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from __future__ import annotations
def lowercase ( __A : list[float] , __A : list[float] ) -> float:
'''simple docstring'''
snake_case : str = sorted(numsa + numsa )
snake_case , snake_case : Any = divmod(len(__A ) , 2 )
if mod == 1:
return all_numbers[div]
else:
return (all_numbers[div] + all_numbers[div - 1]) / 2
if __name__ == "__main__":
import doctest
doctest.testmod()
__lowercase : Any = [float(x) for x in input('''Enter the elements of first array: ''').split()]
__lowercase : List[str] = [float(x) for x in input('''Enter the elements of second array: ''').split()]
print(f'''The median of two arrays is: {median_of_two_arrays(array_a, array_a)}''')
| 36 |
from __future__ import annotations
def lowercase ( __A : list ) -> float:
'''simple docstring'''
if not nums:
raise ValueError("""List is empty""" )
return sum(__A ) / len(__A )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 36 | 1 |
def lowercase ( __A : int = 400_0000 ) -> int:
'''simple docstring'''
snake_case : int = []
snake_case , snake_case : Any = 0, 1
while b <= n:
if b % 2 == 0:
even_fibs.append(__A )
snake_case , snake_case : Optional[Any] = b, a + b
return sum(__A )
if __name__ == "__main__":
print(f'''{solution() = }''')
| 36 |
import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig
if TYPE_CHECKING:
from ... import PreTrainedTokenizerBase, TensorType
__lowercase : List[str] = logging.get_logger(__name__)
class _A ( snake_case ):
'''simple docstring'''
__lowerCamelCase : Tuple = '''vision-encoder-decoder'''
__lowerCamelCase : List[Any] = True
def __init__( self ,**SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
super().__init__(**SCREAMING_SNAKE_CASE_ )
if "encoder" not in kwargs or "decoder" not in kwargs:
raise ValueError(
F"""A configuraton of type {self.model_type} cannot be instantiated because """
F"""not both `encoder` and `decoder` sub-configurations are passed, but only {kwargs}""" )
snake_case : Union[str, Any] = kwargs.pop("""encoder""" )
snake_case : Any = encoder_config.pop("""model_type""" )
snake_case : Optional[Any] = kwargs.pop("""decoder""" )
snake_case : Union[str, Any] = decoder_config.pop("""model_type""" )
snake_case : Any = AutoConfig.for_model(SCREAMING_SNAKE_CASE_ ,**SCREAMING_SNAKE_CASE_ )
snake_case : Union[str, Any] = AutoConfig.for_model(SCREAMING_SNAKE_CASE_ ,**SCREAMING_SNAKE_CASE_ )
snake_case : int = True
@classmethod
def snake_case_ ( cls ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,**SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
logger.info("""Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config""" )
snake_case : Tuple = True
snake_case : Union[str, Any] = True
return cls(encoder=encoder_config.to_dict() ,decoder=decoder_config.to_dict() ,**SCREAMING_SNAKE_CASE_ )
def snake_case_ ( self ):
'''simple docstring'''
snake_case : Union[str, Any] = copy.deepcopy(self.__dict__ )
snake_case : Union[str, Any] = self.encoder.to_dict()
snake_case : Union[str, Any] = self.decoder.to_dict()
snake_case : Dict = self.__class__.model_type
return output
class _A ( snake_case ):
'''simple docstring'''
__lowerCamelCase : Optional[Any] = version.parse('''1.11''' )
@property
def snake_case_ ( self ):
'''simple docstring'''
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
] )
@property
def snake_case_ ( self ):
'''simple docstring'''
return 1E-4
@property
def snake_case_ ( self ):
'''simple docstring'''
return OrderedDict({"""last_hidden_state""": {0: """batch""", 1: """encoder_sequence"""}} )
class _A ( snake_case ):
'''simple docstring'''
@property
def snake_case_ ( self ):
'''simple docstring'''
snake_case : Tuple = OrderedDict()
snake_case : Optional[int] = {0: """batch""", 1: """past_decoder_sequence + sequence"""}
snake_case : Union[str, Any] = {0: """batch""", 1: """past_decoder_sequence + sequence"""}
snake_case : Optional[Any] = {0: """batch""", 1: """encoder_sequence"""}
return common_inputs
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ = -1 ,SCREAMING_SNAKE_CASE_ = -1 ,SCREAMING_SNAKE_CASE_ = False ,SCREAMING_SNAKE_CASE_ = None ,):
'''simple docstring'''
import torch
snake_case : Optional[Any] = OrderedDict()
snake_case : Tuple = super().generate_dummy_inputs(
SCREAMING_SNAKE_CASE_ ,batch_size=SCREAMING_SNAKE_CASE_ ,seq_length=SCREAMING_SNAKE_CASE_ ,is_pair=SCREAMING_SNAKE_CASE_ ,framework=SCREAMING_SNAKE_CASE_ )
snake_case , snake_case : List[Any] = dummy_input["""input_ids"""].shape
snake_case : Optional[int] = (batch, encoder_sequence, self._config.encoder_hidden_size)
snake_case : List[str] = dummy_input.pop("""input_ids""" )
snake_case : int = dummy_input.pop("""attention_mask""" )
snake_case : Dict = torch.zeros(SCREAMING_SNAKE_CASE_ )
return common_inputs
class _A ( snake_case ):
'''simple docstring'''
@property
def snake_case_ ( self ):
'''simple docstring'''
pass
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
return VisionEncoderDecoderEncoderOnnxConfig(SCREAMING_SNAKE_CASE_ )
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ = "default" ):
'''simple docstring'''
snake_case : int = encoder_config.hidden_size
return VisionEncoderDecoderDecoderOnnxConfig(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )
| 36 | 1 |
import unittest
from transformers import is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_available():
from PIL import Image
else:
class _A :
'''simple docstring'''
@staticmethod
def snake_case_ ( *SCREAMING_SNAKE_CASE_ ,**SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
pass
@is_pipeline_test
@require_vision
class _A ( unittest.TestCase ):
'''simple docstring'''
@require_torch
def snake_case_ ( self ):
'''simple docstring'''
snake_case : Dict = pipeline(
model="""hf-internal-testing/tiny-random-clip-zero-shot-image-classification""" ,)
snake_case : Tuple = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
snake_case : List[str] = image_classifier(SCREAMING_SNAKE_CASE_ ,candidate_labels=["""a""", """b""", """c"""] )
# The floating scores are so close, we enter floating error approximation and the order is not guaranteed across
# python and torch versions.
self.assertIn(
nested_simplify(SCREAMING_SNAKE_CASE_ ) ,[
[{"""score""": 0.3_33, """label""": """a"""}, {"""score""": 0.3_33, """label""": """b"""}, {"""score""": 0.3_33, """label""": """c"""}],
[{"""score""": 0.3_33, """label""": """a"""}, {"""score""": 0.3_33, """label""": """c"""}, {"""score""": 0.3_33, """label""": """b"""}],
] ,)
snake_case : Optional[Any] = image_classifier([image] * 5 ,candidate_labels=["""A""", """B""", """C"""] ,batch_size=2 )
self.assertEqual(
nested_simplify(SCREAMING_SNAKE_CASE_ ) ,[
[
{"""score""": 0.3_33, """label""": ANY(SCREAMING_SNAKE_CASE_ )},
{"""score""": 0.3_33, """label""": ANY(SCREAMING_SNAKE_CASE_ )},
{"""score""": 0.3_33, """label""": ANY(SCREAMING_SNAKE_CASE_ )},
],
[
{"""score""": 0.3_33, """label""": ANY(SCREAMING_SNAKE_CASE_ )},
{"""score""": 0.3_33, """label""": ANY(SCREAMING_SNAKE_CASE_ )},
{"""score""": 0.3_33, """label""": ANY(SCREAMING_SNAKE_CASE_ )},
],
[
{"""score""": 0.3_33, """label""": ANY(SCREAMING_SNAKE_CASE_ )},
{"""score""": 0.3_33, """label""": ANY(SCREAMING_SNAKE_CASE_ )},
{"""score""": 0.3_33, """label""": ANY(SCREAMING_SNAKE_CASE_ )},
],
[
{"""score""": 0.3_33, """label""": ANY(SCREAMING_SNAKE_CASE_ )},
{"""score""": 0.3_33, """label""": ANY(SCREAMING_SNAKE_CASE_ )},
{"""score""": 0.3_33, """label""": ANY(SCREAMING_SNAKE_CASE_ )},
],
[
{"""score""": 0.3_33, """label""": ANY(SCREAMING_SNAKE_CASE_ )},
{"""score""": 0.3_33, """label""": ANY(SCREAMING_SNAKE_CASE_ )},
{"""score""": 0.3_33, """label""": ANY(SCREAMING_SNAKE_CASE_ )},
],
] ,)
@require_tf
def snake_case_ ( self ):
'''simple docstring'''
snake_case : Optional[int] = pipeline(
model="""hf-internal-testing/tiny-random-clip-zero-shot-image-classification""" ,framework="""tf""" )
snake_case : Optional[int] = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
snake_case : Any = image_classifier(SCREAMING_SNAKE_CASE_ ,candidate_labels=["""a""", """b""", """c"""] )
self.assertEqual(
nested_simplify(SCREAMING_SNAKE_CASE_ ) ,[{"""score""": 0.3_33, """label""": """a"""}, {"""score""": 0.3_33, """label""": """b"""}, {"""score""": 0.3_33, """label""": """c"""}] ,)
snake_case : int = image_classifier([image] * 5 ,candidate_labels=["""A""", """B""", """C"""] ,batch_size=2 )
self.assertEqual(
nested_simplify(SCREAMING_SNAKE_CASE_ ) ,[
[
{"""score""": 0.3_33, """label""": ANY(SCREAMING_SNAKE_CASE_ )},
{"""score""": 0.3_33, """label""": ANY(SCREAMING_SNAKE_CASE_ )},
{"""score""": 0.3_33, """label""": ANY(SCREAMING_SNAKE_CASE_ )},
],
[
{"""score""": 0.3_33, """label""": ANY(SCREAMING_SNAKE_CASE_ )},
{"""score""": 0.3_33, """label""": ANY(SCREAMING_SNAKE_CASE_ )},
{"""score""": 0.3_33, """label""": ANY(SCREAMING_SNAKE_CASE_ )},
],
[
{"""score""": 0.3_33, """label""": ANY(SCREAMING_SNAKE_CASE_ )},
{"""score""": 0.3_33, """label""": ANY(SCREAMING_SNAKE_CASE_ )},
{"""score""": 0.3_33, """label""": ANY(SCREAMING_SNAKE_CASE_ )},
],
[
{"""score""": 0.3_33, """label""": ANY(SCREAMING_SNAKE_CASE_ )},
{"""score""": 0.3_33, """label""": ANY(SCREAMING_SNAKE_CASE_ )},
{"""score""": 0.3_33, """label""": ANY(SCREAMING_SNAKE_CASE_ )},
],
[
{"""score""": 0.3_33, """label""": ANY(SCREAMING_SNAKE_CASE_ )},
{"""score""": 0.3_33, """label""": ANY(SCREAMING_SNAKE_CASE_ )},
{"""score""": 0.3_33, """label""": ANY(SCREAMING_SNAKE_CASE_ )},
],
] ,)
@slow
@require_torch
def snake_case_ ( self ):
'''simple docstring'''
snake_case : Optional[int] = pipeline(
task="""zero-shot-image-classification""" ,model="""openai/clip-vit-base-patch32""" ,)
# This is an image of 2 cats with remotes and no planes
snake_case : Any = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
snake_case : str = image_classifier(SCREAMING_SNAKE_CASE_ ,candidate_labels=["""cat""", """plane""", """remote"""] )
self.assertEqual(
nested_simplify(SCREAMING_SNAKE_CASE_ ) ,[
{"""score""": 0.5_11, """label""": """remote"""},
{"""score""": 0.4_85, """label""": """cat"""},
{"""score""": 0.0_04, """label""": """plane"""},
] ,)
snake_case : Optional[Any] = image_classifier([image] * 5 ,candidate_labels=["""cat""", """plane""", """remote"""] ,batch_size=2 )
self.assertEqual(
nested_simplify(SCREAMING_SNAKE_CASE_ ) ,[
[
{"""score""": 0.5_11, """label""": """remote"""},
{"""score""": 0.4_85, """label""": """cat"""},
{"""score""": 0.0_04, """label""": """plane"""},
],
]
* 5 ,)
@slow
@require_tf
def snake_case_ ( self ):
'''simple docstring'''
snake_case : Any = pipeline(
task="""zero-shot-image-classification""" ,model="""openai/clip-vit-base-patch32""" ,framework="""tf""" )
# This is an image of 2 cats with remotes and no planes
snake_case : List[str] = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
snake_case : Dict = image_classifier(SCREAMING_SNAKE_CASE_ ,candidate_labels=["""cat""", """plane""", """remote"""] )
self.assertEqual(
nested_simplify(SCREAMING_SNAKE_CASE_ ) ,[
{"""score""": 0.5_11, """label""": """remote"""},
