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"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : List[str] = logging.get_logger(__name__)
__A : Optional[int] = {
'''vinvino02/glpn-kitti''': '''https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json''',
# See all GLPN models at https://huggingface.co/models?filter=glpn
}
class _UpperCAmelCase ( _A ):
SCREAMING_SNAKE_CASE_ : int = "glpn"
def __init__( self : int , A : int=3 , A : Dict=4 , A : Union[str, Any]=[2, 2, 2, 2] , A : str=[8, 4, 2, 1] , A : Optional[Any]=[32, 64, 1_60, 2_56] , A : int=[7, 3, 3, 3] , A : int=[4, 2, 2, 2] , A : Optional[int]=[1, 2, 5, 8] , A : List[Any]=[4, 4, 4, 4] , A : Dict="gelu" , A : Union[str, Any]=0.0 , A : List[Any]=0.0 , A : int=0.02 , A : Optional[int]=0.1 , A : Dict=1e-6 , A : Union[str, Any]=64 , A : Any=10 , A : Any=-1 , **A : Tuple , ) -> str:
super().__init__(**A )
lowercase_ : int = num_channels
lowercase_ : Any = num_encoder_blocks
lowercase_ : Optional[int] = depths
lowercase_ : Optional[int] = sr_ratios
lowercase_ : Dict = hidden_sizes
lowercase_ : Dict = patch_sizes
lowercase_ : Dict = strides
lowercase_ : List[str] = mlp_ratios
lowercase_ : Optional[Any] = num_attention_heads
lowercase_ : Optional[int] = hidden_act
lowercase_ : Union[str, Any] = hidden_dropout_prob
lowercase_ : str = attention_probs_dropout_prob
lowercase_ : Optional[int] = initializer_range
lowercase_ : List[Any] = drop_path_rate
lowercase_ : Dict = layer_norm_eps
lowercase_ : Union[str, Any] = decoder_hidden_size
lowercase_ : List[Any] = max_depth
lowercase_ : Optional[int] = head_in_index
| 33
|
"""simple docstring"""
def lowercase ( __snake_case : list[int] ):
lowercase_ : List[Any] = len(__snake_case )
for i in range(__snake_case ):
for j in range(i + 1 , __snake_case ):
if numbers[j] < numbers[i]:
lowercase_ , lowercase_ : Optional[int] = numbers[j], numbers[i]
return numbers
if __name__ == "__main__":
__A : int = input('''Enter numbers separated by a comma:\n''').strip()
__A : Any = [int(item) for item in user_input.split(''',''')]
print(exchange_sort(unsorted))
| 33
| 1
|
"""simple docstring"""
__A : str = tuple[float, float, float]
__A : str = tuple[float, float, float]
def lowercase ( __snake_case : Pointad , __snake_case : Pointad ):
lowercase_ : List[Any] = end_pointa[0] - end_pointa[0]
lowercase_ : List[Any] = end_pointa[1] - end_pointa[1]
lowercase_ : List[str] = end_pointa[2] - end_pointa[2]
return (x, y, z)
def lowercase ( __snake_case : Vectorad , __snake_case : Vectorad ):
lowercase_ : Any = ab[1] * ac[2] - ab[2] * ac[1] # *i
lowercase_ : Optional[Any] = (ab[0] * ac[2] - ab[2] * ac[0]) * -1 # *j
lowercase_ : int = ab[0] * ac[1] - ab[1] * ac[0] # *k
return (x, y, z)
def lowercase ( __snake_case : Vectorad , __snake_case : int ):
return tuple(round(__snake_case , __snake_case ) for x in vector ) == (0, 0, 0)
def lowercase ( __snake_case : Pointad , __snake_case : Pointad , __snake_case : Pointad , __snake_case : int = 1_0 ):
lowercase_ : Any = create_vector(__snake_case , __snake_case )
lowercase_ : Any = create_vector(__snake_case , __snake_case )
return is_zero_vector(get_ad_vectors_cross(__snake_case , __snake_case ) , __snake_case )
| 33
|
"""simple docstring"""
import json
import pathlib
import unittest
import numpy as np
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, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import YolosImageProcessor
class _UpperCAmelCase ( unittest.TestCase ):
def __init__( self : List[Any] , A : Any , A : Tuple=7 , A : Tuple=3 , A : Optional[Any]=30 , A : List[Any]=4_00 , A : Tuple=True , A : Dict=None , A : List[str]=True , A : Optional[int]=[0.5, 0.5, 0.5] , A : Tuple=[0.5, 0.5, 0.5] , A : List[str]=True , A : List[Any]=1 / 2_55 , A : Union[str, Any]=True , ) -> Tuple:
# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
lowercase_ : Optional[int] = size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 13_33}
lowercase_ : Optional[int] = parent
lowercase_ : str = batch_size
lowercase_ : Tuple = num_channels
lowercase_ : str = min_resolution
lowercase_ : Any = max_resolution
lowercase_ : str = do_resize
lowercase_ : Any = size
lowercase_ : Optional[int] = do_normalize
lowercase_ : List[str] = image_mean
lowercase_ : Optional[Any] = image_std
lowercase_ : int = do_rescale
lowercase_ : List[str] = rescale_factor
lowercase_ : int = do_pad
def A ( self : Any ) -> str:
return {
"do_resize": self.do_resize,
"size": self.size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_rescale": self.do_rescale,
"rescale_factor": self.rescale_factor,
"do_pad": self.do_pad,
}
def A ( self : Optional[Any] , A : int , A : int=False ) -> Tuple:
if not batched:
lowercase_ : Optional[int] = image_inputs[0]
if isinstance(A , Image.Image ):
lowercase_ , lowercase_ : int = image.size
else:
lowercase_ , lowercase_ : Tuple = image.shape[1], image.shape[2]
if w < h:
lowercase_ : int = int(self.size['''shortest_edge'''] * h / w )
lowercase_ : Optional[Any] = self.size['''shortest_edge''']
elif w > h:
lowercase_ : Optional[Any] = self.size['''shortest_edge''']
lowercase_ : Optional[int] = int(self.size['''shortest_edge'''] * w / h )
else:
lowercase_ : Any = self.size['''shortest_edge''']
lowercase_ : Any = self.size['''shortest_edge''']
else:
lowercase_ : Tuple = []
for image in image_inputs:
lowercase_ , lowercase_ : Optional[Any] = self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
lowercase_ : Union[str, Any] = max(A , key=lambda A : item[0] )[0]
lowercase_ : Optional[Any] = max(A , key=lambda A : item[1] )[1]
return expected_height, expected_width
@require_torch
@require_vision
class _UpperCAmelCase ( _A , unittest.TestCase ):
SCREAMING_SNAKE_CASE_ : Optional[Any] = YolosImageProcessor if is_vision_available() else None
def A ( self : Optional[int] ) -> Optional[int]:
lowercase_ : Optional[Any] = YolosImageProcessingTester(self )
@property
def A ( self : str ) -> Any:
return self.image_processor_tester.prepare_image_processor_dict()
def A ( self : Optional[int] ) -> List[str]:
lowercase_ : Tuple = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(A , '''image_mean''' ) )
self.assertTrue(hasattr(A , '''image_std''' ) )
self.assertTrue(hasattr(A , '''do_normalize''' ) )
self.assertTrue(hasattr(A , '''do_resize''' ) )
self.assertTrue(hasattr(A , '''size''' ) )
def A ( self : Dict ) -> Tuple:
lowercase_ : Dict = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'''shortest_edge''': 18, '''longest_edge''': 13_33} )
self.assertEqual(image_processor.do_pad , A )
lowercase_ : Tuple = self.image_processing_class.from_dict(
self.image_processor_dict , size=42 , max_size=84 , pad_and_return_pixel_mask=A )
self.assertEqual(image_processor.size , {'''shortest_edge''': 42, '''longest_edge''': 84} )
self.assertEqual(image_processor.do_pad , A )
def A ( self : Optional[int] ) -> Tuple:
pass
def A ( self : Tuple ) -> int:
# Initialize image_processing
lowercase_ : Dict = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
lowercase_ : str = prepare_image_inputs(self.image_processor_tester , equal_resolution=A )
for image in image_inputs:
self.assertIsInstance(A , Image.Image )
# Test not batched input
lowercase_ : Optional[int] = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
lowercase_ , lowercase_ : Union[str, Any] = self.image_processor_tester.get_expected_values(A )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
lowercase_ , lowercase_ : Dict = self.image_processor_tester.get_expected_values(A , batched=A )
lowercase_ : str = 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,
expected_height,
expected_width,
) , )
def A ( self : str ) -> Any:
# Initialize image_processing
lowercase_ : Any = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
lowercase_ : List[Any] = 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
lowercase_ : List[Any] = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
lowercase_ , lowercase_ : int = self.image_processor_tester.get_expected_values(A )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
lowercase_ : Optional[int] = image_processing(A , return_tensors='''pt''' ).pixel_values
lowercase_ , lowercase_ : List[Any] = self.image_processor_tester.get_expected_values(A , batched=A )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def A ( self : Tuple ) -> Optional[int]:
# Initialize image_processing
lowercase_ : Dict = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
lowercase_ : Optional[int] = 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
lowercase_ : List[str] = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
lowercase_ , lowercase_ : Union[str, Any] = self.image_processor_tester.get_expected_values(A )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
lowercase_ : Any = image_processing(A , return_tensors='''pt''' ).pixel_values
lowercase_ , lowercase_ : List[str] = self.image_processor_tester.get_expected_values(A , batched=A )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def A ( self : Tuple ) -> Optional[Any]:
# Initialize image_processings
lowercase_ : Optional[Any] = self.image_processing_class(**self.image_processor_dict )
lowercase_ : Tuple = self.image_processing_class(do_resize=A , do_normalize=A , do_rescale=A )
# create random PyTorch tensors
lowercase_ : Union[str, Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=A , torchify=A )
for image in image_inputs:
self.assertIsInstance(A , torch.Tensor )
# Test whether the method "pad" and calling the image processor return the same tensors
lowercase_ : Union[str, Any] = image_processing_a.pad(A , return_tensors='''pt''' )
lowercase_ : List[Any] = image_processing_a(A , return_tensors='''pt''' )
self.assertTrue(
torch.allclose(encoded_images_with_method['''pixel_values'''] , encoded_images['''pixel_values'''] , atol=1e-4 ) )
@slow
def A ( self : str ) -> List[Any]:
# prepare image and target
lowercase_ : Dict = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt''' , '''r''' ) as f:
lowercase_ : List[Any] = json.loads(f.read() )
lowercase_ : Tuple = {'''image_id''': 3_97_69, '''annotations''': target}
# encode them
lowercase_ : Union[str, Any] = YolosImageProcessor.from_pretrained('''hustvl/yolos-small''' )
lowercase_ : List[Any] = image_processing(images=A , annotations=A , return_tensors='''pt''' )
# verify pixel values
lowercase_ : Union[str, Any] = torch.Size([1, 3, 8_00, 10_66] )
self.assertEqual(encoding['''pixel_values'''].shape , A )
lowercase_ : Union[str, Any] = torch.tensor([0.2796, 0.3138, 0.3481] )
self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3] , A , atol=1e-4 ) )
# verify area
lowercase_ : Tuple = torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area'''] , A ) )
# verify boxes
lowercase_ : List[str] = torch.Size([6, 4] )
self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape , A )
lowercase_ : Any = torch.tensor([0.5503, 0.2765, 0.0604, 0.2215] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0] , A , atol=1e-3 ) )
# verify image_id
lowercase_ : List[Any] = torch.tensor([3_97_69] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''] , A ) )
# verify is_crowd
lowercase_ : Any = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''] , A ) )
# verify class_labels
lowercase_ : Optional[Any] = torch.tensor([75, 75, 63, 65, 17, 17] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''] , A ) )
# verify orig_size
lowercase_ : List[str] = torch.tensor([4_80, 6_40] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''] , A ) )
# verify size
lowercase_ : Optional[Any] = torch.tensor([8_00, 10_66] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''] , A ) )
@slow
def A ( self : List[Any] ) -> Dict:
# prepare image, target and masks_path
lowercase_ : Tuple = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt''' , '''r''' ) as f:
lowercase_ : str = json.loads(f.read() )
lowercase_ : int = {'''file_name''': '''000000039769.png''', '''image_id''': 3_97_69, '''segments_info''': target}
lowercase_ : List[Any] = pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic''' )
# encode them
lowercase_ : int = YolosImageProcessor(format='''coco_panoptic''' )
lowercase_ : Any = image_processing(images=A , annotations=A , masks_path=A , return_tensors='''pt''' )
# verify pixel values
lowercase_ : Optional[Any] = torch.Size([1, 3, 8_00, 10_66] )
self.assertEqual(encoding['''pixel_values'''].shape , A )
lowercase_ : Tuple = torch.tensor([0.2796, 0.3138, 0.3481] )
self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3] , A , atol=1e-4 ) )
# verify area
lowercase_ : List[Any] = torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area'''] , A ) )
# verify boxes
lowercase_ : str = torch.Size([6, 4] )
self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape , A )
lowercase_ : List[str] = torch.tensor([0.2625, 0.5437, 0.4688, 0.8625] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0] , A , atol=1e-3 ) )
# verify image_id
lowercase_ : List[str] = torch.tensor([3_97_69] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''] , A ) )
# verify is_crowd
lowercase_ : List[str] = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''] , A ) )
# verify class_labels
lowercase_ : Any = torch.tensor([17, 17, 63, 75, 75, 93] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''] , A ) )
# verify masks
lowercase_ : Dict = 82_28_73
self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item() , A )
# verify orig_size
lowercase_ : Tuple = torch.tensor([4_80, 6_40] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''] , A ) )
# verify size
lowercase_ : List[str] = torch.tensor([8_00, 10_66] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''] , A ) )
| 33
| 1
|
"""simple docstring"""
import json
import os
import unittest
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class _UpperCAmelCase ( _A , unittest.TestCase ):
SCREAMING_SNAKE_CASE_ : Any = BioGptTokenizer
SCREAMING_SNAKE_CASE_ : int = False
def A ( self : Any ) -> Tuple:
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
lowercase_ : Dict = [
'''l''',
'''o''',
'''w''',
'''e''',
'''r''',
'''s''',
'''t''',
'''i''',
'''d''',
'''n''',
'''w</w>''',
'''r</w>''',
'''t</w>''',
'''lo''',
'''low''',
'''er</w>''',
'''low</w>''',
'''lowest</w>''',
'''newer</w>''',
'''wider</w>''',
'''<unk>''',
]
lowercase_ : Dict = dict(zip(A , range(len(A ) ) ) )
lowercase_ : List[str] = ['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']
lowercase_ : Union[str, Any] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
lowercase_ : Any = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''merges_file'''] )
with open(self.vocab_file , '''w''' ) as fp:
fp.write(json.dumps(A ) )
with open(self.merges_file , '''w''' ) as fp:
fp.write('''\n'''.join(A ) )
def A ( self : Tuple , A : Dict ) -> int:
lowercase_ : List[str] = '''lower newer'''
lowercase_ : List[str] = '''lower newer'''
return input_text, output_text
def A ( self : Any ) -> str:
lowercase_ : Dict = BioGptTokenizer(self.vocab_file , self.merges_file )
lowercase_ : List[Any] = '''lower'''
lowercase_ : Dict = ['''low''', '''er</w>''']
lowercase_ : Any = tokenizer.tokenize(A )
self.assertListEqual(A , A )
lowercase_ : List[Any] = tokens + ['''<unk>''']
lowercase_ : Dict = [14, 15, 20]
self.assertListEqual(tokenizer.convert_tokens_to_ids(A ) , A )
@slow
def A ( self : int ) -> List[str]:
lowercase_ : Dict = BioGptTokenizer.from_pretrained('''microsoft/biogpt''' )
lowercase_ : int = tokenizer.encode('''sequence builders''' , add_special_tokens=A )
lowercase_ : Tuple = tokenizer.encode('''multi-sequence build''' , add_special_tokens=A )
lowercase_ : Tuple = tokenizer.build_inputs_with_special_tokens(A )
lowercase_ : Tuple = tokenizer.build_inputs_with_special_tokens(A , A )
self.assertTrue(encoded_sentence == [2] + text )
self.assertTrue(encoded_pair == [2] + text + [2] + text_a )
| 33
|
"""simple docstring"""
def lowercase ( __snake_case : int = 1_0_0 ):
lowercase_ : str = 0
lowercase_ : List[Any] = 0
for i in range(1 , n + 1 ):
sum_of_squares += i**2
sum_of_ints += i
return sum_of_ints**2 - sum_of_squares
if __name__ == "__main__":
print(F"""{solution() = }""")
| 33
| 1
|
"""simple docstring"""
import argparse
from transformers import (
TapasConfig,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
TapasModel,
TapasTokenizer,
load_tf_weights_in_tapas,
)
from transformers.utils import logging
logging.set_verbosity_info()
def lowercase ( __snake_case : List[Any] , __snake_case : Any , __snake_case : Dict , __snake_case : Optional[Any] , __snake_case : Dict ):
# Initialise PyTorch model.
# If you want to convert a checkpoint that uses absolute position embeddings, make sure to set reset_position_index_per_cell of
# TapasConfig to False.
# initialize configuration from json file
lowercase_ : Optional[Any] = TapasConfig.from_json_file(__snake_case )
# set absolute/relative position embeddings parameter
lowercase_ : Any = reset_position_index_per_cell
# set remaining parameters of TapasConfig as well as the model based on the task
if task == "SQA":
lowercase_ : List[Any] = TapasForQuestionAnswering(config=__snake_case )
elif task == "WTQ":
# run_task_main.py hparams
lowercase_ : int = 4
lowercase_ : str = True
# hparam_utils.py hparams
lowercase_ : int = 0.664694
lowercase_ : Any = 0.207951
lowercase_ : List[Any] = 0.121194
lowercase_ : Optional[int] = True
lowercase_ : List[Any] = True
lowercase_ : Optional[Any] = False
lowercase_ : Optional[int] = 0.0352513
lowercase_ : str = TapasForQuestionAnswering(config=__snake_case )
elif task == "WIKISQL_SUPERVISED":
# run_task_main.py hparams
lowercase_ : Tuple = 4
lowercase_ : Optional[int] = False
# hparam_utils.py hparams
lowercase_ : Dict = 36.4519
lowercase_ : Tuple = 0.903421
lowercase_ : int = 222.088
lowercase_ : int = True
lowercase_ : List[str] = True
lowercase_ : Optional[Any] = True
lowercase_ : List[str] = 0.763141
lowercase_ : Any = TapasForQuestionAnswering(config=__snake_case )
elif task == "TABFACT":
lowercase_ : Dict = TapasForSequenceClassification(config=__snake_case )
elif task == "MLM":
lowercase_ : List[Any] = TapasForMaskedLM(config=__snake_case )
elif task == "INTERMEDIATE_PRETRAINING":
lowercase_ : Optional[int] = TapasModel(config=__snake_case )
else:
raise ValueError(F'''Task {task} not supported.''' )
print(F'''Building PyTorch model from configuration: {config}''' )
# Load weights from tf checkpoint
load_tf_weights_in_tapas(__snake_case , __snake_case , __snake_case )
# Save pytorch-model (weights and configuration)
print(F'''Save PyTorch model to {pytorch_dump_path}''' )
model.save_pretrained(__snake_case )
# Save tokenizer files
print(F'''Save tokenizer files to {pytorch_dump_path}''' )
lowercase_ : List[str] = TapasTokenizer(vocab_file=tf_checkpoint_path[:-1_0] + '''vocab.txt''' , model_max_length=5_1_2 )
tokenizer.save_pretrained(__snake_case )
print('''Used relative position embeddings:''' , model.config.reset_position_index_per_cell )
if __name__ == "__main__":
__A : List[str] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--task''', default='''SQA''', type=str, help='''Model task for which to convert a checkpoint. Defaults to SQA.'''
)
parser.add_argument(
'''--reset_position_index_per_cell''',
default=False,
action='''store_true''',
help='''Whether to use relative position embeddings or not. Defaults to True.''',
)
parser.add_argument(
'''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
)
parser.add_argument(
'''--tapas_config_file''',
default=None,
type=str,
required=True,
help=(
'''The config json file corresponding to the pre-trained TAPAS 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.'''
)
__A : List[str] = parser.parse_args()
convert_tf_checkpoint_to_pytorch(
args.task,
args.reset_position_index_per_cell,
args.tf_checkpoint_path,
args.tapas_config_file,
args.pytorch_dump_path,
)
| 33
|
"""simple docstring"""
import argparse
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline
if __name__ == "__main__":
__A : str = 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 : str = parser.parse_args()
__A : List[Any] = UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)
__A : Dict = CLIPImageProcessor()
__A : Union[str, Any] = CLIPVisionModelWithProjection.from_pretrained('''openai/clip-vit-large-patch14''')
__A : List[str] = 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)
| 33
| 1
|
"""simple docstring"""
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
OpenAIGPTConfig,
OpenAIGPTDoubleHeadsModel,
OpenAIGPTForSequenceClassification,
OpenAIGPTLMHeadModel,
OpenAIGPTModel,
)
class _UpperCAmelCase :
def __init__( self : Optional[int] , A : Any , A : List[Any]=13 , A : Optional[int]=7 , A : Optional[Any]=True , A : List[Any]=True , A : List[str]=True , A : List[str]=99 , A : Optional[int]=32 , A : int=5 , A : Tuple=4 , A : Optional[Any]=37 , A : List[str]="gelu" , A : str=0.1 , A : Tuple=0.1 , A : Union[str, Any]=5_12 , A : List[str]=16 , A : Optional[Any]=2 , A : int=0.02 , A : Optional[int]=3 , A : Optional[Any]=4 , A : Optional[int]=None , ) -> Optional[Any]:
lowercase_ : List[Any] = parent
lowercase_ : str = batch_size
lowercase_ : str = seq_length
lowercase_ : Dict = is_training
lowercase_ : Optional[int] = use_token_type_ids
lowercase_ : Optional[Any] = use_labels
lowercase_ : int = vocab_size
lowercase_ : List[Any] = hidden_size
lowercase_ : Any = num_hidden_layers
lowercase_ : List[Any] = num_attention_heads
lowercase_ : Any = intermediate_size
lowercase_ : List[Any] = hidden_act
lowercase_ : Tuple = hidden_dropout_prob
lowercase_ : Union[str, Any] = attention_probs_dropout_prob
lowercase_ : Dict = max_position_embeddings
lowercase_ : Any = type_vocab_size
lowercase_ : Dict = type_sequence_label_size
lowercase_ : Dict = initializer_range
lowercase_ : str = num_labels
lowercase_ : Union[str, Any] = num_choices
lowercase_ : Optional[int] = scope
lowercase_ : Tuple = self.vocab_size - 1
def A ( self : int ) -> List[Any]:
lowercase_ : Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowercase_ : List[str] = None
if self.use_token_type_ids:
lowercase_ : Dict = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
lowercase_ : Tuple = None
lowercase_ : Union[str, Any] = None
lowercase_ : int = None
if self.use_labels:
lowercase_ : List[str] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowercase_ : Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
lowercase_ : Optional[int] = ids_tensor([self.batch_size] , self.num_choices )
lowercase_ : List[Any] = OpenAIGPTConfig(
vocab_size=self.vocab_size , n_embd=self.hidden_size , n_layer=self.num_hidden_layers , n_head=self.num_attention_heads , n_positions=self.max_position_embeddings , pad_token_id=self.pad_token_id , )
lowercase_ : Optional[Any] = ids_tensor([self.num_hidden_layers, self.num_attention_heads] , 2 )
return (
config,
input_ids,
head_mask,
token_type_ids,
sequence_labels,
token_labels,
choice_labels,
)
def A ( self : Optional[Any] , A : Dict , A : Optional[int] , A : Union[str, Any] , A : List[Any] , *A : Dict ) -> str:
lowercase_ : str = OpenAIGPTModel(config=A )
model.to(A )
model.eval()
lowercase_ : Tuple = model(A , token_type_ids=A , head_mask=A )
lowercase_ : Dict = model(A , token_type_ids=A )
lowercase_ : Any = model(A )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def A ( self : Any , A : Union[str, Any] , A : Optional[Any] , A : List[Any] , A : Tuple , *A : Optional[int] ) -> Dict:
lowercase_ : List[str] = OpenAIGPTLMHeadModel(A )
model.to(A )
model.eval()
lowercase_ : Tuple = model(A , token_type_ids=A , labels=A )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def A ( self : Any , A : Any , A : int , A : List[str] , A : List[str] , *A : List[str] ) -> Union[str, Any]:
lowercase_ : Optional[Any] = OpenAIGPTDoubleHeadsModel(A )
model.to(A )
model.eval()
lowercase_ : Optional[int] = model(A , token_type_ids=A , labels=A )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def A ( self : int , A : Any , A : Optional[Any] , A : Any , A : int , *A : int ) -> int:
lowercase_ : Optional[Any] = self.num_labels
lowercase_ : Optional[Any] = OpenAIGPTForSequenceClassification(A )
model.to(A )
model.eval()
lowercase_ : List[Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowercase_ : Dict = model(A , token_type_ids=A , labels=A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def A ( self : List[str] ) -> Union[str, Any]:
lowercase_ : Optional[int] = self.prepare_config_and_inputs()
(
(
lowercase_
) , (
lowercase_
) , (
lowercase_
) , (
lowercase_
) , (
lowercase_
) , (
lowercase_
) , (
lowercase_
) ,
) : Dict = config_and_inputs
lowercase_ : Union[str, Any] = {
'''input_ids''': input_ids,
'''token_type_ids''': token_type_ids,
'''head_mask''': head_mask,
}
return config, inputs_dict
@require_torch
class _UpperCAmelCase ( _A , _A , _A , unittest.TestCase ):
SCREAMING_SNAKE_CASE_ : Any = (
(OpenAIGPTModel, OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel, OpenAIGPTForSequenceClassification)
if is_torch_available()
else ()
)
SCREAMING_SNAKE_CASE_ : Union[str, Any] = (
(OpenAIGPTLMHeadModel,) if is_torch_available() else ()
) # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
SCREAMING_SNAKE_CASE_ : List[Any] = (
{
"feature-extraction": OpenAIGPTModel,
"text-classification": OpenAIGPTForSequenceClassification,
"text-generation": OpenAIGPTLMHeadModel,
"zero-shot": OpenAIGPTForSequenceClassification,
}
if is_torch_available()
else {}
)
def A ( self : Optional[int] , A : Dict , A : List[Any] , A : str , A : Tuple , A : Optional[Any] ) -> Optional[int]:
if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
# Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
# `OpenAIGPTConfig` was never used in pipeline tests, either because of a missing checkpoint or because a
# tiny config could not be created.
return True
return False
def A ( self : Optional[Any] , A : str , A : List[Any] , A : List[str]=False ) -> str:
lowercase_ : List[str] = super()._prepare_for_class(A , A , return_labels=A )
if return_labels:
if model_class.__name__ == "OpenAIGPTDoubleHeadsModel":
lowercase_ : Optional[int] = torch.zeros(
(self.model_tester.batch_size, self.model_tester.num_choices, self.model_tester.seq_length) , dtype=torch.long , device=A , )
lowercase_ : Dict = inputs_dict['''labels''']
lowercase_ : Optional[int] = inputs_dict['''labels''']
lowercase_ : Optional[Any] = torch.zeros(
(self.model_tester.batch_size, self.model_tester.num_choices) , dtype=torch.long , device=A , )
lowercase_ : Optional[Any] = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=A )
return inputs_dict
def A ( self : str ) -> Tuple:
lowercase_ : Tuple = OpenAIGPTModelTester(self )
lowercase_ : Dict = ConfigTester(self , config_class=A , n_embd=37 )
def A ( self : int ) -> Union[str, Any]:
self.config_tester.run_common_tests()
def A ( self : int ) -> Union[str, Any]:
lowercase_ : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_openai_gpt_model(*A )
def A ( self : int ) -> int:
lowercase_ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_lm_head_model(*A )
def A ( self : Tuple ) -> Optional[Any]:
lowercase_ : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_double_lm_head_model(*A )
def A ( self : str ) -> Optional[int]:
lowercase_ : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*A )
@slow
def A ( self : Union[str, Any] ) -> Optional[int]:
for model_name in OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase_ : Optional[Any] = OpenAIGPTModel.from_pretrained(A )
self.assertIsNotNone(A )
@require_torch
class _UpperCAmelCase ( unittest.TestCase ):
@slow
def A ( self : List[str] ) -> List[str]:
lowercase_ : Any = OpenAIGPTLMHeadModel.from_pretrained('''openai-gpt''' )
model.to(A )
lowercase_ : str = torch.tensor([[4_81, 47_35, 5_44]] , dtype=torch.long , device=A ) # the president is
lowercase_ : Tuple = [
4_81,
47_35,
5_44,
2_46,
9_63,
8_70,
7_62,
2_39,
2_44,
4_04_77,
2_44,
2_49,
7_19,
8_81,
4_87,
5_44,
2_40,
2_44,
6_03,
4_81,
] # the president is a very good man. " \n " i\'m sure he is, " said the
lowercase_ : Optional[int] = model.generate(A , do_sample=A )
self.assertListEqual(output_ids[0].tolist() , A )
| 33
|
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaControlnetImgaImgPipeline,
KandinskyVaaPriorEmbaEmbPipeline,
UNetaDConditionModel,
VQModel,
)
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 _UpperCAmelCase ( _A , unittest.TestCase ):
SCREAMING_SNAKE_CASE_ : Any = KandinskyVaaControlnetImgaImgPipeline
SCREAMING_SNAKE_CASE_ : Optional[int] = ["image_embeds", "negative_image_embeds", "image", "hint"]
SCREAMING_SNAKE_CASE_ : str = ["image_embeds", "negative_image_embeds", "image", "hint"]
SCREAMING_SNAKE_CASE_ : Dict = [
"generator",
"height",
"width",
"strength",
"guidance_scale",
"num_inference_steps",
"return_dict",
"guidance_scale",
"num_images_per_prompt",
"output_type",
"return_dict",
]
SCREAMING_SNAKE_CASE_ : Dict = False
@property
def A ( self : Any ) -> Any:
return 32
@property
def A ( self : Optional[int] ) -> Any:
return 32
@property
def A ( self : Dict ) -> int:
return self.time_input_dim
@property
def A ( self : Tuple ) -> str:
return self.time_input_dim * 4
@property
def A ( self : Any ) -> str:
return 1_00
@property
def A ( self : str ) -> List[str]:
torch.manual_seed(0 )
lowercase_ : List[Any] = {
'''in_channels''': 8,
# Out channels is double in channels because predicts mean and variance
'''out_channels''': 8,
'''addition_embed_type''': '''image_hint''',
'''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''': '''image_proj''',
'''cross_attention_dim''': self.cross_attention_dim,
'''attention_head_dim''': 4,
'''resnet_time_scale_shift''': '''scale_shift''',
'''class_embed_type''': None,
}
lowercase_ : Dict = UNetaDConditionModel(**A )
return model
@property
def A ( self : Optional[Any] ) -> Union[str, Any]:
return {
"block_out_channels": [32, 32, 64, 64],
"down_block_types": [
"DownEncoderBlock2D",
"DownEncoderBlock2D",
"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", "UpDecoderBlock2D", "UpDecoderBlock2D"],
"vq_embed_dim": 4,
}
@property
def A ( self : List[Any] ) -> Dict:
torch.manual_seed(0 )
lowercase_ : int = VQModel(**self.dummy_movq_kwargs )
return model
def A ( self : Union[str, Any] ) -> Optional[int]:
lowercase_ : Tuple = self.dummy_unet
lowercase_ : int = self.dummy_movq
lowercase_ : List[Any] = {
'''num_train_timesteps''': 10_00,
'''beta_schedule''': '''linear''',
'''beta_start''': 0.00085,
'''beta_end''': 0.012,
'''clip_sample''': False,
'''set_alpha_to_one''': False,
'''steps_offset''': 0,
'''prediction_type''': '''epsilon''',
'''thresholding''': False,
}
lowercase_ : str = DDIMScheduler(**A )
lowercase_ : Tuple = {
'''unet''': unet,
'''scheduler''': scheduler,
'''movq''': movq,
}
return components
def A ( self : Optional[int] , A : int , A : List[str]=0 ) -> int:
lowercase_ : str = floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(A ) ).to(A )
lowercase_ : Tuple = floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(seed + 1 ) ).to(
A )
# create init_image
lowercase_ : Union[str, Any] = floats_tensor((1, 3, 64, 64) , rng=random.Random(A ) ).to(A )
lowercase_ : Any = image.cpu().permute(0 , 2 , 3 , 1 )[0]
lowercase_ : Optional[Any] = Image.fromarray(np.uinta(A ) ).convert('''RGB''' ).resize((2_56, 2_56) )
# create hint
lowercase_ : Optional[int] = floats_tensor((1, 3, 64, 64) , rng=random.Random(A ) ).to(A )
if str(A ).startswith('''mps''' ):
lowercase_ : Optional[Any] = torch.manual_seed(A )
else:
lowercase_ : List[Any] = torch.Generator(device=A ).manual_seed(A )
lowercase_ : Dict = {
'''image''': init_image,
'''image_embeds''': image_embeds,
'''negative_image_embeds''': negative_image_embeds,
'''hint''': hint,
'''generator''': generator,
'''height''': 64,
'''width''': 64,
'''num_inference_steps''': 10,
'''guidance_scale''': 7.0,
'''strength''': 0.2,
'''output_type''': '''np''',
}
return inputs
def A ( self : Any ) -> List[Any]:
lowercase_ : List[str] = '''cpu'''
lowercase_ : Any = self.get_dummy_components()
lowercase_ : Any = self.pipeline_class(**A )
lowercase_ : int = pipe.to(A )
pipe.set_progress_bar_config(disable=A )
lowercase_ : Dict = pipe(**self.get_dummy_inputs(A ) )
lowercase_ : str = output.images
lowercase_ : int = pipe(
**self.get_dummy_inputs(A ) , return_dict=A , )[0]
lowercase_ : Dict = image[0, -3:, -3:, -1]
lowercase_ : Union[str, Any] = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
lowercase_ : List[str] = np.array(
[0.54985034, 0.55509365, 0.52561504, 0.5570494, 0.5593818, 0.5263979, 0.50285643, 0.5069846, 0.51196736] )
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()}'''
@slow
@require_torch_gpu
class _UpperCAmelCase ( unittest.TestCase ):
def A ( self : Tuple ) -> str:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def A ( self : Any ) -> Optional[int]:
lowercase_ : Dict = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy''' )
lowercase_ : Dict = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png''' )
lowercase_ : Optional[int] = init_image.resize((5_12, 5_12) )
lowercase_ : Dict = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/kandinskyv22/hint_image_cat.png''' )
lowercase_ : Optional[int] = torch.from_numpy(np.array(A ) ).float() / 255.0
lowercase_ : Tuple = hint.permute(2 , 0 , 1 ).unsqueeze(0 )
lowercase_ : Optional[Any] = '''A robot, 4k photo'''
lowercase_ : Tuple = KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
'''kandinsky-community/kandinsky-2-2-prior''' , torch_dtype=torch.floataa )
pipe_prior.to(A )
lowercase_ : Dict = KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
'''kandinsky-community/kandinsky-2-2-controlnet-depth''' , torch_dtype=torch.floataa )
lowercase_ : int = pipeline.to(A )
pipeline.set_progress_bar_config(disable=A )
lowercase_ : Tuple = torch.Generator(device='''cpu''' ).manual_seed(0 )
lowercase_ , lowercase_ : int = pipe_prior(
A , image=A , strength=0.85 , generator=A , negative_prompt='''''' , ).to_tuple()
lowercase_ : str = pipeline(
image=A , image_embeds=A , negative_image_embeds=A , hint=A , generator=A , num_inference_steps=1_00 , height=5_12 , width=5_12 , strength=0.5 , output_type='''np''' , )
lowercase_ : Optional[Any] = output.images[0]
assert image.shape == (5_12, 5_12, 3)
assert_mean_pixel_difference(A , A )
| 33
| 1
|
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : Any = logging.get_logger(__name__)
__A : Dict = {
'''weiweishi/roc-bert-base-zh''': '''https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json''',
}
class _UpperCAmelCase ( _A ):
SCREAMING_SNAKE_CASE_ : Dict = "roc_bert"
def __init__( self : Optional[int] , A : Any=3_05_22 , A : List[str]=7_68 , A : List[str]=12 , A : int=12 , A : Any=30_72 , A : Tuple="gelu" , A : Union[str, Any]=0.1 , A : Union[str, Any]=0.1 , A : Optional[int]=5_12 , A : str=2 , A : Dict=0.02 , A : Union[str, Any]=1e-12 , A : Dict=True , A : str=0 , A : Tuple="absolute" , A : Dict=None , A : int=True , A : int=True , A : List[Any]=7_68 , A : List[Any]=9_10 , A : Dict=5_12 , A : Any=2_48_58 , A : Union[str, Any]=True , **A : List[str] , ) -> Dict:
lowercase_ : Union[str, Any] = vocab_size
lowercase_ : int = max_position_embeddings
lowercase_ : Tuple = hidden_size
lowercase_ : List[Any] = num_hidden_layers
lowercase_ : Optional[Any] = num_attention_heads
lowercase_ : Union[str, Any] = intermediate_size
lowercase_ : Dict = hidden_act
lowercase_ : Dict = hidden_dropout_prob
lowercase_ : List[str] = attention_probs_dropout_prob
lowercase_ : str = initializer_range
lowercase_ : List[Any] = type_vocab_size
lowercase_ : Union[str, Any] = layer_norm_eps
lowercase_ : List[Any] = use_cache
lowercase_ : Dict = enable_pronunciation
lowercase_ : Optional[Any] = enable_shape
lowercase_ : List[Any] = pronunciation_embed_dim
lowercase_ : Dict = pronunciation_vocab_size
lowercase_ : Tuple = shape_embed_dim
lowercase_ : int = shape_vocab_size
lowercase_ : int = concat_input
lowercase_ : Dict = position_embedding_type
lowercase_ : str = classifier_dropout
super().__init__(pad_token_id=A , **A )
| 33
|
"""simple docstring"""
def lowercase ( __snake_case : int = 1_0_0_0 ):
lowercase_ , lowercase_ : str = 1, 1
lowercase_ : List[str] = 2
while True:
lowercase_ : Tuple = 0
lowercase_ : List[Any] = fa + fa
lowercase_ , lowercase_ : Optional[int] = fa, f
index += 1
for _ in str(__snake_case ):
i += 1
if i == n:
break
return index
if __name__ == "__main__":
print(solution(int(str(input()).strip())))
| 33
| 1
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__A : Dict = {
'''configuration_lxmert''': ['''LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LxmertConfig'''],
'''tokenization_lxmert''': ['''LxmertTokenizer'''],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : int = ['''LxmertTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : Any = [
'''LxmertEncoder''',
'''LxmertForPreTraining''',
'''LxmertForQuestionAnswering''',
'''LxmertModel''',
'''LxmertPreTrainedModel''',
'''LxmertVisualFeatureEncoder''',
'''LxmertXLayer''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : Any = [
'''TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFLxmertForPreTraining''',
'''TFLxmertMainLayer''',
'''TFLxmertModel''',
'''TFLxmertPreTrainedModel''',
'''TFLxmertVisualFeatureEncoder''',
]
if TYPE_CHECKING:
from .configuration_lxmert import LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP, LxmertConfig
from .tokenization_lxmert import LxmertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_lxmert_fast import LxmertTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_lxmert import (
LxmertEncoder,
LxmertForPreTraining,
LxmertForQuestionAnswering,
LxmertModel,
LxmertPreTrainedModel,
LxmertVisualFeatureEncoder,
LxmertXLayer,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_lxmert import (
TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFLxmertForPreTraining,
TFLxmertMainLayer,
TFLxmertModel,
TFLxmertPreTrainedModel,
TFLxmertVisualFeatureEncoder,
)
else:
import sys
__A : List[str] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 33
|
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : Dict = logging.get_logger(__name__)
__A : Union[str, Any] = {
'''facebook/vit-mae-base''': '''https://huggingface.co/facebook/vit-mae-base/resolve/main/config.json''',
# See all ViT MAE models at https://huggingface.co/models?filter=vit-mae
}
class _UpperCAmelCase ( _A ):
SCREAMING_SNAKE_CASE_ : Union[str, Any] = "vit_mae"
def __init__( self : Dict , A : List[str]=7_68 , A : Any=12 , A : Union[str, Any]=12 , A : Tuple=30_72 , A : Any="gelu" , A : Tuple=0.0 , A : List[str]=0.0 , A : Tuple=0.02 , A : Tuple=1e-12 , A : int=2_24 , A : Dict=16 , A : int=3 , A : Tuple=True , A : Tuple=16 , A : Optional[Any]=5_12 , A : Union[str, Any]=8 , A : List[Any]=20_48 , A : Dict=0.75 , A : Any=False , **A : Optional[int] , ) -> Union[str, Any]:
super().__init__(**A )
lowercase_ : List[Any] = hidden_size
lowercase_ : str = num_hidden_layers
lowercase_ : List[Any] = num_attention_heads
lowercase_ : Any = intermediate_size
lowercase_ : Optional[int] = hidden_act
lowercase_ : List[Any] = hidden_dropout_prob
lowercase_ : int = attention_probs_dropout_prob
lowercase_ : int = initializer_range
lowercase_ : Dict = layer_norm_eps
lowercase_ : Optional[Any] = image_size
lowercase_ : str = patch_size
lowercase_ : Dict = num_channels
lowercase_ : Any = qkv_bias
lowercase_ : Union[str, Any] = decoder_num_attention_heads
lowercase_ : Optional[Any] = decoder_hidden_size
lowercase_ : List[str] = decoder_num_hidden_layers
lowercase_ : List[Any] = decoder_intermediate_size
lowercase_ : Optional[Any] = mask_ratio
lowercase_ : Optional[Any] = norm_pix_loss
| 33
| 1
|
"""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
__A : str = logging.get_logger(__name__)
__A : Any = {
'''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 _UpperCAmelCase ( _A ):
SCREAMING_SNAKE_CASE_ : str = "beit"
def __init__( self : Dict , A : Union[str, Any]=81_92 , A : str=7_68 , A : Optional[int]=12 , A : Union[str, Any]=12 , A : Union[str, Any]=30_72 , A : str="gelu" , A : Dict=0.0 , A : List[Any]=0.0 , A : Optional[int]=0.02 , A : Union[str, Any]=1e-12 , A : Any=2_24 , A : int=16 , A : Dict=3 , A : Tuple=False , A : Optional[Any]=False , A : List[str]=False , A : List[Any]=False , A : List[Any]=0.1 , A : List[Any]=0.1 , A : str=True , A : Tuple=[3, 5, 7, 11] , A : Dict=[1, 2, 3, 6] , A : str=True , A : Tuple=0.4 , A : List[Any]=2_56 , A : str=1 , A : Union[str, Any]=False , A : List[str]=2_55 , **A : Optional[Any] , ) -> Any:
super().__init__(**A )
lowercase_ : int = vocab_size
lowercase_ : Optional[Any] = hidden_size
lowercase_ : int = num_hidden_layers
lowercase_ : Optional[int] = num_attention_heads
lowercase_ : Union[str, Any] = intermediate_size
lowercase_ : Optional[int] = hidden_act
lowercase_ : Optional[int] = hidden_dropout_prob
lowercase_ : int = attention_probs_dropout_prob
lowercase_ : Any = initializer_range
lowercase_ : List[str] = layer_norm_eps
lowercase_ : str = image_size
lowercase_ : Dict = patch_size
lowercase_ : Any = num_channels
lowercase_ : Any = use_mask_token
lowercase_ : Any = use_absolute_position_embeddings
lowercase_ : str = use_relative_position_bias
lowercase_ : Dict = use_shared_relative_position_bias
lowercase_ : List[str] = layer_scale_init_value
lowercase_ : List[Any] = drop_path_rate
lowercase_ : Union[str, Any] = use_mean_pooling
# decode head attributes (semantic segmentation)
lowercase_ : Any = out_indices
lowercase_ : Tuple = pool_scales
# auxiliary head attributes (semantic segmentation)
lowercase_ : Optional[int] = use_auxiliary_head
lowercase_ : List[str] = auxiliary_loss_weight
lowercase_ : Optional[int] = auxiliary_channels
lowercase_ : Tuple = auxiliary_num_convs
lowercase_ : str = auxiliary_concat_input
lowercase_ : Any = semantic_loss_ignore_index
class _UpperCAmelCase ( _A ):
SCREAMING_SNAKE_CASE_ : str = version.parse("1.11" )
@property
def A ( self : int ) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
] )
@property
def A ( self : Dict ) -> float:
return 1e-4
| 33
|
"""simple docstring"""
def lowercase ( __snake_case : int ):
if n == 1 or not isinstance(__snake_case , __snake_case ):
return 0
elif n == 2:
return 1
else:
lowercase_ : Dict = [0, 1]
for i in range(2 , n + 1 ):
sequence.append(sequence[i - 1] + sequence[i - 2] )
return sequence[n]
def lowercase ( __snake_case : int ):
lowercase_ : str = 0
lowercase_ : List[str] = 2
while digits < n:
index += 1
lowercase_ : Any = len(str(fibonacci(__snake_case ) ) )
return index
def lowercase ( __snake_case : int = 1_0_0_0 ):
return fibonacci_digits_index(__snake_case )
if __name__ == "__main__":
print(solution(int(str(input()).strip())))
| 33
| 1
|
"""simple docstring"""
import inspect
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
import torch.utils.checkpoint
from ...models import UNetaDModel, VQModel
from ...schedulers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
def lowercase ( __snake_case : List[Any] ):
lowercase_ , lowercase_ : List[Any] = image.size
lowercase_ , lowercase_ : int = (x - x % 3_2 for x in (w, h)) # resize to integer multiple of 32
lowercase_ : str = image.resize((w, h) , resample=PIL_INTERPOLATION['''lanczos'''] )
lowercase_ : Union[str, Any] = np.array(__snake_case ).astype(np.floataa ) / 255.0
lowercase_ : Union[str, Any] = image[None].transpose(0 , 3 , 1 , 2 )
lowercase_ : Optional[int] = torch.from_numpy(__snake_case )
return 2.0 * image - 1.0
class _UpperCAmelCase ( _A ):
def __init__( self : Tuple , A : VQModel , A : UNetaDModel , A : Union[
DDIMScheduler,
PNDMScheduler,
LMSDiscreteScheduler,
EulerDiscreteScheduler,
EulerAncestralDiscreteScheduler,
DPMSolverMultistepScheduler,
] , ) -> str:
super().__init__()
self.register_modules(vqvae=A , unet=A , scheduler=A )
@torch.no_grad()
def __call__( self : Optional[int] , A : Union[torch.Tensor, PIL.Image.Image] = None , A : Optional[int] = 1 , A : Optional[int] = 1_00 , A : Optional[float] = 0.0 , A : Optional[Union[torch.Generator, List[torch.Generator]]] = None , A : Optional[str] = "pil" , A : bool = True , ) -> Union[Tuple, ImagePipelineOutput]:
if isinstance(A , PIL.Image.Image ):
lowercase_ : Optional[int] = 1
elif isinstance(A , torch.Tensor ):
lowercase_ : str = image.shape[0]
else:
raise ValueError(F'''`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(A )}''' )
if isinstance(A , PIL.Image.Image ):
lowercase_ : Optional[Any] = preprocess(A )
lowercase_ , lowercase_ : List[str] = image.shape[-2:]
# in_channels should be 6: 3 for latents, 3 for low resolution image
lowercase_ : Optional[int] = (batch_size, self.unet.config.in_channels // 2, height, width)
lowercase_ : Dict = next(self.unet.parameters() ).dtype
lowercase_ : Dict = randn_tensor(A , generator=A , device=self.device , dtype=A )
lowercase_ : List[Any] = image.to(device=self.device , dtype=A )
# set timesteps and move to the correct device
self.scheduler.set_timesteps(A , device=self.device )
lowercase_ : str = self.scheduler.timesteps
# scale the initial noise by the standard deviation required by the scheduler
lowercase_ : List[Any] = 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]
lowercase_ : Optional[int] = '''eta''' in set(inspect.signature(self.scheduler.step ).parameters.keys() )
lowercase_ : Any = {}
if accepts_eta:
lowercase_ : Union[str, Any] = eta
for t in self.progress_bar(A ):
# concat latents and low resolution image in the channel dimension.
lowercase_ : Optional[int] = torch.cat([latents, image] , dim=1 )
lowercase_ : Union[str, Any] = self.scheduler.scale_model_input(A , A )
# predict the noise residual
lowercase_ : List[str] = self.unet(A , A ).sample
# compute the previous noisy sample x_t -> x_t-1
lowercase_ : List[str] = self.scheduler.step(A , A , A , **A ).prev_sample
# decode the image latents with the VQVAE
lowercase_ : Union[str, Any] = self.vqvae.decode(A ).sample
lowercase_ : Optional[Any] = torch.clamp(A , -1.0 , 1.0 )
lowercase_ : Dict = image / 2 + 0.5
lowercase_ : int = image.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
lowercase_ : Union[str, Any] = self.numpy_to_pil(A )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=A )
| 33
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__A : List[str] = {
'''configuration_mobilenet_v2''': [
'''MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''MobileNetV2Config''',
'''MobileNetV2OnnxConfig''',
],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : Dict = ['''MobileNetV2FeatureExtractor''']
__A : Optional[int] = ['''MobileNetV2ImageProcessor''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : Optional[Any] = [
'''MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''MobileNetV2ForImageClassification''',
'''MobileNetV2ForSemanticSegmentation''',
'''MobileNetV2Model''',
'''MobileNetV2PreTrainedModel''',
'''load_tf_weights_in_mobilenet_v2''',
]
if TYPE_CHECKING:
from .configuration_mobilenet_va import (
MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
MobileNetVaConfig,
MobileNetVaOnnxConfig,
)
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_mobilenet_va import MobileNetVaFeatureExtractor
from .image_processing_mobilenet_va import MobileNetVaImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mobilenet_va import (
MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
MobileNetVaForImageClassification,
MobileNetVaForSemanticSegmentation,
MobileNetVaModel,
MobileNetVaPreTrainedModel,
load_tf_weights_in_mobilenet_va,
)
else:
import sys
__A : List[Any] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 33
| 1
|
"""simple docstring"""
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DPMSolverMultistepScheduler,
TextToVideoSDPipeline,
UNetaDConditionModel,
)
from diffusers.utils import is_xformers_available, load_numpy, skip_mps, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
@skip_mps
class _UpperCAmelCase ( _A , unittest.TestCase ):
SCREAMING_SNAKE_CASE_ : Tuple = TextToVideoSDPipeline
SCREAMING_SNAKE_CASE_ : Optional[Any] = TEXT_TO_IMAGE_PARAMS
SCREAMING_SNAKE_CASE_ : Optional[int] = TEXT_TO_IMAGE_BATCH_PARAMS
# No `output_type`.
SCREAMING_SNAKE_CASE_ : Optional[Any] = frozenset(
[
"num_inference_steps",
"generator",
"latents",
"return_dict",
"callback",
"callback_steps",
] )
def A ( self : str ) -> int:
torch.manual_seed(0 )
lowercase_ : List[str] = UNetaDConditionModel(
block_out_channels=(32, 64, 64, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=('''CrossAttnDownBlock3D''', '''CrossAttnDownBlock3D''', '''CrossAttnDownBlock3D''', '''DownBlock3D''') , up_block_types=('''UpBlock3D''', '''CrossAttnUpBlock3D''', '''CrossAttnUpBlock3D''', '''CrossAttnUpBlock3D''') , cross_attention_dim=32 , attention_head_dim=4 , )
lowercase_ : Union[str, Any] = DDIMScheduler(
beta_start=0.00085 , beta_end=0.012 , beta_schedule='''scaled_linear''' , clip_sample=A , set_alpha_to_one=A , )
torch.manual_seed(0 )
lowercase_ : str = 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 )
lowercase_ : str = 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=5_12 , )
lowercase_ : Any = CLIPTextModel(A )
lowercase_ : Optional[Any] = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )
lowercase_ : Tuple = {
'''unet''': unet,
'''scheduler''': scheduler,
'''vae''': vae,
'''text_encoder''': text_encoder,
'''tokenizer''': tokenizer,
}
return components
def A ( self : int , A : List[str] , A : List[str]=0 ) -> Any:
if str(A ).startswith('''mps''' ):
lowercase_ : Optional[Any] = torch.manual_seed(A )
else:
lowercase_ : List[Any] = torch.Generator(device=A ).manual_seed(A )
lowercase_ : Union[str, Any] = {
'''prompt''': '''A painting of a squirrel eating a burger''',
'''generator''': generator,
'''num_inference_steps''': 2,
'''guidance_scale''': 6.0,
'''output_type''': '''pt''',
}
return inputs
def A ( self : str ) -> Optional[Any]:
lowercase_ : Tuple = '''cpu''' # ensure determinism for the device-dependent torch.Generator
lowercase_ : int = self.get_dummy_components()
lowercase_ : List[Any] = TextToVideoSDPipeline(**A )
lowercase_ : Optional[Any] = sd_pipe.to(A )
sd_pipe.set_progress_bar_config(disable=A )
lowercase_ : int = self.get_dummy_inputs(A )
lowercase_ : Optional[Any] = '''np'''
lowercase_ : Tuple = sd_pipe(**A ).frames
lowercase_ : Any = frames[0][-3:, -3:, -1]
assert frames[0].shape == (64, 64, 3)
lowercase_ : Tuple = np.array([158.0, 160.0, 153.0, 125.0, 100.0, 121.0, 111.0, 93.0, 113.0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def A ( self : int ) -> int:
self._test_attention_slicing_forward_pass(test_mean_pixel_difference=A , expected_max_diff=3e-3 )
@unittest.skipIf(
torch_device != '''cuda''' or not is_xformers_available() , reason='''XFormers attention is only available with CUDA and `xformers` installed''' , )
def A ( self : List[str] ) -> List[str]:
self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=A , expected_max_diff=1e-2 )
@unittest.skip(reason='''Batching needs to be properly figured out first for this pipeline.''' )
def A ( self : Dict ) -> List[Any]:
pass
@unittest.skip(reason='''Batching needs to be properly figured out first for this pipeline.''' )
def A ( self : Tuple ) -> Optional[Any]:
pass
@unittest.skip(reason='''`num_images_per_prompt` argument is not supported for this pipeline.''' )
def A ( self : Optional[Any] ) -> Tuple:
pass
def A ( self : List[str] ) -> Tuple:
return super().test_progress_bar()
@slow
@skip_mps
class _UpperCAmelCase ( unittest.TestCase ):
def A ( self : Any ) -> Union[str, Any]:
lowercase_ : Optional[Any] = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text_to_video/video.npy''' )
lowercase_ : List[str] = TextToVideoSDPipeline.from_pretrained('''damo-vilab/text-to-video-ms-1.7b''' )
lowercase_ : List[str] = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
lowercase_ : List[str] = pipe.to('''cuda''' )
lowercase_ : List[str] = '''Spiderman is surfing'''
lowercase_ : List[Any] = torch.Generator(device='''cpu''' ).manual_seed(0 )
lowercase_ : int = pipe(A , generator=A , num_inference_steps=25 , output_type='''pt''' ).frames
lowercase_ : Union[str, Any] = video_frames.cpu().numpy()
assert np.abs(expected_video - video ).mean() < 5e-2
def A ( self : Any ) -> Dict:
lowercase_ : int = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text_to_video/video_2step.npy''' )
lowercase_ : int = TextToVideoSDPipeline.from_pretrained('''damo-vilab/text-to-video-ms-1.7b''' )
lowercase_ : List[Any] = pipe.to('''cuda''' )
lowercase_ : Optional[Any] = '''Spiderman is surfing'''
lowercase_ : List[str] = torch.Generator(device='''cpu''' ).manual_seed(0 )
lowercase_ : Union[str, Any] = pipe(A , generator=A , num_inference_steps=2 , output_type='''pt''' ).frames
lowercase_ : Optional[Any] = video_frames.cpu().numpy()
assert np.abs(expected_video - video ).mean() < 5e-2
| 33
|
"""simple docstring"""
from __future__ import annotations
__A : List[Any] = [-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
__A : str = [-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]
def lowercase ( __snake_case : list[float] ):
lowercase_ : List[str] = []
lowercase_ : List[Any] = len(__snake_case )
for i in range(__snake_case ):
lowercase_ : float = -1
for j in range(i + 1 , __snake_case ):
if arr[i] < arr[j]:
lowercase_ : List[str] = arr[j]
break
result.append(__snake_case )
return result
def lowercase ( __snake_case : list[float] ):
lowercase_ : List[str] = []
for i, outer in enumerate(__snake_case ):
lowercase_ : float = -1
for inner in arr[i + 1 :]:
if outer < inner:
lowercase_ : List[Any] = inner
break
result.append(__snake_case )
return result
def lowercase ( __snake_case : list[float] ):
lowercase_ : List[str] = len(__snake_case )
lowercase_ : list[float] = []
lowercase_ : list[float] = [-1] * arr_size
for index in reversed(range(__snake_case ) ):
if stack:
while stack[-1] <= arr[index]:
stack.pop()
if not stack:
break
if stack:
lowercase_ : Optional[Any] = stack[-1]
stack.append(arr[index] )
return result
if __name__ == "__main__":
from doctest import testmod
from timeit import timeit
testmod()
print(next_greatest_element_slow(arr))
print(next_greatest_element_fast(arr))
print(next_greatest_element(arr))
__A : int = (
'''from __main__ import arr, next_greatest_element_slow, '''
'''next_greatest_element_fast, next_greatest_element'''
)
print(
'''next_greatest_element_slow():''',
timeit('''next_greatest_element_slow(arr)''', setup=setup),
)
print(
'''next_greatest_element_fast():''',
timeit('''next_greatest_element_fast(arr)''', setup=setup),
)
print(
''' next_greatest_element():''',
timeit('''next_greatest_element(arr)''', setup=setup),
)
| 33
| 1
|
"""simple docstring"""
from __future__ import annotations
from dataclasses import dataclass
@dataclass
class _UpperCAmelCase :
SCREAMING_SNAKE_CASE_ : float
SCREAMING_SNAKE_CASE_ : TreeNode | None = None
SCREAMING_SNAKE_CASE_ : TreeNode | None = None
def lowercase ( __snake_case : TreeNode | None ):
# Validation
def is_valid_tree(__snake_case : TreeNode | None ) -> bool:
if node is None:
return True
if not isinstance(__snake_case , __snake_case ):
return False
try:
float(node.data )
except (TypeError, ValueError):
return False
return is_valid_tree(node.left ) and is_valid_tree(node.right )
if not is_valid_tree(__snake_case ):
raise ValueError(
'''Each node should be type of TreeNode and data should be float.''' )
def is_binary_search_tree_recursive_check(
__snake_case : TreeNode | None , __snake_case : float , __snake_case : float ) -> bool:
if node is None:
return True
return (
left_bound < node.data < right_bound
and is_binary_search_tree_recursive_check(node.left , __snake_case , node.data )
and is_binary_search_tree_recursive_check(
node.right , node.data , __snake_case )
)
return is_binary_search_tree_recursive_check(__snake_case , -float('''inf''' ) , float('''inf''' ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 33
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__A : Union[str, Any] = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : Dict = ['''NllbTokenizer''']
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : Dict = ['''NllbTokenizerFast''']
if TYPE_CHECKING:
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_nllb import NllbTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_nllb_fast import NllbTokenizerFast
else:
import sys
__A : List[Any] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 33
| 1
|
"""simple docstring"""
def lowercase ( __snake_case : Optional[int] ):
lowercase_ : int = 0
lowercase_ : Optional[Any] = len(__snake_case )
for i in range(n - 1 ):
for j in range(i + 1 , __snake_case ):
if arr[i] > arr[j]:
num_inversions += 1
return num_inversions
def lowercase ( __snake_case : str ):
if len(__snake_case ) <= 1:
return arr, 0
lowercase_ : Optional[Any] = len(__snake_case ) // 2
lowercase_ : List[Any] = arr[0:mid]
lowercase_ : Union[str, Any] = arr[mid:]
lowercase_ , lowercase_ : Tuple = count_inversions_recursive(__snake_case )
lowercase_ , lowercase_ : List[Any] = count_inversions_recursive(__snake_case )
lowercase_ , lowercase_ : List[Any] = _count_cross_inversions(__snake_case , __snake_case )
lowercase_ : List[Any] = inversion_p + inversions_q + cross_inversions
return c, num_inversions
def lowercase ( __snake_case : str , __snake_case : Optional[int] ):
lowercase_ : Optional[Any] = []
lowercase_ : Any = 0
while i < len(__snake_case ) and j < len(__snake_case ):
if p[i] > q[j]:
# if P[1] > Q[j], then P[k] > Q[k] for all i < k <= len(P)
# These are all inversions. The claim emerges from the
# property that P is sorted.
num_inversion += len(__snake_case ) - i
r.append(q[j] )
j += 1
else:
r.append(p[i] )
i += 1
if i < len(__snake_case ):
r.extend(p[i:] )
else:
r.extend(q[j:] )
return r, num_inversion
def lowercase ( ):
lowercase_ : Union[str, Any] = [1_0, 2, 1, 5, 5, 2, 1_1]
# this arr has 8 inversions:
# (10, 2), (10, 1), (10, 5), (10, 5), (10, 2), (2, 1), (5, 2), (5, 2)
lowercase_ : int = count_inversions_bf(__snake_case )
lowercase_ , lowercase_ : Dict = count_inversions_recursive(__snake_case )
assert num_inversions_bf == num_inversions_recursive == 8
print('''number of inversions = ''' , __snake_case )
# testing an array with zero inversion (a sorted arr_1)
arr_a.sort()
lowercase_ : Dict = count_inversions_bf(__snake_case )
lowercase_ , lowercase_ : Dict = count_inversions_recursive(__snake_case )
assert num_inversions_bf == num_inversions_recursive == 0
print('''number of inversions = ''' , __snake_case )
# an empty list should also have zero inversions
lowercase_ : List[Any] = []
lowercase_ : Any = count_inversions_bf(__snake_case )
lowercase_ , lowercase_ : List[str] = count_inversions_recursive(__snake_case )
assert num_inversions_bf == num_inversions_recursive == 0
print('''number of inversions = ''' , __snake_case )
if __name__ == "__main__":
main()
| 33
|
"""simple docstring"""
def lowercase ( __snake_case : int ):
if not isinstance(__snake_case , __snake_case ):
raise ValueError('''Input must be an integer''' )
if input_num <= 0:
raise ValueError('''Input must be positive''' )
return sum(
divisor for divisor in range(1 , input_num // 2 + 1 ) if input_num % divisor == 0 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 33
| 1
|
"""simple docstring"""
import argparse
import os
import torch
from transformers import (
XLNetConfig,
XLNetForQuestionAnswering,
XLNetForSequenceClassification,
XLNetLMHeadModel,
load_tf_weights_in_xlnet,
)
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
__A : Tuple = {
'''cola''': 2,
'''mnli''': 3,
'''mrpc''': 2,
'''sst-2''': 2,
'''sts-b''': 1,
'''qqp''': 2,
'''qnli''': 2,
'''rte''': 2,
'''wnli''': 2,
}
logging.set_verbosity_info()
def lowercase ( __snake_case : Optional[int] , __snake_case : Dict , __snake_case : int , __snake_case : Optional[int]=None ):
# Initialise PyTorch model
lowercase_ : List[Any] = XLNetConfig.from_json_file(__snake_case )
lowercase_ : List[str] = finetuning_task.lower() if finetuning_task is not None else ''''''
if finetuning_task in GLUE_TASKS_NUM_LABELS:
print(F'''Building PyTorch XLNetForSequenceClassification model from configuration: {config}''' )
lowercase_ : Any = finetuning_task
lowercase_ : Optional[Any] = GLUE_TASKS_NUM_LABELS[finetuning_task]
lowercase_ : Tuple = XLNetForSequenceClassification(__snake_case )
elif "squad" in finetuning_task:
lowercase_ : Union[str, Any] = finetuning_task
lowercase_ : Optional[int] = XLNetForQuestionAnswering(__snake_case )
else:
lowercase_ : Optional[Any] = XLNetLMHeadModel(__snake_case )
# Load weights from tf checkpoint
load_tf_weights_in_xlnet(__snake_case , __snake_case , __snake_case )
# Save pytorch-model
lowercase_ : Tuple = os.path.join(__snake_case , __snake_case )
lowercase_ : Dict = os.path.join(__snake_case , __snake_case )
print(F'''Save PyTorch model to {os.path.abspath(__snake_case )}''' )
torch.save(model.state_dict() , __snake_case )
print(F'''Save configuration file to {os.path.abspath(__snake_case )}''' )
with open(__snake_case , '''w''' , encoding='''utf-8''' ) as f:
f.write(config.to_json_string() )
if __name__ == "__main__":
__A : 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(
'''--xlnet_config_file''',
default=None,
type=str,
required=True,
help=(
'''The config json file corresponding to the pre-trained XLNet model. \n'''
'''This specifies the model architecture.'''
),
)
parser.add_argument(
'''--pytorch_dump_folder_path''',
default=None,
type=str,
required=True,
help='''Path to the folder to store the PyTorch model or dataset/vocab.''',
)
parser.add_argument(
'''--finetuning_task''',
default=None,
type=str,
help='''Name of a task on which the XLNet TensorFlow model was fine-tuned''',
)
__A : List[Any] = parser.parse_args()
print(args)
convert_xlnet_checkpoint_to_pytorch(
args.tf_checkpoint_path, args.xlnet_config_file, args.pytorch_dump_folder_path, args.finetuning_task
)
| 33
|
"""simple docstring"""
def lowercase ( __snake_case : Optional[int] ):
lowercase_ : int = 0
lowercase_ : Optional[Any] = len(__snake_case )
for i in range(n - 1 ):
for j in range(i + 1 , __snake_case ):
if arr[i] > arr[j]:
num_inversions += 1
return num_inversions
def lowercase ( __snake_case : str ):
if len(__snake_case ) <= 1:
return arr, 0
lowercase_ : Optional[Any] = len(__snake_case ) // 2
lowercase_ : List[Any] = arr[0:mid]
lowercase_ : Union[str, Any] = arr[mid:]
lowercase_ , lowercase_ : Tuple = count_inversions_recursive(__snake_case )
lowercase_ , lowercase_ : List[Any] = count_inversions_recursive(__snake_case )
lowercase_ , lowercase_ : List[Any] = _count_cross_inversions(__snake_case , __snake_case )
lowercase_ : List[Any] = inversion_p + inversions_q + cross_inversions
return c, num_inversions
def lowercase ( __snake_case : str , __snake_case : Optional[int] ):
lowercase_ : Optional[Any] = []
lowercase_ : Any = 0
while i < len(__snake_case ) and j < len(__snake_case ):
if p[i] > q[j]:
# if P[1] > Q[j], then P[k] > Q[k] for all i < k <= len(P)
# These are all inversions. The claim emerges from the
# property that P is sorted.
num_inversion += len(__snake_case ) - i
r.append(q[j] )
j += 1
else:
r.append(p[i] )
i += 1
if i < len(__snake_case ):
r.extend(p[i:] )
else:
r.extend(q[j:] )
return r, num_inversion
def lowercase ( ):
lowercase_ : Union[str, Any] = [1_0, 2, 1, 5, 5, 2, 1_1]
# this arr has 8 inversions:
# (10, 2), (10, 1), (10, 5), (10, 5), (10, 2), (2, 1), (5, 2), (5, 2)
lowercase_ : int = count_inversions_bf(__snake_case )
lowercase_ , lowercase_ : Dict = count_inversions_recursive(__snake_case )
assert num_inversions_bf == num_inversions_recursive == 8
print('''number of inversions = ''' , __snake_case )
# testing an array with zero inversion (a sorted arr_1)
arr_a.sort()
lowercase_ : Dict = count_inversions_bf(__snake_case )
lowercase_ , lowercase_ : Dict = count_inversions_recursive(__snake_case )
assert num_inversions_bf == num_inversions_recursive == 0
print('''number of inversions = ''' , __snake_case )
# an empty list should also have zero inversions
lowercase_ : List[Any] = []
lowercase_ : Any = count_inversions_bf(__snake_case )
lowercase_ , lowercase_ : List[str] = count_inversions_recursive(__snake_case )
assert num_inversions_bf == num_inversions_recursive == 0
print('''number of inversions = ''' , __snake_case )
if __name__ == "__main__":
main()
| 33
| 1
|
"""simple docstring"""
import re
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class _UpperCAmelCase ( _A ):
SCREAMING_SNAKE_CASE_ : Union[str, Any] = ["image_processor", "tokenizer"]
SCREAMING_SNAKE_CASE_ : Dict = "AutoImageProcessor"
SCREAMING_SNAKE_CASE_ : Optional[int] = "AutoTokenizer"
def __init__( self : Optional[Any] , A : Any=None , A : List[Any]=None , **A : List[str] ) -> Optional[int]:
lowercase_ : Union[str, Any] = None
if "feature_extractor" in kwargs:
warnings.warn(
'''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
''' instead.''' , A , )
lowercase_ : Optional[Any] = kwargs.pop('''feature_extractor''' )
lowercase_ : Optional[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__(A , A )
lowercase_ : List[Any] = self.image_processor
lowercase_ : int = False
def __call__( self : Tuple , *A : Optional[Any] , **A : List[str] ) -> int:
# For backward compatibility
if self._in_target_context_manager:
return self.current_processor(*A , **A )
lowercase_ : int = kwargs.pop('''images''' , A )
lowercase_ : List[str] = kwargs.pop('''text''' , A )
if len(A ) > 0:
lowercase_ : List[Any] = args[0]
lowercase_ : Optional[int] = args[1:]
if images is None and text is None:
raise ValueError('''You need to specify either an `images` or `text` input to process.''' )
if images is not None:
lowercase_ : str = self.image_processor(A , *A , **A )
if text is not None:
lowercase_ : Dict = self.tokenizer(A , **A )
if text is None:
return inputs
elif images is None:
return encodings
else:
lowercase_ : int = encodings['''input_ids''']
return inputs
def A ( self : int , *A : Tuple , **A : Optional[Any] ) -> List[str]:
return self.tokenizer.batch_decode(*A , **A )
def A ( self : Any , *A : List[str] , **A : Optional[Any] ) -> Optional[Any]:
return self.tokenizer.decode(*A , **A )
@contextmanager
def A ( self : Dict ) -> Union[str, Any]:
warnings.warn(
'''`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '''
'''labels by using the argument `text` of the regular `__call__` method (either in the same call as '''
'''your images inputs, or in a separate call.''' )
lowercase_ : Union[str, Any] = True
lowercase_ : Optional[int] = self.tokenizer
yield
lowercase_ : Dict = self.image_processor
lowercase_ : List[Any] = False
def A ( self : Any , A : Optional[Any] , A : Optional[int]=False , A : Any=None ) -> Dict:
if added_vocab is None:
lowercase_ : Tuple = self.tokenizer.get_added_vocab()
lowercase_ : Optional[int] = {}
while tokens:
lowercase_ : int = re.search(R'''<s_(.*?)>''' , A , re.IGNORECASE )
if start_token is None:
break
lowercase_ : Tuple = start_token.group(1 )
lowercase_ : Optional[Any] = re.search(RF'''</s_{key}>''' , A , re.IGNORECASE )
lowercase_ : int = start_token.group()
if end_token is None:
lowercase_ : int = tokens.replace(A , '''''' )
else:
lowercase_ : Any = end_token.group()
lowercase_ : int = re.escape(A )
lowercase_ : Tuple = re.escape(A )
lowercase_ : Optional[int] = re.search(F'''{start_token_escaped}(.*?){end_token_escaped}''' , A , re.IGNORECASE )
if content is not None:
lowercase_ : int = content.group(1 ).strip()
if r"<s_" in content and r"</s_" in content: # non-leaf node
lowercase_ : List[Any] = self.tokenajson(A , is_inner_value=A , added_vocab=A )
if value:
if len(A ) == 1:
lowercase_ : Optional[int] = value[0]
lowercase_ : str = value
else: # leaf nodes
lowercase_ : int = []
for leaf in content.split(R'''<sep/>''' ):
lowercase_ : Optional[int] = leaf.strip()
if leaf in added_vocab and leaf[0] == "<" and leaf[-2:] == "/>":
lowercase_ : List[Any] = leaf[1:-2] # for categorical special tokens
output[key].append(A )
if len(output[key] ) == 1:
lowercase_ : Dict = output[key][0]
lowercase_ : int = tokens[tokens.find(A ) + len(A ) :].strip()
if tokens[:6] == r"<sep/>": # non-leaf nodes
return [output] + self.tokenajson(tokens[6:] , is_inner_value=A , added_vocab=A )
if len(A ):
return [output] if is_inner_value else output
else:
return [] if is_inner_value else {"text_sequence": tokens}
@property
def A ( self : Any ) -> str:
warnings.warn(
'''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.''' , A , )
return self.image_processor_class
@property
def A ( self : Optional[int] ) -> Union[str, Any]:
warnings.warn(
'''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.''' , A , )
return self.image_processor
| 33
|
"""simple docstring"""
__A : Any = {
'''Pillow''': '''Pillow''',
'''accelerate''': '''accelerate>=0.11.0''',
'''compel''': '''compel==0.1.8''',
'''black''': '''black~=23.1''',
'''datasets''': '''datasets''',
'''filelock''': '''filelock''',
'''flax''': '''flax>=0.4.1''',
'''hf-doc-builder''': '''hf-doc-builder>=0.3.0''',
'''huggingface-hub''': '''huggingface-hub>=0.13.2''',
'''requests-mock''': '''requests-mock==1.10.0''',
'''importlib_metadata''': '''importlib_metadata''',
'''invisible-watermark''': '''invisible-watermark''',
'''isort''': '''isort>=5.5.4''',
'''jax''': '''jax>=0.2.8,!=0.3.2''',
'''jaxlib''': '''jaxlib>=0.1.65''',
'''Jinja2''': '''Jinja2''',
'''k-diffusion''': '''k-diffusion>=0.0.12''',
'''torchsde''': '''torchsde''',
'''note_seq''': '''note_seq''',
'''librosa''': '''librosa''',
'''numpy''': '''numpy''',
'''omegaconf''': '''omegaconf''',
'''parameterized''': '''parameterized''',
'''protobuf''': '''protobuf>=3.20.3,<4''',
'''pytest''': '''pytest''',
'''pytest-timeout''': '''pytest-timeout''',
'''pytest-xdist''': '''pytest-xdist''',
'''ruff''': '''ruff>=0.0.241''',
'''safetensors''': '''safetensors''',
'''sentencepiece''': '''sentencepiece>=0.1.91,!=0.1.92''',
'''scipy''': '''scipy''',
'''onnx''': '''onnx''',
'''regex''': '''regex!=2019.12.17''',
'''requests''': '''requests''',
'''tensorboard''': '''tensorboard''',
'''torch''': '''torch>=1.4''',
'''torchvision''': '''torchvision''',
'''transformers''': '''transformers>=4.25.1''',
'''urllib3''': '''urllib3<=2.0.0''',
}
| 33
| 1
|
"""simple docstring"""
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import BatchEncoding, MarianTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import is_sentencepiece_available, is_tf_available, is_torch_available
if is_sentencepiece_available():
from transformers.models.marian.tokenization_marian import VOCAB_FILES_NAMES, save_json
from ...test_tokenization_common import TokenizerTesterMixin
__A : int = get_tests_dir('''fixtures/test_sentencepiece.model''')
__A : List[str] = {'''target_lang''': '''fi''', '''source_lang''': '''en'''}
__A : int = '''>>zh<<'''
__A : Any = '''Helsinki-NLP/'''
if is_torch_available():
__A : List[Any] = '''pt'''
elif is_tf_available():
__A : Union[str, Any] = '''tf'''
else:
__A : Union[str, Any] = '''jax'''
@require_sentencepiece
class _UpperCAmelCase ( _A , unittest.TestCase ):
SCREAMING_SNAKE_CASE_ : Tuple = MarianTokenizer
SCREAMING_SNAKE_CASE_ : List[str] = False
SCREAMING_SNAKE_CASE_ : Tuple = True
def A ( self : Dict ) -> Union[str, Any]:
super().setUp()
lowercase_ : Optional[int] = ['''</s>''', '''<unk>''', '''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''', '''\u0120''', '''<pad>''']
lowercase_ : Dict = dict(zip(A , range(len(A ) ) ) )
lowercase_ : Dict = Path(self.tmpdirname )
save_json(A , save_dir / VOCAB_FILES_NAMES['''vocab'''] )
save_json(A , save_dir / VOCAB_FILES_NAMES['''tokenizer_config_file'''] )
if not (save_dir / VOCAB_FILES_NAMES["source_spm"]).exists():
copyfile(A , save_dir / VOCAB_FILES_NAMES['''source_spm'''] )
copyfile(A , save_dir / VOCAB_FILES_NAMES['''target_spm'''] )
lowercase_ : List[Any] = MarianTokenizer.from_pretrained(self.tmpdirname )
tokenizer.save_pretrained(self.tmpdirname )
def A ( self : str , **A : List[Any] ) -> MarianTokenizer:
return MarianTokenizer.from_pretrained(self.tmpdirname , **A )
def A ( self : List[str] , A : int ) -> int:
return (
"This is a test",
"This is a test",
)
def A ( self : int ) -> int:
lowercase_ : Tuple = '''</s>'''
lowercase_ : Dict = 0
self.assertEqual(self.get_tokenizer()._convert_token_to_id(A ) , A )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(A ) , A )
def A ( self : List[str] ) -> str:
lowercase_ : Any = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '''</s>''' )
self.assertEqual(vocab_keys[1] , '''<unk>''' )
self.assertEqual(vocab_keys[-1] , '''<pad>''' )
self.assertEqual(len(A ) , 9 )
def A ( self : str ) -> Any:
self.assertEqual(self.get_tokenizer().vocab_size , 9 )
def A ( self : Any ) -> Optional[int]:
lowercase_ : List[Any] = MarianTokenizer.from_pretrained(F'''{ORG_NAME}opus-mt-en-de''' )
lowercase_ : Dict = en_de_tokenizer(['''I am a small frog'''] , return_tensors=A )
self.assertIsInstance(A , A )
lowercase_ : Optional[int] = [38, 1_21, 14, 6_97, 3_88_48, 0]
self.assertListEqual(A , batch.input_ids[0] )
lowercase_ : Optional[int] = tempfile.mkdtemp()
en_de_tokenizer.save_pretrained(A )
lowercase_ : int = [x.name for x in Path(A ).glob('''*''' )]
self.assertIn('''source.spm''' , A )
MarianTokenizer.from_pretrained(A )
def A ( self : List[Any] ) -> int:
lowercase_ : int = self.get_tokenizer()
lowercase_ : Optional[Any] = tok(
['''I am a small frog''' * 10_00, '''I am a small frog'''] , padding=A , truncation=A , return_tensors=A )
self.assertIsInstance(A , A )
self.assertEqual(batch.input_ids.shape , (2, 5_12) )
def A ( self : Optional[int] ) -> str:
lowercase_ : Tuple = self.get_tokenizer()
lowercase_ : List[Any] = tok(['''I am a tiny frog''', '''I am a small frog'''] , padding=A , return_tensors=A )
self.assertIsInstance(A , A )
self.assertEqual(batch_smaller.input_ids.shape , (2, 10) )
@slow
def A ( self : Optional[Any] ) -> Union[str, Any]:
# fmt: off
lowercase_ : Dict = {'''input_ids''': [[4_34_95, 4_62, 20, 4_21_64, 13_69, 52, 4_64, 1_32, 17_03, 4_92, 13, 74_91, 3_89_99, 6, 8, 4_64, 1_32, 17_03, 4_92, 13, 46_69, 3_78_67, 13, 75_25, 27, 15_93, 9_88, 13, 3_39_72, 70_29, 6, 20, 82_51, 3_83, 2, 2_70, 58_66, 37_88, 2, 23_53, 82_51, 1_23_38, 2, 1_39_58, 3_87, 2, 36_29, 69_53, 1_88, 29_00, 2, 1_39_58, 80_11, 1_15_01, 23, 84_60, 40_73, 3_40_09, 20, 4_35, 1_14_39, 27, 8, 84_60, 40_73, 60_04, 20, 99_88, 3_75, 27, 33, 2_66, 19_45, 10_76, 13_50, 3_78_67, 32_88, 5, 5_77, 10_76, 43_74, 8, 50_82, 5, 2_64_53, 2_57, 5_56, 4_03, 2, 2_42, 1_32, 3_83, 3_16, 4_92, 8, 1_07_67, 6, 3_16, 3_04, 42_39, 3, 0], [1_48, 1_57_22, 19, 18_39, 12, 13_50, 13, 2_23_27, 50_82, 54_18, 4_75_67, 3_59_38, 59, 3_18, 1_95_52, 1_08, 21_83, 54, 1_49_76, 48_35, 32, 5_47, 11_14, 8, 3_15, 24_17, 5, 92, 1_90_88, 3, 0, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00], [36, 63_95, 1_25_70, 3_91_47, 1_15_97, 6, 2_66, 4, 4_54_05, 72_96, 3, 0, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00]], '''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, 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, 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=A , model_name='''Helsinki-NLP/opus-mt-en-de''' , revision='''1a8c2263da11e68e50938f97e10cd57820bd504c''' , decode_kwargs={'''use_source_tokenizer''': True} , )
def A ( self : Optional[int] ) -> List[str]:
lowercase_ : Any = MarianTokenizer.from_pretrained('''hf-internal-testing/test-marian-two-vocabs''' )
lowercase_ : Optional[Any] = '''Tämä on testi'''
lowercase_ : Union[str, Any] = '''This is a test'''
lowercase_ : str = [76, 7, 20_47, 2]
lowercase_ : Union[str, Any] = [69, 12, 11, 9_40, 2]
lowercase_ : int = tokenizer(A ).input_ids
self.assertListEqual(A , A )
lowercase_ : int = tokenizer(text_target=A ).input_ids
self.assertListEqual(A , A )
lowercase_ : Optional[Any] = tokenizer.decode(A , skip_special_tokens=A )
self.assertEqual(A , A )
| 33
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
__A : List[Any] = {
'''configuration_mega''': ['''MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MegaConfig''', '''MegaOnnxConfig'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : List[str] = [
'''MEGA_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''MegaForCausalLM''',
'''MegaForMaskedLM''',
'''MegaForMultipleChoice''',
'''MegaForQuestionAnswering''',
'''MegaForSequenceClassification''',
'''MegaForTokenClassification''',
'''MegaModel''',
'''MegaPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_mega import MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP, MegaConfig, MegaOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mega import (
MEGA_PRETRAINED_MODEL_ARCHIVE_LIST,
MegaForCausalLM,
MegaForMaskedLM,
MegaForMultipleChoice,
MegaForQuestionAnswering,
MegaForSequenceClassification,
MegaForTokenClassification,
MegaModel,
MegaPreTrainedModel,
)
else:
import sys
__A : Any = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 33
| 1
|
"""simple docstring"""
import json
import os
import re
import sys
import urllib.request
import requests
from bsa import BeautifulSoup
__A : Tuple = {
'''User-Agent''': '''Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36'''
''' (KHTML, like Gecko) Chrome/70.0.3538.102 Safari/537.36 Edge/18.19582'''
}
def lowercase ( __snake_case : str = "dhaka" , __snake_case : int = 5 ):
lowercase_ : Dict = min(__snake_case , 5_0 ) # Prevent abuse!
lowercase_ : Optional[int] = {
'''q''': query,
'''tbm''': '''isch''',
'''hl''': '''en''',
'''ijn''': '''0''',
}
lowercase_ : int = requests.get('''https://www.google.com/search''' , params=__snake_case , headers=__snake_case )
lowercase_ : List[Any] = BeautifulSoup(html.text , '''html.parser''' )
lowercase_ : Tuple = ''''''.join(
re.findall(r'''AF_initDataCallback\(([^<]+)\);''' , str(soup.select('''script''' ) ) ) )
lowercase_ : Union[str, Any] = json.dumps(__snake_case )
lowercase_ : Optional[int] = json.loads(__snake_case )
lowercase_ : Dict = re.findall(
r'''\[\"GRID_STATE0\",null,\[\[1,\[0,\".*?\",(.*),\"All\",''' , __snake_case , )
if not matched_google_image_data:
return 0
lowercase_ : Optional[int] = re.sub(
r'''\[\"(https\:\/\/encrypted-tbn0\.gstatic\.com\/images\?.*?)\",\d+,\d+\]''' , '''''' , str(__snake_case ) , )
lowercase_ : Optional[Any] = re.findall(
r'''(?:\'|,),\[\"(https:|http.*?)\",\d+,\d+\]''' , __snake_case , )
for index, fixed_full_res_image in enumerate(__snake_case ):
if index >= max_images:
return index
lowercase_ : List[str] = bytes(__snake_case , '''ascii''' ).decode(
'''unicode-escape''' )
lowercase_ : Any = bytes(__snake_case , '''ascii''' ).decode(
'''unicode-escape''' )
lowercase_ : Any = urllib.request.build_opener()
lowercase_ : List[str] = [
(
'''User-Agent''',
'''Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36'''
''' (KHTML, like Gecko) Chrome/70.0.3538.102 Safari/537.36 Edge/18.19582''',
)
]
urllib.request.install_opener(__snake_case )
lowercase_ : Dict = F'''query_{query.replace(' ' , '_' )}'''
if not os.path.exists(__snake_case ):
os.makedirs(__snake_case )
urllib.request.urlretrieve( # noqa: S310
__snake_case , F'''{path_name}/original_size_img_{index}.jpg''' )
return index
if __name__ == "__main__":
try:
__A : Union[str, Any] = download_images_from_google_query(sys.argv[1])
print(F"""{image_count} images were downloaded to disk.""")
