model.to(lowerCAmelCase_ )
model.eval()
with torch.no_grad():
_a = model(**self._prepare_for_class(lowerCAmelCase_ , lowerCAmelCase_ ) )
_a = outputs.hidden_states
_a = self.model_tester.num_hidden_layers + 1
self.assertEqual(len(lowerCAmelCase_ ) , lowerCAmelCase_ )
_a = self.model_tester.seq_length - self.model_tester.num_masks
_a = num_visible_patches if model_class == VideoMAEForPreTraining else self.model_tester.seq_length
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [seq_length, self.model_tester.hidden_size] , )
_a , _a = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_a = True
check_hidden_states_output(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
_a = True
check_hidden_states_output(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )
@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''' )
def __lowerCAmelCase ( self : Optional[int] ) -> Dict:
"""simple docstring"""
pass
def snake_case_ ():
'''simple docstring'''
_a = hf_hub_download(
repo_id='''hf-internal-testing/spaghetti-video''' , filename='''eating_spaghetti.npy''' , repo_type='''dataset''' )
_a = np.load(UpperCamelCase )
return list(UpperCamelCase )
@require_torch
@require_vision
class A ( unittest.TestCase ):
@cached_property
def __lowerCAmelCase ( self : str ) -> List[Any]:
"""simple docstring"""
return (
VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5] , image_std=[0.5, 0.5, 0.5] )
if is_vision_available()
else None
)
@slow
def __lowerCAmelCase ( self : Dict ) -> Dict:
"""simple docstring"""
_a = VideoMAEForVideoClassification.from_pretrained('''MCG-NJU/videomae-base-finetuned-kinetics''' ).to(
lowerCAmelCase_ )
_a = self.default_image_processor
_a = prepare_video()
_a = image_processor(lowerCAmelCase_ , return_tensors='''pt''' ).to(lowerCAmelCase_ )
# forward pass
with torch.no_grad():
_a = model(**lowerCAmelCase_ )
# verify the logits
_a = torch.Size((1, 4_00) )
self.assertEqual(outputs.logits.shape , lowerCAmelCase_ )
_a = torch.tensor([0.3_6_6_9, -0.0_6_8_8, -0.2_4_2_1] ).to(lowerCAmelCase_ )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , lowerCAmelCase_ , atol=1e-4 ) )
@slow
def __lowerCAmelCase ( self : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
_a = VideoMAEForPreTraining.from_pretrained('''MCG-NJU/videomae-base-short''' ).to(lowerCAmelCase_ )
_a = self.default_image_processor
_a = prepare_video()
_a = image_processor(lowerCAmelCase_ , return_tensors='''pt''' ).to(lowerCAmelCase_ )
# add boolean mask, indicating which patches to mask
_a = hf_hub_download(repo_id='''hf-internal-testing/bool-masked-pos''' , filename='''bool_masked_pos.pt''' )
_a = torch.load(lowerCAmelCase_ )
# forward pass
with torch.no_grad():
_a = model(**lowerCAmelCase_ )
# verify the logits
_a = torch.Size([1, 14_08, 15_36] )
_a = torch.tensor(
[[0.7_9_9_4, 0.9_6_1_2, 0.8_5_0_8], [0.7_4_0_1, 0.8_9_5_8, 0.8_3_0_2], [0.5_8_6_2, 0.7_4_6_8, 0.7_3_2_5]] , device=lowerCAmelCase_ )
self.assertEqual(outputs.logits.shape , lowerCAmelCase_ )
self.assertTrue(torch.allclose(outputs.logits[0, :3, :3] , lowerCAmelCase_ , atol=1e-4 ) )
# verify the loss (`config.norm_pix_loss` = `True`)
_a = torch.tensor([0.5_1_4_2] , device=lowerCAmelCase_ )
self.assertTrue(torch.allclose(outputs.loss , lowerCAmelCase_ , atol=1e-4 ) )
# verify the loss (`config.norm_pix_loss` = `False`)
_a = VideoMAEForPreTraining.from_pretrained('''MCG-NJU/videomae-base-short''' , norm_pix_loss=lowerCAmelCase_ ).to(
lowerCAmelCase_ )
with torch.no_grad():
_a = model(**lowerCAmelCase_ )
_a = torch.tensor(torch.tensor([0.6_4_6_9] ) , device=lowerCAmelCase_ )
self.assertTrue(torch.allclose(outputs.loss , lowerCAmelCase_ , atol=1e-4 ) )
| 179
| 0
|
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