{"""score""": 0.4_85, """label""": """cat"""},
{"""score""": 0.0_04, """label""": """plane"""},
] ,)
snake_case : Optional[int] = image_classifier([image] * 5 ,candidate_labels=["""cat""", """plane""", """remote"""] ,batch_size=2 )
self.assertEqual(
nested_simplify(SCREAMING_SNAKE_CASE_ ) ,[
[
{"""score""": 0.5_11, """label""": """remote"""},
{"""score""": 0.4_85, """label""": """cat"""},
{"""score""": 0.0_04, """label""": """plane"""},
],
]
* 5 ,)
| 36 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers import BitConfig, BitForImageClassification, BitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging
logging.set_verbosity_info()
__lowercase : Any = logging.get_logger(__name__)
def lowercase ( __A : Optional[Any] ) -> Dict:
'''simple docstring'''
snake_case : Dict = """huggingface/label-files"""
snake_case : int = """imagenet-1k-id2label.json"""
snake_case : Tuple = json.load(open(hf_hub_download(__A , __A , repo_type="""dataset""" ) , """r""" ) )
snake_case : Any = {int(__A ): v for k, v in idalabel.items()}
snake_case : Dict = {v: k for k, v in idalabel.items()}
snake_case : Any = """std_conv""" if """bit""" in model_name else False
# note that when using BiT as backbone for ViT-hybrid checkpoints,
# one needs to additionally set config.layer_type = "bottleneck", config.stem_type = "same",
# config.conv_layer = "std_conv_same"
snake_case : List[Any] = BitConfig(
conv_layer=__A , num_labels=1000 , idalabel=__A , labelaid=__A , )
return config
def lowercase ( __A : Optional[int] ) -> Union[str, Any]:
'''simple docstring'''
if "stem.conv" in name:
snake_case : List[str] = name.replace("""stem.conv""" , """bit.embedder.convolution""" )
if "blocks" in name:
snake_case : List[str] = name.replace("""blocks""" , """layers""" )
if "head.fc" in name:
snake_case : Optional[int] = name.replace("""head.fc""" , """classifier.1""" )
if name.startswith("""norm""" ):
snake_case : Optional[Any] = """bit.""" + name
if "bit" not in name and "classifier" not in name:
snake_case : Tuple = """bit.encoder.""" + name
return name
def lowercase ( ) -> Optional[int]:
'''simple docstring'''
snake_case : int = """http://images.cocodataset.org/val2017/000000039769.jpg"""
snake_case : Optional[Any] = Image.open(requests.get(__A , stream=__A ).raw )
return im
@torch.no_grad()
def lowercase ( __A : Any , __A : Union[str, Any] , __A : str=False ) -> Optional[int]:
'''simple docstring'''
snake_case : str = get_config(__A )
# load original model from timm
snake_case : Tuple = create_model(__A , pretrained=__A )
timm_model.eval()
# load state_dict of original model
snake_case : List[str] = timm_model.state_dict()
for key in state_dict.copy().keys():
snake_case : List[Any] = state_dict.pop(__A )
snake_case : Union[str, Any] = val.squeeze() if """head""" in key else val
# load HuggingFace model
snake_case : List[Any] = BitForImageClassification(__A )
model.eval()
model.load_state_dict(__A )
# create image processor
snake_case : Dict = create_transform(**resolve_data_config({} , model=__A ) )
snake_case : Optional[Any] = transform.transforms
snake_case : List[Any] = {
"""bilinear""": PILImageResampling.BILINEAR,
"""bicubic""": PILImageResampling.BICUBIC,
"""nearest""": PILImageResampling.NEAREST,
}
snake_case : Union[str, Any] = BitImageProcessor(
do_resize=__A , size={"""shortest_edge""": timm_transforms[0].size} , resample=pillow_resamplings[timm_transforms[0].interpolation.value] , do_center_crop=__A , crop_size={"""height""": timm_transforms[1].size[0], """width""": timm_transforms[1].size[1]} , do_normalize=__A , image_mean=timm_transforms[-1].mean.tolist() , image_std=timm_transforms[-1].std.tolist() , )
snake_case : Dict = prepare_img()
snake_case : List[str] = transform(__A ).unsqueeze(0 )
snake_case : int = processor(__A , return_tensors="""pt""" ).pixel_values
# verify pixel values
assert torch.allclose(__A , __A )
# verify logits
with torch.no_grad():
snake_case : Optional[int] = model(__A )
snake_case : Dict = outputs.logits
print("""Logits:""" , logits[0, :3] )
print("""Predicted class:""" , model.config.idalabel[logits.argmax(-1 ).item()] )
snake_case : int = timm_model(__A )
assert timm_logits.shape == outputs.logits.shape
assert torch.allclose(__A , outputs.logits , atol=1E-3 )
print("""Looks ok!""" )
if pytorch_dump_folder_path is not None:
Path(__A ).mkdir(exist_ok=__A )
print(f"""Saving model {model_name} and processor to {pytorch_dump_folder_path}""" )
model.save_pretrained(__A )
processor.save_pretrained(__A )
if push_to_hub:
print(f"""Pushing model {model_name} and processor to the hub""" )
model.push_to_hub(f"""ybelkada/{model_name}""" )
processor.push_to_hub(f"""ybelkada/{model_name}""" )
if __name__ == "__main__":
__lowercase : List[str] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--model_name''',
default='''resnetv2_50x1_bitm''',
type=str,
help='''Name of the BiT timm model you\'d like to convert.''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
)
parser.add_argument(
'''--push_to_hub''',
action='''store_true''',
help='''Whether to push the model to the hub.''',
)
__lowercase : Union[str, Any] = parser.parse_args()
convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 36 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowercase : List[str] = logging.get_logger(__name__)
__lowercase : Optional[Any] = {
'''google/vivit-b-16x2-kinetics400''': (
'''https://huggingface.co/google/vivit-b-16x2-kinetics400/resolve/main/config.json'''
),
# See all Vivit models at https://huggingface.co/models?filter=vivit
}
class _A ( snake_case ):
'''simple docstring'''
__lowerCamelCase : Tuple = '''vivit'''
def __init__( self ,SCREAMING_SNAKE_CASE_=224 ,SCREAMING_SNAKE_CASE_=32 ,SCREAMING_SNAKE_CASE_=[2, 16, 16] ,SCREAMING_SNAKE_CASE_=3 ,SCREAMING_SNAKE_CASE_=768 ,SCREAMING_SNAKE_CASE_=12 ,SCREAMING_SNAKE_CASE_=12 ,SCREAMING_SNAKE_CASE_=3072 ,SCREAMING_SNAKE_CASE_="gelu_fast" ,SCREAMING_SNAKE_CASE_=0.0 ,SCREAMING_SNAKE_CASE_=0.0 ,SCREAMING_SNAKE_CASE_=0.02 ,SCREAMING_SNAKE_CASE_=1E-06 ,SCREAMING_SNAKE_CASE_=True ,**SCREAMING_SNAKE_CASE_ ,):
'''simple docstring'''
snake_case : Dict = hidden_size
snake_case : Union[str, Any] = num_hidden_layers
snake_case : Dict = num_attention_heads
snake_case : List[str] = intermediate_size
snake_case : int = hidden_act
snake_case : Tuple = hidden_dropout_prob
snake_case : Optional[int] = attention_probs_dropout_prob
snake_case : Optional[int] = initializer_range
snake_case : str = layer_norm_eps
snake_case : str = image_size
snake_case : Optional[Any] = num_frames
snake_case : List[str] = tubelet_size
snake_case : int = num_channels
snake_case : Optional[int] = qkv_bias
super().__init__(**SCREAMING_SNAKE_CASE_ )
| 36 |
import os
import pytest
from attr import dataclass
__lowercase : Optional[int] = '''us-east-1''' # defaults region
@dataclass
class _A :
'''simple docstring'''
__lowerCamelCase : str
__lowerCamelCase : Dict = '''arn:aws:iam::558105141721:role/sagemaker_execution_role'''
__lowerCamelCase : Optional[Any] = {
'''task_name''': '''mnli''',
'''per_device_train_batch_size''': 1_6,
'''per_device_eval_batch_size''': 1_6,
'''do_train''': True,
'''do_eval''': True,
'''do_predict''': True,
'''output_dir''': '''/opt/ml/model''',
'''overwrite_output_dir''': True,
'''max_steps''': 5_0_0,
'''save_steps''': 5_5_0_0,
}
__lowerCamelCase : List[str] = {**hyperparameters, '''max_steps''': 1_0_0_0}
@property
def snake_case_ ( self ):
'''simple docstring'''
if self.framework == "pytorch":
return [
{"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
{"Name": "eval_accuracy", "Regex": r"eval_accuracy.*=\D*(.*?)$"},
{"Name": "eval_loss", "Regex": r"eval_loss.*=\D*(.*?)$"},
]
else:
return [
{"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
{"Name": "eval_accuracy", "Regex": r"loss.*=\D*(.*?)]?$"},
{"Name": "eval_loss", "Regex": r"sparse_categorical_accuracy.*=\D*(.*?)]?$"},
]
@property
def snake_case_ ( self ):
'''simple docstring'''
return F"""{self.framework}-transfromers-test"""
@property
def snake_case_ ( self ):
'''simple docstring'''
return F"""./tests/sagemaker/scripts/{self.framework}"""
@property
def snake_case_ ( self ):
'''simple docstring'''
if self.framework == "pytorch":
return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-pytorch-training:1.7.1-transformers4.6.1-gpu-py36-cu110-ubuntu18.04"
else:
return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-tensorflow-training:2.4.1-transformers4.6.1-gpu-py37-cu110-ubuntu18.04"
@pytest.fixture(scope="""class""" )
def lowercase ( __A : List[str] ) -> List[str]:
'''simple docstring'''
snake_case : Optional[Any] = SageMakerTestEnvironment(framework=request.cls.framework )
| 36 | 1 |
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
__lowercase : List[Any] = get_tests_dir('''fixtures/test_sentencepiece.model''')
@require_sentencepiece
@require_tokenizers
class _A ( snake_case , unittest.TestCase ):
'''simple docstring'''
__lowerCamelCase : List[Any] = XLMRobertaTokenizer
__lowerCamelCase : Dict = XLMRobertaTokenizerFast
__lowerCamelCase : Optional[Any] = True
__lowerCamelCase : List[str] = True
def snake_case_ ( self ):
'''simple docstring'''
super().setUp()
# We have a SentencePiece fixture for testing
snake_case : Optional[Any] = XLMRobertaTokenizer(SCREAMING_SNAKE_CASE_ ,keep_accents=SCREAMING_SNAKE_CASE_ )
tokenizer.save_pretrained(self.tmpdirname )
def snake_case_ ( self ):
'''simple docstring'''
snake_case : str = """<pad>"""
snake_case : Tuple = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(SCREAMING_SNAKE_CASE_ ) ,SCREAMING_SNAKE_CASE_ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(SCREAMING_SNAKE_CASE_ ) ,SCREAMING_SNAKE_CASE_ )
def snake_case_ ( self ):
'''simple docstring'''
snake_case : Union[str, Any] = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] ,"""<s>""" )
self.assertEqual(vocab_keys[1] ,"""<pad>""" )
self.assertEqual(vocab_keys[-1] ,"""<mask>""" )
self.assertEqual(len(SCREAMING_SNAKE_CASE_ ) ,1002 )
def snake_case_ ( self ):
'''simple docstring'''
self.assertEqual(self.get_tokenizer().vocab_size ,1002 )
def snake_case_ ( self ):
'''simple docstring'''
snake_case : str = XLMRobertaTokenizer(SCREAMING_SNAKE_CASE_ ,keep_accents=SCREAMING_SNAKE_CASE_ )
snake_case : Tuple = tokenizer.tokenize("""This is a test""" )
self.assertListEqual(SCREAMING_SNAKE_CASE_ ,["""▁This""", """▁is""", """▁a""", """▁t""", """est"""] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_ ) ,[value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]] ,)
snake_case : Union[str, Any] = tokenizer.tokenize("""I was born in 92000, and this is falsé.""" )
self.assertListEqual(
SCREAMING_SNAKE_CASE_ ,[
SPIECE_UNDERLINE + """I""",
SPIECE_UNDERLINE + """was""",
SPIECE_UNDERLINE + """b""",
"""or""",
"""n""",
SPIECE_UNDERLINE + """in""",
SPIECE_UNDERLINE + """""",
"""9""",
"""2""",
"""0""",
"""0""",
"""0""",
""",""",
SPIECE_UNDERLINE + """and""",
SPIECE_UNDERLINE + """this""",
SPIECE_UNDERLINE + """is""",
SPIECE_UNDERLINE + """f""",
"""al""",
"""s""",