except IndexError:
print('''Please provide a search term.''')
raise
| 33
|
"""simple docstring"""
import argparse
import os
import re
import packaging.version
__A : List[str] = '''examples/'''
__A : int = {
'''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'''),
}
__A : Dict = {
'''init''': '''src/transformers/__init__.py''',
'''setup''': '''setup.py''',
}
__A : Optional[int] = '''README.md'''
def lowercase ( __snake_case : int , __snake_case : Any , __snake_case : int ):
with open(__snake_case , '''r''' , encoding='''utf-8''' , newline='''\n''' ) as f:
lowercase_ : int = f.read()
lowercase_ , lowercase_ : List[str] = REPLACE_PATTERNS[pattern]
lowercase_ : Union[str, Any] = replace.replace('''VERSION''' , __snake_case )
lowercase_ : Optional[Any] = re_pattern.sub(__snake_case , __snake_case )
with open(__snake_case , '''w''' , encoding='''utf-8''' , newline='''\n''' ) as f:
f.write(__snake_case )
def lowercase ( __snake_case : int ):
for folder, directories, fnames in os.walk(__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(__snake_case , __snake_case ) , __snake_case , pattern='''examples''' )
def lowercase ( __snake_case : Optional[Any] , __snake_case : Optional[Any]=False ):
for pattern, fname in REPLACE_FILES.items():
update_version_in_file(__snake_case , __snake_case , __snake_case )
if not patch:
update_version_in_examples(__snake_case )
def lowercase ( ):
lowercase_ : Union[str, Any] = '''🤗 Transformers currently provides the following architectures'''
lowercase_ : Union[str, Any] = '''1. Want to contribute a new model?'''
with open(__snake_case , '''r''' , encoding='''utf-8''' , newline='''\n''' ) as f:
lowercase_ : List[str] = f.readlines()
# Find the start of the list.
lowercase_ : Optional[Any] = 0
while not lines[start_index].startswith(_start_prompt ):
start_index += 1
start_index += 1
lowercase_ : str = start_index
# Update the lines in the model list.
while not lines[index].startswith(_end_prompt ):
if lines[index].startswith('''1.''' ):
lowercase_ : str = lines[index].replace(
'''https://huggingface.co/docs/transformers/main/model_doc''' , '''https://huggingface.co/docs/transformers/model_doc''' , )
index += 1
with open(__snake_case , '''w''' , encoding='''utf-8''' , newline='''\n''' ) as f:
f.writelines(__snake_case )
def lowercase ( ):
with open(REPLACE_FILES['''init'''] , '''r''' ) as f:
lowercase_ : List[Any] = f.read()
lowercase_ : List[str] = REPLACE_PATTERNS['''init'''][0].search(__snake_case ).groups()[0]
return packaging.version.parse(__snake_case )
def lowercase ( __snake_case : Optional[Any]=False ):
lowercase_ : str = 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:
lowercase_ : Optional[Any] = default_version.base_version
elif patch:
lowercase_ : Optional[int] = F'''{default_version.major}.{default_version.minor}.{default_version.micro + 1}'''
else:
lowercase_ : Optional[int] = F'''{default_version.major}.{default_version.minor + 1}.0'''
# Now let's ask nicely if that's the right one.
lowercase_ : int = input(F'''Which version are you releasing? [{default_version}]''' )
if len(__snake_case ) == 0:
lowercase_ : Dict = default_version
print(F'''Updating version to {version}.''' )
global_version_update(__snake_case , patch=__snake_case )
if not patch:
print('''Cleaning main README, don\'t forget to run `make fix-copies`.''' )
clean_main_ref_in_model_list()
def lowercase ( ):
lowercase_ : List[Any] = get_version()
lowercase_ : List[str] = F'''{current_version.major}.{current_version.minor + 1}.0.dev0'''
lowercase_ : Any = current_version.base_version
# Check with the user we got that right.
lowercase_ : Tuple = input(F'''Which version are we developing now? [{dev_version}]''' )
if len(__snake_case ) == 0:
lowercase_ : str = dev_version
print(F'''Updating version to {version}.''' )
global_version_update(__snake_case )
print('''Cleaning main README, don\'t forget to run `make fix-copies`.''' )
clean_main_ref_in_model_list()
if __name__ == "__main__":
__A : int = 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.''')
__A : Any = 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()
| 33
| 1
|
"""simple docstring"""
import argparse
import fairseq
import torch
from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging
logging.set_verbosity_info()
__A : Tuple = logging.get_logger(__name__)
__A : Dict = {
'''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''',
'''encoder.layer_norm_for_extract''': '''layer_norm_for_extract''',
'''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''',
'''label_embs_concat''': '''label_embeddings_concat''',
'''mask_emb''': '''masked_spec_embed''',
'''spk_proj''': '''speaker_proj''',
}
__A : Tuple = [
'''lm_head''',
'''quantizer.weight_proj''',
'''quantizer.codevectors''',
'''project_q''',
'''project_hid''',
'''label_embeddings_concat''',
'''speaker_proj''',
'''layer_norm_for_extract''',
]
def lowercase ( __snake_case : Optional[Any] , __snake_case : Dict , __snake_case : Dict , __snake_case : Optional[Any] , __snake_case : List[Any] ):
for attribute in key.split('''.''' ):
lowercase_ : Union[str, Any] = getattr(__snake_case , __snake_case )
if weight_type is not None:
lowercase_ : List[str] = getattr(__snake_case , __snake_case ).shape
else:
lowercase_ : Optional[Any] = 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":
lowercase_ : Union[str, Any] = value
elif weight_type == "weight_g":
lowercase_ : Union[str, Any] = value
elif weight_type == "weight_v":
lowercase_ : Optional[Any] = value
elif weight_type == "bias":
lowercase_ : Tuple = value
else:
lowercase_ : List[Any] = value
logger.info(F'''{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.''' )
def lowercase ( __snake_case : Tuple , __snake_case : Dict ):
lowercase_ : str = []
lowercase_ : int = fairseq_model.state_dict()
lowercase_ : List[str] = hf_model.unispeech_sat.feature_extractor
for name, value in fairseq_dict.items():
lowercase_ : List[Any] = False
if "conv_layers" in name:
load_conv_layer(
__snake_case , __snake_case , __snake_case , __snake_case , hf_model.config.feat_extract_norm == '''group''' , )
lowercase_ : Union[str, Any] = True
else:
for key, mapped_key in MAPPING.items():
lowercase_ : Dict = '''unispeech_sat.''' + 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]:
if "layer_norm_for_extract" in name and (".".join(name.split('''.''' )[:-1] ) != key):
# special case since naming is very similar
continue
lowercase_ : Optional[int] = True
if "*" in mapped_key:
lowercase_ : Optional[Any] = name.split(__snake_case )[0].split('''.''' )[-2]
lowercase_ : Optional[Any] = mapped_key.replace('''*''' , __snake_case )
if "weight_g" in name:
lowercase_ : List[str] = '''weight_g'''
elif "weight_v" in name:
lowercase_ : str = '''weight_v'''
elif "bias" in name:
lowercase_ : Optional[Any] = '''bias'''
elif "weight" in name:
# TODO: don't match quantizer.weight_proj
lowercase_ : str = '''weight'''
else:
lowercase_ : Union[str, Any] = None
set_recursively(__snake_case , __snake_case , __snake_case , __snake_case , __snake_case )
continue
if not is_used:
unused_weights.append(__snake_case )
logger.warning(F'''Unused weights: {unused_weights}''' )
def lowercase ( __snake_case : List[Any] , __snake_case : Any , __snake_case : Dict , __snake_case : Dict , __snake_case : Optional[Any] ):
lowercase_ : Tuple = full_name.split('''conv_layers.''' )[-1]
lowercase_ : Union[str, Any] = name.split('''.''' )
lowercase_ : str = int(items[0] )
lowercase_ : Dict = 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.''' )
lowercase_ : Optional[Any] = 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.''' )
lowercase_ : Dict = 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[layer_id].layer_norm.bias.data.shape} was found.''' )
lowercase_ : Optional[int] = 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[layer_id].layer_norm.weight.data.shape} was found.''' )
lowercase_ : Optional[Any] = value
logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' )
else:
unused_weights.append(__snake_case )
@torch.no_grad()
def lowercase ( __snake_case : str , __snake_case : Optional[Any] , __snake_case : List[Any]=None , __snake_case : Optional[Any]=None , __snake_case : List[Any]=True ):
if config_path is not None:
lowercase_ : List[Any] = UniSpeechSatConfig.from_pretrained(__snake_case )
else:
lowercase_ : List[str] = UniSpeechSatConfig()
lowercase_ : Union[str, Any] = ''''''
if is_finetuned:
lowercase_ : List[Any] = UniSpeechSatForCTC(__snake_case )
else:
lowercase_ : Dict = UniSpeechSatForPreTraining(__snake_case )
lowercase_ , lowercase_ , lowercase_ : str = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={'''data''': '''/'''.join(dict_path.split('''/''' )[:-1] )} )
lowercase_ : Any = model[0].eval()
recursively_load_weights(__snake_case , __snake_case )
hf_wavavec.save_pretrained(__snake_case )
if __name__ == "__main__":
__A : Tuple = 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'''
)
__A : Optional[int] = parser.parse_args()
convert_unispeech_sat_checkpoint(
args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
)
| 33
|
"""simple docstring"""
from typing import Optional
from urllib.parse import quote
import huggingface_hub as hfh
from packaging import version
def lowercase ( __snake_case : str , __snake_case : str , __snake_case : Optional[str] = None ):
if version.parse(hfh.__version__ ).release < version.parse('''0.11.0''' ).release:
# old versions of hfh don't url-encode the file path
lowercase_ : Union[str, Any] = quote(__snake_case )
return hfh.hf_hub_url(__snake_case , __snake_case , repo_type='''dataset''' , revision=__snake_case )
| 33
| 1
|
"""simple docstring"""
import json
from typing import Dict, List, Optional, Tuple, Union
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_led import LEDTokenizer
__A : List[str] = logging.get_logger(__name__)
__A : Optional[Any] = {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}
__A : Optional[Any] = {
'''vocab_file''': {
'''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json''',
},
'''merges_file''': {
'''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt''',
},
'''tokenizer_file''': {
'''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json''',
},
}
__A : int = {
'''allenai/led-base-16384''': 16_384,
}
class _UpperCAmelCase ( _A ):
SCREAMING_SNAKE_CASE_ : Optional[Any] = VOCAB_FILES_NAMES
SCREAMING_SNAKE_CASE_ : Optional[Any] = PRETRAINED_VOCAB_FILES_MAP
SCREAMING_SNAKE_CASE_ : Tuple = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
SCREAMING_SNAKE_CASE_ : List[str] = LEDTokenizer
SCREAMING_SNAKE_CASE_ : Optional[Any] = ["input_ids", "attention_mask"]
def __init__( self : int , A : Any=None , A : Optional[Any]=None , A : List[Any]=None , A : Optional[int]="replace" , A : Tuple="<s>" , A : List[str]="</s>" , A : Optional[int]="</s>" , A : List[Any]="<s>" , A : Optional[Any]="<unk>" , A : Optional[int]="<pad>" , A : Optional[Any]="<mask>" , A : Any=False , A : Union[str, Any]=True , **A : Union[str, Any] , ) -> int:
super().__init__(
A , A , tokenizer_file=A , errors=A , bos_token=A , eos_token=A , sep_token=A , cls_token=A , unk_token=A , pad_token=A , mask_token=A , add_prefix_space=A , trim_offsets=A , **A , )
lowercase_ : List[Any] = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get('''add_prefix_space''' , A ) != add_prefix_space:
lowercase_ : List[str] = getattr(A , pre_tok_state.pop('''type''' ) )
lowercase_ : int = add_prefix_space
lowercase_ : Tuple = pre_tok_class(**A )
lowercase_ : int = add_prefix_space
# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
lowercase_ : List[str] = '''post_processor'''
lowercase_ : List[Any] = getattr(self.backend_tokenizer , A , A )
if tokenizer_component_instance:
lowercase_ : Tuple = json.loads(tokenizer_component_instance.__getstate__() )
# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
if "sep" in state:
lowercase_ : Dict = tuple(state['''sep'''] )
if "cls" in state:
lowercase_ : Union[str, Any] = tuple(state['''cls'''] )
lowercase_ : Dict = False
if state.get('''add_prefix_space''' , A ) != add_prefix_space:
lowercase_ : Union[str, Any] = add_prefix_space
lowercase_ : Union[str, Any] = True
if state.get('''trim_offsets''' , A ) != trim_offsets:
lowercase_ : Union[str, Any] = trim_offsets
lowercase_ : str = True
if changes_to_apply:
lowercase_ : int = getattr(A , state.pop('''type''' ) )
lowercase_ : Tuple = component_class(**A )
setattr(self.backend_tokenizer , A , A )
@property
# Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
def A ( self : Optional[Any] ) -> str:
if self._mask_token is None:
if self.verbose:
logger.error('''Using mask_token, but it is not set yet.''' )
return None
return str(self._mask_token )
@mask_token.setter
def A ( self : Optional[int] , A : int ) -> str:
lowercase_ : str = AddedToken(A , lstrip=A , rstrip=A ) if isinstance(A , A ) else value
lowercase_ : Optional[Any] = value
def A ( self : Optional[Any] , *A : str , **A : Optional[Any] ) -> BatchEncoding:
lowercase_ : int = kwargs.get('''is_split_into_words''' , A )
if is_split_into_words and not self.add_prefix_space:
raise ValueError(
F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
'''to use it with pretokenized inputs.''' )
return super()._batch_encode_plus(*A , **A )
def A ( self : List[Any] , *A : int , **A : List[str] ) -> BatchEncoding:
lowercase_ : str = kwargs.get('''is_split_into_words''' , A )
if is_split_into_words and not self.add_prefix_space:
raise ValueError(
F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
'''to use it with pretokenized inputs.''' )
return super()._encode_plus(*A , **A )
def A ( self : Any , A : str , A : Optional[str] = None ) -> Tuple[str]:
lowercase_ : str = self._tokenizer.model.save(A , name=A )
return tuple(A )
def A ( self : Any , A : str , A : Union[str, Any]=None ) -> Any:
lowercase_ : Any = [self.bos_token_id] + token_ids_a + [self.eos_token_id]
if token_ids_a is None:
return output
return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]
def A ( self : Union[str, Any] , A : List[int] , A : Optional[List[int]] = None ) -> List[int]:
lowercase_ : Optional[Any] = [self.sep_token_id]
lowercase_ : Optional[int] = [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 A ( self : Optional[Any] , A : Union[Dict[str, EncodedInput], BatchEncoding] , A : Optional[int] = None , A : PaddingStrategy = PaddingStrategy.DO_NOT_PAD , A : Optional[int] = None , A : Optional[bool] = None , ) -> dict:
lowercase_ : Any = super()._pad(
encoded_inputs=A , max_length=A , padding_strategy=A , pad_to_multiple_of=A , return_attention_mask=A , )
# Load from model defaults
if return_attention_mask is None:
lowercase_ : Optional[Any] = '''attention_mask''' in self.model_input_names
if return_attention_mask and "global_attention_mask" in encoded_inputs:
lowercase_ : Optional[int] = encoded_inputs[self.model_input_names[0]]
# `global_attention_mask` need to have the same length as other (sequential) inputs.
lowercase_ : Tuple = len(encoded_inputs['''global_attention_mask'''] ) != len(A )
if needs_to_be_padded:
lowercase_ : Union[str, Any] = len(A ) - len(encoded_inputs['''global_attention_mask'''] )
if self.padding_side == "right":
# Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
lowercase_ : Dict = (
encoded_inputs['''global_attention_mask'''] + [-1] * difference
)
elif self.padding_side == "left":
lowercase_ : str = [-1] * difference + encoded_inputs[
'''global_attention_mask'''
]
else:
raise ValueError('''Invalid padding strategy:''' + str(self.padding_side ) )
return encoded_inputs
| 33
|
"""simple docstring"""
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFResNetForImageClassification, TFResNetModel
from transformers.models.resnet.modeling_tf_resnet import TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class _UpperCAmelCase :
def __init__( self : int , A : Tuple , A : int=3 , A : List[str]=32 , A : Dict=3 , A : Any=10 , A : Dict=[10, 20, 30, 40] , A : Optional[Any]=[1, 1, 2, 1] , A : Union[str, Any]=True , A : Optional[Any]=True , A : Any="relu" , A : Optional[Any]=3 , A : Tuple=None , ) -> Dict:
lowercase_ : str = parent
lowercase_ : List[Any] = batch_size
lowercase_ : Optional[int] = image_size
lowercase_ : int = num_channels
lowercase_ : int = embeddings_size
lowercase_ : str = hidden_sizes
lowercase_ : List[str] = depths
lowercase_ : Dict = is_training
lowercase_ : int = use_labels
lowercase_ : Any = hidden_act
lowercase_ : List[Any] = num_labels
lowercase_ : Tuple = scope
lowercase_ : Optional[Any] = len(A )
def A ( self : str ) -> Tuple:
lowercase_ : Optional[int] = 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[str] = ids_tensor([self.batch_size] , self.num_labels )
lowercase_ : Optional[int] = self.get_config()
return config, pixel_values, labels
def A ( self : Dict ) -> int:
return ResNetConfig(
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 , image_size=self.image_size , )
def A ( self : str , A : Tuple , A : str , A : str ) -> str:
lowercase_ : str = TFResNetModel(config=A )
lowercase_ : Union[str, Any] = model(A )
# expected last hidden states: B, C, H // 32, W // 32
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32) , )
def A ( self : Any , A : int , A : List[Any] , A : Optional[Any] ) -> Optional[Any]:
lowercase_ : Tuple = self.num_labels
lowercase_ : Union[str, Any] = TFResNetForImageClassification(A )
lowercase_ : Tuple = model(A , labels=A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def A ( self : Union[str, Any] ) -> Tuple:
lowercase_ : Tuple = self.prepare_config_and_inputs()
lowercase_ , lowercase_ , lowercase_ : Dict = config_and_inputs
lowercase_ : int = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_tf
class _UpperCAmelCase ( _A , _A , unittest.TestCase ):
SCREAMING_SNAKE_CASE_ : Union[str, Any] = (TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
SCREAMING_SNAKE_CASE_ : List[Any] = (
{"feature-extraction": TFResNetModel, "image-classification": TFResNetForImageClassification}
if is_tf_available()
else {}
)
SCREAMING_SNAKE_CASE_ : Optional[int] = False
SCREAMING_SNAKE_CASE_ : Optional[int] = False
SCREAMING_SNAKE_CASE_ : str = False
SCREAMING_SNAKE_CASE_ : Optional[int] = False
SCREAMING_SNAKE_CASE_ : Any = False
def A ( self : Union[str, Any] ) -> List[Any]:
lowercase_ : int = TFResNetModelTester(self )
lowercase_ : str = ConfigTester(self , config_class=A , has_text_modality=A )
def A ( self : Dict ) -> Optional[Any]:
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 A ( self : Dict ) -> List[Any]:
return
@unittest.skip(reason='''ResNet does not use inputs_embeds''' )
def A ( self : Any ) -> Any:
pass
@unittest.skip(reason='''ResNet does not support input and output embeddings''' )
def A ( self : List[str] ) -> Optional[Any]:
pass
def A ( self : str ) -> Tuple:
lowercase_ , lowercase_ : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase_ : int = model_class(A )
lowercase_ : str = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowercase_ : str = [*signature.parameters.keys()]
lowercase_ : str = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , A )
def A ( self : List[str] ) -> Tuple:
lowercase_ : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*A )
def A ( self : List[Any] ) -> List[str]:
def check_hidden_states_output(A : Union[str, Any] , A : int , A : List[Any] ):
lowercase_ : int = model_class(A )
lowercase_ : Optional[Any] = model(**self._prepare_for_class(A , A ) )
lowercase_ : Union[str, Any] = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
lowercase_ : Any = self.model_tester.num_stages
self.assertEqual(len(A ) , expected_num_stages + 1 )
# ResNet'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] , )
lowercase_ , lowercase_ : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
lowercase_ : Union[str, Any] = ['''basic''', '''bottleneck''']
for model_class in self.all_model_classes:
for layer_type in layers_type:
lowercase_ : List[str] = layer_type
lowercase_ : Tuple = True
check_hidden_states_output(A , A , A )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
lowercase_ : Optional[Any] = True
check_hidden_states_output(A , A , A )
def A ( self : Optional[int] ) -> Tuple:
lowercase_ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*A )
@slow
def A ( self : List[str] ) -> Optional[int]:
for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase_ : Tuple = TFResNetModel.from_pretrained(A )
self.assertIsNotNone(A )
def lowercase ( ):
lowercase_ : List[str] = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_tf
@require_vision
class _UpperCAmelCase ( unittest.TestCase ):
@cached_property
def A ( self : Any ) -> Optional[int]:
return (
AutoImageProcessor.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
if is_vision_available()
else None
)
@slow
def A ( self : Any ) -> Optional[int]:
lowercase_ : Optional[int] = TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
lowercase_ : List[Any] = self.default_image_processor
lowercase_ : Dict = prepare_img()
lowercase_ : List[str] = image_processor(images=A , return_tensors='''tf''' )
# forward pass
lowercase_ : Tuple = model(**A )
# verify the logits
lowercase_ : Optional[int] = tf.TensorShape((1, 10_00) )
self.assertEqual(outputs.logits.shape , A )
lowercase_ : Optional[Any] = tf.constant([-11.1069, -9.7877, -8.3777] )
self.assertTrue(np.allclose(outputs.logits[0, :3].numpy() , A , atol=1e-4 ) )
| 33
| 1
|
"""simple docstring"""
import math
class _UpperCAmelCase :
def __init__( self : Union[str, Any] , A : Optional[int]=0 ) -> Union[str, Any]: # a graph with Node 0,1,...,N-1
lowercase_ : Any = n
lowercase_ : Any = [
[math.inf for j in range(0 , A )] for i in range(0 , A )
] # adjacency matrix for weight
lowercase_ : List[Any] = [
[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 A ( self : List[str] , A : Dict , A : str , A : Optional[Any] ) -> Any:
lowercase_ : Any = w
def A ( self : Optional[Any] ) -> Tuple:
for k in range(0 , self.n ):
for i in range(0 , self.n ):
for j in range(0 , self.n ):
lowercase_ : str = min(self.dp[i][j] , self.dp[i][k] + self.dp[k][j] )
def A ( self : Any , A : Tuple , A : Dict ) -> str:
return self.dp[u][v]
if __name__ == "__main__":
__A : str = 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)
| 33
|
"""simple docstring"""
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
__A : Dict = '''
Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that developed a chatbot app targeted at teenagers.[2] After open-sourcing the model behind the chatbot, the company pivoted to focus on being a platform for machine learning.
In March 2021, Hugging Face raised $40 million in a Series B funding round.[3]
On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model.[4] In 2022, the workshop concluded with the announcement of BLOOM, a multilingual large language model with 176 billion parameters.[5]
'''
class _UpperCAmelCase ( unittest.TestCase , _A ):
def A ( self : List[Any] ) -> Dict:
lowercase_ : Optional[int] = load_tool('''text-question-answering''' )
self.tool.setup()
lowercase_ : Union[str, Any] = load_tool('''text-question-answering''' , remote=A )
def A ( self : Any ) -> List[str]:
lowercase_ : Union[str, Any] = self.tool(A , '''What did Hugging Face do in April 2021?''' )
self.assertEqual(A , '''launched the BigScience Research Workshop''' )
def A ( self : str ) -> List[str]:
lowercase_ : int = self.remote_tool(A , '''What did Hugging Face do in April 2021?''' )
self.assertEqual(A , '''launched the BigScience Research Workshop''' )
def A ( self : List[Any] ) -> int:
lowercase_ : Optional[Any] = self.tool(text=A , question='''What did Hugging Face do in April 2021?''' )
self.assertEqual(A , '''launched the BigScience Research Workshop''' )
def A ( self : List[str] ) -> Optional[int]:
lowercase_ : int = self.remote_tool(text=A , question='''What did Hugging Face do in April 2021?''' )
self.assertEqual(A , '''launched the BigScience Research Workshop''' )
| 33
| 1
|
"""simple docstring"""
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_video_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import VivitImageProcessor
class _UpperCAmelCase ( unittest.TestCase ):
def __init__( self : Any , A : Union[str, Any] , A : Union[str, Any]=7 , A : Dict=3 , A : Any=10 , A : Optional[int]=18 , A : List[str]=30 , A : str=4_00 , A : Any=True , A : Union[str, Any]=None , A : Optional[int]=True , A : List[str]=[0.5, 0.5, 0.5] , A : Union[str, Any]=[0.5, 0.5, 0.5] , A : Tuple=None , ) -> Tuple:
lowercase_ : int = size if size is not None else {'''shortest_edge''': 18}
lowercase_ : str = crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
lowercase_ : List[Any] = parent
lowercase_ : List[Any] = batch_size
lowercase_ : Tuple = num_channels
lowercase_ : Union[str, Any] = num_frames
lowercase_ : Dict = image_size
lowercase_ : List[Any] = min_resolution
lowercase_ : Dict = max_resolution
lowercase_ : Optional[Any] = do_resize
lowercase_ : Any = size
lowercase_ : Dict = do_normalize
lowercase_ : Optional[Any] = image_mean
lowercase_ : Optional[Any] = image_std
lowercase_ : List[Any] = crop_size
def A ( self : List[str] ) -> Tuple:
return {
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_normalize": self.do_normalize,
"do_resize": self.do_resize,
"size": self.size,
"crop_size": self.crop_size,
}
@require_torch
@require_vision
class _UpperCAmelCase ( _A , unittest.TestCase ):
SCREAMING_SNAKE_CASE_ : Any = VivitImageProcessor if is_vision_available() else None
def A ( self : List[Any] ) -> List[Any]:
lowercase_ : Optional[Any] = VivitImageProcessingTester(self )
@property
def A ( self : Optional[Any] ) -> Optional[int]:
return self.image_processor_tester.prepare_image_processor_dict()
def A ( self : Union[str, Any] ) -> Optional[Any]:
lowercase_ : Optional[int] = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(A , '''image_mean''' ) )
self.assertTrue(hasattr(A , '''image_std''' ) )
self.assertTrue(hasattr(A , '''do_normalize''' ) )
self.assertTrue(hasattr(A , '''do_resize''' ) )
self.assertTrue(hasattr(A , '''do_center_crop''' ) )
self.assertTrue(hasattr(A , '''size''' ) )
def A ( self : List[Any] ) -> List[str]:
lowercase_ : Optional[int] = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'''shortest_edge''': 18} )
self.assertEqual(image_processor.crop_size , {'''height''': 18, '''width''': 18} )
lowercase_ : int = 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 : int ) -> Optional[Any]:
# Initialize image_processing
lowercase_ : List[str] = self.image_processing_class(**self.image_processor_dict )
# create random PIL videos
lowercase_ : List[Any] = prepare_video_inputs(self.image_processor_tester , equal_resolution=A )
for video in video_inputs:
self.assertIsInstance(A , A )
self.assertIsInstance(video[0] , Image.Image )
# Test not batched input
lowercase_ : Optional[Any] = image_processing(video_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_videos.shape , (
1,
self.image_processor_tester.num_frames,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
# Test batched
lowercase_ : Dict = image_processing(A , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_videos.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_frames,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
def A ( self : int ) -> Optional[int]:
# Initialize image_processing
lowercase_ : Optional[Any] = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
lowercase_ : Optional[Any] = prepare_video_inputs(self.image_processor_tester , equal_resolution=A , numpify=A )
for video in video_inputs:
self.assertIsInstance(A , A )
self.assertIsInstance(video[0] , np.ndarray )
# Test not batched input
lowercase_ : Any = image_processing(video_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_videos.shape , (
1,
self.image_processor_tester.num_frames,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
# Test batched
lowercase_ : List[Any] = image_processing(A , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_videos.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_frames,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
def A ( self : Tuple ) -> Dict:
# Initialize image_processing
lowercase_ : List[Any] = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
lowercase_ : Any = prepare_video_inputs(self.image_processor_tester , equal_resolution=A , torchify=A )
for video in video_inputs:
self.assertIsInstance(A , A )
self.assertIsInstance(video[0] , torch.Tensor )
# Test not batched input
lowercase_ : Any = image_processing(video_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_videos.shape , (
1,
self.image_processor_tester.num_frames,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
# Test batched
lowercase_ : Any = image_processing(A , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_videos.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_frames,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
| 33
|
"""simple docstring"""
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING:
import torch
class _UpperCAmelCase ( TensorFormatter[Mapping, "torch.Tensor", Mapping] ):
def __init__( self : Any , A : int=None , **A : str ) -> Union[str, Any]:
super().__init__(features=A )
lowercase_ : Union[str, Any] = torch_tensor_kwargs
import torch # noqa import torch at initialization
def A ( self : Dict , A : int ) -> List[Any]:
import torch
if isinstance(A , A ) and column:
if all(
isinstance(A , torch.Tensor ) and x.shape == column[0].shape and x.dtype == column[0].dtype
for x in column ):
return torch.stack(A )
return column
def A ( self : int , A : Any ) -> Optional[Any]:
import torch
if isinstance(A , (str, bytes, type(A )) ):
return value
elif isinstance(A , (np.character, np.ndarray) ) and np.issubdtype(value.dtype , np.character ):
return value.tolist()
lowercase_ : Any = {}
if isinstance(A , (np.number, np.ndarray) ) and np.issubdtype(value.dtype , np.integer ):
lowercase_ : Any = {'''dtype''': torch.intaa}
elif isinstance(A , (np.number, np.ndarray) ) and np.issubdtype(value.dtype , np.floating ):
lowercase_ : Dict = {'''dtype''': torch.floataa}
elif config.PIL_AVAILABLE and "PIL" in sys.modules:
import PIL.Image
if isinstance(A , PIL.Image.Image ):
lowercase_ : Dict = np.asarray(A )
return torch.tensor(A , **{**default_dtype, **self.torch_tensor_kwargs} )
def A ( self : Union[str, Any] , A : Optional[int] ) -> str:
import torch
# support for torch, tf, jax etc.
if hasattr(A , '''__array__''' ) and not isinstance(A , torch.Tensor ):
lowercase_ : Optional[int] = data_struct.__array__()
# support for nested types like struct of list of struct
if isinstance(A , np.ndarray ):
if data_struct.dtype == object: # torch tensors cannot be instantied from an array of objects
return self._consolidate([self.recursive_tensorize(A ) for substruct in data_struct] )
elif isinstance(A , (list, tuple) ):
return self._consolidate([self.recursive_tensorize(A ) for substruct in data_struct] )
return self._tensorize(A )
def A ( self : Dict , A : dict ) -> Tuple:
return map_nested(self._recursive_tensorize , A , map_list=A )
def A ( self : str , A : pa.Table ) -> Mapping:
lowercase_ : Optional[Any] = self.numpy_arrow_extractor().extract_row(A )
lowercase_ : str = self.python_features_decoder.decode_row(A )
return self.recursive_tensorize(A )
def A ( self : List[Any] , A : pa.Table ) -> "torch.Tensor":
lowercase_ : List[str] = self.numpy_arrow_extractor().extract_column(A )
lowercase_ : str = self.python_features_decoder.decode_column(A , pa_table.column_names[0] )
lowercase_ : Optional[int] = self.recursive_tensorize(A )
lowercase_ : Any = self._consolidate(A )
return column
def A ( self : List[str] , A : pa.Table ) -> Mapping:
lowercase_ : Optional[int] = self.numpy_arrow_extractor().extract_batch(A )
lowercase_ : int = self.python_features_decoder.decode_batch(A )
lowercase_ : Dict = self.recursive_tensorize(A )
for column_name in batch:
lowercase_ : Optional[Any] = self._consolidate(batch[column_name] )
return batch
| 33
| 1
|
"""simple docstring"""
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Sequence, Value
from .base import TaskTemplate
@dataclass(frozen=_A )
class _UpperCAmelCase ( _A ):
# `task` is not a ClassVar since we want it to be part of the `asdict` output for JSON serialization
SCREAMING_SNAKE_CASE_ : str = field(default="question-answering-extractive" , metadata={"include_in_asdict_even_if_is_default": True} )
SCREAMING_SNAKE_CASE_ : ClassVar[Features] = Features({"question": Value("string" ), "context": Value("string" )} )
SCREAMING_SNAKE_CASE_ : ClassVar[Features] = Features(
{
"answers": Sequence(
{
"text": Value("string" ),
"answer_start": Value("int32" ),
} )
} )
SCREAMING_SNAKE_CASE_ : str = "question"
SCREAMING_SNAKE_CASE_ : str = "context"
SCREAMING_SNAKE_CASE_ : str = "answers"
@property
def A ( self : Any ) -> Dict[str, str]:
return {self.question_column: "question", self.context_column: "context", self.answers_column: "answers"}
| 33
|
"""simple docstring"""
from ...utils import is_torch_available, is_transformers_available
if is_transformers_available() and is_torch_available():
from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline
| 33
| 1
|
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class _UpperCAmelCase ( metaclass=_A ):
SCREAMING_SNAKE_CASE_ : Any = ["keras_nlp"]
def __init__( self : Dict , *A : str , **A : List[str] ) -> str:
requires_backends(self , ['''keras_nlp'''] )
| 33
|
"""simple docstring"""
from typing import Optional, Union
import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention
from ...modeling_utils import PreTrainedModel
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_mobilenet_va import MobileNetVaConfig
__A : Union[str, Any] = logging.get_logger(__name__)
# General docstring
__A : Tuple = '''MobileNetV1Config'''
# Base docstring
__A : Union[str, Any] = '''google/mobilenet_v1_1.0_224'''
__A : Union[str, Any] = [1, 1_024, 7, 7]
# Image classification docstring
__A : Optional[Any] = '''google/mobilenet_v1_1.0_224'''
__A : List[Any] = '''tabby, tabby cat'''
__A : Union[str, Any] = [
'''google/mobilenet_v1_1.0_224''',
'''google/mobilenet_v1_0.75_192''',
# See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
]
def lowercase ( __snake_case : List[str] , __snake_case : Union[str, Any] , __snake_case : Dict=None ):
lowercase_ : str = {}
if isinstance(__snake_case , __snake_case ):
lowercase_ : Union[str, Any] = model.mobilenet_va
else:
lowercase_ : Optional[Any] = model
lowercase_ : Union[str, Any] = '''MobilenetV1/Conv2d_0/'''
lowercase_ : Union[str, Any] = backbone.conv_stem.convolution.weight
lowercase_ : Optional[Any] = backbone.conv_stem.normalization.bias
lowercase_ : Union[str, Any] = backbone.conv_stem.normalization.weight
lowercase_ : Any = backbone.conv_stem.normalization.running_mean
lowercase_ : int = backbone.conv_stem.normalization.running_var
for i in range(1_3 ):
lowercase_ : Optional[int] = i + 1
lowercase_ : Union[str, Any] = i * 2
lowercase_ : Optional[Any] = backbone.layer[pt_index]
lowercase_ : Union[str, Any] = F'''MobilenetV1/Conv2d_{tf_index}_depthwise/'''
lowercase_ : str = pointer.convolution.weight
lowercase_ : int = pointer.normalization.bias
lowercase_ : Any = pointer.normalization.weight
lowercase_ : Dict = pointer.normalization.running_mean
lowercase_ : Union[str, Any] = pointer.normalization.running_var
lowercase_ : Any = backbone.layer[pt_index + 1]
lowercase_ : Union[str, Any] = F'''MobilenetV1/Conv2d_{tf_index}_pointwise/'''
lowercase_ : int = pointer.convolution.weight
lowercase_ : str = pointer.normalization.bias
lowercase_ : Tuple = pointer.normalization.weight
lowercase_ : Dict = pointer.normalization.running_mean
lowercase_ : Any = pointer.normalization.running_var
if isinstance(__snake_case , __snake_case ):
lowercase_ : Optional[Any] = '''MobilenetV1/Logits/Conv2d_1c_1x1/'''
lowercase_ : Any = model.classifier.weight
lowercase_ : Optional[int] = model.classifier.bias
return tf_to_pt_map
def lowercase ( __snake_case : Optional[int] , __snake_case : int , __snake_case : Dict ):
try:
import numpy as np
import tensorflow as tf
except ImportError:
logger.error(
'''Loading a TensorFlow models in PyTorch, requires TensorFlow to be installed. Please see '''
'''https://www.tensorflow.org/install/ for installation instructions.''' )
raise
# Load weights from TF model
lowercase_ : Tuple = tf.train.list_variables(__snake_case )
lowercase_ : int = {}
for name, shape in init_vars:
logger.info(F'''Loading TF weight {name} with shape {shape}''' )
lowercase_ : Optional[Any] = tf.train.load_variable(__snake_case , __snake_case )
lowercase_ : Optional[int] = array
# Build TF to PyTorch weights loading map
lowercase_ : Any = _build_tf_to_pytorch_map(__snake_case , __snake_case , __snake_case )
for name, pointer in tf_to_pt_map.items():
logger.info(F'''Importing {name}''' )
if name not in tf_weights:
logger.info(F'''{name} not in tf pre-trained weights, skipping''' )
continue
lowercase_ : Union[str, Any] = tf_weights[name]
if "depthwise_weights" in name:
logger.info('''Transposing depthwise''' )
lowercase_ : Any = np.transpose(__snake_case , (2, 3, 0, 1) )
elif "weights" in name:
logger.info('''Transposing''' )
if len(pointer.shape ) == 2: # copying into linear layer
lowercase_ : Optional[int] = array.squeeze().transpose()
else:
lowercase_ : Optional[int] = np.transpose(__snake_case , (3, 2, 0, 1) )
if pointer.shape != array.shape:
raise ValueError(F'''Pointer shape {pointer.shape} and array shape {array.shape} mismatched''' )
logger.info(F'''Initialize PyTorch weight {name} {array.shape}''' )
lowercase_ : str = torch.from_numpy(__snake_case )
tf_weights.pop(__snake_case , __snake_case )
tf_weights.pop(name + '''/RMSProp''' , __snake_case )
tf_weights.pop(name + '''/RMSProp_1''' , __snake_case )
tf_weights.pop(name + '''/ExponentialMovingAverage''' , __snake_case )
logger.info(F'''Weights not copied to PyTorch model: {', '.join(tf_weights.keys() )}''' )
return model
def lowercase ( __snake_case : torch.Tensor , __snake_case : nn.Convad ):
lowercase_ , lowercase_ : Optional[int] = features.shape[-2:]
lowercase_ , lowercase_ : str = conv_layer.stride
lowercase_ , lowercase_ : Tuple = conv_layer.kernel_size
if in_height % stride_height == 0:
lowercase_ : Dict = max(kernel_height - stride_height , 0 )
else:
lowercase_ : List[Any] = max(kernel_height - (in_height % stride_height) , 0 )
if in_width % stride_width == 0:
lowercase_ : str = max(kernel_width - stride_width , 0 )
else:
lowercase_ : int = max(kernel_width - (in_width % stride_width) , 0 )
lowercase_ : int = pad_along_width // 2
lowercase_ : Union[str, Any] = pad_along_width - pad_left
lowercase_ : Tuple = pad_along_height // 2
lowercase_ : List[str] = pad_along_height - pad_top
lowercase_ : str = (pad_left, pad_right, pad_top, pad_bottom)
return nn.functional.pad(__snake_case , __snake_case , '''constant''' , 0.0 )
class _UpperCAmelCase ( nn.Module ):
def __init__( self : List[Any] , A : MobileNetVaConfig , A : int , A : int , A : int , A : Optional[int] = 1 , A : Optional[int] = 1 , A : bool = False , A : Optional[bool] = True , A : Optional[bool or str] = True , ) -> None:
super().__init__()
lowercase_ : int = config
if in_channels % groups != 0:
raise ValueError(F'''Input channels ({in_channels}) are not divisible by {groups} groups.''' )
if out_channels % groups != 0:
raise ValueError(F'''Output channels ({out_channels}) are not divisible by {groups} groups.''' )
lowercase_ : Tuple = 0 if config.tf_padding else int((kernel_size - 1) / 2 )
lowercase_ : int = nn.Convad(
in_channels=A , out_channels=A , kernel_size=A , stride=A , padding=A , groups=A , bias=A , padding_mode='''zeros''' , )
if use_normalization:
lowercase_ : Optional[Any] = nn.BatchNormad(
num_features=A , eps=config.layer_norm_eps , momentum=0.9997 , affine=A , track_running_stats=A , )
else:
lowercase_ : Union[str, Any] = None
if use_activation:
if isinstance(A , A ):
lowercase_ : str = ACTaFN[use_activation]
elif isinstance(config.hidden_act , A ):
lowercase_ : Any = ACTaFN[config.hidden_act]
else:
lowercase_ : Tuple = config.hidden_act
else:
lowercase_ : Tuple = None
def A ( self : str , A : torch.Tensor ) -> torch.Tensor:
if self.config.tf_padding:
lowercase_ : List[Any] = apply_tf_padding(A , self.convolution )
lowercase_ : Optional[int] = self.convolution(A )
if self.normalization is not None:
lowercase_ : Union[str, Any] = self.normalization(A )
if self.activation is not None:
lowercase_ : Optional[int] = self.activation(A )
return features
class _UpperCAmelCase ( _A ):
SCREAMING_SNAKE_CASE_ : Optional[int] = MobileNetVaConfig
SCREAMING_SNAKE_CASE_ : int = load_tf_weights_in_mobilenet_va
SCREAMING_SNAKE_CASE_ : Optional[Any] = "mobilenet_v1"
SCREAMING_SNAKE_CASE_ : Union[str, Any] = "pixel_values"
SCREAMING_SNAKE_CASE_ : List[str] = False
def A ( self : Any , A : Union[nn.Linear, nn.Convad] ) -> None:
if isinstance(A , (nn.Linear, nn.Convad) ):
module.weight.data.normal_(mean=0.0 , std=self.config.initializer_range )
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(A , nn.BatchNormad ):
module.bias.data.zero_()
module.weight.data.fill_(1.0 )
__A : Union[str, Any] = R'''
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parameters:
config ([`MobileNetV1Config`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
'''
__A : List[str] = R'''
Args:
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
[`MobileNetV1ImageProcessor.__call__`] for details.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
'''
@add_start_docstrings(
"The bare MobileNetV1 model outputting raw hidden-states without any specific head on top." , _A , )
class _UpperCAmelCase ( _A ):
def __init__( self : str , A : MobileNetVaConfig , A : bool = True ) -> int:
super().__init__(A )
lowercase_ : Union[str, Any] = config
lowercase_ : List[str] = 32
lowercase_ : str = max(int(depth * config.depth_multiplier ) , config.min_depth )
lowercase_ : Union[str, Any] = MobileNetVaConvLayer(
A , in_channels=config.num_channels , out_channels=A , kernel_size=3 , stride=2 , )
lowercase_ : Optional[Any] = [1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 2, 1]
lowercase_ : List[Any] = nn.ModuleList()
for i in range(13 ):
lowercase_ : Dict = out_channels
if strides[i] == 2 or i == 0:
depth *= 2
lowercase_ : str = max(int(depth * config.depth_multiplier ) , config.min_depth )
self.layer.append(
MobileNetVaConvLayer(
A , in_channels=A , out_channels=A , kernel_size=3 , stride=strides[i] , groups=A , ) )
self.layer.append(
MobileNetVaConvLayer(
A , in_channels=A , out_channels=A , kernel_size=1 , ) )
lowercase_ : int = nn.AdaptiveAvgPoolad((1, 1) ) if add_pooling_layer else None
# Initialize weights and apply final processing
self.post_init()
def A ( self : Any , A : Optional[Any] ) -> Optional[int]:
raise NotImplementedError
@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 A ( self : List[Any] , A : Optional[torch.Tensor] = None , A : Optional[bool] = None , A : Optional[bool] = None , ) -> Union[tuple, BaseModelOutputWithPoolingAndNoAttention]:
lowercase_ : str = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
lowercase_ : Union[str, Any] = return_dict if return_dict is not None else self.config.use_return_dict
if pixel_values is None:
raise ValueError('''You have to specify pixel_values''' )
lowercase_ : List[str] = self.conv_stem(A )
lowercase_ : Dict = () if output_hidden_states else None
for i, layer_module in enumerate(self.layer ):
lowercase_ : Optional[int] = layer_module(A )
if output_hidden_states:
lowercase_ : str = all_hidden_states + (hidden_states,)
lowercase_ : Tuple = hidden_states
if self.pooler is not None:
lowercase_ : Dict = torch.flatten(self.pooler(A ) , start_dim=1 )
else:
lowercase_ : Optional[Any] = None
if not return_dict:
return tuple(v for v in [last_hidden_state, pooled_output, all_hidden_states] if v is not None )
return BaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=A , pooler_output=A , hidden_states=A , )
@add_start_docstrings(
"\n MobileNetV1 model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n ImageNet.\n " , _A , )
class _UpperCAmelCase ( _A ):
def __init__( self : List[str] , A : MobileNetVaConfig ) -> None:
super().__init__(A )
lowercase_ : int = config.num_labels
lowercase_ : List[str] = MobileNetVaModel(A )
lowercase_ : Union[str, Any] = self.mobilenet_va.layer[-1].convolution.out_channels
# Classifier head
lowercase_ : Tuple = nn.Dropout(config.classifier_dropout_prob , inplace=A )
lowercase_ : int = nn.Linear(A , 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 A ( self : Optional[Any] , A : Optional[torch.Tensor] = None , A : Optional[bool] = None , A : Optional[torch.Tensor] = None , A : Optional[bool] = None , ) -> Union[tuple, ImageClassifierOutputWithNoAttention]:
lowercase_ : Union[str, Any] = return_dict if return_dict is not None else self.config.use_return_dict
lowercase_ : List[Any] = self.mobilenet_va(A , output_hidden_states=A , return_dict=A )
lowercase_ : Union[str, Any] = outputs.pooler_output if return_dict else outputs[1]
lowercase_ : Dict = self.classifier(self.dropout(A ) )
lowercase_ : int = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
lowercase_ : List[str] = '''regression'''
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
lowercase_ : Optional[Any] = '''single_label_classification'''
else:
lowercase_ : Tuple = '''multi_label_classification'''
if self.config.problem_type == "regression":
lowercase_ : str = MSELoss()
if self.num_labels == 1:
lowercase_ : List[str] = loss_fct(logits.squeeze() , labels.squeeze() )
else:
lowercase_ : List[str] = loss_fct(A , A )
elif self.config.problem_type == "single_label_classification":
lowercase_ : List[Any] = CrossEntropyLoss()
lowercase_ : str = loss_fct(logits.view(-1 , self.num_labels ) , labels.view(-1 ) )
elif self.config.problem_type == "multi_label_classification":
lowercase_ : str = BCEWithLogitsLoss()
lowercase_ : List[Any] = loss_fct(A , A )
if not return_dict:
lowercase_ : Tuple = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return ImageClassifierOutputWithNoAttention(
loss=A , logits=A , hidden_states=outputs.hidden_states , )
| 33
| 1
|
"""simple docstring"""
from __future__ import annotations
import unittest
import numpy as np
from transformers import LayoutLMConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers.models.layoutlm.modeling_tf_layoutlm import (
TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFLayoutLMForMaskedLM,
TFLayoutLMForQuestionAnswering,
TFLayoutLMForSequenceClassification,
TFLayoutLMForTokenClassification,
TFLayoutLMModel,
)
class _UpperCAmelCase :
def __init__( self : Any , A : Optional[int] , A : Dict=13 , A : List[Any]=7 , A : List[str]=True , A : Any=True , A : List[Any]=True , A : List[Any]=True , A : Optional[int]=99 , A : Tuple=32 , A : Union[str, Any]=2 , A : Dict=4 , A : Any=37 , A : List[str]="gelu" , A : Optional[int]=0.1 , A : Union[str, Any]=0.1 , A : str=5_12 , A : str=16 , A : Optional[int]=2 , A : Optional[Any]=0.02 , A : Dict=3 , A : Union[str, Any]=4 , A : Dict=None , A : List[Any]=10_00 , ) -> List[str]:
lowercase_ : str = parent
lowercase_ : Optional[int] = batch_size
lowercase_ : str = seq_length
lowercase_ : int = is_training
lowercase_ : Union[str, Any] = use_input_mask
lowercase_ : int = use_token_type_ids
lowercase_ : List[str] = use_labels
lowercase_ : str = vocab_size
lowercase_ : Union[str, Any] = hidden_size
lowercase_ : str = num_hidden_layers
lowercase_ : Tuple = num_attention_heads
lowercase_ : List[str] = intermediate_size
lowercase_ : Dict = hidden_act
lowercase_ : Tuple = hidden_dropout_prob
lowercase_ : Any = attention_probs_dropout_prob
lowercase_ : Union[str, Any] = max_position_embeddings
lowercase_ : Tuple = type_vocab_size
lowercase_ : Tuple = type_sequence_label_size
lowercase_ : Dict = initializer_range
lowercase_ : List[Any] = num_labels
lowercase_ : Dict = num_choices
lowercase_ : int = scope
lowercase_ : str = range_bbox
def A ( self : str ) -> Tuple:
lowercase_ : Any = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
# convert bbox to numpy since TF does not support item assignment
lowercase_ : Any = ids_tensor([self.batch_size, self.seq_length, 4] , self.range_bbox ).numpy()
# Ensure that bbox is legal
for i in range(bbox.shape[0] ):
for j in range(bbox.shape[1] ):
if bbox[i, j, 3] < bbox[i, j, 1]:
lowercase_ : Any = bbox[i, j, 3]
lowercase_ : str = bbox[i, j, 1]
lowercase_ : List[str] = t
if bbox[i, j, 2] < bbox[i, j, 0]:
lowercase_ : str = bbox[i, j, 2]
lowercase_ : Tuple = bbox[i, j, 0]
lowercase_ : Optional[int] = t
lowercase_ : Optional[int] = tf.convert_to_tensor(A )
lowercase_ : Optional[int] = None
if self.use_input_mask:
lowercase_ : List[str] = random_attention_mask([self.batch_size, self.seq_length] )
lowercase_ : List[Any] = None
if self.use_token_type_ids:
lowercase_ : Optional[Any] = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
lowercase_ : List[Any] = None
lowercase_ : Any = None
lowercase_ : Union[str, Any] = None
if self.use_labels:
lowercase_ : Union[str, Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowercase_ : Optional[Any] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
lowercase_ : List[Any] = ids_tensor([self.batch_size] , self.num_choices )
lowercase_ : Tuple = LayoutLMConfig(
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 , )
return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def A ( self : Tuple , A : Dict , A : List[str] , A : str , A : Optional[Any] , A : List[str] , A : Tuple , A : Tuple , A : Union[str, Any] ) -> Any:
lowercase_ : Optional[Any] = TFLayoutLMModel(config=A )
lowercase_ : Any = model(A , A , attention_mask=A , token_type_ids=A )
lowercase_ : Optional[Any] = model(A , A , token_type_ids=A )
lowercase_ : str = model(A , A )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size) )
def A ( self : str , A : Optional[int] , A : int , A : Any , A : Optional[int] , A : int , A : Any , A : int , A : Dict ) -> List[str]:
lowercase_ : Optional[int] = TFLayoutLMForMaskedLM(config=A )
lowercase_ : Tuple = model(A , A , attention_mask=A , token_type_ids=A , labels=A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def A ( self : Tuple , A : List[str] , A : Any , A : List[str] , A : Union[str, Any] , A : List[str] , A : Dict , A : Optional[Any] , A : List[Any] ) -> Union[str, Any]:
lowercase_ : List[Any] = self.num_labels
lowercase_ : Optional[Any] = TFLayoutLMForSequenceClassification(config=A )
lowercase_ : Tuple = model(A , A , attention_mask=A , token_type_ids=A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def A ( self : List[Any] , A : List[str] , A : Optional[int] , A : Union[str, Any] , A : Union[str, Any] , A : int , A : str , A : Tuple , A : Union[str, Any] ) -> Dict:
lowercase_ : Tuple = self.num_labels
lowercase_ : List[Any] = TFLayoutLMForTokenClassification(config=A )
lowercase_ : List[Any] = model(A , A , attention_mask=A , token_type_ids=A , labels=A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def A ( self : str , A : Tuple , A : Any , A : Dict , A : Tuple , A : Optional[int] , A : Optional[int] , A : List[str] , A : Optional[int] ) -> int:
lowercase_ : Any = TFLayoutLMForQuestionAnswering(config=A )
lowercase_ : Any = model(A , A , attention_mask=A , token_type_ids=A )
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 A ( self : List[Any] ) -> Dict:
lowercase_ : Any = self.prepare_config_and_inputs()
(
(
lowercase_
) , (
lowercase_
) , (
lowercase_
) , (
lowercase_
) , (
lowercase_
) , (
lowercase_
) , (
lowercase_
) , (
lowercase_
) ,
) : Union[str, Any] = config_and_inputs
lowercase_ : List[Any] = {
'''input_ids''': input_ids,
'''bbox''': bbox,
'''token_type_ids''': token_type_ids,
'''attention_mask''': input_mask,
}
return config, inputs_dict
@require_tf
class _UpperCAmelCase ( _A , _A , unittest.TestCase ):
SCREAMING_SNAKE_CASE_ : Dict = (
(
TFLayoutLMModel,
TFLayoutLMForMaskedLM,
TFLayoutLMForTokenClassification,
TFLayoutLMForSequenceClassification,
TFLayoutLMForQuestionAnswering,
)
if is_tf_available()
else ()
)
SCREAMING_SNAKE_CASE_ : List[str] = (
{
"feature-extraction": TFLayoutLMModel,
"fill-mask": TFLayoutLMForMaskedLM,
"text-classification": TFLayoutLMForSequenceClassification,
"token-classification": TFLayoutLMForTokenClassification,
"zero-shot": TFLayoutLMForSequenceClassification,
}
if is_tf_available()
else {}
)
SCREAMING_SNAKE_CASE_ : int = False
SCREAMING_SNAKE_CASE_ : List[Any] = True
SCREAMING_SNAKE_CASE_ : Any = 10
def A ( self : List[Any] ) -> Union[str, Any]:
lowercase_ : Tuple = TFLayoutLMModelTester(self )
lowercase_ : Optional[int] = ConfigTester(self , config_class=A , hidden_size=37 )
def A ( self : Dict ) -> Tuple:
self.config_tester.run_common_tests()
def A ( self : List[Any] ) -> Union[str, Any]:
lowercase_ : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*A )
def A ( self : int ) -> List[str]:
lowercase_ : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*A )
def A ( self : Dict ) -> str:
lowercase_ : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*A )
def A ( self : int ) -> Optional[int]:
lowercase_ : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*A )
def A ( self : Union[str, Any] ) -> List[Any]:
lowercase_ : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*A )
@slow
def A ( self : Any ) -> List[Any]:
for model_name in TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase_ : Optional[int] = TFLayoutLMModel.from_pretrained(A )
self.assertIsNotNone(A )
@unittest.skip('''Onnx compliancy broke with TF 2.10''' )
def A ( self : int ) -> Tuple:
pass
def lowercase ( ):
# Here we prepare a batch of 2 sequences to test a LayoutLM forward pass on:
# fmt: off
lowercase_ : Union[str, Any] = tf.convert_to_tensor([[1_0_1,1_0_1_9,1_0_1_4,1_0_1_6,1_0_3_7,1_2_8_4_9,4_7_4_7,1_0_0_4,1_4_2_4_6,2_2_7_8,5_4_3_9,4_5_2_4,5_0_0_2,2_9_3_0,2_1_9_3,2_9_3_0,4_3_4_1,3_2_0_8,1_0_0_5,1_0_5_5,2_1_7_1,2_8_4_8,1_1_3_0_0,3_5_3_1,1_0_2],[1_0_1,4_0_7_0,4_0_3_4,7_0_2_0,1_0_2_4,3_0_5_8,1_0_1_5,1_0_1_3,2_8_6_1,1_0_1_3,6_0_7_0,1_9_2_7_4,2_7_7_2,6_2_0_5,2_7_8_1_4,1_6_1_4_7,1_6_1_4_7,4_3_4_3,2_0_4_7,1_0_2_8_3,1_0_9_6_9,1_4_3_8_9,1_0_1_2,2_3_3_8,1_0_2]] ) # noqa: E231
lowercase_ : Any = tf.convert_to_tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],[1, 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: E231
lowercase_ : Tuple = tf.convert_to_tensor([[[0,0,0,0],[4_2_3,2_3_7,4_4_0,2_5_1],[4_2_7,2_7_2,4_4_1,2_8_7],[4_1_9,1_1_5,4_3_7,1_2_9],[9_6_1,8_8_5,9_9_2,9_1_2],[2_5_6,3_8,3_3_0,5_8],[2_5_6,3_8,3_3_0,5_8],[3_3_6,4_2,3_5_3,5_7],[3_6_0,3_9,4_0_1,5_6],[3_6_0,3_9,4_0_1,5_6],[4_1_1,3_9,4_7_1,5_9],[4_7_9,4_1,5_2_8,5_9],[5_3_3,3_9,6_3_0,6_0],[6_7,1_1_3,1_3_4,1_3_1],[1_4_1,1_1_5,2_0_9,1_3_2],[6_8,1_4_9,1_3_3,1_6_6],[1_4_1,1_4_9,1_8_7,1_6_4],[1_9_5,1_4_8,2_8_7,1_6_5],[1_9_5,1_4_8,2_8_7,1_6_5],[1_9_5,1_4_8,2_8_7,1_6_5],[2_9_5,1_4_8,3_4_9,1_6_5],[4_4_1,1_4_9,4_9_2,1_6_6],[4_9_7,1_4_9,5_4_6,1_6_4],[6_4,2_0_1,1_2_5,2_1_8],[1_0_0_0,1_0_0_0,1_0_0_0,1_0_0_0]],[[0,0,0,0],[6_6_2,1_5_0,7_5_4,1_6_6],[6_6_5,1_9_9,7_4_2,2_1_1],[5_1_9,2_1_3,5_5_4,2_2_8],[5_1_9,2_1_3,5_5_4,2_2_8],[1_3_4,4_3_3,1_8_7,4_5_4],[1_3_0,4_6_7,2_0_4,4_8_0],[1_3_0,4_6_7,2_0_4,4_8_0],[1_3_0,4_6_7,2_0_4,4_8_0],[1_3_0,4_6_7,2_0_4,4_8_0],[1_3_0,4_6_7,2_0_4,4_8_0],[3_1_4,4_6_9,3_7_6,4_8_2],[5_0_4,6_8_4,5_8_2,7_0_6],[9_4_1,8_2_5,9_7_3,9_0_0],[9_4_1,8_2_5,9_7_3,9_0_0],[9_4_1,8_2_5,9_7_3,9_0_0],[9_4_1,8_2_5,9_7_3,9_0_0],[6_1_0,7_4_9,6_5_2,7_6_5],[1_3_0,6_5_9,1_6_8,6_7_2],[1_7_6,6_5_7,2_3_7,6_7_2],[2_3_8,6_5_7,3_1_2,6_7_2],[4_4_3,6_5_3,6_2_8,6_7_2],[4_4_3,6_5_3,6_2_8,6_7_2],[7_1_6,3_0_1,8_2_5,3_1_7],[1_0_0_0,1_0_0_0,1_0_0_0,1_0_0_0]]] ) # noqa: E231
lowercase_ : List[Any] = tf.convert_to_tensor([[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: E231
# these are sequence labels (i.e. at the token level)
lowercase_ : str = tf.convert_to_tensor([[-1_0_0,1_0,1_0,1_0,9,1,-1_0_0,7,7,-1_0_0,7,7,4,2,5,2,8,8,-1_0_0,-1_0_0,5,0,3,2,-1_0_0],[-1_0_0,1_2,1_2,1_2,-1_0_0,1_2,1_0,-1_0_0,-1_0_0,-1_0_0,-1_0_0,1_0,1_2,9,-1_0_0,-1_0_0,-1_0_0,1_0,1_0,1_0,9,1_2,-1_0_0,1_0,-1_0_0]] ) # noqa: E231
# fmt: on
return input_ids, attention_mask, bbox, token_type_ids, labels
@require_tf
class _UpperCAmelCase ( unittest.TestCase ):
@slow
def A ( self : List[str] ) -> Optional[Any]:
lowercase_ : str = TFLayoutLMModel.from_pretrained('''microsoft/layoutlm-base-uncased''' )
lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ : Optional[int] = prepare_layoutlm_batch_inputs()
# forward pass
lowercase_ : Tuple = model(input_ids=A , bbox=A , attention_mask=A , token_type_ids=A )
# test the sequence output on [0, :3, :3]
lowercase_ : Dict = tf.convert_to_tensor(
[[0.1785, -0.1947, -0.0425], [-0.3254, -0.2807, 0.2553], [-0.5391, -0.3322, 0.3364]] , )
self.assertTrue(np.allclose(outputs.last_hidden_state[0, :3, :3] , A , atol=1e-3 ) )
# test the pooled output on [1, :3]
lowercase_ : List[str] = tf.convert_to_tensor([-0.6580, -0.0214, 0.8552] )
self.assertTrue(np.allclose(outputs.pooler_output[1, :3] , A , atol=1e-3 ) )
@slow
def A ( self : List[str] ) -> List[Any]:
# initialize model with randomly initialized sequence classification head
lowercase_ : Optional[int] = TFLayoutLMForSequenceClassification.from_pretrained('''microsoft/layoutlm-base-uncased''' , num_labels=2 )
lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ : Union[str, Any] = prepare_layoutlm_batch_inputs()
# forward pass
lowercase_ : Tuple = model(
input_ids=A , bbox=A , attention_mask=A , token_type_ids=A , labels=tf.convert_to_tensor([1, 1] ) , )
# test whether we get a loss as a scalar
lowercase_ : Dict = outputs.loss
lowercase_ : List[str] = (2,)
self.assertEqual(loss.shape , A )
# test the shape of the logits
lowercase_ : Optional[Any] = outputs.logits
lowercase_ : Union[str, Any] = (2, 2)
self.assertEqual(logits.shape , A )
@slow
def A ( self : List[Any] ) -> List[str]:
# initialize model with randomly initialized token classification head
lowercase_ : str = TFLayoutLMForTokenClassification.from_pretrained('''microsoft/layoutlm-base-uncased''' , num_labels=13 )
lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ : Optional[Any] = prepare_layoutlm_batch_inputs()
# forward pass
lowercase_ : Union[str, Any] = model(
input_ids=A , bbox=A , attention_mask=A , token_type_ids=A , labels=A )
# test the shape of the logits
lowercase_ : List[str] = outputs.logits
lowercase_ : Dict = tf.convert_to_tensor((2, 25, 13) )
self.assertEqual(logits.shape , A )
@slow
def A ( self : int ) -> Any:
# initialize model with randomly initialized token classification head
lowercase_ : Tuple = TFLayoutLMForQuestionAnswering.from_pretrained('''microsoft/layoutlm-base-uncased''' )
lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ : str = prepare_layoutlm_batch_inputs()
# forward pass
lowercase_ : List[Any] = model(input_ids=A , bbox=A , attention_mask=A , token_type_ids=A )
# test the shape of the logits
lowercase_ : Optional[Any] = tf.convert_to_tensor((2, 25) )
self.assertEqual(outputs.start_logits.shape , A )
self.assertEqual(outputs.end_logits.shape , A )
| 33
|
"""simple docstring"""
def lowercase ( __snake_case : list[int] ):
lowercase_ : List[Any] = len(__snake_case )
for i in range(__snake_case ):
for j in range(i + 1 , __snake_case ):
if numbers[j] < numbers[i]:
lowercase_ , lowercase_ : Optional[int] = numbers[j], numbers[i]
return numbers
if __name__ == "__main__":
__A : int = input('''Enter numbers separated by a comma:\n''').strip()
__A : Any = [int(item) for item in user_input.split(''',''')]
print(exchange_sort(unsorted))
| 33
| 1
|
"""simple docstring"""
import os
from tempfile import TemporaryDirectory
from unittest import TestCase
import pytest
from absl.testing import parameterized
from datasets import config
from datasets.arrow_reader import HF_GCP_BASE_URL
from datasets.builder import DatasetBuilder
from datasets.dataset_dict import IterableDatasetDict
from datasets.iterable_dataset import IterableDataset
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.file_utils import cached_path
__A : Union[str, Any] = [
{'''dataset''': '''wikipedia''', '''config_name''': '''20220301.de'''},
{'''dataset''': '''wikipedia''', '''config_name''': '''20220301.en'''},
{'''dataset''': '''wikipedia''', '''config_name''': '''20220301.fr'''},
{'''dataset''': '''wikipedia''', '''config_name''': '''20220301.frr'''},
{'''dataset''': '''wikipedia''', '''config_name''': '''20220301.it'''},
{'''dataset''': '''wikipedia''', '''config_name''': '''20220301.simple'''},
{'''dataset''': '''snli''', '''config_name''': '''plain_text'''},
{'''dataset''': '''eli5''', '''config_name''': '''LFQA_reddit'''},
{'''dataset''': '''wiki40b''', '''config_name''': '''en'''},
{'''dataset''': '''wiki_dpr''', '''config_name''': '''psgs_w100.nq.compressed'''},
{'''dataset''': '''wiki_dpr''', '''config_name''': '''psgs_w100.nq.no_index'''},
{'''dataset''': '''wiki_dpr''', '''config_name''': '''psgs_w100.multiset.no_index'''},
{'''dataset''': '''natural_questions''', '''config_name''': '''default'''},
]
def lowercase ( __snake_case : Union[str, Any]=True ):
if with_config:
return [
{
"testcase_name": d["dataset"] + "/" + d["config_name"],
"dataset": d["dataset"],
"config_name": d["config_name"],
}
for d in DATASETS_ON_HF_GCP
]
else:
return [
{"testcase_name": dataset, "dataset": dataset} for dataset in {d["dataset"] for d in DATASETS_ON_HF_GCP}
]
@parameterized.named_parameters(list_datasets_on_hf_gcp_parameters(with_config=_A ) )
class _UpperCAmelCase ( _A ):
SCREAMING_SNAKE_CASE_ : int = None
SCREAMING_SNAKE_CASE_ : Optional[Any] = None
def A ( self : List[Any] , A : int , A : Tuple ) -> Tuple:
with TemporaryDirectory() as tmp_dir:
lowercase_ : List[Any] = dataset_module_factory(A , cache_dir=A )
lowercase_ : Optional[Any] = import_main_class(dataset_module.module_path , dataset=A )
lowercase_ : DatasetBuilder = builder_cls(
cache_dir=A , config_name=A , hash=dataset_module.hash , )
lowercase_ : Optional[Any] = '''/'''.join(
[
HF_GCP_BASE_URL,
builder_instance._relative_data_dir(with_hash=A ).replace(os.sep , '''/''' ),
config.DATASET_INFO_FILENAME,
] )
lowercase_ : str = cached_path(A , cache_dir=A )
self.assertTrue(os.path.exists(A ) )
@pytest.mark.integration
def lowercase ( __snake_case : int ):
lowercase_ : int = tmp_path_factory.mktemp('''test_hf_gcp''' ) / '''test_wikipedia_simple'''
lowercase_ : str = dataset_module_factory('''wikipedia''' , cache_dir=__snake_case )
lowercase_ : List[str] = import_main_class(dataset_module.module_path )
lowercase_ : DatasetBuilder = builder_cls(
cache_dir=__snake_case , config_name='''20220301.frr''' , hash=dataset_module.hash , )
# use the HF cloud storage, not the original download_and_prepare that uses apache-beam
lowercase_ : List[Any] = None
builder_instance.download_and_prepare()
lowercase_ : Union[str, Any] = builder_instance.as_dataset()
assert ds
@pytest.mark.integration
def lowercase ( __snake_case : List[str] ):
lowercase_ : Optional[Any] = dataset_module_factory('''wikipedia''' , cache_dir=__snake_case )
lowercase_ : List[str] = import_main_class(dataset_module.module_path , dataset=__snake_case )
lowercase_ : DatasetBuilder = builder_cls(
cache_dir=__snake_case , config_name='''20220301.frr''' , hash=dataset_module.hash , )
lowercase_ : str = builder_instance.as_streaming_dataset()
assert ds
assert isinstance(__snake_case , __snake_case )
assert "train" in ds
assert isinstance(ds['''train'''] , __snake_case )
assert next(iter(ds['''train'''] ) )
| 33
|
"""simple docstring"""
import json
import pathlib
import unittest
import numpy as np
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, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import YolosImageProcessor
class _UpperCAmelCase ( unittest.TestCase ):
def __init__( self : List[Any] , A : Any , A : Tuple=7 , A : Tuple=3 , A : Optional[Any]=30 , A : List[Any]=4_00 , A : Tuple=True , A : Dict=None , A : List[str]=True , A : Optional[int]=[0.5, 0.5, 0.5] , A : Tuple=[0.5, 0.5, 0.5] , A : List[str]=True , A : List[Any]=1 / 2_55 , A : Union[str, Any]=True , ) -> Tuple:
# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
lowercase_ : Optional[int] = size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 13_33}
lowercase_ : Optional[int] = parent
lowercase_ : str = batch_size
lowercase_ : Tuple = num_channels
lowercase_ : str = min_resolution
lowercase_ : Any = max_resolution
lowercase_ : str = do_resize
lowercase_ : Any = size
lowercase_ : Optional[int] = do_normalize
lowercase_ : List[str] = image_mean
lowercase_ : Optional[Any] = image_std
lowercase_ : int = do_rescale
lowercase_ : List[str] = rescale_factor
lowercase_ : int = do_pad
def A ( self : Any ) -> str:
return {
"do_resize": self.do_resize,
"size": self.size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_rescale": self.do_rescale,
"rescale_factor": self.rescale_factor,
"do_pad": self.do_pad,
}
def A ( self : Optional[Any] , A : int , A : int=False ) -> Tuple:
if not batched:
lowercase_ : Optional[int] = image_inputs[0]
if isinstance(A , Image.Image ):
lowercase_ , lowercase_ : int = image.size
else:
lowercase_ , lowercase_ : Tuple = image.shape[1], image.shape[2]
if w < h:
lowercase_ : int = int(self.size['''shortest_edge'''] * h / w )
lowercase_ : Optional[Any] = self.size['''shortest_edge''']
elif w > h:
lowercase_ : Optional[Any] = self.size['''shortest_edge''']
lowercase_ : Optional[int] = int(self.size['''shortest_edge'''] * w / h )
else:
lowercase_ : Any = self.size['''shortest_edge''']
lowercase_ : Any = self.size['''shortest_edge''']
else:
lowercase_ : Tuple = []
for image in image_inputs:
lowercase_ , lowercase_ : Optional[Any] = self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
lowercase_ : Union[str, Any] = max(A , key=lambda A : item[0] )[0]
lowercase_ : Optional[Any] = max(A , key=lambda A : item[1] )[1]
return expected_height, expected_width
@require_torch
@require_vision
class _UpperCAmelCase ( _A , unittest.TestCase ):
SCREAMING_SNAKE_CASE_ : Optional[Any] = YolosImageProcessor if is_vision_available() else None
def A ( self : Optional[int] ) -> Optional[int]:
lowercase_ : Optional[Any] = YolosImageProcessingTester(self )
@property
def A ( self : str ) -> Any:
return self.image_processor_tester.prepare_image_processor_dict()
def A ( self : Optional[int] ) -> List[str]:
lowercase_ : Tuple = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(A , '''image_mean''' ) )
self.assertTrue(hasattr(A , '''image_std''' ) )
self.assertTrue(hasattr(A , '''do_normalize''' ) )
self.assertTrue(hasattr(A , '''do_resize''' ) )
self.assertTrue(hasattr(A , '''size''' ) )
def A ( self : Dict ) -> Tuple:
lowercase_ : Dict = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'''shortest_edge''': 18, '''longest_edge''': 13_33} )
self.assertEqual(image_processor.do_pad , A )
lowercase_ : Tuple = self.image_processing_class.from_dict(
self.image_processor_dict , size=42 , max_size=84 , pad_and_return_pixel_mask=A )
self.assertEqual(image_processor.size , {'''shortest_edge''': 42, '''longest_edge''': 84} )
self.assertEqual(image_processor.do_pad , A )
def A ( self : Optional[int] ) -> Tuple:
pass
def A ( self : Tuple ) -> int:
# Initialize image_processing
lowercase_ : Dict = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
lowercase_ : str = prepare_image_inputs(self.image_processor_tester , equal_resolution=A )
for image in image_inputs:
self.assertIsInstance(A , Image.Image )
# Test not batched input
lowercase_ : Optional[int] = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
lowercase_ , lowercase_ : Union[str, Any] = self.image_processor_tester.get_expected_values(A )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
lowercase_ , lowercase_ : Dict = self.image_processor_tester.get_expected_values(A , batched=A )
lowercase_ : str = 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,
expected_height,
expected_width,
) , )
def A ( self : str ) -> Any:
# Initialize image_processing
lowercase_ : Any = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
lowercase_ : List[Any] = 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
lowercase_ : List[Any] = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
lowercase_ , lowercase_ : int = self.image_processor_tester.get_expected_values(A )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
lowercase_ : Optional[int] = image_processing(A , return_tensors='''pt''' ).pixel_values
lowercase_ , lowercase_ : List[Any] = self.image_processor_tester.get_expected_values(A , batched=A )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def A ( self : Tuple ) -> Optional[int]:
# Initialize image_processing
lowercase_ : Dict = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
lowercase_ : Optional[int] = 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
lowercase_ : List[str] = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
lowercase_ , lowercase_ : Union[str, Any] = self.image_processor_tester.get_expected_values(A )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
lowercase_ : Any = image_processing(A , return_tensors='''pt''' ).pixel_values
lowercase_ , lowercase_ : List[str] = self.image_processor_tester.get_expected_values(A , batched=A )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def A ( self : Tuple ) -> Optional[Any]:
# Initialize image_processings
lowercase_ : Optional[Any] = self.image_processing_class(**self.image_processor_dict )
lowercase_ : Tuple = self.image_processing_class(do_resize=A , do_normalize=A , do_rescale=A )
# create random PyTorch tensors
lowercase_ : Union[str, Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=A , torchify=A )
for image in image_inputs:
self.assertIsInstance(A , torch.Tensor )
# Test whether the method "pad" and calling the image processor return the same tensors
lowercase_ : Union[str, Any] = image_processing_a.pad(A , return_tensors='''pt''' )
lowercase_ : List[Any] = image_processing_a(A , return_tensors='''pt''' )
self.assertTrue(
torch.allclose(encoded_images_with_method['''pixel_values'''] , encoded_images['''pixel_values'''] , atol=1e-4 ) )
@slow
def A ( self : str ) -> List[Any]:
# prepare image and target
lowercase_ : Dict = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt''' , '''r''' ) as f:
lowercase_ : List[Any] = json.loads(f.read() )
lowercase_ : Tuple = {'''image_id''': 3_97_69, '''annotations''': target}
# encode them
lowercase_ : Union[str, Any] = YolosImageProcessor.from_pretrained('''hustvl/yolos-small''' )
lowercase_ : List[Any] = image_processing(images=A , annotations=A , return_tensors='''pt''' )
# verify pixel values
lowercase_ : Union[str, Any] = torch.Size([1, 3, 8_00, 10_66] )
self.assertEqual(encoding['''pixel_values'''].shape , A )
lowercase_ : Union[str, Any] = torch.tensor([0.2796, 0.3138, 0.3481] )
self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3] , A , atol=1e-4 ) )
# verify area
lowercase_ : Tuple = torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area'''] , A ) )
# verify boxes
lowercase_ : List[str] = torch.Size([6, 4] )
self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape , A )
lowercase_ : Any = torch.tensor([0.5503, 0.2765, 0.0604, 0.2215] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0] , A , atol=1e-3 ) )
# verify image_id
lowercase_ : List[Any] = torch.tensor([3_97_69] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''] , A ) )
# verify is_crowd
lowercase_ : Any = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''] , A ) )
# verify class_labels
lowercase_ : Optional[Any] = torch.tensor([75, 75, 63, 65, 17, 17] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''] , A ) )
# verify orig_size
lowercase_ : List[str] = torch.tensor([4_80, 6_40] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''] , A ) )
# verify size
lowercase_ : Optional[Any] = torch.tensor([8_00, 10_66] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''] , A ) )
@slow
def A ( self : List[Any] ) -> Dict:
# prepare image, target and masks_path
lowercase_ : Tuple = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt''' , '''r''' ) as f:
lowercase_ : str = json.loads(f.read() )
lowercase_ : int = {'''file_name''': '''000000039769.png''', '''image_id''': 3_97_69, '''segments_info''': target}
lowercase_ : List[Any] = pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic''' )
# encode them
lowercase_ : int = YolosImageProcessor(format='''coco_panoptic''' )
lowercase_ : Any = image_processing(images=A , annotations=A , masks_path=A , return_tensors='''pt''' )
# verify pixel values
lowercase_ : Optional[Any] = torch.Size([1, 3, 8_00, 10_66] )
self.assertEqual(encoding['''pixel_values'''].shape , A )
lowercase_ : Tuple = torch.tensor([0.2796, 0.3138, 0.3481] )
self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3] , A , atol=1e-4 ) )
# verify area
lowercase_ : List[Any] = torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area'''] , A ) )
# verify boxes
lowercase_ : str = torch.Size([6, 4] )
self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape , A )
lowercase_ : List[str] = torch.tensor([0.2625, 0.5437, 0.4688, 0.8625] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0] , A , atol=1e-3 ) )
# verify image_id
lowercase_ : List[str] = torch.tensor([3_97_69] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''] , A ) )
# verify is_crowd
lowercase_ : List[str] = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''] , A ) )
# verify class_labels
lowercase_ : Any = torch.tensor([17, 17, 63, 75, 75, 93] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''] , A ) )
# verify masks
lowercase_ : Dict = 82_28_73
self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item() , A )
# verify orig_size
lowercase_ : Tuple = torch.tensor([4_80, 6_40] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''] , A ) )
# verify size
lowercase_ : List[str] = torch.tensor([8_00, 10_66] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''] , A ) )
| 33
| 1
|
"""simple docstring"""
def lowercase ( __snake_case : int ):
if number < 0:
raise ValueError('''number must not be negative''' )
return number & (number - 1) == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 33
|
"""simple docstring"""
def lowercase ( __snake_case : int = 1_0_0 ):
lowercase_ : str = 0
lowercase_ : List[Any] = 0
for i in range(1 , n + 1 ):
sum_of_squares += i**2
sum_of_ints += i
return sum_of_ints**2 - sum_of_squares
if __name__ == "__main__":
print(F"""{solution() = }""")
| 33
| 1
|
"""simple docstring"""
def lowercase ( __snake_case : int = 1_0_0_0 ):
return sum(2 * a * ((a - 1) // 2) for a in range(3 , n + 1 ) )
if __name__ == "__main__":
print(solution())
| 33
|
"""simple docstring"""
import argparse
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline
if __name__ == "__main__":
__A : str = 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 : str = parser.parse_args()
__A : List[Any] = UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)
__A : Dict = CLIPImageProcessor()
__A : Union[str, Any] = CLIPVisionModelWithProjection.from_pretrained('''openai/clip-vit-large-patch14''')
__A : List[str] = 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)
| 33
| 1
|
"""simple docstring"""
from ...utils import is_note_seq_available, is_transformers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import * # noqa F403
else:
from .notes_encoder import SpectrogramNotesEncoder
from .continous_encoder import SpectrogramContEncoder
from .pipeline_spectrogram_diffusion import (
SpectrogramContEncoder,
SpectrogramDiffusionPipeline,
TaFilmDecoder,
)
try:
if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_transformers_and_torch_and_note_seq_objects import * # noqa F403
else:
from .midi_utils import MidiProcessor
| 33
|
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaControlnetImgaImgPipeline,
KandinskyVaaPriorEmbaEmbPipeline,
UNetaDConditionModel,
VQModel,
)
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 _UpperCAmelCase ( _A , unittest.TestCase ):
SCREAMING_SNAKE_CASE_ : Any = KandinskyVaaControlnetImgaImgPipeline
SCREAMING_SNAKE_CASE_ : Optional[int] = ["image_embeds", "negative_image_embeds", "image", "hint"]
SCREAMING_SNAKE_CASE_ : str = ["image_embeds", "negative_image_embeds", "image", "hint"]
SCREAMING_SNAKE_CASE_ : Dict = [
"generator",
"height",
"width",
"strength",
"guidance_scale",
"num_inference_steps",
"return_dict",
"guidance_scale",
"num_images_per_prompt",
"output_type",
"return_dict",
]
SCREAMING_SNAKE_CASE_ : Dict = False
@property
def A ( self : Any ) -> Any:
return 32
@property
def A ( self : Optional[int] ) -> Any:
return 32
@property
def A ( self : Dict ) -> int:
return self.time_input_dim
@property
def A ( self : Tuple ) -> str:
return self.time_input_dim * 4
@property
def A ( self : Any ) -> str:
return 1_00
@property
def A ( self : str ) -> List[str]:
torch.manual_seed(0 )
lowercase_ : List[Any] = {
'''in_channels''': 8,
# Out channels is double in channels because predicts mean and variance
'''out_channels''': 8,
'''addition_embed_type''': '''image_hint''',
'''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''': '''image_proj''',
'''cross_attention_dim''': self.cross_attention_dim,
'''attention_head_dim''': 4,
'''resnet_time_scale_shift''': '''scale_shift''',
'''class_embed_type''': None,
}
lowercase_ : Dict = UNetaDConditionModel(**A )
return model
@property
def A ( self : Optional[Any] ) -> Union[str, Any]:
return {
"block_out_channels": [32, 32, 64, 64],
"down_block_types": [
"DownEncoderBlock2D",
"DownEncoderBlock2D",
"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", "UpDecoderBlock2D", "UpDecoderBlock2D"],
"vq_embed_dim": 4,
}
@property
def A ( self : List[Any] ) -> Dict:
torch.manual_seed(0 )
lowercase_ : int = VQModel(**self.dummy_movq_kwargs )
return model
def A ( self : Union[str, Any] ) -> Optional[int]:
lowercase_ : Tuple = self.dummy_unet
lowercase_ : int = self.dummy_movq
lowercase_ : List[Any] = {
'''num_train_timesteps''': 10_00,
'''beta_schedule''': '''linear''',
'''beta_start''': 0.00085,
'''beta_end''': 0.012,
'''clip_sample''': False,
'''set_alpha_to_one''': False,
'''steps_offset''': 0,
'''prediction_type''': '''epsilon''',
'''thresholding''': False,
}
lowercase_ : str = DDIMScheduler(**A )
lowercase_ : Tuple = {
'''unet''': unet,
'''scheduler''': scheduler,
'''movq''': movq,
}
return components
def A ( self : Optional[int] , A : int , A : List[str]=0 ) -> int:
lowercase_ : str = floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(A ) ).to(A )
lowercase_ : Tuple = floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(seed + 1 ) ).to(
A )
# create init_image
lowercase_ : Union[str, Any] = floats_tensor((1, 3, 64, 64) , rng=random.Random(A ) ).to(A )
lowercase_ : Any = image.cpu().permute(0 , 2 , 3 , 1 )[0]
lowercase_ : Optional[Any] = Image.fromarray(np.uinta(A ) ).convert('''RGB''' ).resize((2_56, 2_56) )
# create hint
lowercase_ : Optional[int] = floats_tensor((1, 3, 64, 64) , rng=random.Random(A ) ).to(A )
if str(A ).startswith('''mps''' ):
lowercase_ : Optional[Any] = torch.manual_seed(A )
else:
lowercase_ : List[Any] = torch.Generator(device=A ).manual_seed(A )
lowercase_ : Dict = {
'''image''': init_image,
'''image_embeds''': image_embeds,
'''negative_image_embeds''': negative_image_embeds,
'''hint''': hint,
'''generator''': generator,
'''height''': 64,
'''width''': 64,
'''num_inference_steps''': 10,
'''guidance_scale''': 7.0,
'''strength''': 0.2,
'''output_type''': '''np''',
}
return inputs
def A ( self : Any ) -> List[Any]:
lowercase_ : List[str] = '''cpu'''
lowercase_ : Any = self.get_dummy_components()
lowercase_ : Any = self.pipeline_class(**A )
lowercase_ : int = pipe.to(A )
pipe.set_progress_bar_config(disable=A )
lowercase_ : Dict = pipe(**self.get_dummy_inputs(A ) )
lowercase_ : str = output.images
lowercase_ : int = pipe(
**self.get_dummy_inputs(A ) , return_dict=A , )[0]
lowercase_ : Dict = image[0, -3:, -3:, -1]
lowercase_ : Union[str, Any] = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
lowercase_ : List[str] = np.array(
[0.54985034, 0.55509365, 0.52561504, 0.5570494, 0.5593818, 0.5263979, 0.50285643, 0.5069846, 0.51196736] )
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()}'''
@slow
@require_torch_gpu
class _UpperCAmelCase ( unittest.TestCase ):
def A ( self : Tuple ) -> str:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def A ( self : Any ) -> Optional[int]:
lowercase_ : Dict = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy''' )
lowercase_ : Dict = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png''' )
lowercase_ : Optional[int] = init_image.resize((5_12, 5_12) )
lowercase_ : Dict = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/kandinskyv22/hint_image_cat.png''' )
lowercase_ : Optional[int] = torch.from_numpy(np.array(A ) ).float() / 255.0
lowercase_ : Tuple = hint.permute(2 , 0 , 1 ).unsqueeze(0 )
lowercase_ : Optional[Any] = '''A robot, 4k photo'''
lowercase_ : Tuple = KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
'''kandinsky-community/kandinsky-2-2-prior''' , torch_dtype=torch.floataa )
pipe_prior.to(A )
lowercase_ : Dict = KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
'''kandinsky-community/kandinsky-2-2-controlnet-depth''' , torch_dtype=torch.floataa )
lowercase_ : int = pipeline.to(A )
pipeline.set_progress_bar_config(disable=A )
lowercase_ : Tuple = torch.Generator(device='''cpu''' ).manual_seed(0 )
lowercase_ , lowercase_ : int = pipe_prior(
A , image=A , strength=0.85 , generator=A , negative_prompt='''''' , ).to_tuple()
lowercase_ : str = pipeline(
image=A , image_embeds=A , negative_image_embeds=A , hint=A , generator=A , num_inference_steps=1_00 , height=5_12 , width=5_12 , strength=0.5 , output_type='''np''' , )
lowercase_ : Optional[Any] = output.images[0]
assert image.shape == (5_12, 5_12, 3)
assert_mean_pixel_difference(A , A )
| 33
| 1
|
"""simple docstring"""
from __future__ import annotations
import math
import numpy as np
from numpy.linalg import norm
def lowercase ( __snake_case : np.ndarray , __snake_case : np.ndarray ):
return math.sqrt(sum(pow(a - b , 2 ) for a, b in zip(__snake_case , __snake_case ) ) )
def lowercase ( __snake_case : np.ndarray , __snake_case : np.ndarray ):
if dataset.ndim != value_array.ndim:
lowercase_ : str = (
'''Wrong input data\'s dimensions... '''
F'''dataset : {dataset.ndim}, value_array : {value_array.ndim}'''
)
raise ValueError(__snake_case )
try:
if dataset.shape[1] != value_array.shape[1]:
lowercase_ : Tuple = (
'''Wrong input data\'s shape... '''
F'''dataset : {dataset.shape[1]}, value_array : {value_array.shape[1]}'''
)
raise ValueError(__snake_case )
except IndexError:
if dataset.ndim != value_array.ndim:
raise TypeError('''Wrong shape''' )
if dataset.dtype != value_array.dtype:
lowercase_ : Dict = (
'''Input data have different datatype... '''
F'''dataset : {dataset.dtype}, value_array : {value_array.dtype}'''
)
raise TypeError(__snake_case )
lowercase_ : Union[str, Any] = []
for value in value_array:
lowercase_ : List[str] = euclidean(__snake_case , dataset[0] )
lowercase_ : Tuple = dataset[0].tolist()
for dataset_value in dataset[1:]:
lowercase_ : Any = euclidean(__snake_case , __snake_case )
if dist > temp_dist:
lowercase_ : Optional[int] = temp_dist
lowercase_ : Any = dataset_value.tolist()
answer.append([vector, dist] )
return answer
def lowercase ( __snake_case : np.ndarray , __snake_case : np.ndarray ):
return np.dot(__snake_case , __snake_case ) / (norm(__snake_case ) * norm(__snake_case ))
if __name__ == "__main__":
import doctest
doctest.testmod()
| 33
|
"""simple docstring"""
def lowercase ( __snake_case : int = 1_0_0_0 ):
lowercase_ , lowercase_ : str = 1, 1
lowercase_ : List[str] = 2
while True:
lowercase_ : Tuple = 0
lowercase_ : List[Any] = fa + fa
lowercase_ , lowercase_ : Optional[int] = fa, f
index += 1
for _ in str(__snake_case ):
i += 1
if i == n:
break
return index
if __name__ == "__main__":
print(solution(int(str(input()).strip())))
| 33
| 1
|
"""simple docstring"""
import os
import numpy
import onnx
def lowercase ( __snake_case : Tuple , __snake_case : Union[str, Any] ):
lowercase_ : str = a.name
lowercase_ : List[str] = b.name
lowercase_ : List[str] = ''''''
lowercase_ : int = ''''''
lowercase_ : Any = a == b
lowercase_ : Dict = name_a
lowercase_ : Union[str, Any] = name_b
return res
def lowercase ( __snake_case : Any , __snake_case : Any , __snake_case : List[Any] ):
for i, input_name in enumerate(node_proto.input ):
if input_name == name:
node_proto.input.insert(__snake_case , __snake_case )
node_proto.input.pop(i + 1 )
if node_proto.op_type == "If":
_graph_replace_input_with(node_proto.attribute[0].g , __snake_case , __snake_case )
_graph_replace_input_with(node_proto.attribute[1].g , __snake_case , __snake_case )
if node_proto.op_type == "Loop":
_graph_replace_input_with(node_proto.attribute[0].g , __snake_case , __snake_case )
def lowercase ( __snake_case : Optional[Any] , __snake_case : Dict , __snake_case : Optional[int] ):
for n in graph_proto.node:
_node_replace_input_with(__snake_case , __snake_case , __snake_case )
def lowercase ( __snake_case : List[Any] , __snake_case : Optional[Any] , __snake_case : Optional[Any] ):
lowercase_ : Any = list(model.graph.initializer )
lowercase_ : List[str] = list(model_without_ext.graph.initializer )
for i, ref_i in ind_to_replace:
assert inits_with_data[i].name == inits[i].name
assert inits_with_data[ref_i].name == inits[ref_i].name
assert i > ref_i
lowercase_ : str = inits[i].name
lowercase_ : List[Any] = inits[ref_i].name
model_without_ext.graph.initializer.remove(inits[i] )
# for n in model.graph.node:
_graph_replace_input_with(model_without_ext.graph , __snake_case , __snake_case )
def lowercase ( __snake_case : Union[str, Any] ):
lowercase_ : str = os.path.dirname(__snake_case )
lowercase_ : Optional[int] = os.path.basename(__snake_case )
lowercase_ : Optional[Any] = onnx.load(os.path.join(__snake_case , __snake_case ) )
lowercase_ : Any = list(model.graph.initializer )
lowercase_ : List[Any] = set()
lowercase_ : List[Any] = {}
lowercase_ : List[str] = []
lowercase_ : Optional[int] = 0
for i in range(len(__snake_case ) ):
if i in dup_set:
continue
for j in range(i + 1 , len(__snake_case ) ):
if j in dup_set:
continue
if _is_equal_tensor_proto(inits[i] , inits[j] ):
dup_set.add(__snake_case )
dup_set.add(__snake_case )
lowercase_ : Dict = inits[j].data_type
lowercase_ : Dict = numpy.prod(inits[j].dims )
if dtype == 1:
mem_size *= 4
elif dtype == 6:
mem_size *= 4
elif dtype == 7 or dtype == 1_1:
mem_size *= 8
else:
print('''unexpected data type: ''' , __snake_case )
total_reduced_size += mem_size
lowercase_ : List[Any] = inits[i].name
lowercase_ : Dict = inits[j].name
if name_i in dup_map:
dup_map[name_i].append(__snake_case )
else:
lowercase_ : Optional[int] = [name_j]
ind_to_replace.append((j, i) )
print('''total reduced size: ''' , total_reduced_size / 1_0_2_4 / 1_0_2_4 / 1_0_2_4 , '''GB''' )
lowercase_ : str = sorted(__snake_case )
_remove_dup_initializers_from_model(__snake_case , __snake_case , __snake_case )
lowercase_ : Dict = '''optimized_''' + model_file_name
lowercase_ : List[Any] = os.path.join(__snake_case , __snake_case )
onnx.save(__snake_case , __snake_case )
return new_model
| 33
|
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : Dict = logging.get_logger(__name__)
__A : Union[str, Any] = {
'''facebook/vit-mae-base''': '''https://huggingface.co/facebook/vit-mae-base/resolve/main/config.json''',
# See all ViT MAE models at https://huggingface.co/models?filter=vit-mae
}
class _UpperCAmelCase ( _A ):
SCREAMING_SNAKE_CASE_ : Union[str, Any] = "vit_mae"
def __init__( self : Dict , A : List[str]=7_68 , A : Any=12 , A : Union[str, Any]=12 , A : Tuple=30_72 , A : Any="gelu" , A : Tuple=0.0 , A : List[str]=0.0 , A : Tuple=0.02 , A : Tuple=1e-12 , A : int=2_24 , A : Dict=16 , A : int=3 , A : Tuple=True , A : Tuple=16 , A : Optional[Any]=5_12 , A : Union[str, Any]=8 , A : List[Any]=20_48 , A : Dict=0.75 , A : Any=False , **A : Optional[int] , ) -> Union[str, Any]:
super().__init__(**A )
lowercase_ : List[Any] = hidden_size
lowercase_ : str = num_hidden_layers
lowercase_ : List[Any] = num_attention_heads
lowercase_ : Any = intermediate_size
lowercase_ : Optional[int] = hidden_act
lowercase_ : List[Any] = hidden_dropout_prob
lowercase_ : int = attention_probs_dropout_prob
lowercase_ : int = initializer_range
lowercase_ : Dict = layer_norm_eps
lowercase_ : Optional[Any] = image_size
lowercase_ : str = patch_size
lowercase_ : Dict = num_channels
lowercase_ : Any = qkv_bias
lowercase_ : Union[str, Any] = decoder_num_attention_heads
lowercase_ : Optional[Any] = decoder_hidden_size
lowercase_ : List[str] = decoder_num_hidden_layers
lowercase_ : List[Any] = decoder_intermediate_size
lowercase_ : Optional[Any] = mask_ratio
lowercase_ : Optional[Any] = norm_pix_loss
| 33
| 1
|
"""simple docstring"""
from __future__ import annotations
def lowercase ( __snake_case : list[int] , __snake_case : int ):
if len(__snake_case ) == 0:
return False
lowercase_ : int = 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__":
__A : Tuple = input('''Enter numbers separated by comma:\n''').strip()
__A : List[Any] = [int(item.strip()) for item in user_input.split(''',''')]
__A : str = int(input('''Enter the number to be found in the list:\n''').strip())
__A : Union[str, Any] = '''''' if binary_search(sequence, target) else '''not '''
print(F"""{target} was {not_str}found in {sequence}""")
| 33
|
"""simple docstring"""
def lowercase ( __snake_case : int ):
if n == 1 or not isinstance(__snake_case , __snake_case ):
return 0
elif n == 2:
return 1
else:
lowercase_ : Dict = [0, 1]
for i in range(2 , n + 1 ):
sequence.append(sequence[i - 1] + sequence[i - 2] )
return sequence[n]
def lowercase ( __snake_case : int ):
lowercase_ : str = 0
lowercase_ : List[str] = 2
while digits < n:
index += 1
lowercase_ : Any = len(str(fibonacci(__snake_case ) ) )
return index
def lowercase ( __snake_case : int = 1_0_0_0 ):
return fibonacci_digits_index(__snake_case )
if __name__ == "__main__":
print(solution(int(str(input()).strip())))
| 33
| 1
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__A : Optional[int] = {
'''configuration_instructblip''': [
'''INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''InstructBlipConfig''',
'''InstructBlipQFormerConfig''',
'''InstructBlipVisionConfig''',
],
'''processing_instructblip''': ['''InstructBlipProcessor'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : Optional[Any] = [
'''INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''InstructBlipQFormerModel''',
'''InstructBlipPreTrainedModel''',
'''InstructBlipForConditionalGeneration''',
'''InstructBlipVisionModel''',
]
if TYPE_CHECKING:
from .configuration_instructblip import (
INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
InstructBlipConfig,
InstructBlipQFormerConfig,
InstructBlipVisionConfig,
)
from .processing_instructblip import InstructBlipProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_instructblip import (
INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
InstructBlipForConditionalGeneration,
InstructBlipPreTrainedModel,
InstructBlipQFormerModel,
InstructBlipVisionModel,
)
else:
import sys
__A : Dict = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 33
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__A : List[str] = {
'''configuration_mobilenet_v2''': [
'''MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''MobileNetV2Config''',
'''MobileNetV2OnnxConfig''',
],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : Dict = ['''MobileNetV2FeatureExtractor''']
__A : Optional[int] = ['''MobileNetV2ImageProcessor''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : Optional[Any] = [
'''MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''MobileNetV2ForImageClassification''',
'''MobileNetV2ForSemanticSegmentation''',
'''MobileNetV2Model''',
'''MobileNetV2PreTrainedModel''',
'''load_tf_weights_in_mobilenet_v2''',
]
if TYPE_CHECKING:
from .configuration_mobilenet_va import (
MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
MobileNetVaConfig,
MobileNetVaOnnxConfig,
)
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_mobilenet_va import MobileNetVaFeatureExtractor
from .image_processing_mobilenet_va import MobileNetVaImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mobilenet_va import (
MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
MobileNetVaForImageClassification,
MobileNetVaForSemanticSegmentation,
MobileNetVaModel,
MobileNetVaPreTrainedModel,
load_tf_weights_in_mobilenet_va,
)
else:
import sys
__A : List[Any] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 33
| 1
|
"""simple docstring"""
import argparse
import torch
from transformers import GPTaLMHeadModel, RobertaForMaskedLM
if __name__ == "__main__":
__A : Any = argparse.ArgumentParser(
description=(
'''Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transfer Learned'''
''' Distillation'''
)
)
parser.add_argument('''--model_type''', default='''roberta''', choices=['''roberta''', '''gpt2'''])
parser.add_argument('''--model_name''', default='''roberta-large''', type=str)
parser.add_argument('''--dump_checkpoint''', default='''serialization_dir/tf_roberta_048131723.pth''', type=str)
parser.add_argument('''--vocab_transform''', action='''store_true''')
__A : Union[str, Any] = parser.parse_args()
if args.model_type == "roberta":
__A : Any = RobertaForMaskedLM.from_pretrained(args.model_name)
__A : Optional[Any] = '''roberta'''
elif args.model_type == "gpt2":
__A : Tuple = GPTaLMHeadModel.from_pretrained(args.model_name)
__A : List[Any] = '''transformer'''
__A : Any = model.state_dict()
__A : Any = {}
# Embeddings #
if args.model_type == "gpt2":
for param_name in ["wte.weight", "wpe.weight"]:
__A : Optional[int] = state_dict[F"""{prefix}.{param_name}"""]
else:
for w in ["word_embeddings", "position_embeddings", "token_type_embeddings"]:
__A : Tuple = F"""{prefix}.embeddings.{w}.weight"""
__A : Union[str, Any] = state_dict[param_name]
for w in ["weight", "bias"]:
__A : Union[str, Any] = F"""{prefix}.embeddings.LayerNorm.{w}"""
__A : List[str] = state_dict[param_name]
# Transformer Blocks #
__A : Optional[int] = 0
for teacher_idx in [0, 2, 4, 7, 9, 11]:
if args.model_type == "gpt2":
for layer in ["ln_1", "attn.c_attn", "attn.c_proj", "ln_2", "mlp.c_fc", "mlp.c_proj"]:
for w in ["weight", "bias"]:
__A : Union[str, Any] = state_dict[
F"""{prefix}.h.{teacher_idx}.{layer}.{w}"""
]
__A : int = state_dict[F"""{prefix}.h.{teacher_idx}.attn.bias"""]
else:
for layer in [
"attention.self.query",
"attention.self.key",
"attention.self.value",
"attention.output.dense",
"attention.output.LayerNorm",
"intermediate.dense",
"output.dense",
"output.LayerNorm",
]:
for w in ["weight", "bias"]:
__A : Optional[Any] = state_dict[
F"""{prefix}.encoder.layer.{teacher_idx}.{layer}.{w}"""
]
std_idx += 1
# Language Modeling Head ###s
if args.model_type == "roberta":
for layer in ["lm_head.decoder.weight", "lm_head.bias"]:
__A : List[str] = state_dict[F"""{layer}"""]
if args.vocab_transform:
for w in ["weight", "bias"]:
__A : List[str] = state_dict[F"""lm_head.dense.{w}"""]
__A : Optional[int] = state_dict[F"""lm_head.layer_norm.{w}"""]
elif args.model_type == "gpt2":
for w in ["weight", "bias"]:
__A : Union[str, Any] = state_dict[F"""{prefix}.ln_f.{w}"""]
__A : Optional[int] = state_dict['''lm_head.weight''']
print(F"""N layers selected for distillation: {std_idx}""")
print(F"""Number of params transferred for distillation: {len(compressed_sd.keys())}""")
print(F"""Save transferred checkpoint to {args.dump_checkpoint}.""")