"""é""",
""".""",
] ,)
snake_case : str = tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_ )
self.assertListEqual(
SCREAMING_SNAKE_CASE_ ,[
value + tokenizer.fairseq_offset
for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
# ^ unk: 2 + 1 = 3 unk: 2 + 1 = 3 ^
] ,)
snake_case : List[str] = tokenizer.convert_ids_to_tokens(SCREAMING_SNAKE_CASE_ )
self.assertListEqual(
SCREAMING_SNAKE_CASE_ ,[
SPIECE_UNDERLINE + """I""",
SPIECE_UNDERLINE + """was""",
SPIECE_UNDERLINE + """b""",
"""or""",
"""n""",
SPIECE_UNDERLINE + """in""",
SPIECE_UNDERLINE + """""",
"""<unk>""",
"""2""",
"""0""",
"""0""",
"""0""",
""",""",
SPIECE_UNDERLINE + """and""",
SPIECE_UNDERLINE + """this""",
SPIECE_UNDERLINE + """is""",
SPIECE_UNDERLINE + """f""",
"""al""",
"""s""",
"""<unk>""",
""".""",
] ,)
def snake_case_ ( self ):
'''simple docstring'''
if not self.test_slow_tokenizer:
# as we don't have a slow version, we can't compare the outputs between slow and fast versions
return
snake_case : Dict = (self.rust_tokenizer_class, """hf-internal-testing/tiny-xlm-roberta""", {})
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
snake_case : List[Any] = self.rust_tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE_ ,**SCREAMING_SNAKE_CASE_ )
snake_case : Dict = self.tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE_ ,**SCREAMING_SNAKE_CASE_ )
snake_case : Dict = tempfile.mkdtemp()
snake_case : str = tokenizer_r.save_pretrained(SCREAMING_SNAKE_CASE_ )
snake_case : Any = tokenizer_p.save_pretrained(SCREAMING_SNAKE_CASE_ )
# Checks it save with the same files + the tokenizer.json file for the fast one
self.assertTrue(any("""tokenizer.json""" in f for f in tokenizer_r_files ) )
snake_case : str = tuple(f for f in tokenizer_r_files if """tokenizer.json""" not in f )
self.assertSequenceEqual(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )
# Checks everything loads correctly in the same way
snake_case : str = tokenizer_r.from_pretrained(SCREAMING_SNAKE_CASE_ )
snake_case : Tuple = tokenizer_p.from_pretrained(SCREAMING_SNAKE_CASE_ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ) )
# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))
shutil.rmtree(SCREAMING_SNAKE_CASE_ )
# Save tokenizer rust, legacy_format=True
snake_case : Union[str, Any] = tempfile.mkdtemp()
snake_case : Optional[Any] = tokenizer_r.save_pretrained(SCREAMING_SNAKE_CASE_ ,legacy_format=SCREAMING_SNAKE_CASE_ )
snake_case : List[str] = tokenizer_p.save_pretrained(SCREAMING_SNAKE_CASE_ )
# Checks it save with the same files
self.assertSequenceEqual(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )
# Checks everything loads correctly in the same way
snake_case : int = tokenizer_r.from_pretrained(SCREAMING_SNAKE_CASE_ )
snake_case : Tuple = tokenizer_p.from_pretrained(SCREAMING_SNAKE_CASE_ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ) )
shutil.rmtree(SCREAMING_SNAKE_CASE_ )
# Save tokenizer rust, legacy_format=False
snake_case : Union[str, Any] = tempfile.mkdtemp()
snake_case : List[Any] = tokenizer_r.save_pretrained(SCREAMING_SNAKE_CASE_ ,legacy_format=SCREAMING_SNAKE_CASE_ )
snake_case : Tuple = tokenizer_p.save_pretrained(SCREAMING_SNAKE_CASE_ )
# Checks it saved the tokenizer.json file
self.assertTrue(any("""tokenizer.json""" in f for f in tokenizer_r_files ) )
# Checks everything loads correctly in the same way
snake_case : List[str] = tokenizer_r.from_pretrained(SCREAMING_SNAKE_CASE_ )
snake_case : List[str] = tokenizer_p.from_pretrained(SCREAMING_SNAKE_CASE_ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ) )
shutil.rmtree(SCREAMING_SNAKE_CASE_ )
@cached_property
def snake_case_ ( self ):
'''simple docstring'''
return XLMRobertaTokenizer.from_pretrained("""xlm-roberta-base""" )
def snake_case_ ( self ):
'''simple docstring'''
with tempfile.NamedTemporaryFile() as f:
shutil.copyfile(SCREAMING_SNAKE_CASE_ ,f.name )
snake_case : Dict = XLMRobertaTokenizer(f.name ,keep_accents=SCREAMING_SNAKE_CASE_ )
snake_case : Union[str, Any] = pickle.dumps(SCREAMING_SNAKE_CASE_ )
pickle.loads(SCREAMING_SNAKE_CASE_ )
def snake_case_ ( self ):
'''simple docstring'''
if not self.test_rust_tokenizer:
return
snake_case : Optional[Any] = self.get_tokenizer()
snake_case : str = self.get_rust_tokenizer()
snake_case : Optional[int] = """I was born in 92000, and this is falsé."""
snake_case : Any = tokenizer.tokenize(SCREAMING_SNAKE_CASE_ )
snake_case : List[str] = rust_tokenizer.tokenize(SCREAMING_SNAKE_CASE_ )
self.assertListEqual(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )
snake_case : List[str] = tokenizer.encode(SCREAMING_SNAKE_CASE_ ,add_special_tokens=SCREAMING_SNAKE_CASE_ )
snake_case : Optional[Any] = rust_tokenizer.encode(SCREAMING_SNAKE_CASE_ ,add_special_tokens=SCREAMING_SNAKE_CASE_ )
self.assertListEqual(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )
snake_case : Tuple = self.get_rust_tokenizer()
snake_case : Dict = tokenizer.encode(SCREAMING_SNAKE_CASE_ )
snake_case : Tuple = rust_tokenizer.encode(SCREAMING_SNAKE_CASE_ )
self.assertListEqual(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )
@slow
def snake_case_ ( self ):
'''simple docstring'''
snake_case : Dict = """Hello World!"""
snake_case : Any = [0, 35378, 6661, 38, 2]
# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base') # xlmr.large has same tokenizer
# xlmr.eval()
# xlmr.encode(symbols)
self.assertListEqual(SCREAMING_SNAKE_CASE_ ,self.big_tokenizer.encode(SCREAMING_SNAKE_CASE_ ) )
@slow
def snake_case_ ( self ):
'''simple docstring'''
snake_case : Tuple = (
"""This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"""
""" add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"""
)
snake_case : Union[str, Any] = [
0,
3293,
83,
10,
4552,
4989,
7986,
678,
10,
5915,
111,
179459,
124850,
4,
6044,
237,
12,
6,
5,
6,
4,
6780,
705,
15,
1388,
44,
378,
10114,
711,
152,
20,
6,
5,
22376,
642,
1221,
15190,
34153,
450,
5608,
959,
1119,
57702,
136,
186,
47,
1098,
29367,
47,
# 4426, # What fairseq tokenizes from "<unk>": "_<"
# 3678, # What fairseq tokenizes from "<unk>": "unk"
# 2740, # What fairseq tokenizes from "<unk>": ">"
3, # What we tokenize from "<unk>": "<unk>"
6, # Residue from the tokenization: an extra sentencepiece underline
4,
6044,
237,
6284,
50901,
528,
31,
90,
34,
927,
2,
]
# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base') # xlmr.large has same tokenizer
# xlmr.eval()
# xlmr.encode(symbols)
self.assertListEqual(SCREAMING_SNAKE_CASE_ ,self.big_tokenizer.encode(SCREAMING_SNAKE_CASE_ ) )
@slow
def snake_case_ ( self ):
'''simple docstring'''
# fmt: off
snake_case : List[Any] = {"""input_ids""": [[0, 11062, 82772, 7, 15, 82772, 538, 51529, 237, 17198, 1290, 206, 9, 215175, 1314, 136, 17198, 1290, 206, 9, 56359, 42, 122009, 9, 16466, 16, 87344, 4537, 9, 4717, 78381, 6, 159958, 7, 15, 24480, 618, 4, 527, 22693, 5428, 4, 2777, 24480, 9874, 4, 43523, 594, 4, 803, 18392, 33189, 18, 4, 43523, 24447, 12399, 100, 24955, 83658, 9626, 144057, 15, 839, 22335, 16, 136, 24955, 83658, 83479, 15, 39102, 724, 16, 678, 645, 2789, 1328, 4589, 42, 122009, 115774, 23, 805, 1328, 46876, 7, 136, 53894, 1940, 42227, 41159, 17721, 823, 425, 4, 27512, 98722, 206, 136, 5531, 4970, 919, 17336, 5, 2], [0, 20080, 618, 83, 82775, 47, 479, 9, 1517, 73, 53894, 333, 80581, 110117, 18811, 5256, 1295, 51, 152526, 297, 7986, 390, 124416, 538, 35431, 214, 98, 15044, 25737, 136, 7108, 43701, 23, 756, 135355, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 581, 63773, 119455, 6, 147797, 88203, 7, 645, 70, 21, 3285, 10269, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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=SCREAMING_SNAKE_CASE_ ,model_name="""xlm-roberta-base""" ,revision="""d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3""" ,)
| 36 |
from .imports import is_rich_available
if is_rich_available():
from rich.traceback import install
install(show_locals=False)
else:
raise ModuleNotFoundError('''To use the rich extension, install rich with `pip install rich`''')
| 36 | 1 |
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging
__lowercase : Optional[int] = logging.get_logger(__name__)
__lowercase : List[str] = {
'''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/config.json''',
# See all BlenderbotSmall models at https://huggingface.co/models?filter=blenderbot_small
}
class _A ( snake_case ):
'''simple docstring'''
__lowerCamelCase : List[Any] = '''blenderbot-small'''
__lowerCamelCase : List[str] = ['''past_key_values''']
__lowerCamelCase : Tuple = {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}
def __init__( self ,SCREAMING_SNAKE_CASE_=50265 ,SCREAMING_SNAKE_CASE_=512 ,SCREAMING_SNAKE_CASE_=8 ,SCREAMING_SNAKE_CASE_=2048 ,SCREAMING_SNAKE_CASE_=16 ,SCREAMING_SNAKE_CASE_=8 ,SCREAMING_SNAKE_CASE_=2048 ,SCREAMING_SNAKE_CASE_=16 ,SCREAMING_SNAKE_CASE_=0.0 ,SCREAMING_SNAKE_CASE_=0.0 ,SCREAMING_SNAKE_CASE_=True ,SCREAMING_SNAKE_CASE_=True ,SCREAMING_SNAKE_CASE_="gelu" ,SCREAMING_SNAKE_CASE_=512 ,SCREAMING_SNAKE_CASE_=0.1 ,SCREAMING_SNAKE_CASE_=0.0 ,SCREAMING_SNAKE_CASE_=0.0 ,SCREAMING_SNAKE_CASE_=0.02 ,SCREAMING_SNAKE_CASE_=1 ,SCREAMING_SNAKE_CASE_=False ,SCREAMING_SNAKE_CASE_=0 ,SCREAMING_SNAKE_CASE_=1 ,SCREAMING_SNAKE_CASE_=2 ,SCREAMING_SNAKE_CASE_=2 ,**SCREAMING_SNAKE_CASE_ ,):
'''simple docstring'''
snake_case : int = vocab_size
snake_case : List[Any] = max_position_embeddings
snake_case : int = d_model
snake_case : List[Any] = encoder_ffn_dim
snake_case : str = encoder_layers
snake_case : List[Any] = encoder_attention_heads
snake_case : Tuple = decoder_ffn_dim
snake_case : List[Any] = decoder_layers
snake_case : List[Any] = decoder_attention_heads
snake_case : str = dropout
snake_case : Union[str, Any] = attention_dropout
snake_case : Any = activation_dropout
snake_case : str = activation_function
snake_case : str = init_std
snake_case : Union[str, Any] = encoder_layerdrop
snake_case : List[Any] = decoder_layerdrop
snake_case : List[str] = use_cache
snake_case : Optional[Any] = encoder_layers
snake_case : Dict = scale_embedding # scale factor will be sqrt(d_model) if True
super().__init__(
pad_token_id=SCREAMING_SNAKE_CASE_ ,bos_token_id=SCREAMING_SNAKE_CASE_ ,eos_token_id=SCREAMING_SNAKE_CASE_ ,is_encoder_decoder=SCREAMING_SNAKE_CASE_ ,decoder_start_token_id=SCREAMING_SNAKE_CASE_ ,forced_eos_token_id=SCREAMING_SNAKE_CASE_ ,**SCREAMING_SNAKE_CASE_ ,)
class _A ( snake_case ):
'''simple docstring'''
@property
def snake_case_ ( self ):
'''simple docstring'''
if self.task in ["default", "seq2seq-lm"]:
snake_case : str = OrderedDict(
[
("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
] )
if self.use_past:
snake_case : int = {0: """batch"""}
snake_case : List[Any] = {0: """batch""", 1: """past_decoder_sequence + sequence"""}
else:
snake_case : Any = {0: """batch""", 1: """decoder_sequence"""}
snake_case : List[Any] = {0: """batch""", 1: """decoder_sequence"""}
if self.use_past:
self.fill_with_past_key_values_(SCREAMING_SNAKE_CASE_ ,direction="""inputs""" )
elif self.task == "causal-lm":