torch.save(compressed_sd, args.dump_checkpoint)
| 33
|
"""simple docstring"""
from __future__ import annotations
__A : List[Any] = [-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
__A : str = [-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]
def lowercase ( __snake_case : list[float] ):
lowercase_ : List[str] = []
lowercase_ : List[Any] = len(__snake_case )
for i in range(__snake_case ):
lowercase_ : float = -1
for j in range(i + 1 , __snake_case ):
if arr[i] < arr[j]:
lowercase_ : List[str] = arr[j]
break
result.append(__snake_case )
return result
def lowercase ( __snake_case : list[float] ):
lowercase_ : List[str] = []
for i, outer in enumerate(__snake_case ):
lowercase_ : float = -1
for inner in arr[i + 1 :]:
if outer < inner:
lowercase_ : List[Any] = inner
break
result.append(__snake_case )
return result
def lowercase ( __snake_case : list[float] ):
lowercase_ : List[str] = len(__snake_case )
lowercase_ : list[float] = []
lowercase_ : list[float] = [-1] * arr_size
for index in reversed(range(__snake_case ) ):
if stack:
while stack[-1] <= arr[index]:
stack.pop()
if not stack:
break
if stack:
lowercase_ : Optional[Any] = stack[-1]
stack.append(arr[index] )
return result
if __name__ == "__main__":
from doctest import testmod
from timeit import timeit
testmod()
print(next_greatest_element_slow(arr))
print(next_greatest_element_fast(arr))
print(next_greatest_element(arr))
__A : int = (
'''from __main__ import arr, next_greatest_element_slow, '''
'''next_greatest_element_fast, next_greatest_element'''
)
print(
'''next_greatest_element_slow():''',
timeit('''next_greatest_element_slow(arr)''', setup=setup),
)
print(
'''next_greatest_element_fast():''',
timeit('''next_greatest_element_fast(arr)''', setup=setup),
)
print(
''' next_greatest_element():''',
timeit('''next_greatest_element(arr)''', setup=setup),
)
| 33
| 1
|
"""simple docstring"""
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class _UpperCAmelCase ( _A ):
SCREAMING_SNAKE_CASE_ : List[Any] = ["image_processor", "tokenizer"]
SCREAMING_SNAKE_CASE_ : Union[str, Any] = "BlipImageProcessor"
SCREAMING_SNAKE_CASE_ : int = ("BertTokenizer", "BertTokenizerFast")
def __init__( self : int , A : Optional[int] , A : Union[str, Any] ) -> Tuple:
lowercase_ : int = False
super().__init__(A , A )
lowercase_ : str = self.image_processor
def __call__( self : List[str] , A : ImageInput = None , A : Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None , A : bool = True , A : Union[bool, str, PaddingStrategy] = False , A : Union[bool, str, TruncationStrategy] = None , A : Optional[int] = None , A : int = 0 , A : Optional[int] = None , A : Optional[bool] = None , A : bool = False , A : bool = False , A : bool = False , A : bool = False , A : bool = False , A : bool = True , A : Optional[Union[str, TensorType]] = None , **A : Dict , ) -> BatchEncoding:
if images is None and text is None:
raise ValueError('''You have to specify either images or text.''' )
# Get only text
if images is None:
lowercase_ : Optional[Any] = self.tokenizer
lowercase_ : Tuple = self.tokenizer(
text=A , add_special_tokens=A , padding=A , truncation=A , max_length=A , stride=A , pad_to_multiple_of=A , return_attention_mask=A , return_overflowing_tokens=A , return_special_tokens_mask=A , return_offsets_mapping=A , return_token_type_ids=A , return_length=A , verbose=A , return_tensors=A , **A , )
return text_encoding
# add pixel_values
lowercase_ : List[str] = self.image_processor(A , return_tensors=A )
if text is not None:
lowercase_ : Optional[int] = self.tokenizer(
text=A , add_special_tokens=A , padding=A , truncation=A , max_length=A , stride=A , pad_to_multiple_of=A , return_attention_mask=A , return_overflowing_tokens=A , return_special_tokens_mask=A , return_offsets_mapping=A , return_token_type_ids=A , return_length=A , verbose=A , return_tensors=A , **A , )
else:
lowercase_ : str = None
if text_encoding is not None:
encoding_image_processor.update(A )
return encoding_image_processor
def A ( self : Union[str, Any] , *A : str , **A : List[Any] ) -> Optional[int]:
return self.tokenizer.batch_decode(*A , **A )
def A ( self : int , *A : Optional[int] , **A : Any ) -> int:
return self.tokenizer.decode(*A , **A )
@property
def A ( self : Optional[Any] ) -> Any:
lowercase_ : Optional[Any] = self.tokenizer.model_input_names
lowercase_ : List[str] = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
| 33
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__A : Union[str, Any] = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : Dict = ['''NllbTokenizer''']
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : Dict = ['''NllbTokenizerFast''']
if TYPE_CHECKING:
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_nllb import NllbTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_nllb_fast import NllbTokenizerFast
else:
import sys
__A : List[Any] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 33
| 1
|
"""simple docstring"""
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 _UpperCAmelCase ( unittest.TestCase ):
def A ( self : List[str] , A : Union[str, Any] ) -> int:
for model_result in results.values():
for batch_size, sequence_length in zip(model_result['''bs'''] , model_result['''ss'''] ):
lowercase_ : Tuple = model_result['''result'''][batch_size][sequence_length]
self.assertIsNotNone(A )
def A ( self : int ) -> Optional[Any]:
lowercase_ : Optional[Any] = '''sshleifer/tiny-gpt2'''
lowercase_ : int = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=A , inference=A , sequence_lengths=[8] , batch_sizes=[1] , multi_process=A , )
lowercase_ : List[str] = PyTorchBenchmark(A )
lowercase_ : Optional[Any] = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def A ( self : int ) -> Optional[Any]:
lowercase_ : List[Any] = '''sgugger/tiny-distilbert-classification'''
lowercase_ : Optional[Any] = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=A , inference=A , sequence_lengths=[8] , batch_sizes=[1] , multi_process=A , only_pretrain_model=A , )
lowercase_ : Tuple = PyTorchBenchmark(A )
lowercase_ : Optional[int] = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def A ( self : List[Any] ) -> str:
lowercase_ : str = '''sshleifer/tiny-gpt2'''
lowercase_ : Any = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=A , inference=A , torchscript=A , sequence_lengths=[8] , batch_sizes=[1] , multi_process=A , )
lowercase_ : List[Any] = PyTorchBenchmark(A )
lowercase_ : Dict = 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 A ( self : List[Any] ) -> str:
lowercase_ : int = '''sshleifer/tiny-gpt2'''
lowercase_ : Tuple = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=A , inference=A , fpaa=A , sequence_lengths=[8] , batch_sizes=[1] , multi_process=A , )
lowercase_ : Any = PyTorchBenchmark(A )
lowercase_ : Dict = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def A ( self : Union[str, Any] ) -> Optional[int]:
lowercase_ : Union[str, Any] = '''sshleifer/tiny-gpt2'''
lowercase_ : Optional[int] = AutoConfig.from_pretrained(A )
# set architectures equal to `None`
lowercase_ : str = None
lowercase_ : Tuple = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=A , inference=A , sequence_lengths=[8] , batch_sizes=[1] , multi_process=A , )
lowercase_ : Any = PyTorchBenchmark(A , configs=[config] )
lowercase_ : 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 A ( self : Optional[Any] ) -> List[Any]:
lowercase_ : Optional[Any] = '''sshleifer/tiny-gpt2'''
lowercase_ : str = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=A , inference=A , sequence_lengths=[8] , batch_sizes=[1] , multi_process=A , )
lowercase_ : List[str] = PyTorchBenchmark(A )
lowercase_ : Dict = 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 A ( self : Optional[Any] ) -> Dict:
lowercase_ : Optional[Any] = '''sshleifer/tiny-gpt2'''
lowercase_ : str = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=A , inference=A , sequence_lengths=[8] , batch_sizes=[1] , fpaa=A , multi_process=A , )
lowercase_ : int = PyTorchBenchmark(A )
lowercase_ : Optional[int] = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result )
self.check_results_dict_not_empty(results.memory_train_result )
def A ( self : int ) -> Optional[Any]:
lowercase_ : List[Any] = '''sshleifer/tiny-gpt2'''
lowercase_ : Any = AutoConfig.from_pretrained(A )
lowercase_ : Any = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=A , inference=A , sequence_lengths=[8] , batch_sizes=[1] , multi_process=A , )
lowercase_ : Optional[Any] = PyTorchBenchmark(A , configs=[config] )
lowercase_ : 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 A ( self : Any ) -> List[Any]:
lowercase_ : Union[str, Any] = '''sshleifer/tinier_bart'''
lowercase_ : Optional[Any] = AutoConfig.from_pretrained(A )
lowercase_ : int = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=A , inference=A , sequence_lengths=[8] , batch_sizes=[1] , multi_process=A , )
lowercase_ : Any = PyTorchBenchmark(A , configs=[config] )
lowercase_ : int = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def A ( self : List[str] ) -> Optional[int]:
lowercase_ : str = '''sshleifer/tiny-gpt2'''
lowercase_ : Union[str, Any] = AutoConfig.from_pretrained(A )
lowercase_ : List[Any] = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=A , inference=A , sequence_lengths=[8] , batch_sizes=[1] , multi_process=A , )
lowercase_ : List[str] = PyTorchBenchmark(A , configs=[config] )
lowercase_ : str = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result )
self.check_results_dict_not_empty(results.memory_train_result )
def A ( self : Optional[Any] ) -> int:
lowercase_ : Optional[Any] = '''sshleifer/tinier_bart'''
lowercase_ : Optional[Any] = AutoConfig.from_pretrained(A )
lowercase_ : Tuple = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=A , inference=A , sequence_lengths=[8] , batch_sizes=[1] , multi_process=A , )
lowercase_ : str = PyTorchBenchmark(A , configs=[config] )
lowercase_ : str = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result )
self.check_results_dict_not_empty(results.memory_train_result )
def A ( self : Union[str, Any] ) -> Optional[int]:
lowercase_ : List[Any] = '''sshleifer/tiny-gpt2'''
with tempfile.TemporaryDirectory() as tmp_dir:
lowercase_ : Any = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=A , inference=A , save_to_csv=A , sequence_lengths=[8] , batch_sizes=[1] , inference_time_csv_file=os.path.join(A , '''inf_time.csv''' ) , train_memory_csv_file=os.path.join(A , '''train_mem.csv''' ) , inference_memory_csv_file=os.path.join(A , '''inf_mem.csv''' ) , train_time_csv_file=os.path.join(A , '''train_time.csv''' ) , env_info_csv_file=os.path.join(A , '''env.csv''' ) , multi_process=A , )
lowercase_ : str = PyTorchBenchmark(A )
benchmark.run()
self.assertTrue(Path(os.path.join(A , '''inf_time.csv''' ) ).exists() )
self.assertTrue(Path(os.path.join(A , '''train_time.csv''' ) ).exists() )
self.assertTrue(Path(os.path.join(A , '''inf_mem.csv''' ) ).exists() )
self.assertTrue(Path(os.path.join(A , '''train_mem.csv''' ) ).exists() )
self.assertTrue(Path(os.path.join(A , '''env.csv''' ) ).exists() )
def A ( self : str ) -> Tuple:
lowercase_ : str = '''sshleifer/tiny-gpt2'''
def _check_summary_is_not_empty(A : List[Any] ):
self.assertTrue(hasattr(A , '''sequential''' ) )
self.assertTrue(hasattr(A , '''cumulative''' ) )
self.assertTrue(hasattr(A , '''current''' ) )
self.assertTrue(hasattr(A , '''total''' ) )
with tempfile.TemporaryDirectory() as tmp_dir:
lowercase_ : Any = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=A , inference=A , sequence_lengths=[8] , batch_sizes=[1] , log_filename=os.path.join(A , '''log.txt''' ) , log_print=A , trace_memory_line_by_line=A , multi_process=A , )
lowercase_ : Optional[int] = PyTorchBenchmark(A )
lowercase_ : Dict = benchmark.run()
_check_summary_is_not_empty(result.inference_summary )
_check_summary_is_not_empty(result.train_summary )
self.assertTrue(Path(os.path.join(A , '''log.txt''' ) ).exists() )
| 33
|
"""simple docstring"""
def lowercase ( __snake_case : int ):
if not isinstance(__snake_case , __snake_case ):
raise ValueError('''Input must be an integer''' )
if input_num <= 0:
raise ValueError('''Input must be positive''' )
return sum(
divisor for divisor in range(1 , input_num // 2 + 1 ) if input_num % divisor == 0 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 33
| 1
|
"""simple docstring"""
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data import Dataset
from transformers import BartTokenizer, RagTokenizer, TaTokenizer
def lowercase ( __snake_case : int , __snake_case : Dict , __snake_case : List[str] , __snake_case : Optional[int] , __snake_case : str=True , __snake_case : int="pt" ):
lowercase_ : int = {'''add_prefix_space''': True} if isinstance(__snake_case , __snake_case ) and not line.startswith(''' ''' ) else {}
lowercase_ : str = padding_side
return tokenizer(
[line] , max_length=__snake_case , padding='''max_length''' if pad_to_max_length else None , truncation=__snake_case , return_tensors=__snake_case , add_special_tokens=__snake_case , **__snake_case , )
def lowercase ( __snake_case : Dict , __snake_case : Dict , __snake_case : int=None , ):
lowercase_ : Optional[Any] = input_ids.ne(__snake_case ).any(dim=0 )
if attention_mask is None:
return input_ids[:, keep_column_mask]
else:
return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])
class _UpperCAmelCase ( _A ):
def __init__( self : Any , A : str , A : Optional[Any] , A : int , A : Optional[Any] , A : Optional[int]="train" , A : Dict=None , A : int=None , A : Union[str, Any]=None , A : List[str]="" , ) -> List[str]:
super().__init__()
lowercase_ : Any = Path(A ).joinpath(type_path + '''.source''' )
lowercase_ : int = Path(A ).joinpath(type_path + '''.target''' )
lowercase_ : Optional[Any] = self.get_char_lens(self.src_file )
lowercase_ : str = max_source_length
lowercase_ : Any = max_target_length
assert min(self.src_lens ) > 0, F'''found empty line in {self.src_file}'''
lowercase_ : Union[str, Any] = tokenizer
lowercase_ : Optional[int] = prefix
if n_obs is not None:
lowercase_ : Union[str, Any] = self.src_lens[:n_obs]
lowercase_ : str = src_lang
lowercase_ : Any = tgt_lang
def __len__( self : List[Any] ) -> str:
return len(self.src_lens )
def __getitem__( self : Any , A : List[str] ) -> Dict[str, torch.Tensor]:
lowercase_ : List[str] = index + 1 # linecache starts at 1
lowercase_ : int = self.prefix + linecache.getline(str(self.src_file ) , A ).rstrip('''\n''' )
lowercase_ : int = linecache.getline(str(self.tgt_file ) , A ).rstrip('''\n''' )
assert source_line, F'''empty source line for index {index}'''
assert tgt_line, F'''empty tgt line for index {index}'''
# Need to add eos token manually for T5
if isinstance(self.tokenizer , A ):
source_line += self.tokenizer.eos_token
tgt_line += self.tokenizer.eos_token
# Pad source and target to the right
lowercase_ : Tuple = (
self.tokenizer.question_encoder if isinstance(self.tokenizer , A ) else self.tokenizer
)
lowercase_ : Any = self.tokenizer.generator if isinstance(self.tokenizer , A ) else self.tokenizer
lowercase_ : str = encode_line(A , A , self.max_source_length , '''right''' )
lowercase_ : Optional[Any] = encode_line(A , A , self.max_target_length , '''right''' )
lowercase_ : Any = source_inputs['''input_ids'''].squeeze()
lowercase_ : List[Any] = target_inputs['''input_ids'''].squeeze()
lowercase_ : List[str] = source_inputs['''attention_mask'''].squeeze()
return {
"input_ids": source_ids,
"attention_mask": src_mask,
"decoder_input_ids": target_ids,
}
@staticmethod
def A ( A : Tuple ) -> List[str]:
return [len(A ) for x in Path(A ).open().readlines()]
def A ( self : Dict , A : Union[str, Any] ) -> Dict[str, torch.Tensor]:
lowercase_ : List[Any] = torch.stack([x['''input_ids'''] for x in batch] )
lowercase_ : str = torch.stack([x['''attention_mask'''] for x in batch] )
lowercase_ : int = torch.stack([x['''decoder_input_ids'''] for x in batch] )
lowercase_ : str = (
self.tokenizer.generator.pad_token_id
if isinstance(self.tokenizer , A )
else self.tokenizer.pad_token_id
)
lowercase_ : Optional[Any] = (
self.tokenizer.question_encoder.pad_token_id
if isinstance(self.tokenizer , A )
else self.tokenizer.pad_token_id
)
lowercase_ : int = trim_batch(A , A )
lowercase_ , lowercase_ : List[Any] = trim_batch(A , A , attention_mask=A )
lowercase_ : List[str] = {
'''input_ids''': source_ids,
'''attention_mask''': source_mask,
'''decoder_input_ids''': y,
}
return batch
__A : Tuple = getLogger(__name__)
def lowercase ( __snake_case : List[List] ):
return list(itertools.chain.from_iterable(__snake_case ) )
def lowercase ( __snake_case : str ):
lowercase_ : str = get_git_info()
save_json(__snake_case , os.path.join(__snake_case , '''git_log.json''' ) )
def lowercase ( __snake_case : int , __snake_case : List[Any] , __snake_case : Any=4 , **__snake_case : Dict ):
with open(__snake_case , '''w''' ) as f:
json.dump(__snake_case , __snake_case , indent=__snake_case , **__snake_case )
def lowercase ( __snake_case : Tuple ):
with open(__snake_case ) as f:
return json.load(__snake_case )
def lowercase ( ):
lowercase_ : Union[str, Any] = git.Repo(search_parent_directories=__snake_case )
lowercase_ : Optional[Any] = {
'''repo_id''': str(__snake_case ),
'''repo_sha''': str(repo.head.object.hexsha ),
'''repo_branch''': str(repo.active_branch ),
'''hostname''': str(socket.gethostname() ),
}
return repo_infos
def lowercase ( __snake_case : Callable , __snake_case : Iterable ):
return list(map(__snake_case , __snake_case ) )
def lowercase ( __snake_case : Any , __snake_case : List[str] ):
with open(__snake_case , '''wb''' ) as f:
return pickle.dump(__snake_case , __snake_case )
def lowercase ( __snake_case : Any ):
def remove_articles(__snake_case : List[Any] ):
return re.sub(r'''\b(a|an|the)\b''' , ''' ''' , __snake_case )
def white_space_fix(__snake_case : List[str] ):
return " ".join(text.split() )
def remove_punc(__snake_case : Optional[int] ):
lowercase_ : str = set(string.punctuation )
return "".join(ch for ch in text if ch not in exclude )
def lower(__snake_case : Tuple ):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(__snake_case ) ) ) )
def lowercase ( __snake_case : List[Any] , __snake_case : Dict ):
lowercase_ : Optional[Any] = normalize_answer(__snake_case ).split()
lowercase_ : List[Any] = normalize_answer(__snake_case ).split()
lowercase_ : str = Counter(__snake_case ) & Counter(__snake_case )
lowercase_ : Union[str, Any] = sum(common.values() )
if num_same == 0:
return 0
lowercase_ : Dict = 1.0 * num_same / len(__snake_case )
lowercase_ : str = 1.0 * num_same / len(__snake_case )
lowercase_ : Union[str, Any] = (2 * precision * recall) / (precision + recall)
return fa
def lowercase ( __snake_case : Optional[Any] , __snake_case : Any ):
return normalize_answer(__snake_case ) == normalize_answer(__snake_case )
def lowercase ( __snake_case : List[str] , __snake_case : List[str] ):
assert len(__snake_case ) == len(__snake_case )
lowercase_ : Union[str, Any] = 0
for hypo, pred in zip(__snake_case , __snake_case ):
em += exact_match_score(__snake_case , __snake_case )
if len(__snake_case ) > 0:
em /= len(__snake_case )
return {"em": em}
def lowercase ( __snake_case : List[str] ):
return model_prefix.startswith('''rag''' )
def lowercase ( __snake_case : Optional[int] , __snake_case : str , __snake_case : Union[str, Any] ):
lowercase_ : Union[str, Any] = {p: p for p in extra_params}
# T5 models don't have `dropout` param, they have `dropout_rate` instead
lowercase_ : List[str] = '''dropout_rate'''
for p in extra_params:
if getattr(__snake_case , __snake_case , __snake_case ):
if not hasattr(__snake_case , __snake_case ) and not hasattr(__snake_case , equivalent_param[p] ):
logger.info('''config doesn\'t have a `{}` attribute'''.format(__snake_case ) )
delattr(__snake_case , __snake_case )
continue
lowercase_ : Tuple = p if hasattr(__snake_case , __snake_case ) else equivalent_param[p]
setattr(__snake_case , __snake_case , getattr(__snake_case , __snake_case ) )
delattr(__snake_case , __snake_case )
return hparams, config
| 33
|
"""simple docstring"""
def lowercase ( __snake_case : Optional[int] ):
lowercase_ : int = 0
lowercase_ : Optional[Any] = len(__snake_case )
for i in range(n - 1 ):
for j in range(i + 1 , __snake_case ):
if arr[i] > arr[j]:
num_inversions += 1
return num_inversions
def lowercase ( __snake_case : str ):
if len(__snake_case ) <= 1:
return arr, 0
lowercase_ : Optional[Any] = len(__snake_case ) // 2
lowercase_ : List[Any] = arr[0:mid]
lowercase_ : Union[str, Any] = arr[mid:]
lowercase_ , lowercase_ : Tuple = count_inversions_recursive(__snake_case )
lowercase_ , lowercase_ : List[Any] = count_inversions_recursive(__snake_case )
lowercase_ , lowercase_ : List[Any] = _count_cross_inversions(__snake_case , __snake_case )
lowercase_ : List[Any] = inversion_p + inversions_q + cross_inversions
return c, num_inversions
def lowercase ( __snake_case : str , __snake_case : Optional[int] ):
lowercase_ : Optional[Any] = []
lowercase_ : Any = 0
while i < len(__snake_case ) and j < len(__snake_case ):
if p[i] > q[j]:
# if P[1] > Q[j], then P[k] > Q[k] for all i < k <= len(P)
# These are all inversions. The claim emerges from the
# property that P is sorted.
num_inversion += len(__snake_case ) - i
r.append(q[j] )
j += 1
else:
r.append(p[i] )
i += 1
if i < len(__snake_case ):
r.extend(p[i:] )
else:
r.extend(q[j:] )
return r, num_inversion
def lowercase ( ):
lowercase_ : Union[str, Any] = [1_0, 2, 1, 5, 5, 2, 1_1]
# this arr has 8 inversions:
# (10, 2), (10, 1), (10, 5), (10, 5), (10, 2), (2, 1), (5, 2), (5, 2)
lowercase_ : int = count_inversions_bf(__snake_case )
lowercase_ , lowercase_ : Dict = count_inversions_recursive(__snake_case )
assert num_inversions_bf == num_inversions_recursive == 8
print('''number of inversions = ''' , __snake_case )
# testing an array with zero inversion (a sorted arr_1)
arr_a.sort()
lowercase_ : Dict = count_inversions_bf(__snake_case )
lowercase_ , lowercase_ : Dict = count_inversions_recursive(__snake_case )
assert num_inversions_bf == num_inversions_recursive == 0
print('''number of inversions = ''' , __snake_case )
# an empty list should also have zero inversions
lowercase_ : List[Any] = []
lowercase_ : Any = count_inversions_bf(__snake_case )
lowercase_ , lowercase_ : List[str] = count_inversions_recursive(__snake_case )
assert num_inversions_bf == num_inversions_recursive == 0
print('''number of inversions = ''' , __snake_case )
if __name__ == "__main__":
main()
| 33
| 1
|
"""simple docstring"""
import argparse
import json
import os
import re
import shutil
import torch
from transformers import BioGptConfig, BioGptForCausalLM
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEIGHTS_NAME, logging
logging.set_verbosity_warning()
__A : List[Any] = 2
class _UpperCAmelCase :
def __init__( self : Tuple , *, # begin keyword-only arguments
A : Tuple="<s>" , A : List[str]="<pad>" , A : Optional[Any]="</s>" , A : str="<unk>" , A : int=None , ) -> Union[str, Any]:
lowercase_ , lowercase_ , lowercase_ , lowercase_ : List[str] = bos, unk, pad, eos
lowercase_ : Tuple = []
lowercase_ : Union[str, Any] = []
lowercase_ : Dict = {}
lowercase_ : List[Any] = self.add_symbol(A )
lowercase_ : Optional[Any] = self.add_symbol(A )
lowercase_ : Optional[Any] = self.add_symbol(A )
lowercase_ : str = self.add_symbol(A )
if extra_special_symbols:
for s in extra_special_symbols:
self.add_symbol(A )
lowercase_ : int = len(self.symbols )
def __eq__( self : str , A : Tuple ) -> Any:
return self.indices == other.indices
def __getitem__( self : int , A : Tuple ) -> Any:
if idx < len(self.symbols ):
return self.symbols[idx]
return self.unk_word
def __len__( self : Any ) -> Union[str, Any]:
return len(self.symbols )
def __contains__( self : Optional[Any] , A : Optional[int] ) -> Dict:
return sym in self.indices
@classmethod
def A ( cls : Optional[int] , A : Dict ) -> Any:
lowercase_ : Any = cls()
d.add_from_file(A )
return d
def A ( self : List[Any] , A : int , A : List[Any]=1 , A : List[str]=False ) -> Dict:
if word in self.indices and not overwrite:
lowercase_ : Optional[int] = self.indices[word]
lowercase_ : Tuple = self.count[idx] + n
return idx
else:
lowercase_ : Dict = len(self.symbols )
lowercase_ : int = idx
self.symbols.append(A )
self.count.append(A )
return idx
def A ( self : int , A : Tuple ) -> List[str]:
return 0
def A ( self : str , A : str ) -> Tuple:
if isinstance(A , A ):
try:
with open(A , '''r''' , encoding='''utf-8''' ) as fd:
self.add_from_file(A )
except FileNotFoundError as fnfe:
raise fnfe
except UnicodeError:
raise Exception('''Incorrect encoding detected in {}, please rebuild the dataset'''.format(A ) )
return
lowercase_ : Any = f.readlines()
lowercase_ : int = self._load_meta(A )
for line in lines[indices_start_line:]:
try:
lowercase_ , lowercase_ : Any = line.rstrip().rsplit(''' ''' , 1 )
if field == "#fairseq:overwrite":
lowercase_ : str = True
lowercase_ , lowercase_ : Union[str, Any] = line.rsplit(''' ''' , 1 )
else:
lowercase_ : Tuple = False
lowercase_ : Optional[int] = int(A )
lowercase_ : Optional[int] = line
if word in self and not overwrite:
raise RuntimeError(
'''Duplicate word found when loading Dictionary: \'{}\'. '''
'''Duplicate words can overwrite earlier ones by adding the '''
'''#fairseq:overwrite flag at the end of the corresponding row '''
'''in the dictionary file. If using the Camembert model, please '''
'''download an updated copy of the model file.'''.format(A ) )
self.add_symbol(A , n=A , overwrite=A )
except ValueError:
raise ValueError('''Incorrect dictionary format, expected \'<token> <cnt> [flags]\'''' )
def lowercase ( __snake_case : Dict ):
# (1) remove word breaking symbol, (2) add word ending symbol where the word is not broken up,
# e.g.: d = {'le@@': 5, 'tt@@': 6, 'er': 7} => {'le': 5, 'tt': 6, 'er</w>': 7}
lowercase_ : Dict = dict((re.sub(r'''@@$''' , '''''' , __snake_case ), v) if k.endswith('''@@''' ) else (re.sub(r'''$''' , '''</w>''' , __snake_case ), v) for k, v in d.items() )
lowercase_ : int = '''<s> <pad> </s> <unk>'''.split()
# restore the special tokens
for k in keep_keys:
del da[F'''{k}</w>''']
lowercase_ : Union[str, Any] = d[k] # restore
return da
def lowercase ( __snake_case : Tuple , __snake_case : Any ):
# prep
if not os.path.exists(__snake_case ):
raise ValueError(F'''path {biogpt_checkpoint_path} does not exist!''' )
os.makedirs(__snake_case , exist_ok=__snake_case )
print(F'''Writing results to {pytorch_dump_folder_path}''' )
# handle various types of models
lowercase_ : Optional[Any] = os.path.join(__snake_case , '''checkpoint.pt''' )
if not os.path.isfile(__snake_case ):
raise ValueError(F'''path to the file {checkpoint_file} does not exist!''' )
lowercase_ : Union[str, Any] = torch.load(__snake_case , map_location='''cpu''' )
lowercase_ : int = chkpt['''cfg''']['''model''']
# dicts
lowercase_ : int = os.path.join(__snake_case , '''dict.txt''' )
if not os.path.isfile(__snake_case ):
raise ValueError(F'''path to the file {dict_file} does not exist!''' )
lowercase_ : str = Dictionary.load(__snake_case )
lowercase_ : List[str] = rewrite_dict_keys(src_dict.indices )
lowercase_ : Dict = len(__snake_case )
lowercase_ : int = os.path.join(__snake_case , VOCAB_FILES_NAMES['''vocab_file'''] )
print(F'''Generating {src_vocab_file} of {src_vocab_size} records''' )
with open(__snake_case , '''w''' , encoding='''utf-8''' ) as f:
f.write(json.dumps(__snake_case , ensure_ascii=__snake_case , indent=__snake_case ) )
# merges_file (bpecodes)
lowercase_ : Optional[int] = os.path.join(__snake_case , '''bpecodes''' )
if not os.path.isfile(__snake_case ):
raise ValueError(F'''path to the file {bpecodes_file} does not exist!''' )
lowercase_ : List[Any] = os.path.join(__snake_case , VOCAB_FILES_NAMES['''merges_file'''] )
shutil.copyfile(__snake_case , __snake_case )
# model config
lowercase_ : Union[str, Any] = os.path.join(__snake_case , '''config.json''' )
lowercase_ : Dict = {
'''activation_dropout''': args['''activation_dropout'''],
'''architectures''': ['''BioGptForCausalLM'''],
'''attention_probs_dropout_prob''': args['''attention_dropout'''],
'''bos_token_id''': 0,
'''eos_token_id''': 2,
'''hidden_act''': args['''activation_fn'''],
'''hidden_dropout_prob''': args['''dropout'''],
'''hidden_size''': args['''decoder_embed_dim'''],
'''initializer_range''': 0.02,
'''intermediate_size''': args['''decoder_ffn_embed_dim'''],
'''layer_norm_eps''': 1e-12,
'''layerdrop''': args['''decoder_layerdrop'''],
'''max_position_embeddings''': args['''max_target_positions'''],
'''model_type''': '''biogpt''',
'''num_attention_heads''': args['''decoder_attention_heads'''],
'''num_hidden_layers''': args['''decoder_layers'''],
'''pad_token_id''': 1,
'''scale_embedding''': not args['''no_scale_embedding'''],
'''tie_word_embeddings''': args['''share_decoder_input_output_embed'''],
'''vocab_size''': src_vocab_size,
}
# good hparam defaults to start with
print(F'''Generating {biogpt_model_config_file}''' )
with open(__snake_case , '''w''' , encoding='''utf-8''' ) as f:
f.write(json.dumps(__snake_case , ensure_ascii=__snake_case , indent=__snake_case ) )
# tokenizer config
lowercase_ : Optional[int] = os.path.join(__snake_case , __snake_case )
lowercase_ : str = {
'''bos_token''': '''<s>''',
'''eos_token''': '''</s>''',
'''model_max_length''': 1_0_2_4,
'''pad_token''': '''<pad>''',
'''special_tokens_map_file''': None,
'''tokenizer_class''': '''BioGptTokenizer''',
'''unk_token''': '''<unk>''',
}
print(F'''Generating {biogpt_tokenizer_config_file}''' )
with open(__snake_case , '''w''' , encoding='''utf-8''' ) as f:
f.write(json.dumps(__snake_case , ensure_ascii=__snake_case , indent=__snake_case ) )
# model
lowercase_ : Tuple = chkpt['''model''']
# remove unneeded keys
lowercase_ : Dict = [
'''decoder.version''',
]
for k in ignore_keys:
model_state_dict.pop(__snake_case , __snake_case )
lowercase_ : List[Any] = list(model_state_dict.keys() )
for layer_name in layer_names:
if layer_name.endswith('''output_projection.weight''' ):
lowercase_ : Optional[int] = model_state_dict.pop(__snake_case )
else:
lowercase_ : str = model_state_dict.pop(__snake_case )
lowercase_ : int = BioGptConfig.from_pretrained(__snake_case )
lowercase_ : int = BioGptForCausalLM(__snake_case )
# check that it loads ok
model_new.load_state_dict(__snake_case )
# save
lowercase_ : Optional[int] = os.path.join(__snake_case , __snake_case )
print(F'''Generating {pytorch_weights_dump_path}''' )
torch.save(__snake_case , __snake_case )
print('''Conversion is done!''' )
if __name__ == "__main__":
__A : Any = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--biogpt_checkpoint_path''',
default=None,
type=str,
required=True,
help=(
'''Path to the official PyTorch checkpoint file which is expected to reside in the dump dir with dicts,'''
''' bpecodes, etc.'''
),
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
__A : Dict = parser.parse_args()
convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)
| 33
|
"""simple docstring"""
__A : Any = {
'''Pillow''': '''Pillow''',
'''accelerate''': '''accelerate>=0.11.0''',
'''compel''': '''compel==0.1.8''',
'''black''': '''black~=23.1''',
'''datasets''': '''datasets''',
'''filelock''': '''filelock''',
'''flax''': '''flax>=0.4.1''',
'''hf-doc-builder''': '''hf-doc-builder>=0.3.0''',
'''huggingface-hub''': '''huggingface-hub>=0.13.2''',
'''requests-mock''': '''requests-mock==1.10.0''',
'''importlib_metadata''': '''importlib_metadata''',
'''invisible-watermark''': '''invisible-watermark''',
'''isort''': '''isort>=5.5.4''',
'''jax''': '''jax>=0.2.8,!=0.3.2''',
'''jaxlib''': '''jaxlib>=0.1.65''',
'''Jinja2''': '''Jinja2''',
'''k-diffusion''': '''k-diffusion>=0.0.12''',
'''torchsde''': '''torchsde''',
'''note_seq''': '''note_seq''',
'''librosa''': '''librosa''',
'''numpy''': '''numpy''',
'''omegaconf''': '''omegaconf''',
'''parameterized''': '''parameterized''',
'''protobuf''': '''protobuf>=3.20.3,<4''',
'''pytest''': '''pytest''',
'''pytest-timeout''': '''pytest-timeout''',
'''pytest-xdist''': '''pytest-xdist''',
'''ruff''': '''ruff>=0.0.241''',
'''safetensors''': '''safetensors''',
'''sentencepiece''': '''sentencepiece>=0.1.91,!=0.1.92''',
'''scipy''': '''scipy''',
'''onnx''': '''onnx''',
'''regex''': '''regex!=2019.12.17''',
'''requests''': '''requests''',
'''tensorboard''': '''tensorboard''',
'''torch''': '''torch>=1.4''',
'''torchvision''': '''torchvision''',
'''transformers''': '''transformers>=4.25.1''',
'''urllib3''': '''urllib3<=2.0.0''',
}
| 33
| 1
|
"""simple docstring"""
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 _UpperCAmelCase ( _A ):
def __init__( self : int , A : Tuple , A : Dict=13 , A : Any=7 , A : Dict=True , A : Optional[int]=True , A : Optional[Any]=True , A : List[Any]=True , A : Tuple=99 , A : List[str]=32 , A : str=5 , A : int=4 , A : int=37 , A : str="gelu" , A : Tuple=0.1 , A : Optional[int]=0.1 , A : List[str]=5_12 , A : Any=16 , A : Any=2 , A : Optional[Any]=0.02 , A : Any=False , A : Any=True , A : List[Any]="None" , A : str=3 , A : List[Any]=4 , A : str=None , ) -> Union[str, Any]:
lowercase_ : Optional[int] = parent
lowercase_ : Dict = batch_size
lowercase_ : Any = seq_length
lowercase_ : List[Any] = is_training
lowercase_ : Tuple = use_input_mask
lowercase_ : str = use_token_type_ids
lowercase_ : str = use_labels
lowercase_ : Optional[Any] = vocab_size
lowercase_ : List[Any] = hidden_size
lowercase_ : Dict = num_hidden_layers
lowercase_ : List[Any] = num_attention_heads
lowercase_ : Tuple = intermediate_size
lowercase_ : Union[str, Any] = hidden_act
lowercase_ : Optional[int] = hidden_dropout_prob
lowercase_ : List[Any] = attention_probs_dropout_prob
lowercase_ : List[Any] = max_position_embeddings
lowercase_ : Optional[int] = type_vocab_size
lowercase_ : int = type_sequence_label_size
lowercase_ : List[str] = initializer_range
lowercase_ : int = num_labels
lowercase_ : List[str] = num_choices
lowercase_ : int = relative_attention
lowercase_ : Union[str, Any] = position_biased_input
lowercase_ : List[str] = pos_att_type
lowercase_ : str = scope
def A ( self : List[Any] ) -> List[str]:
lowercase_ : List[Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowercase_ : Optional[Any] = None
if self.use_input_mask:
lowercase_ : int = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
lowercase_ : str = None
if self.use_token_type_ids:
lowercase_ : Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
lowercase_ : Optional[Any] = None
lowercase_ : Tuple = None
lowercase_ : Any = None
if self.use_labels:
lowercase_ : Tuple = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowercase_ : Optional[Any] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
lowercase_ : Optional[Any] = ids_tensor([self.batch_size] , self.num_choices )
lowercase_ : List[Any] = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def A ( self : List[str] ) -> Any:
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 A ( self : Dict ) -> Tuple:
lowercase_ : Any = self.get_config()
lowercase_ : Union[str, Any] = 3_00
return config
def A ( self : Tuple , A : int ) -> str:
self.parent.assertListEqual(list(result.loss.size() ) , [] )
def A ( self : Optional[int] , A : int , A : Tuple , A : int , A : Union[str, Any] , A : Any , A : Tuple , A : List[str] ) -> List[str]:
lowercase_ : List[str] = DebertaModel(config=A )
model.to(A )
model.eval()
lowercase_ : str = model(A , attention_mask=A , token_type_ids=A )[0]
lowercase_ : Optional[int] = model(A , token_type_ids=A )[0]
lowercase_ : List[Any] = model(A )[0]
self.parent.assertListEqual(list(sequence_output.size() ) , [self.batch_size, self.seq_length, self.hidden_size] )
def A ( self : Tuple , A : str , A : List[str] , A : Any , A : Optional[Any] , A : List[Any] , A : int , A : List[Any] ) -> Optional[int]:
lowercase_ : str = DebertaForMaskedLM(config=A )
model.to(A )
model.eval()
lowercase_ : Any = model(A , attention_mask=A , token_type_ids=A , labels=A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def A ( self : Tuple , A : List[str] , A : Optional[Any] , A : Union[str, Any] , A : Dict , A : Dict , A : List[Any] , A : List[Any] ) -> Optional[int]:
lowercase_ : Optional[int] = self.num_labels
lowercase_ : Optional[int] = DebertaForSequenceClassification(A )
model.to(A )
model.eval()
lowercase_ : Tuple = model(A , attention_mask=A , token_type_ids=A , labels=A )
self.parent.assertListEqual(list(result.logits.size() ) , [self.batch_size, self.num_labels] )
self.check_loss_output(A )
def A ( self : Optional[Any] , A : Union[str, Any] , A : Optional[Any] , A : Union[str, Any] , A : Any , A : str , A : Optional[int] , A : Tuple ) -> Optional[int]:
lowercase_ : Tuple = self.num_labels
lowercase_ : Any = DebertaForTokenClassification(config=A )
model.to(A )
model.eval()
lowercase_ : Tuple = model(A , attention_mask=A , token_type_ids=A , labels=A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def A ( self : Optional[int] , A : Dict , A : int , A : str , A : str , A : List[str] , A : int , A : str ) -> Tuple:
lowercase_ : str = DebertaForQuestionAnswering(config=A )
model.to(A )
model.eval()
lowercase_ : Optional[int] = model(
A , attention_mask=A , token_type_ids=A , start_positions=A , end_positions=A , )
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 A ( self : Optional[int] ) -> Any:
lowercase_ : int = self.prepare_config_and_inputs()
(
(
lowercase_
) , (
lowercase_
) , (
lowercase_
) , (
lowercase_
) , (
lowercase_
) , (
lowercase_
) , (
lowercase_
) ,
) : str = config_and_inputs
lowercase_ : Optional[Any] = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_torch
class _UpperCAmelCase ( _A , _A , unittest.TestCase ):
SCREAMING_SNAKE_CASE_ : Union[str, Any] = (
(
DebertaModel,
DebertaForMaskedLM,
DebertaForSequenceClassification,
DebertaForTokenClassification,
DebertaForQuestionAnswering,
)
if is_torch_available()
else ()
)
SCREAMING_SNAKE_CASE_ : str = (
{
"feature-extraction": DebertaModel,
"fill-mask": DebertaForMaskedLM,
"question-answering": DebertaForQuestionAnswering,
"text-classification": DebertaForSequenceClassification,
"token-classification": DebertaForTokenClassification,
"zero-shot": DebertaForSequenceClassification,
}
if is_torch_available()
else {}
)
SCREAMING_SNAKE_CASE_ : Tuple = True
SCREAMING_SNAKE_CASE_ : Optional[Any] = False
SCREAMING_SNAKE_CASE_ : Any = False
SCREAMING_SNAKE_CASE_ : Union[str, Any] = False
SCREAMING_SNAKE_CASE_ : Tuple = False
def A ( self : int ) -> Dict:
lowercase_ : List[Any] = DebertaModelTester(self )
lowercase_ : Optional[int] = ConfigTester(self , config_class=A , hidden_size=37 )
def A ( self : Dict ) -> Any:
self.config_tester.run_common_tests()
def A ( self : int ) -> List[str]:
lowercase_ : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_model(*A )
def A ( self : str ) -> Optional[int]:
lowercase_ : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_sequence_classification(*A )
def A ( self : str ) -> Tuple:
lowercase_ : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_masked_lm(*A )
def A ( self : Dict ) -> Dict:
lowercase_ : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_question_answering(*A )
def A ( self : List[Any] ) -> Dict:
lowercase_ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_token_classification(*A )
@slow
def A ( self : Optional[int] ) -> Union[str, Any]:
for model_name in DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase_ : List[Any] = DebertaModel.from_pretrained(A )
self.assertIsNotNone(A )
@require_torch
@require_sentencepiece
@require_tokenizers
class _UpperCAmelCase ( unittest.TestCase ):
@unittest.skip(reason='''Model not available yet''' )
def A ( self : List[str] ) -> Optional[Any]:
pass
@slow
def A ( self : int ) -> Dict:
lowercase_ : List[str] = DebertaModel.from_pretrained('''microsoft/deberta-base''' )
lowercase_ : List[Any] = torch.tensor([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]] )
lowercase_ : Dict = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
with torch.no_grad():
lowercase_ : Optional[Any] = model(A , attention_mask=A )[0]
# compare the actual values for a slice.
lowercase_ : int = torch.tensor(
[[[-0.5986, -0.8055, -0.8462], [1.4484, -0.9348, -0.8059], [0.3123, 0.0032, -1.4131]]] )
self.assertTrue(torch.allclose(output[:, 1:4, 1:4] , A , atol=1e-4 ) , F'''{output[:, 1:4, 1:4]}''' )
| 33
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
__A : List[Any] = {
'''configuration_mega''': ['''MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MegaConfig''', '''MegaOnnxConfig'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : List[str] = [
'''MEGA_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''MegaForCausalLM''',
'''MegaForMaskedLM''',
'''MegaForMultipleChoice''',
'''MegaForQuestionAnswering''',
'''MegaForSequenceClassification''',
'''MegaForTokenClassification''',
'''MegaModel''',
'''MegaPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_mega import MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP, MegaConfig, MegaOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mega import (
MEGA_PRETRAINED_MODEL_ARCHIVE_LIST,
MegaForCausalLM,
MegaForMaskedLM,
MegaForMultipleChoice,
MegaForQuestionAnswering,
MegaForSequenceClassification,
MegaForTokenClassification,
MegaModel,
MegaPreTrainedModel,
)
else:
import sys
__A : Any = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 33
| 1
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
__A : Tuple = {'''configuration_dpt''': ['''DPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DPTConfig''']}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : List[str] = ['''DPTFeatureExtractor''']
__A : Any = ['''DPTImageProcessor''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : int = [
'''DPT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''DPTForDepthEstimation''',
'''DPTForSemanticSegmentation''',
'''DPTModel''',
'''DPTPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_dpt import DPT_PRETRAINED_CONFIG_ARCHIVE_MAP, DPTConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_dpt import DPTFeatureExtractor
from .image_processing_dpt import DPTImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_dpt import (
DPT_PRETRAINED_MODEL_ARCHIVE_LIST,
DPTForDepthEstimation,
DPTForSemanticSegmentation,
DPTModel,
DPTPreTrainedModel,
)
else:
import sys
__A : Dict = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 33
|
"""simple docstring"""
import argparse
import os
import re
import packaging.version
__A : List[str] = '''examples/'''
__A : int = {
'''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'''),
}
__A : Dict = {
'''init''': '''src/transformers/__init__.py''',
'''setup''': '''setup.py''',
}
__A : Optional[int] = '''README.md'''
def lowercase ( __snake_case : int , __snake_case : Any , __snake_case : int ):
with open(__snake_case , '''r''' , encoding='''utf-8''' , newline='''\n''' ) as f:
lowercase_ : int = f.read()
lowercase_ , lowercase_ : List[str] = REPLACE_PATTERNS[pattern]
lowercase_ : Union[str, Any] = replace.replace('''VERSION''' , __snake_case )
lowercase_ : Optional[Any] = re_pattern.sub(__snake_case , __snake_case )
with open(__snake_case , '''w''' , encoding='''utf-8''' , newline='''\n''' ) as f:
f.write(__snake_case )
def lowercase ( __snake_case : int ):
for folder, directories, fnames in os.walk(__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(__snake_case , __snake_case ) , __snake_case , pattern='''examples''' )
def lowercase ( __snake_case : Optional[Any] , __snake_case : Optional[Any]=False ):
for pattern, fname in REPLACE_FILES.items():
update_version_in_file(__snake_case , __snake_case , __snake_case )
if not patch:
update_version_in_examples(__snake_case )
def lowercase ( ):
lowercase_ : Union[str, Any] = '''🤗 Transformers currently provides the following architectures'''
lowercase_ : Union[str, Any] = '''1. Want to contribute a new model?'''
with open(__snake_case , '''r''' , encoding='''utf-8''' , newline='''\n''' ) as f:
lowercase_ : List[str] = f.readlines()
# Find the start of the list.
lowercase_ : Optional[Any] = 0
while not lines[start_index].startswith(_start_prompt ):
start_index += 1
start_index += 1
lowercase_ : str = start_index
# Update the lines in the model list.
while not lines[index].startswith(_end_prompt ):
if lines[index].startswith('''1.''' ):
lowercase_ : str = lines[index].replace(
'''https://huggingface.co/docs/transformers/main/model_doc''' , '''https://huggingface.co/docs/transformers/model_doc''' , )
index += 1
with open(__snake_case , '''w''' , encoding='''utf-8''' , newline='''\n''' ) as f:
f.writelines(__snake_case )
def lowercase ( ):
with open(REPLACE_FILES['''init'''] , '''r''' ) as f:
lowercase_ : List[Any] = f.read()
lowercase_ : List[str] = REPLACE_PATTERNS['''init'''][0].search(__snake_case ).groups()[0]
return packaging.version.parse(__snake_case )
def lowercase ( __snake_case : Optional[Any]=False ):
lowercase_ : str = 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:
lowercase_ : Optional[Any] = default_version.base_version
elif patch:
lowercase_ : Optional[int] = F'''{default_version.major}.{default_version.minor}.{default_version.micro + 1}'''
else:
lowercase_ : Optional[int] = F'''{default_version.major}.{default_version.minor + 1}.0'''
# Now let's ask nicely if that's the right one.
lowercase_ : int = input(F'''Which version are you releasing? [{default_version}]''' )
if len(__snake_case ) == 0:
lowercase_ : Dict = default_version
print(F'''Updating version to {version}.''' )
global_version_update(__snake_case , patch=__snake_case )
if not patch:
print('''Cleaning main README, don\'t forget to run `make fix-copies`.''' )
clean_main_ref_in_model_list()
def lowercase ( ):
lowercase_ : List[Any] = get_version()
lowercase_ : List[str] = F'''{current_version.major}.{current_version.minor + 1}.0.dev0'''
lowercase_ : Any = current_version.base_version
# Check with the user we got that right.
lowercase_ : Tuple = input(F'''Which version are we developing now? [{dev_version}]''' )
if len(__snake_case ) == 0:
lowercase_ : str = dev_version
print(F'''Updating version to {version}.''' )
global_version_update(__snake_case )
print('''Cleaning main README, don\'t forget to run `make fix-copies`.''' )
clean_main_ref_in_model_list()
if __name__ == "__main__":
__A : int = 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.''')
__A : Any = 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()
| 33
| 1
|
"""simple docstring"""
import importlib
import inspect
import json
import os
import re
import shutil
import sys
from pathlib import Path
from typing import Dict, Optional, Union
from urllib import request
from huggingface_hub import HfFolder, cached_download, hf_hub_download, model_info
from packaging import version
from .. import __version__
from . import DIFFUSERS_DYNAMIC_MODULE_NAME, HF_MODULES_CACHE, logging
__A : Tuple = (
'''https://raw.githubusercontent.com/huggingface/diffusers/{revision}/examples/community/{pipeline}.py'''
)
__A : int = logging.get_logger(__name__) # pylint: disable=invalid-name
def lowercase ( ):
lowercase_ : Tuple = '''https://pypi.org/pypi/diffusers/json'''
lowercase_ : str = json.loads(request.urlopen(__snake_case ).read() )['''releases'''].keys()
return sorted(__snake_case , key=lambda __snake_case : version.Version(__snake_case ) )
def lowercase ( ):
# This function has already been executed if HF_MODULES_CACHE already is in the Python path.
if HF_MODULES_CACHE in sys.path:
return
sys.path.append(__snake_case )
os.makedirs(__snake_case , exist_ok=__snake_case )
lowercase_ : List[str] = Path(__snake_case ) / '''__init__.py'''
if not init_path.exists():
init_path.touch()
def lowercase ( __snake_case : Union[str, os.PathLike] ):
init_hf_modules()
lowercase_ : List[str] = Path(__snake_case ) / name
# If the parent module does not exist yet, recursively create it.
if not dynamic_module_path.parent.exists():
create_dynamic_module(dynamic_module_path.parent )
os.makedirs(__snake_case , exist_ok=__snake_case )
lowercase_ : str = dynamic_module_path / '''__init__.py'''
if not init_path.exists():
init_path.touch()
def lowercase ( __snake_case : Union[str, Any] ):
with open(__snake_case , '''r''' , encoding='''utf-8''' ) as f:
lowercase_ : int = f.read()
# Imports of the form `import .xxx`
lowercase_ : List[Any] = re.findall('''^\s*import\s+\.(\S+)\s*$''' , __snake_case , flags=re.MULTILINE )
# Imports of the form `from .xxx import yyy`
relative_imports += re.findall('''^\s*from\s+\.(\S+)\s+import''' , __snake_case , flags=re.MULTILINE )
# Unique-ify
return list(set(__snake_case ) )
def lowercase ( __snake_case : int ):
lowercase_ : List[Any] = False
lowercase_ : Dict = [module_file]
lowercase_ : List[str] = []
# Let's recurse through all relative imports
while not no_change:
lowercase_ : Union[str, Any] = []
for f in files_to_check:
new_imports.extend(get_relative_imports(__snake_case ) )
lowercase_ : Optional[Any] = Path(__snake_case ).parent
lowercase_ : Any = [str(module_path / m ) for m in new_imports]
lowercase_ : str = [f for f in new_import_files if f not in all_relative_imports]
lowercase_ : List[Any] = [F'''{f}.py''' for f in new_import_files]
lowercase_ : Union[str, Any] = len(__snake_case ) == 0
all_relative_imports.extend(__snake_case )
return all_relative_imports
def lowercase ( __snake_case : List[Any] ):
with open(__snake_case , '''r''' , encoding='''utf-8''' ) as f:
lowercase_ : Optional[Any] = f.read()
# Imports of the form `import xxx`
lowercase_ : Optional[int] = re.findall('''^\s*import\s+(\S+)\s*$''' , __snake_case , flags=re.MULTILINE )
# Imports of the form `from xxx import yyy`
imports += re.findall('''^\s*from\s+(\S+)\s+import''' , __snake_case , flags=re.MULTILINE )
# Only keep the top-level module
lowercase_ : str = [imp.split('''.''' )[0] for imp in imports if not imp.startswith('''.''' )]
# Unique-ify and test we got them all
lowercase_ : List[Any] = list(set(__snake_case ) )
lowercase_ : List[str] = []
for imp in imports:
try:
importlib.import_module(__snake_case )
except ImportError:
missing_packages.append(__snake_case )
if len(__snake_case ) > 0:
raise ImportError(
'''This modeling file requires the following packages that were not found in your environment: '''
F'''{', '.join(__snake_case )}. Run `pip install {' '.join(__snake_case )}`''' )
return get_relative_imports(__snake_case )
def lowercase ( __snake_case : Any , __snake_case : List[str] ):
lowercase_ : Tuple = module_path.replace(os.path.sep , '''.''' )
lowercase_ : List[Any] = importlib.import_module(__snake_case )
if class_name is None:
return find_pipeline_class(__snake_case )
return getattr(__snake_case , __snake_case )
def lowercase ( __snake_case : List[Any] ):
from ..pipelines import DiffusionPipeline
lowercase_ : Any = dict(inspect.getmembers(__snake_case , inspect.isclass ) )
lowercase_ : Union[str, Any] = None
for cls_name, cls in cls_members.items():
if (
cls_name != DiffusionPipeline.__name__
and issubclass(cls , __snake_case )
and cls.__module__.split('''.''' )[0] != "diffusers"
):
if pipeline_class is not None:
raise ValueError(
F'''Multiple classes that inherit from {DiffusionPipeline.__name__} have been found:'''
F''' {pipeline_class.__name__}, and {cls_name}. Please make sure to define only one in'''
F''' {loaded_module}.''' )
lowercase_ : List[str] = cls
return pipeline_class
def lowercase ( __snake_case : Union[str, os.PathLike] , __snake_case : str , __snake_case : Optional[Union[str, os.PathLike]] = None , __snake_case : bool = False , __snake_case : bool = False , __snake_case : Optional[Dict[str, str]] = None , __snake_case : Optional[Union[bool, str]] = None , __snake_case : Optional[str] = None , __snake_case : bool = False , ):
lowercase_ : Optional[Any] = str(__snake_case )
lowercase_ : Optional[Any] = os.path.join(__snake_case , __snake_case )
if os.path.isfile(__snake_case ):
lowercase_ : Union[str, Any] = module_file_or_url
lowercase_ : Tuple = '''local'''
elif pretrained_model_name_or_path.count('''/''' ) == 0:
lowercase_ : str = get_diffusers_versions()
# cut ".dev0"
lowercase_ : Optional[int] = '''v''' + '''.'''.join(__version__.split('''.''' )[:3] )
# retrieve github version that matches
if revision is None:
lowercase_ : Any = latest_version if latest_version[1:] in available_versions else '''main'''
logger.info(F'''Defaulting to latest_version: {revision}.''' )
elif revision in available_versions:
lowercase_ : Optional[int] = F'''v{revision}'''
elif revision == "main":
lowercase_ : Optional[int] = revision
else:
raise ValueError(
F'''`custom_revision`: {revision} does not exist. Please make sure to choose one of'''
F''' {', '.join(available_versions + ['main'] )}.''' )
# community pipeline on GitHub
lowercase_ : Union[str, Any] = COMMUNITY_PIPELINES_URL.format(revision=__snake_case , pipeline=__snake_case )
try:
lowercase_ : Any = cached_download(
__snake_case , cache_dir=__snake_case , force_download=__snake_case , proxies=__snake_case , resume_download=__snake_case , local_files_only=__snake_case , use_auth_token=__snake_case , )
lowercase_ : Union[str, Any] = '''git'''
lowercase_ : str = pretrained_model_name_or_path + '''.py'''
except EnvironmentError:
logger.error(F'''Could not locate the {module_file} inside {pretrained_model_name_or_path}.''' )
raise
else:
try:
# Load from URL or cache if already cached
lowercase_ : int = hf_hub_download(
__snake_case , __snake_case , cache_dir=__snake_case , force_download=__snake_case , proxies=__snake_case , resume_download=__snake_case , local_files_only=__snake_case , use_auth_token=__snake_case , )
lowercase_ : Optional[Any] = os.path.join('''local''' , '''--'''.join(pretrained_model_name_or_path.split('''/''' ) ) )
except EnvironmentError:
logger.error(F'''Could not locate the {module_file} inside {pretrained_model_name_or_path}.''' )
raise
# Check we have all the requirements in our environment
lowercase_ : List[str] = check_imports(__snake_case )
# Now we move the module inside our cached dynamic modules.
lowercase_ : str = DIFFUSERS_DYNAMIC_MODULE_NAME + os.path.sep + submodule
create_dynamic_module(__snake_case )
lowercase_ : List[Any] = Path(__snake_case ) / full_submodule
if submodule == "local" or submodule == "git":
# We always copy local files (we could hash the file to see if there was a change, and give them the name of
# that hash, to only copy when there is a modification but it seems overkill for now).
# The only reason we do the copy is to avoid putting too many folders in sys.path.
shutil.copy(__snake_case , submodule_path / module_file )
for module_needed in modules_needed:
lowercase_ : List[str] = F'''{module_needed}.py'''
shutil.copy(os.path.join(__snake_case , __snake_case ) , submodule_path / module_needed )
else:
# Get the commit hash
# TODO: we will get this info in the etag soon, so retrieve it from there and not here.
if isinstance(__snake_case , __snake_case ):
lowercase_ : Any = use_auth_token
elif use_auth_token is True:
lowercase_ : Optional[Any] = HfFolder.get_token()
else:
lowercase_ : List[Any] = None
lowercase_ : Dict = model_info(__snake_case , revision=__snake_case , token=__snake_case ).sha
# The module file will end up being placed in a subfolder with the git hash of the repo. This way we get the
# benefit of versioning.
lowercase_ : int = submodule_path / commit_hash
lowercase_ : Optional[int] = full_submodule + os.path.sep + commit_hash
create_dynamic_module(__snake_case )
if not (submodule_path / module_file).exists():
shutil.copy(__snake_case , submodule_path / module_file )
# Make sure we also have every file with relative
for module_needed in modules_needed:
if not (submodule_path / module_needed).exists():
get_cached_module_file(
__snake_case , F'''{module_needed}.py''' , cache_dir=__snake_case , force_download=__snake_case , resume_download=__snake_case , proxies=__snake_case , use_auth_token=__snake_case , revision=__snake_case , local_files_only=__snake_case , )
return os.path.join(__snake_case , __snake_case )
def lowercase ( __snake_case : Union[str, os.PathLike] , __snake_case : str , __snake_case : Optional[str] = None , __snake_case : Optional[Union[str, os.PathLike]] = None , __snake_case : bool = False , __snake_case : bool = False , __snake_case : Optional[Dict[str, str]] = None , __snake_case : Optional[Union[bool, str]] = None , __snake_case : Optional[str] = None , __snake_case : bool = False , **__snake_case : Dict , ):
lowercase_ : int = get_cached_module_file(
__snake_case , __snake_case , cache_dir=__snake_case , force_download=__snake_case , resume_download=__snake_case , proxies=__snake_case , use_auth_token=__snake_case , revision=__snake_case , local_files_only=__snake_case , )
return get_class_in_module(__snake_case , final_module.replace('''.py''' , '''''' ) )
| 33
|
"""simple docstring"""
from typing import Optional
from urllib.parse import quote
import huggingface_hub as hfh
from packaging import version
def lowercase ( __snake_case : str , __snake_case : str , __snake_case : Optional[str] = None ):
if version.parse(hfh.__version__ ).release < version.parse('''0.11.0''' ).release:
# old versions of hfh don't url-encode the file path
lowercase_ : Union[str, Any] = quote(__snake_case )
return hfh.hf_hub_url(__snake_case , __snake_case , repo_type='''dataset''' , revision=__snake_case )
| 33
| 1
|
"""simple docstring"""
import os
from typing import List, Optional, Union
from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging
__A : Any = logging.get_logger(__name__)
__A : Optional[int] = {'''vocab_file''': '''vocab.txt'''}
__A : int = {
'''vocab_file''': {
'''facebook/esm2_t6_8M_UR50D''': '''https://huggingface.co/facebook/esm2_t6_8M_UR50D/resolve/main/vocab.txt''',
'''facebook/esm2_t12_35M_UR50D''': '''https://huggingface.co/facebook/esm2_t12_35M_UR50D/resolve/main/vocab.txt''',
},
}
__A : int = {
'''facebook/esm2_t6_8M_UR50D''': 1_024,
'''facebook/esm2_t12_35M_UR50D''': 1_024,
}
def lowercase ( __snake_case : str ):
with open(__snake_case , '''r''' ) as f:
lowercase_ : int = f.read().splitlines()
return [l.strip() for l in lines]
class _UpperCAmelCase ( _A ):
SCREAMING_SNAKE_CASE_ : Optional[Any] = VOCAB_FILES_NAMES
SCREAMING_SNAKE_CASE_ : Dict = PRETRAINED_VOCAB_FILES_MAP
SCREAMING_SNAKE_CASE_ : Dict = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
SCREAMING_SNAKE_CASE_ : str = ["input_ids", "attention_mask"]
def __init__( self : Union[str, Any] , A : Dict , A : Tuple="<unk>" , A : List[Any]="<cls>" , A : int="<pad>" , A : Optional[Any]="<mask>" , A : List[Any]="<eos>" , **A : Tuple , ) -> Union[str, Any]:
super().__init__(**A )
lowercase_ : Optional[Any] = load_vocab_file(A )
lowercase_ : str = dict(enumerate(self.all_tokens ) )
lowercase_ : str = {tok: ind for ind, tok in enumerate(self.all_tokens )}
lowercase_ : Union[str, Any] = unk_token
lowercase_ : Union[str, Any] = cls_token
lowercase_ : Optional[int] = pad_token
lowercase_ : List[Any] = mask_token
lowercase_ : str = eos_token
lowercase_ : Optional[Any] = self.all_tokens
self._create_trie(self.unique_no_split_tokens )
def A ( self : Optional[int] , A : int ) -> str:
return self._id_to_token.get(A , self.unk_token )
def A ( self : Optional[Any] , A : str ) -> int:
return self._token_to_id.get(A , self._token_to_id.get(self.unk_token ) )
def A ( self : str , A : List[str] , **A : Tuple ) -> Any:
return text.split()
def A ( self : Union[str, Any] , A : int=False ) -> List[Any]:
return len(self._id_to_token )
def A ( self : List[Any] ) -> Any:
return {token: i for i, token in enumerate(self.all_tokens )}
def A ( self : int , A : str ) -> int:
return self._token_to_id.get(A , self._token_to_id.get(self.unk_token ) )
def A ( self : List[Any] , A : int ) -> str:
return self._id_to_token.get(A , self.unk_token )
def A ( self : Dict , A : List[int] , A : Optional[List[int]] = None ) -> List[int]:
lowercase_ : str = [self.cls_token_id]
lowercase_ : Any = [self.eos_token_id] # No sep token in ESM vocabulary
if token_ids_a is None:
if self.eos_token_id is None:
return cls + token_ids_a
else:
return cls + token_ids_a + sep
elif self.eos_token_id is None:
raise ValueError('''Cannot tokenize multiple sequences when EOS token is not set!''' )
return cls + token_ids_a + sep + token_ids_a + sep # Multiple inputs always have an EOS token
def A ( self : Union[str, Any] , A : List , A : Optional[List] = None , A : bool = False ) -> List[int]:
if already_has_special_tokens:
if token_ids_a is not None:
raise ValueError(
'''You should not supply a second sequence if the provided sequence of '''
'''ids is already formatted with special tokens for the model.''' )
return [1 if token in self.all_special_ids else 0 for token in token_ids_a]
lowercase_ : Any = [1] + ([0] * len(A )) + [1]
if token_ids_a is not None:
mask += [0] * len(A ) + [1]
return mask
def A ( self : Union[str, Any] , A : Union[str, Any] , A : int ) -> List[str]:
lowercase_ : Union[str, Any] = os.path.join(A , (filename_prefix + '''-''' if filename_prefix else '''''') + '''vocab.txt''' )
with open(A , '''w''' ) as f:
f.write('''\n'''.join(self.all_tokens ) )
return (vocab_file,)
@property
def A ( self : Optional[Any] ) -> int:
return self.get_vocab_size(with_added_tokens=A )
def A ( self : Dict , A : Union[List[str], List[AddedToken]] , A : bool = False ) -> int:
return super()._add_tokens(A , special_tokens=A )
| 33
|
"""simple docstring"""
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFResNetForImageClassification, TFResNetModel
from transformers.models.resnet.modeling_tf_resnet import TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class _UpperCAmelCase :
def __init__( self : int , A : Tuple , A : int=3 , A : List[str]=32 , A : Dict=3 , A : Any=10 , A : Dict=[10, 20, 30, 40] , A : Optional[Any]=[1, 1, 2, 1] , A : Union[str, Any]=True , A : Optional[Any]=True , A : Any="relu" , A : Optional[Any]=3 , A : Tuple=None , ) -> Dict:
lowercase_ : str = parent
lowercase_ : List[Any] = batch_size
lowercase_ : Optional[int] = image_size
lowercase_ : int = num_channels
lowercase_ : int = embeddings_size
lowercase_ : str = hidden_sizes
lowercase_ : List[str] = depths
lowercase_ : Dict = is_training
lowercase_ : int = use_labels
lowercase_ : Any = hidden_act
lowercase_ : List[Any] = num_labels
lowercase_ : Tuple = scope
lowercase_ : Optional[Any] = len(A )
def A ( self : str ) -> Tuple:
lowercase_ : Optional[int] = 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[str] = ids_tensor([self.batch_size] , self.num_labels )
lowercase_ : Optional[int] = self.get_config()
return config, pixel_values, labels
def A ( self : Dict ) -> int:
return ResNetConfig(
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 , image_size=self.image_size , )
def A ( self : str , A : Tuple , A : str , A : str ) -> str:
lowercase_ : str = TFResNetModel(config=A )
lowercase_ : Union[str, Any] = model(A )
# expected last hidden states: B, C, H // 32, W // 32
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32) , )
def A ( self : Any , A : int , A : List[Any] , A : Optional[Any] ) -> Optional[Any]:
lowercase_ : Tuple = self.num_labels
lowercase_ : Union[str, Any] = TFResNetForImageClassification(A )
lowercase_ : Tuple = model(A , labels=A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def A ( self : Union[str, Any] ) -> Tuple:
lowercase_ : Tuple = self.prepare_config_and_inputs()
lowercase_ , lowercase_ , lowercase_ : Dict = config_and_inputs
lowercase_ : int = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_tf
class _UpperCAmelCase ( _A , _A , unittest.TestCase ):
SCREAMING_SNAKE_CASE_ : Union[str, Any] = (TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
SCREAMING_SNAKE_CASE_ : List[Any] = (
{"feature-extraction": TFResNetModel, "image-classification": TFResNetForImageClassification}
if is_tf_available()
else {}
)
SCREAMING_SNAKE_CASE_ : Optional[int] = False
SCREAMING_SNAKE_CASE_ : Optional[int] = False
SCREAMING_SNAKE_CASE_ : str = False
SCREAMING_SNAKE_CASE_ : Optional[int] = False
SCREAMING_SNAKE_CASE_ : Any = False
def A ( self : Union[str, Any] ) -> List[Any]:
lowercase_ : int = TFResNetModelTester(self )
lowercase_ : str = ConfigTester(self , config_class=A , has_text_modality=A )
def A ( self : Dict ) -> Optional[Any]:
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 A ( self : Dict ) -> List[Any]:
return
@unittest.skip(reason='''ResNet does not use inputs_embeds''' )
def A ( self : Any ) -> Any:
pass
@unittest.skip(reason='''ResNet does not support input and output embeddings''' )
def A ( self : List[str] ) -> Optional[Any]:
pass
def A ( self : str ) -> Tuple:
lowercase_ , lowercase_ : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase_ : int = model_class(A )
lowercase_ : str = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowercase_ : str = [*signature.parameters.keys()]
lowercase_ : str = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , A )
def A ( self : List[str] ) -> Tuple:
lowercase_ : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*A )
def A ( self : List[Any] ) -> List[str]:
def check_hidden_states_output(A : Union[str, Any] , A : int , A : List[Any] ):
lowercase_ : int = model_class(A )
lowercase_ : Optional[Any] = model(**self._prepare_for_class(A , A ) )
lowercase_ : Union[str, Any] = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
lowercase_ : Any = self.model_tester.num_stages
self.assertEqual(len(A ) , expected_num_stages + 1 )
# ResNet'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] , )
lowercase_ , lowercase_ : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
lowercase_ : Union[str, Any] = ['''basic''', '''bottleneck''']
for model_class in self.all_model_classes:
for layer_type in layers_type:
lowercase_ : List[str] = layer_type
lowercase_ : Tuple = True
check_hidden_states_output(A , A , A )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
lowercase_ : Optional[Any] = True
check_hidden_states_output(A , A , A )
def A ( self : Optional[int] ) -> Tuple:
lowercase_ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*A )
@slow
def A ( self : List[str] ) -> Optional[int]:
for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase_ : Tuple = TFResNetModel.from_pretrained(A )
self.assertIsNotNone(A )
def lowercase ( ):
lowercase_ : List[str] = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_tf
@require_vision
class _UpperCAmelCase ( unittest.TestCase ):
@cached_property
def A ( self : Any ) -> Optional[int]:
return (
AutoImageProcessor.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
if is_vision_available()
else None
)
@slow
def A ( self : Any ) -> Optional[int]:
lowercase_ : Optional[int] = TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
lowercase_ : List[Any] = self.default_image_processor
lowercase_ : Dict = prepare_img()
lowercase_ : List[str] = image_processor(images=A , return_tensors='''tf''' )
# forward pass
lowercase_ : Tuple = model(**A )
# verify the logits
lowercase_ : Optional[int] = tf.TensorShape((1, 10_00) )
self.assertEqual(outputs.logits.shape , A )
lowercase_ : Optional[Any] = tf.constant([-11.1069, -9.7877, -8.3777] )
self.assertTrue(np.allclose(outputs.logits[0, :3].numpy() , A , atol=1e-4 ) )
| 33
| 1
|
def _a ( a :int = 1_000 ) -> int:
a , a = 1, 1
a = []
for i in range(1 , n + 1 ):
a = prev_numerator + 2 * prev_denominator
a = prev_numerator + prev_denominator
if len(str(a ) ) > len(str(a ) ):
result.append(a )
a = numerator
a = denominator
return len(a )
if __name__ == "__main__":
print(f"""{solution() = }""")
| 0
|
"""simple docstring"""
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
__A : Dict = '''
Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that developed a chatbot app targeted at teenagers.[2] After open-sourcing the model behind the chatbot, the company pivoted to focus on being a platform for machine learning.
In March 2021, Hugging Face raised $40 million in a Series B funding round.[3]
On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model.[4] In 2022, the workshop concluded with the announcement of BLOOM, a multilingual large language model with 176 billion parameters.[5]
'''
class _UpperCAmelCase ( unittest.TestCase , _A ):
def A ( self : List[Any] ) -> Dict:
lowercase_ : Optional[int] = load_tool('''text-question-answering''' )
self.tool.setup()
lowercase_ : Union[str, Any] = load_tool('''text-question-answering''' , remote=A )
def A ( self : Any ) -> List[str]:
lowercase_ : Union[str, Any] = self.tool(A , '''What did Hugging Face do in April 2021?''' )
self.assertEqual(A , '''launched the BigScience Research Workshop''' )
def A ( self : str ) -> List[str]:
lowercase_ : int = self.remote_tool(A , '''What did Hugging Face do in April 2021?''' )
self.assertEqual(A , '''launched the BigScience Research Workshop''' )
def A ( self : List[Any] ) -> int:
lowercase_ : Optional[Any] = self.tool(text=A , question='''What did Hugging Face do in April 2021?''' )
self.assertEqual(A , '''launched the BigScience Research Workshop''' )
def A ( self : List[str] ) -> Optional[int]:
lowercase_ : int = self.remote_tool(text=A , question='''What did Hugging Face do in April 2021?''' )
self.assertEqual(A , '''launched the BigScience Research Workshop''' )
| 33
| 0
|
'''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class __A ( unittest.TestCase ):
@property
def _lowercase (self : Tuple ):
torch.manual_seed(0 )
UpperCAmelCase_ = UNetaDModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=3 , out_channels=3 , down_block_types=("DownBlock2D", "AttnDownBlock2D") , up_block_types=("AttnUpBlock2D", "UpBlock2D") , )
return model
def _lowercase (self : Union[str, Any] ):
UpperCAmelCase_ = self.dummy_uncond_unet
UpperCAmelCase_ = ScoreSdeVeScheduler()
UpperCAmelCase_ = ScoreSdeVePipeline(unet=__a , scheduler=__a )
sde_ve.to(__a )
sde_ve.set_progress_bar_config(disable=__a )
UpperCAmelCase_ = torch.manual_seed(0 )
UpperCAmelCase_ = sde_ve(num_inference_steps=2 , output_type="numpy" , generator=__a ).images
UpperCAmelCase_ = torch.manual_seed(0 )
UpperCAmelCase_ = sde_ve(num_inference_steps=2 , output_type="numpy" , generator=__a , return_dict=__a )[
0
]
UpperCAmelCase_ = image[0, -3:, -3:, -1]
UpperCAmelCase_ = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
UpperCAmelCase_ = np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
@slow
@require_torch
class __A ( unittest.TestCase ):
def _lowercase (self : Tuple ):
UpperCAmelCase_ = "google/ncsnpp-church-256"
UpperCAmelCase_ = UNetaDModel.from_pretrained(__a )
UpperCAmelCase_ = ScoreSdeVeScheduler.from_pretrained(__a )
UpperCAmelCase_ = ScoreSdeVePipeline(unet=__a , scheduler=__a )
sde_ve.to(__a )
sde_ve.set_progress_bar_config(disable=__a )
UpperCAmelCase_ = torch.manual_seed(0 )
UpperCAmelCase_ = sde_ve(num_inference_steps=10 , output_type="numpy" , generator=__a ).images
UpperCAmelCase_ = image[0, -3:, -3:, -1]
assert image.shape == (1, 256, 256, 3)
UpperCAmelCase_ = np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
| 1
|
"""simple docstring"""
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING:
import torch
class _UpperCAmelCase ( TensorFormatter[Mapping, "torch.Tensor", Mapping] ):
def __init__( self : Any , A : int=None , **A : str ) -> Union[str, Any]:
super().__init__(features=A )
lowercase_ : Union[str, Any] = torch_tensor_kwargs
import torch # noqa import torch at initialization
def A ( self : Dict , A : int ) -> List[Any]:
import torch
if isinstance(A , A ) and column:
if all(
isinstance(A , torch.Tensor ) and x.shape == column[0].shape and x.dtype == column[0].dtype
for x in column ):
return torch.stack(A )
return column
def A ( self : int , A : Any ) -> Optional[Any]:
import torch
if isinstance(A , (str, bytes, type(A )) ):
return value
elif isinstance(A , (np.character, np.ndarray) ) and np.issubdtype(value.dtype , np.character ):
return value.tolist()
lowercase_ : Any = {}
if isinstance(A , (np.number, np.ndarray) ) and np.issubdtype(value.dtype , np.integer ):
lowercase_ : Any = {'''dtype''': torch.intaa}
elif isinstance(A , (np.number, np.ndarray) ) and np.issubdtype(value.dtype , np.floating ):
lowercase_ : Dict = {'''dtype''': torch.floataa}
elif config.PIL_AVAILABLE and "PIL" in sys.modules:
import PIL.Image
if isinstance(A , PIL.Image.Image ):
lowercase_ : Dict = np.asarray(A )
return torch.tensor(A , **{**default_dtype, **self.torch_tensor_kwargs} )
def A ( self : Union[str, Any] , A : Optional[int] ) -> str:
import torch
# support for torch, tf, jax etc.
if hasattr(A , '''__array__''' ) and not isinstance(A , torch.Tensor ):
lowercase_ : Optional[int] = data_struct.__array__()
# support for nested types like struct of list of struct
if isinstance(A , np.ndarray ):
if data_struct.dtype == object: # torch tensors cannot be instantied from an array of objects
return self._consolidate([self.recursive_tensorize(A ) for substruct in data_struct] )
elif isinstance(A , (list, tuple) ):
return self._consolidate([self.recursive_tensorize(A ) for substruct in data_struct] )
return self._tensorize(A )
def A ( self : Dict , A : dict ) -> Tuple:
return map_nested(self._recursive_tensorize , A , map_list=A )
def A ( self : str , A : pa.Table ) -> Mapping:
lowercase_ : Optional[Any] = self.numpy_arrow_extractor().extract_row(A )
lowercase_ : str = self.python_features_decoder.decode_row(A )
return self.recursive_tensorize(A )
def A ( self : List[Any] , A : pa.Table ) -> "torch.Tensor":
lowercase_ : List[str] = self.numpy_arrow_extractor().extract_column(A )
lowercase_ : str = self.python_features_decoder.decode_column(A , pa_table.column_names[0] )
lowercase_ : Optional[int] = self.recursive_tensorize(A )
lowercase_ : Any = self._consolidate(A )
return column
def A ( self : List[str] , A : pa.Table ) -> Mapping:
lowercase_ : Optional[int] = self.numpy_arrow_extractor().extract_batch(A )
lowercase_ : int = self.python_features_decoder.decode_batch(A )
lowercase_ : Dict = self.recursive_tensorize(A )
for column_name in batch:
lowercase_ : Optional[Any] = self._consolidate(batch[column_name] )
return batch
| 33
| 0
|
'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional
from packaging import version
if TYPE_CHECKING:
from ... import PreTrainedTokenizer, TensorType
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import is_torch_available, logging
lowerCamelCase : Optional[int] = logging.get_logger(__name__)
lowerCamelCase : int = {
'bigscience/bloom': 'https://huggingface.co/bigscience/bloom/resolve/main/config.json',
'bigscience/bloom-560m': 'https://huggingface.co/bigscience/bloom-560m/blob/main/config.json',
'bigscience/bloom-1b1': 'https://huggingface.co/bigscience/bloom-1b1/blob/main/config.json',
'bigscience/bloom-1b7': 'https://huggingface.co/bigscience/bloom-1b7/blob/main/config.json',
'bigscience/bloom-3b': 'https://huggingface.co/bigscience/bloom-3b/blob/main/config.json',
'bigscience/bloom-7b1': 'https://huggingface.co/bigscience/bloom-7b1/blob/main/config.json',
}
class __lowerCAmelCase (lowercase_ ):
'''simple docstring'''
lowerCAmelCase__ : Any = """bloom"""
lowerCAmelCase__ : Optional[Any] = ["""past_key_values"""]
lowerCAmelCase__ : Optional[int] = {
"""num_hidden_layers""": """n_layer""",
"""num_attention_heads""": """n_head""",
}
def __init__(self : Dict , UpperCamelCase : int=250880 , UpperCamelCase : Any=64 , UpperCamelCase : List[Any]=2 , UpperCamelCase : Any=8 , UpperCamelCase : Any=1E-5 , UpperCamelCase : Any=0.02 , UpperCamelCase : str=True , UpperCamelCase : Optional[int]=1 , UpperCamelCase : Optional[int]=2 , UpperCamelCase : Union[str, Any]=False , UpperCamelCase : List[str]=0.0 , UpperCamelCase : Any=0.0 , UpperCamelCase : Optional[Any]=1 , UpperCamelCase : List[Any]=False , **UpperCamelCase : Tuple , ):
'''simple docstring'''
lowercase__ = vocab_size
# Backward compatibility with n_embed kwarg
lowercase__ = kwargs.pop('''n_embed''' , UpperCamelCase )
lowercase__ = hidden_size if n_embed is None else n_embed
lowercase__ = n_layer
lowercase__ = n_head
lowercase__ = layer_norm_epsilon
lowercase__ = initializer_range
lowercase__ = use_cache
lowercase__ = pretraining_tp
lowercase__ = apply_residual_connection_post_layernorm
lowercase__ = hidden_dropout
lowercase__ = attention_dropout
lowercase__ = bos_token_id
lowercase__ = eos_token_id
lowercase__ = slow_but_exact
super().__init__(bos_token_id=UpperCamelCase , eos_token_id=UpperCamelCase , **UpperCamelCase )
class __lowerCAmelCase (lowercase_ ):
'''simple docstring'''
lowerCAmelCase__ : Tuple = version.parse("""1.12""" )
def __init__(self : Optional[Any] , UpperCamelCase : PretrainedConfig , UpperCamelCase : str = "default" , UpperCamelCase : List[PatchingSpec] = None , UpperCamelCase : bool = False , ):
'''simple docstring'''
super().__init__(UpperCamelCase , task=UpperCamelCase , patching_specs=UpperCamelCase , use_past=UpperCamelCase )
if not getattr(self._config , '''pad_token_id''' , UpperCamelCase ):
# TODO: how to do that better?
lowercase__ = 0
@property
def UpperCamelCase__ (self : str ):
'''simple docstring'''
lowercase__ = OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}} )
if self.use_past:
# BLOOM stores values on dynamic axis 2. For more details see: https://github.com/huggingface/transformers/pull/18344
self.fill_with_past_key_values_(UpperCamelCase , direction='''inputs''' , inverted_values_shape=UpperCamelCase )
lowercase__ = {0: '''batch''', 1: '''past_sequence + sequence'''}
else:
lowercase__ = {0: '''batch''', 1: '''sequence'''}
return common_inputs
@property
def UpperCamelCase__ (self : Optional[Any] ):
'''simple docstring'''
return self._config.n_layer
@property
def UpperCamelCase__ (self : int ):
'''simple docstring'''
return self._config.n_head
@property
def UpperCamelCase__ (self : List[Any] ):
'''simple docstring'''
return 1E-3
def UpperCamelCase__ (self : str , UpperCamelCase : "PreTrainedTokenizer" , UpperCamelCase : int = -1 , UpperCamelCase : int = -1 , UpperCamelCase : bool = False , UpperCamelCase : Optional["TensorType"] = None , ):
'''simple docstring'''
lowercase__ = super(UpperCamelCase , self ).generate_dummy_inputs(
UpperCamelCase , batch_size=UpperCamelCase , seq_length=UpperCamelCase , is_pair=UpperCamelCase , framework=UpperCamelCase )
# We need to order the input in the way they appears in the forward()
lowercase__ = OrderedDict({'''input_ids''': common_inputs['''input_ids''']} )
# Need to add the past_keys
if self.use_past:
if not is_torch_available():
raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.''' )
else:
import torch
lowercase__ ,lowercase__ = common_inputs['''input_ids'''].shape
# Not using the same length for past_key_values
lowercase__ = seqlen + 2
lowercase__ = self._config.hidden_size // self.num_attention_heads
lowercase__ = (
batch * self.num_attention_heads,
head_dim,
past_key_values_length,
)
lowercase__ = (
batch * self.num_attention_heads,
past_key_values_length,
head_dim,
)
lowercase__ = [
(torch.zeros(UpperCamelCase ), torch.zeros(UpperCamelCase )) for _ in range(self.num_layers )
]
lowercase__ = common_inputs['''attention_mask''']
if self.use_past:
lowercase__ = ordered_inputs['''attention_mask'''].dtype
lowercase__ = torch.cat(
[ordered_inputs['''attention_mask'''], torch.ones(UpperCamelCase , UpperCamelCase , dtype=UpperCamelCase )] , dim=1 )
return ordered_inputs
@property
def UpperCamelCase__ (self : Any ):
'''simple docstring'''
return 13
| 2
|
"""simple docstring"""
from ...utils import is_torch_available, is_transformers_available
if is_transformers_available() and is_torch_available():
from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline
| 33
| 0
|
'''simple docstring'''
from .integrations import (
is_optuna_available,
is_ray_available,
is_sigopt_available,
is_wandb_available,
run_hp_search_optuna,
run_hp_search_ray,
run_hp_search_sigopt,
run_hp_search_wandb,
)
from .trainer_utils import (
HPSearchBackend,
default_hp_space_optuna,
default_hp_space_ray,
default_hp_space_sigopt,
default_hp_space_wandb,
)
from .utils import logging
lowercase : Union[str, Any] = logging.get_logger(__name__)
class A :
__magic_name__ = 42
__magic_name__ = None
@staticmethod
def __lowerCAmelCase ( ) -> List[Any]:
"""simple docstring"""
raise NotImplementedError
def __lowerCAmelCase ( self , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE ) -> Tuple:
"""simple docstring"""
raise NotImplementedError
def __lowerCAmelCase ( self , SCREAMING_SNAKE_CASE ) -> Optional[Any]:
"""simple docstring"""
raise NotImplementedError
def __lowerCAmelCase ( self ) -> List[Any]:
"""simple docstring"""
if not self.is_available():
raise RuntimeError(
F'You picked the {self.name} backend, but it is not installed. Run {self.pip_install()}.' )
@classmethod
def __lowerCAmelCase ( cls ) -> Dict:
"""simple docstring"""
return F'`pip install {cls.pip_package or cls.name}`'
class A ( __snake_case ):
__magic_name__ = '''optuna'''
@staticmethod
def __lowerCAmelCase ( ) -> str:
"""simple docstring"""
return is_optuna_available()
def __lowerCAmelCase ( self , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE ) -> Any:
"""simple docstring"""
return run_hp_search_optuna(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE )
def __lowerCAmelCase ( self , SCREAMING_SNAKE_CASE ) -> List[str]:
"""simple docstring"""
return default_hp_space_optuna(SCREAMING_SNAKE_CASE )
class A ( __snake_case ):
__magic_name__ = '''ray'''
__magic_name__ = '''\'ray[tune]\''''
@staticmethod
def __lowerCAmelCase ( ) -> int:
"""simple docstring"""
return is_ray_available()
def __lowerCAmelCase ( self , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE ) -> Any:
"""simple docstring"""
return run_hp_search_ray(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE )
def __lowerCAmelCase ( self , SCREAMING_SNAKE_CASE ) -> int:
"""simple docstring"""
return default_hp_space_ray(SCREAMING_SNAKE_CASE )
class A ( __snake_case ):
__magic_name__ = '''sigopt'''
@staticmethod
def __lowerCAmelCase ( ) -> Dict:
"""simple docstring"""
return is_sigopt_available()
def __lowerCAmelCase ( self , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE ) -> Optional[Any]:
"""simple docstring"""
return run_hp_search_sigopt(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE )
def __lowerCAmelCase ( self , SCREAMING_SNAKE_CASE ) -> List[str]:
"""simple docstring"""
return default_hp_space_sigopt(SCREAMING_SNAKE_CASE )
class A ( __snake_case ):
__magic_name__ = '''wandb'''
@staticmethod
def __lowerCAmelCase ( ) -> List[Any]:
"""simple docstring"""
return is_wandb_available()
def __lowerCAmelCase ( self , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE ) -> Any:
"""simple docstring"""
return run_hp_search_wandb(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE )
def __lowerCAmelCase ( self , SCREAMING_SNAKE_CASE ) -> Optional[Any]:
"""simple docstring"""
return default_hp_space_wandb(SCREAMING_SNAKE_CASE )
lowercase : Dict = {
HPSearchBackend(backend.name): backend for backend in [OptunaBackend, RayTuneBackend, SigOptBackend, WandbBackend]
}
def lowerCAmelCase_ ( ):
'''simple docstring'''
A : List[Any] = [backend for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() if backend.is_available()]
if len(snake_case__ ) > 0:
A : Dict = available_backends[0].name
if len(snake_case__ ) > 1:
logger.info(
F'{len(snake_case__ )} hyperparameter search backends available. Using {name} as the default.' )
return name
raise RuntimeError(
'''No hyperparameter search backend available.\n'''
+ '''\n'''.join(
F' - To install {backend.name} run {backend.pip_install()}'
for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() ) )
| 3
|
"""simple docstring"""
from typing import Optional, Union
import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention
from ...modeling_utils import PreTrainedModel
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_mobilenet_va import MobileNetVaConfig
__A : Union[str, Any] = logging.get_logger(__name__)
# General docstring
__A : Tuple = '''MobileNetV1Config'''
# Base docstring
__A : Union[str, Any] = '''google/mobilenet_v1_1.0_224'''
__A : Union[str, Any] = [1, 1_024, 7, 7]
# Image classification docstring
__A : Optional[Any] = '''google/mobilenet_v1_1.0_224'''
__A : List[Any] = '''tabby, tabby cat'''
__A : Union[str, Any] = [
'''google/mobilenet_v1_1.0_224''',
'''google/mobilenet_v1_0.75_192''',
# See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
]
def lowercase ( __snake_case : List[str] , __snake_case : Union[str, Any] , __snake_case : Dict=None ):
lowercase_ : str = {}
if isinstance(__snake_case , __snake_case ):
lowercase_ : Union[str, Any] = model.mobilenet_va
else:
lowercase_ : Optional[Any] = model
lowercase_ : Union[str, Any] = '''MobilenetV1/Conv2d_0/'''
lowercase_ : Union[str, Any] = backbone.conv_stem.convolution.weight
lowercase_ : Optional[Any] = backbone.conv_stem.normalization.bias
lowercase_ : Union[str, Any] = backbone.conv_stem.normalization.weight
lowercase_ : Any = backbone.conv_stem.normalization.running_mean
lowercase_ : int = backbone.conv_stem.normalization.running_var
for i in range(1_3 ):
lowercase_ : Optional[int] = i + 1
lowercase_ : Union[str, Any] = i * 2
lowercase_ : Optional[Any] = backbone.layer[pt_index]
lowercase_ : Union[str, Any] = F'''MobilenetV1/Conv2d_{tf_index}_depthwise/'''
lowercase_ : str = pointer.convolution.weight
lowercase_ : int = pointer.normalization.bias
lowercase_ : Any = pointer.normalization.weight
lowercase_ : Dict = pointer.normalization.running_mean
lowercase_ : Union[str, Any] = pointer.normalization.running_var
lowercase_ : Any = backbone.layer[pt_index + 1]
lowercase_ : Union[str, Any] = F'''MobilenetV1/Conv2d_{tf_index}_pointwise/'''
lowercase_ : int = pointer.convolution.weight
lowercase_ : str = pointer.normalization.bias
lowercase_ : Tuple = pointer.normalization.weight
lowercase_ : Dict = pointer.normalization.running_mean
lowercase_ : Any = pointer.normalization.running_var
if isinstance(__snake_case , __snake_case ):
lowercase_ : Optional[Any] = '''MobilenetV1/Logits/Conv2d_1c_1x1/'''
lowercase_ : Any = model.classifier.weight
lowercase_ : Optional[int] = model.classifier.bias
return tf_to_pt_map
def lowercase ( __snake_case : Optional[int] , __snake_case : int , __snake_case : Dict ):
try:
import numpy as np
import tensorflow as tf
except ImportError:
logger.error(
'''Loading a TensorFlow models in PyTorch, requires TensorFlow to be installed. Please see '''
'''https://www.tensorflow.org/install/ for installation instructions.''' )
raise
# Load weights from TF model
lowercase_ : Tuple = tf.train.list_variables(__snake_case )
lowercase_ : int = {}
for name, shape in init_vars:
logger.info(F'''Loading TF weight {name} with shape {shape}''' )
lowercase_ : Optional[Any] = tf.train.load_variable(__snake_case , __snake_case )
lowercase_ : Optional[int] = array
# Build TF to PyTorch weights loading map
lowercase_ : Any = _build_tf_to_pytorch_map(__snake_case , __snake_case , __snake_case )
for name, pointer in tf_to_pt_map.items():
logger.info(F'''Importing {name}''' )
if name not in tf_weights:
logger.info(F'''{name} not in tf pre-trained weights, skipping''' )
continue
lowercase_ : Union[str, Any] = tf_weights[name]
if "depthwise_weights" in name:
logger.info('''Transposing depthwise''' )
lowercase_ : Any = np.transpose(__snake_case , (2, 3, 0, 1) )
elif "weights" in name:
logger.info('''Transposing''' )
if len(pointer.shape ) == 2: # copying into linear layer
lowercase_ : Optional[int] = array.squeeze().transpose()
else:
lowercase_ : Optional[int] = np.transpose(__snake_case , (3, 2, 0, 1) )
if pointer.shape != array.shape:
raise ValueError(F'''Pointer shape {pointer.shape} and array shape {array.shape} mismatched''' )
logger.info(F'''Initialize PyTorch weight {name} {array.shape}''' )
lowercase_ : str = torch.from_numpy(__snake_case )
tf_weights.pop(__snake_case , __snake_case )
tf_weights.pop(name + '''/RMSProp''' , __snake_case )
tf_weights.pop(name + '''/RMSProp_1''' , __snake_case )
tf_weights.pop(name + '''/ExponentialMovingAverage''' , __snake_case )
logger.info(F'''Weights not copied to PyTorch model: {', '.join(tf_weights.keys() )}''' )
return model
def lowercase ( __snake_case : torch.Tensor , __snake_case : nn.Convad ):
lowercase_ , lowercase_ : Optional[int] = features.shape[-2:]
lowercase_ , lowercase_ : str = conv_layer.stride
lowercase_ , lowercase_ : Tuple = conv_layer.kernel_size
if in_height % stride_height == 0:
lowercase_ : Dict = max(kernel_height - stride_height , 0 )
else:
lowercase_ : List[Any] = max(kernel_height - (in_height % stride_height) , 0 )
if in_width % stride_width == 0:
lowercase_ : str = max(kernel_width - stride_width , 0 )
else:
lowercase_ : int = max(kernel_width - (in_width % stride_width) , 0 )
lowercase_ : int = pad_along_width // 2
lowercase_ : Union[str, Any] = pad_along_width - pad_left
lowercase_ : Tuple = pad_along_height // 2
lowercase_ : List[str] = pad_along_height - pad_top
lowercase_ : str = (pad_left, pad_right, pad_top, pad_bottom)
return nn.functional.pad(__snake_case , __snake_case , '''constant''' , 0.0 )
class _UpperCAmelCase ( nn.Module ):
def __init__( self : List[Any] , A : MobileNetVaConfig , A : int , A : int , A : int , A : Optional[int] = 1 , A : Optional[int] = 1 , A : bool = False , A : Optional[bool] = True , A : Optional[bool or str] = True , ) -> None:
super().__init__()
lowercase_ : int = config
if in_channels % groups != 0:
raise ValueError(F'''Input channels ({in_channels}) are not divisible by {groups} groups.''' )
if out_channels % groups != 0:
raise ValueError(F'''Output channels ({out_channels}) are not divisible by {groups} groups.''' )
lowercase_ : Tuple = 0 if config.tf_padding else int((kernel_size - 1) / 2 )
lowercase_ : int = nn.Convad(
in_channels=A , out_channels=A , kernel_size=A , stride=A , padding=A , groups=A , bias=A , padding_mode='''zeros''' , )
if use_normalization:
lowercase_ : Optional[Any] = nn.BatchNormad(
num_features=A , eps=config.layer_norm_eps , momentum=0.9997 , affine=A , track_running_stats=A , )
else:
lowercase_ : Union[str, Any] = None
if use_activation:
if isinstance(A , A ):
lowercase_ : str = ACTaFN[use_activation]
elif isinstance(config.hidden_act , A ):
lowercase_ : Any = ACTaFN[config.hidden_act]
else:
lowercase_ : Tuple = config.hidden_act
else:
lowercase_ : Tuple = None
def A ( self : str , A : torch.Tensor ) -> torch.Tensor:
if self.config.tf_padding:
lowercase_ : List[Any] = apply_tf_padding(A , self.convolution )
lowercase_ : Optional[int] = self.convolution(A )
if self.normalization is not None:
lowercase_ : Union[str, Any] = self.normalization(A )
if self.activation is not None:
lowercase_ : Optional[int] = self.activation(A )
return features
class _UpperCAmelCase ( _A ):
SCREAMING_SNAKE_CASE_ : Optional[int] = MobileNetVaConfig
SCREAMING_SNAKE_CASE_ : int = load_tf_weights_in_mobilenet_va
SCREAMING_SNAKE_CASE_ : Optional[Any] = "mobilenet_v1"
SCREAMING_SNAKE_CASE_ : Union[str, Any] = "pixel_values"
SCREAMING_SNAKE_CASE_ : List[str] = False
def A ( self : Any , A : Union[nn.Linear, nn.Convad] ) -> None:
if isinstance(A , (nn.Linear, nn.Convad) ):
module.weight.data.normal_(mean=0.0 , std=self.config.initializer_range )
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(A , nn.BatchNormad ):
module.bias.data.zero_()
module.weight.data.fill_(1.0 )
__A : Union[str, Any] = R'''
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parameters:
config ([`MobileNetV1Config`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
'''
__A : List[str] = R'''
Args:
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
[`MobileNetV1ImageProcessor.__call__`] for details.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
'''
@add_start_docstrings(
"The bare MobileNetV1 model outputting raw hidden-states without any specific head on top." , _A , )
class _UpperCAmelCase ( _A ):
def __init__( self : str , A : MobileNetVaConfig , A : bool = True ) -> int:
super().__init__(A )
lowercase_ : Union[str, Any] = config
lowercase_ : List[str] = 32
lowercase_ : str = max(int(depth * config.depth_multiplier ) , config.min_depth )
lowercase_ : Union[str, Any] = MobileNetVaConvLayer(
A , in_channels=config.num_channels , out_channels=A , kernel_size=3 , stride=2 , )
lowercase_ : Optional[Any] = [1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 2, 1]
lowercase_ : List[Any] = nn.ModuleList()
for i in range(13 ):
lowercase_ : Dict = out_channels
if strides[i] == 2 or i == 0:
depth *= 2
lowercase_ : str = max(int(depth * config.depth_multiplier ) , config.min_depth )
self.layer.append(
MobileNetVaConvLayer(
A , in_channels=A , out_channels=A , kernel_size=3 , stride=strides[i] , groups=A , ) )
self.layer.append(
MobileNetVaConvLayer(
A , in_channels=A , out_channels=A , kernel_size=1 , ) )
lowercase_ : int = nn.AdaptiveAvgPoolad((1, 1) ) if add_pooling_layer else None
# Initialize weights and apply final processing
self.post_init()
def A ( self : Any , A : Optional[Any] ) -> Optional[int]:
raise NotImplementedError
@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 A ( self : List[Any] , A : Optional[torch.Tensor] = None , A : Optional[bool] = None , A : Optional[bool] = None , ) -> Union[tuple, BaseModelOutputWithPoolingAndNoAttention]:
lowercase_ : str = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
lowercase_ : Union[str, Any] = return_dict if return_dict is not None else self.config.use_return_dict
if pixel_values is None:
raise ValueError('''You have to specify pixel_values''' )
lowercase_ : List[str] = self.conv_stem(A )
lowercase_ : Dict = () if output_hidden_states else None
for i, layer_module in enumerate(self.layer ):
lowercase_ : Optional[int] = layer_module(A )
if output_hidden_states:
lowercase_ : str = all_hidden_states + (hidden_states,)
lowercase_ : Tuple = hidden_states
if self.pooler is not None:
lowercase_ : Dict = torch.flatten(self.pooler(A ) , start_dim=1 )
else:
lowercase_ : Optional[Any] = None
if not return_dict:
return tuple(v for v in [last_hidden_state, pooled_output, all_hidden_states] if v is not None )
return BaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=A , pooler_output=A , hidden_states=A , )
@add_start_docstrings(
"\n MobileNetV1 model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n ImageNet.\n " , _A , )
class _UpperCAmelCase ( _A ):
def __init__( self : List[str] , A : MobileNetVaConfig ) -> None:
super().__init__(A )
lowercase_ : int = config.num_labels
lowercase_ : List[str] = MobileNetVaModel(A )
lowercase_ : Union[str, Any] = self.mobilenet_va.layer[-1].convolution.out_channels
# Classifier head
lowercase_ : Tuple = nn.Dropout(config.classifier_dropout_prob , inplace=A )
lowercase_ : int = nn.Linear(A , 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 A ( self : Optional[Any] , A : Optional[torch.Tensor] = None , A : Optional[bool] = None , A : Optional[torch.Tensor] = None , A : Optional[bool] = None , ) -> Union[tuple, ImageClassifierOutputWithNoAttention]:
lowercase_ : Union[str, Any] = return_dict if return_dict is not None else self.config.use_return_dict
lowercase_ : List[Any] = self.mobilenet_va(A , output_hidden_states=A , return_dict=A )
lowercase_ : Union[str, Any] = outputs.pooler_output if return_dict else outputs[1]
lowercase_ : Dict = self.classifier(self.dropout(A ) )
lowercase_ : int = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
lowercase_ : List[str] = '''regression'''
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
lowercase_ : Optional[Any] = '''single_label_classification'''
else:
lowercase_ : Tuple = '''multi_label_classification'''
if self.config.problem_type == "regression":
lowercase_ : str = MSELoss()
if self.num_labels == 1:
lowercase_ : List[str] = loss_fct(logits.squeeze() , labels.squeeze() )
else:
lowercase_ : List[str] = loss_fct(A , A )
elif self.config.problem_type == "single_label_classification":
lowercase_ : List[Any] = CrossEntropyLoss()
lowercase_ : str = loss_fct(logits.view(-1 , self.num_labels ) , labels.view(-1 ) )
elif self.config.problem_type == "multi_label_classification":
lowercase_ : str = BCEWithLogitsLoss()
lowercase_ : List[Any] = loss_fct(A , A )
if not return_dict:
lowercase_ : Tuple = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return ImageClassifierOutputWithNoAttention(
loss=A , logits=A , hidden_states=outputs.hidden_states , )
| 33
| 0
|
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__snake_case =logging.get_logger(__name__)
__snake_case ={
"""roberta-base""": """https://huggingface.co/roberta-base/resolve/main/config.json""",
"""roberta-large""": """https://huggingface.co/roberta-large/resolve/main/config.json""",
"""roberta-large-mnli""": """https://huggingface.co/roberta-large-mnli/resolve/main/config.json""",
"""distilroberta-base""": """https://huggingface.co/distilroberta-base/resolve/main/config.json""",
"""roberta-base-openai-detector""": """https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json""",
"""roberta-large-openai-detector""": """https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json""",
}
class UpperCAmelCase_ ( __lowercase ):
lowerCamelCase : int = '''roberta'''
def __init__( self : Optional[Any] , UpperCAmelCase__ : Tuple=5_0_2_6_5 , UpperCAmelCase__ : str=7_6_8 , UpperCAmelCase__ : Optional[Any]=1_2 , UpperCAmelCase__ : Optional[int]=1_2 , UpperCAmelCase__ : int=3_0_7_2 , UpperCAmelCase__ : Any="gelu" , UpperCAmelCase__ : Union[str, Any]=0.1 , UpperCAmelCase__ : List[Any]=0.1 , UpperCAmelCase__ : Any=5_1_2 , UpperCAmelCase__ : List[Any]=2 , UpperCAmelCase__ : List[str]=0.02 , UpperCAmelCase__ : Optional[Any]=1E-12 , UpperCAmelCase__ : str=1 , UpperCAmelCase__ : Optional[int]=0 , UpperCAmelCase__ : str=2 , UpperCAmelCase__ : List[str]="absolute" , UpperCAmelCase__ : Optional[int]=True , UpperCAmelCase__ : List[str]=None , **UpperCAmelCase__ : List[Any] , ) -> Any:
super().__init__(pad_token_id=UpperCAmelCase__ , bos_token_id=UpperCAmelCase__ , eos_token_id=UpperCAmelCase__ , **UpperCAmelCase__ )
lowerCAmelCase = vocab_size
lowerCAmelCase = hidden_size
lowerCAmelCase = num_hidden_layers
lowerCAmelCase = num_attention_heads
lowerCAmelCase = hidden_act
lowerCAmelCase = intermediate_size
lowerCAmelCase = hidden_dropout_prob
lowerCAmelCase = attention_probs_dropout_prob
lowerCAmelCase = max_position_embeddings
lowerCAmelCase = type_vocab_size
lowerCAmelCase = initializer_range
lowerCAmelCase = layer_norm_eps
lowerCAmelCase = position_embedding_type
lowerCAmelCase = use_cache
lowerCAmelCase = classifier_dropout
class UpperCAmelCase_ ( __lowercase ):
@property
def __UpperCAmelCase ( self : List[Any] ) -> Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
lowerCAmelCase = {0: 'batch', 1: 'choice', 2: 'sequence'}
else:
lowerCAmelCase = {0: 'batch', 1: 'sequence'}
return OrderedDict(
[
('input_ids', dynamic_axis),
('attention_mask', dynamic_axis),
] )
| 4
|
"""simple docstring"""
def lowercase ( __snake_case : list[int] ):
lowercase_ : List[Any] = len(__snake_case )
for i in range(__snake_case ):
for j in range(i + 1 , __snake_case ):
if numbers[j] < numbers[i]:
lowercase_ , lowercase_ : Optional[int] = numbers[j], numbers[i]
return numbers
if __name__ == "__main__":
__A : int = input('''Enter numbers separated by a comma:\n''').strip()
__A : Any = [int(item) for item in user_input.split(''',''')]
print(exchange_sort(unsorted))
| 33
| 0
|
import argparse
import glob
import logging
import os
import time
from argparse import Namespace
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from torch.utils.data import DataLoader, TensorDataset
from transformers import glue_compute_metrics as compute_metrics
from transformers import glue_convert_examples_to_features as convert_examples_to_features
from transformers import glue_output_modes, glue_tasks_num_labels
from transformers import glue_processors as processors
UpperCAmelCase__ = logging.getLogger(__name__)
class lowerCamelCase__ ( lowerCAmelCase):
SCREAMING_SNAKE_CASE__ = '''sequence-classification'''
def __init__(self , UpperCAmelCase ) -> Union[str, Any]:
if type(UpperCAmelCase ) == dict:
_lowercase =Namespace(**UpperCAmelCase )
_lowercase =glue_output_modes[hparams.task]
_lowercase =glue_tasks_num_labels[hparams.task]
super().__init__(UpperCAmelCase , UpperCAmelCase , self.mode )
def __A (self , **UpperCAmelCase ) -> List[Any]:
return self.model(**UpperCAmelCase )
def __A (self , UpperCAmelCase , UpperCAmelCase ) -> Optional[Any]:
_lowercase ={'''input_ids''': batch[0], '''attention_mask''': batch[1], '''labels''': batch[3]}
if self.config.model_type not in ["distilbert", "bart"]:
_lowercase =batch[2] if self.config.model_type in ['''bert''', '''xlnet''', '''albert'''] else None
_lowercase =self(**UpperCAmelCase )
_lowercase =outputs[0]
_lowercase =self.trainer.lr_schedulers[0]['''scheduler''']
_lowercase ={'''loss''': loss, '''rate''': lr_scheduler.get_last_lr()[-1]}
return {"loss": loss, "log": tensorboard_logs}
def __A (self ) -> Dict:
_lowercase =self.hparams
_lowercase =processors[args.task]()
_lowercase =processor.get_labels()
for mode in ["train", "dev"]:
_lowercase =self._feature_file(UpperCAmelCase )
if os.path.exists(UpperCAmelCase ) and not args.overwrite_cache:
logger.info('''Loading features from cached file %s''' , UpperCAmelCase )
else:
logger.info('''Creating features from dataset file at %s''' , args.data_dir )
_lowercase =(
processor.get_dev_examples(args.data_dir )
if mode == '''dev'''
else processor.get_train_examples(args.data_dir )
)
_lowercase =convert_examples_to_features(
UpperCAmelCase , self.tokenizer , max_length=args.max_seq_length , label_list=self.labels , output_mode=args.glue_output_mode , )
logger.info('''Saving features into cached file %s''' , UpperCAmelCase )
torch.save(UpperCAmelCase , UpperCAmelCase )
def __A (self , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = False ) -> DataLoader:
_lowercase ='''dev''' if mode == '''test''' else mode
_lowercase =self._feature_file(UpperCAmelCase )
logger.info('''Loading features from cached file %s''' , UpperCAmelCase )
_lowercase =torch.load(UpperCAmelCase )
_lowercase =torch.tensor([f.input_ids for f in features] , dtype=torch.long )
_lowercase =torch.tensor([f.attention_mask for f in features] , dtype=torch.long )
_lowercase =torch.tensor([f.token_type_ids for f in features] , dtype=torch.long )
if self.hparams.glue_output_mode == "classification":
_lowercase =torch.tensor([f.label for f in features] , dtype=torch.long )
elif self.hparams.glue_output_mode == "regression":
_lowercase =torch.tensor([f.label for f in features] , dtype=torch.float )
return DataLoader(
TensorDataset(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) , batch_size=UpperCAmelCase , shuffle=UpperCAmelCase , )
def __A (self , UpperCAmelCase , UpperCAmelCase ) -> int:
_lowercase ={'''input_ids''': batch[0], '''attention_mask''': batch[1], '''labels''': batch[3]}
if self.config.model_type not in ["distilbert", "bart"]:
_lowercase =batch[2] if self.config.model_type in ['''bert''', '''xlnet''', '''albert'''] else None
_lowercase =self(**UpperCAmelCase )
_lowercase , _lowercase =outputs[:2]
_lowercase =logits.detach().cpu().numpy()
_lowercase =inputs['''labels'''].detach().cpu().numpy()
return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}
def __A (self , UpperCAmelCase ) -> tuple:
_lowercase =torch.stack([x['''val_loss'''] for x in outputs] ).mean().detach().cpu().item()
_lowercase =np.concatenate([x['''pred'''] for x in outputs] , axis=0 )
if self.hparams.glue_output_mode == "classification":
_lowercase =np.argmax(UpperCAmelCase , axis=1 )
elif self.hparams.glue_output_mode == "regression":
_lowercase =np.squeeze(UpperCAmelCase )
_lowercase =np.concatenate([x['''target'''] for x in outputs] , axis=0 )
_lowercase =[[] for _ in range(out_label_ids.shape[0] )]
_lowercase =[[] for _ in range(out_label_ids.shape[0] )]
_lowercase ={**{'''val_loss''': val_loss_mean}, **compute_metrics(self.hparams.task , UpperCAmelCase , UpperCAmelCase )}
_lowercase =dict(results.items() )
_lowercase =results
return ret, preds_list, out_label_list
def __A (self , UpperCAmelCase ) -> dict:
_lowercase , _lowercase , _lowercase =self._eval_end(UpperCAmelCase )
_lowercase =ret['''log''']
return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}
def __A (self , UpperCAmelCase ) -> dict:
_lowercase , _lowercase , _lowercase =self._eval_end(UpperCAmelCase )
_lowercase =ret['''log''']
# `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}
@staticmethod
def __A (UpperCAmelCase , UpperCAmelCase ) -> List[Any]:
BaseTransformer.add_model_specific_args(UpperCAmelCase , UpperCAmelCase )
parser.add_argument(
'''--max_seq_length''' , default=1_2_8 , type=UpperCAmelCase , help=(
'''The maximum total input sequence length after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
) , )
parser.add_argument(
'''--task''' , default='''''' , type=UpperCAmelCase , required=UpperCAmelCase , help='''The GLUE task to run''' , )
parser.add_argument(
'''--gpus''' , default=0 , type=UpperCAmelCase , help='''The number of GPUs allocated for this, it is by default 0 meaning none''' , )
parser.add_argument(
'''--overwrite_cache''' , action='''store_true''' , help='''Overwrite the cached training and evaluation sets''' )
return parser
def UpperCAmelCase_ ( ) -> Dict:
"""simple docstring"""
_lowercase =argparse.ArgumentParser()
add_generic_args(__snake_case , os.getcwd() )
_lowercase =GLUETransformer.add_model_specific_args(__snake_case , os.getcwd() )
_lowercase =parser.parse_args()
# If output_dir not provided, a folder will be generated in pwd
if args.output_dir is None:
_lowercase =os.path.join(
'''./results''' , F"{args.task}_{time.strftime('%Y%m%d_%H%M%S' )}" , )
os.makedirs(args.output_dir )
_lowercase =GLUETransformer(__snake_case )
_lowercase =generic_train(__snake_case , __snake_case )
# Optionally, predict on dev set and write to output_dir
if args.do_predict:
_lowercase =sorted(glob.glob(os.path.join(args.output_dir , '''checkpoint-epoch=*.ckpt''' ) , recursive=__snake_case ) )
_lowercase =model.load_from_checkpoint(checkpoints[-1] )
return trainer.test(__snake_case )
if __name__ == "__main__":
main()
| 5
|
"""simple docstring"""
import json
import pathlib
import unittest
import numpy as np
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, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import YolosImageProcessor
class _UpperCAmelCase ( unittest.TestCase ):
def __init__( self : List[Any] , A : Any , A : Tuple=7 , A : Tuple=3 , A : Optional[Any]=30 , A : List[Any]=4_00 , A : Tuple=True , A : Dict=None , A : List[str]=True , A : Optional[int]=[0.5, 0.5, 0.5] , A : Tuple=[0.5, 0.5, 0.5] , A : List[str]=True , A : List[Any]=1 / 2_55 , A : Union[str, Any]=True , ) -> Tuple:
# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
lowercase_ : Optional[int] = size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 13_33}
lowercase_ : Optional[int] = parent
lowercase_ : str = batch_size
lowercase_ : Tuple = num_channels
lowercase_ : str = min_resolution
lowercase_ : Any = max_resolution
lowercase_ : str = do_resize
lowercase_ : Any = size
lowercase_ : Optional[int] = do_normalize
lowercase_ : List[str] = image_mean
lowercase_ : Optional[Any] = image_std
lowercase_ : int = do_rescale
lowercase_ : List[str] = rescale_factor
lowercase_ : int = do_pad
def A ( self : Any ) -> str:
return {
"do_resize": self.do_resize,
"size": self.size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_rescale": self.do_rescale,
"rescale_factor": self.rescale_factor,
"do_pad": self.do_pad,
}
def A ( self : Optional[Any] , A : int , A : int=False ) -> Tuple:
if not batched:
lowercase_ : Optional[int] = image_inputs[0]
if isinstance(A , Image.Image ):
lowercase_ , lowercase_ : int = image.size
else:
lowercase_ , lowercase_ : Tuple = image.shape[1], image.shape[2]
if w < h:
lowercase_ : int = int(self.size['''shortest_edge'''] * h / w )
lowercase_ : Optional[Any] = self.size['''shortest_edge''']
elif w > h:
lowercase_ : Optional[Any] = self.size['''shortest_edge''']
lowercase_ : Optional[int] = int(self.size['''shortest_edge'''] * w / h )
else:
lowercase_ : Any = self.size['''shortest_edge''']
lowercase_ : Any = self.size['''shortest_edge''']
else:
lowercase_ : Tuple = []
for image in image_inputs:
lowercase_ , lowercase_ : Optional[Any] = self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
lowercase_ : Union[str, Any] = max(A , key=lambda A : item[0] )[0]
lowercase_ : Optional[Any] = max(A , key=lambda A : item[1] )[1]
return expected_height, expected_width
@require_torch
@require_vision
class _UpperCAmelCase ( _A , unittest.TestCase ):
SCREAMING_SNAKE_CASE_ : Optional[Any] = YolosImageProcessor if is_vision_available() else None
def A ( self : Optional[int] ) -> Optional[int]:
lowercase_ : Optional[Any] = YolosImageProcessingTester(self )
@property
def A ( self : str ) -> Any:
return self.image_processor_tester.prepare_image_processor_dict()
def A ( self : Optional[int] ) -> List[str]:
lowercase_ : Tuple = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(A , '''image_mean''' ) )
self.assertTrue(hasattr(A , '''image_std''' ) )
self.assertTrue(hasattr(A , '''do_normalize''' ) )
self.assertTrue(hasattr(A , '''do_resize''' ) )
self.assertTrue(hasattr(A , '''size''' ) )
def A ( self : Dict ) -> Tuple:
lowercase_ : Dict = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'''shortest_edge''': 18, '''longest_edge''': 13_33} )
self.assertEqual(image_processor.do_pad , A )
lowercase_ : Tuple = self.image_processing_class.from_dict(
self.image_processor_dict , size=42 , max_size=84 , pad_and_return_pixel_mask=A )
self.assertEqual(image_processor.size , {'''shortest_edge''': 42, '''longest_edge''': 84} )
self.assertEqual(image_processor.do_pad , A )
def A ( self : Optional[int] ) -> Tuple:
pass
def A ( self : Tuple ) -> int:
# Initialize image_processing
lowercase_ : Dict = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
lowercase_ : str = prepare_image_inputs(self.image_processor_tester , equal_resolution=A )
for image in image_inputs:
self.assertIsInstance(A , Image.Image )
# Test not batched input
lowercase_ : Optional[int] = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
lowercase_ , lowercase_ : Union[str, Any] = self.image_processor_tester.get_expected_values(A )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
lowercase_ , lowercase_ : Dict = self.image_processor_tester.get_expected_values(A , batched=A )
lowercase_ : str = 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,
expected_height,
expected_width,
) , )
def A ( self : str ) -> Any:
# Initialize image_processing
lowercase_ : Any = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
lowercase_ : List[Any] = 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
lowercase_ : List[Any] = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
lowercase_ , lowercase_ : int = self.image_processor_tester.get_expected_values(A )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
lowercase_ : Optional[int] = image_processing(A , return_tensors='''pt''' ).pixel_values
lowercase_ , lowercase_ : List[Any] = self.image_processor_tester.get_expected_values(A , batched=A )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def A ( self : Tuple ) -> Optional[int]:
# Initialize image_processing
lowercase_ : Dict = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
lowercase_ : Optional[int] = 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
lowercase_ : List[str] = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
lowercase_ , lowercase_ : Union[str, Any] = self.image_processor_tester.get_expected_values(A )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
lowercase_ : Any = image_processing(A , return_tensors='''pt''' ).pixel_values
lowercase_ , lowercase_ : List[str] = self.image_processor_tester.get_expected_values(A , batched=A )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def A ( self : Tuple ) -> Optional[Any]:
# Initialize image_processings
lowercase_ : Optional[Any] = self.image_processing_class(**self.image_processor_dict )
lowercase_ : Tuple = self.image_processing_class(do_resize=A , do_normalize=A , do_rescale=A )
# create random PyTorch tensors
lowercase_ : Union[str, Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=A , torchify=A )
for image in image_inputs:
self.assertIsInstance(A , torch.Tensor )
# Test whether the method "pad" and calling the image processor return the same tensors
lowercase_ : Union[str, Any] = image_processing_a.pad(A , return_tensors='''pt''' )
lowercase_ : List[Any] = image_processing_a(A , return_tensors='''pt''' )
self.assertTrue(
torch.allclose(encoded_images_with_method['''pixel_values'''] , encoded_images['''pixel_values'''] , atol=1e-4 ) )
@slow
def A ( self : str ) -> List[Any]:
# prepare image and target
lowercase_ : Dict = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt''' , '''r''' ) as f:
lowercase_ : List[Any] = json.loads(f.read() )
lowercase_ : Tuple = {'''image_id''': 3_97_69, '''annotations''': target}
# encode them
lowercase_ : Union[str, Any] = YolosImageProcessor.from_pretrained('''hustvl/yolos-small''' )
lowercase_ : List[Any] = image_processing(images=A , annotations=A , return_tensors='''pt''' )
# verify pixel values
lowercase_ : Union[str, Any] = torch.Size([1, 3, 8_00, 10_66] )
self.assertEqual(encoding['''pixel_values'''].shape , A )
lowercase_ : Union[str, Any] = torch.tensor([0.2796, 0.3138, 0.3481] )
self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3] , A , atol=1e-4 ) )
# verify area
lowercase_ : Tuple = torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area'''] , A ) )
# verify boxes
lowercase_ : List[str] = torch.Size([6, 4] )
self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape , A )
lowercase_ : Any = torch.tensor([0.5503, 0.2765, 0.0604, 0.2215] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0] , A , atol=1e-3 ) )
# verify image_id
lowercase_ : List[Any] = torch.tensor([3_97_69] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''] , A ) )
# verify is_crowd
lowercase_ : Any = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''] , A ) )
# verify class_labels
lowercase_ : Optional[Any] = torch.tensor([75, 75, 63, 65, 17, 17] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''] , A ) )
# verify orig_size
lowercase_ : List[str] = torch.tensor([4_80, 6_40] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''] , A ) )
# verify size
lowercase_ : Optional[Any] = torch.tensor([8_00, 10_66] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''] , A ) )
@slow
def A ( self : List[Any] ) -> Dict:
# prepare image, target and masks_path
lowercase_ : Tuple = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt''' , '''r''' ) as f:
lowercase_ : str = json.loads(f.read() )
lowercase_ : int = {'''file_name''': '''000000039769.png''', '''image_id''': 3_97_69, '''segments_info''': target}
lowercase_ : List[Any] = pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic''' )
# encode them
lowercase_ : int = YolosImageProcessor(format='''coco_panoptic''' )
lowercase_ : Any = image_processing(images=A , annotations=A , masks_path=A , return_tensors='''pt''' )
# verify pixel values
lowercase_ : Optional[Any] = torch.Size([1, 3, 8_00, 10_66] )
self.assertEqual(encoding['''pixel_values'''].shape , A )
lowercase_ : Tuple = torch.tensor([0.2796, 0.3138, 0.3481] )
self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3] , A , atol=1e-4 ) )
# verify area
lowercase_ : List[Any] = torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area'''] , A ) )
# verify boxes
lowercase_ : str = torch.Size([6, 4] )
self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape , A )
lowercase_ : List[str] = torch.tensor([0.2625, 0.5437, 0.4688, 0.8625] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0] , A , atol=1e-3 ) )
# verify image_id
lowercase_ : List[str] = torch.tensor([3_97_69] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''] , A ) )
# verify is_crowd
lowercase_ : List[str] = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''] , A ) )
# verify class_labels
lowercase_ : Any = torch.tensor([17, 17, 63, 75, 75, 93] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''] , A ) )
# verify masks
lowercase_ : Dict = 82_28_73
self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item() , A )
# verify orig_size
lowercase_ : Tuple = torch.tensor([4_80, 6_40] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''] , A ) )
# verify size
lowercase_ : List[str] = torch.tensor([8_00, 10_66] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''] , A ) )
| 33
| 0
|
from unittest import TestCase
from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters
def __lowerCAmelCase ( ) -> Dict:
__a = {
'''repo_name''': ['''test_repo1''', '''test_repo2''', '''test_repo3'''],
'''path''': ['''test_1.py''', '''test_2.py''', '''unit_test.py'''],
'''content''': ['''a ''' * 20, '''a ''' * 30, '''b ''' * 7],
}
__a = Dataset.from_dict(a__ )
return dataset
class __A( a ):
def SCREAMING_SNAKE_CASE_ ( self ) -> Dict:
'''simple docstring'''
__a = get_dataset()
__a = make_duplicate_clusters(_snake_case , 0.85 )
self.assertEqual(len(duplicate_clusters[0] ) , 2 )
def SCREAMING_SNAKE_CASE_ ( self ) -> Tuple:
'''simple docstring'''
__a = get_dataset()
__a , __a = deduplicate_dataset(_snake_case )
self.assertEqual(len(_snake_case ) , 2 )
print(_snake_case )
self.assertEqual(duplicate_clusters[0][0]['''copies'''] , 2 )
self.assertEqual(duplicate_clusters[0][0]['''is_extreme'''] , _snake_case )
| 6
|
"""simple docstring"""
def lowercase ( __snake_case : int = 1_0_0 ):
lowercase_ : str = 0
lowercase_ : List[Any] = 0
for i in range(1 , n + 1 ):
sum_of_squares += i**2
sum_of_ints += i
return sum_of_ints**2 - sum_of_squares
if __name__ == "__main__":
print(F"""{solution() = }""")
| 33
| 0
|
import random
import torch
from huggingface_hub import HfApi
from diffusers import UNetaDModel
lowercase_ = HfApi()
lowercase_ = {}
# fmt: off
lowercase_ = torch.tensor([
-0.7_515, -1.6_883, 0.2_420, 0.0_300, 0.6_347, 1.3_433, -1.1_743, -3.7_467,
1.2_342, -2.2_485, 0.4_636, 0.8_076, -0.7_991, 0.3_969, 0.8_498, 0.9_189,
-1.8_887, -3.3_522, 0.7_639, 0.2_040, 0.6_271, -2.7_148, -1.6_316, 3.0_839,
0.3_186, 0.2_721, -0.9_759, -1.2_461, 2.6_257, 1.3_557
])
lowercase_ = torch.tensor([
-2.3_639, -2.5_344, 0.0_054, -0.6_674, 1.5_990, 1.0_158, 0.3_124, -2.1_436,
1.8_795, -2.5_429, -0.1_566, -0.3_973, 1.2_490, 2.6_447, 1.2_283, -0.5_208,
-2.8_154, -3.5_119, 2.3_838, 1.2_033, 1.7_201, -2.1_256, -1.4_576, 2.7_948,
2.4_204, -0.9_752, -1.2_546, 0.8_027, 3.2_758, 3.1_365
])
lowercase_ = torch.tensor([
-0.6_531, -0.6_891, -0.3_172, -0.5_375, -0.9_140, -0.5_367, -0.1_175, -0.7_869,
-0.3_808, -0.4_513, -0.2_098, -0.0_083, 0.3_183, 0.5_140, 0.2_247, -0.1_304,
-0.1_302, -0.2_802, -0.2_084, -0.2_025, -0.4_967, -0.4_873, -0.0_861, 0.6_925,
0.0_250, 0.1_290, -0.1_543, 0.6_316, 1.0_460, 1.4_943
])
lowercase_ = torch.tensor([
0.0_911, 0.1_107, 0.0_182, 0.0_435, -0.0_805, -0.0_608, 0.0_381, 0.2_172,
-0.0_280, 0.1_327, -0.0_299, -0.0_255, -0.0_050, -0.1_170, -0.1_046, 0.0_309,
0.1_367, 0.1_728, -0.0_533, -0.0_748, -0.0_534, 0.1_624, 0.0_384, -0.1_805,
-0.0_707, 0.0_642, 0.0_220, -0.0_134, -0.1_333, -0.1_505
])
lowercase_ = torch.tensor([
0.1_321, 0.1_337, 0.0_440, 0.0_622, -0.0_591, -0.0_370, 0.0_503, 0.2_133,
-0.0_177, 0.1_415, -0.0_116, -0.0_112, 0.0_044, -0.0_980, -0.0_789, 0.0_395,
0.1_502, 0.1_785, -0.0_488, -0.0_514, -0.0_404, 0.1_539, 0.0_454, -0.1_559,
-0.0_665, 0.0_659, 0.0_383, -0.0_005, -0.1_266, -0.1_386
])
lowercase_ = torch.tensor([
0.1_154, 0.1_218, 0.0_307, 0.0_526, -0.0_711, -0.0_541, 0.0_366, 0.2_078,
-0.0_267, 0.1_317, -0.0_226, -0.0_193, -0.0_014, -0.1_055, -0.0_902, 0.0_330,
0.1_391, 0.1_709, -0.0_562, -0.0_693, -0.0_560, 0.1_482, 0.0_381, -0.1_683,
-0.0_681, 0.0_661, 0.0_331, -0.0_046, -0.1_268, -0.1_431
])
lowercase_ = torch.tensor([
0.1_192, 0.1_240, 0.0_414, 0.0_606, -0.0_557, -0.0_412, 0.0_430, 0.2_042,
-0.0_200, 0.1_385, -0.0_115, -0.0_132, 0.0_017, -0.0_965, -0.0_802, 0.0_398,
0.1_433, 0.1_747, -0.0_458, -0.0_533, -0.0_407, 0.1_545, 0.0_419, -0.1_574,
-0.0_645, 0.0_626, 0.0_341, -0.0_010, -0.1_199, -0.1_390
])
lowercase_ = torch.tensor([
0.1_075, 0.1_074, 0.0_205, 0.0_431, -0.0_774, -0.0_607, 0.0_298, 0.2_042,
-0.0_320, 0.1_267, -0.0_281, -0.0_250, -0.0_064, -0.1_091, -0.0_946, 0.0_290,
0.1_328, 0.1_650, -0.0_580, -0.0_738, -0.0_586, 0.1_440, 0.0_337, -0.1_746,
-0.0_712, 0.0_605, 0.0_250, -0.0_099, -0.1_316, -0.1_473
])
lowercase_ = torch.tensor([
-1.4_572, -2.0_481, -0.0_414, -0.6_005, 1.4_136, 0.5_848, 0.4_028, -2.7_330,
1.2_212, -2.1_228, 0.2_155, 0.4_039, 0.7_662, 2.0_535, 0.7_477, -0.3_243,
-2.1_758, -2.7_648, 1.6_947, 0.7_026, 1.2_338, -1.6_078, -0.8_682, 2.2_810,
1.8_574, -0.5_718, -0.5_586, -0.0_186, 2.3_415, 2.1_251])
lowercase_ = torch.tensor([
-1.3_690, -1.9_720, -0.4_090, -0.6_966, 1.4_660, 0.9_938, -0.1_385, -2.7_324,
0.7_736, -1.8_917, 0.2_923, 0.4_293, 0.1_693, 1.4_112, 1.1_887, -0.3_181,
-2.2_160, -2.6_381, 1.3_170, 0.8_163, 0.9_240, -1.6_544, -0.6_099, 2.5_259,
1.6_430, -0.9_090, -0.9_392, -0.0_126, 2.4_268, 2.3_266
])
lowercase_ = torch.tensor([
-1.3_525, -1.9_628, -0.3_956, -0.6_860, 1.4_664, 1.0_014, -0.1_259, -2.7_212,
0.7_772, -1.8_811, 0.2_996, 0.4_388, 0.1_704, 1.4_029, 1.1_701, -0.3_027,
-2.2_053, -2.6_287, 1.3_350, 0.8_131, 0.9_274, -1.6_292, -0.6_098, 2.5_131,
1.6_505, -0.8_958, -0.9_298, -0.0_151, 2.4_257, 2.3_355
])
lowercase_ = torch.tensor([
-2.0_585, -2.7_897, -0.2_850, -0.8_940, 1.9_052, 0.5_702, 0.6_345, -3.8_959,
1.5_932, -3.2_319, 0.1_974, 0.0_287, 1.7_566, 2.6_543, 0.8_387, -0.5_351,
-3.2_736, -4.3_375, 2.9_029, 1.6_390, 1.4_640, -2.1_701, -1.9_013, 2.9_341,
3.4_981, -0.6_255, -1.1_644, -0.1_591, 3.7_097, 3.2_066
])
lowercase_ = torch.tensor([
-2.3_139, -2.5_594, -0.0_197, -0.6_785, 1.7_001, 1.1_606, 0.3_075, -2.1_740,
1.8_071, -2.5_630, -0.0_926, -0.3_811, 1.2_116, 2.6_246, 1.2_731, -0.5_398,
-2.8_153, -3.6_140, 2.3_893, 1.3_262, 1.6_258, -2.1_856, -1.3_267, 2.8_395,
2.3_779, -1.0_623, -1.2_468, 0.8_959, 3.3_367, 3.2_243
])
lowercase_ = torch.tensor([
-2.0_628, -2.7_667, -0.2_089, -0.8_263, 2.0_539, 0.5_992, 0.6_495, -3.8_336,
1.6_025, -3.2_817, 0.1_721, -0.0_633, 1.7_516, 2.7_039, 0.8_100, -0.5_908,
-3.2_113, -4.4_343, 2.9_257, 1.3_632, 1.5_562, -2.1_489, -1.9_894, 3.0_560,
3.3_396, -0.7_328, -1.0_417, 0.0_383, 3.7_093, 3.2_343
])
lowercase_ = torch.tensor([
-1.4_574, -2.0_569, -0.0_473, -0.6_117, 1.4_018, 0.5_769, 0.4_129, -2.7_344,
1.2_241, -2.1_397, 0.2_000, 0.3_937, 0.7_616, 2.0_453, 0.7_324, -0.3_391,
-2.1_746, -2.7_744, 1.6_963, 0.6_921, 1.2_187, -1.6_172, -0.8_877, 2.2_439,
1.8_471, -0.5_839, -0.5_605, -0.0_464, 2.3_250, 2.1_219
])
# fmt: on
lowercase_ = api.list_models(filter="diffusers")
for mod in models:
if "google" in mod.author or mod.modelId == "CompVis/ldm-celebahq-256":
lowercase_ = "/home/patrick/google_checkpoints/" + mod.modelId.split("/")[-1]
print(f"""Started running {mod.modelId}!!!""")
if mod.modelId.startswith("CompVis"):
lowercase_ = UNetaDModel.from_pretrained(local_checkpoint, subfolder="unet")
else:
lowercase_ = UNetaDModel.from_pretrained(local_checkpoint)
torch.manual_seed(0)
random.seed(0)
lowercase_ = torch.randn(1, model.config.in_channels, model.config.sample_size, model.config.sample_size)
lowercase_ = torch.tensor([10] * noise.shape[0])
with torch.no_grad():
lowercase_ = model(noise, time_step).sample
assert torch.allclose(
logits[0, 0, 0, :30], results["_".join("_".join(mod.modelId.split("/")).split("-"))], atol=1e-3
)
print(f"""{mod.modelId} has passed successfully!!!""")
| 7
|
"""simple docstring"""
import argparse
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline
if __name__ == "__main__":
__A : str = 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 : str = parser.parse_args()
__A : List[Any] = UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)
__A : Dict = CLIPImageProcessor()
__A : Union[str, Any] = CLIPVisionModelWithProjection.from_pretrained('''openai/clip-vit-large-patch14''')
__A : List[str] = 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)
| 33
| 0
|
from typing import Optional
from torch import nn
from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class snake_case_ ( nn.Module ):
'''simple docstring'''
def __init__( self : Dict , _UpperCamelCase : int = 1_6 , _UpperCamelCase : int = 8_8 , _UpperCamelCase : Optional[int] = None , _UpperCamelCase : int = 1 , _UpperCamelCase : float = 0.0 , _UpperCamelCase : int = 3_2 , _UpperCamelCase : Optional[int] = None , _UpperCamelCase : bool = False , _UpperCamelCase : Optional[int] = None , _UpperCamelCase : Optional[int] = None , _UpperCamelCase : str = "geglu" , _UpperCamelCase : Optional[int] = None , ) ->Any:
super().__init__()
snake_case_ = nn.ModuleList(
[
TransformeraDModel(
num_attention_heads=_UpperCamelCase , attention_head_dim=_UpperCamelCase , in_channels=_UpperCamelCase , num_layers=_UpperCamelCase , dropout=_UpperCamelCase , norm_num_groups=_UpperCamelCase , cross_attention_dim=_UpperCamelCase , attention_bias=_UpperCamelCase , sample_size=_UpperCamelCase , num_vector_embeds=_UpperCamelCase , activation_fn=_UpperCamelCase , num_embeds_ada_norm=_UpperCamelCase , )
for _ in range(2 )
] )
# Variables that can be set by a pipeline:
# The ratio of transformer1 to transformer2's output states to be combined during inference
snake_case_ = 0.5
# The shape of `encoder_hidden_states` is expected to be
# `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
snake_case_ = [7_7, 2_5_7]
# Which transformer to use to encode which condition.
# E.g. `(1, 0)` means that we'll use `transformers[1](conditions[0])` and `transformers[0](conditions[1])`
snake_case_ = [1, 0]
def snake_case__( self : Dict , _UpperCamelCase : str , _UpperCamelCase : str , _UpperCamelCase : Optional[Any]=None , _UpperCamelCase : List[Any]=None , _UpperCamelCase : List[str]=None , _UpperCamelCase : bool = True , ) ->Optional[Any]:
snake_case_ = hidden_states
snake_case_ = []
snake_case_ = 0
# attention_mask is not used yet
for i in range(2 ):
# for each of the two transformers, pass the corresponding condition tokens
snake_case_ = encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
snake_case_ = self.transformer_index_for_condition[i]
snake_case_ = self.transformers[transformer_index](
_UpperCamelCase , encoder_hidden_states=_UpperCamelCase , timestep=_UpperCamelCase , cross_attention_kwargs=_UpperCamelCase , return_dict=_UpperCamelCase , )[0]
encoded_states.append(encoded_state - input_states )
tokens_start += self.condition_lengths[i]
snake_case_ = encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
snake_case_ = output_states + input_states
if not return_dict:
return (output_states,)
return TransformeraDModelOutput(sample=_UpperCamelCase )
| 8
|
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaControlnetImgaImgPipeline,
KandinskyVaaPriorEmbaEmbPipeline,
UNetaDConditionModel,
VQModel,
)
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 _UpperCAmelCase ( _A , unittest.TestCase ):
SCREAMING_SNAKE_CASE_ : Any = KandinskyVaaControlnetImgaImgPipeline
SCREAMING_SNAKE_CASE_ : Optional[int] = ["image_embeds", "negative_image_embeds", "image", "hint"]
SCREAMING_SNAKE_CASE_ : str = ["image_embeds", "negative_image_embeds", "image", "hint"]
SCREAMING_SNAKE_CASE_ : Dict = [
"generator",
"height",
"width",
"strength",
"guidance_scale",
"num_inference_steps",
"return_dict",
"guidance_scale",
"num_images_per_prompt",
"output_type",
"return_dict",
]
SCREAMING_SNAKE_CASE_ : Dict = False
@property
def A ( self : Any ) -> Any:
return 32
@property
def A ( self : Optional[int] ) -> Any:
return 32
@property
def A ( self : Dict ) -> int:
return self.time_input_dim
@property
def A ( self : Tuple ) -> str:
return self.time_input_dim * 4
@property
def A ( self : Any ) -> str:
return 1_00
@property
def A ( self : str ) -> List[str]:
torch.manual_seed(0 )
lowercase_ : List[Any] = {
'''in_channels''': 8,
# Out channels is double in channels because predicts mean and variance
'''out_channels''': 8,
'''addition_embed_type''': '''image_hint''',
'''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''': '''image_proj''',
'''cross_attention_dim''': self.cross_attention_dim,
'''attention_head_dim''': 4,
'''resnet_time_scale_shift''': '''scale_shift''',
'''class_embed_type''': None,
}
lowercase_ : Dict = UNetaDConditionModel(**A )
return model
@property
def A ( self : Optional[Any] ) -> Union[str, Any]:
return {
"block_out_channels": [32, 32, 64, 64],
"down_block_types": [
"DownEncoderBlock2D",
"DownEncoderBlock2D",
"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", "UpDecoderBlock2D", "UpDecoderBlock2D"],
"vq_embed_dim": 4,
}
@property
def A ( self : List[Any] ) -> Dict:
torch.manual_seed(0 )
lowercase_ : int = VQModel(**self.dummy_movq_kwargs )
return model
def A ( self : Union[str, Any] ) -> Optional[int]:
lowercase_ : Tuple = self.dummy_unet
lowercase_ : int = self.dummy_movq
lowercase_ : List[Any] = {
'''num_train_timesteps''': 10_00,
'''beta_schedule''': '''linear''',
'''beta_start''': 0.00085,
'''beta_end''': 0.012,
'''clip_sample''': False,
'''set_alpha_to_one''': False,
'''steps_offset''': 0,
'''prediction_type''': '''epsilon''',
'''thresholding''': False,
}
lowercase_ : str = DDIMScheduler(**A )
lowercase_ : Tuple = {
'''unet''': unet,
'''scheduler''': scheduler,
'''movq''': movq,
}
return components
def A ( self : Optional[int] , A : int , A : List[str]=0 ) -> int:
lowercase_ : str = floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(A ) ).to(A )
lowercase_ : Tuple = floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(seed + 1 ) ).to(
A )
# create init_image
lowercase_ : Union[str, Any] = floats_tensor((1, 3, 64, 64) , rng=random.Random(A ) ).to(A )
lowercase_ : Any = image.cpu().permute(0 , 2 , 3 , 1 )[0]
lowercase_ : Optional[Any] = Image.fromarray(np.uinta(A ) ).convert('''RGB''' ).resize((2_56, 2_56) )
# create hint
lowercase_ : Optional[int] = floats_tensor((1, 3, 64, 64) , rng=random.Random(A ) ).to(A )
if str(A ).startswith('''mps''' ):
lowercase_ : Optional[Any] = torch.manual_seed(A )
else:
lowercase_ : List[Any] = torch.Generator(device=A ).manual_seed(A )
lowercase_ : Dict = {
'''image''': init_image,
'''image_embeds''': image_embeds,
'''negative_image_embeds''': negative_image_embeds,
'''hint''': hint,
'''generator''': generator,
'''height''': 64,
'''width''': 64,
'''num_inference_steps''': 10,
'''guidance_scale''': 7.0,
'''strength''': 0.2,
'''output_type''': '''np''',
}
return inputs
def A ( self : Any ) -> List[Any]:
lowercase_ : List[str] = '''cpu'''
lowercase_ : Any = self.get_dummy_components()
lowercase_ : Any = self.pipeline_class(**A )
lowercase_ : int = pipe.to(A )
pipe.set_progress_bar_config(disable=A )
lowercase_ : Dict = pipe(**self.get_dummy_inputs(A ) )
lowercase_ : str = output.images
lowercase_ : int = pipe(
**self.get_dummy_inputs(A ) , return_dict=A , )[0]
lowercase_ : Dict = image[0, -3:, -3:, -1]
lowercase_ : Union[str, Any] = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
lowercase_ : List[str] = np.array(
[0.54985034, 0.55509365, 0.52561504, 0.5570494, 0.5593818, 0.5263979, 0.50285643, 0.5069846, 0.51196736] )
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()}'''
@slow
@require_torch_gpu
class _UpperCAmelCase ( unittest.TestCase ):
def A ( self : Tuple ) -> str:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def A ( self : Any ) -> Optional[int]:
lowercase_ : Dict = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy''' )
lowercase_ : Dict = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png''' )
lowercase_ : Optional[int] = init_image.resize((5_12, 5_12) )
lowercase_ : Dict = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/kandinskyv22/hint_image_cat.png''' )
lowercase_ : Optional[int] = torch.from_numpy(np.array(A ) ).float() / 255.0
lowercase_ : Tuple = hint.permute(2 , 0 , 1 ).unsqueeze(0 )
lowercase_ : Optional[Any] = '''A robot, 4k photo'''
lowercase_ : Tuple = KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
'''kandinsky-community/kandinsky-2-2-prior''' , torch_dtype=torch.floataa )
pipe_prior.to(A )
lowercase_ : Dict = KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
'''kandinsky-community/kandinsky-2-2-controlnet-depth''' , torch_dtype=torch.floataa )
lowercase_ : int = pipeline.to(A )
pipeline.set_progress_bar_config(disable=A )
lowercase_ : Tuple = torch.Generator(device='''cpu''' ).manual_seed(0 )
lowercase_ , lowercase_ : int = pipe_prior(
A , image=A , strength=0.85 , generator=A , negative_prompt='''''' , ).to_tuple()
lowercase_ : str = pipeline(
image=A , image_embeds=A , negative_image_embeds=A , hint=A , generator=A , num_inference_steps=1_00 , height=5_12 , width=5_12 , strength=0.5 , output_type='''np''' , )
lowercase_ : Optional[Any] = output.images[0]
assert image.shape == (5_12, 5_12, 3)
assert_mean_pixel_difference(A , A )
| 33
| 0
|
def _UpperCamelCase ( lowercase__ , lowercase__ ):
if len(lowercase__ ) != len(lowercase__ ):
raise ValueError('''String lengths must match!''' )
__SCREAMING_SNAKE_CASE : List[Any] = 0
for chara, chara in zip(lowercase__ , lowercase__ ):
if chara != chara:
count += 1
return count
if __name__ == "__main__":
import doctest
doctest.testmod()
| 9
|
"""simple docstring"""
def lowercase ( __snake_case : int = 1_0_0_0 ):
lowercase_ , lowercase_ : str = 1, 1
lowercase_ : List[str] = 2
while True:
lowercase_ : Tuple = 0
lowercase_ : List[Any] = fa + fa
lowercase_ , lowercase_ : Optional[int] = fa, f
index += 1
for _ in str(__snake_case ):
i += 1
if i == n:
break
return index
if __name__ == "__main__":
print(solution(int(str(input()).strip())))
| 33
| 0
|
import unittest
import numpy as np
import timeout_decorator # noqa
from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
if is_flax_available():
import os
# The slow tests are often failing with OOM error on GPU
# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
__A = "platform"
import jax
import jax.numpy as jnp
from transformers import BlenderbotTokenizer
from transformers.models.blenderbot.modeling_flax_blenderbot import (
FlaxBlenderbotForConditionalGeneration,
FlaxBlenderbotModel,
shift_tokens_right,
)
def lowerCAmelCase_ ( __a , __a , __a=None , __a=None , __a=None , __a=None , __a=None , __a=None , ) -> Any:
"""simple docstring"""
if attention_mask is None:
lowerCamelCase__: Optional[Any] =np.where(input_ids != config.pad_token_id , 1 , 0 )
if decoder_attention_mask is None:
lowerCamelCase__: Dict =np.where(decoder_input_ids != config.pad_token_id , 1 , 0 )
if head_mask is None:
lowerCamelCase__: Optional[Any] =np.ones((config.encoder_layers, config.encoder_attention_heads) )
if decoder_head_mask is None:
lowerCamelCase__: Any =np.ones((config.decoder_layers, config.decoder_attention_heads) )
if cross_attn_head_mask is None:
lowerCamelCase__: List[str] =np.ones((config.decoder_layers, config.decoder_attention_heads) )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": attention_mask,
}
class _SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__(self : Tuple , UpperCAmelCase_ : List[Any] , UpperCAmelCase_ : Dict=13 , UpperCAmelCase_ : List[Any]=7 , UpperCAmelCase_ : str=True , UpperCAmelCase_ : Optional[int]=False , UpperCAmelCase_ : Union[str, Any]=99 , UpperCAmelCase_ : Any=16 , UpperCAmelCase_ : Dict=2 , UpperCAmelCase_ : Any=4 , UpperCAmelCase_ : List[Any]=4 , UpperCAmelCase_ : int="gelu" , UpperCAmelCase_ : int=0.1 , UpperCAmelCase_ : str=0.1 , UpperCAmelCase_ : Tuple=32 , UpperCAmelCase_ : int=2 , UpperCAmelCase_ : int=1 , UpperCAmelCase_ : Union[str, Any]=0 , UpperCAmelCase_ : Any=0.02 , ) ->Optional[int]:
'''simple docstring'''
lowerCamelCase__: int =parent
lowerCamelCase__: List[str] =batch_size
lowerCamelCase__: Optional[int] =seq_length
lowerCamelCase__: Optional[Any] =is_training
lowerCamelCase__: str =use_labels
lowerCamelCase__: Optional[Any] =vocab_size
lowerCamelCase__: int =hidden_size
lowerCamelCase__: Dict =num_hidden_layers
lowerCamelCase__: Any =num_attention_heads
lowerCamelCase__: str =intermediate_size
lowerCamelCase__: int =hidden_act
lowerCamelCase__: Tuple =hidden_dropout_prob
lowerCamelCase__: List[str] =attention_probs_dropout_prob
lowerCamelCase__: Optional[int] =max_position_embeddings
lowerCamelCase__: int =eos_token_id
lowerCamelCase__: Union[str, Any] =pad_token_id
lowerCamelCase__: List[str] =bos_token_id
lowerCamelCase__: int =initializer_range
def SCREAMING_SNAKE_CASE_ (self : Any) ->Optional[int]:
'''simple docstring'''
lowerCamelCase__: Optional[Any] =np.clip(ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size) , 3 , self.vocab_size)
lowerCamelCase__: str =np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1) , dtype=np.intaa)) , -1)
lowerCamelCase__: int =shift_tokens_right(UpperCAmelCase_ , 1 , 2)
lowerCamelCase__: Dict =BlenderbotConfig(
vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , eos_token_id=self.eos_token_id , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , initializer_range=self.initializer_range , use_cache=UpperCAmelCase_ , )
lowerCamelCase__: Any =prepare_blenderbot_inputs_dict(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_)
return config, inputs_dict
def SCREAMING_SNAKE_CASE_ (self : int) ->Optional[int]:
'''simple docstring'''
lowerCamelCase__ , lowerCamelCase__: Dict =self.prepare_config_and_inputs()
return config, inputs_dict
def SCREAMING_SNAKE_CASE_ (self : Dict , UpperCAmelCase_ : Optional[int] , UpperCAmelCase_ : List[str] , UpperCAmelCase_ : Dict) ->Optional[Any]:
'''simple docstring'''
lowerCamelCase__: Optional[Any] =20
lowerCamelCase__: Optional[int] =model_class_name(UpperCAmelCase_)
lowerCamelCase__: str =model.encode(inputs_dict["input_ids"])
lowerCamelCase__ , lowerCamelCase__: List[Any] =(
inputs_dict["decoder_input_ids"],
inputs_dict["decoder_attention_mask"],
)
lowerCamelCase__: Union[str, Any] =model.init_cache(decoder_input_ids.shape[0] , UpperCAmelCase_ , UpperCAmelCase_)
lowerCamelCase__: Union[str, Any] =jnp.ones((decoder_input_ids.shape[0], max_decoder_length) , dtype="i4")
lowerCamelCase__: Tuple =jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :] , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) , )
lowerCamelCase__: Union[str, Any] =model.decode(
decoder_input_ids[:, :-1] , UpperCAmelCase_ , decoder_attention_mask=UpperCAmelCase_ , past_key_values=UpperCAmelCase_ , decoder_position_ids=UpperCAmelCase_ , )
lowerCamelCase__: Union[str, Any] =jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] , dtype="i4")
lowerCamelCase__: Dict =model.decode(
decoder_input_ids[:, -1:] , UpperCAmelCase_ , decoder_attention_mask=UpperCAmelCase_ , past_key_values=outputs_cache.past_key_values , decoder_position_ids=UpperCAmelCase_ , )
lowerCamelCase__: List[Any] =model.decode(UpperCAmelCase_ , UpperCAmelCase_)
lowerCamelCase__: Optional[Any] =np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
self.parent.assertTrue(diff < 1E-3 , msg=F"""Max diff is {diff}""")
def SCREAMING_SNAKE_CASE_ (self : List[str] , UpperCAmelCase_ : Tuple , UpperCAmelCase_ : Optional[Any] , UpperCAmelCase_ : Tuple) ->Optional[int]:
'''simple docstring'''
lowerCamelCase__: List[str] =20
lowerCamelCase__: Optional[Any] =model_class_name(UpperCAmelCase_)
lowerCamelCase__: Any =model.encode(inputs_dict["input_ids"])
lowerCamelCase__ , lowerCamelCase__: Union[str, Any] =(
inputs_dict["decoder_input_ids"],
inputs_dict["decoder_attention_mask"],
)
lowerCamelCase__: Optional[int] =jnp.concatenate(
[
decoder_attention_mask,
jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])),
] , axis=-1 , )
lowerCamelCase__: Union[str, Any] =model.init_cache(decoder_input_ids.shape[0] , UpperCAmelCase_ , UpperCAmelCase_)
lowerCamelCase__: Tuple =jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :] , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) , )
lowerCamelCase__: List[Any] =model.decode(
decoder_input_ids[:, :-1] , UpperCAmelCase_ , decoder_attention_mask=UpperCAmelCase_ , past_key_values=UpperCAmelCase_ , decoder_position_ids=UpperCAmelCase_ , )
lowerCamelCase__: Dict =jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] , dtype="i4")
lowerCamelCase__: str =model.decode(
decoder_input_ids[:, -1:] , UpperCAmelCase_ , past_key_values=outputs_cache.past_key_values , decoder_attention_mask=UpperCAmelCase_ , decoder_position_ids=UpperCAmelCase_ , )
lowerCamelCase__: Union[str, Any] =model.decode(UpperCAmelCase_ , UpperCAmelCase_ , decoder_attention_mask=UpperCAmelCase_)
lowerCamelCase__: str =np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
self.parent.assertTrue(diff < 1E-3 , msg=F"""Max diff is {diff}""")
@require_flax
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
lowercase_ = 99
def SCREAMING_SNAKE_CASE_ (self : Any) ->int:
'''simple docstring'''
lowerCamelCase__: Union[str, Any] =np.array(
[
[71, 82, 18, 33, 46, 91, 2],
[68, 34, 26, 58, 30, 82, 2],
[5, 97, 17, 39, 94, 40, 2],
[76, 83, 94, 25, 70, 78, 2],
[87, 59, 41, 35, 48, 66, 2],
[55, 13, 16, 58, 5, 2, 1], # note padding
[64, 27, 31, 51, 12, 75, 2],
[52, 64, 86, 17, 83, 39, 2],
[48, 61, 9, 24, 71, 82, 2],
[26, 1, 60, 48, 22, 13, 2],
[21, 5, 62, 28, 14, 76, 2],
[45, 98, 37, 86, 59, 48, 2],
[70, 70, 50, 9, 28, 0, 2],
] , dtype=np.intaa , )
lowerCamelCase__: Optional[Any] =input_ids.shape[0]
lowerCamelCase__: List[str] =BlenderbotConfig(
vocab_size=self.vocab_size , d_model=24 , encoder_layers=2 , decoder_layers=2 , encoder_attention_heads=2 , decoder_attention_heads=2 , encoder_ffn_dim=32 , decoder_ffn_dim=32 , max_position_embeddings=48 , eos_token_id=2 , pad_token_id=1 , bos_token_id=0 , )
return config, input_ids, batch_size
def SCREAMING_SNAKE_CASE_ (self : Union[str, Any]) ->Optional[Any]:
'''simple docstring'''
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__: Any =self._get_config_and_data()
lowerCamelCase__: Dict =FlaxBlenderbotForConditionalGeneration(UpperCAmelCase_)
lowerCamelCase__: Dict =lm_model(input_ids=UpperCAmelCase_)
lowerCamelCase__: Dict =(batch_size, input_ids.shape[1], config.vocab_size)
self.assertEqual(outputs["logits"].shape , UpperCAmelCase_)
def SCREAMING_SNAKE_CASE_ (self : Dict) ->str:
'''simple docstring'''
lowerCamelCase__: Optional[int] =BlenderbotConfig(
vocab_size=self.vocab_size , d_model=14 , encoder_layers=2 , decoder_layers=2 , encoder_attention_heads=2 , decoder_attention_heads=2 , encoder_ffn_dim=8 , decoder_ffn_dim=8 , max_position_embeddings=48 , )
lowerCamelCase__: str =FlaxBlenderbotForConditionalGeneration(UpperCAmelCase_)
lowerCamelCase__: Optional[int] =np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]] , dtype=np.intaa)
lowerCamelCase__: Optional[int] =np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]] , dtype=np.intaa)
lowerCamelCase__: List[str] =lm_model(input_ids=UpperCAmelCase_ , decoder_input_ids=UpperCAmelCase_)
lowerCamelCase__: Optional[int] =(*summary.shape, config.vocab_size)
self.assertEqual(outputs["logits"].shape , UpperCAmelCase_)
def SCREAMING_SNAKE_CASE_ (self : Any) ->Tuple:
'''simple docstring'''
lowerCamelCase__: Optional[int] =np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]] , dtype=np.intaa)
lowerCamelCase__: Optional[int] =shift_tokens_right(UpperCAmelCase_ , 1 , 2)
lowerCamelCase__: List[str] =np.equal(UpperCAmelCase_ , 1).astype(np.floataa).sum()
lowerCamelCase__: Tuple =np.equal(UpperCAmelCase_ , 1).astype(np.floataa).sum()
self.assertEqual(shifted.shape , input_ids.shape)
self.assertEqual(UpperCAmelCase_ , n_pad_before - 1)
self.assertTrue(np.equal(shifted[:, 0] , 2).all())
@require_flax
class _SCREAMING_SNAKE_CASE ( __SCREAMING_SNAKE_CASE , unittest.TestCase , __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
lowercase_ = True
lowercase_ = (
(
FlaxBlenderbotModel,
FlaxBlenderbotForConditionalGeneration,
)
if is_flax_available()
else ()
)
lowercase_ = (FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()
def SCREAMING_SNAKE_CASE_ (self : List[str]) ->List[Any]:
'''simple docstring'''
lowerCamelCase__: List[Any] =FlaxBlenderbotModelTester(self)
def SCREAMING_SNAKE_CASE_ (self : Union[str, Any]) ->List[str]:
'''simple docstring'''
lowerCamelCase__ , lowerCamelCase__: List[str] =self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_)
def SCREAMING_SNAKE_CASE_ (self : Tuple) ->List[Any]:
'''simple docstring'''
lowerCamelCase__ , lowerCamelCase__: List[str] =self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward_with_attn_mask(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_)
def SCREAMING_SNAKE_CASE_ (self : Union[str, Any]) ->str:
'''simple docstring'''
lowerCamelCase__ , lowerCamelCase__: Union[str, Any] =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__):
lowerCamelCase__: List[str] =self._prepare_for_class(UpperCAmelCase_ , UpperCAmelCase_)
lowerCamelCase__: Optional[int] =model_class(UpperCAmelCase_)
@jax.jit
def encode_jitted(UpperCAmelCase_ : List[Any] , UpperCAmelCase_ : Any=None , **UpperCAmelCase_ : List[str]):
return model.encode(input_ids=UpperCAmelCase_ , attention_mask=UpperCAmelCase_)
with self.subTest("JIT Enabled"):
lowerCamelCase__: Any =encode_jitted(**UpperCAmelCase_).to_tuple()
with self.subTest("JIT Disabled"):
with jax.disable_jit():
lowerCamelCase__: Tuple =encode_jitted(**UpperCAmelCase_).to_tuple()
self.assertEqual(len(UpperCAmelCase_) , len(UpperCAmelCase_))
for jitted_output, output in zip(UpperCAmelCase_ , UpperCAmelCase_):
self.assertEqual(jitted_output.shape , output.shape)
def SCREAMING_SNAKE_CASE_ (self : List[Any]) ->List[Any]:
'''simple docstring'''
lowerCamelCase__ , lowerCamelCase__: List[Any] =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__):
lowerCamelCase__: Optional[Any] =model_class(UpperCAmelCase_)
lowerCamelCase__: List[Any] =model.encode(inputs_dict["input_ids"] , inputs_dict["attention_mask"])
lowerCamelCase__: int ={
"decoder_input_ids": inputs_dict["decoder_input_ids"],
"decoder_attention_mask": inputs_dict["decoder_attention_mask"],
"encoder_outputs": encoder_outputs,
}
@jax.jit
def decode_jitted(UpperCAmelCase_ : Optional[Any] , UpperCAmelCase_ : Dict , UpperCAmelCase_ : Optional[int]):
return model.decode(
decoder_input_ids=UpperCAmelCase_ , decoder_attention_mask=UpperCAmelCase_ , encoder_outputs=UpperCAmelCase_ , )
with self.subTest("JIT Enabled"):
lowerCamelCase__: int =decode_jitted(**UpperCAmelCase_).to_tuple()
with self.subTest("JIT Disabled"):
with jax.disable_jit():
lowerCamelCase__: int =decode_jitted(**UpperCAmelCase_).to_tuple()
self.assertEqual(len(UpperCAmelCase_) , len(UpperCAmelCase_))
for jitted_output, output in zip(UpperCAmelCase_ , UpperCAmelCase_):
self.assertEqual(jitted_output.shape , output.shape)
@slow
def SCREAMING_SNAKE_CASE_ (self : Any) ->Union[str, Any]:
'''simple docstring'''
for model_class_name in self.all_model_classes:
lowerCamelCase__: Optional[int] =model_class_name.from_pretrained("facebook/blenderbot-400M-distill")
# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
lowerCamelCase__: int =np.ones((1, 1)) * model.config.eos_token_id
lowerCamelCase__: str =model(UpperCAmelCase_)
self.assertIsNotNone(UpperCAmelCase_)
@unittest.skipUnless(jax_device != "cpu" , "3B test too slow on CPU.")