# TODO: figure this case out.
snake_case : int = OrderedDict(
[
("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
] )
if self.use_past:
snake_case , snake_case : int = self.num_layers
for i in range(SCREAMING_SNAKE_CASE_ ):
snake_case : List[str] = {0: """batch""", 2: """past_sequence + sequence"""}
snake_case : Any = {0: """batch""", 2: """past_sequence + sequence"""}
else:
snake_case : Union[str, Any] = OrderedDict(
[
("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
("""decoder_input_ids""", {0: """batch""", 1: """decoder_sequence"""}),
("""decoder_attention_mask""", {0: """batch""", 1: """decoder_sequence"""}),
] )
return common_inputs
@property
def snake_case_ ( self ):
'''simple docstring'''
if self.task in ["default", "seq2seq-lm"]:
snake_case : Union[str, Any] = super().outputs
else:
snake_case : Optional[Any] = super(SCREAMING_SNAKE_CASE_ ,self ).outputs
if self.use_past:
snake_case , snake_case : List[str] = self.num_layers
for i in range(SCREAMING_SNAKE_CASE_ ):
snake_case : Union[str, Any] = {0: """batch""", 2: """past_sequence + sequence"""}
snake_case : str = {0: """batch""", 2: """past_sequence + sequence"""}
return common_outputs
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ = -1 ,SCREAMING_SNAKE_CASE_ = -1 ,SCREAMING_SNAKE_CASE_ = False ,SCREAMING_SNAKE_CASE_ = None ,):
'''simple docstring'''
snake_case : Union[str, Any] = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )
# Generate decoder inputs
snake_case : Optional[int] = seq_length if not self.use_past else 1
snake_case : Union[str, Any] = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )
snake_case : List[Any] = {F"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
snake_case : int = dict(**SCREAMING_SNAKE_CASE_ ,**SCREAMING_SNAKE_CASE_ )
if self.use_past:
if not is_torch_available():
raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed.""" )
else:
import torch
snake_case , snake_case : List[Any] = common_inputs["""input_ids"""].shape
snake_case : Tuple = common_inputs["""decoder_input_ids"""].shape[1]
snake_case , snake_case : Optional[int] = self.num_attention_heads
snake_case : Optional[int] = (
batch,
num_encoder_attention_heads,
encoder_seq_length,
self._config.hidden_size // num_encoder_attention_heads,
)
snake_case : Optional[Any] = decoder_seq_length + 3
snake_case : Dict = (
batch,
num_decoder_attention_heads,
decoder_past_length,
self._config.hidden_size // num_decoder_attention_heads,
)
snake_case : Dict = torch.cat(
[common_inputs["""decoder_attention_mask"""], torch.ones(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )] ,dim=1 )
snake_case : Union[str, Any] = []
# If the number of encoder and decoder layers are present in the model configuration, both are considered
snake_case , snake_case : List[str] = self.num_layers
snake_case : Dict = min(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )
snake_case : Optional[Any] = max(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ) - min_num_layers
snake_case : Union[str, Any] = """encoder""" if num_encoder_layers > num_decoder_layers else """decoder"""
for _ in range(SCREAMING_SNAKE_CASE_ ):
common_inputs["past_key_values"].append(
(
torch.zeros(SCREAMING_SNAKE_CASE_ ),
torch.zeros(SCREAMING_SNAKE_CASE_ ),
torch.zeros(SCREAMING_SNAKE_CASE_ ),
torch.zeros(SCREAMING_SNAKE_CASE_ ),
) )
# TODO: test this.
snake_case : List[Any] = encoder_shape if remaining_side_name == """encoder""" else decoder_shape
for _ in range(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ):
common_inputs["past_key_values"].append((torch.zeros(SCREAMING_SNAKE_CASE_ ), torch.zeros(SCREAMING_SNAKE_CASE_ )) )
return common_inputs
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ = -1 ,SCREAMING_SNAKE_CASE_ = -1 ,SCREAMING_SNAKE_CASE_ = False ,SCREAMING_SNAKE_CASE_ = None ,):
'''simple docstring'''
snake_case : int = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )
if self.use_past:
if not is_torch_available():
raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed.""" )
else:
import torch
snake_case , snake_case : Optional[Any] = common_inputs["""input_ids"""].shape
# Not using the same length for past_key_values
snake_case : List[Any] = seqlen + 2
snake_case , snake_case : Tuple = self.num_layers
snake_case , snake_case : Optional[int] = self.num_attention_heads
snake_case : Any = (
batch,
num_encoder_attention_heads,
past_key_values_length,
self._config.hidden_size // num_encoder_attention_heads,
)
snake_case : Any = common_inputs["""attention_mask"""].dtype
snake_case : int = torch.cat(
[common_inputs["""attention_mask"""], torch.ones(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,dtype=SCREAMING_SNAKE_CASE_ )] ,dim=1 )
snake_case : Dict = [
(torch.zeros(SCREAMING_SNAKE_CASE_ ), torch.zeros(SCREAMING_SNAKE_CASE_ )) for _ in range(SCREAMING_SNAKE_CASE_ )
]
return common_inputs
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ = -1 ,SCREAMING_SNAKE_CASE_ = -1 ,SCREAMING_SNAKE_CASE_ = False ,SCREAMING_SNAKE_CASE_ = None ,):
'''simple docstring'''
# Copied from OnnxConfig.generate_dummy_inputs
# Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
snake_case : Tuple = compute_effective_axis_dimension(
SCREAMING_SNAKE_CASE_ ,fixed_dimension=OnnxConfig.default_fixed_batch ,num_token_to_add=0 )
# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
snake_case : List[str] = tokenizer.num_special_tokens_to_add(SCREAMING_SNAKE_CASE_ )
snake_case : Optional[int] = compute_effective_axis_dimension(
SCREAMING_SNAKE_CASE_ ,fixed_dimension=OnnxConfig.default_fixed_sequence ,num_token_to_add=SCREAMING_SNAKE_CASE_ )
# Generate dummy inputs according to compute batch and sequence
snake_case : str = [""" """.join([tokenizer.unk_token] ) * seq_length] * batch_size
snake_case : Optional[int] = dict(tokenizer(SCREAMING_SNAKE_CASE_ ,return_tensors=SCREAMING_SNAKE_CASE_ ) )
return common_inputs
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ = -1 ,SCREAMING_SNAKE_CASE_ = -1 ,SCREAMING_SNAKE_CASE_ = False ,SCREAMING_SNAKE_CASE_ = None ,):
'''simple docstring'''
if self.task in ["default", "seq2seq-lm"]:
snake_case : str = self._generate_dummy_inputs_for_default_and_seqaseq_lm(
SCREAMING_SNAKE_CASE_ ,batch_size=SCREAMING_SNAKE_CASE_ ,seq_length=SCREAMING_SNAKE_CASE_ ,is_pair=SCREAMING_SNAKE_CASE_ ,framework=SCREAMING_SNAKE_CASE_ )
elif self.task == "causal-lm":
snake_case : List[Any] = self._generate_dummy_inputs_for_causal_lm(
SCREAMING_SNAKE_CASE_ ,batch_size=SCREAMING_SNAKE_CASE_ ,seq_length=SCREAMING_SNAKE_CASE_ ,is_pair=SCREAMING_SNAKE_CASE_ ,framework=SCREAMING_SNAKE_CASE_ )
else:
snake_case : Any = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
SCREAMING_SNAKE_CASE_ ,batch_size=SCREAMING_SNAKE_CASE_ ,seq_length=SCREAMING_SNAKE_CASE_ ,is_pair=SCREAMING_SNAKE_CASE_ ,framework=SCREAMING_SNAKE_CASE_ )
return common_inputs
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
if self.task in ["default", "seq2seq-lm"]:
snake_case : int = super()._flatten_past_key_values_(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )
else:
snake_case : Tuple = super(SCREAMING_SNAKE_CASE_ ,self )._flatten_past_key_values_(
SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )
| 36 |
import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union
import tqdm
from filelock import FileLock
from transformers import (
BartTokenizer,
BartTokenizerFast,
DataProcessor,
PreTrainedTokenizer,
RobertaTokenizer,
RobertaTokenizerFast,
XLMRobertaTokenizer,
is_tf_available,
is_torch_available,
)
__lowercase : Any = logging.getLogger(__name__)
@dataclass(frozen=snake_case )
class _A :
'''simple docstring'''
__lowerCamelCase : str
__lowerCamelCase : str
__lowerCamelCase : Optional[str] = None
__lowerCamelCase : Optional[str] = None
__lowerCamelCase : Optional[str] = None
@dataclass(frozen=snake_case )
class _A :
'''simple docstring'''
__lowerCamelCase : List[int]
__lowerCamelCase : Optional[List[int]] = None
__lowerCamelCase : Optional[List[int]] = None
__lowerCamelCase : Optional[Union[int, float]] = None
__lowerCamelCase : Optional[int] = None
if is_torch_available():
import torch
from torch.utils.data import Dataset
class _A ( snake_case ):
'''simple docstring'''
__lowerCamelCase : List[InputFeatures]
def __init__( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_=False ,SCREAMING_SNAKE_CASE_ = False ,):
'''simple docstring'''
snake_case : str = hans_processors[task]()
snake_case : str = os.path.join(
SCREAMING_SNAKE_CASE_ ,"""cached_{}_{}_{}_{}""".format(
"""dev""" if evaluate else """train""" ,tokenizer.__class__.__name__ ,str(SCREAMING_SNAKE_CASE_ ) ,SCREAMING_SNAKE_CASE_ ,) ,)
snake_case : Dict = processor.get_labels()
if tokenizer.__class__ in (
RobertaTokenizer,
RobertaTokenizerFast,
XLMRobertaTokenizer,
BartTokenizer,
BartTokenizerFast,
):
# HACK(label indices are swapped in RoBERTa pretrained model)
snake_case , snake_case : List[Any] = label_list[2], label_list[1]