@slow
def SCREAMING_SNAKE_CASE_ (self : Tuple) ->Dict:
'''simple docstring'''
lowerCamelCase__: Dict ={"num_beams": 1, "early_stopping": True, "min_length": 15, "max_length": 25}
lowerCamelCase__: Union[str, Any] ={"skip_special_tokens": True, "clean_up_tokenization_spaces": True}
lowerCamelCase__: Dict =FlaxBlenderbotForConditionalGeneration.from_pretrained("facebook/blenderbot-3B" , from_pt=UpperCAmelCase_)
lowerCamelCase__: List[str] =BlenderbotTokenizer.from_pretrained("facebook/blenderbot-3B")
lowerCamelCase__: Any =["Sam"]
lowerCamelCase__: Tuple =tokenizer(UpperCAmelCase_ , return_tensors="jax")
lowerCamelCase__: Optional[Any] =model.generate(**UpperCAmelCase_ , **UpperCAmelCase_)
lowerCamelCase__: Any ="Sam is a great name. It means \"sun\" in Gaelic."
lowerCamelCase__: Optional[Any] =tokenizer.batch_decode(UpperCAmelCase_ , **UpperCAmelCase_)
assert generated_txt[0].strip() == tgt_text
| 10
|
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : Dict = logging.get_logger(__name__)
__A : Union[str, Any] = {
'''facebook/vit-mae-base''': '''https://huggingface.co/facebook/vit-mae-base/resolve/main/config.json''',
# See all ViT MAE models at https://huggingface.co/models?filter=vit-mae
}
class _UpperCAmelCase ( _A ):
SCREAMING_SNAKE_CASE_ : Union[str, Any] = "vit_mae"
def __init__( self : Dict , A : List[str]=7_68 , A : Any=12 , A : Union[str, Any]=12 , A : Tuple=30_72 , A : Any="gelu" , A : Tuple=0.0 , A : List[str]=0.0 , A : Tuple=0.02 , A : Tuple=1e-12 , A : int=2_24 , A : Dict=16 , A : int=3 , A : Tuple=True , A : Tuple=16 , A : Optional[Any]=5_12 , A : Union[str, Any]=8 , A : List[Any]=20_48 , A : Dict=0.75 , A : Any=False , **A : Optional[int] , ) -> Union[str, Any]:
super().__init__(**A )
lowercase_ : List[Any] = hidden_size
lowercase_ : str = num_hidden_layers
lowercase_ : List[Any] = num_attention_heads
lowercase_ : Any = intermediate_size
lowercase_ : Optional[int] = hidden_act
lowercase_ : List[Any] = hidden_dropout_prob
lowercase_ : int = attention_probs_dropout_prob
lowercase_ : int = initializer_range
lowercase_ : Dict = layer_norm_eps
lowercase_ : Optional[Any] = image_size
lowercase_ : str = patch_size
lowercase_ : Dict = num_channels
lowercase_ : Any = qkv_bias
lowercase_ : Union[str, Any] = decoder_num_attention_heads
lowercase_ : Optional[Any] = decoder_hidden_size
lowercase_ : List[str] = decoder_num_hidden_layers
lowercase_ : List[Any] = decoder_intermediate_size
lowercase_ : Optional[Any] = mask_ratio
lowercase_ : Optional[Any] = norm_pix_loss
| 33
| 0
|
from ..utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_scipy_available,
is_torch_available,
is_torchsde_available,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ..utils.dummy_pt_objects import * # noqa F403
else:
from .scheduling_consistency_models import CMStochasticIterativeScheduler
from .scheduling_ddim import DDIMScheduler
from .scheduling_ddim_inverse import DDIMInverseScheduler
from .scheduling_ddim_parallel import DDIMParallelScheduler
from .scheduling_ddpm import DDPMScheduler
from .scheduling_ddpm_parallel import DDPMParallelScheduler
from .scheduling_deis_multistep import DEISMultistepScheduler
from .scheduling_dpmsolver_multistep import DPMSolverMultistepScheduler
from .scheduling_dpmsolver_multistep_inverse import DPMSolverMultistepInverseScheduler
from .scheduling_dpmsolver_singlestep import DPMSolverSinglestepScheduler
from .scheduling_euler_ancestral_discrete import EulerAncestralDiscreteScheduler
from .scheduling_euler_discrete import EulerDiscreteScheduler
from .scheduling_heun_discrete import HeunDiscreteScheduler
from .scheduling_ipndm import IPNDMScheduler
from .scheduling_k_dpm_2_ancestral_discrete import KDPMaAncestralDiscreteScheduler
from .scheduling_k_dpm_2_discrete import KDPMaDiscreteScheduler
from .scheduling_karras_ve import KarrasVeScheduler
from .scheduling_pndm import PNDMScheduler
from .scheduling_repaint import RePaintScheduler
from .scheduling_sde_ve import ScoreSdeVeScheduler
from .scheduling_sde_vp import ScoreSdeVpScheduler
from .scheduling_unclip import UnCLIPScheduler
from .scheduling_unipc_multistep import UniPCMultistepScheduler
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin
from .scheduling_vq_diffusion import VQDiffusionScheduler
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ..utils.dummy_flax_objects import * # noqa F403
else:
from .scheduling_ddim_flax import FlaxDDIMScheduler
from .scheduling_ddpm_flax import FlaxDDPMScheduler
from .scheduling_dpmsolver_multistep_flax import FlaxDPMSolverMultistepScheduler
from .scheduling_karras_ve_flax import FlaxKarrasVeScheduler
from .scheduling_lms_discrete_flax import FlaxLMSDiscreteScheduler
from .scheduling_pndm_flax import FlaxPNDMScheduler
from .scheduling_sde_ve_flax import FlaxScoreSdeVeScheduler
from .scheduling_utils_flax import (
FlaxKarrasDiffusionSchedulers,
FlaxSchedulerMixin,
FlaxSchedulerOutput,
broadcast_to_shape_from_left,
)
try:
if not (is_torch_available() and is_scipy_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ..utils.dummy_torch_and_scipy_objects import * # noqa F403
else:
from .scheduling_lms_discrete import LMSDiscreteScheduler
try:
if not (is_torch_available() and is_torchsde_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ..utils.dummy_torch_and_torchsde_objects import * # noqa F403
else:
from .scheduling_dpmsolver_sde import DPMSolverSDEScheduler
| 11
|
"""simple docstring"""
def lowercase ( __snake_case : int ):
if n == 1 or not isinstance(__snake_case , __snake_case ):
return 0
elif n == 2:
return 1
else:
lowercase_ : Dict = [0, 1]
for i in range(2 , n + 1 ):
sequence.append(sequence[i - 1] + sequence[i - 2] )
return sequence[n]
def lowercase ( __snake_case : int ):
lowercase_ : str = 0
lowercase_ : List[str] = 2
while digits < n:
index += 1
lowercase_ : Any = len(str(fibonacci(__snake_case ) ) )
return index
def lowercase ( __snake_case : int = 1_0_0_0 ):
return fibonacci_digits_index(__snake_case )
if __name__ == "__main__":
print(solution(int(str(input()).strip())))
| 33
| 0
|
# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that there is no edge that connects
# vertices of same set.
def lowerCamelCase__ ( A__ : Optional[Any] ):
'''simple docstring'''
__lowerCamelCase = [False] * len(A__ )
__lowerCamelCase = [-1] * len(A__ )
def dfs(A__ : str , A__ : Tuple ):
__lowerCamelCase = True
__lowerCamelCase = c
for u in graph[v]:
if not visited[u]:
dfs(A__ , 1 - c )
for i in range(len(A__ ) ):
if not visited[i]:
dfs(A__ , 0 )
for i in range(len(A__ ) ):
for j in graph[i]:
if color[i] == color[j]:
return False
return True
# Adjacency list of graph
UpperCAmelCase_ = {0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2], 4: []}
print(check_bipartite_dfs(graph))
| 12
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__A : List[str] = {
'''configuration_mobilenet_v2''': [
'''MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''MobileNetV2Config''',
'''MobileNetV2OnnxConfig''',
],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : Dict = ['''MobileNetV2FeatureExtractor''']
__A : Optional[int] = ['''MobileNetV2ImageProcessor''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : Optional[Any] = [
'''MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''MobileNetV2ForImageClassification''',
'''MobileNetV2ForSemanticSegmentation''',
'''MobileNetV2Model''',
'''MobileNetV2PreTrainedModel''',
'''load_tf_weights_in_mobilenet_v2''',
]
if TYPE_CHECKING:
from .configuration_mobilenet_va import (
MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
MobileNetVaConfig,
MobileNetVaOnnxConfig,
)
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_mobilenet_va import MobileNetVaFeatureExtractor
from .image_processing_mobilenet_va import MobileNetVaImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mobilenet_va import (
MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
MobileNetVaForImageClassification,
MobileNetVaForSemanticSegmentation,
MobileNetVaModel,
MobileNetVaPreTrainedModel,
load_tf_weights_in_mobilenet_va,
)
else:
import sys
__A : List[Any] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 33
| 0
|
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
lowerCAmelCase : Optional[int] = logging.get_logger(__name__)
lowerCAmelCase : Optional[int] = {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt"""}
# See all BART models at https://huggingface.co/models?filter=bart
lowerCAmelCase : int = {
"""vocab_file""": {
"""facebook/bart-base""": """https://huggingface.co/facebook/bart-base/resolve/main/vocab.json""",
"""facebook/bart-large""": """https://huggingface.co/facebook/bart-large/resolve/main/vocab.json""",
"""facebook/bart-large-mnli""": """https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json""",
"""facebook/bart-large-cnn""": """https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json""",
"""facebook/bart-large-xsum""": """https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json""",
"""yjernite/bart_eli5""": """https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json""",
},
"""merges_file""": {
"""facebook/bart-base""": """https://huggingface.co/facebook/bart-base/resolve/main/merges.txt""",
"""facebook/bart-large""": """https://huggingface.co/facebook/bart-large/resolve/main/merges.txt""",
"""facebook/bart-large-mnli""": """https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt""",
"""facebook/bart-large-cnn""": """https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt""",
"""facebook/bart-large-xsum""": """https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt""",
"""yjernite/bart_eli5""": """https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt""",
},
}
lowerCAmelCase : List[str] = {
"""facebook/bart-base""": 1024,
"""facebook/bart-large""": 1024,
"""facebook/bart-large-mnli""": 1024,
"""facebook/bart-large-cnn""": 1024,
"""facebook/bart-large-xsum""": 1024,
"""yjernite/bart_eli5""": 1024,
}
@lru_cache()
def A_ ( ):
SCREAMING_SNAKE_CASE_: List[Any] = (
list(range(ord("!" ) , ord("~" ) + 1 ) ) + list(range(ord("¡" ) , ord("¬" ) + 1 ) ) + list(range(ord("®" ) , ord("ÿ" ) + 1 ) )
)
SCREAMING_SNAKE_CASE_: Optional[Any] = bs[:]
SCREAMING_SNAKE_CASE_: int = 0
for b in range(2**8 ):
if b not in bs:
bs.append(_UpperCAmelCase )
cs.append(2**8 + n )
n += 1
SCREAMING_SNAKE_CASE_: Any = [chr(_UpperCAmelCase ) for n in cs]
return dict(zip(_UpperCAmelCase , _UpperCAmelCase ) )
def A_ ( _UpperCAmelCase ):
SCREAMING_SNAKE_CASE_: Optional[int] = set()
SCREAMING_SNAKE_CASE_: int = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
SCREAMING_SNAKE_CASE_: Tuple = char
return pairs
class __lowercase ( UpperCAmelCase_ ):
"""simple docstring"""
_UpperCAmelCase : Tuple = VOCAB_FILES_NAMES
_UpperCAmelCase : Any = PRETRAINED_VOCAB_FILES_MAP
_UpperCAmelCase : Union[str, Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_UpperCAmelCase : str = ['''input_ids''', '''attention_mask''']
def __init__( self : Optional[int] , lowerCAmelCase__ : Optional[Any] , lowerCAmelCase__ : Dict , lowerCAmelCase__ : Optional[int]="replace" , lowerCAmelCase__ : List[Any]="<s>" , lowerCAmelCase__ : List[Any]="</s>" , lowerCAmelCase__ : str="</s>" , lowerCAmelCase__ : Dict="<s>" , lowerCAmelCase__ : int="<unk>" , lowerCAmelCase__ : Union[str, Any]="<pad>" , lowerCAmelCase__ : str="<mask>" , lowerCAmelCase__ : Optional[int]=False , **lowerCAmelCase__ : Optional[int] , ):
SCREAMING_SNAKE_CASE_: Optional[Any] = AddedToken(lowerCAmelCase__ , lstrip=lowerCAmelCase__ , rstrip=lowerCAmelCase__) if isinstance(lowerCAmelCase__ , lowerCAmelCase__) else bos_token
SCREAMING_SNAKE_CASE_: str = AddedToken(lowerCAmelCase__ , lstrip=lowerCAmelCase__ , rstrip=lowerCAmelCase__) if isinstance(lowerCAmelCase__ , lowerCAmelCase__) else eos_token
SCREAMING_SNAKE_CASE_: Union[str, Any] = AddedToken(lowerCAmelCase__ , lstrip=lowerCAmelCase__ , rstrip=lowerCAmelCase__) if isinstance(lowerCAmelCase__ , lowerCAmelCase__) else sep_token
SCREAMING_SNAKE_CASE_: Any = 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_: str = 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_: Any = 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_: Dict = {v: k for k, v in self.encoder.items()}
SCREAMING_SNAKE_CASE_: Optional[Any] = errors # how to handle errors in decoding
SCREAMING_SNAKE_CASE_: Optional[Any] = bytes_to_unicode()
SCREAMING_SNAKE_CASE_: List[Any] = {v: k for k, v in self.byte_encoder.items()}
with open(lowerCAmelCase__ , encoding="utf-8") as merges_handle:
SCREAMING_SNAKE_CASE_: Any = merges_handle.read().split("\n")[1:-1]
SCREAMING_SNAKE_CASE_: List[Any] = [tuple(merge.split()) for merge in bpe_merges]
SCREAMING_SNAKE_CASE_: List[str] = dict(zip(lowerCAmelCase__ , range(len(lowerCAmelCase__))))
SCREAMING_SNAKE_CASE_: str = {}
SCREAMING_SNAKE_CASE_: Union[str, Any] = add_prefix_space
# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
SCREAMING_SNAKE_CASE_: List[str] = re.compile(R"'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+")
@property
def _SCREAMING_SNAKE_CASE ( self : str):
return len(self.encoder)
def _SCREAMING_SNAKE_CASE ( self : int):
return dict(self.encoder , **self.added_tokens_encoder)
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] , lowerCAmelCase__ : Dict):
if token in self.cache:
return self.cache[token]
SCREAMING_SNAKE_CASE_: str = tuple(lowerCAmelCase__)
SCREAMING_SNAKE_CASE_: str = get_pairs(lowerCAmelCase__)
if not pairs:
return token
while True:
SCREAMING_SNAKE_CASE_: str = 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_: Union[str, Any] = []
SCREAMING_SNAKE_CASE_: Any = 0
while i < len(lowerCAmelCase__):
try:
SCREAMING_SNAKE_CASE_: Optional[Any] = word.index(lowerCAmelCase__ , lowerCAmelCase__)
except ValueError:
new_word.extend(word[i:])
break
else:
new_word.extend(word[i:j])
SCREAMING_SNAKE_CASE_: Union[str, Any] = 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_: Tuple = tuple(lowerCAmelCase__)
SCREAMING_SNAKE_CASE_: int = new_word
if len(lowerCAmelCase__) == 1:
break
else:
SCREAMING_SNAKE_CASE_: int = get_pairs(lowerCAmelCase__)
SCREAMING_SNAKE_CASE_: Union[str, Any] = " ".join(lowerCAmelCase__)
SCREAMING_SNAKE_CASE_: Any = word
return word
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] , lowerCAmelCase__ : List[Any]):
SCREAMING_SNAKE_CASE_: List[Any] = []
for token in re.findall(self.pat , lowerCAmelCase__):
SCREAMING_SNAKE_CASE_: Optional[Any] = "".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 _SCREAMING_SNAKE_CASE ( self : Tuple , lowerCAmelCase__ : List[Any]):
return self.encoder.get(lowerCAmelCase__ , self.encoder.get(self.unk_token))
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] , lowerCAmelCase__ : Dict):
return self.decoder.get(lowerCAmelCase__)
def _SCREAMING_SNAKE_CASE ( self : Any , lowerCAmelCase__ : int):
SCREAMING_SNAKE_CASE_: Optional[Any] = "".join(lowerCAmelCase__)
SCREAMING_SNAKE_CASE_: Optional[int] = bytearray([self.byte_decoder[c] for c in text]).decode("utf-8" , errors=self.errors)
return text
def _SCREAMING_SNAKE_CASE ( self : List[str] , lowerCAmelCase__ : str , lowerCAmelCase__ : Optional[str] = None):
if not os.path.isdir(lowerCAmelCase__):
logger.error(F"Vocabulary path ({save_directory}) should be a directory")
return
SCREAMING_SNAKE_CASE_: List[Any] = os.path.join(
lowerCAmelCase__ , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"])
SCREAMING_SNAKE_CASE_: 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_: Union[str, Any] = 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_: str = token_index
writer.write(" ".join(lowerCAmelCase__) + "\n")
index += 1
return vocab_file, merge_file
def _SCREAMING_SNAKE_CASE ( self : str , lowerCAmelCase__ : List[int] , lowerCAmelCase__ : Optional[List[int]] = None):
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
SCREAMING_SNAKE_CASE_: List[Any] = [self.cls_token_id]
SCREAMING_SNAKE_CASE_: Dict = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def _SCREAMING_SNAKE_CASE ( self : str , lowerCAmelCase__ : List[int] , lowerCAmelCase__ : Optional[List[int]] = None , lowerCAmelCase__ : bool = False):
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 _SCREAMING_SNAKE_CASE ( self : Tuple , lowerCAmelCase__ : List[int] , lowerCAmelCase__ : Optional[List[int]] = None):
SCREAMING_SNAKE_CASE_: Any = [self.sep_token_id]
SCREAMING_SNAKE_CASE_: str = [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 _SCREAMING_SNAKE_CASE ( self : Any , lowerCAmelCase__ : Dict , lowerCAmelCase__ : Union[str, Any]=False , **lowerCAmelCase__ : Optional[Any]):
SCREAMING_SNAKE_CASE_: List[Any] = 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_: Optional[int] = " " + text
return (text, kwargs)
| 13
|
"""simple docstring"""
from __future__ import annotations
__A : List[Any] = [-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
__A : str = [-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]
def lowercase ( __snake_case : list[float] ):
lowercase_ : List[str] = []
lowercase_ : List[Any] = len(__snake_case )
for i in range(__snake_case ):
lowercase_ : float = -1
for j in range(i + 1 , __snake_case ):
if arr[i] < arr[j]:
lowercase_ : List[str] = arr[j]
break
result.append(__snake_case )
return result
def lowercase ( __snake_case : list[float] ):
lowercase_ : List[str] = []
for i, outer in enumerate(__snake_case ):
lowercase_ : float = -1
for inner in arr[i + 1 :]:
if outer < inner:
lowercase_ : List[Any] = inner
break
result.append(__snake_case )
return result
def lowercase ( __snake_case : list[float] ):
lowercase_ : List[str] = len(__snake_case )
lowercase_ : list[float] = []
lowercase_ : list[float] = [-1] * arr_size
for index in reversed(range(__snake_case ) ):
if stack:
while stack[-1] <= arr[index]:
stack.pop()
if not stack:
break
if stack:
lowercase_ : Optional[Any] = stack[-1]
stack.append(arr[index] )
return result
if __name__ == "__main__":
from doctest import testmod
from timeit import timeit
testmod()
print(next_greatest_element_slow(arr))
print(next_greatest_element_fast(arr))
print(next_greatest_element(arr))
__A : int = (
'''from __main__ import arr, next_greatest_element_slow, '''
'''next_greatest_element_fast, next_greatest_element'''
)
print(
'''next_greatest_element_slow():''',
timeit('''next_greatest_element_slow(arr)''', setup=setup),
)
print(
'''next_greatest_element_fast():''',
timeit('''next_greatest_element_fast(arr)''', setup=setup),
)
print(
''' next_greatest_element():''',
timeit('''next_greatest_element(arr)''', setup=setup),
)
| 33
| 0
|
from __future__ import annotations
import queue
class UpperCamelCase_ :
'''simple docstring'''
def __init__( self : str , UpperCAmelCase__ : str) ->Tuple:
'''simple docstring'''
A__ = data
A__ = None
A__ = None
def SCREAMING_SNAKE_CASE ( ) -> TreeNode:
"""simple docstring"""
print('''\n********Press N to stop entering at any point of time********\n''' )
A__ = input('''Enter the value of the root node: ''' ).strip().lower()
A__ = queue.Queue()
A__ = TreeNode(int(lowercase_ ) )
q.put(lowercase_ )
while not q.empty():
A__ = q.get()
A__ = f"""Enter the left node of {node_found.data}: """
A__ = input(lowercase_ ).strip().lower() or '''n'''
if check == "n":
return tree_node
A__ = TreeNode(int(lowercase_ ) )
A__ = left_node
q.put(lowercase_ )
A__ = f"""Enter the right node of {node_found.data}: """
A__ = input(lowercase_ ).strip().lower() or '''n'''
if check == "n":
return tree_node
A__ = TreeNode(int(lowercase_ ) )
A__ = right_node
q.put(lowercase_ )
raise
def SCREAMING_SNAKE_CASE ( lowercase_ ) -> None:
"""simple docstring"""
if not isinstance(lowercase_ , lowercase_ ) or not node:
return
print(node.data , end=''',''' )
pre_order(node.left )
pre_order(node.right )
def SCREAMING_SNAKE_CASE ( lowercase_ ) -> None:
"""simple docstring"""
if not isinstance(lowercase_ , lowercase_ ) or not node:
return
in_order(node.left )
print(node.data , end=''',''' )
in_order(node.right )
def SCREAMING_SNAKE_CASE ( lowercase_ ) -> None:
"""simple docstring"""
if not isinstance(lowercase_ , lowercase_ ) or not node:
return
post_order(node.left )
post_order(node.right )
print(node.data , end=''',''' )
def SCREAMING_SNAKE_CASE ( lowercase_ ) -> None:
"""simple docstring"""
if not isinstance(lowercase_ , lowercase_ ) or not node:
return
A__ = queue.Queue()
q.put(lowercase_ )
while not q.empty():
A__ = q.get()
print(node_dequeued.data , end=''',''' )
if node_dequeued.left:
q.put(node_dequeued.left )
if node_dequeued.right:
q.put(node_dequeued.right )
def SCREAMING_SNAKE_CASE ( lowercase_ ) -> None:
"""simple docstring"""
if not isinstance(lowercase_ , lowercase_ ) or not node:
return
A__ = queue.Queue()
q.put(lowercase_ )
while not q.empty():
A__ = []
while not q.empty():
A__ = q.get()
print(node_dequeued.data , end=''',''' )
if node_dequeued.left:
list_.append(node_dequeued.left )
if node_dequeued.right:
list_.append(node_dequeued.right )
print()
for node in list_:
q.put(lowercase_ )
def SCREAMING_SNAKE_CASE ( lowercase_ ) -> None:
"""simple docstring"""
if not isinstance(lowercase_ , lowercase_ ) or not node:
return
A__ = []
A__ = node
while n or stack:
while n: # start from root node, find its left child
print(n.data , end=''',''' )
stack.append(lowercase_ )
A__ = n.left
# end of while means current node doesn't have left child
A__ = stack.pop()
# start to traverse its right child
A__ = n.right
def SCREAMING_SNAKE_CASE ( lowercase_ ) -> None:
"""simple docstring"""
if not isinstance(lowercase_ , lowercase_ ) or not node:
return
A__ = []
A__ = node
while n or stack:
while n:
stack.append(lowercase_ )
A__ = n.left
A__ = stack.pop()
print(n.data , end=''',''' )
A__ = n.right
def SCREAMING_SNAKE_CASE ( lowercase_ ) -> None:
"""simple docstring"""
if not isinstance(lowercase_ , lowercase_ ) or not node:
return
A__ , A__ = [], []
A__ = node
stacka.append(lowercase_ )
while stacka: # to find the reversed order of post order, store it in stack2
A__ = stacka.pop()
if n.left:
stacka.append(n.left )
if n.right:
stacka.append(n.right )
stacka.append(lowercase_ )
while stacka: # pop up from stack2 will be the post order
print(stacka.pop().data , end=''',''' )
def SCREAMING_SNAKE_CASE ( lowercase_ = "" , lowercase_=50 , lowercase_="*" ) -> str:
"""simple docstring"""
if not s:
return "\n" + width * char
A__ , A__ = divmod(width - len(lowercase_ ) - 2 , 2 )
return f"""{left * char} {s} {(left + extra) * char}"""
if __name__ == "__main__":
import doctest
doctest.testmod()
print(prompt("""Binary Tree Traversals"""))
_lowerCamelCase : TreeNode = build_tree()
print(prompt("""Pre Order Traversal"""))
pre_order(node)
print(prompt() + """\n""")
print(prompt("""In Order Traversal"""))
in_order(node)
print(prompt() + """\n""")
print(prompt("""Post Order Traversal"""))
post_order(node)
print(prompt() + """\n""")
print(prompt("""Level Order Traversal"""))
level_order(node)
print(prompt() + """\n""")
print(prompt("""Actual Level Order Traversal"""))
level_order_actual(node)
print("""*""" * 50 + """\n""")
print(prompt("""Pre Order Traversal - Iteration Version"""))
pre_order_iter(node)
print(prompt() + """\n""")
print(prompt("""In Order Traversal - Iteration Version"""))
in_order_iter(node)
print(prompt() + """\n""")
print(prompt("""Post Order Traversal - Iteration Version"""))
post_order_iter(node)
print(prompt())
| 14
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__A : Union[str, Any] = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : Dict = ['''NllbTokenizer''']
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : Dict = ['''NllbTokenizerFast''']
if TYPE_CHECKING:
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_nllb import NllbTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_nllb_fast import NllbTokenizerFast
else:
import sys
__A : List[Any] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 33
| 0
|
import argparse
import math
import traceback
import dateutil.parser as date_parser
import requests
def UpperCAmelCase ( a_ ) -> str:
"""simple docstring"""
__A = {}
__A = job["started_at"]
__A = job["completed_at"]
__A = date_parser.parse(a_ )
__A = date_parser.parse(a_ )
__A = round((end_datetime - start_datetime).total_seconds() / 60.0 )
__A = start
__A = end
__A = duration_in_min
return job_info
def UpperCAmelCase ( a_ , a_=None ) -> str:
"""simple docstring"""
__A = None
if token is not None:
__A = {"Accept": "application/vnd.github+json", "Authorization": F'''Bearer {token}'''}
__A = F'''https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100'''
__A = requests.get(a_ , headers=a_ ).json()
__A = {}
try:
job_time.update({job["name"]: extract_time_from_single_job(a_ ) for job in result["jobs"]} )
__A = math.ceil((result["total_count"] - 1_0_0) / 1_0_0 )
for i in range(a_ ):
__A = requests.get(url + F'''&page={i + 2}''' , headers=a_ ).json()
job_time.update({job["name"]: extract_time_from_single_job(a_ ) 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__":
SCREAMING_SNAKE_CASE :Union[str, Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument('--workflow_run_id', type=str, required=True, help='A GitHub Actions workflow run id.')
SCREAMING_SNAKE_CASE :Optional[int] = parser.parse_args()
SCREAMING_SNAKE_CASE :Union[str, Any] = get_job_time(args.workflow_run_id)
SCREAMING_SNAKE_CASE :Optional[int] = 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"]}''')
| 15
|
"""simple docstring"""
def lowercase ( __snake_case : int ):
if not isinstance(__snake_case , __snake_case ):
raise ValueError('''Input must be an integer''' )
if input_num <= 0:
raise ValueError('''Input must be positive''' )
return sum(
divisor for divisor in range(1 , input_num // 2 + 1 ) if input_num % divisor == 0 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 33
| 0
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase_ = {'configuration_plbart': ['PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PLBartConfig']}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = ['PLBartTokenizer']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = [
'PLBART_PRETRAINED_MODEL_ARCHIVE_LIST',
'PLBartForCausalLM',
'PLBartForConditionalGeneration',
'PLBartForSequenceClassification',
'PLBartModel',
'PLBartPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_plbart import PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP, PLBartConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_plbart import PLBartTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_plbart import (
PLBART_PRETRAINED_MODEL_ARCHIVE_LIST,
PLBartForCausalLM,
PLBartForConditionalGeneration,
PLBartForSequenceClassification,
PLBartModel,
PLBartPreTrainedModel,
)
else:
import sys
lowerCAmelCase_ = _LazyModule(__name__, globals()['__file__'], _import_structure)
| 16
|
"""simple docstring"""
def lowercase ( __snake_case : Optional[int] ):
lowercase_ : int = 0
lowercase_ : Optional[Any] = len(__snake_case )
for i in range(n - 1 ):
for j in range(i + 1 , __snake_case ):
if arr[i] > arr[j]:
num_inversions += 1
return num_inversions
def lowercase ( __snake_case : str ):
if len(__snake_case ) <= 1:
return arr, 0
lowercase_ : Optional[Any] = len(__snake_case ) // 2
lowercase_ : List[Any] = arr[0:mid]
lowercase_ : Union[str, Any] = arr[mid:]
lowercase_ , lowercase_ : Tuple = count_inversions_recursive(__snake_case )
lowercase_ , lowercase_ : List[Any] = count_inversions_recursive(__snake_case )
lowercase_ , lowercase_ : List[Any] = _count_cross_inversions(__snake_case , __snake_case )
lowercase_ : List[Any] = inversion_p + inversions_q + cross_inversions
return c, num_inversions
def lowercase ( __snake_case : str , __snake_case : Optional[int] ):
lowercase_ : Optional[Any] = []
lowercase_ : Any = 0
while i < len(__snake_case ) and j < len(__snake_case ):
if p[i] > q[j]:
# if P[1] > Q[j], then P[k] > Q[k] for all i < k <= len(P)
# These are all inversions. The claim emerges from the
# property that P is sorted.
num_inversion += len(__snake_case ) - i
r.append(q[j] )
j += 1
else:
r.append(p[i] )
i += 1
if i < len(__snake_case ):
r.extend(p[i:] )
else:
r.extend(q[j:] )
return r, num_inversion
def lowercase ( ):
lowercase_ : Union[str, Any] = [1_0, 2, 1, 5, 5, 2, 1_1]
# this arr has 8 inversions:
# (10, 2), (10, 1), (10, 5), (10, 5), (10, 2), (2, 1), (5, 2), (5, 2)
lowercase_ : int = count_inversions_bf(__snake_case )
lowercase_ , lowercase_ : Dict = count_inversions_recursive(__snake_case )
assert num_inversions_bf == num_inversions_recursive == 8
print('''number of inversions = ''' , __snake_case )
# testing an array with zero inversion (a sorted arr_1)
arr_a.sort()
lowercase_ : Dict = count_inversions_bf(__snake_case )
lowercase_ , lowercase_ : Dict = count_inversions_recursive(__snake_case )
assert num_inversions_bf == num_inversions_recursive == 0
print('''number of inversions = ''' , __snake_case )
# an empty list should also have zero inversions
lowercase_ : List[Any] = []
lowercase_ : Any = count_inversions_bf(__snake_case )
lowercase_ , lowercase_ : List[str] = count_inversions_recursive(__snake_case )
assert num_inversions_bf == num_inversions_recursive == 0
print('''number of inversions = ''' , __snake_case )
if __name__ == "__main__":
main()
| 33
| 0
|
"""simple docstring"""
def _A ( UpperCamelCase_ : int, UpperCamelCase_ : int) -> str:
'''simple docstring'''
if a < 0 or b < 0:
raise ValueError("the value of both inputs must be positive")
__lowercase = str(bin(UpperCamelCase_))[2:] # remove the leading "0b"
__lowercase = str(bin(UpperCamelCase_))[2:] # remove the leading "0b"
__lowercase = max(len(UpperCamelCase_), len(UpperCamelCase_))
return "0b" + "".join(
str(int(char_a == "1" and char_b == "1"))
for char_a, char_b in zip(a_binary.zfill(UpperCamelCase_), b_binary.zfill(UpperCamelCase_)))
if __name__ == "__main__":
import doctest
doctest.testmod()
| 17
|
"""simple docstring"""
__A : Any = {
'''Pillow''': '''Pillow''',
'''accelerate''': '''accelerate>=0.11.0''',
'''compel''': '''compel==0.1.8''',
'''black''': '''black~=23.1''',
'''datasets''': '''datasets''',
'''filelock''': '''filelock''',
'''flax''': '''flax>=0.4.1''',
'''hf-doc-builder''': '''hf-doc-builder>=0.3.0''',
'''huggingface-hub''': '''huggingface-hub>=0.13.2''',
'''requests-mock''': '''requests-mock==1.10.0''',
'''importlib_metadata''': '''importlib_metadata''',
'''invisible-watermark''': '''invisible-watermark''',
'''isort''': '''isort>=5.5.4''',
'''jax''': '''jax>=0.2.8,!=0.3.2''',
'''jaxlib''': '''jaxlib>=0.1.65''',
'''Jinja2''': '''Jinja2''',
'''k-diffusion''': '''k-diffusion>=0.0.12''',
'''torchsde''': '''torchsde''',
'''note_seq''': '''note_seq''',
'''librosa''': '''librosa''',
'''numpy''': '''numpy''',
'''omegaconf''': '''omegaconf''',
'''parameterized''': '''parameterized''',
'''protobuf''': '''protobuf>=3.20.3,<4''',
'''pytest''': '''pytest''',
'''pytest-timeout''': '''pytest-timeout''',
'''pytest-xdist''': '''pytest-xdist''',
'''ruff''': '''ruff>=0.0.241''',
'''safetensors''': '''safetensors''',
'''sentencepiece''': '''sentencepiece>=0.1.91,!=0.1.92''',
'''scipy''': '''scipy''',
'''onnx''': '''onnx''',
'''regex''': '''regex!=2019.12.17''',
'''requests''': '''requests''',
'''tensorboard''': '''tensorboard''',
'''torch''': '''torch>=1.4''',
'''torchvision''': '''torchvision''',
'''transformers''': '''transformers>=4.25.1''',
'''urllib3''': '''urllib3<=2.0.0''',
}
| 33
| 0
|
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 PoolFormerImageProcessor
class a__ ( unittest.TestCase ):
def __init__( self : Any,_A : List[Any],_A : List[Any]=7,_A : Tuple=3,_A : Optional[Any]=30,_A : Optional[Any]=400,_A : Union[str, Any]=True,_A : Optional[int]=None,_A : str=0.9,_A : str=None,_A : str=True,_A : int=[0.5, 0.5, 0.5],_A : Dict=[0.5, 0.5, 0.5],):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[int] = size if size is not None else {"shortest_edge": 30}
SCREAMING_SNAKE_CASE_ : int = crop_size if crop_size is not None else {"height": 30, "width": 30}
SCREAMING_SNAKE_CASE_ : Optional[Any] = parent
SCREAMING_SNAKE_CASE_ : Any = batch_size
SCREAMING_SNAKE_CASE_ : int = num_channels
SCREAMING_SNAKE_CASE_ : Any = min_resolution
SCREAMING_SNAKE_CASE_ : Union[str, Any] = max_resolution
SCREAMING_SNAKE_CASE_ : int = do_resize_and_center_crop
SCREAMING_SNAKE_CASE_ : Union[str, Any] = size
SCREAMING_SNAKE_CASE_ : List[str] = crop_pct
SCREAMING_SNAKE_CASE_ : str = crop_size
SCREAMING_SNAKE_CASE_ : List[str] = do_normalize
SCREAMING_SNAKE_CASE_ : Dict = image_mean
SCREAMING_SNAKE_CASE_ : Any = image_std
def __UpperCamelCase ( self : int ):
"""simple docstring"""
return {
"size": self.size,
"do_resize_and_center_crop": self.do_resize_and_center_crop,
"crop_pct": self.crop_pct,
"crop_size": self.crop_size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
}
@require_torch
@require_vision
class a__ ( A__ , unittest.TestCase ):
A = PoolFormerImageProcessor if is_vision_available() else None
def __UpperCamelCase ( self : Tuple ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Any = PoolFormerImageProcessingTester(self )
@property
def __UpperCamelCase ( self : Tuple ):
"""simple docstring"""
return self.image_processor_tester.prepare_image_processor_dict()
def __UpperCamelCase ( self : List[str] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : int = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(_A,"do_resize_and_center_crop" ) )
self.assertTrue(hasattr(_A,"size" ) )
self.assertTrue(hasattr(_A,"crop_pct" ) )
self.assertTrue(hasattr(_A,"do_normalize" ) )
self.assertTrue(hasattr(_A,"image_mean" ) )
self.assertTrue(hasattr(_A,"image_std" ) )
def __UpperCamelCase ( self : List[str] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Dict = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size,{"shortest_edge": 30} )
self.assertEqual(image_processor.crop_size,{"height": 30, "width": 30} )
SCREAMING_SNAKE_CASE_ : List[Any] = 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 __UpperCamelCase ( self : Union[str, Any] ):
"""simple docstring"""
pass
def __UpperCamelCase ( self : Tuple ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Any = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
SCREAMING_SNAKE_CASE_ : Optional[Any] = 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_ : Optional[Any] = 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_ : Union[str, Any] = 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 __UpperCamelCase ( self : Optional[Any] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[int] = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
SCREAMING_SNAKE_CASE_ : Optional[Any] = 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_ : List[Any] = 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_ : Dict = 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 __UpperCamelCase ( self : List[Any] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Union[str, Any] = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
SCREAMING_SNAKE_CASE_ : Dict = 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_ : List[str] = 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_ : Dict = 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"],
),)
| 18
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
__A : List[Any] = {
'''configuration_mega''': ['''MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MegaConfig''', '''MegaOnnxConfig'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : List[str] = [
'''MEGA_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''MegaForCausalLM''',
'''MegaForMaskedLM''',
'''MegaForMultipleChoice''',
'''MegaForQuestionAnswering''',
'''MegaForSequenceClassification''',
'''MegaForTokenClassification''',
'''MegaModel''',
'''MegaPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_mega import MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP, MegaConfig, MegaOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mega import (
MEGA_PRETRAINED_MODEL_ARCHIVE_LIST,
MegaForCausalLM,
MegaForMaskedLM,
MegaForMultipleChoice,
MegaForQuestionAnswering,
MegaForSequenceClassification,
MegaForTokenClassification,
MegaModel,
MegaPreTrainedModel,
)
else:
import sys
__A : Any = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 33
| 0
|
import argparse
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration
__A =[
# tf -> hf
('''/''', '''.'''),
('''layer_''', '''layers.'''),
('''kernel''', '''weight'''),
('''beta''', '''bias'''),
('''gamma''', '''weight'''),
('''pegasus''', '''model'''),
]
__A =[
('''.output.dense''', '''.fc2'''),
('''intermediate.LayerNorm''', '''final_layer_norm'''),
('''intermediate.dense''', '''fc1'''),
]
__A =(
INIT_COMMON
+ [
('''attention.self.LayerNorm''', '''self_attn_layer_norm'''),
('''attention.output.dense''', '''self_attn.out_proj'''),
('''attention.self''', '''self_attn'''),
('''attention.encdec.LayerNorm''', '''encoder_attn_layer_norm'''),
('''attention.encdec_output.dense''', '''encoder_attn.out_proj'''),
('''attention.encdec''', '''encoder_attn'''),
('''key''', '''k_proj'''),
('''value''', '''v_proj'''),
('''query''', '''q_proj'''),
('''decoder.LayerNorm''', '''decoder.layernorm_embedding'''),
]
+ END_COMMON
)
__A =(
INIT_COMMON
+ [
('''embeddings.word_embeddings''', '''shared.weight'''),
('''embeddings.position_embeddings''', '''embed_positions.weight'''),
('''attention.self.LayerNorm''', '''self_attn_layer_norm'''),
('''attention.output.dense''', '''self_attn.output'''),
('''attention.self''', '''self_attn.self'''),
('''encoder.LayerNorm''', '''encoder.layernorm_embedding'''),
]
+ END_COMMON
)
__A =[
'''encdec/key/bias''',
'''encdec/query/bias''',
'''encdec/value/bias''',
'''self/key/bias''',
'''self/query/bias''',
'''self/value/bias''',
'''encdec_output/dense/bias''',
'''attention/output/dense/bias''',
]
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ ):
for tf_name, hf_name in patterns:
lowerCamelCase_ = k.replace(lowerCamelCase__ , lowerCamelCase__ )
return k
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ ):
lowerCamelCase_ = BigBirdPegasusConfig(**lowerCamelCase__ )
lowerCamelCase_ = BigBirdPegasusForConditionalGeneration(lowerCamelCase__ )
lowerCamelCase_ = torch_model.state_dict()
lowerCamelCase_ = {}
# separating decoder weights
lowerCamelCase_ = {k: tf_weights[k] for k in tf_weights if k.startswith("pegasus/decoder" )}
lowerCamelCase_ = {k: tf_weights[k] for k in tf_weights if not k.startswith("pegasus/decoder" )}
for k, v in tqdm(decoder_weights.items() , "tf -> hf conversion" ):
lowerCamelCase_ = [k.endswith(lowerCamelCase__ ) for ending in KEYS_TO_IGNORE]
if any(lowerCamelCase__ ):
continue
lowerCamelCase_ = DECODER_PATTERNS
lowerCamelCase_ = rename_state_dict_key(lowerCamelCase__ , lowerCamelCase__ )
if new_k not in state_dict:
raise ValueError(F'could not find new key {new_k} in state dict. (converted from {k})' )
if any(True if i in k else False for i in ["dense", "query", "key", "value"] ):
lowerCamelCase_ = v.T
lowerCamelCase_ = torch.from_numpy(lowerCamelCase__ )
assert v.shape == state_dict[new_k].shape, F'{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}'
for k, v in tqdm(remaining_weights.items() , "tf -> hf conversion" ):
lowerCamelCase_ = [k.endswith(lowerCamelCase__ ) for ending in KEYS_TO_IGNORE]
if any(lowerCamelCase__ ):
continue
lowerCamelCase_ = REMAINING_PATTERNS
lowerCamelCase_ = rename_state_dict_key(lowerCamelCase__ , lowerCamelCase__ )
if new_k not in state_dict and k != "pegasus/embeddings/position_embeddings":
raise ValueError(F'could not find new key {new_k} in state dict. (converted from {k})' )
if any(True if i in k else False for i in ["dense", "query", "key", "value"] ):
lowerCamelCase_ = v.T
lowerCamelCase_ = torch.from_numpy(lowerCamelCase__ )
if k != "pegasus/embeddings/position_embeddings":
assert v.shape == state_dict[new_k].shape, F'{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}'
lowerCamelCase_ = mapping["model.embed_positions.weight"]
lowerCamelCase_ = mapping.pop("model.embed_positions.weight" )
lowerCamelCase_ , lowerCamelCase_ = torch_model.load_state_dict(lowerCamelCase__ , strict=lowerCamelCase__ )
lowerCamelCase_ = [
k
for k in missing
if k
not in [
"final_logits_bias",
"model.encoder.embed_tokens.weight",
"model.decoder.embed_tokens.weight",
"lm_head.weight",
]
]
assert unexpected_missing == [], F'no matches found for the following torch keys {unexpected_missing}'
assert extra == [], F'no matches found for the following tf keys {extra}'
return torch_model
def lowerCamelCase_ ( lowerCamelCase__ ):
lowerCamelCase_ = tf.train.list_variables(lowerCamelCase__ )
lowerCamelCase_ = {}
lowerCamelCase_ = ["global_step"]
for name, shape in tqdm(lowerCamelCase__ , desc="converting tf checkpoint to dict" ):
lowerCamelCase_ = any(pat in name for pat in ignore_name )
if skip_key:
continue
lowerCamelCase_ = tf.train.load_variable(lowerCamelCase__ , lowerCamelCase__ )
lowerCamelCase_ = array
return tf_weights
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
lowerCamelCase_ = get_tf_weights_as_numpy(lowerCamelCase__ )
lowerCamelCase_ = convert_bigbird_pegasus(lowerCamelCase__ , lowerCamelCase__ )
torch_model.save_pretrained(lowerCamelCase__ )
if __name__ == "__main__":
__A =argparse.ArgumentParser()
parser.add_argument('''--tf_ckpt_path''', type=str, help='''passed to tf.train.list_variables''')
parser.add_argument('''--save_dir''', default=None, type=str, help='''Path to the output PyTorch model.''')
__A =parser.parse_args()
__A ={}
convert_bigbird_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir, config_update=config_update)
| 19
|
"""simple docstring"""
import argparse
import os
import re
import packaging.version
__A : List[str] = '''examples/'''
__A : int = {
'''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'''),
}
__A : Dict = {
'''init''': '''src/transformers/__init__.py''',
'''setup''': '''setup.py''',
}
__A : Optional[int] = '''README.md'''
def lowercase ( __snake_case : int , __snake_case : Any , __snake_case : int ):
with open(__snake_case , '''r''' , encoding='''utf-8''' , newline='''\n''' ) as f:
lowercase_ : int = f.read()
lowercase_ , lowercase_ : List[str] = REPLACE_PATTERNS[pattern]
lowercase_ : Union[str, Any] = replace.replace('''VERSION''' , __snake_case )
lowercase_ : Optional[Any] = re_pattern.sub(__snake_case , __snake_case )
with open(__snake_case , '''w''' , encoding='''utf-8''' , newline='''\n''' ) as f:
f.write(__snake_case )
def lowercase ( __snake_case : int ):
for folder, directories, fnames in os.walk(__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(__snake_case , __snake_case ) , __snake_case , pattern='''examples''' )
def lowercase ( __snake_case : Optional[Any] , __snake_case : Optional[Any]=False ):
for pattern, fname in REPLACE_FILES.items():
update_version_in_file(__snake_case , __snake_case , __snake_case )
if not patch:
update_version_in_examples(__snake_case )
def lowercase ( ):
lowercase_ : Union[str, Any] = '''🤗 Transformers currently provides the following architectures'''
lowercase_ : Union[str, Any] = '''1. Want to contribute a new model?'''
with open(__snake_case , '''r''' , encoding='''utf-8''' , newline='''\n''' ) as f:
lowercase_ : List[str] = f.readlines()
# Find the start of the list.
lowercase_ : Optional[Any] = 0
while not lines[start_index].startswith(_start_prompt ):
start_index += 1
start_index += 1
lowercase_ : str = start_index
# Update the lines in the model list.
while not lines[index].startswith(_end_prompt ):
if lines[index].startswith('''1.''' ):
lowercase_ : str = lines[index].replace(
'''https://huggingface.co/docs/transformers/main/model_doc''' , '''https://huggingface.co/docs/transformers/model_doc''' , )
index += 1
with open(__snake_case , '''w''' , encoding='''utf-8''' , newline='''\n''' ) as f:
f.writelines(__snake_case )
def lowercase ( ):
with open(REPLACE_FILES['''init'''] , '''r''' ) as f:
lowercase_ : List[Any] = f.read()
lowercase_ : List[str] = REPLACE_PATTERNS['''init'''][0].search(__snake_case ).groups()[0]
return packaging.version.parse(__snake_case )
def lowercase ( __snake_case : Optional[Any]=False ):
lowercase_ : str = 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:
lowercase_ : Optional[Any] = default_version.base_version
elif patch:
lowercase_ : Optional[int] = F'''{default_version.major}.{default_version.minor}.{default_version.micro + 1}'''
else:
lowercase_ : Optional[int] = F'''{default_version.major}.{default_version.minor + 1}.0'''
# Now let's ask nicely if that's the right one.
lowercase_ : int = input(F'''Which version are you releasing? [{default_version}]''' )
if len(__snake_case ) == 0:
lowercase_ : Dict = default_version
print(F'''Updating version to {version}.''' )
global_version_update(__snake_case , patch=__snake_case )
if not patch:
print('''Cleaning main README, don\'t forget to run `make fix-copies`.''' )
clean_main_ref_in_model_list()
def lowercase ( ):
lowercase_ : List[Any] = get_version()
lowercase_ : List[str] = F'''{current_version.major}.{current_version.minor + 1}.0.dev0'''
lowercase_ : Any = current_version.base_version
# Check with the user we got that right.
lowercase_ : Tuple = input(F'''Which version are we developing now? [{dev_version}]''' )
if len(__snake_case ) == 0:
lowercase_ : str = dev_version
print(F'''Updating version to {version}.''' )
global_version_update(__snake_case )
print('''Cleaning main README, don\'t forget to run `make fix-copies`.''' )
clean_main_ref_in_model_list()
if __name__ == "__main__":
__A : int = 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.''')
__A : Any = 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()
| 33
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|
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mobilebert import MobileBertTokenizer
lowercase : List[str] = logging.get_logger(__name__)
lowercase : Optional[int] = {"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}
lowercase : Dict = {
"""vocab_file""": {"""mobilebert-uncased""": """https://huggingface.co/google/mobilebert-uncased/resolve/main/vocab.txt"""},
"""tokenizer_file""": {
"""mobilebert-uncased""": """https://huggingface.co/google/mobilebert-uncased/resolve/main/tokenizer.json"""
},
}
lowercase : Dict = {"""mobilebert-uncased""": 512}
lowercase : int = {}
class __snake_case ( lowerCAmelCase ):
_a : Optional[Any]= VOCAB_FILES_NAMES
_a : Tuple= PRETRAINED_VOCAB_FILES_MAP
_a : List[Any]= PRETRAINED_INIT_CONFIGURATION
_a : Dict= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_a : int= MobileBertTokenizer
def __init__( self ,snake_case=None ,snake_case=None ,snake_case=True ,snake_case="[UNK]" ,snake_case="[SEP]" ,snake_case="[PAD]" ,snake_case="[CLS]" ,snake_case="[MASK]" ,snake_case=True ,snake_case=None ,**snake_case ,):
'''simple docstring'''
super().__init__(
snake_case ,tokenizer_file=snake_case ,do_lower_case=snake_case ,unk_token=snake_case ,sep_token=snake_case ,pad_token=snake_case ,cls_token=snake_case ,mask_token=snake_case ,tokenize_chinese_chars=snake_case ,strip_accents=snake_case ,**snake_case ,)
lowercase : Tuple = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get("""lowercase""" ,snake_case ) != do_lower_case
or normalizer_state.get("""strip_accents""" ,snake_case ) != strip_accents
or normalizer_state.get("""handle_chinese_chars""" ,snake_case ) != tokenize_chinese_chars
):
lowercase : List[Any] = getattr(snake_case ,normalizer_state.pop("""type""" ) )
lowercase : int = do_lower_case
lowercase : int = strip_accents
lowercase : int = tokenize_chinese_chars
lowercase : int = normalizer_class(**snake_case )
lowercase : Any = do_lower_case
def _SCREAMING_SNAKE_CASE ( self ,snake_case ,snake_case=None ):
'''simple docstring'''
lowercase : List[Any] = [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 _SCREAMING_SNAKE_CASE ( self ,snake_case ,snake_case = None ):
'''simple docstring'''
lowercase : List[Any] = [self.sep_token_id]
lowercase : 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 ) * [0] + len(token_ids_a + sep ) * [1]
def _SCREAMING_SNAKE_CASE ( self ,snake_case ,snake_case = None ):
'''simple docstring'''
lowercase : Dict = self._tokenizer.model.save(snake_case ,name=snake_case )
return tuple(snake_case )
| 20
|
"""simple docstring"""
from typing import Optional
from urllib.parse import quote
import huggingface_hub as hfh
from packaging import version
def lowercase ( __snake_case : str , __snake_case : str , __snake_case : Optional[str] = None ):
if version.parse(hfh.__version__ ).release < version.parse('''0.11.0''' ).release:
# old versions of hfh don't url-encode the file path
lowercase_ : Union[str, Any] = quote(__snake_case )
return hfh.hf_hub_url(__snake_case , __snake_case , repo_type='''dataset''' , revision=__snake_case )
| 33
| 0
|
from __future__ import annotations
class _lowerCamelCase:
def __init__( self, lowerCamelCase=None) -> Optional[int]:
"""simple docstring"""
_lowercase : Optional[Any] = data
_lowercase : List[str] = None
def __repr__( self) -> Optional[int]:
"""simple docstring"""
_lowercase : Tuple = []
_lowercase : Dict = self
while temp:
string_rep.append(F'''{temp.data}''')
_lowercase : int = temp.next
return "->".join(lowerCamelCase)
def UpperCamelCase_( lowerCamelCase_ ) -> List[str]:
if not elements_list:
raise Exception('The Elements List is empty' )
_lowercase : List[str] = Node(elements_list[0] )
for i in range(1 , len(lowerCamelCase_ ) ):
_lowercase : Optional[int] = Node(elements_list[i] )
_lowercase : str = current.next
return head
def UpperCamelCase_( lowerCamelCase_ ) -> None:
if head_node is not None and isinstance(lowerCamelCase_ , lowerCamelCase_ ):
print_reverse(head_node.next )
print(head_node.data )
def UpperCamelCase_( ) -> Dict:
from doctest import testmod
testmod()
_lowercase : str = make_linked_list([14, 52, 14, 12, 43] )
print('Linked List:' )
print(lowerCamelCase_ )
print('Elements in Reverse:' )
print_reverse(lowerCamelCase_ )
if __name__ == "__main__":
main()
| 21
|
"""simple docstring"""
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFResNetForImageClassification, TFResNetModel
from transformers.models.resnet.modeling_tf_resnet import TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class _UpperCAmelCase :
def __init__( self : int , A : Tuple , A : int=3 , A : List[str]=32 , A : Dict=3 , A : Any=10 , A : Dict=[10, 20, 30, 40] , A : Optional[Any]=[1, 1, 2, 1] , A : Union[str, Any]=True , A : Optional[Any]=True , A : Any="relu" , A : Optional[Any]=3 , A : Tuple=None , ) -> Dict:
lowercase_ : str = parent
lowercase_ : List[Any] = batch_size
lowercase_ : Optional[int] = image_size
lowercase_ : int = num_channels
lowercase_ : int = embeddings_size
lowercase_ : str = hidden_sizes
lowercase_ : List[str] = depths
lowercase_ : Dict = is_training
lowercase_ : int = use_labels
lowercase_ : Any = hidden_act
lowercase_ : List[Any] = num_labels
lowercase_ : Tuple = scope
lowercase_ : Optional[Any] = len(A )
def A ( self : str ) -> Tuple:
lowercase_ : Optional[int] = 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[str] = ids_tensor([self.batch_size] , self.num_labels )
lowercase_ : Optional[int] = self.get_config()
return config, pixel_values, labels
def A ( self : Dict ) -> int:
return ResNetConfig(
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 , image_size=self.image_size , )
def A ( self : str , A : Tuple , A : str , A : str ) -> str:
lowercase_ : str = TFResNetModel(config=A )
lowercase_ : Union[str, Any] = model(A )
# expected last hidden states: B, C, H // 32, W // 32
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32) , )
def A ( self : Any , A : int , A : List[Any] , A : Optional[Any] ) -> Optional[Any]:
lowercase_ : Tuple = self.num_labels
lowercase_ : Union[str, Any] = TFResNetForImageClassification(A )
lowercase_ : Tuple = model(A , labels=A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def A ( self : Union[str, Any] ) -> Tuple:
lowercase_ : Tuple = self.prepare_config_and_inputs()
lowercase_ , lowercase_ , lowercase_ : Dict = config_and_inputs
lowercase_ : int = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_tf
class _UpperCAmelCase ( _A , _A , unittest.TestCase ):
SCREAMING_SNAKE_CASE_ : Union[str, Any] = (TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
SCREAMING_SNAKE_CASE_ : List[Any] = (
{"feature-extraction": TFResNetModel, "image-classification": TFResNetForImageClassification}
if is_tf_available()
else {}
)
SCREAMING_SNAKE_CASE_ : Optional[int] = False
SCREAMING_SNAKE_CASE_ : Optional[int] = False
SCREAMING_SNAKE_CASE_ : str = False
SCREAMING_SNAKE_CASE_ : Optional[int] = False
SCREAMING_SNAKE_CASE_ : Any = False
def A ( self : Union[str, Any] ) -> List[Any]:
lowercase_ : int = TFResNetModelTester(self )
lowercase_ : str = ConfigTester(self , config_class=A , has_text_modality=A )
def A ( self : Dict ) -> Optional[Any]:
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 A ( self : Dict ) -> List[Any]:
return
@unittest.skip(reason='''ResNet does not use inputs_embeds''' )
def A ( self : Any ) -> Any:
pass
@unittest.skip(reason='''ResNet does not support input and output embeddings''' )
def A ( self : List[str] ) -> Optional[Any]:
pass
def A ( self : str ) -> Tuple:
lowercase_ , lowercase_ : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase_ : int = model_class(A )
lowercase_ : str = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowercase_ : str = [*signature.parameters.keys()]
lowercase_ : str = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , A )
def A ( self : List[str] ) -> Tuple:
lowercase_ : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*A )
def A ( self : List[Any] ) -> List[str]:
def check_hidden_states_output(A : Union[str, Any] , A : int , A : List[Any] ):
lowercase_ : int = model_class(A )
lowercase_ : Optional[Any] = model(**self._prepare_for_class(A , A ) )
lowercase_ : Union[str, Any] = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
lowercase_ : Any = self.model_tester.num_stages
self.assertEqual(len(A ) , expected_num_stages + 1 )
# ResNet'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] , )
lowercase_ , lowercase_ : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
lowercase_ : Union[str, Any] = ['''basic''', '''bottleneck''']
for model_class in self.all_model_classes:
for layer_type in layers_type:
lowercase_ : List[str] = layer_type
lowercase_ : Tuple = True
check_hidden_states_output(A , A , A )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
lowercase_ : Optional[Any] = True
check_hidden_states_output(A , A , A )
def A ( self : Optional[int] ) -> Tuple:
lowercase_ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*A )
@slow
def A ( self : List[str] ) -> Optional[int]:
for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase_ : Tuple = TFResNetModel.from_pretrained(A )
self.assertIsNotNone(A )
def lowercase ( ):
lowercase_ : List[str] = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_tf
@require_vision
class _UpperCAmelCase ( unittest.TestCase ):
@cached_property
def A ( self : Any ) -> Optional[int]:
return (
AutoImageProcessor.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
if is_vision_available()
else None
)
@slow
def A ( self : Any ) -> Optional[int]:
lowercase_ : Optional[int] = TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
lowercase_ : List[Any] = self.default_image_processor
lowercase_ : Dict = prepare_img()
lowercase_ : List[str] = image_processor(images=A , return_tensors='''tf''' )
# forward pass
lowercase_ : Tuple = model(**A )
# verify the logits
lowercase_ : Optional[int] = tf.TensorShape((1, 10_00) )
self.assertEqual(outputs.logits.shape , A )
lowercase_ : Optional[Any] = tf.constant([-11.1069, -9.7877, -8.3777] )
self.assertTrue(np.allclose(outputs.logits[0, :3].numpy() , A , atol=1e-4 ) )
| 33
| 0
|
'''simple docstring'''
def UpperCAmelCase_ ( __lowercase : str , __lowercase : str ) -> list:
'''simple docstring'''
_UpperCAmelCase = len(__lowercase )
_UpperCAmelCase = []
for i in range(len(__lowercase ) - pat_len + 1 ):
_UpperCAmelCase = True
for j in range(__lowercase ):
if s[i + j] != pattern[j]:
_UpperCAmelCase = False
break
if match_found:
position.append(__lowercase )
return position
if __name__ == "__main__":
assert naive_pattern_search('''ABCDEFG''', '''DE''') == [3]
print(naive_pattern_search('''ABAAABCDBBABCDDEBCABC''', '''ABC'''))
| 22
|
"""simple docstring"""
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
__A : Dict = '''
Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that developed a chatbot app targeted at teenagers.[2] After open-sourcing the model behind the chatbot, the company pivoted to focus on being a platform for machine learning.
In March 2021, Hugging Face raised $40 million in a Series B funding round.[3]
On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model.[4] In 2022, the workshop concluded with the announcement of BLOOM, a multilingual large language model with 176 billion parameters.[5]
'''
class _UpperCAmelCase ( unittest.TestCase , _A ):
def A ( self : List[Any] ) -> Dict:
lowercase_ : Optional[int] = load_tool('''text-question-answering''' )
self.tool.setup()
lowercase_ : Union[str, Any] = load_tool('''text-question-answering''' , remote=A )
def A ( self : Any ) -> List[str]:
lowercase_ : Union[str, Any] = self.tool(A , '''What did Hugging Face do in April 2021?''' )
self.assertEqual(A , '''launched the BigScience Research Workshop''' )
def A ( self : str ) -> List[str]:
lowercase_ : int = self.remote_tool(A , '''What did Hugging Face do in April 2021?''' )
self.assertEqual(A , '''launched the BigScience Research Workshop''' )
def A ( self : List[Any] ) -> int:
lowercase_ : Optional[Any] = self.tool(text=A , question='''What did Hugging Face do in April 2021?''' )
self.assertEqual(A , '''launched the BigScience Research Workshop''' )
def A ( self : List[str] ) -> Optional[int]:
lowercase_ : int = self.remote_tool(text=A , question='''What did Hugging Face do in April 2021?''' )
self.assertEqual(A , '''launched the BigScience Research Workshop''' )
| 33
| 0
|
'''simple docstring'''
import json
import os
from pathlib import Path
import pytest
from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadManager
from datasets.utils.file_utils import hash_url_to_filename
UpperCamelCase__: Union[str, Any] = "http://www.mocksite.com/file1.txt"
UpperCamelCase__: Tuple = "\"text\": [\"foo\", \"foo\"]"
UpperCamelCase__: Dict = "6d8ce9aa78a471c7477201efbeabd3bb01ac2e7d100a6dc024ba1608361f90a8"
class SCREAMING_SNAKE_CASE:
"""simple docstring"""
lowerCamelCase__ = 200
lowerCamelCase__ = {"""Content-Length""": """100"""}
lowerCamelCase__ = {}
def A ( self : Tuple , **__snake_case : Dict ) -> List[Any]:
return [bytes(__snake_case , '''utf-8''' )]
def snake_case_ ( *_lowerCAmelCase : Optional[Any] , **_lowerCAmelCase : str ) -> List[str]:
return MockResponse()
@pytest.mark.parametrize('''urls_type''' , [str, list, dict] )
def snake_case_ ( _lowerCAmelCase : str , _lowerCAmelCase : Any , _lowerCAmelCase : Dict ) -> str:
import requests
monkeypatch.setattr(_lowerCAmelCase , '''request''' , _lowerCAmelCase )
UpperCAmelCase : Optional[Any] = URL
if issubclass(_lowerCAmelCase , _lowerCAmelCase ):
UpperCAmelCase : List[Any] = url
elif issubclass(_lowerCAmelCase , _lowerCAmelCase ):
UpperCAmelCase : int = [url]
elif issubclass(_lowerCAmelCase , _lowerCAmelCase ):
UpperCAmelCase : str = {'''train''': url}
UpperCAmelCase : Dict = '''dummy'''
UpperCAmelCase : Optional[int] = '''downloads'''
UpperCAmelCase : List[str] = tmp_path
UpperCAmelCase : Optional[Any] = DownloadConfig(
cache_dir=os.path.join(_lowerCAmelCase , _lowerCAmelCase ) , use_etag=_lowerCAmelCase , )
UpperCAmelCase : Tuple = DownloadManager(dataset_name=_lowerCAmelCase , download_config=_lowerCAmelCase )
UpperCAmelCase : Any = dl_manager.download(_lowerCAmelCase )
UpperCAmelCase : Any = urls
for downloaded_paths in [downloaded_paths]:
if isinstance(_lowerCAmelCase , _lowerCAmelCase ):
UpperCAmelCase : Optional[Any] = [downloaded_paths]
UpperCAmelCase : Any = [urls]
elif isinstance(_lowerCAmelCase , _lowerCAmelCase ):
assert "train" in downloaded_paths.keys()
UpperCAmelCase : Any = downloaded_paths.values()
UpperCAmelCase : Tuple = urls.values()
assert downloaded_paths
for downloaded_path, input_url in zip(_lowerCAmelCase , _lowerCAmelCase ):
assert downloaded_path == dl_manager.downloaded_paths[input_url]
UpperCAmelCase : List[str] = Path(_lowerCAmelCase )
UpperCAmelCase : List[Any] = downloaded_path.parts
assert parts[-1] == HASH
assert parts[-2] == cache_subdir
assert downloaded_path.exists()
UpperCAmelCase : List[Any] = downloaded_path.read_text()
assert content == CONTENT
UpperCAmelCase : Union[str, Any] = downloaded_path.with_suffix('''.json''' )
assert metadata_downloaded_path.exists()
UpperCAmelCase : Any = json.loads(metadata_downloaded_path.read_text() )
assert metadata_content == {"url": URL, "etag": None}
@pytest.mark.parametrize('''paths_type''' , [str, list, dict] )
def snake_case_ ( _lowerCAmelCase : List[str] , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : List[Any] ) -> int:
UpperCAmelCase : Optional[int] = str(_lowerCAmelCase )
if issubclass(_lowerCAmelCase , _lowerCAmelCase ):
UpperCAmelCase : Union[str, Any] = filename
elif issubclass(_lowerCAmelCase , _lowerCAmelCase ):
UpperCAmelCase : int = [filename]
elif issubclass(_lowerCAmelCase , _lowerCAmelCase ):
UpperCAmelCase : Union[str, Any] = {'''train''': filename}
UpperCAmelCase : Optional[int] = '''dummy'''
UpperCAmelCase : Union[str, Any] = xz_file.parent
UpperCAmelCase : Tuple = '''extracted'''
UpperCAmelCase : List[Any] = DownloadConfig(
cache_dir=_lowerCAmelCase , use_etag=_lowerCAmelCase , )
UpperCAmelCase : Optional[Any] = DownloadManager(dataset_name=_lowerCAmelCase , download_config=_lowerCAmelCase )
UpperCAmelCase : List[Any] = dl_manager.extract(_lowerCAmelCase )
UpperCAmelCase : Union[str, Any] = paths
for extracted_paths in [extracted_paths]:
if isinstance(_lowerCAmelCase , _lowerCAmelCase ):
UpperCAmelCase : int = [extracted_paths]
UpperCAmelCase : int = [paths]
elif isinstance(_lowerCAmelCase , _lowerCAmelCase ):
assert "train" in extracted_paths.keys()
UpperCAmelCase : Any = extracted_paths.values()
UpperCAmelCase : Tuple = paths.values()
assert extracted_paths
for extracted_path, input_path in zip(_lowerCAmelCase , _lowerCAmelCase ):
assert extracted_path == dl_manager.extracted_paths[input_path]
UpperCAmelCase : Union[str, Any] = Path(_lowerCAmelCase )
UpperCAmelCase : List[Any] = extracted_path.parts
assert parts[-1] == hash_url_to_filename(_lowerCAmelCase , etag=_lowerCAmelCase )
assert parts[-2] == extracted_subdir
assert extracted_path.exists()
UpperCAmelCase : Union[str, Any] = extracted_path.read_text()
UpperCAmelCase : List[str] = text_file.read_text()
assert extracted_file_content == expected_file_content
def snake_case_ ( _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : List[Any] ) -> str:
assert path.endswith('''.jsonl''' )
for num_items, line in enumerate(_lowerCAmelCase , start=1 ):
UpperCAmelCase : str = json.loads(line.decode('''utf-8''' ) )
assert item.keys() == {"col_1", "col_2", "col_3"}
assert num_items == 4
@pytest.mark.parametrize('''archive_jsonl''' , ['''tar_jsonl_path''', '''zip_jsonl_path'''] )
def snake_case_ ( _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : str ) -> Optional[int]:
UpperCAmelCase : Optional[Any] = request.getfixturevalue(_lowerCAmelCase )
UpperCAmelCase : Any = DownloadManager()
for num_jsonl, (path, file) in enumerate(dl_manager.iter_archive(_lowerCAmelCase ) , start=1 ):
_test_jsonl(_lowerCAmelCase , _lowerCAmelCase )
assert num_jsonl == 2
@pytest.mark.parametrize('''archive_nested_jsonl''' , ['''tar_nested_jsonl_path''', '''zip_nested_jsonl_path'''] )
def snake_case_ ( _lowerCAmelCase : Any , _lowerCAmelCase : Union[str, Any] ) -> Tuple:
UpperCAmelCase : int = request.getfixturevalue(_lowerCAmelCase )
UpperCAmelCase : str = DownloadManager()
for num_tar, (path, file) in enumerate(dl_manager.iter_archive(_lowerCAmelCase ) , start=1 ):
for num_jsonl, (subpath, subfile) in enumerate(dl_manager.iter_archive(_lowerCAmelCase ) , start=1 ):
_test_jsonl(_lowerCAmelCase , _lowerCAmelCase )
assert num_tar == 1
assert num_jsonl == 2
def snake_case_ ( _lowerCAmelCase : List[Any] ) -> Union[str, Any]:
UpperCAmelCase : Optional[Any] = DownloadManager()
for num_file, file in enumerate(dl_manager.iter_files(_lowerCAmelCase ) , start=1 ):
assert os.path.basename(_lowerCAmelCase ) == ("test.txt" if num_file == 1 else "train.txt")
assert num_file == 2
| 23
|
"""simple docstring"""
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING:
import torch
class _UpperCAmelCase ( TensorFormatter[Mapping, "torch.Tensor", Mapping] ):
def __init__( self : Any , A : int=None , **A : str ) -> Union[str, Any]:
super().__init__(features=A )
lowercase_ : Union[str, Any] = torch_tensor_kwargs
import torch # noqa import torch at initialization
def A ( self : Dict , A : int ) -> List[Any]:
import torch
if isinstance(A , A ) and column:
if all(
isinstance(A , torch.Tensor ) and x.shape == column[0].shape and x.dtype == column[0].dtype
for x in column ):
return torch.stack(A )
return column
def A ( self : int , A : Any ) -> Optional[Any]:
import torch
if isinstance(A , (str, bytes, type(A )) ):
return value
elif isinstance(A , (np.character, np.ndarray) ) and np.issubdtype(value.dtype , np.character ):
return value.tolist()
lowercase_ : Any = {}
if isinstance(A , (np.number, np.ndarray) ) and np.issubdtype(value.dtype , np.integer ):
lowercase_ : Any = {'''dtype''': torch.intaa}
elif isinstance(A , (np.number, np.ndarray) ) and np.issubdtype(value.dtype , np.floating ):
lowercase_ : Dict = {'''dtype''': torch.floataa}
elif config.PIL_AVAILABLE and "PIL" in sys.modules:
import PIL.Image
if isinstance(A , PIL.Image.Image ):
lowercase_ : Dict = np.asarray(A )
return torch.tensor(A , **{**default_dtype, **self.torch_tensor_kwargs} )
def A ( self : Union[str, Any] , A : Optional[int] ) -> str:
import torch
# support for torch, tf, jax etc.
if hasattr(A , '''__array__''' ) and not isinstance(A , torch.Tensor ):
lowercase_ : Optional[int] = data_struct.__array__()
# support for nested types like struct of list of struct
if isinstance(A , np.ndarray ):
if data_struct.dtype == object: # torch tensors cannot be instantied from an array of objects
return self._consolidate([self.recursive_tensorize(A ) for substruct in data_struct] )
elif isinstance(A , (list, tuple) ):
return self._consolidate([self.recursive_tensorize(A ) for substruct in data_struct] )
return self._tensorize(A )
def A ( self : Dict , A : dict ) -> Tuple:
return map_nested(self._recursive_tensorize , A , map_list=A )
def A ( self : str , A : pa.Table ) -> Mapping:
lowercase_ : Optional[Any] = self.numpy_arrow_extractor().extract_row(A )
lowercase_ : str = self.python_features_decoder.decode_row(A )
return self.recursive_tensorize(A )
def A ( self : List[Any] , A : pa.Table ) -> "torch.Tensor":
lowercase_ : List[str] = self.numpy_arrow_extractor().extract_column(A )
lowercase_ : str = self.python_features_decoder.decode_column(A , pa_table.column_names[0] )
lowercase_ : Optional[int] = self.recursive_tensorize(A )
lowercase_ : Any = self._consolidate(A )
return column
def A ( self : List[str] , A : pa.Table ) -> Mapping:
lowercase_ : Optional[int] = self.numpy_arrow_extractor().extract_batch(A )
lowercase_ : int = self.python_features_decoder.decode_batch(A )
lowercase_ : Dict = self.recursive_tensorize(A )
for column_name in batch:
lowercase_ : Optional[Any] = self._consolidate(batch[column_name] )
return batch
| 33
| 0
|
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
snake_case_ = logging.get_logger(__name__)
snake_case_ = {
'sail/poolformer_s12': 'https://huggingface.co/sail/poolformer_s12/resolve/main/config.json',
# See all PoolFormer models at https://huggingface.co/models?filter=poolformer
}
class SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase ):
A_ : Optional[int] = 'poolformer'
def __init__(self : Optional[Any] , a__ : int=3 , a__ : Union[str, Any]=16 , a__ : Tuple=16 , a__ : List[Any]=3 , a__ : List[str]=4.0 , a__ : Optional[int]=[2, 2, 6, 2] , a__ : str=[64, 128, 320, 512] , a__ : int=[7, 3, 3, 3] , a__ : Optional[int]=[4, 2, 2, 2] , a__ : Optional[int]=[2, 1, 1, 1] , a__ : List[Any]=4 , a__ : Any=0.0 , a__ : Dict="gelu" , a__ : Tuple=True , a__ : Optional[Any]=1E-5 , a__ : str=0.0_2 , **a__ : Dict , ):
"""simple docstring"""
__snake_case = num_channels
__snake_case = patch_size
__snake_case = stride
__snake_case = padding
__snake_case = pool_size
__snake_case = hidden_sizes
__snake_case = mlp_ratio
__snake_case = depths
__snake_case = patch_sizes
__snake_case = strides
__snake_case = num_encoder_blocks
__snake_case = drop_path_rate
__snake_case = hidden_act
__snake_case = use_layer_scale
__snake_case = layer_scale_init_value
__snake_case = initializer_range
super().__init__(**a__ )
class SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase ):
A_ : List[Any] = version.parse('1.11' )
@property
def a (self : Union[str, Any] ):
"""simple docstring"""
return OrderedDict(
[
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
] )
@property
def a (self : int ):
"""simple docstring"""
return 2E-3
| 24
|
"""simple docstring"""
from ...utils import is_torch_available, is_transformers_available
if is_transformers_available() and is_torch_available():
from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline
| 33
| 0
|
"""simple docstring"""
import argparse
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
########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to properly calculate the metrics on the
# validation dataset when in a distributed system, and builds off the
# `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
UpperCAmelCase__ : int = 1_6
UpperCAmelCase__ : int = 3_2
def lowercase_ ( _snake_case ,_snake_case = 16 ):
SCREAMING_SNAKE_CASE__ : Dict = AutoTokenizer.from_pretrained("""bert-base-cased""" )
SCREAMING_SNAKE_CASE__ : Tuple = load_dataset("""glue""" ,"""mrpc""" )
def tokenize_function(_snake_case ):
# max_length=None => use the model max length (it's actually the default)
SCREAMING_SNAKE_CASE__ : Tuple = tokenizer(examples["""sentence1"""] ,examples["""sentence2"""] ,truncation=_snake_case ,max_length=_snake_case )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
SCREAMING_SNAKE_CASE__ : List[str] = datasets.map(
_snake_case ,batched=_snake_case ,remove_columns=["""idx""", """sentence1""", """sentence2"""] ,)
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
SCREAMING_SNAKE_CASE__ : Any = tokenized_datasets.rename_column("""label""" ,"""labels""" )
def collate_fn(_snake_case ):
# On TPU it's best to pad everything to the same length or training will be very slow.
SCREAMING_SNAKE_CASE__ : Any = 128 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
SCREAMING_SNAKE_CASE__ : Optional[Any] = 16
elif accelerator.mixed_precision != "no":
SCREAMING_SNAKE_CASE__ : Any = 8
else:
SCREAMING_SNAKE_CASE__ : Optional[Any] = None
return tokenizer.pad(
_snake_case ,padding="""longest""" ,max_length=_snake_case ,pad_to_multiple_of=_snake_case ,return_tensors="""pt""" ,)
# Instantiate dataloaders.
SCREAMING_SNAKE_CASE__ : Union[str, Any] = DataLoader(
tokenized_datasets["""train"""] ,shuffle=_snake_case ,collate_fn=_snake_case ,batch_size=_snake_case )
SCREAMING_SNAKE_CASE__ : int = DataLoader(
tokenized_datasets["""validation"""] ,shuffle=_snake_case ,collate_fn=_snake_case ,batch_size=_snake_case )
return train_dataloader, eval_dataloader
# For testing only
if os.environ.get('TESTING_MOCKED_DATALOADERS', None) == "1":
from accelerate.test_utils.training import mocked_dataloaders
UpperCAmelCase__ : Union[str, Any] = mocked_dataloaders # noqa: F811
def lowercase_ ( _snake_case ,_snake_case ):
# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""" ,_snake_case ) == "1":
SCREAMING_SNAKE_CASE__ : Optional[int] = 2
# Initialize accelerator
SCREAMING_SNAKE_CASE__ : int = Accelerator(cpu=args.cpu ,mixed_precision=args.mixed_precision )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
SCREAMING_SNAKE_CASE__ : List[str] = config["""lr"""]
SCREAMING_SNAKE_CASE__ : Union[str, Any] = int(config["""num_epochs"""] )
SCREAMING_SNAKE_CASE__ : List[str] = int(config["""seed"""] )
SCREAMING_SNAKE_CASE__ : Optional[Any] = int(config["""batch_size"""] )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = evaluate.load("""glue""" ,"""mrpc""" )
# If the batch size is too big we use gradient accumulation
SCREAMING_SNAKE_CASE__ : Dict = 1
if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
SCREAMING_SNAKE_CASE__ : Tuple = batch_size // MAX_GPU_BATCH_SIZE
SCREAMING_SNAKE_CASE__ : Any = MAX_GPU_BATCH_SIZE
set_seed(_snake_case )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[Any] = get_dataloaders(_snake_case ,_snake_case )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
SCREAMING_SNAKE_CASE__ : List[Any] = AutoModelForSequenceClassification.from_pretrained("""bert-base-cased""" ,return_dict=_snake_case )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
SCREAMING_SNAKE_CASE__ : Dict = model.to(accelerator.device )
# Instantiate optimizer
SCREAMING_SNAKE_CASE__ : Dict = AdamW(params=model.parameters() ,lr=_snake_case )
# Instantiate scheduler
SCREAMING_SNAKE_CASE__ : Dict = get_linear_schedule_with_warmup(
optimizer=_snake_case ,num_warmup_steps=100 ,num_training_steps=(len(_snake_case ) * num_epochs) // gradient_accumulation_steps ,)
# 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.
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : str = accelerator.prepare(
_snake_case ,_snake_case ,_snake_case ,_snake_case ,_snake_case )
# Now we train the model
for epoch in range(_snake_case ):
model.train()
for step, batch in enumerate(_snake_case ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
SCREAMING_SNAKE_CASE__ : List[str] = model(**_snake_case )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = outputs.loss
SCREAMING_SNAKE_CASE__ : List[Any] = loss / gradient_accumulation_steps
accelerator.backward(_snake_case )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
SCREAMING_SNAKE_CASE__ : Optional[int] = 0
for step, batch in enumerate(_snake_case ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
SCREAMING_SNAKE_CASE__ : List[str] = model(**_snake_case )
SCREAMING_SNAKE_CASE__ : Optional[int] = outputs.logits.argmax(dim=-1 )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[str] = accelerator.gather((predictions, batch["""labels"""]) )
# New Code #
# First we check if it's a distributed system
if accelerator.use_distributed:
# Then see if we're on the last batch of our eval dataloader
if step == len(_snake_case ) - 1:
# Last batch needs to be truncated on distributed systems as it contains additional samples
SCREAMING_SNAKE_CASE__ : Any = predictions[: len(eval_dataloader.dataset ) - samples_seen]
SCREAMING_SNAKE_CASE__ : Optional[Any] = references[: len(eval_dataloader.dataset ) - samples_seen]
else:
# Otherwise we add the number of samples seen
samples_seen += references.shape[0]
# All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
# accelerator.gather_for_metrics((predictions, batch["labels"]))
metric.add_batch(
predictions=_snake_case ,references=_snake_case ,)
SCREAMING_SNAKE_CASE__ : Optional[int] = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(f'''epoch {epoch}:''' ,_snake_case )
def lowercase_ ( ):
SCREAMING_SNAKE_CASE__ : str = argparse.ArgumentParser(description="""Simple example of training script.""" )
parser.add_argument(
"""--mixed_precision""" ,type=_snake_case ,default=_snake_case ,choices=["""no""", """fp16""", """bf16""", """fp8"""] ,help="""Whether to use mixed precision. Choose"""
"""between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."""