snake_case : List[Any] = label_list
# Make sure only the first process in distributed training processes the dataset,
# and the others will use the cache.
snake_case : Any = cached_features_file + """.lock"""
with FileLock(SCREAMING_SNAKE_CASE_ ):
if os.path.exists(SCREAMING_SNAKE_CASE_ ) and not overwrite_cache:
logger.info(F"""Loading features from cached file {cached_features_file}""" )
snake_case : int = torch.load(SCREAMING_SNAKE_CASE_ )
else:
logger.info(F"""Creating features from dataset file at {data_dir}""" )
snake_case : Union[str, Any] = (
processor.get_dev_examples(SCREAMING_SNAKE_CASE_ ) if evaluate else processor.get_train_examples(SCREAMING_SNAKE_CASE_ )
)
logger.info("""Training examples: %s""" ,len(SCREAMING_SNAKE_CASE_ ) )
snake_case : Dict = hans_convert_examples_to_features(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )
logger.info("""Saving features into cached file %s""" ,SCREAMING_SNAKE_CASE_ )
torch.save(self.features ,SCREAMING_SNAKE_CASE_ )
def __len__( self ):
'''simple docstring'''
return len(self.features )
def __getitem__( self ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
return self.features[i]
def snake_case_ ( self ):
'''simple docstring'''
return self.label_list
if is_tf_available():
import tensorflow as tf
class _A :
'''simple docstring'''
__lowerCamelCase : List[InputFeatures]
def __init__( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ = 128 ,SCREAMING_SNAKE_CASE_=False ,SCREAMING_SNAKE_CASE_ = False ,):
'''simple docstring'''
snake_case : Any = hans_processors[task]()
snake_case : List[str] = processor.get_labels()
if tokenizer.__class__ in (
RobertaTokenizer,
RobertaTokenizerFast,
XLMRobertaTokenizer,
BartTokenizer,
BartTokenizerFast,
):
# HACK(label indices are swapped in RoBERTa pretrained model)
snake_case , snake_case : int = label_list[2], label_list[1]
snake_case : List[str] = label_list
snake_case : int = processor.get_dev_examples(SCREAMING_SNAKE_CASE_ ) if evaluate else processor.get_train_examples(SCREAMING_SNAKE_CASE_ )
snake_case : Any = hans_convert_examples_to_features(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )
def gen():
for ex_index, ex in tqdm.tqdm(enumerate(self.features ) ,desc="""convert examples to features""" ):
if ex_index % 10000 == 0:
logger.info("""Writing example %d of %d""" % (ex_index, len(SCREAMING_SNAKE_CASE_ )) )
yield (
{
"example_id": 0,
"input_ids": ex.input_ids,
"attention_mask": ex.attention_mask,
"token_type_ids": ex.token_type_ids,
},
ex.label,
)
snake_case : Any = tf.data.Dataset.from_generator(
SCREAMING_SNAKE_CASE_ ,(
{
"""example_id""": tf.intaa,
"""input_ids""": tf.intaa,
"""attention_mask""": tf.intaa,
"""token_type_ids""": tf.intaa,
},
tf.intaa,
) ,(
{
"""example_id""": tf.TensorShape([] ),
"""input_ids""": tf.TensorShape([None, None] ),
"""attention_mask""": tf.TensorShape([None, None] ),
"""token_type_ids""": tf.TensorShape([None, None] ),
},
tf.TensorShape([] ),
) ,)
def snake_case_ ( self ):
'''simple docstring'''
return self.dataset
def __len__( self ):
'''simple docstring'''
return len(self.features )
def __getitem__( self ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
return self.features[i]
def snake_case_ ( self ):
'''simple docstring'''
return self.label_list
class _A ( snake_case ):
'''simple docstring'''
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
return self._create_examples(self._read_tsv(os.path.join(SCREAMING_SNAKE_CASE_ ,"""heuristics_train_set.txt""" ) ) ,"""train""" )
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
return self._create_examples(self._read_tsv(os.path.join(SCREAMING_SNAKE_CASE_ ,"""heuristics_evaluation_set.txt""" ) ) ,"""dev""" )
def snake_case_ ( self ):
'''simple docstring'''
return ["contradiction", "entailment", "neutral"]
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
snake_case : List[str] = []
for i, line in enumerate(SCREAMING_SNAKE_CASE_ ):
if i == 0:
continue
snake_case : Any = """%s-%s""" % (set_type, line[0])
snake_case : Optional[int] = line[5]
snake_case : Union[str, Any] = line[6]
snake_case : Optional[Any] = line[7][2:] if line[7].startswith("""ex""" ) else line[7]
snake_case : Dict = line[0]
examples.append(InputExample(guid=SCREAMING_SNAKE_CASE_ ,text_a=SCREAMING_SNAKE_CASE_ ,text_b=SCREAMING_SNAKE_CASE_ ,label=SCREAMING_SNAKE_CASE_ ,pairID=SCREAMING_SNAKE_CASE_ ) )
return examples
def lowercase ( __A : List[InputExample] , __A : List[str] , __A : int , __A : PreTrainedTokenizer , ) -> Tuple:
'''simple docstring'''
snake_case : List[Any] = {label: i for i, label in enumerate(__A )}
snake_case : Union[str, Any] = []
for ex_index, example in tqdm.tqdm(enumerate(__A ) , desc="""convert examples to features""" ):
if ex_index % 1_0000 == 0:
logger.info("""Writing example %d""" % (ex_index) )
snake_case : Union[str, Any] = tokenizer(
example.text_a , example.text_b , add_special_tokens=__A , max_length=__A , padding="""max_length""" , truncation=__A , return_overflowing_tokens=__A , )
snake_case : Tuple = label_map[example.label] if example.label in label_map else 0
snake_case : Tuple = int(example.pairID )
features.append(InputFeatures(**__A , label=__A , pairID=__A ) )
for i, example in enumerate(examples[:5] ):
logger.info("""*** Example ***""" )
logger.info(f"""guid: {example}""" )
logger.info(f"""features: {features[i]}""" )
return features
__lowercase : Dict = {
'''hans''': 3,
}
__lowercase : Union[str, Any] = {
'''hans''': HansProcessor,
}
| 36 | 1 |
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
def lowercase ( __A : dict ) -> tuple:
'''simple docstring'''
return (data["data"], data["target"])
def lowercase ( __A : np.ndarray , __A : np.ndarray ) -> XGBClassifier:
'''simple docstring'''
snake_case : List[str] = XGBClassifier()
classifier.fit(__A , __A )
return classifier
def lowercase ( ) -> None:
'''simple docstring'''
snake_case : Any = load_iris()
snake_case , snake_case : str = data_handling(__A )
snake_case , snake_case , snake_case , snake_case : Optional[int] = train_test_split(
__A , __A , test_size=0.25 )
snake_case : Union[str, Any] = iris["""target_names"""]
# Create an XGBoost Classifier from the training data
snake_case : int = xgboost(__A , __A )
# Display the confusion matrix of the classifier with both training and test sets
ConfusionMatrixDisplay.from_estimator(
__A , __A , __A , display_labels=__A , cmap="""Blues""" , normalize="""true""" , )
plt.title("""Normalized Confusion Matrix - IRIS Dataset""" )
plt.show()
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
main()
| 36 |
from __future__ import annotations
def lowercase ( __A : int ) -> list[int]:
'''simple docstring'''
snake_case : Dict = 2
snake_case : int = []
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.append(__A )
if n > 1:
factors.append(__A )
return factors
if __name__ == "__main__":
import doctest
doctest.testmod()
| 36 | 1 |
import json
import logging
import os
import sys
from pathlib import Path
import finetune_rag
from transformers.file_utils import is_apex_available
from transformers.testing_utils import (
TestCasePlus,
execute_subprocess_async,
require_ray,
require_torch_gpu,
require_torch_multi_gpu,
)
logging.basicConfig(level=logging.DEBUG)
__lowercase : Optional[Any] = logging.getLogger()
__lowercase : List[Any] = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
class _A ( snake_case ):
'''simple docstring'''
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
os.makedirs(SCREAMING_SNAKE_CASE_ ,exist_ok=SCREAMING_SNAKE_CASE_ )
snake_case : Dict = {"""source""": """What is love ?""", """target""": """life"""}
snake_case : List[str] = {"""train""": 12, """val""": 2, """test""": 2}
for split in ["train", "test", "val"]:
for field in ["source", "target"]:
snake_case : Optional[int] = """\n""".join([contents[field]] * n_lines[split] )
with open(os.path.join(SCREAMING_SNAKE_CASE_ ,F"""{split}.{field}""" ) ,"""w""" ) as f:
f.write(SCREAMING_SNAKE_CASE_ )
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ = "pytorch" ):
'''simple docstring'''
snake_case : List[Any] = self.get_auto_remove_tmp_dir()
snake_case : Tuple = os.path.join(SCREAMING_SNAKE_CASE_ ,"""output""" )
snake_case : Any = os.path.join(SCREAMING_SNAKE_CASE_ ,"""data""" )
self._create_dummy_data(data_dir=SCREAMING_SNAKE_CASE_ )
snake_case : int = F"""
--data_dir {data_dir} \
--output_dir {output_dir} \
--model_name_or_path facebook/rag-sequence-base \
--model_type rag_sequence \
--do_train \
--do_predict \
--n_val -1 \
--val_check_interval 1.0 \
--train_batch_size 2 \
--eval_batch_size 1 \
--max_source_length 25 \
--max_target_length 25 \
--val_max_target_length 25 \
--test_max_target_length 25 \
--label_smoothing 0.1 \
--dropout 0.1 \
--attention_dropout 0.1 \
--weight_decay 0.001 \
--adam_epsilon 1e-08 \
--max_grad_norm 0.1 \
--lr_scheduler polynomial \
--learning_rate 3e-04 \
--num_train_epochs 1 \
--warmup_steps 4 \
--gradient_accumulation_steps 1 \
--distributed-port 8787 \
--use_dummy_dataset 1 \
--distributed_retriever {distributed_retriever} \
""".split()
if gpus > 0:
testargs.append(F"""--gpus={gpus}""" )
if is_apex_available():
testargs.append("""--fp16""" )
else:
testargs.append("""--gpus=0""" )
testargs.append("""--distributed_backend=ddp_cpu""" )
testargs.append("""--num_processes=2""" )
snake_case : Dict = [sys.executable, str(Path(finetune_rag.__file__ ).resolve() )] + testargs
execute_subprocess_async(SCREAMING_SNAKE_CASE_ ,env=self.get_env() )
snake_case : Optional[Any] = os.path.join(SCREAMING_SNAKE_CASE_ ,"""metrics.json""" )
with open(SCREAMING_SNAKE_CASE_ ) as f:
snake_case : Any = json.load(SCREAMING_SNAKE_CASE_ )
return result
@require_torch_gpu
def snake_case_ ( self ):
'''simple docstring'''
snake_case : Union[str, Any] = self._run_finetune(gpus=1 )
self.assertGreaterEqual(result["""test"""][0]["""test_avg_em"""] ,0.2 )
@require_torch_multi_gpu
def snake_case_ ( self ):
'''simple docstring'''
snake_case : str = self._run_finetune(gpus=2 )
self.assertGreaterEqual(result["""test"""][0]["""test_avg_em"""] ,0.2 )
@require_torch_gpu
@require_ray
def snake_case_ ( self ):
'''simple docstring'''
snake_case : Tuple = self._run_finetune(gpus=1 ,distributed_retriever="""ray""" )
self.assertGreaterEqual(result["""test"""][0]["""test_avg_em"""] ,0.2 )
@require_torch_multi_gpu
@require_ray
def snake_case_ ( self ):
'''simple docstring'''
snake_case : Any = self._run_finetune(gpus=1 ,distributed_retriever="""ray""" )
self.assertGreaterEqual(result["""test"""][0]["""test_avg_em"""] ,0.2 )
| 36 |
import numpy as np
def lowercase ( __A : np.array ) -> np.array:
'''simple docstring'''
return (2 / (1 + np.exp(-2 * vector ))) - 1
if __name__ == "__main__":
import doctest
doctest.testmod()
| 36 | 1 |
from typing import Optional
from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
class _A ( snake_case ):
'''simple docstring'''
def __init__( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_ = False ,SCREAMING_SNAKE_CASE_ = False ,SCREAMING_SNAKE_CASE_ = None ,**SCREAMING_SNAKE_CASE_ ,):
'''simple docstring'''
super().__init__(
SCREAMING_SNAKE_CASE_ ,split=SCREAMING_SNAKE_CASE_ ,features=SCREAMING_SNAKE_CASE_ ,cache_dir=SCREAMING_SNAKE_CASE_ ,keep_in_memory=SCREAMING_SNAKE_CASE_ ,streaming=SCREAMING_SNAKE_CASE_ ,num_proc=SCREAMING_SNAKE_CASE_ ,**SCREAMING_SNAKE_CASE_ ,)
snake_case : Any = path_or_paths if isinstance(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ) else {self.split: path_or_paths}
snake_case : str = Text(
cache_dir=SCREAMING_SNAKE_CASE_ ,data_files=SCREAMING_SNAKE_CASE_ ,features=SCREAMING_SNAKE_CASE_ ,**SCREAMING_SNAKE_CASE_ ,)
def snake_case_ ( self ):
'''simple docstring'''
# Build iterable dataset
if self.streaming:
snake_case : List[str] = self.builder.as_streaming_dataset(split=self.split )
# Build regular (map-style) dataset
else:
snake_case : int = None
snake_case : Tuple = None
snake_case : str = None
snake_case : List[str] = None
self.builder.download_and_prepare(
download_config=SCREAMING_SNAKE_CASE_ ,download_mode=SCREAMING_SNAKE_CASE_ ,verification_mode=SCREAMING_SNAKE_CASE_ ,base_path=SCREAMING_SNAKE_CASE_ ,num_proc=self.num_proc ,)
snake_case : Any = self.builder.as_dataset(
split=self.split ,verification_mode=SCREAMING_SNAKE_CASE_ ,in_memory=self.keep_in_memory )
return dataset
| 36 |
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
__lowercase : Optional[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 ( __A : Union[str, Any] ) -> Optional[int]:
'''simple docstring'''
for pegasus_name, hf_name in PATTERNS:
snake_case : Dict = k.replace(__A , __A )
return k
def lowercase ( __A : dict , __A : dict ) -> PegasusForConditionalGeneration:
'''simple docstring'''
snake_case : Dict = DEFAULTS.copy()
cfg_kwargs.update(__A )
snake_case : int = PegasusConfig(**__A )
snake_case : List[Any] = PegasusForConditionalGeneration(__A )
snake_case : Optional[Any] = torch_model.model.state_dict()
snake_case : Optional[int] = {}
for k, v in tf_weights.items():
snake_case : str = rename_state_dict_key(__A )
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:
snake_case : Optional[Any] = v.T
snake_case : List[Any] = torch.tensor(__A , 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
snake_case : List[str] = torch.zeros_like(mapping["""shared.weight"""][cfg.pad_token_id + 1] )
snake_case : Optional[Any] = mapping["""shared.weight"""]
snake_case : Optional[Any] = mapping["""shared.weight"""]
snake_case : Tuple = {k: torch.zeros_like(__A ) for k, v in sd.items() if k.endswith("""bias""" ) and k not in mapping}
mapping.update(**__A )
snake_case , snake_case : Union[str, Any] = torch_model.model.load_state_dict(__A , strict=__A )
snake_case : Union[str, Any] = [
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 ( __A : int="./ckpt/aeslc/model.ckpt-32000" ) -> Dict:
'''simple docstring'''
snake_case : Optional[Any] = tf.train.list_variables(__A )
snake_case : Union[str, Any] = {}
snake_case : List[str] = ["""Adafactor""", """global_step"""]
for name, shape in tqdm(__A , desc="""converting tf checkpoint to dict""" ):
snake_case : str = any(pat in name for pat in ignore_name )
if skip_key:
continue
snake_case : List[str] = tf.train.load_variable(__A , __A )
snake_case : Optional[Any] = array
return tf_weights
def lowercase ( __A : str , __A : str ) -> Optional[int]:
'''simple docstring'''
snake_case : Dict = Path(__A ).parent.name
snake_case : Dict = task_specific_params[f"""summarization_{dataset}"""]["""max_position_embeddings"""]
snake_case : Any = PegasusTokenizer.from_pretrained("""sshleifer/pegasus""" , model_max_length=__A )
assert tok.model_max_length == desired_max_model_length
tok.save_pretrained(__A )
# convert model
snake_case : Dict = get_tf_weights_as_numpy(__A )
snake_case : List[Any] = task_specific_params[f"""summarization_{dataset}"""]
if dataset == "large":
snake_case : Optional[int] = task_specific_params
snake_case : Optional[int] = convert_pegasus(__A , __A )
torch_model.save_pretrained(__A )
snake_case : int = torch_model.state_dict()
sd.pop("""model.decoder.embed_positions.weight""" )
sd.pop("""model.encoder.embed_positions.weight""" )
torch.save(__A , Path(__A ) / """pytorch_model.bin""" )
if __name__ == "__main__":
__lowercase : Tuple = 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.''')
__lowercase : List[Any] = parser.parse_args()
if args.save_dir is None:
__lowercase : Optional[Any] = Path(args.tf_ckpt_path).parent.name
__lowercase : Union[str, Any] = os.path.join('''pegasus''', dataset)
convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)
| 36 | 1 |
from __future__ import annotations
__lowercase : List[Any] = [True] * 1_000_001
__lowercase : Union[str, Any] = 2
while i * i <= 1_000_000:
if seive[i]:
for j in range(i * i, 1_000_001, i):
__lowercase : List[Any] = False
i += 1
def lowercase ( __A : int ) -> bool:
'''simple docstring'''
return seive[n]
def lowercase ( __A : int ) -> bool:
'''simple docstring'''
return any(digit in """02468""" for digit in str(__A ) )
def lowercase ( __A : int = 100_0000 ) -> list[int]:
'''simple docstring'''
snake_case : Optional[int] = [2] # result already includes the number 2.
for num in range(3 , limit + 1 , 2 ):
if is_prime(__A ) and not contains_an_even_digit(__A ):
snake_case : Any = str(__A )
snake_case : Tuple = [int(str_num[j:] + str_num[:j] ) for j in range(len(__A ) )]
if all(is_prime(__A ) for i in list_nums ):
result.append(__A )
return result
def lowercase ( ) -> int:
'''simple docstring'''
return len(find_circular_primes() )
if __name__ == "__main__":
print(f'''{len(find_circular_primes()) = }''')
| 36 |
import argparse
import pytorch_lightning as pl
import torch
from torch import nn
from transformers import LongformerForQuestionAnswering, LongformerModel
class _A ( pl.LightningModule ):
'''simple docstring'''
def __init__( self ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
super().__init__()
snake_case : Dict = model
snake_case : Optional[int] = 2
snake_case : Optional[Any] = nn.Linear(self.model.config.hidden_size ,self.num_labels )
def snake_case_ ( self ):
'''simple docstring'''
pass
def lowercase ( __A : str , __A : str , __A : str ) -> Optional[Any]:
'''simple docstring'''
snake_case : Optional[Any] = LongformerModel.from_pretrained(__A )
snake_case : Tuple = LightningModel(__A )
snake_case : Optional[int] = torch.load(__A , map_location=torch.device("""cpu""" ) )
lightning_model.load_state_dict(ckpt["""state_dict"""] )
# init longformer question answering model
snake_case : Dict = LongformerForQuestionAnswering.from_pretrained(__A )
# transfer weights
longformer_for_qa.longformer.load_state_dict(lightning_model.model.state_dict() )
longformer_for_qa.qa_outputs.load_state_dict(lightning_model.qa_outputs.state_dict() )
longformer_for_qa.eval()
# save model
longformer_for_qa.save_pretrained(__A )
print(f"""Conversion successful. Model saved under {pytorch_dump_folder_path}""" )
if __name__ == "__main__":
__lowercase : str = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--longformer_model''',
default=None,
type=str,
required=True,
help='''model identifier of longformer. Should be either `longformer-base-4096` or `longformer-large-4096`.''',
)
parser.add_argument(
'''--longformer_question_answering_ckpt_path''',
default=None,
type=str,
required=True,
help='''Path the official PyTorch Lightning Checkpoint.''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
__lowercase : List[str] = parser.parse_args()
convert_longformer_qa_checkpoint_to_pytorch(
args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
)
| 36 | 1 |
import time
from dataclasses import dataclass
from multiprocessing import Pool
from unittest import TestCase
from unittest.mock import patch
import multiprocess
import numpy as np
import pytest
from datasets.utils.py_utils import (
NestedDataStructure,
asdict,
iflatmap_unordered,
map_nested,
temp_seed,
temporary_assignment,
zip_dict,
)
from .utils import require_tf, require_torch
def lowercase ( __A : List[Any] ) -> int: # picklable for multiprocessing
'''simple docstring'''
return x.sum()
def lowercase ( __A : Any ) -> int: # picklable for multiprocessing
'''simple docstring'''
return i + 1
@dataclass
class _A :
'''simple docstring'''
__lowerCamelCase : int
__lowerCamelCase : str
class _A ( snake_case ):
'''simple docstring'''
def snake_case_ ( self ):
'''simple docstring'''
snake_case : List[str] = {}
snake_case : List[str] = []
snake_case : List[Any] = 1
snake_case : Tuple = [1, 2]
snake_case : Optional[int] = {"""a""": 1, """b""": 2}
snake_case : Union[str, Any] = {"""a""": [1, 2], """b""": [3, 4]}
snake_case : Union[str, Any] = {"""a""": {"""1""": 1}, """b""": 2}
snake_case : Union[str, Any] = {"""a""": 1, """b""": 2, """c""": 3, """d""": 4}
snake_case : str = {}
snake_case : Optional[Any] = []
snake_case : Optional[int] = 2
snake_case : str = [2, 3]
snake_case : Optional[Any] = {"""a""": 2, """b""": 3}
snake_case : List[str] = {"""a""": [2, 3], """b""": [4, 5]}
snake_case : Optional[Any] = {"""a""": {"""1""": 2}, """b""": 3}
snake_case : Tuple = {"""a""": 2, """b""": 3, """c""": 4, """d""": 5}
self.assertEqual(map_nested(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ) ,SCREAMING_SNAKE_CASE_ )
self.assertEqual(map_nested(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ) ,SCREAMING_SNAKE_CASE_ )
self.assertEqual(map_nested(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ) ,SCREAMING_SNAKE_CASE_ )
self.assertEqual(map_nested(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ) ,SCREAMING_SNAKE_CASE_ )
self.assertEqual(map_nested(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ) ,SCREAMING_SNAKE_CASE_ )
self.assertEqual(map_nested(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ) ,SCREAMING_SNAKE_CASE_ )
self.assertEqual(map_nested(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ) ,SCREAMING_SNAKE_CASE_ )