"""and an Nvidia Ampere GPU.""" ,)
parser.add_argument("""--cpu""" ,action="""store_true""" ,help="""If passed, will train on the CPU.""" )
SCREAMING_SNAKE_CASE__ : Dict = parser.parse_args()
SCREAMING_SNAKE_CASE__ : List[str] = {"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
training_function(_snake_case ,_snake_case )
if __name__ == "__main__":
main()
| 25
|
"""simple docstring"""
from typing import Optional, Union
import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention
from ...modeling_utils import PreTrainedModel
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_mobilenet_va import MobileNetVaConfig
__A : Union[str, Any] = logging.get_logger(__name__)
# General docstring
__A : Tuple = '''MobileNetV1Config'''
# Base docstring
__A : Union[str, Any] = '''google/mobilenet_v1_1.0_224'''
__A : Union[str, Any] = [1, 1_024, 7, 7]
# Image classification docstring
__A : Optional[Any] = '''google/mobilenet_v1_1.0_224'''
__A : List[Any] = '''tabby, tabby cat'''
__A : Union[str, Any] = [
'''google/mobilenet_v1_1.0_224''',
'''google/mobilenet_v1_0.75_192''',
# See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
]
def lowercase ( __snake_case : List[str] , __snake_case : Union[str, Any] , __snake_case : Dict=None ):
lowercase_ : str = {}
if isinstance(__snake_case , __snake_case ):
lowercase_ : Union[str, Any] = model.mobilenet_va
else:
lowercase_ : Optional[Any] = model
lowercase_ : Union[str, Any] = '''MobilenetV1/Conv2d_0/'''
lowercase_ : Union[str, Any] = backbone.conv_stem.convolution.weight
lowercase_ : Optional[Any] = backbone.conv_stem.normalization.bias
lowercase_ : Union[str, Any] = backbone.conv_stem.normalization.weight
lowercase_ : Any = backbone.conv_stem.normalization.running_mean
lowercase_ : int = backbone.conv_stem.normalization.running_var
for i in range(1_3 ):
lowercase_ : Optional[int] = i + 1
lowercase_ : Union[str, Any] = i * 2
lowercase_ : Optional[Any] = backbone.layer[pt_index]
lowercase_ : Union[str, Any] = F'''MobilenetV1/Conv2d_{tf_index}_depthwise/'''
lowercase_ : str = pointer.convolution.weight
lowercase_ : int = pointer.normalization.bias
lowercase_ : Any = pointer.normalization.weight
lowercase_ : Dict = pointer.normalization.running_mean
lowercase_ : Union[str, Any] = pointer.normalization.running_var
lowercase_ : Any = backbone.layer[pt_index + 1]
lowercase_ : Union[str, Any] = F'''MobilenetV1/Conv2d_{tf_index}_pointwise/'''
lowercase_ : int = pointer.convolution.weight
lowercase_ : str = pointer.normalization.bias
lowercase_ : Tuple = pointer.normalization.weight
lowercase_ : Dict = pointer.normalization.running_mean
lowercase_ : Any = pointer.normalization.running_var
if isinstance(__snake_case , __snake_case ):
lowercase_ : Optional[Any] = '''MobilenetV1/Logits/Conv2d_1c_1x1/'''
lowercase_ : Any = model.classifier.weight
lowercase_ : Optional[int] = model.classifier.bias
return tf_to_pt_map
def lowercase ( __snake_case : Optional[int] , __snake_case : int , __snake_case : Dict ):
try:
import numpy as np
import tensorflow as tf
except ImportError:
logger.error(
'''Loading a TensorFlow models in PyTorch, requires TensorFlow to be installed. Please see '''
'''https://www.tensorflow.org/install/ for installation instructions.''' )
raise
# Load weights from TF model
lowercase_ : Tuple = tf.train.list_variables(__snake_case )
lowercase_ : int = {}
for name, shape in init_vars:
logger.info(F'''Loading TF weight {name} with shape {shape}''' )
lowercase_ : Optional[Any] = tf.train.load_variable(__snake_case , __snake_case )
lowercase_ : Optional[int] = array
# Build TF to PyTorch weights loading map
lowercase_ : Any = _build_tf_to_pytorch_map(__snake_case , __snake_case , __snake_case )
for name, pointer in tf_to_pt_map.items():
logger.info(F'''Importing {name}''' )
if name not in tf_weights:
logger.info(F'''{name} not in tf pre-trained weights, skipping''' )
continue
lowercase_ : Union[str, Any] = tf_weights[name]
if "depthwise_weights" in name:
logger.info('''Transposing depthwise''' )
lowercase_ : Any = np.transpose(__snake_case , (2, 3, 0, 1) )
elif "weights" in name:
logger.info('''Transposing''' )
if len(pointer.shape ) == 2: # copying into linear layer
lowercase_ : Optional[int] = array.squeeze().transpose()
else:
lowercase_ : Optional[int] = np.transpose(__snake_case , (3, 2, 0, 1) )
if pointer.shape != array.shape:
raise ValueError(F'''Pointer shape {pointer.shape} and array shape {array.shape} mismatched''' )
logger.info(F'''Initialize PyTorch weight {name} {array.shape}''' )
lowercase_ : str = torch.from_numpy(__snake_case )
tf_weights.pop(__snake_case , __snake_case )
tf_weights.pop(name + '''/RMSProp''' , __snake_case )
tf_weights.pop(name + '''/RMSProp_1''' , __snake_case )
tf_weights.pop(name + '''/ExponentialMovingAverage''' , __snake_case )
logger.info(F'''Weights not copied to PyTorch model: {', '.join(tf_weights.keys() )}''' )
return model
def lowercase ( __snake_case : torch.Tensor , __snake_case : nn.Convad ):
lowercase_ , lowercase_ : Optional[int] = features.shape[-2:]
lowercase_ , lowercase_ : str = conv_layer.stride
lowercase_ , lowercase_ : Tuple = conv_layer.kernel_size
if in_height % stride_height == 0:
lowercase_ : Dict = max(kernel_height - stride_height , 0 )
else:
lowercase_ : List[Any] = max(kernel_height - (in_height % stride_height) , 0 )
if in_width % stride_width == 0:
lowercase_ : str = max(kernel_width - stride_width , 0 )
else:
lowercase_ : int = max(kernel_width - (in_width % stride_width) , 0 )
lowercase_ : int = pad_along_width // 2
lowercase_ : Union[str, Any] = pad_along_width - pad_left
lowercase_ : Tuple = pad_along_height // 2
lowercase_ : List[str] = pad_along_height - pad_top
lowercase_ : str = (pad_left, pad_right, pad_top, pad_bottom)
return nn.functional.pad(__snake_case , __snake_case , '''constant''' , 0.0 )
class _UpperCAmelCase ( nn.Module ):
def __init__( self : List[Any] , A : MobileNetVaConfig , A : int , A : int , A : int , A : Optional[int] = 1 , A : Optional[int] = 1 , A : bool = False , A : Optional[bool] = True , A : Optional[bool or str] = True , ) -> None:
super().__init__()
lowercase_ : int = config
if in_channels % groups != 0:
raise ValueError(F'''Input channels ({in_channels}) are not divisible by {groups} groups.''' )
if out_channels % groups != 0:
raise ValueError(F'''Output channels ({out_channels}) are not divisible by {groups} groups.''' )
lowercase_ : Tuple = 0 if config.tf_padding else int((kernel_size - 1) / 2 )
lowercase_ : int = nn.Convad(
in_channels=A , out_channels=A , kernel_size=A , stride=A , padding=A , groups=A , bias=A , padding_mode='''zeros''' , )
if use_normalization:
lowercase_ : Optional[Any] = nn.BatchNormad(
num_features=A , eps=config.layer_norm_eps , momentum=0.9997 , affine=A , track_running_stats=A , )
else:
lowercase_ : Union[str, Any] = None
if use_activation:
if isinstance(A , A ):
lowercase_ : str = ACTaFN[use_activation]
elif isinstance(config.hidden_act , A ):
lowercase_ : Any = ACTaFN[config.hidden_act]
else:
lowercase_ : Tuple = config.hidden_act
else:
lowercase_ : Tuple = None
def A ( self : str , A : torch.Tensor ) -> torch.Tensor:
if self.config.tf_padding:
lowercase_ : List[Any] = apply_tf_padding(A , self.convolution )
lowercase_ : Optional[int] = self.convolution(A )
if self.normalization is not None:
lowercase_ : Union[str, Any] = self.normalization(A )
if self.activation is not None:
lowercase_ : Optional[int] = self.activation(A )
return features
class _UpperCAmelCase ( _A ):
SCREAMING_SNAKE_CASE_ : Optional[int] = MobileNetVaConfig
SCREAMING_SNAKE_CASE_ : int = load_tf_weights_in_mobilenet_va
SCREAMING_SNAKE_CASE_ : Optional[Any] = "mobilenet_v1"
SCREAMING_SNAKE_CASE_ : Union[str, Any] = "pixel_values"
SCREAMING_SNAKE_CASE_ : List[str] = False
def A ( self : Any , A : Union[nn.Linear, nn.Convad] ) -> None:
if isinstance(A , (nn.Linear, nn.Convad) ):
module.weight.data.normal_(mean=0.0 , std=self.config.initializer_range )
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(A , nn.BatchNormad ):
module.bias.data.zero_()
module.weight.data.fill_(1.0 )
__A : Union[str, Any] = R'''
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parameters:
config ([`MobileNetV1Config`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
'''
__A : List[str] = R'''
Args:
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
[`MobileNetV1ImageProcessor.__call__`] for details.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
'''
@add_start_docstrings(
"The bare MobileNetV1 model outputting raw hidden-states without any specific head on top." , _A , )
class _UpperCAmelCase ( _A ):
def __init__( self : str , A : MobileNetVaConfig , A : bool = True ) -> int:
super().__init__(A )
lowercase_ : Union[str, Any] = config
lowercase_ : List[str] = 32
lowercase_ : str = max(int(depth * config.depth_multiplier ) , config.min_depth )
lowercase_ : Union[str, Any] = MobileNetVaConvLayer(
A , in_channels=config.num_channels , out_channels=A , kernel_size=3 , stride=2 , )
lowercase_ : Optional[Any] = [1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 2, 1]
lowercase_ : List[Any] = nn.ModuleList()
for i in range(13 ):
lowercase_ : Dict = out_channels
if strides[i] == 2 or i == 0:
depth *= 2
lowercase_ : str = max(int(depth * config.depth_multiplier ) , config.min_depth )
self.layer.append(
MobileNetVaConvLayer(
A , in_channels=A , out_channels=A , kernel_size=3 , stride=strides[i] , groups=A , ) )
self.layer.append(
MobileNetVaConvLayer(
A , in_channels=A , out_channels=A , kernel_size=1 , ) )
lowercase_ : int = nn.AdaptiveAvgPoolad((1, 1) ) if add_pooling_layer else None
# Initialize weights and apply final processing
self.post_init()
def A ( self : Any , A : Optional[Any] ) -> Optional[int]:
raise NotImplementedError
@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 A ( self : List[Any] , A : Optional[torch.Tensor] = None , A : Optional[bool] = None , A : Optional[bool] = None , ) -> Union[tuple, BaseModelOutputWithPoolingAndNoAttention]:
lowercase_ : str = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
lowercase_ : Union[str, Any] = return_dict if return_dict is not None else self.config.use_return_dict
if pixel_values is None:
raise ValueError('''You have to specify pixel_values''' )
lowercase_ : List[str] = self.conv_stem(A )
lowercase_ : Dict = () if output_hidden_states else None
for i, layer_module in enumerate(self.layer ):
lowercase_ : Optional[int] = layer_module(A )
if output_hidden_states:
lowercase_ : str = all_hidden_states + (hidden_states,)
lowercase_ : Tuple = hidden_states
if self.pooler is not None:
lowercase_ : Dict = torch.flatten(self.pooler(A ) , start_dim=1 )
else:
lowercase_ : Optional[Any] = None
if not return_dict:
return tuple(v for v in [last_hidden_state, pooled_output, all_hidden_states] if v is not None )
return BaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=A , pooler_output=A , hidden_states=A , )
@add_start_docstrings(
"\n MobileNetV1 model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n ImageNet.\n " , _A , )
class _UpperCAmelCase ( _A ):
def __init__( self : List[str] , A : MobileNetVaConfig ) -> None:
super().__init__(A )
lowercase_ : int = config.num_labels
lowercase_ : List[str] = MobileNetVaModel(A )
lowercase_ : Union[str, Any] = self.mobilenet_va.layer[-1].convolution.out_channels
# Classifier head
lowercase_ : Tuple = nn.Dropout(config.classifier_dropout_prob , inplace=A )
lowercase_ : int = nn.Linear(A , 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 A ( self : Optional[Any] , A : Optional[torch.Tensor] = None , A : Optional[bool] = None , A : Optional[torch.Tensor] = None , A : Optional[bool] = None , ) -> Union[tuple, ImageClassifierOutputWithNoAttention]:
lowercase_ : Union[str, Any] = return_dict if return_dict is not None else self.config.use_return_dict
lowercase_ : List[Any] = self.mobilenet_va(A , output_hidden_states=A , return_dict=A )
lowercase_ : Union[str, Any] = outputs.pooler_output if return_dict else outputs[1]
lowercase_ : Dict = self.classifier(self.dropout(A ) )
lowercase_ : int = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
lowercase_ : List[str] = '''regression'''
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
lowercase_ : Optional[Any] = '''single_label_classification'''
else:
lowercase_ : Tuple = '''multi_label_classification'''
if self.config.problem_type == "regression":
lowercase_ : str = MSELoss()
if self.num_labels == 1:
lowercase_ : List[str] = loss_fct(logits.squeeze() , labels.squeeze() )
else:
lowercase_ : List[str] = loss_fct(A , A )
elif self.config.problem_type == "single_label_classification":
lowercase_ : List[Any] = CrossEntropyLoss()
lowercase_ : str = loss_fct(logits.view(-1 , self.num_labels ) , labels.view(-1 ) )
elif self.config.problem_type == "multi_label_classification":
lowercase_ : str = BCEWithLogitsLoss()
lowercase_ : List[Any] = loss_fct(A , A )
if not return_dict:
lowercase_ : Tuple = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return ImageClassifierOutputWithNoAttention(
loss=A , logits=A , hidden_states=outputs.hidden_states , )
| 33
| 0
|
def lowerCAmelCase_ ( snake_case_ ):
if n_term == "":
return []
_A : list = []
for temp in range(int(snake_case_ ) ):
series.append(f'''1/{temp + 1}''' if series else """1""" )
return series
if __name__ == "__main__":
_snake_case = input("Enter the last number (nth term) of the Harmonic Series")
print("Formula of Harmonic Series => 1+1/2+1/3 ..... 1/n")
print(harmonic_series(nth_term))
| 26
|
"""simple docstring"""
def lowercase ( __snake_case : list[int] ):
lowercase_ : List[Any] = len(__snake_case )
for i in range(__snake_case ):
for j in range(i + 1 , __snake_case ):
if numbers[j] < numbers[i]:
lowercase_ , lowercase_ : Optional[int] = numbers[j], numbers[i]
return numbers
if __name__ == "__main__":
__A : int = input('''Enter numbers separated by a comma:\n''').strip()
__A : Any = [int(item) for item in user_input.split(''',''')]
print(exchange_sort(unsorted))
| 33
| 0
|
'''simple docstring'''
import uuid
from typing import Any, Dict, List, Optional, Union
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
if is_torch_available():
import torch
__lowercase : Union[str, Any] = logging.get_logger(__name__)
class __UpperCamelCase :
def __init__( self , __a = None , __a = None , __a=None , __a=None ):
'''simple docstring'''
if not conversation_id:
__a : Tuple = uuid.uuida()
if past_user_inputs is None:
__a : Optional[Any] = []
if generated_responses is None:
__a : List[str] = []
__a : uuid.UUID = conversation_id
__a : List[str] = past_user_inputs
__a : List[str] = generated_responses
__a : Optional[str] = text
def __eq__( self , __a ):
'''simple docstring'''
if not isinstance(__a , __a ):
return False
if self.uuid == other.uuid:
return True
return (
self.new_user_input == other.new_user_input
and self.past_user_inputs == other.past_user_inputs
and self.generated_responses == other.generated_responses
)
def __UpperCAmelCase ( self , __a , __a = False ):
'''simple docstring'''
if self.new_user_input:
if overwrite:
logger.warning(
f"""User input added while unprocessed input was existing: \"{self.new_user_input}\" was overwritten """
f"""with: \"{text}\".""" )
__a : Tuple = text
else:
logger.warning(
f"""User input added while unprocessed input was existing: \"{self.new_user_input}\" new input """
f"""ignored: \"{text}\". Set `overwrite` to True to overwrite unprocessed user input""" )
else:
__a : Optional[Any] = text
def __UpperCAmelCase ( self ):
'''simple docstring'''
if self.new_user_input:
self.past_user_inputs.append(self.new_user_input )
__a : List[Any] = None
def __UpperCAmelCase ( self , __a ):
'''simple docstring'''
self.generated_responses.append(__a )
def __UpperCAmelCase ( self ):
'''simple docstring'''
for user_input, generated_response in zip(self.past_user_inputs , self.generated_responses ):
yield True, user_input
yield False, generated_response
if self.new_user_input:
yield True, self.new_user_input
def __repr__( self ):
'''simple docstring'''
__a : Any = f"""Conversation id: {self.uuid} \n"""
for is_user, text in self.iter_texts():
__a : Any = 'user' if is_user else 'bot'
output += f"""{name} >> {text} \n"""
return output
@add_end_docstrings(
lowerCAmelCase_ , r"\n min_length_for_response (`int`, *optional*, defaults to 32):\n The minimum length (in number of tokens) for a response.\n minimum_tokens (`int`, *optional*, defaults to 10):\n The minimum length of tokens to leave for a response.\n " , )
class __UpperCamelCase ( lowerCAmelCase_ ):
def __init__( self , *__a , **__a ):
'''simple docstring'''
super().__init__(*__a , **__a )
if self.tokenizer.pad_token_id is None:
__a : Any = self.tokenizer.eos_token
def __UpperCAmelCase ( self , __a=None , __a=None , __a=None , **__a ):
'''simple docstring'''
__a : str = {}
__a : List[Any] = {}
__a : Union[str, Any] = {}
if min_length_for_response is not None:
__a : List[str] = min_length_for_response
if minimum_tokens is not None:
__a : Optional[Any] = minimum_tokens
if "max_length" in generate_kwargs:
__a : Union[str, Any] = generate_kwargs['max_length']
# self.max_length = generate_kwargs.get("max_length", self.model.config.max_length)
if clean_up_tokenization_spaces is not None:
__a : int = clean_up_tokenization_spaces
if generate_kwargs:
forward_params.update(__a )
return preprocess_params, forward_params, postprocess_params
def __call__( self , __a , __a=0 , **__a ):
'''simple docstring'''
__a : Tuple = super().__call__(__a , num_workers=__a , **__a )
if isinstance(__a , __a ) and len(__a ) == 1:
return outputs[0]
return outputs
def __UpperCAmelCase ( self , __a , __a=32 ):
'''simple docstring'''
if not isinstance(__a , __a ):
raise ValueError('ConversationalPipeline, expects Conversation as inputs' )
if conversation.new_user_input is None:
raise ValueError(
f"""Conversation with UUID {type(conversation.uuid )} does not contain new user input to process. """
'Add user inputs with the conversation\'s `add_user_input` method' )
if hasattr(self.tokenizer , '_build_conversation_input_ids' ):
__a : List[Any] = self.tokenizer._build_conversation_input_ids(__a )
else:
# If the tokenizer cannot handle conversations, we default to only the old version
__a : Any = self._legacy_parse_and_tokenize(__a )
if self.framework == "pt":
__a : List[Any] = torch.LongTensor([input_ids] )
elif self.framework == "tf":
__a : Dict = tf.constant([input_ids] )
return {"input_ids": input_ids, "conversation": conversation}
def __UpperCAmelCase ( self , __a , __a=10 , **__a ):
'''simple docstring'''
__a : Optional[int] = generate_kwargs.get('max_length' , self.model.config.max_length )
__a : Optional[int] = model_inputs['input_ids'].shape[1]
if max_length - minimum_tokens < n:
logger.warning(f"""Conversation input is to long ({n}), trimming it to ({max_length} - {minimum_tokens})""" )
__a : Optional[Any] = max_length - minimum_tokens
__a : str = model_inputs['input_ids'][:, -trim:]
if "attention_mask" in model_inputs:
__a : Tuple = model_inputs['attention_mask'][:, -trim:]
__a : str = model_inputs.pop('conversation' )
__a : Optional[Any] = max_length
__a : List[str] = self.model.generate(**__a , **__a )
if self.model.config.is_encoder_decoder:
__a : List[Any] = 1
else:
__a : str = n
return {"output_ids": output_ids[:, start_position:], "conversation": conversation}
def __UpperCAmelCase ( self , __a , __a=True ):
'''simple docstring'''
__a : List[Any] = model_outputs['output_ids']
__a : Tuple = self.tokenizer.decode(
output_ids[0] , skip_special_tokens=__a , clean_up_tokenization_spaces=__a , )
__a : Optional[int] = model_outputs['conversation']
conversation.mark_processed()
conversation.append_response(__a )
return conversation
def __UpperCAmelCase ( self , __a ):
'''simple docstring'''
__a : List[str] = self.tokenizer.eos_token_id
__a : Optional[Any] = []
for is_user, text in conversation.iter_texts():
if eos_token_id is not None:
input_ids.extend(self.tokenizer.encode(__a , add_special_tokens=__a ) + [eos_token_id] )
else:
input_ids.extend(self.tokenizer.encode(__a , add_special_tokens=__a ) )
if len(__a ) > self.tokenizer.model_max_length:
__a : Optional[Any] = input_ids[-self.tokenizer.model_max_length :]
return input_ids
| 27
|
"""simple docstring"""
import json
import pathlib
import unittest
import numpy as np
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, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import YolosImageProcessor
class _UpperCAmelCase ( unittest.TestCase ):
def __init__( self : List[Any] , A : Any , A : Tuple=7 , A : Tuple=3 , A : Optional[Any]=30 , A : List[Any]=4_00 , A : Tuple=True , A : Dict=None , A : List[str]=True , A : Optional[int]=[0.5, 0.5, 0.5] , A : Tuple=[0.5, 0.5, 0.5] , A : List[str]=True , A : List[Any]=1 / 2_55 , A : Union[str, Any]=True , ) -> Tuple:
# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
lowercase_ : Optional[int] = size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 13_33}
lowercase_ : Optional[int] = parent
lowercase_ : str = batch_size
lowercase_ : Tuple = num_channels
lowercase_ : str = min_resolution
lowercase_ : Any = max_resolution
lowercase_ : str = do_resize
lowercase_ : Any = size
lowercase_ : Optional[int] = do_normalize
lowercase_ : List[str] = image_mean
lowercase_ : Optional[Any] = image_std
lowercase_ : int = do_rescale
lowercase_ : List[str] = rescale_factor
lowercase_ : int = do_pad
def A ( self : Any ) -> str:
return {
"do_resize": self.do_resize,
"size": self.size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_rescale": self.do_rescale,
"rescale_factor": self.rescale_factor,
"do_pad": self.do_pad,
}
def A ( self : Optional[Any] , A : int , A : int=False ) -> Tuple:
if not batched:
lowercase_ : Optional[int] = image_inputs[0]
if isinstance(A , Image.Image ):
lowercase_ , lowercase_ : int = image.size
else:
lowercase_ , lowercase_ : Tuple = image.shape[1], image.shape[2]
if w < h:
lowercase_ : int = int(self.size['''shortest_edge'''] * h / w )
lowercase_ : Optional[Any] = self.size['''shortest_edge''']
elif w > h:
lowercase_ : Optional[Any] = self.size['''shortest_edge''']
lowercase_ : Optional[int] = int(self.size['''shortest_edge'''] * w / h )
else:
lowercase_ : Any = self.size['''shortest_edge''']
lowercase_ : Any = self.size['''shortest_edge''']
else:
lowercase_ : Tuple = []
for image in image_inputs:
lowercase_ , lowercase_ : Optional[Any] = self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
lowercase_ : Union[str, Any] = max(A , key=lambda A : item[0] )[0]
lowercase_ : Optional[Any] = max(A , key=lambda A : item[1] )[1]
return expected_height, expected_width
@require_torch
@require_vision
class _UpperCAmelCase ( _A , unittest.TestCase ):
SCREAMING_SNAKE_CASE_ : Optional[Any] = YolosImageProcessor if is_vision_available() else None
def A ( self : Optional[int] ) -> Optional[int]:
lowercase_ : Optional[Any] = YolosImageProcessingTester(self )
@property
def A ( self : str ) -> Any:
return self.image_processor_tester.prepare_image_processor_dict()
def A ( self : Optional[int] ) -> List[str]:
lowercase_ : Tuple = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(A , '''image_mean''' ) )
self.assertTrue(hasattr(A , '''image_std''' ) )
self.assertTrue(hasattr(A , '''do_normalize''' ) )
self.assertTrue(hasattr(A , '''do_resize''' ) )
self.assertTrue(hasattr(A , '''size''' ) )
def A ( self : Dict ) -> Tuple:
lowercase_ : Dict = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'''shortest_edge''': 18, '''longest_edge''': 13_33} )
self.assertEqual(image_processor.do_pad , A )
lowercase_ : Tuple = self.image_processing_class.from_dict(
self.image_processor_dict , size=42 , max_size=84 , pad_and_return_pixel_mask=A )
self.assertEqual(image_processor.size , {'''shortest_edge''': 42, '''longest_edge''': 84} )
self.assertEqual(image_processor.do_pad , A )
def A ( self : Optional[int] ) -> Tuple:
pass
def A ( self : Tuple ) -> int:
# Initialize image_processing
lowercase_ : Dict = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
lowercase_ : str = prepare_image_inputs(self.image_processor_tester , equal_resolution=A )
for image in image_inputs:
self.assertIsInstance(A , Image.Image )
# Test not batched input
lowercase_ : Optional[int] = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
lowercase_ , lowercase_ : Union[str, Any] = self.image_processor_tester.get_expected_values(A )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
lowercase_ , lowercase_ : Dict = self.image_processor_tester.get_expected_values(A , batched=A )
lowercase_ : str = 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,
expected_height,
expected_width,
) , )
def A ( self : str ) -> Any:
# Initialize image_processing
lowercase_ : Any = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
lowercase_ : List[Any] = 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
lowercase_ : List[Any] = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
lowercase_ , lowercase_ : int = self.image_processor_tester.get_expected_values(A )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
lowercase_ : Optional[int] = image_processing(A , return_tensors='''pt''' ).pixel_values
lowercase_ , lowercase_ : List[Any] = self.image_processor_tester.get_expected_values(A , batched=A )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def A ( self : Tuple ) -> Optional[int]:
# Initialize image_processing
lowercase_ : Dict = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
lowercase_ : Optional[int] = 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
lowercase_ : List[str] = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
lowercase_ , lowercase_ : Union[str, Any] = self.image_processor_tester.get_expected_values(A )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
lowercase_ : Any = image_processing(A , return_tensors='''pt''' ).pixel_values
lowercase_ , lowercase_ : List[str] = self.image_processor_tester.get_expected_values(A , batched=A )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def A ( self : Tuple ) -> Optional[Any]:
# Initialize image_processings
lowercase_ : Optional[Any] = self.image_processing_class(**self.image_processor_dict )
lowercase_ : Tuple = self.image_processing_class(do_resize=A , do_normalize=A , do_rescale=A )
# create random PyTorch tensors
lowercase_ : Union[str, Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=A , torchify=A )
for image in image_inputs:
self.assertIsInstance(A , torch.Tensor )
# Test whether the method "pad" and calling the image processor return the same tensors
lowercase_ : Union[str, Any] = image_processing_a.pad(A , return_tensors='''pt''' )
lowercase_ : List[Any] = image_processing_a(A , return_tensors='''pt''' )
self.assertTrue(
torch.allclose(encoded_images_with_method['''pixel_values'''] , encoded_images['''pixel_values'''] , atol=1e-4 ) )
@slow
def A ( self : str ) -> List[Any]:
# prepare image and target
lowercase_ : Dict = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt''' , '''r''' ) as f:
lowercase_ : List[Any] = json.loads(f.read() )
lowercase_ : Tuple = {'''image_id''': 3_97_69, '''annotations''': target}
# encode them
lowercase_ : Union[str, Any] = YolosImageProcessor.from_pretrained('''hustvl/yolos-small''' )
lowercase_ : List[Any] = image_processing(images=A , annotations=A , return_tensors='''pt''' )
# verify pixel values
lowercase_ : Union[str, Any] = torch.Size([1, 3, 8_00, 10_66] )
self.assertEqual(encoding['''pixel_values'''].shape , A )
lowercase_ : Union[str, Any] = torch.tensor([0.2796, 0.3138, 0.3481] )
self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3] , A , atol=1e-4 ) )
# verify area
lowercase_ : Tuple = torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area'''] , A ) )
# verify boxes
lowercase_ : List[str] = torch.Size([6, 4] )
self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape , A )
lowercase_ : Any = torch.tensor([0.5503, 0.2765, 0.0604, 0.2215] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0] , A , atol=1e-3 ) )
# verify image_id
lowercase_ : List[Any] = torch.tensor([3_97_69] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''] , A ) )
# verify is_crowd
lowercase_ : Any = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''] , A ) )
# verify class_labels
lowercase_ : Optional[Any] = torch.tensor([75, 75, 63, 65, 17, 17] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''] , A ) )
# verify orig_size
lowercase_ : List[str] = torch.tensor([4_80, 6_40] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''] , A ) )
# verify size
lowercase_ : Optional[Any] = torch.tensor([8_00, 10_66] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''] , A ) )
@slow
def A ( self : List[Any] ) -> Dict:
# prepare image, target and masks_path
lowercase_ : Tuple = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt''' , '''r''' ) as f:
lowercase_ : str = json.loads(f.read() )
lowercase_ : int = {'''file_name''': '''000000039769.png''', '''image_id''': 3_97_69, '''segments_info''': target}
lowercase_ : List[Any] = pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic''' )
# encode them
lowercase_ : int = YolosImageProcessor(format='''coco_panoptic''' )
lowercase_ : Any = image_processing(images=A , annotations=A , masks_path=A , return_tensors='''pt''' )
# verify pixel values
lowercase_ : Optional[Any] = torch.Size([1, 3, 8_00, 10_66] )
self.assertEqual(encoding['''pixel_values'''].shape , A )
lowercase_ : Tuple = torch.tensor([0.2796, 0.3138, 0.3481] )
self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3] , A , atol=1e-4 ) )
# verify area
lowercase_ : List[Any] = torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area'''] , A ) )
# verify boxes
lowercase_ : str = torch.Size([6, 4] )
self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape , A )
lowercase_ : List[str] = torch.tensor([0.2625, 0.5437, 0.4688, 0.8625] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0] , A , atol=1e-3 ) )
# verify image_id
lowercase_ : List[str] = torch.tensor([3_97_69] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''] , A ) )
# verify is_crowd
lowercase_ : List[str] = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''] , A ) )
# verify class_labels
lowercase_ : Any = torch.tensor([17, 17, 63, 75, 75, 93] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''] , A ) )
# verify masks
lowercase_ : Dict = 82_28_73
self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item() , A )
# verify orig_size
lowercase_ : Tuple = torch.tensor([4_80, 6_40] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''] , A ) )
# verify size
lowercase_ : List[str] = torch.tensor([8_00, 10_66] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''] , A ) )
| 33
| 0
|
'''simple docstring'''
import json
import os
import tempfile
from unittest.mock import patch
import torch
from torch.utils.data import DataLoader, TensorDataset
from accelerate import DistributedType, infer_auto_device_map, init_empty_weights
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState, PartialState
from accelerate.test_utils import require_bnb, require_multi_gpu, slow
from accelerate.test_utils.testing import AccelerateTestCase, require_cuda
from accelerate.utils import patch_environment
def __lowerCamelCase ( ) -> List[Any]:
"""simple docstring"""
UpperCamelCase = torch.nn.Linear(2 , 4 )
UpperCamelCase = torch.optim.AdamW(model.parameters() , lr=1.0 )
UpperCamelCase = torch.optim.lr_scheduler.OneCycleLR(A__ , max_lr=0.01 , steps_per_epoch=2 , epochs=1 )
UpperCamelCase = DataLoader(TensorDataset(torch.tensor([1, 2, 3] ) ) )
UpperCamelCase = DataLoader(TensorDataset(torch.tensor([4, 5, 6] ) ) )
return model, optimizer, scheduler, train_dl, valid_dl
def __lowerCamelCase ( A__ ) -> str:
"""simple docstring"""
return (model.weight.abs().sum() + model.bias.abs().sum()).item()
def __lowerCamelCase ( A__ ) -> Dict:
"""simple docstring"""
UpperCamelCase = torch.nn.Linear(*tuple(model.weight.T.shape ) ).state_dict()
model.load_state_dict(A__ )
class SCREAMING_SNAKE_CASE ( _a ):
"""simple docstring"""
@require_cuda
def A ( self : Any ):
"""simple docstring"""
UpperCamelCase = Accelerator()
assert PartialState._shared_state["_cpu"] is False
assert PartialState._shared_state["device"].type == "cuda"
with self.assertRaises(UpperCamelCase__ ):
UpperCamelCase = Accelerator(cpu=UpperCamelCase__ )
def A ( self : Any ):
"""simple docstring"""
UpperCamelCase = Accelerator()
UpperCamelCase = GradientState()
assert state.num_steps == 1
UpperCamelCase = 4
assert state.num_steps == 4
assert state.sync_gradients is True
UpperCamelCase = False
assert state.sync_gradients is False
GradientState._reset_state()
def A ( self : List[str] ):
"""simple docstring"""
UpperCamelCase = Accelerator()
UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase = create_components()
(
(
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) ,
) = accelerator.prepare(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
self.assertTrue(prepared_model in accelerator._models )
self.assertTrue(prepared_optimizer in accelerator._optimizers )
self.assertTrue(prepared_scheduler in accelerator._schedulers )
self.assertTrue(prepared_train_dl in accelerator._dataloaders )
self.assertTrue(prepared_valid_dl in accelerator._dataloaders )
def A ( self : List[str] ):
"""simple docstring"""
UpperCamelCase = Accelerator()
UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase = create_components()
accelerator.prepare(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
accelerator.free_memory()
self.assertTrue(len(accelerator._models ) == 0 )
self.assertTrue(len(accelerator._optimizers ) == 0 )
self.assertTrue(len(accelerator._schedulers ) == 0 )
self.assertTrue(len(accelerator._dataloaders ) == 0 )
def A ( self : Optional[Any] ):
"""simple docstring"""
PartialState._reset_state()
# Mock torch.cuda.set_device to avoid an exception as the device doesn't exist
def noop(*UpperCamelCase__ : Dict , **UpperCamelCase__ : int ):
pass
with patch('torch.cuda.set_device' , UpperCamelCase__ ), patch_environment(ACCELERATE_TORCH_DEVICE='cuda:64' ):
UpperCamelCase = Accelerator()
self.assertEqual(str(accelerator.state.device ) , 'cuda:64' )
def A ( self : Optional[int] ):
"""simple docstring"""
UpperCamelCase = Accelerator()
UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase = create_components()
accelerator.prepare(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
UpperCamelCase = get_signature(UpperCamelCase__ )
with tempfile.TemporaryDirectory() as tmpdirname:
accelerator.save_state(UpperCamelCase__ )
# make sure random weights don't match
load_random_weights(UpperCamelCase__ )
self.assertTrue(abs(model_signature - get_signature(UpperCamelCase__ ) ) > 1E-3 )
# make sure loaded weights match
accelerator.load_state(UpperCamelCase__ )
self.assertTrue(abs(model_signature - get_signature(UpperCamelCase__ ) ) < 1E-3 )
def A ( self : List[Any] ):
"""simple docstring"""
UpperCamelCase = Accelerator()
UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase = create_components()
accelerator.prepare(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
UpperCamelCase = get_signature(UpperCamelCase__ )
# saving hook
def save_config(UpperCamelCase__ : str , UpperCamelCase__ : Union[str, Any] , UpperCamelCase__ : str ):
UpperCamelCase = {'class_name': models[0].__class__.__name__}
with open(os.path.join(UpperCamelCase__ , 'data.json' ) , 'w' ) as f:
json.dump(UpperCamelCase__ , UpperCamelCase__ )
# loading hook
def load_config(UpperCamelCase__ : Tuple , UpperCamelCase__ : Union[str, Any] ):
with open(os.path.join(UpperCamelCase__ , 'data.json' ) , 'r' ) as f:
UpperCamelCase = json.load(UpperCamelCase__ )
UpperCamelCase = config['class_name']
UpperCamelCase = accelerator.register_save_state_pre_hook(UpperCamelCase__ )
UpperCamelCase = accelerator.register_load_state_pre_hook(UpperCamelCase__ )
with tempfile.TemporaryDirectory() as tmpdirname:
accelerator.save_state(UpperCamelCase__ )
# make sure random weights don't match with hooks
load_random_weights(UpperCamelCase__ )
self.assertTrue(abs(model_signature - get_signature(UpperCamelCase__ ) ) > 1E-3 )
# random class name to verify correct one is loaded
UpperCamelCase = 'random'
# make sure loaded weights match with hooks
accelerator.load_state(UpperCamelCase__ )
self.assertTrue(abs(model_signature - get_signature(UpperCamelCase__ ) ) < 1E-3 )
# mode.class_name is loaded from config
self.assertTrue(model.class_name == model.__class__.__name__ )
# remove hooks
save_hook.remove()
load_hook.remove()
with tempfile.TemporaryDirectory() as tmpdirname:
accelerator.save_state(UpperCamelCase__ )
# make sure random weights don't match with hooks removed
load_random_weights(UpperCamelCase__ )
self.assertTrue(abs(model_signature - get_signature(UpperCamelCase__ ) ) > 1E-3 )
# random class name to verify correct one is loaded
UpperCamelCase = 'random'
# make sure loaded weights match with hooks removed
accelerator.load_state(UpperCamelCase__ )
self.assertTrue(abs(model_signature - get_signature(UpperCamelCase__ ) ) < 1E-3 )
# mode.class_name is NOT loaded from config
self.assertTrue(model.class_name != model.__class__.__name__ )
def A ( self : str ):
"""simple docstring"""
UpperCamelCase = Accelerator()
UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase = create_components()
UpperCamelCase = None
# This should work
UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase = accelerator.prepare(
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
self.assertTrue(dummy_obj is None )
def A ( self : Any ):
"""simple docstring"""
UpperCamelCase = Accelerator()
UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase = create_components()
UpperCamelCase = [1, 2, 3]
# This should work
UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase = accelerator.prepare(
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
self.assertEqual(
getattr(UpperCamelCase__ , '_is_accelerate_prepared' , UpperCamelCase__ ) , UpperCamelCase__ , 'Dummy object should have `_is_accelerate_prepared` set to `True`' , )
self.assertEqual(
getattr(UpperCamelCase__ , '_is_accelerate_prepared' , UpperCamelCase__ ) , UpperCamelCase__ , 'Model is missing `_is_accelerator_prepared` or is set to `False`' , )
self.assertEqual(
getattr(UpperCamelCase__ , '_is_accelerate_prepared' , UpperCamelCase__ ) , UpperCamelCase__ , 'Optimizer is missing `_is_accelerator_prepared` or is set to `False`' , )
self.assertEqual(
getattr(UpperCamelCase__ , '_is_accelerate_prepared' , UpperCamelCase__ ) , UpperCamelCase__ , 'Scheduler is missing `_is_accelerator_prepared` or is set to `False`' , )
self.assertEqual(
getattr(UpperCamelCase__ , '_is_accelerate_prepared' , UpperCamelCase__ ) , UpperCamelCase__ , 'Train Dataloader is missing `_is_accelerator_prepared` or is set to `False`' , )
self.assertEqual(
getattr(UpperCamelCase__ , '_is_accelerate_prepared' , UpperCamelCase__ ) , UpperCamelCase__ , 'Valid Dataloader is missing `_is_accelerator_prepared` or is set to `False`' , )
@slow
@require_bnb
def A ( self : List[str] ):
"""simple docstring"""
from transformers import AutoModelForCausalLM
UpperCamelCase = AutoModelForCausalLM.from_pretrained(
'EleutherAI/gpt-neo-125m' , load_in_abit=UpperCamelCase__ , device_map={'': 0} , )
UpperCamelCase = Accelerator()
# This should work
UpperCamelCase = accelerator.prepare(UpperCamelCase__ )
@slow
@require_bnb
def A ( self : Tuple ):
"""simple docstring"""
from transformers import AutoModelForCausalLM
UpperCamelCase = Accelerator()
with init_empty_weights():
UpperCamelCase = AutoModelForCausalLM.from_pretrained(
'EleutherAI/gpt-neo-125m' , )
model.tie_weights()
UpperCamelCase = infer_auto_device_map(UpperCamelCase__ )
UpperCamelCase = 'cpu'
UpperCamelCase = AutoModelForCausalLM.from_pretrained(
'EleutherAI/gpt-neo-125m' , device_map=UpperCamelCase__ , load_in_abit=UpperCamelCase__ , llm_inta_enable_fpaa_cpu_offload=UpperCamelCase__ )
# This should not work and get value error
with self.assertRaises(UpperCamelCase__ ):
UpperCamelCase = accelerator.prepare(UpperCamelCase__ )
@slow
@require_bnb
@require_multi_gpu
def A ( self : Optional[Any] ):
"""simple docstring"""
from transformers import AutoModelForCausalLM
UpperCamelCase = {'distributed_type': DistributedType.MULTI_GPU}
with init_empty_weights():
UpperCamelCase = AutoModelForCausalLM.from_pretrained(
'EleutherAI/gpt-neo-125m' , )
model.tie_weights()
UpperCamelCase = infer_auto_device_map(UpperCamelCase__ )
UpperCamelCase = 1
UpperCamelCase = AutoModelForCausalLM.from_pretrained(
'EleutherAI/gpt-neo-125m' , load_in_abit=UpperCamelCase__ , device_map=UpperCamelCase__ , )
UpperCamelCase = Accelerator()
# This should not work and get value error
with self.assertRaises(UpperCamelCase__ ):
UpperCamelCase = accelerator.prepare(UpperCamelCase__ )
PartialState._reset_state()
@slow
@require_bnb
@require_multi_gpu
def A ( self : Optional[Any] ):
"""simple docstring"""
from transformers import AutoModelForCausalLM
with init_empty_weights():
UpperCamelCase = AutoModelForCausalLM.from_pretrained(
'EleutherAI/gpt-neo-125m' , )
UpperCamelCase = infer_auto_device_map(UpperCamelCase__ )
UpperCamelCase = 1
UpperCamelCase = AutoModelForCausalLM.from_pretrained(
'EleutherAI/gpt-neo-125m' , load_in_abit=UpperCamelCase__ , device_map=UpperCamelCase__ , )
UpperCamelCase = Accelerator()
# This should work
UpperCamelCase = accelerator.prepare(UpperCamelCase__ )
@require_cuda
def A ( self : Union[str, Any] ):
"""simple docstring"""
UpperCamelCase = torch.nn.Linear(1_0 , 1_0 )
UpperCamelCase = torch.optim.SGD(model.parameters() , lr=0.0_1 )
UpperCamelCase = Accelerator(cpu=UpperCamelCase__ )
UpperCamelCase = accelerator.prepare(UpperCamelCase__ )
| 28
|
"""simple docstring"""
def lowercase ( __snake_case : int = 1_0_0 ):
lowercase_ : str = 0
lowercase_ : List[Any] = 0
for i in range(1 , n + 1 ):
sum_of_squares += i**2
sum_of_ints += i
return sum_of_ints**2 - sum_of_squares
if __name__ == "__main__":
print(F"""{solution() = }""")
| 33
| 0
|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
is_vision_available,
)
__UpperCAmelCase = {'configuration_vit': ['VIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTConfig', 'ViTOnnxConfig']}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase = ['ViTFeatureExtractor']
__UpperCAmelCase = ['ViTImageProcessor']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase = [
'VIT_PRETRAINED_MODEL_ARCHIVE_LIST',
'ViTForImageClassification',
'ViTForMaskedImageModeling',
'ViTModel',
'ViTPreTrainedModel',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase = [
'TFViTForImageClassification',
'TFViTModel',
'TFViTPreTrainedModel',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase = [
'FlaxViTForImageClassification',
'FlaxViTModel',
'FlaxViTPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_vit import VIT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTConfig, ViTOnnxConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_vit import ViTFeatureExtractor
from .image_processing_vit import ViTImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_vit import (
VIT_PRETRAINED_MODEL_ARCHIVE_LIST,
ViTForImageClassification,
ViTForMaskedImageModeling,
ViTModel,
ViTPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_vit import TFViTForImageClassification, TFViTModel, TFViTPreTrainedModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel, FlaxViTPreTrainedModel
else:
import sys
__UpperCAmelCase = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 29
|
"""simple docstring"""
import argparse
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline
if __name__ == "__main__":
__A : str = 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 : str = parser.parse_args()
__A : List[Any] = UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)
__A : Dict = CLIPImageProcessor()
__A : Union[str, Any] = CLIPVisionModelWithProjection.from_pretrained('''openai/clip-vit-large-patch14''')
__A : List[str] = 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)
| 33
| 0
|
import unittest
from diffusers.models.unet_ad_blocks import * # noqa F403
from diffusers.utils import torch_device
from .test_unet_blocks_common import UNetBlockTesterMixin
class lowercase__( UpperCAmelCase , unittest.TestCase ):
"""simple docstring"""
a :Optional[Any] = DownBlockaD # noqa F405
a :Any = 'down'
def _lowercase ( self : Dict ) -> str:
lowercase_ = [-0.02_32, -0.98_69, 0.80_54, -0.06_37, -0.16_88, -1.42_64, 0.44_70, -1.33_94, 0.09_04]
super().test_output(SCREAMING_SNAKE_CASE_ )
class lowercase__( UpperCAmelCase , unittest.TestCase ):
"""simple docstring"""
a :int = ResnetDownsampleBlockaD # noqa F405
a :Dict = 'down'
def _lowercase ( self : Dict ) -> int:
lowercase_ = [0.07_10, 0.24_10, -0.73_20, -1.07_57, -1.13_43, 0.35_40, -0.01_33, -0.25_76, 0.09_48]
super().test_output(SCREAMING_SNAKE_CASE_ )
class lowercase__( UpperCAmelCase , unittest.TestCase ):
"""simple docstring"""
a :int = AttnDownBlockaD # noqa F405
a :Tuple = 'down'
def _lowercase ( self : Optional[Any] ) -> Optional[Any]:
lowercase_ = [0.06_36, 0.89_64, -0.62_34, -1.01_31, 0.08_44, 0.49_35, 0.34_37, 0.09_11, -0.29_57]
super().test_output(SCREAMING_SNAKE_CASE_ )
class lowercase__( UpperCAmelCase , unittest.TestCase ):
"""simple docstring"""
a :str = CrossAttnDownBlockaD # noqa F405
a :str = 'down'
def _lowercase ( self : List[Any] ) -> Optional[Any]:
lowercase_ , lowercase_ = super().prepare_init_args_and_inputs_for_common()
lowercase_ = 3_2
return init_dict, inputs_dict
def _lowercase ( self : List[Any] ) -> Dict:
lowercase_ = [0.22_38, -0.73_96, -0.22_55, -0.38_29, 0.19_25, 1.16_65, 0.06_03, -0.72_95, 0.19_83]
super().test_output(SCREAMING_SNAKE_CASE_ )
class lowercase__( UpperCAmelCase , unittest.TestCase ):
"""simple docstring"""
a :List[str] = SimpleCrossAttnDownBlockaD # noqa F405
a :List[Any] = 'down'
@property
def _lowercase ( self : Tuple ) -> Dict:
return super().get_dummy_input(include_encoder_hidden_states=SCREAMING_SNAKE_CASE_ )
def _lowercase ( self : Optional[Any] ) -> List[Any]:
lowercase_ , lowercase_ = super().prepare_init_args_and_inputs_for_common()
lowercase_ = 3_2
return init_dict, inputs_dict
@unittest.skipIf(torch_device == '''mps''' , '''MPS result is not consistent''' )
def _lowercase ( self : Union[str, Any] ) -> Union[str, Any]:
lowercase_ = [0.79_21, -0.09_92, -0.19_62, -0.76_95, -0.42_42, 0.78_04, 0.47_37, 0.27_65, 0.33_38]
super().test_output(SCREAMING_SNAKE_CASE_ )
class lowercase__( UpperCAmelCase , unittest.TestCase ):
"""simple docstring"""
a :Dict = SkipDownBlockaD # noqa F405
a :str = 'down'
@property
def _lowercase ( self : int ) -> Optional[int]:
return super().get_dummy_input(include_skip_sample=SCREAMING_SNAKE_CASE_ )
def _lowercase ( self : Optional[int] ) -> List[str]:
lowercase_ = [-0.08_45, -0.20_87, -0.24_65, 0.09_71, 0.19_00, -0.04_84, 0.26_64, 0.41_79, 0.50_69]
super().test_output(SCREAMING_SNAKE_CASE_ )
class lowercase__( UpperCAmelCase , unittest.TestCase ):
"""simple docstring"""
a :Optional[Any] = AttnSkipDownBlockaD # noqa F405
a :Optional[Any] = 'down'
@property
def _lowercase ( self : Optional[int] ) -> List[str]:
return super().get_dummy_input(include_skip_sample=SCREAMING_SNAKE_CASE_ )
def _lowercase ( self : Any ) -> Dict:
lowercase_ = [0.55_39, 0.16_09, 0.49_24, 0.05_37, -0.19_95, 0.40_50, 0.09_79, -0.27_21, -0.06_42]
super().test_output(SCREAMING_SNAKE_CASE_ )
class lowercase__( UpperCAmelCase , unittest.TestCase ):
"""simple docstring"""
a :Dict = DownEncoderBlockaD # noqa F405
a :Tuple = 'down'
@property
def _lowercase ( self : List[Any] ) -> Optional[int]:
return super().get_dummy_input(include_temb=SCREAMING_SNAKE_CASE_ )
def _lowercase ( self : str ) -> Dict:
lowercase_ = {
'''in_channels''': 3_2,
'''out_channels''': 3_2,
}
lowercase_ = self.dummy_input
return init_dict, inputs_dict
def _lowercase ( self : List[Any] ) -> Tuple:
lowercase_ = [1.11_02, 0.53_02, 0.48_72, -0.00_23, -0.80_42, 0.04_83, -0.34_89, -0.56_32, 0.76_26]
super().test_output(SCREAMING_SNAKE_CASE_ )
class lowercase__( UpperCAmelCase , unittest.TestCase ):
"""simple docstring"""
a :Optional[int] = AttnDownEncoderBlockaD # noqa F405
a :Optional[Any] = 'down'
@property
def _lowercase ( self : List[str] ) -> Dict:
return super().get_dummy_input(include_temb=SCREAMING_SNAKE_CASE_ )
def _lowercase ( self : List[str] ) -> List[Any]:
lowercase_ = {
'''in_channels''': 3_2,
'''out_channels''': 3_2,
}
lowercase_ = self.dummy_input
return init_dict, inputs_dict
def _lowercase ( self : str ) -> Any:
lowercase_ = [0.89_66, -0.14_86, 0.85_68, 0.81_41, -0.90_46, -0.13_42, -0.09_72, -0.74_17, 0.15_38]
super().test_output(SCREAMING_SNAKE_CASE_ )
class lowercase__( UpperCAmelCase , unittest.TestCase ):
"""simple docstring"""
a :Dict = UNetMidBlockaD # noqa F405
a :str = 'mid'
def _lowercase ( self : Any ) -> int:
lowercase_ = {
'''in_channels''': 3_2,
'''temb_channels''': 1_2_8,
}
lowercase_ = self.dummy_input
return init_dict, inputs_dict
def _lowercase ( self : Optional[Any] ) -> Any:
lowercase_ = [-0.10_62, 1.72_48, 0.34_94, 1.45_69, -0.09_10, -1.24_21, -0.99_84, 0.67_36, 1.00_28]
super().test_output(SCREAMING_SNAKE_CASE_ )
class lowercase__( UpperCAmelCase , unittest.TestCase ):
"""simple docstring"""
a :List[Any] = UNetMidBlockaDCrossAttn # noqa F405
a :str = 'mid'
def _lowercase ( self : List[Any] ) -> List[Any]:
lowercase_ , lowercase_ = super().prepare_init_args_and_inputs_for_common()
lowercase_ = 3_2
return init_dict, inputs_dict
def _lowercase ( self : Any ) -> str:
lowercase_ = [0.01_87, 2.42_20, 0.44_84, 1.12_03, -0.61_21, -1.51_22, -0.82_70, 0.78_51, 1.83_35]
super().test_output(SCREAMING_SNAKE_CASE_ )
class lowercase__( UpperCAmelCase , unittest.TestCase ):
"""simple docstring"""
a :str = UNetMidBlockaDSimpleCrossAttn # noqa F405
a :List[str] = 'mid'
@property
def _lowercase ( self : Any ) -> int:
return super().get_dummy_input(include_encoder_hidden_states=SCREAMING_SNAKE_CASE_ )
def _lowercase ( self : Optional[Any] ) -> List[Any]:
lowercase_ , lowercase_ = super().prepare_init_args_and_inputs_for_common()
lowercase_ = 3_2
return init_dict, inputs_dict
def _lowercase ( self : Tuple ) -> int:
lowercase_ = [0.71_43, 1.99_74, 0.54_48, 1.39_77, 0.12_82, -1.12_37, -1.42_38, 0.55_30, 0.88_80]
super().test_output(SCREAMING_SNAKE_CASE_ )
class lowercase__( UpperCAmelCase , unittest.TestCase ):
"""simple docstring"""
a :str = UpBlockaD # noqa F405
a :Optional[int] = 'up'
@property
def _lowercase ( self : List[str] ) -> Dict:
return super().get_dummy_input(include_res_hidden_states_tuple=SCREAMING_SNAKE_CASE_ )
def _lowercase ( self : Optional[Any] ) -> List[Any]:
lowercase_ = [-0.20_41, -0.41_65, -0.30_22, 0.00_41, -0.66_28, -0.70_53, 0.19_28, -0.03_25, 0.05_23]
super().test_output(SCREAMING_SNAKE_CASE_ )
class lowercase__( UpperCAmelCase , unittest.TestCase ):
"""simple docstring"""
a :Optional[Any] = ResnetUpsampleBlockaD # noqa F405
a :Tuple = 'up'
@property
def _lowercase ( self : int ) -> Dict:
return super().get_dummy_input(include_res_hidden_states_tuple=SCREAMING_SNAKE_CASE_ )
def _lowercase ( self : int ) -> Optional[Any]:
lowercase_ = [0.22_87, 0.35_49, -0.13_46, 0.47_97, -0.17_15, -0.96_49, 0.73_05, -0.58_64, -0.62_44]
super().test_output(SCREAMING_SNAKE_CASE_ )
class lowercase__( UpperCAmelCase , unittest.TestCase ):
"""simple docstring"""
a :Any = CrossAttnUpBlockaD # noqa F405
a :Optional[Any] = 'up'
@property
def _lowercase ( self : Optional[Any] ) -> Optional[int]:
return super().get_dummy_input(include_res_hidden_states_tuple=SCREAMING_SNAKE_CASE_ )
def _lowercase ( self : List[str] ) -> Optional[Any]:
lowercase_ , lowercase_ = super().prepare_init_args_and_inputs_for_common()
lowercase_ = 3_2
return init_dict, inputs_dict
def _lowercase ( self : Optional[Any] ) -> Optional[int]:
lowercase_ = [-0.14_03, -0.35_15, -0.04_20, -0.14_25, 0.31_67, 0.50_94, -0.21_81, 0.59_31, 0.55_82]
super().test_output(SCREAMING_SNAKE_CASE_ )
class lowercase__( UpperCAmelCase , unittest.TestCase ):
"""simple docstring"""
a :Union[str, Any] = SimpleCrossAttnUpBlockaD # noqa F405
a :List[str] = 'up'
@property
def _lowercase ( self : Tuple ) -> List[str]:
return super().get_dummy_input(include_res_hidden_states_tuple=SCREAMING_SNAKE_CASE_ , include_encoder_hidden_states=SCREAMING_SNAKE_CASE_ )
def _lowercase ( self : str ) -> List[str]:
lowercase_ , lowercase_ = super().prepare_init_args_and_inputs_for_common()
lowercase_ = 3_2
return init_dict, inputs_dict
def _lowercase ( self : Dict ) -> Any:
lowercase_ = [0.26_45, 0.14_80, 0.09_09, 0.80_44, -0.97_58, -0.90_83, 0.09_94, -1.14_53, -0.74_02]
super().test_output(SCREAMING_SNAKE_CASE_ )
class lowercase__( UpperCAmelCase , unittest.TestCase ):
"""simple docstring"""
a :Optional[int] = AttnUpBlockaD # noqa F405
a :Tuple = 'up'
@property
def _lowercase ( self : Any ) -> str:
return super().get_dummy_input(include_res_hidden_states_tuple=SCREAMING_SNAKE_CASE_ )
@unittest.skipIf(torch_device == '''mps''' , '''MPS result is not consistent''' )
def _lowercase ( self : Any ) -> Union[str, Any]:
lowercase_ = [0.09_79, 0.13_26, 0.00_21, 0.06_59, 0.22_49, 0.00_59, 0.11_32, 0.59_52, 0.10_33]
super().test_output(SCREAMING_SNAKE_CASE_ )
class lowercase__( UpperCAmelCase , unittest.TestCase ):
"""simple docstring"""
a :Optional[int] = SkipUpBlockaD # noqa F405
a :Tuple = 'up'
@property
def _lowercase ( self : Tuple ) -> Any:
return super().get_dummy_input(include_res_hidden_states_tuple=SCREAMING_SNAKE_CASE_ )
def _lowercase ( self : str ) -> Optional[int]:
lowercase_ = [-0.08_93, -0.12_34, -0.15_06, -0.03_32, 0.01_23, -0.02_11, 0.05_66, 0.01_43, 0.03_62]
super().test_output(SCREAMING_SNAKE_CASE_ )
class lowercase__( UpperCAmelCase , unittest.TestCase ):
"""simple docstring"""
a :Union[str, Any] = AttnSkipUpBlockaD # noqa F405
a :List[Any] = 'up'
@property
def _lowercase ( self : Optional[Any] ) -> Tuple:
return super().get_dummy_input(include_res_hidden_states_tuple=SCREAMING_SNAKE_CASE_ )
def _lowercase ( self : Any ) -> List[str]:
lowercase_ = [0.03_61, 0.06_17, 0.27_87, -0.03_50, 0.03_42, 0.34_21, -0.08_43, 0.09_13, 0.30_15]
super().test_output(SCREAMING_SNAKE_CASE_ )
class lowercase__( UpperCAmelCase , unittest.TestCase ):
"""simple docstring"""
a :Any = UpDecoderBlockaD # noqa F405
a :Optional[Any] = 'up'
@property
def _lowercase ( self : Dict ) -> Union[str, Any]:
return super().get_dummy_input(include_temb=SCREAMING_SNAKE_CASE_ )
def _lowercase ( self : str ) -> Tuple:
lowercase_ = {'''in_channels''': 3_2, '''out_channels''': 3_2}
lowercase_ = self.dummy_input
return init_dict, inputs_dict
def _lowercase ( self : int ) -> Tuple:
lowercase_ = [0.44_04, 0.19_98, -0.98_86, -0.33_20, -0.31_28, -0.70_34, -0.69_55, -0.23_38, -0.31_37]
super().test_output(SCREAMING_SNAKE_CASE_ )
class lowercase__( UpperCAmelCase , unittest.TestCase ):
"""simple docstring"""
a :List[Any] = AttnUpDecoderBlockaD # noqa F405
a :List[str] = 'up'
@property
def _lowercase ( self : Union[str, Any] ) -> Optional[int]:
return super().get_dummy_input(include_temb=SCREAMING_SNAKE_CASE_ )
def _lowercase ( self : Optional[int] ) -> str:
lowercase_ = {'''in_channels''': 3_2, '''out_channels''': 3_2}
lowercase_ = self.dummy_input
return init_dict, inputs_dict
def _lowercase ( self : Union[str, Any] ) -> Optional[Any]:
lowercase_ = [0.67_38, 0.44_91, 0.10_55, 1.07_10, 0.73_16, 0.33_39, 0.33_52, 0.10_23, 0.35_68]
super().test_output(SCREAMING_SNAKE_CASE_ )
| 30
|
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaControlnetImgaImgPipeline,
KandinskyVaaPriorEmbaEmbPipeline,
UNetaDConditionModel,
VQModel,
)
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 _UpperCAmelCase ( _A , unittest.TestCase ):
SCREAMING_SNAKE_CASE_ : Any = KandinskyVaaControlnetImgaImgPipeline
SCREAMING_SNAKE_CASE_ : Optional[int] = ["image_embeds", "negative_image_embeds", "image", "hint"]
SCREAMING_SNAKE_CASE_ : str = ["image_embeds", "negative_image_embeds", "image", "hint"]
SCREAMING_SNAKE_CASE_ : Dict = [
"generator",
"height",
"width",
"strength",
"guidance_scale",
"num_inference_steps",
"return_dict",
"guidance_scale",
"num_images_per_prompt",
"output_type",
"return_dict",
]
SCREAMING_SNAKE_CASE_ : Dict = False
@property
def A ( self : Any ) -> Any:
return 32
@property
def A ( self : Optional[int] ) -> Any:
return 32
@property
def A ( self : Dict ) -> int:
return self.time_input_dim
@property
def A ( self : Tuple ) -> str:
return self.time_input_dim * 4
@property
def A ( self : Any ) -> str:
return 1_00
@property
def A ( self : str ) -> List[str]:
torch.manual_seed(0 )
lowercase_ : List[Any] = {
'''in_channels''': 8,
# Out channels is double in channels because predicts mean and variance
'''out_channels''': 8,
'''addition_embed_type''': '''image_hint''',
'''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''': '''image_proj''',
'''cross_attention_dim''': self.cross_attention_dim,
'''attention_head_dim''': 4,
'''resnet_time_scale_shift''': '''scale_shift''',
'''class_embed_type''': None,
}
lowercase_ : Dict = UNetaDConditionModel(**A )
return model
@property
def A ( self : Optional[Any] ) -> Union[str, Any]:
return {
"block_out_channels": [32, 32, 64, 64],
"down_block_types": [
"DownEncoderBlock2D",
"DownEncoderBlock2D",
"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", "UpDecoderBlock2D", "UpDecoderBlock2D"],
"vq_embed_dim": 4,
}
@property
def A ( self : List[Any] ) -> Dict:
torch.manual_seed(0 )
lowercase_ : int = VQModel(**self.dummy_movq_kwargs )
return model
def A ( self : Union[str, Any] ) -> Optional[int]:
lowercase_ : Tuple = self.dummy_unet
lowercase_ : int = self.dummy_movq
lowercase_ : List[Any] = {
'''num_train_timesteps''': 10_00,
'''beta_schedule''': '''linear''',
'''beta_start''': 0.00085,
'''beta_end''': 0.012,
'''clip_sample''': False,
'''set_alpha_to_one''': False,
'''steps_offset''': 0,
'''prediction_type''': '''epsilon''',
'''thresholding''': False,
}
lowercase_ : str = DDIMScheduler(**A )
lowercase_ : Tuple = {
'''unet''': unet,
'''scheduler''': scheduler,
'''movq''': movq,
}
return components
def A ( self : Optional[int] , A : int , A : List[str]=0 ) -> int:
lowercase_ : str = floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(A ) ).to(A )
lowercase_ : Tuple = floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(seed + 1 ) ).to(
A )
# create init_image
lowercase_ : Union[str, Any] = floats_tensor((1, 3, 64, 64) , rng=random.Random(A ) ).to(A )
lowercase_ : Any = image.cpu().permute(0 , 2 , 3 , 1 )[0]
lowercase_ : Optional[Any] = Image.fromarray(np.uinta(A ) ).convert('''RGB''' ).resize((2_56, 2_56) )
# create hint
lowercase_ : Optional[int] = floats_tensor((1, 3, 64, 64) , rng=random.Random(A ) ).to(A )
if str(A ).startswith('''mps''' ):
lowercase_ : Optional[Any] = torch.manual_seed(A )
else:
lowercase_ : List[Any] = torch.Generator(device=A ).manual_seed(A )
lowercase_ : Dict = {
'''image''': init_image,
'''image_embeds''': image_embeds,
'''negative_image_embeds''': negative_image_embeds,
'''hint''': hint,
'''generator''': generator,
'''height''': 64,
'''width''': 64,
'''num_inference_steps''': 10,
'''guidance_scale''': 7.0,
'''strength''': 0.2,
'''output_type''': '''np''',
}
return inputs
def A ( self : Any ) -> List[Any]:
lowercase_ : List[str] = '''cpu'''
lowercase_ : Any = self.get_dummy_components()
lowercase_ : Any = self.pipeline_class(**A )
lowercase_ : int = pipe.to(A )
pipe.set_progress_bar_config(disable=A )
lowercase_ : Dict = pipe(**self.get_dummy_inputs(A ) )
lowercase_ : str = output.images
lowercase_ : int = pipe(
**self.get_dummy_inputs(A ) , return_dict=A , )[0]
lowercase_ : Dict = image[0, -3:, -3:, -1]
lowercase_ : Union[str, Any] = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
lowercase_ : List[str] = np.array(
[0.54985034, 0.55509365, 0.52561504, 0.5570494, 0.5593818, 0.5263979, 0.50285643, 0.5069846, 0.51196736] )
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()}'''
@slow
@require_torch_gpu
class _UpperCAmelCase ( unittest.TestCase ):
def A ( self : Tuple ) -> str:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def A ( self : Any ) -> Optional[int]:
lowercase_ : Dict = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy''' )
lowercase_ : Dict = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png''' )
lowercase_ : Optional[int] = init_image.resize((5_12, 5_12) )
lowercase_ : Dict = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/kandinskyv22/hint_image_cat.png''' )
lowercase_ : Optional[int] = torch.from_numpy(np.array(A ) ).float() / 255.0
lowercase_ : Tuple = hint.permute(2 , 0 , 1 ).unsqueeze(0 )
lowercase_ : Optional[Any] = '''A robot, 4k photo'''
lowercase_ : Tuple = KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
'''kandinsky-community/kandinsky-2-2-prior''' , torch_dtype=torch.floataa )
pipe_prior.to(A )
lowercase_ : Dict = KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
'''kandinsky-community/kandinsky-2-2-controlnet-depth''' , torch_dtype=torch.floataa )
lowercase_ : int = pipeline.to(A )
pipeline.set_progress_bar_config(disable=A )
lowercase_ : Tuple = torch.Generator(device='''cpu''' ).manual_seed(0 )
lowercase_ , lowercase_ : int = pipe_prior(
A , image=A , strength=0.85 , generator=A , negative_prompt='''''' , ).to_tuple()
lowercase_ : str = pipeline(
image=A , image_embeds=A , negative_image_embeds=A , hint=A , generator=A , num_inference_steps=1_00 , height=5_12 , width=5_12 , strength=0.5 , output_type='''np''' , )
lowercase_ : Optional[Any] = output.images[0]
assert image.shape == (5_12, 5_12, 3)
assert_mean_pixel_difference(A , A )
| 33
| 0
|
'''simple docstring'''
from __future__ import annotations
import math
from collections.abc import Callable
def UpperCamelCase_ ( _UpperCAmelCase : Callable[[int | float], int | float] , _UpperCAmelCase : int | float , _UpperCAmelCase : int | float , _UpperCAmelCase : int = 100 , ) -> float:
"""simple docstring"""
_UpperCAmelCase : Tuple = x_start
_UpperCAmelCase : int = fnc(_UpperCAmelCase )
_UpperCAmelCase : str = 0.0
for _ in range(_UpperCAmelCase ):
# Approximates curve as a sequence of linear lines and sums their length
_UpperCAmelCase : List[str] = (x_end - x_start) / steps + xa
_UpperCAmelCase : str = fnc(_UpperCAmelCase )
length += math.hypot(xa - xa , fxa - fxa )
# Increment step
_UpperCAmelCase : Dict = xa
_UpperCAmelCase : Union[str, Any] = fxa
return length
if __name__ == "__main__":
def UpperCamelCase_ ( _UpperCAmelCase : int ) -> List[str]:
"""simple docstring"""
return math.sin(10 * x )
print("""f(x) = sin(10 * x)""")
print("""The length of the curve from x = -10 to x = 10 is:""")
__SCREAMING_SNAKE_CASE : str = 10
while i <= 100_000:
print(F'With {i} steps: {line_length(f, -10, 10, i)}')
i *= 10
| 31
|
"""simple docstring"""
def lowercase ( __snake_case : int = 1_0_0_0 ):
lowercase_ , lowercase_ : str = 1, 1
lowercase_ : List[str] = 2
while True:
lowercase_ : Tuple = 0
lowercase_ : List[Any] = fa + fa
lowercase_ , lowercase_ : Optional[int] = fa, f
index += 1
for _ in str(__snake_case ):
i += 1
if i == n:
break
return index
if __name__ == "__main__":
print(solution(int(str(input()).strip())))
| 33
| 0
|
import math
def SCREAMING_SNAKE_CASE_ ( __A : int ) -> bool:
"""simple docstring"""
a_ : Dict = math.loga(math.sqrt(4 * positive_integer + 1 ) / 2 + 1 / 2 )
return exponent == int(__A )
def SCREAMING_SNAKE_CASE_ ( __A : float = 1 / 1_23_45 ) -> int:
"""simple docstring"""
a_ : Optional[int] = 0
a_ : Dict = 0
a_ : Any = 3
while True:
a_ : int = (integer**2 - 1) / 4
# if candidate is an integer, then there is a partition for k
if partition_candidate == int(__A ):
a_ : Optional[Any] = int(__A )
total_partitions += 1
if check_partition_perfect(__A ):
perfect_partitions += 1
if perfect_partitions > 0:
if perfect_partitions / total_partitions < max_proportion:
return int(__A )
integer += 1
if __name__ == "__main__":
print(F'{solution() = }')
| 32
|
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : Dict = logging.get_logger(__name__)
__A : Union[str, Any] = {
'''facebook/vit-mae-base''': '''https://huggingface.co/facebook/vit-mae-base/resolve/main/config.json''',
# See all ViT MAE models at https://huggingface.co/models?filter=vit-mae
}
class _UpperCAmelCase ( _A ):
SCREAMING_SNAKE_CASE_ : Union[str, Any] = "vit_mae"
def __init__( self : Dict , A : List[str]=7_68 , A : Any=12 , A : Union[str, Any]=12 , A : Tuple=30_72 , A : Any="gelu" , A : Tuple=0.0 , A : List[str]=0.0 , A : Tuple=0.02 , A : Tuple=1e-12 , A : int=2_24 , A : Dict=16 , A : int=3 , A : Tuple=True , A : Tuple=16 , A : Optional[Any]=5_12 , A : Union[str, Any]=8 , A : List[Any]=20_48 , A : Dict=0.75 , A : Any=False , **A : Optional[int] , ) -> Union[str, Any]:
super().__init__(**A )
lowercase_ : List[Any] = hidden_size
lowercase_ : str = num_hidden_layers
lowercase_ : List[Any] = num_attention_heads
lowercase_ : Any = intermediate_size
lowercase_ : Optional[int] = hidden_act
lowercase_ : List[Any] = hidden_dropout_prob
lowercase_ : int = attention_probs_dropout_prob
lowercase_ : int = initializer_range
lowercase_ : Dict = layer_norm_eps
lowercase_ : Optional[Any] = image_size
lowercase_ : str = patch_size
lowercase_ : Dict = num_channels
lowercase_ : Any = qkv_bias
lowercase_ : Union[str, Any] = decoder_num_attention_heads
lowercase_ : Optional[Any] = decoder_hidden_size
lowercase_ : List[str] = decoder_num_hidden_layers
lowercase_ : List[Any] = decoder_intermediate_size
lowercase_ : Optional[Any] = mask_ratio
lowercase_ : Optional[Any] = norm_pix_loss
| 33
| 0
|
'''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _a ( __a ):
__a : int = ["""image_processor""", """tokenizer"""]
__a : Union[str, Any] = """ChineseCLIPImageProcessor"""
__a : List[Any] = ("""BertTokenizer""", """BertTokenizerFast""")
def __init__( self : Dict , lowercase : Union[str, Any]=None , lowercase : Dict=None , **lowercase : Optional[Any] ):
'''simple docstring'''
UpperCAmelCase = 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 , )
UpperCAmelCase = kwargs.pop('''feature_extractor''' )
UpperCAmelCase = 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 )
UpperCAmelCase = self.image_processor
def __call__( self : Tuple , lowercase : Optional[Any]=None , lowercase : Union[str, Any]=None , lowercase : int=None , **lowercase : Dict ):
'''simple docstring'''
if text is None and images is None:
raise ValueError('''You have to specify either text or images. Both cannot be none.''' )
if text is not None:
UpperCAmelCase = self.tokenizer(lowercase , return_tensors=lowercase , **lowercase )
if images is not None:
UpperCAmelCase = self.image_processor(lowercase , return_tensors=lowercase , **lowercase )
if text is not None and images is not None:
UpperCAmelCase = image_features.pixel_values
return encoding
elif text is not None:
return encoding
else:
return BatchEncoding(data=dict(**lowercase ) , tensor_type=lowercase )
def A ( self : int , *lowercase : Tuple , **lowercase : List[str] ):
'''simple docstring'''
return self.tokenizer.batch_decode(*lowercase , **lowercase )
def A ( self : Optional[Any] , *lowercase : int , **lowercase : Optional[int] ):
'''simple docstring'''
return self.tokenizer.decode(*lowercase , **lowercase )
@property
def A ( self : Optional[int] ):
'''simple docstring'''
UpperCAmelCase = self.tokenizer.model_input_names
UpperCAmelCase = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
@property
def A ( self : List[Any] ):
'''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
| 34
|
"""simple docstring"""
def lowercase ( __snake_case : int ):
if n == 1 or not isinstance(__snake_case , __snake_case ):
return 0
elif n == 2:
return 1
else:
lowercase_ : Dict = [0, 1]
for i in range(2 , n + 1 ):
sequence.append(sequence[i - 1] + sequence[i - 2] )
return sequence[n]
def lowercase ( __snake_case : int ):
lowercase_ : str = 0
lowercase_ : List[str] = 2
while digits < n:
index += 1
lowercase_ : Any = len(str(fibonacci(__snake_case ) ) )
return index
def lowercase ( __snake_case : int = 1_0_0_0 ):
return fibonacci_digits_index(__snake_case )
if __name__ == "__main__":
print(solution(int(str(input()).strip())))
| 33
| 0
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__a = {
"configuration_mvp": ["MVP_PRETRAINED_CONFIG_ARCHIVE_MAP", "MvpConfig", "MvpOnnxConfig"],
"tokenization_mvp": ["MvpTokenizer"],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = ["MvpTokenizerFast"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = [
"MVP_PRETRAINED_MODEL_ARCHIVE_LIST",
"MvpForCausalLM",
"MvpForConditionalGeneration",
"MvpForQuestionAnswering",
"MvpForSequenceClassification",
"MvpModel",
"MvpPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_mvp import MVP_PRETRAINED_CONFIG_ARCHIVE_MAP, MvpConfig, MvpOnnxConfig
from .tokenization_mvp import MvpTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mvp_fast import MvpTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mvp import (
MVP_PRETRAINED_MODEL_ARCHIVE_LIST,
MvpForCausalLM,
MvpForConditionalGeneration,
MvpForQuestionAnswering,
MvpForSequenceClassification,
MvpModel,
MvpPreTrainedModel,
)
else:
import sys
__a = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 35
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__A : List[str] = {
'''configuration_mobilenet_v2''': [
'''MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''MobileNetV2Config''',
'''MobileNetV2OnnxConfig''',
],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : Dict = ['''MobileNetV2FeatureExtractor''']
__A : Optional[int] = ['''MobileNetV2ImageProcessor''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : Optional[Any] = [
'''MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''MobileNetV2ForImageClassification''',
'''MobileNetV2ForSemanticSegmentation''',
'''MobileNetV2Model''',
'''MobileNetV2PreTrainedModel''',
'''load_tf_weights_in_mobilenet_v2''',
]
if TYPE_CHECKING:
from .configuration_mobilenet_va import (
MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
MobileNetVaConfig,
MobileNetVaOnnxConfig,
)
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_mobilenet_va import MobileNetVaFeatureExtractor
from .image_processing_mobilenet_va import MobileNetVaImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mobilenet_va import (
MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
MobileNetVaForImageClassification,
MobileNetVaForSemanticSegmentation,
MobileNetVaModel,
MobileNetVaPreTrainedModel,
load_tf_weights_in_mobilenet_va,
)
else:
import sys
__A : List[Any] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 33
| 0
|
def A ( _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : Dict = int(_lowerCamelCase )
if n_element < 1:
_lowerCAmelCase : Tuple = ValueError("a should be a positive number" )
raise my_error
_lowerCAmelCase : str = [1]
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase : Tuple = (0, 0, 0)
_lowerCAmelCase : List[Any] = 1
while index < n_element:
while hamming_list[i] * 2 <= hamming_list[-1]:
i += 1
while hamming_list[j] * 3 <= hamming_list[-1]:
j += 1
while hamming_list[k] * 5 <= hamming_list[-1]:
k += 1
hamming_list.append(
min(hamming_list[i] * 2 , hamming_list[j] * 3 , hamming_list[k] * 5 ) )
index += 1
return hamming_list
if __name__ == "__main__":
_snake_case = input("Enter the last number (nth term) of the Hamming Number Series: ")
print("Formula of Hamming Number Series => 2^i * 3^j * 5^k")
_snake_case = hamming(int(n))
print("-----------------------------------------------------")
print(f'''The list with nth numbers is: {hamming_numbers}''')
print("-----------------------------------------------------")
| 36
|
"""simple docstring"""
from __future__ import annotations
__A : List[Any] = [-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
__A : str = [-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]
def lowercase ( __snake_case : list[float] ):
lowercase_ : List[str] = []
lowercase_ : List[Any] = len(__snake_case )
for i in range(__snake_case ):
lowercase_ : float = -1
for j in range(i + 1 , __snake_case ):
if arr[i] < arr[j]:
lowercase_ : List[str] = arr[j]
break
result.append(__snake_case )
return result
def lowercase ( __snake_case : list[float] ):
lowercase_ : List[str] = []
for i, outer in enumerate(__snake_case ):
lowercase_ : float = -1
for inner in arr[i + 1 :]:
if outer < inner:
lowercase_ : List[Any] = inner
break
result.append(__snake_case )
return result
def lowercase ( __snake_case : list[float] ):
lowercase_ : List[str] = len(__snake_case )
lowercase_ : list[float] = []
lowercase_ : list[float] = [-1] * arr_size
for index in reversed(range(__snake_case ) ):
if stack:
while stack[-1] <= arr[index]:
stack.pop()
if not stack:
break
if stack:
lowercase_ : Optional[Any] = stack[-1]
stack.append(arr[index] )
return result
if __name__ == "__main__":
from doctest import testmod
from timeit import timeit
testmod()
print(next_greatest_element_slow(arr))
print(next_greatest_element_fast(arr))
print(next_greatest_element(arr))
__A : int = (
'''from __main__ import arr, next_greatest_element_slow, '''
'''next_greatest_element_fast, next_greatest_element'''
)
print(
'''next_greatest_element_slow():''',
timeit('''next_greatest_element_slow(arr)''', setup=setup),
)
print(
'''next_greatest_element_fast():''',
timeit('''next_greatest_element_fast(arr)''', setup=setup),
)
print(
''' next_greatest_element():''',
timeit('''next_greatest_element(arr)''', setup=setup),
)
| 33
| 0
|
'''simple docstring'''
import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image
if version.parse(version.parse(PIL.__version__).base_version) >= version.parse('''9.1.0'''):
_lowerCAmelCase = {
'''linear''': PIL.Image.Resampling.BILINEAR,
'''bilinear''': PIL.Image.Resampling.BILINEAR,
'''bicubic''': PIL.Image.Resampling.BICUBIC,
'''lanczos''': PIL.Image.Resampling.LANCZOS,
'''nearest''': PIL.Image.Resampling.NEAREST,
}
else:
_lowerCAmelCase = {
'''linear''': PIL.Image.LINEAR,
'''bilinear''': PIL.Image.BILINEAR,
'''bicubic''': PIL.Image.BICUBIC,
'''lanczos''': PIL.Image.LANCZOS,
'''nearest''': PIL.Image.NEAREST,
}
def _SCREAMING_SNAKE_CASE ( UpperCamelCase ):
"""simple docstring"""
lowerCAmelCase__ : List[str] = (images / 2 + 0.5).clamp(0 , 1 )
lowerCAmelCase__ : Optional[Any] = images.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
lowerCAmelCase__ : Optional[int] = numpy_to_pil(UpperCamelCase )
return images
def _SCREAMING_SNAKE_CASE ( UpperCamelCase ):
"""simple docstring"""
if images.ndim == 3:
lowerCAmelCase__ : List[str] = images[None, ...]
lowerCAmelCase__ : int = (images * 255).round().astype("""uint8""" )
if images.shape[-1] == 1:
# special case for grayscale (single channel) images
lowerCAmelCase__ : int = [Image.fromarray(image.squeeze() , mode="""L""" ) for image in images]
else:
lowerCAmelCase__ : Tuple = [Image.fromarray(UpperCamelCase ) for image in images]
return pil_images
| 37
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__A : Union[str, Any] = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : Dict = ['''NllbTokenizer''']
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : Dict = ['''NllbTokenizerFast''']
if TYPE_CHECKING:
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_nllb import NllbTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_nllb_fast import NllbTokenizerFast
else:
import sys
__A : List[Any] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 33
| 0
|
import argparse
import torch
# Step 1. clone https://github.com/microsoft/unilm
# Step 2. git checkout to https://github.com/microsoft/unilm/commit/b94ec76c36f02fb2b0bf0dcb0b8554a2185173cd
# Step 3. cd unilm
# Step 4. ln -s $(realpath wavlm/modules.py) ./ # create simlink
# import classes
from unilm.wavlm.WavLM import WavLM as WavLMOrig
from unilm.wavlm.WavLM import WavLMConfig as WavLMConfigOrig
from transformers import WavLMConfig, WavLMModel, logging
logging.set_verbosity_info()
UpperCAmelCase_ : Optional[Any] = logging.get_logger(__name__)
UpperCAmelCase_ : Optional[Any] = {
'''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.grep_linear''': '''encoder.layers.*.attention.gru_rel_pos_linear''',
'''self_attn.relative_attention_bias''': '''encoder.layers.*.attention.rel_attn_embed''',
'''self_attn.grep_a''': '''encoder.layers.*.attention.gru_rel_pos_const''',
'''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''',
'''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''': '''ctc_proj''',
'''mask_emb''': '''masked_spec_embed''',
}
UpperCAmelCase_ : int = [
'''ctc_proj''',
'''quantizer.weight_proj''',
'''quantizer.codevectors''',
'''project_q''',
'''project_hid''',
]
def SCREAMING_SNAKE_CASE_ ( __magic_name__ : Dict , __magic_name__ : Dict , __magic_name__ : Union[str, Any] , __magic_name__ : Tuple , __magic_name__ : Optional[int] ) -> Dict:
"""simple docstring"""
for attribute in key.split(""".""" ):
UpperCamelCase :Dict = getattr(__magic_name__ , __magic_name__ )
if weight_type is not None:
UpperCamelCase :Optional[int] = getattr(__magic_name__ , __magic_name__ ).shape
else:
UpperCamelCase :Optional[int] = hf_pointer.shape
assert hf_shape == value.shape, (
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":
UpperCamelCase :str = value
elif weight_type == "weight_g":
UpperCamelCase :int = value
elif weight_type == "weight_v":
UpperCamelCase :int = value
elif weight_type == "bias":
UpperCamelCase :List[Any] = value
else:
UpperCamelCase :Any = value
logger.info(f"""{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.""" )
def SCREAMING_SNAKE_CASE_ ( __magic_name__ : Optional[Any] , __magic_name__ : List[str] ) -> Optional[Any]:
"""simple docstring"""
UpperCamelCase :Union[str, Any] = []
UpperCamelCase :Dict = fairseq_model.state_dict()
UpperCamelCase :int = hf_model.feature_extractor
for name, value in fairseq_dict.items():
UpperCamelCase :str = False
if "conv_layers" in name:
load_conv_layer(
__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , hf_model.config.feat_extract_norm == """group""" , )
UpperCamelCase :Union[str, Any] = True
else:
for key, mapped_key in MAPPING.items():
if key in name or key.split("""w2v_model.""" )[-1] == name.split(""".""" )[0]:
UpperCamelCase :Optional[int] = True
if "*" in mapped_key:
UpperCamelCase :List[Any] = name.split(__magic_name__ )[0].split(""".""" )[-2]
UpperCamelCase :int = mapped_key.replace("""*""" , __magic_name__ )
if "weight_g" in name:
UpperCamelCase :List[Any] = """weight_g"""
elif "weight_v" in name:
UpperCamelCase :List[Any] = """weight_v"""
elif "bias" in name and "relative_attention_bias" not in name:
UpperCamelCase :Any = """bias"""
elif "weight" in name:
# TODO: don't match quantizer.weight_proj
UpperCamelCase :List[str] = """weight"""
else:
UpperCamelCase :Optional[int] = None
set_recursively(__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ )
continue
if not is_used:
unused_weights.append(__magic_name__ )
logger.warning(f"""Unused weights: {unused_weights}""" )
def SCREAMING_SNAKE_CASE_ ( __magic_name__ : Any , __magic_name__ : str , __magic_name__ : int , __magic_name__ : int , __magic_name__ : List[str] ) -> Dict:
"""simple docstring"""
UpperCamelCase :Dict = full_name.split("""conv_layers.""" )[-1]
UpperCamelCase :int = name.split(""".""" )
UpperCamelCase :str = int(items[0] )
UpperCamelCase :str = int(items[1] )
if type_id == 0:
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""
)
UpperCamelCase :Tuple = value
logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""
)
UpperCamelCase :Dict = 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:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
f"""{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"""
" found."