self.assertEqual(map_nested(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ) ,SCREAMING_SNAKE_CASE_ )
snake_case : int = 2
self.assertEqual(map_nested(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,num_proc=SCREAMING_SNAKE_CASE_ ) ,SCREAMING_SNAKE_CASE_ )
self.assertEqual(map_nested(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,num_proc=SCREAMING_SNAKE_CASE_ ) ,SCREAMING_SNAKE_CASE_ )
self.assertEqual(map_nested(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,num_proc=SCREAMING_SNAKE_CASE_ ) ,SCREAMING_SNAKE_CASE_ )
self.assertEqual(map_nested(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,num_proc=SCREAMING_SNAKE_CASE_ ) ,SCREAMING_SNAKE_CASE_ )
self.assertEqual(map_nested(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,num_proc=SCREAMING_SNAKE_CASE_ ) ,SCREAMING_SNAKE_CASE_ )
self.assertEqual(map_nested(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,num_proc=SCREAMING_SNAKE_CASE_ ) ,SCREAMING_SNAKE_CASE_ )
self.assertEqual(map_nested(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,num_proc=SCREAMING_SNAKE_CASE_ ) ,SCREAMING_SNAKE_CASE_ )
self.assertEqual(map_nested(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,num_proc=SCREAMING_SNAKE_CASE_ ) ,SCREAMING_SNAKE_CASE_ )
snake_case : Optional[Any] = {"""a""": np.eye(2 ), """b""": np.zeros(3 ), """c""": np.ones(2 )}
snake_case : Union[str, Any] = {"""a""": 2, """b""": 0, """c""": 2}
snake_case : Optional[Any] = {
"""a""": np.eye(2 ).astype(SCREAMING_SNAKE_CASE_ ),
"""b""": np.zeros(3 ).astype(SCREAMING_SNAKE_CASE_ ),
"""c""": np.ones(2 ).astype(SCREAMING_SNAKE_CASE_ ),
}
self.assertEqual(map_nested(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,map_numpy=SCREAMING_SNAKE_CASE_ ) ,SCREAMING_SNAKE_CASE_ )
self.assertEqual(
{k: v.tolist() for k, v in map_nested(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,map_numpy=SCREAMING_SNAKE_CASE_ ).items()} ,{k: v.tolist() for k, v in expected_map_nested_sna_int.items()} ,)
self.assertEqual(map_nested(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,map_numpy=SCREAMING_SNAKE_CASE_ ,num_proc=SCREAMING_SNAKE_CASE_ ) ,SCREAMING_SNAKE_CASE_ )
self.assertEqual(
{k: v.tolist() for k, v in map_nested(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,map_numpy=SCREAMING_SNAKE_CASE_ ,num_proc=SCREAMING_SNAKE_CASE_ ).items()} ,{k: v.tolist() for k, v in expected_map_nested_sna_int.items()} ,)
with self.assertRaises(SCREAMING_SNAKE_CASE_ ): # can't pickle a local lambda
map_nested(lambda SCREAMING_SNAKE_CASE_ : x + 1 ,SCREAMING_SNAKE_CASE_ ,num_proc=SCREAMING_SNAKE_CASE_ )
def snake_case_ ( self ):
'''simple docstring'''
snake_case : Any = {"""a""": 1, """b""": 2}
snake_case : List[Any] = {"""a""": 3, """b""": 4}
snake_case : List[Any] = {"""a""": 5, """b""": 6}
snake_case : Optional[int] = sorted([("""a""", (1, 3, 5)), ("""b""", (2, 4, 6))] )
self.assertEqual(sorted(zip_dict(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ) ) ,SCREAMING_SNAKE_CASE_ )
def snake_case_ ( self ):
'''simple docstring'''
class _A :
'''simple docstring'''
__lowerCamelCase : Tuple = '''bar'''
snake_case : Optional[Any] = Foo()
self.assertEqual(foo.my_attr ,"""bar""" )
with temporary_assignment(SCREAMING_SNAKE_CASE_ ,"""my_attr""" ,"""BAR""" ):
self.assertEqual(foo.my_attr ,"""BAR""" )
self.assertEqual(foo.my_attr ,"""bar""" )
@pytest.mark.parametrize(
"""iterable_length, num_proc, expected_num_proc""" , [
(1, None, 1),
(1, 1, 1),
(2, None, 1),
(2, 1, 1),
(2, 2, 1),
(2, 3, 1),
(3, 2, 1),
(16, 16, 16),
(16, 17, 16),
(17, 16, 16),
] , )
def lowercase ( __A : List[Any] , __A : Optional[Any] , __A : Dict ) -> Tuple:
'''simple docstring'''
with patch("""datasets.utils.py_utils._single_map_nested""" ) as mock_single_map_nested, patch(
"""datasets.parallel.parallel.Pool""" ) as mock_multiprocessing_pool:
snake_case : int = {f"""{i}""": i for i in range(__A )}
snake_case : str = map_nested(lambda __A : x + 10 , __A , num_proc=__A , parallel_min_length=16 )
if expected_num_proc == 1:
assert mock_single_map_nested.called
assert not mock_multiprocessing_pool.called
else:
assert not mock_single_map_nested.called
assert mock_multiprocessing_pool.called
assert mock_multiprocessing_pool.call_args[0][0] == expected_num_proc
class _A ( snake_case ):
'''simple docstring'''
@require_tf
def snake_case_ ( self ):
'''simple docstring'''
import tensorflow as tf
from tensorflow.keras import layers
snake_case : Any = layers.Dense(2 )
def gen_random_output():
snake_case : List[Any] = tf.random.uniform((1, 3) )
return model(SCREAMING_SNAKE_CASE_ ).numpy()
with temp_seed(42 ,set_tensorflow=SCREAMING_SNAKE_CASE_ ):
snake_case : Tuple = gen_random_output()
with temp_seed(42 ,set_tensorflow=SCREAMING_SNAKE_CASE_ ):
snake_case : int = gen_random_output()
snake_case : Tuple = gen_random_output()
np.testing.assert_equal(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )
self.assertGreater(np.abs(outa - outa ).sum() ,0 )
@require_torch
def snake_case_ ( self ):
'''simple docstring'''
import torch
def gen_random_output():
snake_case : Optional[Any] = torch.nn.Linear(3 ,2 )
snake_case : Union[str, Any] = torch.rand(1 ,3 )
return model(SCREAMING_SNAKE_CASE_ ).detach().numpy()
with temp_seed(42 ,set_pytorch=SCREAMING_SNAKE_CASE_ ):
snake_case : Union[str, Any] = gen_random_output()
with temp_seed(42 ,set_pytorch=SCREAMING_SNAKE_CASE_ ):
snake_case : Tuple = gen_random_output()
snake_case : Any = gen_random_output()
np.testing.assert_equal(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )
self.assertGreater(np.abs(outa - outa ).sum() ,0 )
def snake_case_ ( self ):
'''simple docstring'''
def gen_random_output():
return np.random.rand(1 ,3 )
with temp_seed(42 ):
snake_case : Union[str, Any] = gen_random_output()
with temp_seed(42 ):
snake_case : Union[str, Any] = gen_random_output()
snake_case : List[Any] = gen_random_output()
np.testing.assert_equal(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )
self.assertGreater(np.abs(outa - outa ).sum() ,0 )
@pytest.mark.parametrize("""input_data""" , [{}] )
def lowercase ( __A : Any ) -> Any:
'''simple docstring'''
snake_case : Tuple = NestedDataStructure(__A ).data
assert output_data == input_data
@pytest.mark.parametrize(
"""data, expected_output""" , [
({}, []),
([], []),
("""foo""", ["""foo"""]),
(["""foo""", """bar"""], ["""foo""", """bar"""]),
([["""foo""", """bar"""]], ["""foo""", """bar"""]),
([[["""foo"""], ["""bar"""]]], ["""foo""", """bar"""]),
([[["""foo"""], """bar"""]], ["""foo""", """bar"""]),
({"""a""": 1, """b""": 2}, [1, 2]),
({"""a""": [1, 2], """b""": [3, 4]}, [1, 2, 3, 4]),
({"""a""": [[1, 2]], """b""": [[3, 4]]}, [1, 2, 3, 4]),
({"""a""": [[1, 2]], """b""": [3, 4]}, [1, 2, 3, 4]),
({"""a""": [[[1], [2]]], """b""": [[[3], [4]]]}, [1, 2, 3, 4]),
({"""a""": [[[1], [2]]], """b""": [[3, 4]]}, [1, 2, 3, 4]),
({"""a""": [[[1], [2]]], """b""": [3, 4]}, [1, 2, 3, 4]),
({"""a""": [[[1], [2]]], """b""": [3, [4]]}, [1, 2, 3, 4]),
({"""a""": {"""1""": 1}, """b""": 2}, [1, 2]),
({"""a""": {"""1""": [1]}, """b""": 2}, [1, 2]),
({"""a""": {"""1""": [1]}, """b""": [2]}, [1, 2]),
] , )
def lowercase ( __A : str , __A : Any ) -> Optional[int]:
'''simple docstring'''
snake_case : List[Any] = NestedDataStructure(__A ).flatten()
assert output == expected_output
def lowercase ( ) -> Any:
'''simple docstring'''
snake_case : str = A(x=1 , y="""foobar""" )
snake_case : Tuple = {"""x""": 1, """y""": """foobar"""}
assert asdict(__A ) == expected_output
snake_case : Any = {"""a""": {"""b""": A(x=10 , y="""foo""" )}, """c""": [A(x=20 , y="""bar""" )]}
snake_case : Union[str, Any] = {"""a""": {"""b""": {"""x""": 10, """y""": """foo"""}}, """c""": [{"""x""": 20, """y""": """bar"""}]}
assert asdict(__A ) == expected_output
with pytest.raises(__A ):
asdict([1, A(x=10 , y="""foo""" )] )
def lowercase ( __A : str ) -> Union[str, Any]:
'''simple docstring'''
return text.split()
def lowercase ( __A : Any ) -> List[Any]:
'''simple docstring'''
yield (time.time(), content)
time.sleep(2 )
yield (time.time(), content)
def lowercase ( ) -> int:
'''simple docstring'''
with Pool(2 ) as pool:
snake_case : int = list(iflatmap_unordered(__A , _split_text , kwargs_iterable=[{"""text""": """hello there"""}] * 10 ) )
assert out.count("""hello""" ) == 10
assert out.count("""there""" ) == 10
assert len(__A ) == 20
# check multiprocess from pathos (uses dill for pickling)
with multiprocess.Pool(2 ) as pool:
snake_case : Optional[Any] = list(iflatmap_unordered(__A , _split_text , kwargs_iterable=[{"""text""": """hello there"""}] * 10 ) )
assert out.count("""hello""" ) == 10
assert out.count("""there""" ) == 10
assert len(__A ) == 20
# check that we get items as fast as possible
with Pool(2 ) as pool:
snake_case : str = []
for yield_time, content in iflatmap_unordered(
__A , _aseconds_generator_of_aitems_with_timing , kwargs_iterable=[{"""content""": """a"""}, {"""content""": """b"""}] ):
assert yield_time < time.time() + 0.1, "we should each item directly after it was yielded"
out.append(__A )
assert out.count("""a""" ) == 2
assert out.count("""b""" ) == 2
assert len(__A ) == 4
| 36 |
import argparse
import collections
import json
import os
import re
import string
import sys
import numpy as np
__lowercase : Optional[Any] = re.compile(r'''\b(a|an|the)\b''', re.UNICODE)
__lowercase : Optional[int] = None
def lowercase ( ) -> Optional[Any]:
'''simple docstring'''
snake_case : int = argparse.ArgumentParser("""Official evaluation script for SQuAD version 2.0.""" )
parser.add_argument("""data_file""" , metavar="""data.json""" , help="""Input data JSON file.""" )
parser.add_argument("""pred_file""" , metavar="""pred.json""" , help="""Model predictions.""" )
parser.add_argument(
"""--out-file""" , """-o""" , metavar="""eval.json""" , help="""Write accuracy metrics to file (default is stdout).""" )
parser.add_argument(
"""--na-prob-file""" , """-n""" , metavar="""na_prob.json""" , help="""Model estimates of probability of no answer.""" )
parser.add_argument(
"""--na-prob-thresh""" , """-t""" , type=__A , default=1.0 , help="""Predict \"\" if no-answer probability exceeds this (default = 1.0).""" , )
parser.add_argument(
"""--out-image-dir""" , """-p""" , metavar="""out_images""" , default=__A , help="""Save precision-recall curves to directory.""" )
parser.add_argument("""--verbose""" , """-v""" , action="""store_true""" )
if len(sys.argv ) == 1:
parser.print_help()