)
UpperCamelCase :Tuple = value
logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""
)
UpperCamelCase :Union[str, Any] = value
logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
else:
unused_weights.append(__magic_name__ )
@torch.no_grad()
def SCREAMING_SNAKE_CASE_ ( __magic_name__ : List[str] , __magic_name__ : List[Any] , __magic_name__ : str=None ) -> int:
"""simple docstring"""
UpperCamelCase :List[Any] = torch.load(__magic_name__ )
UpperCamelCase :List[Any] = WavLMConfigOrig(checkpoint["""cfg"""] )
UpperCamelCase :int = WavLMOrig(__magic_name__ )
model.load_state_dict(checkpoint["""model"""] )
model.eval()
if config_path is not None:
UpperCamelCase :List[Any] = WavLMConfig.from_pretrained(__magic_name__ )
else:
UpperCamelCase :Any = WavLMConfig()
UpperCamelCase :Dict = WavLMModel(__magic_name__ )
recursively_load_weights(__magic_name__ , __magic_name__ )
hf_wavlm.save_pretrained(__magic_name__ )
if __name__ == "__main__":
UpperCAmelCase_ : Union[str, Any] = 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('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
UpperCAmelCase_ : Optional[int] = parser.parse_args()
convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)
| 38
|
"""simple docstring"""
def lowercase ( __snake_case : int ):
if not isinstance(__snake_case , __snake_case ):
raise ValueError('''Input must be an integer''' )
if input_num <= 0:
raise ValueError('''Input must be positive''' )
return sum(
divisor for divisor in range(1 , input_num // 2 + 1 ) if input_num % divisor == 0 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 33
| 0
|
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextModel,
CLIPTokenizer,
WhisperForConditionalGeneration,
WhisperProcessor,
)
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
LMSDiscreteScheduler,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import logging
_a = logging.get_logger(__name__) # pylint: disable=invalid-name
class __lowerCamelCase ( snake_case__):
"""simple docstring"""
def __init__( self , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , ):
"""simple docstring"""
super().__init__()
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(
speech_model=UpperCAmelCase , speech_processor=UpperCAmelCase , vae=UpperCAmelCase , text_encoder=UpperCAmelCase , tokenizer=UpperCAmelCase , unet=UpperCAmelCase , scheduler=UpperCAmelCase , feature_extractor=UpperCAmelCase , )
def UpperCamelCase ( self , UpperCAmelCase = "auto" ):
"""simple docstring"""
if slice_size == "auto":
_UpperCAmelCase = self.unet.config.attention_head_dim // 2
self.unet.set_attention_slice(UpperCAmelCase )
def UpperCamelCase ( self ):
"""simple docstring"""
self.enable_attention_slicing(UpperCAmelCase )
@torch.no_grad()
def __call__( self , UpperCAmelCase , UpperCAmelCase=1_6000 , UpperCAmelCase = 512 , UpperCAmelCase = 512 , UpperCAmelCase = 50 , UpperCAmelCase = 7.5 , UpperCAmelCase = None , UpperCAmelCase = 1 , UpperCAmelCase = 0.0 , UpperCAmelCase = None , UpperCAmelCase = None , UpperCAmelCase = "pil" , UpperCAmelCase = True , UpperCAmelCase = None , UpperCAmelCase = 1 , **UpperCAmelCase , ):
"""simple docstring"""
_UpperCAmelCase = self.speech_processor.feature_extractor(
UpperCAmelCase , return_tensors='pt' , sampling_rate=UpperCAmelCase ).input_features.to(self.device )
_UpperCAmelCase = self.speech_model.generate(UpperCAmelCase , max_length=48_0000 )
_UpperCAmelCase = self.speech_processor.tokenizer.batch_decode(UpperCAmelCase , skip_special_tokens=UpperCAmelCase , normalize=UpperCAmelCase )[
0
]
if isinstance(UpperCAmelCase , UpperCAmelCase ):
_UpperCAmelCase = 1
elif isinstance(UpperCAmelCase , UpperCAmelCase ):
_UpperCAmelCase = len(UpperCAmelCase )
else:
raise ValueError(F"""`prompt` has to be of type `str` or `list` but is {type(UpperCAmelCase )}""" )
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(UpperCAmelCase , UpperCAmelCase ) or callback_steps <= 0)
):
raise ValueError(
F"""`callback_steps` has to be a positive integer but is {callback_steps} of type"""
F""" {type(UpperCAmelCase )}.""" )
# get prompt text embeddings
_UpperCAmelCase = self.tokenizer(
UpperCAmelCase , 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]
_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 , UpperCAmelCase , 1 )
_UpperCAmelCase = text_embeddings.view(bs_embed * num_images_per_prompt , UpperCAmelCase , -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 = [''] * batch_size
elif type(UpperCAmelCase ) is not type(UpperCAmelCase ):
raise TypeError(
F"""`negative_prompt` should be the same type to `prompt`, but got {type(UpperCAmelCase )} !="""
F""" {type(UpperCAmelCase )}.""" )
elif isinstance(UpperCAmelCase , UpperCAmelCase ):
_UpperCAmelCase = [negative_prompt]
elif batch_size != len(UpperCAmelCase ):
raise ValueError(
F"""`negative_prompt`: {negative_prompt} has batch size {len(UpperCAmelCase )}, 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(
UpperCAmelCase , padding='max_length' , max_length=UpperCAmelCase , truncation=UpperCAmelCase , 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(1 , UpperCAmelCase , 1 )
_UpperCAmelCase = uncond_embeddings.view(batch_size * num_images_per_prompt , UpperCAmelCase , -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 = text_embeddings.dtype
if latents is None:
if self.device.type == "mps":
# randn does not exist on mps
_UpperCAmelCase = torch.randn(UpperCAmelCase , generator=UpperCAmelCase , device='cpu' , dtype=UpperCAmelCase ).to(
self.device )
else:
_UpperCAmelCase = torch.randn(UpperCAmelCase , generator=UpperCAmelCase , device=self.device , dtype=UpperCAmelCase )
else:
if latents.shape != latents_shape:
raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {latents_shape}""" )
_UpperCAmelCase = latents.to(self.device )
# set timesteps
self.scheduler.set_timesteps(UpperCAmelCase )
# 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(UpperCAmelCase ) ):
# 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(UpperCAmelCase , UpperCAmelCase )
# predict the noise residual
_UpperCAmelCase = self.unet(UpperCAmelCase , UpperCAmelCase , encoder_hidden_states=UpperCAmelCase ).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(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , **UpperCAmelCase ).prev_sample
# call the callback, if provided
if callback is not None and i % callback_steps == 0:
callback(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
_UpperCAmelCase = 1 / 0.1_82_15 * latents
_UpperCAmelCase = self.vae.decode(UpperCAmelCase ).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 output_type == "pil":
_UpperCAmelCase = self.numpy_to_pil(UpperCAmelCase )
if not return_dict:
return image
return StableDiffusionPipelineOutput(images=UpperCAmelCase , nsfw_content_detected=UpperCAmelCase )
| 39
|
"""simple docstring"""
def lowercase ( __snake_case : Optional[int] ):
lowercase_ : int = 0
lowercase_ : Optional[Any] = len(__snake_case )
for i in range(n - 1 ):
for j in range(i + 1 , __snake_case ):
if arr[i] > arr[j]:
num_inversions += 1
return num_inversions
def lowercase ( __snake_case : str ):
if len(__snake_case ) <= 1:
return arr, 0
lowercase_ : Optional[Any] = len(__snake_case ) // 2
lowercase_ : List[Any] = arr[0:mid]
lowercase_ : Union[str, Any] = arr[mid:]
lowercase_ , lowercase_ : Tuple = count_inversions_recursive(__snake_case )
lowercase_ , lowercase_ : List[Any] = count_inversions_recursive(__snake_case )
lowercase_ , lowercase_ : List[Any] = _count_cross_inversions(__snake_case , __snake_case )
lowercase_ : List[Any] = inversion_p + inversions_q + cross_inversions
return c, num_inversions
def lowercase ( __snake_case : str , __snake_case : Optional[int] ):
lowercase_ : Optional[Any] = []
lowercase_ : Any = 0
while i < len(__snake_case ) and j < len(__snake_case ):
if p[i] > q[j]:
# if P[1] > Q[j], then P[k] > Q[k] for all i < k <= len(P)
# These are all inversions. The claim emerges from the
# property that P is sorted.
num_inversion += len(__snake_case ) - i
r.append(q[j] )
j += 1
else:
r.append(p[i] )
i += 1
if i < len(__snake_case ):
r.extend(p[i:] )
else:
r.extend(q[j:] )
return r, num_inversion
def lowercase ( ):
lowercase_ : Union[str, Any] = [1_0, 2, 1, 5, 5, 2, 1_1]
# this arr has 8 inversions:
# (10, 2), (10, 1), (10, 5), (10, 5), (10, 2), (2, 1), (5, 2), (5, 2)
lowercase_ : int = count_inversions_bf(__snake_case )
lowercase_ , lowercase_ : Dict = count_inversions_recursive(__snake_case )
assert num_inversions_bf == num_inversions_recursive == 8
print('''number of inversions = ''' , __snake_case )
# testing an array with zero inversion (a sorted arr_1)
arr_a.sort()
lowercase_ : Dict = count_inversions_bf(__snake_case )
lowercase_ , lowercase_ : Dict = count_inversions_recursive(__snake_case )
assert num_inversions_bf == num_inversions_recursive == 0
print('''number of inversions = ''' , __snake_case )
# an empty list should also have zero inversions
lowercase_ : List[Any] = []
lowercase_ : Any = count_inversions_bf(__snake_case )
lowercase_ , lowercase_ : List[str] = count_inversions_recursive(__snake_case )
assert num_inversions_bf == num_inversions_recursive == 0
print('''number of inversions = ''' , __snake_case )
if __name__ == "__main__":
main()
| 33
| 0
|
"""simple docstring"""
# Copyright 2023 The HuggingFace Inc. 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 torch
from ..models.speechta import SpeechTaForTextToSpeech, SpeechTaHifiGan, SpeechTaProcessor
from ..utils import is_datasets_available
from .base import PipelineTool
if is_datasets_available():
from datasets import load_dataset
class _A ( _a ):
"""simple docstring"""
UpperCAmelCase : Union[str, Any] = """microsoft/speecht5_tts"""
UpperCAmelCase : Optional[Any] = (
"""This is a tool that reads an English text out loud. It takes an input named `text` which should contain the """
"""text to read (in English) and returns a waveform object containing the sound."""
)
UpperCAmelCase : str = """text_reader"""
UpperCAmelCase : str = SpeechTaProcessor
UpperCAmelCase : Tuple = SpeechTaForTextToSpeech
UpperCAmelCase : Tuple = SpeechTaHifiGan
UpperCAmelCase : Optional[Any] = ["""text"""]
UpperCAmelCase : List[Any] = ["""audio"""]
def __snake_case ( self : Tuple):
if self.post_processor is None:
a : Tuple = "microsoft/speecht5_hifigan"
super().setup()
def __snake_case ( self : List[Any] , __UpperCAmelCase : List[Any] , __UpperCAmelCase : List[Any]=None):
a : Any = self.pre_processor(text=__UpperCAmelCase , return_tensors="pt" , truncation=__UpperCAmelCase)
if speaker_embeddings is None:
if not is_datasets_available():
raise ImportError("Datasets needs to be installed if not passing speaker embeddings.")
a : List[str] = load_dataset("Matthijs/cmu-arctic-xvectors" , split="validation")
a : Optional[int] = torch.tensor(embeddings_dataset[7305]["xvector"]).unsqueeze(0)
return {"input_ids": inputs["input_ids"], "speaker_embeddings": speaker_embeddings}
def __snake_case ( self : Union[str, Any] , __UpperCAmelCase : Optional[int]):
with torch.no_grad():
return self.model.generate_speech(**__UpperCAmelCase)
def __snake_case ( self : Tuple , __UpperCAmelCase : str):
with torch.no_grad():
return self.post_processor(__UpperCAmelCase).cpu().detach()
| 40
|
"""simple docstring"""
__A : Any = {
'''Pillow''': '''Pillow''',
'''accelerate''': '''accelerate>=0.11.0''',
'''compel''': '''compel==0.1.8''',
'''black''': '''black~=23.1''',
'''datasets''': '''datasets''',
'''filelock''': '''filelock''',
'''flax''': '''flax>=0.4.1''',
'''hf-doc-builder''': '''hf-doc-builder>=0.3.0''',
'''huggingface-hub''': '''huggingface-hub>=0.13.2''',
'''requests-mock''': '''requests-mock==1.10.0''',
'''importlib_metadata''': '''importlib_metadata''',
'''invisible-watermark''': '''invisible-watermark''',
'''isort''': '''isort>=5.5.4''',
'''jax''': '''jax>=0.2.8,!=0.3.2''',
'''jaxlib''': '''jaxlib>=0.1.65''',
'''Jinja2''': '''Jinja2''',
'''k-diffusion''': '''k-diffusion>=0.0.12''',
'''torchsde''': '''torchsde''',
'''note_seq''': '''note_seq''',
'''librosa''': '''librosa''',
'''numpy''': '''numpy''',
'''omegaconf''': '''omegaconf''',
'''parameterized''': '''parameterized''',
'''protobuf''': '''protobuf>=3.20.3,<4''',
'''pytest''': '''pytest''',
'''pytest-timeout''': '''pytest-timeout''',
'''pytest-xdist''': '''pytest-xdist''',
'''ruff''': '''ruff>=0.0.241''',
'''safetensors''': '''safetensors''',
'''sentencepiece''': '''sentencepiece>=0.1.91,!=0.1.92''',
'''scipy''': '''scipy''',
'''onnx''': '''onnx''',
'''regex''': '''regex!=2019.12.17''',
'''requests''': '''requests''',
'''tensorboard''': '''tensorboard''',
'''torch''': '''torch>=1.4''',
'''torchvision''': '''torchvision''',
'''transformers''': '''transformers>=4.25.1''',
'''urllib3''': '''urllib3<=2.0.0''',
}
| 33
| 0
|
'''simple docstring'''
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...feature_extraction_utils import FeatureExtractionMixin
from ...utils import CONFIG_NAME, FEATURE_EXTRACTOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
CONFIG_MAPPING_NAMES,
AutoConfig,
model_type_to_module_name,
replace_list_option_in_docstrings,
)
_A : Any =logging.get_logger(__name__)
_A : List[str] =OrderedDict(
[
('''audio-spectrogram-transformer''', '''ASTFeatureExtractor'''),
('''beit''', '''BeitFeatureExtractor'''),
('''chinese_clip''', '''ChineseCLIPFeatureExtractor'''),
('''clap''', '''ClapFeatureExtractor'''),
('''clip''', '''CLIPFeatureExtractor'''),
('''clipseg''', '''ViTFeatureExtractor'''),
('''conditional_detr''', '''ConditionalDetrFeatureExtractor'''),
('''convnext''', '''ConvNextFeatureExtractor'''),
('''cvt''', '''ConvNextFeatureExtractor'''),
('''data2vec-audio''', '''Wav2Vec2FeatureExtractor'''),
('''data2vec-vision''', '''BeitFeatureExtractor'''),
('''deformable_detr''', '''DeformableDetrFeatureExtractor'''),
('''deit''', '''DeiTFeatureExtractor'''),
('''detr''', '''DetrFeatureExtractor'''),
('''dinat''', '''ViTFeatureExtractor'''),
('''donut-swin''', '''DonutFeatureExtractor'''),
('''dpt''', '''DPTFeatureExtractor'''),
('''encodec''', '''EncodecFeatureExtractor'''),
('''flava''', '''FlavaFeatureExtractor'''),
('''glpn''', '''GLPNFeatureExtractor'''),
('''groupvit''', '''CLIPFeatureExtractor'''),
('''hubert''', '''Wav2Vec2FeatureExtractor'''),
('''imagegpt''', '''ImageGPTFeatureExtractor'''),
('''layoutlmv2''', '''LayoutLMv2FeatureExtractor'''),
('''layoutlmv3''', '''LayoutLMv3FeatureExtractor'''),
('''levit''', '''LevitFeatureExtractor'''),
('''maskformer''', '''MaskFormerFeatureExtractor'''),
('''mctct''', '''MCTCTFeatureExtractor'''),
('''mobilenet_v1''', '''MobileNetV1FeatureExtractor'''),
('''mobilenet_v2''', '''MobileNetV2FeatureExtractor'''),
('''mobilevit''', '''MobileViTFeatureExtractor'''),
('''nat''', '''ViTFeatureExtractor'''),
('''owlvit''', '''OwlViTFeatureExtractor'''),
('''perceiver''', '''PerceiverFeatureExtractor'''),
('''poolformer''', '''PoolFormerFeatureExtractor'''),
('''regnet''', '''ConvNextFeatureExtractor'''),
('''resnet''', '''ConvNextFeatureExtractor'''),
('''segformer''', '''SegformerFeatureExtractor'''),
('''sew''', '''Wav2Vec2FeatureExtractor'''),
('''sew-d''', '''Wav2Vec2FeatureExtractor'''),
('''speech_to_text''', '''Speech2TextFeatureExtractor'''),
('''speecht5''', '''SpeechT5FeatureExtractor'''),
('''swiftformer''', '''ViTFeatureExtractor'''),
('''swin''', '''ViTFeatureExtractor'''),
('''swinv2''', '''ViTFeatureExtractor'''),
('''table-transformer''', '''DetrFeatureExtractor'''),
('''timesformer''', '''VideoMAEFeatureExtractor'''),
('''tvlt''', '''TvltFeatureExtractor'''),
('''unispeech''', '''Wav2Vec2FeatureExtractor'''),
('''unispeech-sat''', '''Wav2Vec2FeatureExtractor'''),
('''van''', '''ConvNextFeatureExtractor'''),
('''videomae''', '''VideoMAEFeatureExtractor'''),
('''vilt''', '''ViltFeatureExtractor'''),
('''vit''', '''ViTFeatureExtractor'''),
('''vit_mae''', '''ViTFeatureExtractor'''),
('''vit_msn''', '''ViTFeatureExtractor'''),
('''wav2vec2''', '''Wav2Vec2FeatureExtractor'''),
('''wav2vec2-conformer''', '''Wav2Vec2FeatureExtractor'''),
('''wavlm''', '''Wav2Vec2FeatureExtractor'''),
('''whisper''', '''WhisperFeatureExtractor'''),
('''xclip''', '''CLIPFeatureExtractor'''),
('''yolos''', '''YolosFeatureExtractor'''),
]
)
_A : Dict =_LazyAutoMapping(CONFIG_MAPPING_NAMES, FEATURE_EXTRACTOR_MAPPING_NAMES)
def SCREAMING_SNAKE_CASE_ (UpperCamelCase ) -> str:
for module_name, extractors in FEATURE_EXTRACTOR_MAPPING_NAMES.items():
if class_name in extractors:
lowerCamelCase__ : List[Any] = model_type_to_module_name(UpperCamelCase )
lowerCamelCase__ : Any = importlib.import_module(f'''.{module_name}''' , """transformers.models""" )
try:
return getattr(UpperCamelCase , UpperCamelCase )
except AttributeError:
continue
for _, extractor in FEATURE_EXTRACTOR_MAPPING._extra_content.items():
if getattr(UpperCamelCase , """__name__""" , UpperCamelCase ) == class_name:
return extractor
# We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
# init and we return the proper dummy to get an appropriate error message.
lowerCamelCase__ : List[Any] = importlib.import_module("""transformers""" )
if hasattr(UpperCamelCase , UpperCamelCase ):
return getattr(UpperCamelCase , UpperCamelCase )
return None
def SCREAMING_SNAKE_CASE_ (UpperCamelCase , UpperCamelCase = None , UpperCamelCase = False , UpperCamelCase = False , UpperCamelCase = None , UpperCamelCase = None , UpperCamelCase = None , UpperCamelCase = False , **UpperCamelCase , ) -> Optional[Any]:
lowerCamelCase__ : Optional[Any] = get_file_from_repo(
UpperCamelCase , UpperCamelCase , cache_dir=UpperCamelCase , force_download=UpperCamelCase , resume_download=UpperCamelCase , proxies=UpperCamelCase , use_auth_token=UpperCamelCase , revision=UpperCamelCase , local_files_only=UpperCamelCase , )
if resolved_config_file is None:
logger.info(
"""Could not locate the feature extractor configuration file, will try to use the model config instead.""" )
return {}
with open(UpperCamelCase , encoding="""utf-8""" ) as reader:
return json.load(UpperCamelCase )
class _lowercase :
def __init__( self: Optional[int] ):
raise EnvironmentError(
"""AutoFeatureExtractor is designed to be instantiated """
"""using the `AutoFeatureExtractor.from_pretrained(pretrained_model_name_or_path)` method.""" )
@classmethod
@replace_list_option_in_docstrings(UpperCamelCase__ )
def lowerCamelCase_ ( cls: List[Any] , UpperCamelCase__: int , **UpperCamelCase__: str ):
lowerCamelCase__ : List[Any] = kwargs.pop("""config""" , UpperCamelCase__ )
lowerCamelCase__ : int = kwargs.pop("""trust_remote_code""" , UpperCamelCase__ )
lowerCamelCase__ : Dict = True
lowerCamelCase__ , lowerCamelCase__ : Optional[Any] = FeatureExtractionMixin.get_feature_extractor_dict(UpperCamelCase__ , **UpperCamelCase__ )
lowerCamelCase__ : List[str] = config_dict.get("""feature_extractor_type""" , UpperCamelCase__ )
lowerCamelCase__ : Tuple = None
if "AutoFeatureExtractor" in config_dict.get("""auto_map""" , {} ):
lowerCamelCase__ : Any = config_dict["""auto_map"""]["""AutoFeatureExtractor"""]
# If we don't find the feature extractor class in the feature extractor config, let's try the model config.
if feature_extractor_class is None and feature_extractor_auto_map is None:
if not isinstance(UpperCamelCase__ , UpperCamelCase__ ):
lowerCamelCase__ : List[str] = AutoConfig.from_pretrained(UpperCamelCase__ , **UpperCamelCase__ )
# It could be in `config.feature_extractor_type``
lowerCamelCase__ : Tuple = getattr(UpperCamelCase__ , """feature_extractor_type""" , UpperCamelCase__ )
if hasattr(UpperCamelCase__ , """auto_map""" ) and "AutoFeatureExtractor" in config.auto_map:
lowerCamelCase__ : Union[str, Any] = config.auto_map["""AutoFeatureExtractor"""]
if feature_extractor_class is not None:
lowerCamelCase__ : List[Any] = feature_extractor_class_from_name(UpperCamelCase__ )
lowerCamelCase__ : str = feature_extractor_auto_map is not None
lowerCamelCase__ : Optional[Any] = feature_extractor_class is not None or type(UpperCamelCase__ ) in FEATURE_EXTRACTOR_MAPPING
lowerCamelCase__ : Optional[Any] = resolve_trust_remote_code(
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
if has_remote_code and trust_remote_code:
lowerCamelCase__ : str = get_class_from_dynamic_module(
UpperCamelCase__ , UpperCamelCase__ , **UpperCamelCase__ )
lowerCamelCase__ : str = kwargs.pop("""code_revision""" , UpperCamelCase__ )
if os.path.isdir(UpperCamelCase__ ):
feature_extractor_class.register_for_auto_class()
return feature_extractor_class.from_dict(UpperCamelCase__ , **UpperCamelCase__ )
elif feature_extractor_class is not None:
return feature_extractor_class.from_dict(UpperCamelCase__ , **UpperCamelCase__ )
# Last try: we use the FEATURE_EXTRACTOR_MAPPING.
elif type(UpperCamelCase__ ) in FEATURE_EXTRACTOR_MAPPING:
lowerCamelCase__ : int = FEATURE_EXTRACTOR_MAPPING[type(UpperCamelCase__ )]
return feature_extractor_class.from_dict(UpperCamelCase__ , **UpperCamelCase__ )
raise ValueError(
F'''Unrecognized feature extractor in {pretrained_model_name_or_path}. Should have a '''
F'''`feature_extractor_type` key in its {FEATURE_EXTRACTOR_NAME} of {CONFIG_NAME}, or one of the following '''
F'''`model_type` keys in its {CONFIG_NAME}: {', '.join(c for c in FEATURE_EXTRACTOR_MAPPING_NAMES.keys() )}''' )
@staticmethod
def lowerCamelCase_ ( UpperCamelCase__: Optional[Any] , UpperCamelCase__: Optional[Any] ):
FEATURE_EXTRACTOR_MAPPING.register(UpperCamelCase__ , UpperCamelCase__ )
| 41
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
__A : List[Any] = {
'''configuration_mega''': ['''MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MegaConfig''', '''MegaOnnxConfig'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : List[str] = [
'''MEGA_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''MegaForCausalLM''',
'''MegaForMaskedLM''',
'''MegaForMultipleChoice''',
'''MegaForQuestionAnswering''',
'''MegaForSequenceClassification''',
'''MegaForTokenClassification''',
'''MegaModel''',
'''MegaPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_mega import MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP, MegaConfig, MegaOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mega import (
MEGA_PRETRAINED_MODEL_ARCHIVE_LIST,
MegaForCausalLM,
MegaForMaskedLM,
MegaForMultipleChoice,
MegaForQuestionAnswering,
MegaForSequenceClassification,
MegaForTokenClassification,
MegaModel,
MegaPreTrainedModel,
)
else:
import sys
__A : Any = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 33
| 0
|
'''simple docstring'''
from typing import Dict, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import flip_channel_order, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_pytesseract_available, is_vision_available, logging, requires_backends
if is_vision_available():
import PIL
# soft dependency
if is_pytesseract_available():
import pytesseract
lowercase : Dict = logging.get_logger(__name__)
def SCREAMING_SNAKE_CASE__ ( __A , __A , __A ) -> Union[str, Any]:
return [
int(1_000 * (box[0] / width) ),
int(1_000 * (box[1] / height) ),
int(1_000 * (box[2] / width) ),
int(1_000 * (box[3] / height) ),
]
def SCREAMING_SNAKE_CASE__ ( __A , __A , __A = None ) -> Any:
_snake_case = tesseract_config if tesseract_config is not None else ''
# apply OCR
_snake_case = to_pil_image(__A )
_snake_case , _snake_case = pil_image.size
_snake_case = pytesseract.image_to_data(__A , lang=__A , output_type='dict' , config=__A )
_snake_case , _snake_case , _snake_case , _snake_case , _snake_case = data['text'], data['left'], data['top'], data['width'], data['height']
# filter empty words and corresponding coordinates
_snake_case = [idx for idx, word in enumerate(__A ) if not word.strip()]
_snake_case = [word for idx, word in enumerate(__A ) if idx not in irrelevant_indices]
_snake_case = [coord for idx, coord in enumerate(__A ) if idx not in irrelevant_indices]
_snake_case = [coord for idx, coord in enumerate(__A ) if idx not in irrelevant_indices]
_snake_case = [coord for idx, coord in enumerate(__A ) if idx not in irrelevant_indices]
_snake_case = [coord for idx, coord in enumerate(__A ) if idx not in irrelevant_indices]
# turn coordinates into (left, top, left+width, top+height) format
_snake_case = []
for x, y, w, h in zip(__A , __A , __A , __A ):
_snake_case = [x, y, x + w, y + h]
actual_boxes.append(__A )
# finally, normalize the bounding boxes
_snake_case = []
for box in actual_boxes:
normalized_boxes.append(normalize_box(__A , __A , __A ) )
assert len(__A ) == len(__A ), "Not as many words as there are bounding boxes"
return words, normalized_boxes
class __UpperCAmelCase ( _lowerCamelCase ):
__lowercase = ["""pixel_values"""]
def __init__( self , lowerCAmelCase_ = True , lowerCAmelCase_ = None , lowerCAmelCase_ = PILImageResampling.BILINEAR , lowerCAmelCase_ = True , lowerCAmelCase_ = None , lowerCAmelCase_ = "" , **lowerCAmelCase_ , ):
"""simple docstring"""
super().__init__(**lowerCAmelCase_ )
_snake_case = size if size is not None else {'height': 2_24, 'width': 2_24}
_snake_case = get_size_dict(lowerCAmelCase_ )
_snake_case = do_resize
_snake_case = size
_snake_case = resample
_snake_case = apply_ocr
_snake_case = ocr_lang
_snake_case = tesseract_config
def lowerCamelCase ( self , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ = PILImageResampling.BILINEAR , lowerCAmelCase_ = None , **lowerCAmelCase_ , ):
"""simple docstring"""
_snake_case = 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()}' )
_snake_case = (size['height'], size['width'])
return resize(lowerCAmelCase_ , size=lowerCAmelCase_ , resample=lowerCAmelCase_ , data_format=lowerCAmelCase_ , **lowerCAmelCase_ )
def lowerCamelCase ( self , lowerCAmelCase_ , lowerCAmelCase_ = None , lowerCAmelCase_ = None , lowerCAmelCase_ = None , lowerCAmelCase_ = None , lowerCAmelCase_ = None , lowerCAmelCase_ = None , lowerCAmelCase_ = None , lowerCAmelCase_ = ChannelDimension.FIRST , **lowerCAmelCase_ , ):
"""simple docstring"""
_snake_case = do_resize if do_resize is not None else self.do_resize
_snake_case = size if size is not None else self.size
_snake_case = get_size_dict(lowerCAmelCase_ )
_snake_case = resample if resample is not None else self.resample
_snake_case = apply_ocr if apply_ocr is not None else self.apply_ocr
_snake_case = ocr_lang if ocr_lang is not None else self.ocr_lang
_snake_case = tesseract_config if tesseract_config is not None else self.tesseract_config
_snake_case = 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.' )
# All transformations expect numpy arrays.
_snake_case = [to_numpy_array(lowerCAmelCase_ ) for image in images]
if apply_ocr:
requires_backends(self , 'pytesseract' )
_snake_case = []
_snake_case = []
for image in images:
_snake_case , _snake_case = apply_tesseract(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )
words_batch.append(lowerCAmelCase_ )
boxes_batch.append(lowerCAmelCase_ )
if do_resize:
_snake_case = [self.resize(image=lowerCAmelCase_ , size=lowerCAmelCase_ , resample=lowerCAmelCase_ ) for image in images]
# flip color channels from RGB to BGR (as Detectron2 requires this)
_snake_case = [flip_channel_order(lowerCAmelCase_ ) for image in images]
_snake_case = [to_channel_dimension_format(lowerCAmelCase_ , lowerCAmelCase_ ) for image in images]
_snake_case = BatchFeature(data={'pixel_values': images} , tensor_type=lowerCAmelCase_ )
if apply_ocr:
_snake_case = words_batch
_snake_case = boxes_batch
return data
| 42
|
"""simple docstring"""
import argparse
import os
import re
import packaging.version
__A : List[str] = '''examples/'''
__A : int = {
'''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'''),
}
__A : Dict = {
'''init''': '''src/transformers/__init__.py''',
'''setup''': '''setup.py''',
}
__A : Optional[int] = '''README.md'''
def lowercase ( __snake_case : int , __snake_case : Any , __snake_case : int ):
with open(__snake_case , '''r''' , encoding='''utf-8''' , newline='''\n''' ) as f:
lowercase_ : int = f.read()
lowercase_ , lowercase_ : List[str] = REPLACE_PATTERNS[pattern]
lowercase_ : Union[str, Any] = replace.replace('''VERSION''' , __snake_case )
lowercase_ : Optional[Any] = re_pattern.sub(__snake_case , __snake_case )
with open(__snake_case , '''w''' , encoding='''utf-8''' , newline='''\n''' ) as f:
f.write(__snake_case )
def lowercase ( __snake_case : int ):
for folder, directories, fnames in os.walk(__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(__snake_case , __snake_case ) , __snake_case , pattern='''examples''' )
def lowercase ( __snake_case : Optional[Any] , __snake_case : Optional[Any]=False ):
for pattern, fname in REPLACE_FILES.items():
update_version_in_file(__snake_case , __snake_case , __snake_case )
if not patch:
update_version_in_examples(__snake_case )
def lowercase ( ):
lowercase_ : Union[str, Any] = '''🤗 Transformers currently provides the following architectures'''
lowercase_ : Union[str, Any] = '''1. Want to contribute a new model?'''
with open(__snake_case , '''r''' , encoding='''utf-8''' , newline='''\n''' ) as f:
lowercase_ : List[str] = f.readlines()
# Find the start of the list.
lowercase_ : Optional[Any] = 0
while not lines[start_index].startswith(_start_prompt ):
start_index += 1
start_index += 1
lowercase_ : str = start_index
# Update the lines in the model list.
while not lines[index].startswith(_end_prompt ):
if lines[index].startswith('''1.''' ):
lowercase_ : str = lines[index].replace(
'''https://huggingface.co/docs/transformers/main/model_doc''' , '''https://huggingface.co/docs/transformers/model_doc''' , )
index += 1
with open(__snake_case , '''w''' , encoding='''utf-8''' , newline='''\n''' ) as f:
f.writelines(__snake_case )
def lowercase ( ):
with open(REPLACE_FILES['''init'''] , '''r''' ) as f:
lowercase_ : List[Any] = f.read()
lowercase_ : List[str] = REPLACE_PATTERNS['''init'''][0].search(__snake_case ).groups()[0]
return packaging.version.parse(__snake_case )
def lowercase ( __snake_case : Optional[Any]=False ):
lowercase_ : str = 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:
lowercase_ : Optional[Any] = default_version.base_version
elif patch:
lowercase_ : Optional[int] = F'''{default_version.major}.{default_version.minor}.{default_version.micro + 1}'''
else:
lowercase_ : Optional[int] = F'''{default_version.major}.{default_version.minor + 1}.0'''
# Now let's ask nicely if that's the right one.
lowercase_ : int = input(F'''Which version are you releasing? [{default_version}]''' )
if len(__snake_case ) == 0:
lowercase_ : Dict = default_version
print(F'''Updating version to {version}.''' )
global_version_update(__snake_case , patch=__snake_case )
if not patch:
print('''Cleaning main README, don\'t forget to run `make fix-copies`.''' )
clean_main_ref_in_model_list()
def lowercase ( ):
lowercase_ : List[Any] = get_version()
lowercase_ : List[str] = F'''{current_version.major}.{current_version.minor + 1}.0.dev0'''
lowercase_ : Any = current_version.base_version
# Check with the user we got that right.
lowercase_ : Tuple = input(F'''Which version are we developing now? [{dev_version}]''' )
if len(__snake_case ) == 0:
lowercase_ : str = dev_version
print(F'''Updating version to {version}.''' )
global_version_update(__snake_case )
print('''Cleaning main README, don\'t forget to run `make fix-copies`.''' )
clean_main_ref_in_model_list()
if __name__ == "__main__":
__A : int = 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.''')
__A : Any = 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()
| 33
| 0
|
import json
import multiprocessing
import os
import re
from collections import defaultdict
import torch
from accelerate import Accelerator
from accelerate.utils import set_seed
from arguments import HumanEvalArguments
from datasets import load_dataset, load_metric
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from tqdm import tqdm
import transformers
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, StoppingCriteria, StoppingCriteriaList
__lowercase = ['''\nclass''', '''\ndef''', '''\n#''', '''\n@''', '''\nprint''', '''\nif''']
class lowerCamelCase_ ( UpperCAmelCase_ ):
'''simple docstring'''
def __init__( self , __lowercase , __lowercase , __lowercase=None , __lowercase=1) -> List[Any]:
__UpperCamelCase :List[Any] = tokenizer
__UpperCamelCase :Tuple = dataset
__UpperCamelCase :Optional[Any] = len(__lowercase) if n_tasks is None else n_tasks
__UpperCamelCase :List[Any] = n_copies
def __iter__( self) -> List[Any]:
__UpperCamelCase :Tuple = []
for task in range(self.n_tasks):
# without strip, the model generate commented codes ...
prompts.append(self.tokenizer.eos_token + self.dataset[task]['''prompt'''].strip())
__UpperCamelCase :List[str] = self.tokenizer(__lowercase , padding=__lowercase , return_tensors='''pt''')
for task in range(self.n_tasks):
for _ in range(self.n_copies):
yield {
"ids": outputs.input_ids[task],
"task_id": task,
"input_len": outputs.attention_mask[task].sum(),
}
class lowerCamelCase_ ( UpperCAmelCase_ ):
'''simple docstring'''
def __init__( self , __lowercase , __lowercase , __lowercase) -> Any:
__UpperCamelCase :Optional[Any] = start_length
__UpperCamelCase :Any = eof_strings
__UpperCamelCase :int = tokenizer
def __call__( self , __lowercase , __lowercase , **__lowercase) -> Tuple:
__UpperCamelCase :Union[str, Any] = self.tokenizer.batch_decode(input_ids[:, self.start_length :])
__UpperCamelCase :Tuple = []
for decoded_generation in decoded_generations:
done.append(any(stop_string in decoded_generation for stop_string in self.eof_strings))
return all(__lowercase)
def lowerCamelCase ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__UpperCamelCase :Dict = re.split('''(%s)''' % '''|'''.join(SCREAMING_SNAKE_CASE ) , SCREAMING_SNAKE_CASE )
# last string should be ""
return "".join(string_list[:-2] )
def lowerCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE=20 , **SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__UpperCamelCase :Dict = defaultdict(SCREAMING_SNAKE_CASE ) # dict of list of generated tokens
for step, batch in tqdm(enumerate(SCREAMING_SNAKE_CASE ) ):
with torch.no_grad():
__UpperCamelCase :str = batch['''ids'''].shape[-1]
__UpperCamelCase :List[str] = accelerator.unwrap_model(SCREAMING_SNAKE_CASE ).generate(
input_ids=batch['''ids'''][:, : batch['''input_len''']] , num_return_sequences=SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE )
# each task is generated batch_size times
__UpperCamelCase :int = batch['''task_id'''].repeat(SCREAMING_SNAKE_CASE )
__UpperCamelCase :List[str] = accelerator.pad_across_processes(
SCREAMING_SNAKE_CASE , dim=1 , pad_index=tokenizer.pad_token_id )
__UpperCamelCase , __UpperCamelCase :Union[str, Any] = accelerator.gather((generated_tokens, generated_tasks) )
__UpperCamelCase :Tuple = generated_tokens.cpu().numpy()
__UpperCamelCase :Dict = generated_tasks.cpu().numpy()
for task, generated_tokens in zip(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
gen_token_dict[task].append(SCREAMING_SNAKE_CASE )
__UpperCamelCase :str = [[] for _ in range(SCREAMING_SNAKE_CASE )]
for task, generated_tokens in gen_token_dict.items():
for s in generated_tokens:
__UpperCamelCase :Any = tokenizer.decode(SCREAMING_SNAKE_CASE , skip_special_tokens=SCREAMING_SNAKE_CASE , clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE )
code_gens[task].append(remove_last_block(SCREAMING_SNAKE_CASE ) )
return code_gens
def lowerCamelCase ( ):
'''simple docstring'''
__UpperCamelCase :Optional[Any] = HfArgumentParser(SCREAMING_SNAKE_CASE )
__UpperCamelCase :Any = parser.parse_args()
transformers.logging.set_verbosity_error()
# enables code execution in code_eval metric
__UpperCamelCase :Union[str, Any] = args.HF_ALLOW_CODE_EVAL
# make sure tokenizer plays nice with multiprocessing
__UpperCamelCase :Optional[int] = '''false'''
if args.num_workers is None:
__UpperCamelCase :str = multiprocessing.cpu_count()
# Use dataset load to feed to accelerate
__UpperCamelCase :int = Accelerator()
set_seed(args.seed , device_specific=SCREAMING_SNAKE_CASE )
# Load model and tokenizer
__UpperCamelCase :int = AutoTokenizer.from_pretrained(args.model_ckpt )
__UpperCamelCase :Dict = tokenizer.eos_token
__UpperCamelCase :int = AutoModelForCausalLM.from_pretrained(args.model_ckpt )
# Generation settings
__UpperCamelCase :Union[str, Any] = {
'''do_sample''': args.do_sample,
'''temperature''': args.temperature,
'''max_new_tokens''': args.max_new_tokens,
'''top_p''': args.top_p,
'''top_k''': args.top_k,
'''stopping_criteria''': StoppingCriteriaList([EndOfFunctionCriteria(0 , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )] ),
}
# Load evaluation dataset and metric
__UpperCamelCase :Union[str, Any] = load_dataset('''openai_humaneval''' )
__UpperCamelCase :List[Any] = load_metric('''code_eval''' )
__UpperCamelCase :str = args.num_tasks if args.num_tasks is not None else len(human_eval['''test'''] )
__UpperCamelCase :int = args.n_samples // args.batch_size
__UpperCamelCase :int = TokenizedDataset(SCREAMING_SNAKE_CASE , human_eval['''test'''] , n_copies=SCREAMING_SNAKE_CASE , n_tasks=SCREAMING_SNAKE_CASE )
# do not confuse args.batch_size, which is actually the num_return_sequences
__UpperCamelCase :Optional[Any] = DataLoader(SCREAMING_SNAKE_CASE , batch_size=1 )
# Run a quick test to see if code evaluation is enabled
try:
__UpperCamelCase :int = code_eval_metric.compute(references=[''''''] , predictions=[['''''']] )
except ValueError as exception:
print(
'''Code evaluation not enabled. Read the warning below carefully and then use `--HF_ALLOW_CODE_EVAL="1"`'''
''' flag to enable code evaluation.''' )
raise exception
__UpperCamelCase , __UpperCamelCase :List[Any] = accelerator.prepare(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
__UpperCamelCase :Any = complete_code(
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , n_tasks=SCREAMING_SNAKE_CASE , batch_size=args.batch_size , **SCREAMING_SNAKE_CASE , )
if accelerator.is_main_process:
__UpperCamelCase :Optional[int] = []
for task in tqdm(range(SCREAMING_SNAKE_CASE ) ):
__UpperCamelCase :List[Any] = human_eval['''test'''][task]['''test''']
__UpperCamelCase :Any = f"""check({human_eval['test'][task]['entry_point']})"""
references.append('''\n''' + test_func + '''\n''' + entry_point )
# Evaluate completions with "code_eval" metric
__UpperCamelCase , __UpperCamelCase :Optional[Any] = code_eval_metric.compute(
references=SCREAMING_SNAKE_CASE , predictions=SCREAMING_SNAKE_CASE , num_workers=args.num_workers )
print(f"""Results: {pass_at_k}""" )
# Save results to json file
with open(args.output_file , '''w''' ) as fp:
json.dump(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
# For some reason the folliwng seems to be necessary sometimes for code_eval to work nice with multiprocessing
# https://stackoverflow.com/questions/60804599/python-multiprocessing-keeps-spawning-the-whole-script
if __name__ == "__main__":
main()
| 43
|
"""simple docstring"""
from typing import Optional
from urllib.parse import quote
import huggingface_hub as hfh
from packaging import version
def lowercase ( __snake_case : str , __snake_case : str , __snake_case : Optional[str] = None ):
if version.parse(hfh.__version__ ).release < version.parse('''0.11.0''' ).release:
# old versions of hfh don't url-encode the file path
lowercase_ : Union[str, Any] = quote(__snake_case )
return hfh.hf_hub_url(__snake_case , __snake_case , repo_type='''dataset''' , revision=__snake_case )
| 33
| 0
|
"""simple docstring"""
import argparse
import torch
from torch import nn
from transformers import MBartConfig, MBartForConditionalGeneration
def SCREAMING_SNAKE_CASE ( _lowerCamelCase : Tuple ) -> Dict:
_lowerCAmelCase : str = [
"""encoder.version""",
"""decoder.version""",
"""model.encoder.version""",
"""model.decoder.version""",
"""_float_tensor""",
"""decoder.output_projection.weight""",
]
for k in ignore_keys:
state_dict.pop(_lowerCamelCase ,_lowerCamelCase )
def SCREAMING_SNAKE_CASE ( _lowerCamelCase : Tuple ) -> Union[str, Any]:
_lowerCAmelCase , _lowerCAmelCase : List[Any] = emb.weight.shape
_lowerCAmelCase : Any = nn.Linear(_lowerCamelCase ,_lowerCamelCase ,bias=_lowerCamelCase )
_lowerCAmelCase : Optional[Any] = emb.weight.data
return lin_layer
def SCREAMING_SNAKE_CASE ( _lowerCamelCase : Dict ,_lowerCamelCase : Any="facebook/mbart-large-en-ro" ,_lowerCamelCase : List[Any]=False ,_lowerCamelCase : Any=False ) -> int:
_lowerCAmelCase : Dict = torch.load(_lowerCamelCase ,map_location="""cpu""" )["""model"""]
remove_ignore_keys_(_lowerCamelCase )
_lowerCAmelCase : List[str] = state_dict["""encoder.embed_tokens.weight"""].shape[0]
_lowerCAmelCase : Any = MBartConfig.from_pretrained(_lowerCamelCase ,vocab_size=_lowerCamelCase )
if mbart_aa and finetuned:
_lowerCAmelCase : Any = """relu"""
_lowerCAmelCase : Tuple = state_dict["""decoder.embed_tokens.weight"""]
_lowerCAmelCase : Optional[int] = MBartForConditionalGeneration(_lowerCamelCase )
model.model.load_state_dict(_lowerCamelCase )
if finetuned:
_lowerCAmelCase : str = make_linear_from_emb(model.model.shared )
return model
if __name__ == "__main__":
_a : Optional[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'fairseq_path', type=str, help='bart.large, bart.large.cnn or a path to a model.pt on local filesystem.'
)
parser.add_argument('pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
parser.add_argument(
'--hf_config',
default='facebook/mbart-large-cc25',
type=str,
help='Which huggingface architecture to use: mbart-large',
)
parser.add_argument('--mbart_50', action='store_true', help='whether the model is mMART-50 checkpoint')
parser.add_argument('--finetuned', action='store_true', help='whether the model is a fine-tuned checkpoint')
_a : Optional[Any] = parser.parse_args()
_a : List[str] = convert_fairseq_mbart_checkpoint_from_disk(
args.fairseq_path, hf_config_path=args.hf_config, finetuned=args.finetuned, mbart_aa=args.mbart_aa
)
model.save_pretrained(args.pytorch_dump_folder_path)
| 44
|
"""simple docstring"""
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFResNetForImageClassification, TFResNetModel
from transformers.models.resnet.modeling_tf_resnet import TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class _UpperCAmelCase :
def __init__( self : int , A : Tuple , A : int=3 , A : List[str]=32 , A : Dict=3 , A : Any=10 , A : Dict=[10, 20, 30, 40] , A : Optional[Any]=[1, 1, 2, 1] , A : Union[str, Any]=True , A : Optional[Any]=True , A : Any="relu" , A : Optional[Any]=3 , A : Tuple=None , ) -> Dict:
lowercase_ : str = parent
lowercase_ : List[Any] = batch_size
lowercase_ : Optional[int] = image_size
lowercase_ : int = num_channels
lowercase_ : int = embeddings_size
lowercase_ : str = hidden_sizes
lowercase_ : List[str] = depths
lowercase_ : Dict = is_training
lowercase_ : int = use_labels
lowercase_ : Any = hidden_act
lowercase_ : List[Any] = num_labels
lowercase_ : Tuple = scope
lowercase_ : Optional[Any] = len(A )
def A ( self : str ) -> Tuple:
lowercase_ : Optional[int] = 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[str] = ids_tensor([self.batch_size] , self.num_labels )
lowercase_ : Optional[int] = self.get_config()
return config, pixel_values, labels
def A ( self : Dict ) -> int:
return ResNetConfig(
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 , image_size=self.image_size , )
def A ( self : str , A : Tuple , A : str , A : str ) -> str:
lowercase_ : str = TFResNetModel(config=A )
lowercase_ : Union[str, Any] = model(A )
# expected last hidden states: B, C, H // 32, W // 32
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32) , )
def A ( self : Any , A : int , A : List[Any] , A : Optional[Any] ) -> Optional[Any]:
lowercase_ : Tuple = self.num_labels
lowercase_ : Union[str, Any] = TFResNetForImageClassification(A )
lowercase_ : Tuple = model(A , labels=A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def A ( self : Union[str, Any] ) -> Tuple:
lowercase_ : Tuple = self.prepare_config_and_inputs()
lowercase_ , lowercase_ , lowercase_ : Dict = config_and_inputs
lowercase_ : int = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_tf
class _UpperCAmelCase ( _A , _A , unittest.TestCase ):
SCREAMING_SNAKE_CASE_ : Union[str, Any] = (TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
SCREAMING_SNAKE_CASE_ : List[Any] = (
{"feature-extraction": TFResNetModel, "image-classification": TFResNetForImageClassification}
if is_tf_available()
else {}
)
SCREAMING_SNAKE_CASE_ : Optional[int] = False
SCREAMING_SNAKE_CASE_ : Optional[int] = False
SCREAMING_SNAKE_CASE_ : str = False
SCREAMING_SNAKE_CASE_ : Optional[int] = False
SCREAMING_SNAKE_CASE_ : Any = False
def A ( self : Union[str, Any] ) -> List[Any]:
lowercase_ : int = TFResNetModelTester(self )
lowercase_ : str = ConfigTester(self , config_class=A , has_text_modality=A )
def A ( self : Dict ) -> Optional[Any]:
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 A ( self : Dict ) -> List[Any]:
return
@unittest.skip(reason='''ResNet does not use inputs_embeds''' )
def A ( self : Any ) -> Any:
pass
@unittest.skip(reason='''ResNet does not support input and output embeddings''' )
def A ( self : List[str] ) -> Optional[Any]:
pass
def A ( self : str ) -> Tuple:
lowercase_ , lowercase_ : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase_ : int = model_class(A )
lowercase_ : str = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowercase_ : str = [*signature.parameters.keys()]
lowercase_ : str = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , A )
def A ( self : List[str] ) -> Tuple:
lowercase_ : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*A )
def A ( self : List[Any] ) -> List[str]:
def check_hidden_states_output(A : Union[str, Any] , A : int , A : List[Any] ):
lowercase_ : int = model_class(A )
lowercase_ : Optional[Any] = model(**self._prepare_for_class(A , A ) )
lowercase_ : Union[str, Any] = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
lowercase_ : Any = self.model_tester.num_stages
self.assertEqual(len(A ) , expected_num_stages + 1 )
# ResNet'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] , )
lowercase_ , lowercase_ : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
lowercase_ : Union[str, Any] = ['''basic''', '''bottleneck''']
for model_class in self.all_model_classes:
for layer_type in layers_type:
lowercase_ : List[str] = layer_type
lowercase_ : Tuple = True
check_hidden_states_output(A , A , A )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
lowercase_ : Optional[Any] = True
check_hidden_states_output(A , A , A )
def A ( self : Optional[int] ) -> Tuple:
lowercase_ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*A )
@slow
def A ( self : List[str] ) -> Optional[int]:
for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase_ : Tuple = TFResNetModel.from_pretrained(A )
self.assertIsNotNone(A )
def lowercase ( ):
lowercase_ : List[str] = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_tf
@require_vision
class _UpperCAmelCase ( unittest.TestCase ):
@cached_property
def A ( self : Any ) -> Optional[int]:
return (
AutoImageProcessor.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
if is_vision_available()
else None
)
@slow
def A ( self : Any ) -> Optional[int]:
lowercase_ : Optional[int] = TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
lowercase_ : List[Any] = self.default_image_processor
lowercase_ : Dict = prepare_img()
lowercase_ : List[str] = image_processor(images=A , return_tensors='''tf''' )
# forward pass
lowercase_ : Tuple = model(**A )
# verify the logits
lowercase_ : Optional[int] = tf.TensorShape((1, 10_00) )
self.assertEqual(outputs.logits.shape , A )
lowercase_ : Optional[Any] = tf.constant([-11.1069, -9.7877, -8.3777] )
self.assertTrue(np.allclose(outputs.logits[0, :3].numpy() , A , atol=1e-4 ) )
| 33
| 0
|
"""simple docstring"""
from collections.abc import Sequence
def lowercase ( lowerCAmelCase__ : Sequence[int] | None = None ) -> int:
if nums is None or not nums:
raise ValueError('''Input sequence should not be empty''' )
__a = nums[0]
for i in range(1 , len(lowerCAmelCase__ ) ):
__a = nums[i]
__a = max(lowerCAmelCase__ , ans + num , lowerCAmelCase__ )
return ans
if __name__ == "__main__":
import doctest
doctest.testmod()
# Try on a sample input from the user
lowercase_ = int(input("Enter number of elements : ").strip())
lowercase_ = list(map(int, input("\nEnter the numbers : ").strip().split()))[:n]
print(max_subsequence_sum(array))
| 45
|
"""simple docstring"""
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
__A : Dict = '''
Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that developed a chatbot app targeted at teenagers.[2] After open-sourcing the model behind the chatbot, the company pivoted to focus on being a platform for machine learning.
In March 2021, Hugging Face raised $40 million in a Series B funding round.[3]
On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model.[4] In 2022, the workshop concluded with the announcement of BLOOM, a multilingual large language model with 176 billion parameters.[5]
'''
class _UpperCAmelCase ( unittest.TestCase , _A ):
def A ( self : List[Any] ) -> Dict:
lowercase_ : Optional[int] = load_tool('''text-question-answering''' )
self.tool.setup()
lowercase_ : Union[str, Any] = load_tool('''text-question-answering''' , remote=A )
def A ( self : Any ) -> List[str]:
lowercase_ : Union[str, Any] = self.tool(A , '''What did Hugging Face do in April 2021?''' )
self.assertEqual(A , '''launched the BigScience Research Workshop''' )
def A ( self : str ) -> List[str]:
lowercase_ : int = self.remote_tool(A , '''What did Hugging Face do in April 2021?''' )
self.assertEqual(A , '''launched the BigScience Research Workshop''' )
def A ( self : List[Any] ) -> int:
lowercase_ : Optional[Any] = self.tool(text=A , question='''What did Hugging Face do in April 2021?''' )
self.assertEqual(A , '''launched the BigScience Research Workshop''' )
def A ( self : List[str] ) -> Optional[int]:
lowercase_ : int = self.remote_tool(text=A , question='''What did Hugging Face do in April 2021?''' )
self.assertEqual(A , '''launched the BigScience Research Workshop''' )
| 33
| 0
|
"""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
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ = {"vocab_file": "spiece.model"}
SCREAMING_SNAKE_CASE__ = {
"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",
}
}
SCREAMING_SNAKE_CASE__ = {
"AI-Sweden/gpt-sw3-126m": 2_048,
"AI-Sweden/gpt-sw3-350m": 2_048,
"AI-Sweden/gpt-sw3-1.6b": 2_048,
"AI-Sweden/gpt-sw3-6.7b": 2_048,
"AI-Sweden/gpt-sw3-20b": 2_048,
}
class lowercase ( _UpperCAmelCase ):
_SCREAMING_SNAKE_CASE = VOCAB_FILES_NAMES
_SCREAMING_SNAKE_CASE = PRETRAINED_VOCAB_FILES_MAP
_SCREAMING_SNAKE_CASE = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_SCREAMING_SNAKE_CASE = ['input_ids', 'attention_mask']
def __init__( self , lowercase , lowercase=False , lowercase=False , lowercase=False , lowercase=None , lowercase=None , lowercase=None , lowercase=None , lowercase = None , **lowercase , ) -> None:
lowerCAmelCase = {} if sp_model_kwargs is None else sp_model_kwargs
lowerCAmelCase = 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 = """None"""
# Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
lowerCAmelCase = """<|endoftext|>""" if eos_token is None else eos_token
lowerCAmelCase = """<unk>""" if unk_token is None else unk_token
if "gpt-sw3-7b" in name_or_path:
lowerCAmelCase = unk_token if pad_token is None else pad_token
lowerCAmelCase = eos_token if bos_token is None else bos_token
else:
lowerCAmelCase = """<pad>""" if pad_token is None else pad_token
lowerCAmelCase = """<s>""" if bos_token is None else bos_token
super().__init__(
do_lower_case=lowercase , remove_space=lowercase , keep_accents=lowercase , bos_token=lowercase , eos_token=lowercase , unk_token=lowercase , pad_token=lowercase , sp_model_kwargs=self.sp_model_kwargs , **lowercase , )
lowerCAmelCase = do_lower_case
lowerCAmelCase = remove_space
lowerCAmelCase = keep_accents
lowerCAmelCase = vocab_file
lowerCAmelCase = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(lowercase )
# Used for whitespace normalization in input texts
# fmt : off
lowerCAmelCase = {""" """, """ """, """ """, """ """, """ """, """ """, """ """, """ """, """ """, """ """, """""", """"""}
# fmt : on
# Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
lowerCAmelCase = re.compile(
f'[{"".join(map(lowercase , list(range(0 , 9 ) ) + list(range(11 , 32 ) ) + list(range(127 , 160 ) ) + [160, 173, 8_203] ) )}]' )
def __getstate__( self ) -> Optional[int]:
lowerCAmelCase = self.__dict__.copy()
lowerCAmelCase = None
return state
def __setstate__( self , lowercase ) -> str:
lowerCAmelCase = d
# for backward compatibility
if not hasattr(self , """sp_model_kwargs""" ):
lowerCAmelCase = {}
lowerCAmelCase = 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 _snake_case ( self ) -> int:
return len(self.sp_model )
def _snake_case ( self , lowercase ) -> str:
lowerCAmelCase = self.non_printing_characters_re.sub("""""" , lowercase )
# Normalize whitespaces
lowerCAmelCase = """""".join([char if char not in self.whitespaces else """ """ for char in text] )
# NFC Unicode normalization
lowerCAmelCase = unicodedata.normalize("""NFC""" , lowercase )
return text
def _snake_case ( self , lowercase , **lowercase ) -> List[str]:
lowerCAmelCase = self.preprocess_text(lowercase )
return self.sp_model.encode(lowercase , out_type=lowercase )
def _snake_case ( self , lowercase ) -> int:
return self.sp_model.PieceToId(lowercase )
def _snake_case ( self , lowercase ) -> str:
return self.sp_model.IdToPiece(lowercase )
@staticmethod
def _snake_case ( lowercase ) -> str:
return out_string
def _snake_case ( self , lowercase ) -> str:
lowerCAmelCase = []
lowerCAmelCase = """"""
lowerCAmelCase = 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(lowercase ) + token
lowerCAmelCase = True
lowerCAmelCase = []
else:
current_sub_tokens.append(lowercase )
lowerCAmelCase = False
out_string += self.sp_model.decode(lowercase )
return out_string
def _snake_case ( self ) -> Dict[str, int]:
lowerCAmelCase = {self.convert_ids_to_tokens(lowercase ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def _snake_case ( self , lowercase , lowercase = None ) -> Tuple[str]:
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"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(lowercase ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , lowercase )
elif not os.path.isfile(self.vocab_file ):
with open(lowercase , """wb""" ) as fi:
lowerCAmelCase = self.sp_model.serialized_model_proto()
fi.write(lowercase )
return (out_vocab_file,)
def _snake_case ( self , lowercase , lowercase = False ) -> Union[List[int], List[List[int]], "torch.Tensor"]:
if isinstance(lowercase , lowercase ):
lowerCAmelCase = self.preprocess_text(lowercase )
lowerCAmelCase = self.sp_model.encode(lowercase )
else:
lowerCAmelCase = [self.preprocess_text(lowercase ) for t in text]
lowerCAmelCase = self.sp_model.encode(lowercase )
if return_tensors is True or return_tensors == "pt":
lowerCAmelCase = torch.tensor(lowercase )
return token_ids
def _snake_case ( self , lowercase ) -> str:
return self.sp_model.decode(lowercase )
def _snake_case ( self , lowercase ) -> List[int]:
lowerCAmelCase = [f'User: {text}' if is_user else f'Bot: {text}' for is_user, text in conversation.iter_texts()]
lowerCAmelCase = (
f'{self.eos_token}{self.bos_token}' + f'{self.bos_token}'.join(lowercase ) + f'{self.bos_token}Bot:'
)
return self.encode(text=lowercase )
| 46
|
"""simple docstring"""
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING:
import torch
class _UpperCAmelCase ( TensorFormatter[Mapping, "torch.Tensor", Mapping] ):
def __init__( self : Any , A : int=None , **A : str ) -> Union[str, Any]:
super().__init__(features=A )
lowercase_ : Union[str, Any] = torch_tensor_kwargs
import torch # noqa import torch at initialization
def A ( self : Dict , A : int ) -> List[Any]:
import torch
if isinstance(A , A ) and column:
if all(
isinstance(A , torch.Tensor ) and x.shape == column[0].shape and x.dtype == column[0].dtype
for x in column ):
return torch.stack(A )
return column
def A ( self : int , A : Any ) -> Optional[Any]:
import torch
if isinstance(A , (str, bytes, type(A )) ):
return value
elif isinstance(A , (np.character, np.ndarray) ) and np.issubdtype(value.dtype , np.character ):
return value.tolist()
lowercase_ : Any = {}
if isinstance(A , (np.number, np.ndarray) ) and np.issubdtype(value.dtype , np.integer ):
lowercase_ : Any = {'''dtype''': torch.intaa}
elif isinstance(A , (np.number, np.ndarray) ) and np.issubdtype(value.dtype , np.floating ):
lowercase_ : Dict = {'''dtype''': torch.floataa}
elif config.PIL_AVAILABLE and "PIL" in sys.modules:
import PIL.Image
if isinstance(A , PIL.Image.Image ):
lowercase_ : Dict = np.asarray(A )
return torch.tensor(A , **{**default_dtype, **self.torch_tensor_kwargs} )
def A ( self : Union[str, Any] , A : Optional[int] ) -> str:
import torch
# support for torch, tf, jax etc.
if hasattr(A , '''__array__''' ) and not isinstance(A , torch.Tensor ):
lowercase_ : Optional[int] = data_struct.__array__()
# support for nested types like struct of list of struct
if isinstance(A , np.ndarray ):
if data_struct.dtype == object: # torch tensors cannot be instantied from an array of objects
return self._consolidate([self.recursive_tensorize(A ) for substruct in data_struct] )
elif isinstance(A , (list, tuple) ):
return self._consolidate([self.recursive_tensorize(A ) for substruct in data_struct] )
return self._tensorize(A )
def A ( self : Dict , A : dict ) -> Tuple:
return map_nested(self._recursive_tensorize , A , map_list=A )
def A ( self : str , A : pa.Table ) -> Mapping:
lowercase_ : Optional[Any] = self.numpy_arrow_extractor().extract_row(A )
lowercase_ : str = self.python_features_decoder.decode_row(A )
return self.recursive_tensorize(A )
def A ( self : List[Any] , A : pa.Table ) -> "torch.Tensor":
lowercase_ : List[str] = self.numpy_arrow_extractor().extract_column(A )
lowercase_ : str = self.python_features_decoder.decode_column(A , pa_table.column_names[0] )
lowercase_ : Optional[int] = self.recursive_tensorize(A )
lowercase_ : Any = self._consolidate(A )
return column
def A ( self : List[str] , A : pa.Table ) -> Mapping:
lowercase_ : Optional[int] = self.numpy_arrow_extractor().extract_batch(A )
lowercase_ : int = self.python_features_decoder.decode_batch(A )
lowercase_ : Dict = self.recursive_tensorize(A )
for column_name in batch:
lowercase_ : Optional[Any] = self._consolidate(batch[column_name] )
return batch
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'''simple docstring'''
import argparse
import torch
from transformers import OpenAIGPTConfig, OpenAIGPTModel, load_tf_weights_in_openai_gpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def _lowerCAmelCase ( _UpperCamelCase : Optional[int] , _UpperCamelCase : Optional[Any] , _UpperCamelCase : Optional[int] ) -> Optional[Any]:
"""simple docstring"""
if openai_config_file == "":
_SCREAMING_SNAKE_CASE =OpenAIGPTConfig()
else:
_SCREAMING_SNAKE_CASE =OpenAIGPTConfig.from_json_file(_UpperCamelCase )
_SCREAMING_SNAKE_CASE =OpenAIGPTModel(_UpperCamelCase )
# Load weights from numpy
load_tf_weights_in_openai_gpt(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase )
# Save pytorch-model
_SCREAMING_SNAKE_CASE =pytorch_dump_folder_path + '/' + WEIGHTS_NAME
_SCREAMING_SNAKE_CASE =pytorch_dump_folder_path + '/' + CONFIG_NAME
print(f"Save PyTorch model to {pytorch_weights_dump_path}" )
torch.save(model.state_dict() , _UpperCamelCase )
print(f"Save configuration file to {pytorch_config_dump_path}" )
with open(_UpperCamelCase , 'w' , encoding='utf-8' ) as f:
f.write(config.to_json_string() )
if __name__ == "__main__":
lowerCamelCase : Dict = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--openai_checkpoint_folder_path",
default=None,
type=str,
required=True,
help="Path to the TensorFlow checkpoint path.",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
parser.add_argument(
"--openai_config_file",
default="",
type=str,
help=(
"An optional config json file corresponding to the pre-trained OpenAI model. \n"
"This specifies the model architecture."
),
)
lowerCamelCase : str = parser.parse_args()
convert_openai_checkpoint_to_pytorch(
args.openai_checkpoint_folder_path, args.openai_config_file, args.pytorch_dump_folder_path
)
| 47
|
"""simple docstring"""
from ...utils import is_torch_available, is_transformers_available
if is_transformers_available() and is_torch_available():
from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline
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|
import inspect
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class UpperCamelCase__ (lowerCAmelCase__ ):
'''simple docstring'''
def __init__( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> List[Any]:
super().__init__()
self.register_modules(vqvae=UpperCamelCase__ , unet=UpperCamelCase__ , scheduler=UpperCamelCase__ )
@torch.no_grad()
def __call__( self , UpperCamelCase__ = 1 , UpperCamelCase__ = None , UpperCamelCase__ = 0.0 , UpperCamelCase__ = 50 , UpperCamelCase__ = "pil" , UpperCamelCase__ = True , **UpperCamelCase__ , ) -> Union[Tuple, ImagePipelineOutput]:
lowerCamelCase : List[Any] = randn_tensor(
(batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size) , generator=UpperCamelCase__ , )
lowerCamelCase : Any = latents.to(self.device )
# scale the initial noise by the standard deviation required by the scheduler
lowerCamelCase : Any = latents * self.scheduler.init_noise_sigma
self.scheduler.set_timesteps(UpperCamelCase__ )
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
lowerCamelCase : Optional[int] = "eta" in set(inspect.signature(self.scheduler.step ).parameters.keys() )
lowerCamelCase : Union[str, Any] = {}
if accepts_eta:
lowerCamelCase : Tuple = eta
for t in self.progress_bar(self.scheduler.timesteps ):
lowerCamelCase : int = self.scheduler.scale_model_input(UpperCamelCase__ , UpperCamelCase__ )
# predict the noise residual
lowerCamelCase : int = self.unet(UpperCamelCase__ , UpperCamelCase__ ).sample
# compute the previous noisy sample x_t -> x_t-1
lowerCamelCase : Tuple = self.scheduler.step(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , **UpperCamelCase__ ).prev_sample
# decode the image latents with the VAE
lowerCamelCase : List[str] = self.vqvae.decode(UpperCamelCase__ ).sample
lowerCamelCase : List[str] = (image / 2 + 0.5).clamp(0 , 1 )
lowerCamelCase : List[Any] = image.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
lowerCamelCase : str = self.numpy_to_pil(UpperCamelCase__ )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=UpperCamelCase__ )
| 48
|
"""simple docstring"""
from typing import Optional, Union
import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention
from ...modeling_utils import PreTrainedModel
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_mobilenet_va import MobileNetVaConfig
__A : Union[str, Any] = logging.get_logger(__name__)
# General docstring
__A : Tuple = '''MobileNetV1Config'''
# Base docstring
__A : Union[str, Any] = '''google/mobilenet_v1_1.0_224'''
__A : Union[str, Any] = [1, 1_024, 7, 7]
# Image classification docstring
__A : Optional[Any] = '''google/mobilenet_v1_1.0_224'''
__A : List[Any] = '''tabby, tabby cat'''
__A : Union[str, Any] = [
'''google/mobilenet_v1_1.0_224''',
'''google/mobilenet_v1_0.75_192''',
# See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
]
def lowercase ( __snake_case : List[str] , __snake_case : Union[str, Any] , __snake_case : Dict=None ):
lowercase_ : str = {}
if isinstance(__snake_case , __snake_case ):
lowercase_ : Union[str, Any] = model.mobilenet_va
else:
lowercase_ : Optional[Any] = model
lowercase_ : Union[str, Any] = '''MobilenetV1/Conv2d_0/'''
lowercase_ : Union[str, Any] = backbone.conv_stem.convolution.weight
lowercase_ : Optional[Any] = backbone.conv_stem.normalization.bias
lowercase_ : Union[str, Any] = backbone.conv_stem.normalization.weight
lowercase_ : Any = backbone.conv_stem.normalization.running_mean
lowercase_ : int = backbone.conv_stem.normalization.running_var
for i in range(1_3 ):
lowercase_ : Optional[int] = i + 1
lowercase_ : Union[str, Any] = i * 2
lowercase_ : Optional[Any] = backbone.layer[pt_index]
lowercase_ : Union[str, Any] = F'''MobilenetV1/Conv2d_{tf_index}_depthwise/'''
lowercase_ : str = pointer.convolution.weight
lowercase_ : int = pointer.normalization.bias
lowercase_ : Any = pointer.normalization.weight
lowercase_ : Dict = pointer.normalization.running_mean
lowercase_ : Union[str, Any] = pointer.normalization.running_var
lowercase_ : Any = backbone.layer[pt_index + 1]
lowercase_ : Union[str, Any] = F'''MobilenetV1/Conv2d_{tf_index}_pointwise/'''
lowercase_ : int = pointer.convolution.weight
lowercase_ : str = pointer.normalization.bias
lowercase_ : Tuple = pointer.normalization.weight
lowercase_ : Dict = pointer.normalization.running_mean
lowercase_ : Any = pointer.normalization.running_var
if isinstance(__snake_case , __snake_case ):
lowercase_ : Optional[Any] = '''MobilenetV1/Logits/Conv2d_1c_1x1/'''
lowercase_ : Any = model.classifier.weight
lowercase_ : Optional[int] = model.classifier.bias
return tf_to_pt_map
def lowercase ( __snake_case : Optional[int] , __snake_case : int , __snake_case : Dict ):
try:
import numpy as np
import tensorflow as tf
except ImportError:
logger.error(
'''Loading a TensorFlow models in PyTorch, requires TensorFlow to be installed. Please see '''
'''https://www.tensorflow.org/install/ for installation instructions.''' )
raise
# Load weights from TF model
lowercase_ : Tuple = tf.train.list_variables(__snake_case )
lowercase_ : int = {}
for name, shape in init_vars:
logger.info(F'''Loading TF weight {name} with shape {shape}''' )
lowercase_ : Optional[Any] = tf.train.load_variable(__snake_case , __snake_case )
lowercase_ : Optional[int] = array
# Build TF to PyTorch weights loading map
lowercase_ : Any = _build_tf_to_pytorch_map(__snake_case , __snake_case , __snake_case )
for name, pointer in tf_to_pt_map.items():
logger.info(F'''Importing {name}''' )
if name not in tf_weights:
logger.info(F'''{name} not in tf pre-trained weights, skipping''' )
continue
lowercase_ : Union[str, Any] = tf_weights[name]
if "depthwise_weights" in name:
logger.info('''Transposing depthwise''' )
lowercase_ : Any = np.transpose(__snake_case , (2, 3, 0, 1) )
elif "weights" in name:
logger.info('''Transposing''' )
if len(pointer.shape ) == 2: # copying into linear layer
lowercase_ : Optional[int] = array.squeeze().transpose()
else:
lowercase_ : Optional[int] = np.transpose(__snake_case , (3, 2, 0, 1) )
if pointer.shape != array.shape:
raise ValueError(F'''Pointer shape {pointer.shape} and array shape {array.shape} mismatched''' )
logger.info(F'''Initialize PyTorch weight {name} {array.shape}''' )
lowercase_ : str = torch.from_numpy(__snake_case )
tf_weights.pop(__snake_case , __snake_case )
tf_weights.pop(name + '''/RMSProp''' , __snake_case )
tf_weights.pop(name + '''/RMSProp_1''' , __snake_case )
tf_weights.pop(name + '''/ExponentialMovingAverage''' , __snake_case )
logger.info(F'''Weights not copied to PyTorch model: {', '.join(tf_weights.keys() )}''' )
return model
def lowercase ( __snake_case : torch.Tensor , __snake_case : nn.Convad ):
lowercase_ , lowercase_ : Optional[int] = features.shape[-2:]
lowercase_ , lowercase_ : str = conv_layer.stride
lowercase_ , lowercase_ : Tuple = conv_layer.kernel_size
if in_height % stride_height == 0:
lowercase_ : Dict = max(kernel_height - stride_height , 0 )
else:
lowercase_ : List[Any] = max(kernel_height - (in_height % stride_height) , 0 )
if in_width % stride_width == 0:
lowercase_ : str = max(kernel_width - stride_width , 0 )
else:
lowercase_ : int = max(kernel_width - (in_width % stride_width) , 0 )
lowercase_ : int = pad_along_width // 2
lowercase_ : Union[str, Any] = pad_along_width - pad_left
lowercase_ : Tuple = pad_along_height // 2
lowercase_ : List[str] = pad_along_height - pad_top
lowercase_ : str = (pad_left, pad_right, pad_top, pad_bottom)
return nn.functional.pad(__snake_case , __snake_case , '''constant''' , 0.0 )
class _UpperCAmelCase ( nn.Module ):
def __init__( self : List[Any] , A : MobileNetVaConfig , A : int , A : int , A : int , A : Optional[int] = 1 , A : Optional[int] = 1 , A : bool = False , A : Optional[bool] = True , A : Optional[bool or str] = True , ) -> None:
super().__init__()
lowercase_ : int = config
if in_channels % groups != 0:
raise ValueError(F'''Input channels ({in_channels}) are not divisible by {groups} groups.''' )
if out_channels % groups != 0:
raise ValueError(F'''Output channels ({out_channels}) are not divisible by {groups} groups.''' )
lowercase_ : Tuple = 0 if config.tf_padding else int((kernel_size - 1) / 2 )
lowercase_ : int = nn.Convad(
in_channels=A , out_channels=A , kernel_size=A , stride=A , padding=A , groups=A , bias=A , padding_mode='''zeros''' , )
if use_normalization:
lowercase_ : Optional[Any] = nn.BatchNormad(
num_features=A , eps=config.layer_norm_eps , momentum=0.9997 , affine=A , track_running_stats=A , )
else:
lowercase_ : Union[str, Any] = None
if use_activation:
if isinstance(A , A ):
lowercase_ : str = ACTaFN[use_activation]
elif isinstance(config.hidden_act , A ):
lowercase_ : Any = ACTaFN[config.hidden_act]
else:
lowercase_ : Tuple = config.hidden_act
else:
lowercase_ : Tuple = None
def A ( self : str , A : torch.Tensor ) -> torch.Tensor:
if self.config.tf_padding:
lowercase_ : List[Any] = apply_tf_padding(A , self.convolution )
lowercase_ : Optional[int] = self.convolution(A )
if self.normalization is not None:
lowercase_ : Union[str, Any] = self.normalization(A )
if self.activation is not None:
lowercase_ : Optional[int] = self.activation(A )
return features
class _UpperCAmelCase ( _A ):
SCREAMING_SNAKE_CASE_ : Optional[int] = MobileNetVaConfig
SCREAMING_SNAKE_CASE_ : int = load_tf_weights_in_mobilenet_va
SCREAMING_SNAKE_CASE_ : Optional[Any] = "mobilenet_v1"
SCREAMING_SNAKE_CASE_ : Union[str, Any] = "pixel_values"
SCREAMING_SNAKE_CASE_ : List[str] = False
def A ( self : Any , A : Union[nn.Linear, nn.Convad] ) -> None:
if isinstance(A , (nn.Linear, nn.Convad) ):
module.weight.data.normal_(mean=0.0 , std=self.config.initializer_range )
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(A , nn.BatchNormad ):
module.bias.data.zero_()
module.weight.data.fill_(1.0 )
__A : Union[str, Any] = R'''
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parameters:
config ([`MobileNetV1Config`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
'''
__A : List[str] = R'''
Args:
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
[`MobileNetV1ImageProcessor.__call__`] for details.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
'''
@add_start_docstrings(
"The bare MobileNetV1 model outputting raw hidden-states without any specific head on top." , _A , )
class _UpperCAmelCase ( _A ):
def __init__( self : str , A : MobileNetVaConfig , A : bool = True ) -> int:
super().__init__(A )
lowercase_ : Union[str, Any] = config
lowercase_ : List[str] = 32
lowercase_ : str = max(int(depth * config.depth_multiplier ) , config.min_depth )
lowercase_ : Union[str, Any] = MobileNetVaConvLayer(
A , in_channels=config.num_channels , out_channels=A , kernel_size=3 , stride=2 , )
lowercase_ : Optional[Any] = [1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 2, 1]
lowercase_ : List[Any] = nn.ModuleList()
for i in range(13 ):
lowercase_ : Dict = out_channels
if strides[i] == 2 or i == 0:
depth *= 2
lowercase_ : str = max(int(depth * config.depth_multiplier ) , config.min_depth )
self.layer.append(
MobileNetVaConvLayer(
A , in_channels=A , out_channels=A , kernel_size=3 , stride=strides[i] , groups=A , ) )
self.layer.append(
MobileNetVaConvLayer(
A , in_channels=A , out_channels=A , kernel_size=1 , ) )
lowercase_ : int = nn.AdaptiveAvgPoolad((1, 1) ) if add_pooling_layer else None
# Initialize weights and apply final processing
self.post_init()
def A ( self : Any , A : Optional[Any] ) -> Optional[int]:
raise NotImplementedError
@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 A ( self : List[Any] , A : Optional[torch.Tensor] = None , A : Optional[bool] = None , A : Optional[bool] = None , ) -> Union[tuple, BaseModelOutputWithPoolingAndNoAttention]:
lowercase_ : str = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
lowercase_ : Union[str, Any] = return_dict if return_dict is not None else self.config.use_return_dict
if pixel_values is None:
raise ValueError('''You have to specify pixel_values''' )
lowercase_ : List[str] = self.conv_stem(A )
lowercase_ : Dict = () if output_hidden_states else None
for i, layer_module in enumerate(self.layer ):
lowercase_ : Optional[int] = layer_module(A )
if output_hidden_states:
lowercase_ : str = all_hidden_states + (hidden_states,)
lowercase_ : Tuple = hidden_states
if self.pooler is not None:
lowercase_ : Dict = torch.flatten(self.pooler(A ) , start_dim=1 )
else:
lowercase_ : Optional[Any] = None
if not return_dict:
return tuple(v for v in [last_hidden_state, pooled_output, all_hidden_states] if v is not None )
return BaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=A , pooler_output=A , hidden_states=A , )
@add_start_docstrings(
"\n MobileNetV1 model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n ImageNet.\n " , _A , )
class _UpperCAmelCase ( _A ):
def __init__( self : List[str] , A : MobileNetVaConfig ) -> None:
super().__init__(A )
lowercase_ : int = config.num_labels
lowercase_ : List[str] = MobileNetVaModel(A )
lowercase_ : Union[str, Any] = self.mobilenet_va.layer[-1].convolution.out_channels
# Classifier head
lowercase_ : Tuple = nn.Dropout(config.classifier_dropout_prob , inplace=A )
lowercase_ : int = nn.Linear(A , 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 A ( self : Optional[Any] , A : Optional[torch.Tensor] = None , A : Optional[bool] = None , A : Optional[torch.Tensor] = None , A : Optional[bool] = None , ) -> Union[tuple, ImageClassifierOutputWithNoAttention]:
lowercase_ : Union[str, Any] = return_dict if return_dict is not None else self.config.use_return_dict
lowercase_ : List[Any] = self.mobilenet_va(A , output_hidden_states=A , return_dict=A )
lowercase_ : Union[str, Any] = outputs.pooler_output if return_dict else outputs[1]
lowercase_ : Dict = self.classifier(self.dropout(A ) )
lowercase_ : int = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
lowercase_ : List[str] = '''regression'''
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
lowercase_ : Optional[Any] = '''single_label_classification'''
else:
lowercase_ : Tuple = '''multi_label_classification'''
if self.config.problem_type == "regression":
lowercase_ : str = MSELoss()
if self.num_labels == 1:
lowercase_ : List[str] = loss_fct(logits.squeeze() , labels.squeeze() )
else:
lowercase_ : List[str] = loss_fct(A , A )
elif self.config.problem_type == "single_label_classification":
lowercase_ : List[Any] = CrossEntropyLoss()
lowercase_ : str = loss_fct(logits.view(-1 , self.num_labels ) , labels.view(-1 ) )
elif self.config.problem_type == "multi_label_classification":
lowercase_ : str = BCEWithLogitsLoss()
lowercase_ : List[Any] = loss_fct(A , A )
if not return_dict:
lowercase_ : Tuple = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return ImageClassifierOutputWithNoAttention(
loss=A , logits=A , hidden_states=outputs.hidden_states , )
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|
import argparse
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
__snake_case :List[Any] = '''src/diffusers'''
# Matches is_xxx_available()
__snake_case :Optional[Any] = re.compile(r'''is\_([a-z_]*)_available\(\)''')
# Matches from xxx import bla
__snake_case :Optional[int] = re.compile(r'''\s+from\s+\S*\s+import\s+([^\(\s].*)\n''')
__snake_case :Optional[Any] = '''
{0} = None
'''
__snake_case :Tuple = '''
class {0}(metaclass=DummyObject):
_backends = {1}
def __init__(self, *args, **kwargs):
requires_backends(self, {1})
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, {1})
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, {1})
'''
__snake_case :int = '''
def {0}(*args, **kwargs):
requires_backends({0}, {1})
'''
def __snake_case ( _UpperCAmelCase ):
__a = _re_backend.findall(_UpperCAmelCase )
if len(_UpperCAmelCase ) == 0:
return None
return "_and_".join(_UpperCAmelCase )
def __snake_case ( ):
with open(os.path.join(_UpperCAmelCase , '''__init__.py''' ) , '''r''' , encoding='''utf-8''' , newline='''\n''' ) as f:
__a = f.readlines()
# Get to the point we do the actual imports for type checking
__a = 0
__a = {}
# Go through the end of the file
while line_index < len(_UpperCAmelCase ):
# If the line contains is_backend_available, we grab all objects associated with the `else` block
__a = find_backend(lines[line_index] )
if backend is not None:
while not lines[line_index].startswith('''else:''' ):
line_index += 1
line_index += 1
__a = []
# Until we unindent, add backend objects to the list
while line_index < len(_UpperCAmelCase ) and len(lines[line_index] ) > 1:
__a = lines[line_index]
__a = _re_single_line_import.search(_UpperCAmelCase )
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
if len(_UpperCAmelCase ) > 0:
__a = objects
else:
line_index += 1
return backend_specific_objects
def __snake_case ( _UpperCAmelCase , _UpperCAmelCase ):
if name.isupper():
return DUMMY_CONSTANT.format(_UpperCAmelCase )
elif name.islower():
return DUMMY_FUNCTION.format(_UpperCAmelCase , _UpperCAmelCase )
else:
return DUMMY_CLASS.format(_UpperCAmelCase , _UpperCAmelCase )
def __snake_case ( _UpperCAmelCase=None ):
if backend_specific_objects is None:
__a = read_init()
# For special correspondence backend to module name as used in the function requires_modulename
__a = {}
for backend, objects in backend_specific_objects.items():
__a = '''[''' + ''', '''.join(f'"{b}"' for b in backend.split('''_and_''' ) ) + ''']'''
__a = '''# This file is autogenerated by the command `make fix-copies`, do not edit.\n'''
dummy_file += "from ..utils import DummyObject, requires_backends\n\n"
dummy_file += "\n".join([create_dummy_object(_UpperCAmelCase , _UpperCAmelCase ) for o in objects] )
__a = dummy_file
return dummy_files
def __snake_case ( _UpperCAmelCase=False ):
__a = create_dummy_files()
# For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
__a = {'''torch''': '''pt'''}
# Locate actual dummy modules and read their content.
__a = os.path.join(_UpperCAmelCase , '''utils''' )
__a = {
backend: os.path.join(_UpperCAmelCase , f'dummy_{short_names.get(_UpperCAmelCase , _UpperCAmelCase )}_objects.py' )
for backend in dummy_files.keys()
}
__a = {}
for backend, file_path in dummy_file_paths.items():
if os.path.isfile(_UpperCAmelCase ):
with open(_UpperCAmelCase , '''r''' , encoding='''utf-8''' , newline='''\n''' ) as f:
__a = f.read()
else:
__a = ''''''
for backend in dummy_files.keys():
if dummy_files[backend] != actual_dummies[backend]:
if overwrite:
print(
f'Updating diffusers.utils.dummy_{short_names.get(_UpperCAmelCase , _UpperCAmelCase )}_objects.py as the main '
'''__init__ has new objects.''' )
with open(dummy_file_paths[backend] , '''w''' , encoding='''utf-8''' , newline='''\n''' ) as f:
f.write(dummy_files[backend] )
else:
raise ValueError(
'''The main __init__ has objects that are not present in '''
f'diffusers.utils.dummy_{short_names.get(_UpperCAmelCase , _UpperCAmelCase )}_objects.py. Run `make fix-copies` '
'''to fix this.''' )
if __name__ == "__main__":
__snake_case :List[Any] = argparse.ArgumentParser()
parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
__snake_case :List[str] = parser.parse_args()
check_dummies(args.fix_and_overwrite)
| 49
|
"""simple docstring"""
def lowercase ( __snake_case : list[int] ):
lowercase_ : List[Any] = len(__snake_case )
for i in range(__snake_case ):
for j in range(i + 1 , __snake_case ):
if numbers[j] < numbers[i]:
lowercase_ , lowercase_ : Optional[int] = numbers[j], numbers[i]
return numbers
if __name__ == "__main__":
__A : int = input('''Enter numbers separated by a comma:\n''').strip()
__A : Any = [int(item) for item in user_input.split(''',''')]
print(exchange_sort(unsorted))
| 33
| 0
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase : List[Any] = logging.get_logger(__name__)
_UpperCAmelCase : Optional[int] = {
"""SCUT-DLVCLab/lilt-roberta-en-base""": (
"""https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/config.json"""
),
}
class lowerCAmelCase ( __UpperCamelCase ):
UpperCAmelCase__ = """lilt"""
def __init__( self : Dict , UpperCAmelCase : int=30522 , UpperCAmelCase : Tuple=768 , UpperCAmelCase : Optional[int]=12 , UpperCAmelCase : List[str]=12 , UpperCAmelCase : Union[str, Any]=3072 , UpperCAmelCase : Tuple="gelu" , UpperCAmelCase : Optional[int]=0.1 , UpperCAmelCase : List[str]=0.1 , UpperCAmelCase : Any=512 , UpperCAmelCase : List[str]=2 , UpperCAmelCase : str=0.0_2 , UpperCAmelCase : List[Any]=1e-12 , UpperCAmelCase : List[str]=0 , UpperCAmelCase : Union[str, Any]="absolute" , UpperCAmelCase : str=None , UpperCAmelCase : Optional[Any]=4 , UpperCAmelCase : Union[str, Any]=1024 , **UpperCAmelCase : str , ) -> Dict:
super().__init__(pad_token_id=UpperCAmelCase , **UpperCAmelCase )
lowerCamelCase__ : Union[str, Any] = vocab_size
lowerCamelCase__ : Dict = hidden_size
lowerCamelCase__ : Tuple = num_hidden_layers
lowerCamelCase__ : Optional[Any] = num_attention_heads
lowerCamelCase__ : Tuple = hidden_act
lowerCamelCase__ : Union[str, Any] = intermediate_size
lowerCamelCase__ : List[Any] = hidden_dropout_prob
lowerCamelCase__ : Union[str, Any] = attention_probs_dropout_prob
lowerCamelCase__ : Optional[int] = max_position_embeddings
lowerCamelCase__ : Tuple = type_vocab_size
lowerCamelCase__ : Dict = initializer_range
lowerCamelCase__ : Union[str, Any] = layer_norm_eps
lowerCamelCase__ : Any = position_embedding_type
lowerCamelCase__ : List[str] = classifier_dropout
lowerCamelCase__ : int = channel_shrink_ratio
lowerCamelCase__ : int = max_ad_position_embeddings
| 50
|
"""simple docstring"""
import json
import pathlib
import unittest
import numpy as np
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, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import YolosImageProcessor
class _UpperCAmelCase ( unittest.TestCase ):
def __init__( self : List[Any] , A : Any , A : Tuple=7 , A : Tuple=3 , A : Optional[Any]=30 , A : List[Any]=4_00 , A : Tuple=True , A : Dict=None , A : List[str]=True , A : Optional[int]=[0.5, 0.5, 0.5] , A : Tuple=[0.5, 0.5, 0.5] , A : List[str]=True , A : List[Any]=1 / 2_55 , A : Union[str, Any]=True , ) -> Tuple:
# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
lowercase_ : Optional[int] = size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 13_33}
lowercase_ : Optional[int] = parent
lowercase_ : str = batch_size
lowercase_ : Tuple = num_channels
lowercase_ : str = min_resolution
lowercase_ : Any = max_resolution
lowercase_ : str = do_resize
lowercase_ : Any = size
lowercase_ : Optional[int] = do_normalize
lowercase_ : List[str] = image_mean
lowercase_ : Optional[Any] = image_std
lowercase_ : int = do_rescale
lowercase_ : List[str] = rescale_factor
lowercase_ : int = do_pad
def A ( self : Any ) -> str:
return {
"do_resize": self.do_resize,
"size": self.size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_rescale": self.do_rescale,
"rescale_factor": self.rescale_factor,
"do_pad": self.do_pad,
}
def A ( self : Optional[Any] , A : int , A : int=False ) -> Tuple:
if not batched:
lowercase_ : Optional[int] = image_inputs[0]
if isinstance(A , Image.Image ):
lowercase_ , lowercase_ : int = image.size
else:
lowercase_ , lowercase_ : Tuple = image.shape[1], image.shape[2]
if w < h:
lowercase_ : int = int(self.size['''shortest_edge'''] * h / w )
lowercase_ : Optional[Any] = self.size['''shortest_edge''']
elif w > h:
lowercase_ : Optional[Any] = self.size['''shortest_edge''']
lowercase_ : Optional[int] = int(self.size['''shortest_edge'''] * w / h )
else:
lowercase_ : Any = self.size['''shortest_edge''']
lowercase_ : Any = self.size['''shortest_edge''']
else:
lowercase_ : Tuple = []
for image in image_inputs:
lowercase_ , lowercase_ : Optional[Any] = self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
lowercase_ : Union[str, Any] = max(A , key=lambda A : item[0] )[0]
lowercase_ : Optional[Any] = max(A , key=lambda A : item[1] )[1]
return expected_height, expected_width
@require_torch
@require_vision
class _UpperCAmelCase ( _A , unittest.TestCase ):
SCREAMING_SNAKE_CASE_ : Optional[Any] = YolosImageProcessor if is_vision_available() else None
def A ( self : Optional[int] ) -> Optional[int]:
lowercase_ : Optional[Any] = YolosImageProcessingTester(self )
@property
def A ( self : str ) -> Any:
return self.image_processor_tester.prepare_image_processor_dict()
def A ( self : Optional[int] ) -> List[str]:
lowercase_ : Tuple = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(A , '''image_mean''' ) )
self.assertTrue(hasattr(A , '''image_std''' ) )
self.assertTrue(hasattr(A , '''do_normalize''' ) )
self.assertTrue(hasattr(A , '''do_resize''' ) )
self.assertTrue(hasattr(A , '''size''' ) )
def A ( self : Dict ) -> Tuple:
lowercase_ : Dict = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'''shortest_edge''': 18, '''longest_edge''': 13_33} )
self.assertEqual(image_processor.do_pad , A )
lowercase_ : Tuple = self.image_processing_class.from_dict(
self.image_processor_dict , size=42 , max_size=84 , pad_and_return_pixel_mask=A )
self.assertEqual(image_processor.size , {'''shortest_edge''': 42, '''longest_edge''': 84} )
self.assertEqual(image_processor.do_pad , A )
def A ( self : Optional[int] ) -> Tuple:
pass
def A ( self : Tuple ) -> int:
# Initialize image_processing
lowercase_ : Dict = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
lowercase_ : str = prepare_image_inputs(self.image_processor_tester , equal_resolution=A )
for image in image_inputs:
self.assertIsInstance(A , Image.Image )
# Test not batched input
lowercase_ : Optional[int] = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
lowercase_ , lowercase_ : Union[str, Any] = self.image_processor_tester.get_expected_values(A )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
lowercase_ , lowercase_ : Dict = self.image_processor_tester.get_expected_values(A , batched=A )
lowercase_ : str = 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,
expected_height,
expected_width,
) , )
def A ( self : str ) -> Any:
# Initialize image_processing
lowercase_ : Any = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
lowercase_ : List[Any] = 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
lowercase_ : List[Any] = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
lowercase_ , lowercase_ : int = self.image_processor_tester.get_expected_values(A )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
lowercase_ : Optional[int] = image_processing(A , return_tensors='''pt''' ).pixel_values
lowercase_ , lowercase_ : List[Any] = self.image_processor_tester.get_expected_values(A , batched=A )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def A ( self : Tuple ) -> Optional[int]:
# Initialize image_processing
lowercase_ : Dict = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
lowercase_ : Optional[int] = 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
lowercase_ : List[str] = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
lowercase_ , lowercase_ : Union[str, Any] = self.image_processor_tester.get_expected_values(A )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
lowercase_ : Any = image_processing(A , return_tensors='''pt''' ).pixel_values
lowercase_ , lowercase_ : List[str] = self.image_processor_tester.get_expected_values(A , batched=A )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def A ( self : Tuple ) -> Optional[Any]:
# Initialize image_processings
lowercase_ : Optional[Any] = self.image_processing_class(**self.image_processor_dict )
lowercase_ : Tuple = self.image_processing_class(do_resize=A , do_normalize=A , do_rescale=A )
# create random PyTorch tensors
lowercase_ : Union[str, Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=A , torchify=A )
for image in image_inputs:
self.assertIsInstance(A , torch.Tensor )
# Test whether the method "pad" and calling the image processor return the same tensors
lowercase_ : Union[str, Any] = image_processing_a.pad(A , return_tensors='''pt''' )
lowercase_ : List[Any] = image_processing_a(A , return_tensors='''pt''' )
self.assertTrue(
torch.allclose(encoded_images_with_method['''pixel_values'''] , encoded_images['''pixel_values'''] , atol=1e-4 ) )
@slow
def A ( self : str ) -> List[Any]:
# prepare image and target
lowercase_ : Dict = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt''' , '''r''' ) as f:
lowercase_ : List[Any] = json.loads(f.read() )
lowercase_ : Tuple = {'''image_id''': 3_97_69, '''annotations''': target}
# encode them
lowercase_ : Union[str, Any] = YolosImageProcessor.from_pretrained('''hustvl/yolos-small''' )
lowercase_ : List[Any] = image_processing(images=A , annotations=A , return_tensors='''pt''' )
# verify pixel values
lowercase_ : Union[str, Any] = torch.Size([1, 3, 8_00, 10_66] )
self.assertEqual(encoding['''pixel_values'''].shape , A )
lowercase_ : Union[str, Any] = torch.tensor([0.2796, 0.3138, 0.3481] )
self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3] , A , atol=1e-4 ) )
# verify area
lowercase_ : Tuple = torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area'''] , A ) )
# verify boxes
lowercase_ : List[str] = torch.Size([6, 4] )
self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape , A )
lowercase_ : Any = torch.tensor([0.5503, 0.2765, 0.0604, 0.2215] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0] , A , atol=1e-3 ) )
# verify image_id
lowercase_ : List[Any] = torch.tensor([3_97_69] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''] , A ) )
# verify is_crowd
lowercase_ : Any = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''] , A ) )
# verify class_labels
lowercase_ : Optional[Any] = torch.tensor([75, 75, 63, 65, 17, 17] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''] , A ) )
# verify orig_size
lowercase_ : List[str] = torch.tensor([4_80, 6_40] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''] , A ) )
# verify size
lowercase_ : Optional[Any] = torch.tensor([8_00, 10_66] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''] , A ) )
@slow
def A ( self : List[Any] ) -> Dict:
# prepare image, target and masks_path
lowercase_ : Tuple = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt''' , '''r''' ) as f:
lowercase_ : str = json.loads(f.read() )
lowercase_ : int = {'''file_name''': '''000000039769.png''', '''image_id''': 3_97_69, '''segments_info''': target}
lowercase_ : List[Any] = pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic''' )
# encode them
lowercase_ : int = YolosImageProcessor(format='''coco_panoptic''' )
lowercase_ : Any = image_processing(images=A , annotations=A , masks_path=A , return_tensors='''pt''' )
# verify pixel values
lowercase_ : Optional[Any] = torch.Size([1, 3, 8_00, 10_66] )
self.assertEqual(encoding['''pixel_values'''].shape , A )
lowercase_ : Tuple = torch.tensor([0.2796, 0.3138, 0.3481] )
self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3] , A , atol=1e-4 ) )
# verify area
lowercase_ : List[Any] = torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area'''] , A ) )
# verify boxes
lowercase_ : str = torch.Size([6, 4] )
self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape , A )
lowercase_ : List[str] = torch.tensor([0.2625, 0.5437, 0.4688, 0.8625] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0] , A , atol=1e-3 ) )
# verify image_id
lowercase_ : List[str] = torch.tensor([3_97_69] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''] , A ) )
# verify is_crowd
lowercase_ : List[str] = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''] , A ) )
# verify class_labels
lowercase_ : Any = torch.tensor([17, 17, 63, 75, 75, 93] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''] , A ) )
# verify masks
lowercase_ : Dict = 82_28_73
self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item() , A )
# verify orig_size
lowercase_ : Tuple = torch.tensor([4_80, 6_40] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''] , A ) )
# verify size
lowercase_ : List[str] = torch.tensor([8_00, 10_66] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''] , A ) )
| 33
| 0
|
# 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 subprocess
from packaging.version import Version, parse
from accelerate.commands.config.config_args import default_config_file, load_config_from_file
snake_case_ : Optional[int] = "Run commands across TPU VMs for initial setup before running `accelerate launch`."
def A (__A : str=None ) -> Optional[int]:
"""simple docstring"""
if subparsers is not None:
UpperCAmelCase_ = subparsers.add_parser('''tpu-config''' , description=_description )
else:
UpperCAmelCase_ = argparse.ArgumentParser('''Accelerate tpu-config command''' , description=_description )
# Core arguments
UpperCAmelCase_ = parser.add_argument_group(
'''Config Arguments''' , '''Arguments that can be configured through `accelerate config`.''' )
config_args.add_argument(
'''--config_file''' , type=__A , default=__A , help='''Path to the config file to use for accelerate.''' , )
config_args.add_argument(
'''--tpu_name''' , default=__A , help='''The name of the TPU to use. If not specified, will use the TPU specified in the config file.''' , )
config_args.add_argument(
'''--tpu_zone''' , default=__A , help='''The zone of the TPU to use. If not specified, will use the zone specified in the config file.''' , )
UpperCAmelCase_ = parser.add_argument_group('''TPU Arguments''' , '''Arguments for options ran inside the TPU.''' )
pod_args.add_argument(
'''--use_alpha''' , action='''store_true''' , help='''Whether to use `gcloud alpha` when running the TPU training script instead of `gcloud`.''' , )
pod_args.add_argument(
'''--command_file''' , default=__A , help='''The path to the file containing the commands to run on the pod on startup.''' , )
pod_args.add_argument(
'''--command''' , action='''append''' , nargs='''+''' , help='''A command to run on the pod. Can be passed multiple times.''' , )
pod_args.add_argument(
'''--install_accelerate''' , action='''store_true''' , help='''Whether to install accelerate on the pod. Defaults to False.''' , )
pod_args.add_argument(
'''--accelerate_version''' , default='''latest''' , help='''The version of accelerate to install on the pod. If not specified, will use the latest pypi version. Specify \'dev\' to install from GitHub.''' , )
pod_args.add_argument(
'''--debug''' , action='''store_true''' , help='''If set, will print the command that would be run instead of running it.''' )
if subparsers is not None:
parser.set_defaults(func=__A )
return parser
def A (__A : str ) -> Union[str, Any]:
"""simple docstring"""
UpperCAmelCase_ = None
# Get the default from the config file if it exists.
if args.config_file is not None or os.path.isfile(__A ):
UpperCAmelCase_ = load_config_from_file(args.config_file )
if not args.command_file and defaults.command_file is not None and not args.command:
UpperCAmelCase_ = defaults.command_file
if not args.command and defaults.commands is not None:
UpperCAmelCase_ = defaults.commands
if not args.tpu_name:
UpperCAmelCase_ = defaults.tpu_name
if not args.tpu_zone:
UpperCAmelCase_ = defaults.tpu_zone
if args.accelerate_version == "dev":
UpperCAmelCase_ = '''git+https://github.com/huggingface/accelerate.git'''
elif args.accelerate_version == "latest":
UpperCAmelCase_ = '''accelerate -U'''
elif isinstance(parse(args.accelerate_version ) , __A ):
UpperCAmelCase_ = F"""accelerate=={args.accelerate_version}"""
if not args.command_file and not args.command:
raise ValueError('''You must specify either a command file or a command to run on the pod.''' )
if args.command_file:
with open(args.command_file , '''r''' ) as f:
UpperCAmelCase_ = [f.read().splitlines()]
# To turn list of lists into list of strings
if isinstance(args.command[0] , __A ):
UpperCAmelCase_ = [line for cmd in args.command for line in cmd]
# Default to the shared folder and install accelerate
UpperCAmelCase_ = ['''cd /usr/share''']
if args.install_accelerate:
new_cmd += [F"""pip install {args.accelerate_version}"""]
new_cmd += args.command
UpperCAmelCase_ = '''; '''.join(__A )
# Then send it to gcloud
# Eventually try to use google-api-core to do this instead of subprocess
UpperCAmelCase_ = ['''gcloud''']
if args.use_alpha:
cmd += ["alpha"]
cmd += [
"compute",
"tpus",
"tpu-vm",
"ssh",
args.tpu_name,
"--zone",
args.tpu_zone,
"--command",
args.command,
"--worker",
"all",
]
if args.debug:
print(F"""Running {" ".join(__A )}""" )
return
subprocess.run(__A )
print('''Successfully setup pod.''' )
def A () -> Optional[int]:
"""simple docstring"""
UpperCAmelCase_ = tpu_command_parser()
UpperCAmelCase_ = parser.parse_args()
tpu_command_launcher(__A )
| 51
|
"""simple docstring"""
def lowercase ( __snake_case : int = 1_0_0 ):
lowercase_ : str = 0
lowercase_ : List[Any] = 0
for i in range(1 , n + 1 ):
sum_of_squares += i**2
sum_of_ints += i
return sum_of_ints**2 - sum_of_squares
if __name__ == "__main__":
print(F"""{solution() = }""")
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from ....configuration_utils import PretrainedConfig
from ....utils import logging
__lowerCamelCase : Dict = logging.get_logger(__name__)
__lowerCamelCase : Optional[int] = {
"""CarlCochet/trajectory-transformer-halfcheetah-medium-v2""": (
"""https://huggingface.co/CarlCochet/trajectory-transformer-halfcheetah-medium-v2/resolve/main/config.json"""
),
# See all TrajectoryTransformer models at https://huggingface.co/models?filter=trajectory_transformer
}
class A__ ( __snake_case ):
_UpperCAmelCase :Tuple = 'trajectory_transformer'
_UpperCAmelCase :Union[str, Any] = ['past_key_values']
_UpperCAmelCase :Optional[Any] = {
'hidden_size': 'n_embd',
'num_attention_heads': 'n_head',
'num_hidden_layers': 'n_layer',
}
def __init__( self , A_=100 , A_=5 , A_=1 , A_=1 , A_=249 , A_=6 , A_=17 , A_=25 , A_=4 , A_=4 , A_=128 , A_=0.1 , A_=0.1 , A_=0.1 , A_=0.00_06 , A_=512 , A_=0.02 , A_=1e-12 , A_=1 , A_=True , A_=1 , A_=5_0256 , A_=5_0256 , **A_ , ):
'''simple docstring'''
UpperCamelCase : Optional[int] = vocab_size
UpperCamelCase : Optional[int] = action_weight
UpperCamelCase : Optional[int] = reward_weight
UpperCamelCase : Union[str, Any] = value_weight
UpperCamelCase : Optional[int] = max_position_embeddings
UpperCamelCase : Union[str, Any] = block_size
UpperCamelCase : Dict = action_dim
UpperCamelCase : List[Any] = observation_dim
UpperCamelCase : str = transition_dim
UpperCamelCase : Optional[Any] = learning_rate
UpperCamelCase : Optional[int] = n_layer
UpperCamelCase : Optional[int] = n_head
UpperCamelCase : Union[str, Any] = n_embd
UpperCamelCase : int = embd_pdrop
UpperCamelCase : List[Any] = attn_pdrop
UpperCamelCase : Union[str, Any] = resid_pdrop
UpperCamelCase : Optional[int] = initializer_range
UpperCamelCase : int = layer_norm_eps
UpperCamelCase : Union[str, Any] = kaiming_initializer_range
UpperCamelCase : List[str] = use_cache
super().__init__(pad_token_id=A_ , bos_token_id=A_ , eos_token_id=A_ , **A_ )
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|
"""simple docstring"""
import argparse
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline
if __name__ == "__main__":
__A : str = 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 : str = parser.parse_args()
__A : List[Any] = UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)
__A : Dict = CLIPImageProcessor()
__A : Union[str, Any] = CLIPVisionModelWithProjection.from_pretrained('''openai/clip-vit-large-patch14''')
__A : List[str] = 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)
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'''simple docstring'''
import argparse
import os
from pathlib import Path
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_specific_params
a__ : int =[
# replace left string with right string to get the relevant state_dict key (identical state dict to bart)
['''memory_attention''', '''encoder_attn'''],
['''attention''', '''attn'''],
['''/''', '''.'''],
['''.LayerNorm.gamma''', '''_layer_norm.weight'''],
['''.LayerNorm.beta''', '''_layer_norm.bias'''],
['''r.layer_''', '''r.layers.'''],
['''output_proj''', '''out_proj'''],
['''ffn.dense_1.''', '''fc2.'''],
['''ffn.dense.''', '''fc1.'''],
['''ffn_layer_norm''', '''final_layer_norm'''],
['''kernel''', '''weight'''],
['''encoder_layer_norm.''', '''encoder.layer_norm.'''],
['''decoder_layer_norm.''', '''decoder.layer_norm.'''],
['''embeddings.weights''', '''shared.weight'''],
]
def lowercase__ ( __lowercase : Dict ) -> Tuple:
"""simple docstring"""
for pegasus_name, hf_name in PATTERNS:
__UpperCamelCase = k.replace(__lowercase , __lowercase )
return k
def lowercase__ ( __lowercase : dict , __lowercase : dict ) -> PegasusForConditionalGeneration:
"""simple docstring"""
__UpperCamelCase = DEFAULTS.copy()
cfg_kwargs.update(__lowercase )
__UpperCamelCase = PegasusConfig(**__lowercase )
__UpperCamelCase = PegasusForConditionalGeneration(__lowercase )
__UpperCamelCase = torch_model.model.state_dict()
__UpperCamelCase = {}
for k, v in tf_weights.items():
__UpperCamelCase = rename_state_dict_key(__lowercase )
if new_k not in sd:
raise ValueError(F'''could not find new key {new_k} in state dict. (converted from {k})''' )
if "dense" in k or "proj" in new_k:
__UpperCamelCase = v.T
__UpperCamelCase = torch.tensor(__lowercase , dtype=sd[new_k].dtype )
assert v.shape == sd[new_k].shape, F'''{new_k}, {k}, {v.shape}, {sd[new_k].shape}'''
# make sure embedding.padding_idx is respected
__UpperCamelCase = torch.zeros_like(mapping['shared.weight'][cfg.pad_token_id + 1] )
__UpperCamelCase = mapping['shared.weight']
__UpperCamelCase = mapping['shared.weight']
__UpperCamelCase = {k: torch.zeros_like(__lowercase ) for k, v in sd.items() if k.endswith('bias' ) and k not in mapping}
mapping.update(**__lowercase )
__UpperCamelCase , __UpperCamelCase = torch_model.model.load_state_dict(__lowercase , strict=__lowercase )
__UpperCamelCase = [
k for k in missing if k not in ['encoder.embed_positions.weight', 'decoder.embed_positions.weight']
]
assert unexpected_missing == [], F'''no matches found for the following torch keys {unexpected_missing}'''
assert extra == [], F'''no matches found for the following tf keys {extra}'''
return torch_model
def lowercase__ ( __lowercase : Optional[Any]="./ckpt/aeslc/model.ckpt-32000" ) -> Dict:
"""simple docstring"""
__UpperCamelCase = tf.train.list_variables(__lowercase )
__UpperCamelCase = {}
__UpperCamelCase = ['Adafactor', 'global_step']
for name, shape in tqdm(__lowercase , desc='converting tf checkpoint to dict' ):
__UpperCamelCase = any(pat in name for pat in ignore_name )
if skip_key:
continue
__UpperCamelCase = tf.train.load_variable(__lowercase , __lowercase )
__UpperCamelCase = array
return tf_weights
def lowercase__ ( __lowercase : str , __lowercase : str ) -> Optional[Any]:
"""simple docstring"""
__UpperCamelCase = Path(__lowercase ).parent.name
__UpperCamelCase = task_specific_params[F'''summarization_{dataset}''']['max_position_embeddings']
__UpperCamelCase = PegasusTokenizer.from_pretrained('sshleifer/pegasus' , model_max_length=__lowercase )
assert tok.model_max_length == desired_max_model_length
tok.save_pretrained(__lowercase )
# convert model
__UpperCamelCase = get_tf_weights_as_numpy(__lowercase )
__UpperCamelCase = task_specific_params[F'''summarization_{dataset}''']
if dataset == "large":
__UpperCamelCase = task_specific_params
__UpperCamelCase = convert_pegasus(__lowercase , __lowercase )
torch_model.save_pretrained(__lowercase )
__UpperCamelCase = torch_model.state_dict()
sd.pop('model.decoder.embed_positions.weight' )
sd.pop('model.encoder.embed_positions.weight' )
torch.save(__lowercase , Path(__lowercase ) / 'pytorch_model.bin' )
if __name__ == "__main__":
a__ : Union[str, Any] =argparse.ArgumentParser()
# Required parameters
parser.add_argument('''tf_ckpt_path''', type=str, help='''passed to tf.train.list_variables''')
parser.add_argument('''save_dir''', default=None, type=str, help='''Path to the output PyTorch model.''')
a__ : int =parser.parse_args()
if args.save_dir is None:
a__ : str =Path(args.tf_ckpt_path).parent.name
a__ : str =os.path.join('''pegasus''', dataset)
convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)
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"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaControlnetImgaImgPipeline,
KandinskyVaaPriorEmbaEmbPipeline,
UNetaDConditionModel,
VQModel,
)
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 _UpperCAmelCase ( _A , unittest.TestCase ):
SCREAMING_SNAKE_CASE_ : Any = KandinskyVaaControlnetImgaImgPipeline
SCREAMING_SNAKE_CASE_ : Optional[int] = ["image_embeds", "negative_image_embeds", "image", "hint"]
SCREAMING_SNAKE_CASE_ : str = ["image_embeds", "negative_image_embeds", "image", "hint"]
SCREAMING_SNAKE_CASE_ : Dict = [
"generator",
"height",
"width",
"strength",
"guidance_scale",
"num_inference_steps",
"return_dict",
"guidance_scale",
"num_images_per_prompt",
"output_type",
"return_dict",
]
SCREAMING_SNAKE_CASE_ : Dict = False
@property
def A ( self : Any ) -> Any:
return 32
@property
def A ( self : Optional[int] ) -> Any:
return 32
@property
def A ( self : Dict ) -> int:
return self.time_input_dim
@property
def A ( self : Tuple ) -> str:
return self.time_input_dim * 4
@property
def A ( self : Any ) -> str:
return 1_00
@property
def A ( self : str ) -> List[str]:
torch.manual_seed(0 )
lowercase_ : List[Any] = {
'''in_channels''': 8,
# Out channels is double in channels because predicts mean and variance
'''out_channels''': 8,
'''addition_embed_type''': '''image_hint''',
'''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''': '''image_proj''',
'''cross_attention_dim''': self.cross_attention_dim,
'''attention_head_dim''': 4,
'''resnet_time_scale_shift''': '''scale_shift''',
'''class_embed_type''': None,
}
lowercase_ : Dict = UNetaDConditionModel(**A )
return model
@property
def A ( self : Optional[Any] ) -> Union[str, Any]:
return {
"block_out_channels": [32, 32, 64, 64],
"down_block_types": [
"DownEncoderBlock2D",
"DownEncoderBlock2D",
"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", "UpDecoderBlock2D", "UpDecoderBlock2D"],
"vq_embed_dim": 4,
}
@property
def A ( self : List[Any] ) -> Dict:
torch.manual_seed(0 )
lowercase_ : int = VQModel(**self.dummy_movq_kwargs )
return model
def A ( self : Union[str, Any] ) -> Optional[int]:
lowercase_ : Tuple = self.dummy_unet
lowercase_ : int = self.dummy_movq
lowercase_ : List[Any] = {
'''num_train_timesteps''': 10_00,
'''beta_schedule''': '''linear''',
'''beta_start''': 0.00085,
'''beta_end''': 0.012,
'''clip_sample''': False,
'''set_alpha_to_one''': False,
'''steps_offset''': 0,
'''prediction_type''': '''epsilon''',
'''thresholding''': False,
}
lowercase_ : str = DDIMScheduler(**A )
lowercase_ : Tuple = {
'''unet''': unet,
'''scheduler''': scheduler,
'''movq''': movq,
}
return components
def A ( self : Optional[int] , A : int , A : List[str]=0 ) -> int:
lowercase_ : str = floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(A ) ).to(A )
lowercase_ : Tuple = floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(seed + 1 ) ).to(
A )
# create init_image
lowercase_ : Union[str, Any] = floats_tensor((1, 3, 64, 64) , rng=random.Random(A ) ).to(A )
lowercase_ : Any = image.cpu().permute(0 , 2 , 3 , 1 )[0]
lowercase_ : Optional[Any] = Image.fromarray(np.uinta(A ) ).convert('''RGB''' ).resize((2_56, 2_56) )
# create hint
lowercase_ : Optional[int] = floats_tensor((1, 3, 64, 64) , rng=random.Random(A ) ).to(A )
if str(A ).startswith('''mps''' ):
lowercase_ : Optional[Any] = torch.manual_seed(A )
else:
lowercase_ : List[Any] = torch.Generator(device=A ).manual_seed(A )
lowercase_ : Dict = {
'''image''': init_image,
'''image_embeds''': image_embeds,
'''negative_image_embeds''': negative_image_embeds,
'''hint''': hint,
'''generator''': generator,
'''height''': 64,
'''width''': 64,
'''num_inference_steps''': 10,
'''guidance_scale''': 7.0,
'''strength''': 0.2,
'''output_type''': '''np''',
}
return inputs
def A ( self : Any ) -> List[Any]:
lowercase_ : List[str] = '''cpu'''
lowercase_ : Any = self.get_dummy_components()
lowercase_ : Any = self.pipeline_class(**A )
lowercase_ : int = pipe.to(A )
pipe.set_progress_bar_config(disable=A )
lowercase_ : Dict = pipe(**self.get_dummy_inputs(A ) )
lowercase_ : str = output.images
lowercase_ : int = pipe(
**self.get_dummy_inputs(A ) , return_dict=A , )[0]
lowercase_ : Dict = image[0, -3:, -3:, -1]
lowercase_ : Union[str, Any] = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
lowercase_ : List[str] = np.array(
[0.54985034, 0.55509365, 0.52561504, 0.5570494, 0.5593818, 0.5263979, 0.50285643, 0.5069846, 0.51196736] )
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()}'''
@slow
@require_torch_gpu
class _UpperCAmelCase ( unittest.TestCase ):
def A ( self : Tuple ) -> str:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def A ( self : Any ) -> Optional[int]:
lowercase_ : Dict = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy''' )
lowercase_ : Dict = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png''' )
lowercase_ : Optional[int] = init_image.resize((5_12, 5_12) )
lowercase_ : Dict = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/kandinskyv22/hint_image_cat.png''' )
lowercase_ : Optional[int] = torch.from_numpy(np.array(A ) ).float() / 255.0
lowercase_ : Tuple = hint.permute(2 , 0 , 1 ).unsqueeze(0 )
lowercase_ : Optional[Any] = '''A robot, 4k photo'''
lowercase_ : Tuple = KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
'''kandinsky-community/kandinsky-2-2-prior''' , torch_dtype=torch.floataa )
pipe_prior.to(A )
lowercase_ : Dict = KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
'''kandinsky-community/kandinsky-2-2-controlnet-depth''' , torch_dtype=torch.floataa )
lowercase_ : int = pipeline.to(A )
pipeline.set_progress_bar_config(disable=A )
lowercase_ : Tuple = torch.Generator(device='''cpu''' ).manual_seed(0 )
lowercase_ , lowercase_ : int = pipe_prior(
A , image=A , strength=0.85 , generator=A , negative_prompt='''''' , ).to_tuple()
lowercase_ : str = pipeline(
image=A , image_embeds=A , negative_image_embeds=A , hint=A , generator=A , num_inference_steps=1_00 , height=5_12 , width=5_12 , strength=0.5 , output_type='''np''' , )
lowercase_ : Optional[Any] = output.images[0]
assert image.shape == (5_12, 5_12, 3)
assert_mean_pixel_difference(A , A )
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|
"""simple docstring"""
# limitations under the License.
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from .pipelines import DiffusionPipeline, ImagePipelineOutput # noqa: F401
from .utils import deprecate
deprecate(
'''pipelines_utils''',
'''0.22.0''',
'''Importing `DiffusionPipeline` or `ImagePipelineOutput` from diffusers.pipeline_utils is deprecated. Please import from diffusers.pipelines.pipeline_utils instead.''',
standard_warn=False,
stacklevel=3,
)
| 54
|
"""simple docstring"""
def lowercase ( __snake_case : int = 1_0_0_0 ):
lowercase_ , lowercase_ : str = 1, 1
lowercase_ : List[str] = 2
while True:
lowercase_ : Tuple = 0
lowercase_ : List[Any] = fa + fa
lowercase_ , lowercase_ : Optional[int] = fa, f
index += 1
for _ in str(__snake_case ):
i += 1
if i == n:
break
return index
if __name__ == "__main__":
print(solution(int(str(input()).strip())))
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|
'''simple docstring'''
def __snake_case ( UpperCAmelCase_ : Dict ):
if not head:
return True
# split the list to two parts
lowerCamelCase_ ,lowerCamelCase_ = head.next, head
while fast and fast.next:
lowerCamelCase_ = fast.next.next
lowerCamelCase_ = slow.next
lowerCamelCase_ = slow.next
lowerCamelCase_ = None # Don't forget here! But forget still works!
# reverse the second part
lowerCamelCase_ = None
while second:
lowerCamelCase_ = second.next
lowerCamelCase_ = node
lowerCamelCase_ = second
lowerCamelCase_ = nxt
# compare two parts
# second part has the same or one less node
while node:
if node.val != head.val:
return False
lowerCamelCase_ = node.next
lowerCamelCase_ = head.next
return True
def __snake_case ( UpperCAmelCase_ : int ):
if not head or not head.next:
return True
# 1. Get the midpoint (slow)
lowerCamelCase_ = lowerCamelCase_ = lowerCamelCase_ = head
while fast and fast.next:
lowerCamelCase_ ,lowerCamelCase_ = fast.next.next, slow.next
# 2. Push the second half into the stack
lowerCamelCase_ = [slow.val]
while slow.next:
lowerCamelCase_ = slow.next
stack.append(slow.val )
# 3. Comparison
while stack:
if stack.pop() != cur.val:
return False
lowerCamelCase_ = cur.next
return True
def __snake_case ( UpperCAmelCase_ : Optional[int] ):
if not head or not head.next:
return True
lowerCamelCase_ = {}
lowerCamelCase_ = 0
while head:
if head.val in d:
d[head.val].append(UpperCAmelCase_ )
else:
lowerCamelCase_ = [pos]
lowerCamelCase_ = head.next
pos += 1
lowerCamelCase_ = pos - 1
lowerCamelCase_ = 0
for v in d.values():
if len(UpperCAmelCase_ ) % 2 != 0:
middle += 1
else:
lowerCamelCase_ = 0
for i in range(0 , len(UpperCAmelCase_ ) ):
if v[i] + v[len(UpperCAmelCase_ ) - 1 - step] != checksum:
return False
step += 1
if middle > 1:
return False
return True
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|
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : Dict = logging.get_logger(__name__)
__A : Union[str, Any] = {
'''facebook/vit-mae-base''': '''https://huggingface.co/facebook/vit-mae-base/resolve/main/config.json''',
# See all ViT MAE models at https://huggingface.co/models?filter=vit-mae
}
class _UpperCAmelCase ( _A ):
SCREAMING_SNAKE_CASE_ : Union[str, Any] = "vit_mae"
def __init__( self : Dict , A : List[str]=7_68 , A : Any=12 , A : Union[str, Any]=12 , A : Tuple=30_72 , A : Any="gelu" , A : Tuple=0.0 , A : List[str]=0.0 , A : Tuple=0.02 , A : Tuple=1e-12 , A : int=2_24 , A : Dict=16 , A : int=3 , A : Tuple=True , A : Tuple=16 , A : Optional[Any]=5_12 , A : Union[str, Any]=8 , A : List[Any]=20_48 , A : Dict=0.75 , A : Any=False , **A : Optional[int] , ) -> Union[str, Any]:
super().__init__(**A )
lowercase_ : List[Any] = hidden_size
lowercase_ : str = num_hidden_layers
lowercase_ : List[Any] = num_attention_heads
lowercase_ : Any = intermediate_size
lowercase_ : Optional[int] = hidden_act
lowercase_ : List[Any] = hidden_dropout_prob
lowercase_ : int = attention_probs_dropout_prob
lowercase_ : int = initializer_range
lowercase_ : Dict = layer_norm_eps
lowercase_ : Optional[Any] = image_size
lowercase_ : str = patch_size
lowercase_ : Dict = num_channels
lowercase_ : Any = qkv_bias
lowercase_ : Union[str, Any] = decoder_num_attention_heads
lowercase_ : Optional[Any] = decoder_hidden_size
lowercase_ : List[str] = decoder_num_hidden_layers
lowercase_ : List[Any] = decoder_intermediate_size
lowercase_ : Optional[Any] = mask_ratio
lowercase_ : Optional[Any] = norm_pix_loss
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|
'''simple docstring'''
import tempfile
import torch
from diffusers import PNDMScheduler
from .test_schedulers import SchedulerCommonTest
class a ( _lowerCamelCase ):
snake_case_ = (PNDMScheduler,)
snake_case_ = (("num_inference_steps", 50),)
def A_ ( self : Tuple , **lowercase_ : Tuple ):
snake_case_ = {
'''num_train_timesteps''': 1000,
'''beta_start''': 0.0001,
'''beta_end''': 0.02,
'''beta_schedule''': '''linear''',
}
config.update(**lowercase_ )
return config
def A_ ( self : Any , lowercase_ : Optional[int]=0 , **lowercase_ : int ):
snake_case_ = dict(self.forward_default_kwargs )
snake_case_ = kwargs.pop('''num_inference_steps''' , lowercase_ )
snake_case_ = self.dummy_sample
snake_case_ = 0.1 * sample
snake_case_ = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
for scheduler_class in self.scheduler_classes:
snake_case_ = self.get_scheduler_config(**lowercase_ )
snake_case_ = scheduler_class(**lowercase_ )
scheduler.set_timesteps(lowercase_ )
# copy over dummy past residuals
snake_case_ = dummy_past_residuals[:]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(lowercase_ )
snake_case_ = scheduler_class.from_pretrained(lowercase_ )
new_scheduler.set_timesteps(lowercase_ )
# copy over dummy past residuals
snake_case_ = dummy_past_residuals[:]
snake_case_ = scheduler.step_prk(lowercase_ , lowercase_ , lowercase_ , **lowercase_ ).prev_sample
snake_case_ = new_scheduler.step_prk(lowercase_ , lowercase_ , lowercase_ , **lowercase_ ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"
snake_case_ = scheduler.step_plms(lowercase_ , lowercase_ , lowercase_ , **lowercase_ ).prev_sample
snake_case_ = new_scheduler.step_plms(lowercase_ , lowercase_ , lowercase_ , **lowercase_ ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"
def A_ ( self : Any ):
pass
def A_ ( self : Any , lowercase_ : Dict=0 , **lowercase_ : Optional[int] ):
snake_case_ = dict(self.forward_default_kwargs )
snake_case_ = kwargs.pop('''num_inference_steps''' , lowercase_ )
snake_case_ = self.dummy_sample
snake_case_ = 0.1 * sample
snake_case_ = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
for scheduler_class in self.scheduler_classes:
snake_case_ = self.get_scheduler_config()
snake_case_ = scheduler_class(**lowercase_ )
scheduler.set_timesteps(lowercase_ )
# copy over dummy past residuals (must be after setting timesteps)
snake_case_ = dummy_past_residuals[:]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(lowercase_ )
snake_case_ = scheduler_class.from_pretrained(lowercase_ )
# copy over dummy past residuals
new_scheduler.set_timesteps(lowercase_ )
# copy over dummy past residual (must be after setting timesteps)
snake_case_ = dummy_past_residuals[:]
snake_case_ = scheduler.step_prk(lowercase_ , lowercase_ , lowercase_ , **lowercase_ ).prev_sample
snake_case_ = new_scheduler.step_prk(lowercase_ , lowercase_ , lowercase_ , **lowercase_ ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"
snake_case_ = scheduler.step_plms(lowercase_ , lowercase_ , lowercase_ , **lowercase_ ).prev_sample
snake_case_ = new_scheduler.step_plms(lowercase_ , lowercase_ , lowercase_ , **lowercase_ ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"
def A_ ( self : Dict , **lowercase_ : str ):
snake_case_ = self.scheduler_classes[0]
snake_case_ = self.get_scheduler_config(**lowercase_ )
snake_case_ = scheduler_class(**lowercase_ )
snake_case_ = 10
snake_case_ = self.dummy_model()
snake_case_ = self.dummy_sample_deter
scheduler.set_timesteps(lowercase_ )
for i, t in enumerate(scheduler.prk_timesteps ):
snake_case_ = model(lowercase_ , lowercase_ )
snake_case_ = scheduler.step_prk(lowercase_ , lowercase_ , lowercase_ ).prev_sample
for i, t in enumerate(scheduler.plms_timesteps ):
snake_case_ = model(lowercase_ , lowercase_ )
snake_case_ = scheduler.step_plms(lowercase_ , lowercase_ , lowercase_ ).prev_sample
return sample
def A_ ( self : Dict ):
snake_case_ = dict(self.forward_default_kwargs )
snake_case_ = kwargs.pop('''num_inference_steps''' , lowercase_ )
for scheduler_class in self.scheduler_classes:
snake_case_ = self.get_scheduler_config()
snake_case_ = scheduler_class(**lowercase_ )
snake_case_ = self.dummy_sample
snake_case_ = 0.1 * sample
if num_inference_steps is not None and hasattr(lowercase_ , '''set_timesteps''' ):
scheduler.set_timesteps(lowercase_ )
elif num_inference_steps is not None and not hasattr(lowercase_ , '''set_timesteps''' ):
snake_case_ = num_inference_steps
# copy over dummy past residuals (must be done after set_timesteps)
snake_case_ = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
snake_case_ = dummy_past_residuals[:]
snake_case_ = scheduler.step_prk(lowercase_ , 0 , lowercase_ , **lowercase_ ).prev_sample
snake_case_ = scheduler.step_prk(lowercase_ , 1 , lowercase_ , **lowercase_ ).prev_sample
self.assertEqual(output_a.shape , sample.shape )
self.assertEqual(output_a.shape , output_a.shape )
snake_case_ = scheduler.step_plms(lowercase_ , 0 , lowercase_ , **lowercase_ ).prev_sample
snake_case_ = scheduler.step_plms(lowercase_ , 1 , lowercase_ , **lowercase_ ).prev_sample
self.assertEqual(output_a.shape , sample.shape )
self.assertEqual(output_a.shape , output_a.shape )
def A_ ( self : List[Any] ):
for timesteps in [100, 1000]:
self.check_over_configs(num_train_timesteps=lowercase_ )
def A_ ( self : List[str] ):
for steps_offset in [0, 1]:
self.check_over_configs(steps_offset=lowercase_ )
snake_case_ = self.scheduler_classes[0]
snake_case_ = self.get_scheduler_config(steps_offset=1 )
snake_case_ = scheduler_class(**lowercase_ )
scheduler.set_timesteps(10 )
assert torch.equal(
scheduler.timesteps , torch.LongTensor(
[901, 851, 851, 801, 801, 751, 751, 701, 701, 651, 651, 601, 601, 501, 401, 301, 201, 101, 1] ) , )
def A_ ( self : Any ):
for beta_start, beta_end in zip([0.0001, 0.001] , [0.002, 0.02] ):
self.check_over_configs(beta_start=lowercase_ , beta_end=lowercase_ )
def A_ ( self : List[str] ):
for schedule in ["linear", "squaredcos_cap_v2"]:
self.check_over_configs(beta_schedule=lowercase_ )
def A_ ( self : str ):
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=lowercase_ )
def A_ ( self : str ):
for t in [1, 5, 10]:
self.check_over_forward(time_step=lowercase_ )
def A_ ( self : str ):
for t, num_inference_steps in zip([1, 5, 10] , [10, 50, 100] ):
self.check_over_forward(num_inference_steps=lowercase_ )
def A_ ( self : Any ):
# earlier version of set_timesteps() caused an error indexing alpha's with inference steps as power of 3
snake_case_ = 27
for scheduler_class in self.scheduler_classes:
snake_case_ = self.dummy_sample
snake_case_ = 0.1 * sample
snake_case_ = self.get_scheduler_config()
snake_case_ = scheduler_class(**lowercase_ )
scheduler.set_timesteps(lowercase_ )
# before power of 3 fix, would error on first step, so we only need to do two
for i, t in enumerate(scheduler.prk_timesteps[:2] ):
snake_case_ = scheduler.step_prk(lowercase_ , lowercase_ , lowercase_ ).prev_sample
def A_ ( self : Tuple ):
with self.assertRaises(lowercase_ ):
snake_case_ = self.scheduler_classes[0]
snake_case_ = self.get_scheduler_config()
snake_case_ = scheduler_class(**lowercase_ )
scheduler.step_plms(self.dummy_sample , 1 , self.dummy_sample ).prev_sample
def A_ ( self : str ):
snake_case_ = self.full_loop()
snake_case_ = torch.sum(torch.abs(lowercase_ ) )
snake_case_ = torch.mean(torch.abs(lowercase_ ) )
assert abs(result_sum.item() - 198.1318 ) < 1e-2
assert abs(result_mean.item() - 0.2580 ) < 1e-3
def A_ ( self : Any ):
snake_case_ = self.full_loop(prediction_type='''v_prediction''' )
snake_case_ = torch.sum(torch.abs(lowercase_ ) )
snake_case_ = torch.mean(torch.abs(lowercase_ ) )
assert abs(result_sum.item() - 67.3986 ) < 1e-2
assert abs(result_mean.item() - 0.0878 ) < 1e-3
def A_ ( self : Optional[Any] ):
# We specify different beta, so that the first alpha is 0.99
snake_case_ = self.full_loop(set_alpha_to_one=lowercase_ , beta_start=0.01 )
snake_case_ = torch.sum(torch.abs(lowercase_ ) )
snake_case_ = torch.mean(torch.abs(lowercase_ ) )
assert abs(result_sum.item() - 230.0399 ) < 1e-2
assert abs(result_mean.item() - 0.2995 ) < 1e-3
def A_ ( self : str ):
# We specify different beta, so that the first alpha is 0.99
snake_case_ = self.full_loop(set_alpha_to_one=lowercase_ , beta_start=0.01 )
snake_case_ = torch.sum(torch.abs(lowercase_ ) )
snake_case_ = torch.mean(torch.abs(lowercase_ ) )
assert abs(result_sum.item() - 186.9482 ) < 1e-2
assert abs(result_mean.item() - 0.2434 ) < 1e-3
| 56
|
"""simple docstring"""
def lowercase ( __snake_case : int ):
if n == 1 or not isinstance(__snake_case , __snake_case ):
return 0
elif n == 2:
return 1
else:
lowercase_ : Dict = [0, 1]
for i in range(2 , n + 1 ):
sequence.append(sequence[i - 1] + sequence[i - 2] )
return sequence[n]
def lowercase ( __snake_case : int ):
lowercase_ : str = 0
lowercase_ : List[str] = 2
while digits < n:
index += 1
lowercase_ : Any = len(str(fibonacci(__snake_case ) ) )
return index
def lowercase ( __snake_case : int = 1_0_0_0 ):
return fibonacci_digits_index(__snake_case )
if __name__ == "__main__":
print(solution(int(str(input()).strip())))
| 33
| 0
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_tf_available,
is_torch_available,
)
A : Dict = {
"configuration_speech_to_text": ["SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP", "Speech2TextConfig"],
"processing_speech_to_text": ["Speech2TextProcessor"],
}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : Optional[int] = ["Speech2TextTokenizer"]
try:
if not is_speech_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : Optional[int] = ["Speech2TextFeatureExtractor"]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : str = [
"TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFSpeech2TextForConditionalGeneration",
"TFSpeech2TextModel",
"TFSpeech2TextPreTrainedModel",
]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : List[str] = [
"SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST",
"Speech2TextForConditionalGeneration",
"Speech2TextModel",
"Speech2TextPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_speech_to_text import SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextConfig
from .processing_speech_to_text import SpeechaTextProcessor
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_speech_to_text import SpeechaTextTokenizer
try:
if not is_speech_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_speech_to_text import SpeechaTextFeatureExtractor
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_speech_to_text import (
TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFSpeechaTextForConditionalGeneration,
TFSpeechaTextModel,
TFSpeechaTextPreTrainedModel,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_speech_to_text import (
SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
SpeechaTextForConditionalGeneration,
SpeechaTextModel,
SpeechaTextPreTrainedModel,
)
else:
import sys
A : Optional[Any] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 57
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__A : List[str] = {
'''configuration_mobilenet_v2''': [
'''MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''MobileNetV2Config''',
'''MobileNetV2OnnxConfig''',
],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : Dict = ['''MobileNetV2FeatureExtractor''']
__A : Optional[int] = ['''MobileNetV2ImageProcessor''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : Optional[Any] = [
'''MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''MobileNetV2ForImageClassification''',
'''MobileNetV2ForSemanticSegmentation''',
'''MobileNetV2Model''',
'''MobileNetV2PreTrainedModel''',
'''load_tf_weights_in_mobilenet_v2''',
]
if TYPE_CHECKING:
from .configuration_mobilenet_va import (
MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
MobileNetVaConfig,
MobileNetVaOnnxConfig,
)
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_mobilenet_va import MobileNetVaFeatureExtractor
from .image_processing_mobilenet_va import MobileNetVaImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mobilenet_va import (
MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
MobileNetVaForImageClassification,
MobileNetVaForSemanticSegmentation,
MobileNetVaModel,
MobileNetVaPreTrainedModel,
load_tf_weights_in_mobilenet_va,
)
else:
import sys
__A : List[Any] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 33
| 0
|
'''simple docstring'''
from collections import OrderedDict
from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES
lowercase_ = logging.get_logger(__name__)
lowercase_ = OrderedDict(
[
# Base model mapping
("""albert""", """FlaxAlbertModel"""),
("""bart""", """FlaxBartModel"""),
("""beit""", """FlaxBeitModel"""),
("""bert""", """FlaxBertModel"""),
("""big_bird""", """FlaxBigBirdModel"""),
("""blenderbot""", """FlaxBlenderbotModel"""),
("""blenderbot-small""", """FlaxBlenderbotSmallModel"""),
("""clip""", """FlaxCLIPModel"""),
("""distilbert""", """FlaxDistilBertModel"""),
("""electra""", """FlaxElectraModel"""),
("""gpt-sw3""", """FlaxGPT2Model"""),
("""gpt2""", """FlaxGPT2Model"""),
("""gpt_neo""", """FlaxGPTNeoModel"""),
("""gptj""", """FlaxGPTJModel"""),
("""longt5""", """FlaxLongT5Model"""),
("""marian""", """FlaxMarianModel"""),
("""mbart""", """FlaxMBartModel"""),
("""mt5""", """FlaxMT5Model"""),
("""opt""", """FlaxOPTModel"""),
("""pegasus""", """FlaxPegasusModel"""),
("""regnet""", """FlaxRegNetModel"""),
("""resnet""", """FlaxResNetModel"""),
("""roberta""", """FlaxRobertaModel"""),
("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormModel"""),
("""roformer""", """FlaxRoFormerModel"""),
("""t5""", """FlaxT5Model"""),
("""vision-text-dual-encoder""", """FlaxVisionTextDualEncoderModel"""),
("""vit""", """FlaxViTModel"""),
("""wav2vec2""", """FlaxWav2Vec2Model"""),
("""whisper""", """FlaxWhisperModel"""),
("""xglm""", """FlaxXGLMModel"""),
("""xlm-roberta""", """FlaxXLMRobertaModel"""),
]
)
lowercase_ = OrderedDict(
[
# Model for pre-training mapping
("""albert""", """FlaxAlbertForPreTraining"""),
("""bart""", """FlaxBartForConditionalGeneration"""),
("""bert""", """FlaxBertForPreTraining"""),
("""big_bird""", """FlaxBigBirdForPreTraining"""),
("""electra""", """FlaxElectraForPreTraining"""),
("""longt5""", """FlaxLongT5ForConditionalGeneration"""),
("""mbart""", """FlaxMBartForConditionalGeneration"""),
("""mt5""", """FlaxMT5ForConditionalGeneration"""),
("""roberta""", """FlaxRobertaForMaskedLM"""),
("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForMaskedLM"""),
("""roformer""", """FlaxRoFormerForMaskedLM"""),
("""t5""", """FlaxT5ForConditionalGeneration"""),
("""wav2vec2""", """FlaxWav2Vec2ForPreTraining"""),
("""whisper""", """FlaxWhisperForConditionalGeneration"""),
("""xlm-roberta""", """FlaxXLMRobertaForMaskedLM"""),
]
)
lowercase_ = OrderedDict(
[
# Model for Masked LM mapping
("""albert""", """FlaxAlbertForMaskedLM"""),
("""bart""", """FlaxBartForConditionalGeneration"""),
("""bert""", """FlaxBertForMaskedLM"""),
("""big_bird""", """FlaxBigBirdForMaskedLM"""),
("""distilbert""", """FlaxDistilBertForMaskedLM"""),
("""electra""", """FlaxElectraForMaskedLM"""),
("""mbart""", """FlaxMBartForConditionalGeneration"""),
("""roberta""", """FlaxRobertaForMaskedLM"""),
("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForMaskedLM"""),
("""roformer""", """FlaxRoFormerForMaskedLM"""),
("""xlm-roberta""", """FlaxXLMRobertaForMaskedLM"""),
]
)
lowercase_ = OrderedDict(
[
# Model for Seq2Seq Causal LM mapping
("""bart""", """FlaxBartForConditionalGeneration"""),
("""blenderbot""", """FlaxBlenderbotForConditionalGeneration"""),
("""blenderbot-small""", """FlaxBlenderbotSmallForConditionalGeneration"""),
("""encoder-decoder""", """FlaxEncoderDecoderModel"""),
("""longt5""", """FlaxLongT5ForConditionalGeneration"""),
("""marian""", """FlaxMarianMTModel"""),
("""mbart""", """FlaxMBartForConditionalGeneration"""),
("""mt5""", """FlaxMT5ForConditionalGeneration"""),
("""pegasus""", """FlaxPegasusForConditionalGeneration"""),
("""t5""", """FlaxT5ForConditionalGeneration"""),
]
)
lowercase_ = OrderedDict(
[
# Model for Image-classsification
("""beit""", """FlaxBeitForImageClassification"""),
("""regnet""", """FlaxRegNetForImageClassification"""),
("""resnet""", """FlaxResNetForImageClassification"""),
("""vit""", """FlaxViTForImageClassification"""),
]
)
lowercase_ = OrderedDict(
[
("""vision-encoder-decoder""", """FlaxVisionEncoderDecoderModel"""),
]
)
lowercase_ = OrderedDict(
[
# Model for Causal LM mapping
("""bart""", """FlaxBartForCausalLM"""),
("""bert""", """FlaxBertForCausalLM"""),
("""big_bird""", """FlaxBigBirdForCausalLM"""),
("""electra""", """FlaxElectraForCausalLM"""),
("""gpt-sw3""", """FlaxGPT2LMHeadModel"""),
("""gpt2""", """FlaxGPT2LMHeadModel"""),
("""gpt_neo""", """FlaxGPTNeoForCausalLM"""),
("""gptj""", """FlaxGPTJForCausalLM"""),
("""opt""", """FlaxOPTForCausalLM"""),
("""roberta""", """FlaxRobertaForCausalLM"""),
("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForCausalLM"""),
("""xglm""", """FlaxXGLMForCausalLM"""),
("""xlm-roberta""", """FlaxXLMRobertaForCausalLM"""),
]
)
lowercase_ = OrderedDict(
[
# Model for Sequence Classification mapping
("""albert""", """FlaxAlbertForSequenceClassification"""),
("""bart""", """FlaxBartForSequenceClassification"""),
("""bert""", """FlaxBertForSequenceClassification"""),
("""big_bird""", """FlaxBigBirdForSequenceClassification"""),
("""distilbert""", """FlaxDistilBertForSequenceClassification"""),
("""electra""", """FlaxElectraForSequenceClassification"""),
("""mbart""", """FlaxMBartForSequenceClassification"""),
("""roberta""", """FlaxRobertaForSequenceClassification"""),
("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForSequenceClassification"""),
("""roformer""", """FlaxRoFormerForSequenceClassification"""),
("""xlm-roberta""", """FlaxXLMRobertaForSequenceClassification"""),
]
)
lowercase_ = OrderedDict(
[
# Model for Question Answering mapping
("""albert""", """FlaxAlbertForQuestionAnswering"""),
("""bart""", """FlaxBartForQuestionAnswering"""),
("""bert""", """FlaxBertForQuestionAnswering"""),
("""big_bird""", """FlaxBigBirdForQuestionAnswering"""),
("""distilbert""", """FlaxDistilBertForQuestionAnswering"""),
("""electra""", """FlaxElectraForQuestionAnswering"""),
("""mbart""", """FlaxMBartForQuestionAnswering"""),
("""roberta""", """FlaxRobertaForQuestionAnswering"""),
("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForQuestionAnswering"""),
("""roformer""", """FlaxRoFormerForQuestionAnswering"""),
("""xlm-roberta""", """FlaxXLMRobertaForQuestionAnswering"""),
]
)
lowercase_ = OrderedDict(
[
# Model for Token Classification mapping
("""albert""", """FlaxAlbertForTokenClassification"""),
("""bert""", """FlaxBertForTokenClassification"""),
("""big_bird""", """FlaxBigBirdForTokenClassification"""),
("""distilbert""", """FlaxDistilBertForTokenClassification"""),
("""electra""", """FlaxElectraForTokenClassification"""),
("""roberta""", """FlaxRobertaForTokenClassification"""),
("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForTokenClassification"""),
("""roformer""", """FlaxRoFormerForTokenClassification"""),
("""xlm-roberta""", """FlaxXLMRobertaForTokenClassification"""),
]
)
lowercase_ = OrderedDict(
[
# Model for Multiple Choice mapping
("""albert""", """FlaxAlbertForMultipleChoice"""),
("""bert""", """FlaxBertForMultipleChoice"""),
("""big_bird""", """FlaxBigBirdForMultipleChoice"""),
("""distilbert""", """FlaxDistilBertForMultipleChoice"""),
("""electra""", """FlaxElectraForMultipleChoice"""),
("""roberta""", """FlaxRobertaForMultipleChoice"""),
("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForMultipleChoice"""),
("""roformer""", """FlaxRoFormerForMultipleChoice"""),
("""xlm-roberta""", """FlaxXLMRobertaForMultipleChoice"""),
]
)
lowercase_ = OrderedDict(
[
("""bert""", """FlaxBertForNextSentencePrediction"""),
]
)
lowercase_ = OrderedDict(
[
("""speech-encoder-decoder""", """FlaxSpeechEncoderDecoderModel"""),
("""whisper""", """FlaxWhisperForConditionalGeneration"""),
]
)
lowercase_ = OrderedDict(
[
("""whisper""", """FlaxWhisperForAudioClassification"""),
]
)
lowercase_ = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_MAPPING_NAMES)
lowercase_ = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_PRETRAINING_MAPPING_NAMES)
lowercase_ = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MASKED_LM_MAPPING_NAMES)
lowercase_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES
)
lowercase_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES
)
lowercase_ = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES)
lowercase_ = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_CAUSAL_LM_MAPPING_NAMES)
lowercase_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES
)
lowercase_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES
)
lowercase_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES
)
lowercase_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES
)
lowercase_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES
)
lowercase_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES
)
lowercase_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES
)
class a_ ( _BaseAutoModelClass ):
'''simple docstring'''
UpperCamelCase = FLAX_MODEL_MAPPING
lowercase_ = auto_class_update(FlaxAutoModel)
class a_ ( _BaseAutoModelClass ):
'''simple docstring'''
UpperCamelCase = FLAX_MODEL_FOR_PRETRAINING_MAPPING
lowercase_ = auto_class_update(FlaxAutoModelForPreTraining, head_doc="""pretraining""")
class a_ ( _BaseAutoModelClass ):
'''simple docstring'''
UpperCamelCase = FLAX_MODEL_FOR_CAUSAL_LM_MAPPING
lowercase_ = auto_class_update(FlaxAutoModelForCausalLM, head_doc="""causal language modeling""")
class a_ ( _BaseAutoModelClass ):
'''simple docstring'''
UpperCamelCase = FLAX_MODEL_FOR_MASKED_LM_MAPPING
lowercase_ = auto_class_update(FlaxAutoModelForMaskedLM, head_doc="""masked language modeling""")
class a_ ( _BaseAutoModelClass ):
'''simple docstring'''
UpperCamelCase = FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
lowercase_ = auto_class_update(
FlaxAutoModelForSeqaSeqLM, head_doc="""sequence-to-sequence language modeling""", checkpoint_for_example="""t5-base"""
)
class a_ ( _BaseAutoModelClass ):
'''simple docstring'''
UpperCamelCase = FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
lowercase_ = auto_class_update(
FlaxAutoModelForSequenceClassification, head_doc="""sequence classification"""
)
class a_ ( _BaseAutoModelClass ):
'''simple docstring'''
UpperCamelCase = FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING
lowercase_ = auto_class_update(FlaxAutoModelForQuestionAnswering, head_doc="""question answering""")
class a_ ( _BaseAutoModelClass ):
'''simple docstring'''
UpperCamelCase = FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING
lowercase_ = auto_class_update(
FlaxAutoModelForTokenClassification, head_doc="""token classification"""
)
class a_ ( _BaseAutoModelClass ):
'''simple docstring'''
UpperCamelCase = FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING
lowercase_ = auto_class_update(FlaxAutoModelForMultipleChoice, head_doc="""multiple choice""")
class a_ ( _BaseAutoModelClass ):
'''simple docstring'''
UpperCamelCase = FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING
lowercase_ = auto_class_update(
FlaxAutoModelForNextSentencePrediction, head_doc="""next sentence prediction"""
)
class a_ ( _BaseAutoModelClass ):
'''simple docstring'''
UpperCamelCase = FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
lowercase_ = auto_class_update(
FlaxAutoModelForImageClassification, head_doc="""image classification"""
)
class a_ ( _BaseAutoModelClass ):
'''simple docstring'''
UpperCamelCase = FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING
lowercase_ = auto_class_update(FlaxAutoModelForVisionaSeq, head_doc="""vision-to-text modeling""")
class a_ ( _BaseAutoModelClass ):
'''simple docstring'''
UpperCamelCase = FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING
lowercase_ = auto_class_update(
FlaxAutoModelForSpeechSeqaSeq, head_doc="""sequence-to-sequence speech-to-text modeling"""
)
| 58
|
"""simple docstring"""
from __future__ import annotations
__A : List[Any] = [-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
__A : str = [-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]
def lowercase ( __snake_case : list[float] ):
lowercase_ : List[str] = []
lowercase_ : List[Any] = len(__snake_case )
for i in range(__snake_case ):
lowercase_ : float = -1
for j in range(i + 1 , __snake_case ):
if arr[i] < arr[j]:
lowercase_ : List[str] = arr[j]
break
result.append(__snake_case )
return result
def lowercase ( __snake_case : list[float] ):
lowercase_ : List[str] = []
for i, outer in enumerate(__snake_case ):
lowercase_ : float = -1
for inner in arr[i + 1 :]:
if outer < inner:
lowercase_ : List[Any] = inner
break
result.append(__snake_case )
return result
def lowercase ( __snake_case : list[float] ):
lowercase_ : List[str] = len(__snake_case )
lowercase_ : list[float] = []
lowercase_ : list[float] = [-1] * arr_size
for index in reversed(range(__snake_case ) ):
if stack:
while stack[-1] <= arr[index]:
stack.pop()
if not stack:
break
if stack:
lowercase_ : Optional[Any] = stack[-1]
stack.append(arr[index] )
return result
if __name__ == "__main__":
from doctest import testmod
from timeit import timeit
testmod()
print(next_greatest_element_slow(arr))
print(next_greatest_element_fast(arr))
print(next_greatest_element(arr))
__A : int = (
'''from __main__ import arr, next_greatest_element_slow, '''
'''next_greatest_element_fast, next_greatest_element'''
)
print(
'''next_greatest_element_slow():''',
timeit('''next_greatest_element_slow(arr)''', setup=setup),
)
print(
'''next_greatest_element_fast():''',
timeit('''next_greatest_element_fast(arr)''', setup=setup),
)
print(
''' next_greatest_element():''',
timeit('''next_greatest_element(arr)''', setup=setup),
)
| 33
| 0
|
def UpperCamelCase ( __lowerCamelCase : str ):
if not all(char in "01" for char in bin_string ):
raise ValueError("Non-binary value was passed to the function" )
if not bin_string:
raise ValueError("Empty string was passed to the function" )
snake_case : Tuple = ""
while len(__lowerCamelCase ) % 3 != 0:
snake_case : Any = "0" + bin_string
snake_case : Any = [
bin_string[index : index + 3]
for index in range(len(__lowerCamelCase ) )
if index % 3 == 0
]
for bin_group in bin_string_in_3_list:
snake_case : Union[str, Any] = 0
for index, val in enumerate(__lowerCamelCase ):
oct_val += int(2 ** (2 - index) * int(__lowerCamelCase ) )
oct_string += str(__lowerCamelCase )
return oct_string
if __name__ == "__main__":
from doctest import testmod
testmod()
| 59
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__A : Union[str, Any] = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : Dict = ['''NllbTokenizer''']
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : Dict = ['''NllbTokenizerFast''']
if TYPE_CHECKING:
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_nllb import NllbTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_nllb_fast import NllbTokenizerFast
else:
import sys
__A : List[Any] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 33
| 0
|
"""simple docstring"""
import argparse
import glob
import logging
import os
from argparse import Namespace
from importlib import import_module
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch.nn import CrossEntropyLoss
from torch.utils.data import DataLoader, TensorDataset
from utils_ner import TokenClassificationTask
snake_case__ : int = logging.getLogger(__name__)
class snake_case_( a__ ):
__UpperCamelCase = '''token-classification'''
def __init__( self : int , UpperCamelCase_ : Tuple ):
if type(UpperCamelCase_ ) == dict:
lowerCAmelCase : Union[str, Any] = Namespace(**UpperCamelCase_ )
lowerCAmelCase : Dict = import_module('''tasks''' )
try:
lowerCAmelCase : str = getattr(UpperCamelCase_ , hparams.task_type )
lowerCAmelCase : TokenClassificationTask = token_classification_task_clazz()
except AttributeError:
raise ValueError(
F'''Task {hparams.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. '''
F'''Available tasks classes are: {TokenClassificationTask.__subclasses__()}''' )
lowerCAmelCase : Any = self.token_classification_task.get_labels(hparams.labels )
lowerCAmelCase : Optional[Any] = CrossEntropyLoss().ignore_index
super().__init__(UpperCamelCase_ , len(self.labels ) , self.mode )
def lowerCamelCase__ ( self : int , **UpperCamelCase_ : List[Any] ):
return self.model(**UpperCamelCase_ )
def lowerCamelCase__ ( self : Optional[Any] , UpperCamelCase_ : Any , UpperCamelCase_ : int ):
lowerCAmelCase : List[str] = {'''input_ids''': batch[0], '''attention_mask''': batch[1], '''labels''': batch[3]}
if self.config.model_type != "distilbert":
lowerCAmelCase : Dict = (
batch[2] if self.config.model_type in ['''bert''', '''xlnet'''] else None
) # XLM and RoBERTa don"t use token_type_ids
lowerCAmelCase : Optional[int] = self(**UpperCamelCase_ )
lowerCAmelCase : str = outputs[0]
# tensorboard_logs = {"loss": loss, "rate": self.lr_scheduler.get_last_lr()[-1]}
return {"loss": loss}
def lowerCamelCase__ ( self : Tuple ):
lowerCAmelCase : List[str] = self.hparams
for mode in ["train", "dev", "test"]:
lowerCAmelCase : Dict = self._feature_file(UpperCamelCase_ )
if os.path.exists(UpperCamelCase_ ) and not args.overwrite_cache:
logger.info('''Loading features from cached file %s''' , UpperCamelCase_ )
lowerCAmelCase : int = torch.load(UpperCamelCase_ )
else:
logger.info('''Creating features from dataset file at %s''' , args.data_dir )
lowerCAmelCase : Union[str, Any] = self.token_classification_task.read_examples_from_file(args.data_dir , UpperCamelCase_ )
lowerCAmelCase : Optional[Any] = self.token_classification_task.convert_examples_to_features(
UpperCamelCase_ , self.labels , args.max_seq_length , self.tokenizer , cls_token_at_end=bool(self.config.model_type in ['''xlnet'''] ) , cls_token=self.tokenizer.cls_token , cls_token_segment_id=2 if self.config.model_type in ['''xlnet'''] else 0 , sep_token=self.tokenizer.sep_token , sep_token_extra=UpperCamelCase_ , pad_on_left=bool(self.config.model_type in ['''xlnet'''] ) , pad_token=self.tokenizer.pad_token_id , pad_token_segment_id=self.tokenizer.pad_token_type_id , pad_token_label_id=self.pad_token_label_id , )
logger.info('''Saving features into cached file %s''' , UpperCamelCase_ )
torch.save(UpperCamelCase_ , UpperCamelCase_ )
def lowerCamelCase__ ( self : Union[str, Any] , UpperCamelCase_ : int , UpperCamelCase_ : int , UpperCamelCase_ : bool = False ):
lowerCAmelCase : Optional[int] = self._feature_file(UpperCamelCase_ )
logger.info('''Loading features from cached file %s''' , UpperCamelCase_ )
lowerCAmelCase : str = torch.load(UpperCamelCase_ )
lowerCAmelCase : int = torch.tensor([f.input_ids for f in features] , dtype=torch.long )
lowerCAmelCase : Tuple = torch.tensor([f.attention_mask for f in features] , dtype=torch.long )
if features[0].token_type_ids is not None:
lowerCAmelCase : int = torch.tensor([f.token_type_ids for f in features] , dtype=torch.long )
else:
lowerCAmelCase : Optional[Any] = torch.tensor([0 for f in features] , dtype=torch.long )
# HACK(we will not use this anymore soon)
lowerCAmelCase : int = torch.tensor([f.label_ids for f in features] , dtype=torch.long )
return DataLoader(
TensorDataset(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ) , batch_size=UpperCamelCase_ )
def lowerCamelCase__ ( self : Any , UpperCamelCase_ : Union[str, Any] , UpperCamelCase_ : List[Any] ):
"""Compute validation""" ""
lowerCAmelCase : str = {'''input_ids''': batch[0], '''attention_mask''': batch[1], '''labels''': batch[3]}
if self.config.model_type != "distilbert":
lowerCAmelCase : Optional[int] = (
batch[2] if self.config.model_type in ['''bert''', '''xlnet'''] else None
) # XLM and RoBERTa don"t use token_type_ids
lowerCAmelCase : Dict = self(**UpperCamelCase_ )
lowerCAmelCase, lowerCAmelCase : Union[str, Any] = outputs[:2]
lowerCAmelCase : Optional[int] = logits.detach().cpu().numpy()
lowerCAmelCase : Any = inputs['''labels'''].detach().cpu().numpy()
return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}
def lowerCamelCase__ ( self : Union[str, Any] , UpperCamelCase_ : Any ):
lowerCAmelCase : str = torch.stack([x['''val_loss'''] for x in outputs] ).mean()
lowerCAmelCase : List[str] = np.concatenate([x['''pred'''] for x in outputs] , axis=0 )
lowerCAmelCase : Optional[Any] = np.argmax(UpperCamelCase_ , axis=2 )
lowerCAmelCase : Optional[Any] = np.concatenate([x['''target'''] for x in outputs] , axis=0 )
lowerCAmelCase : Dict = dict(enumerate(self.labels ) )
lowerCAmelCase : Tuple = [[] for _ in range(out_label_ids.shape[0] )]
lowerCAmelCase : str = [[] for _ in range(out_label_ids.shape[0] )]
for i in range(out_label_ids.shape[0] ):
for j in range(out_label_ids.shape[1] ):
if out_label_ids[i, j] != self.pad_token_label_id:
out_label_list[i].append(label_map[out_label_ids[i][j]] )
preds_list[i].append(label_map[preds[i][j]] )
lowerCAmelCase : List[Any] = {
'''val_loss''': val_loss_mean,
'''accuracy_score''': accuracy_score(UpperCamelCase_ , UpperCamelCase_ ),
'''precision''': precision_score(UpperCamelCase_ , UpperCamelCase_ ),
'''recall''': recall_score(UpperCamelCase_ , UpperCamelCase_ ),
'''f1''': fa_score(UpperCamelCase_ , UpperCamelCase_ ),
}
lowerCAmelCase : List[Any] = dict(results.items() )
lowerCAmelCase : List[Any] = results
return ret, preds_list, out_label_list
def lowerCamelCase__ ( self : Union[str, Any] , UpperCamelCase_ : List[Any] ):
# when stable
lowerCAmelCase, lowerCAmelCase, lowerCAmelCase : Union[str, Any] = self._eval_end(UpperCamelCase_ )
lowerCAmelCase : Any = ret['''log''']
return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}
def lowerCamelCase__ ( self : Tuple , UpperCamelCase_ : Dict ):
# updating to test_epoch_end instead of deprecated test_end
lowerCAmelCase, lowerCAmelCase, lowerCAmelCase : Optional[Any] = self._eval_end(UpperCamelCase_ )
# Converting to the dict required by pl
# https://github.com/PyTorchLightning/pytorch-lightning/blob/master/\
# pytorch_lightning/trainer/logging.py#L139
lowerCAmelCase : Optional[Any] = ret['''log''']
# `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}
@staticmethod
def lowerCamelCase__ ( UpperCamelCase_ : List[str] , UpperCamelCase_ : List[str] ):
# Add NER specific options
BaseTransformer.add_model_specific_args(UpperCamelCase_ , UpperCamelCase_ )
parser.add_argument(
'''--task_type''' , default='''NER''' , type=UpperCamelCase_ , help='''Task type to fine tune in training (e.g. NER, POS, etc)''' )
parser.add_argument(
'''--max_seq_length''' , default=1_2_8 , type=UpperCamelCase_ , help=(
'''The maximum total input sequence length after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
) , )
parser.add_argument(
'''--labels''' , default='''''' , type=UpperCamelCase_ , help='''Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.''' , )
parser.add_argument(
'''--gpus''' , default=0 , type=UpperCamelCase_ , help='''The number of GPUs allocated for this, it is by default 0 meaning none''' , )
parser.add_argument(
'''--overwrite_cache''' , action='''store_true''' , help='''Overwrite the cached training and evaluation sets''' )
return parser
if __name__ == "__main__":
snake_case__ : Optional[Any] = argparse.ArgumentParser()
add_generic_args(parser, os.getcwd())
snake_case__ : Dict = NERTransformer.add_model_specific_args(parser, os.getcwd())
snake_case__ : str = parser.parse_args()
snake_case__ : Optional[Any] = NERTransformer(args)
snake_case__ : str = generic_train(model, args)
if args.do_predict:
# See https://github.com/huggingface/transformers/issues/3159
# pl use this default format to create a checkpoint:
# https://github.com/PyTorchLightning/pytorch-lightning/blob/master\
# /pytorch_lightning/callbacks/model_checkpoint.py#L322
snake_case__ : Tuple = sorted(glob.glob(os.path.join(args.output_dir, '''checkpoint-epoch=*.ckpt'''), recursive=True))
snake_case__ : str = model.load_from_checkpoint(checkpoints[-1])
trainer.test(model)
| 60
|
"""simple docstring"""
def lowercase ( __snake_case : int ):
if not isinstance(__snake_case , __snake_case ):
raise ValueError('''Input must be an integer''' )
if input_num <= 0:
raise ValueError('''Input must be positive''' )
return sum(
divisor for divisor in range(1 , input_num // 2 + 1 ) if input_num % divisor == 0 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 33
| 0
|
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