sys.exit(1 )
return parser.parse_args()
def lowercase ( __A : Union[str, Any] ) -> int:
'''simple docstring'''
snake_case : Any = {}
for article in dataset:
for p in article["paragraphs"]:
for qa in p["qas"]:
snake_case : int = bool(qa["""answers"""]["""text"""] )
return qid_to_has_ans
def lowercase ( __A : int ) -> Optional[int]:
'''simple docstring'''
def remove_articles(__A : List[Any] ):
return ARTICLES_REGEX.sub(""" """ , __A )
def white_space_fix(__A : Union[str, Any] ):
return " ".join(text.split() )
def remove_punc(__A : Tuple ):
snake_case : Optional[Any] = set(string.punctuation )
return "".join(ch for ch in text if ch not in exclude )
def lower(__A : Any ):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(__A ) ) ) )
def lowercase ( __A : List[str] ) -> Union[str, Any]:
'''simple docstring'''
if not s:
return []
return normalize_answer(__A ).split()
def lowercase ( __A : Optional[int] , __A : int ) -> List[Any]:
'''simple docstring'''
return int(normalize_answer(__A ) == normalize_answer(__A ) )
def lowercase ( __A : Any , __A : Optional[Any] ) -> List[str]:
'''simple docstring'''
snake_case : Tuple = get_tokens(__A )
snake_case : str = get_tokens(__A )
snake_case : Dict = collections.Counter(__A ) & collections.Counter(__A )
snake_case : Optional[int] = sum(common.values() )
if len(__A ) == 0 or len(__A ) == 0:
# If either is no-answer, then F1 is 1 if they agree, 0 otherwise
return int(gold_toks == pred_toks )
if num_same == 0:
return 0
snake_case : List[Any] = 1.0 * num_same / len(__A )
snake_case : int = 1.0 * num_same / len(__A )
snake_case : Dict = (2 * precision * recall) / (precision + recall)
return fa
def lowercase ( __A : List[Any] , __A : int ) -> str:
'''simple docstring'''
snake_case : Tuple = {}
snake_case : Tuple = {}
for article in dataset:
for p in article["paragraphs"]:
for qa in p["qas"]:
snake_case : str = qa["""id"""]
snake_case : Union[str, Any] = [t for t in qa["""answers"""]["""text"""] if normalize_answer(__A )]
if not gold_answers:
# For unanswerable questions, only correct answer is empty string
snake_case : Optional[Any] = [""""""]
if qid not in preds:
print(f"""Missing prediction for {qid}""" )
continue
snake_case : Dict = preds[qid]
# Take max over all gold answers
snake_case : Union[str, Any] = max(compute_exact(__A , __A ) for a in gold_answers )
snake_case : Optional[int] = max(compute_fa(__A , __A ) for a in gold_answers )
return exact_scores, fa_scores
def lowercase ( __A : str , __A : Any , __A : List[Any] , __A : List[Any] ) -> Dict:
'''simple docstring'''
snake_case : Optional[int] = {}
for qid, s in scores.items():
snake_case : Any = na_probs[qid] > na_prob_thresh
if pred_na:
snake_case : str = float(not qid_to_has_ans[qid] )
else:
snake_case : List[Any] = s
return new_scores
def lowercase ( __A : Dict , __A : Union[str, Any] , __A : List[str]=None ) -> int:
'''simple docstring'''
if not qid_list:
snake_case : List[str] = len(__A )
return collections.OrderedDict(
[
("""exact""", 100.0 * sum(exact_scores.values() ) / total),
("""f1""", 100.0 * sum(fa_scores.values() ) / total),
("""total""", total),
] )
else:
snake_case : Any = len(__A )
return collections.OrderedDict(
[
("""exact""", 100.0 * sum(exact_scores[k] for k in qid_list ) / total),
("""f1""", 100.0 * sum(fa_scores[k] for k in qid_list ) / total),
("""total""", total),
] )
def lowercase ( __A : Optional[Any] , __A : Tuple , __A : List[str] ) -> Optional[Any]:
'''simple docstring'''
for k in new_eval:
snake_case : str = new_eval[k]
def lowercase ( __A : Tuple , __A : int , __A : Dict , __A : Dict ) -> int:
'''simple docstring'''
plt.step(__A , __A , color="""b""" , alpha=0.2 , where="""post""" )
plt.fill_between(__A , __A , step="""post""" , alpha=0.2 , color="""b""" )
plt.xlabel("""Recall""" )
plt.ylabel("""Precision""" )
plt.xlim([0.0, 1.05] )
plt.ylim([0.0, 1.05] )
plt.title(__A )
plt.savefig(__A )
plt.clf()
def lowercase ( __A : Optional[Any] , __A : Union[str, Any] , __A : Dict , __A : Tuple , __A : Optional[Any]=None , __A : List[str]=None ) -> Union[str, Any]:
'''simple docstring'''
snake_case : Optional[int] = sorted(__A , key=lambda __A : na_probs[k] )
snake_case : Any = 0.0
snake_case : str = 1.0
snake_case : Tuple = 0.0
snake_case : str = [1.0]
snake_case : Any = [0.0]
snake_case : Dict = 0.0
for i, qid in enumerate(__A ):
if qid_to_has_ans[qid]:
true_pos += scores[qid]
snake_case : str = true_pos / float(i + 1 )
snake_case : List[str] = true_pos / float(__A )
if i == len(__A ) - 1 or na_probs[qid] != na_probs[qid_list[i + 1]]:
# i.e., if we can put a threshold after this point
avg_prec += cur_p * (cur_r - recalls[-1])
precisions.append(__A )
recalls.append(__A )
if out_image:
plot_pr_curve(__A , __A , __A , __A )
return {"ap": 100.0 * avg_prec}
def lowercase ( __A : Any , __A : Optional[int] , __A : Tuple , __A : Tuple , __A : List[Any] , __A : Optional[Any] ) -> Optional[Any]:
'''simple docstring'''
if out_image_dir and not os.path.exists(__A ):
os.makedirs(__A )
snake_case : Tuple = sum(1 for v in qid_to_has_ans.values() if v )
if num_true_pos == 0:
return
snake_case : str = make_precision_recall_eval(
__A , __A , __A , __A , out_image=os.path.join(__A , """pr_exact.png""" ) , title="""Precision-Recall curve for Exact Match score""" , )
snake_case : int = make_precision_recall_eval(
__A , __A , __A , __A , out_image=os.path.join(__A , """pr_f1.png""" ) , title="""Precision-Recall curve for F1 score""" , )
snake_case : Dict = {k: float(__A ) for k, v in qid_to_has_ans.items()}
snake_case : int = make_precision_recall_eval(
__A , __A , __A , __A , out_image=os.path.join(__A , """pr_oracle.png""" ) , title="""Oracle Precision-Recall curve (binary task of HasAns vs. NoAns)""" , )
merge_eval(__A , __A , """pr_exact""" )
merge_eval(__A , __A , """pr_f1""" )
merge_eval(__A , __A , """pr_oracle""" )
def lowercase ( __A : List[Any] , __A : Union[str, Any] , __A : Union[str, Any] , __A : Optional[int] ) -> Union[str, Any]:
'''simple docstring'''
if not qid_list:
return
snake_case : int = [na_probs[k] for k in qid_list]
snake_case : List[str] = np.ones_like(__A ) / float(len(__A ) )
plt.hist(__A , weights=__A , bins=20 , range=(0.0, 1.0) )
plt.xlabel("""Model probability of no-answer""" )
plt.ylabel("""Proportion of dataset""" )
plt.title(f"""Histogram of no-answer probability: {name}""" )
plt.savefig(os.path.join(__A , f"""na_prob_hist_{name}.png""" ) )
plt.clf()
def lowercase ( __A : List[Any] , __A : Tuple , __A : Tuple , __A : Any ) -> Dict:
'''simple docstring'''
snake_case : Optional[int] = sum(1 for k in qid_to_has_ans if not qid_to_has_ans[k] )
snake_case : str = num_no_ans
snake_case : Optional[Any] = cur_score
snake_case : Optional[Any] = 0.0
snake_case : List[Any] = sorted(__A , key=lambda __A : na_probs[k] )
for i, qid in enumerate(__A ):
if qid not in scores:
continue
if qid_to_has_ans[qid]:
snake_case : Dict = scores[qid]
else:
if preds[qid]:
snake_case : Dict = -1
else:
snake_case : str = 0
cur_score += diff
if cur_score > best_score:
snake_case : Union[str, Any] = cur_score
snake_case : List[Any] = na_probs[qid]
return 100.0 * best_score / len(__A ), best_thresh
def lowercase ( __A : Dict , __A : str , __A : str , __A : int , __A : str , __A : Any ) -> List[str]:
'''simple docstring'''
snake_case , snake_case : Optional[int] = find_best_thresh(__A , __A , __A , __A )
snake_case , snake_case : str = find_best_thresh(__A , __A , __A , __A )
snake_case : List[str] = best_exact
snake_case : List[Any] = exact_thresh
snake_case : Optional[Any] = best_fa
snake_case : Optional[int] = fa_thresh
def lowercase ( ) -> Any:
'''simple docstring'''
with open(OPTS.data_file ) as f:
snake_case : Dict = json.load(__A )
snake_case : Union[str, Any] = dataset_json["""data"""]
with open(OPTS.pred_file ) as f:
snake_case : int = json.load(__A )
if OPTS.na_prob_file:
with open(OPTS.na_prob_file ) as f:
snake_case : Any = json.load(__A )
else:
snake_case : Any = {k: 0.0 for k in preds}
snake_case : Optional[int] = make_qid_to_has_ans(__A ) # maps qid to True/False
snake_case : Dict = [k for k, v in qid_to_has_ans.items() if v]
snake_case : Optional[int] = [k for k, v in qid_to_has_ans.items() if not v]
snake_case , snake_case : Optional[Any] = get_raw_scores(__A , __A )
snake_case : Tuple = apply_no_ans_threshold(__A , __A , __A , OPTS.na_prob_thresh )
snake_case : Optional[Any] = apply_no_ans_threshold(__A , __A , __A , OPTS.na_prob_thresh )
snake_case : Optional[int] = make_eval_dict(__A , __A )
if has_ans_qids:
snake_case : Any = make_eval_dict(__A , __A , qid_list=__A )
merge_eval(__A , __A , """HasAns""" )
if no_ans_qids:
snake_case : str = make_eval_dict(__A , __A , qid_list=__A )
merge_eval(__A , __A , """NoAns""" )
if OPTS.na_prob_file:
find_all_best_thresh(__A , __A , __A , __A , __A , __A )
if OPTS.na_prob_file and OPTS.out_image_dir:
run_precision_recall_analysis(__A , __A , __A , __A , __A , OPTS.out_image_dir )
histogram_na_prob(__A , __A , OPTS.out_image_dir , """hasAns""" )
histogram_na_prob(__A , __A , OPTS.out_image_dir , """noAns""" )
if OPTS.out_file:
with open(OPTS.out_file , """w""" ) as f:
json.dump(__A , __A )
else:
print(json.dumps(__A , indent=2 ) )
if __name__ == "__main__":
__lowercase : Union[str, Any] = parse_args()
if OPTS.out_image_dir:
import matplotlib
matplotlib.use('''Agg''')
import matplotlib.pyplot as plt
main()
| 36 | 1 |
import time
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device
from ..test_modeling_common import ids_tensor
if is_torch_available():
import torch
from transformers.generation import (
MaxLengthCriteria,
MaxNewTokensCriteria,
MaxTimeCriteria,
StoppingCriteriaList,
validate_stopping_criteria,
)
@require_torch
class _A ( unittest.TestCase ):
'''simple docstring'''
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
snake_case : List[str] = 3
snake_case : Optional[Any] = 250
snake_case : Union[str, Any] = ids_tensor((batch_size, length) ,SCREAMING_SNAKE_CASE_ )
snake_case : Tuple = torch.ones((batch_size, length) ,device=SCREAMING_SNAKE_CASE_ ,dtype=torch.float ) / length
return input_ids, scores
def snake_case_ ( self ):
'''simple docstring'''
snake_case , snake_case : str = self._get_tensors(5 )
snake_case : Optional[Any] = StoppingCriteriaList(
[
MaxLengthCriteria(max_length=10 ),
MaxTimeCriteria(max_time=0.1 ),
] )
self.assertFalse(criteria(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ) )
snake_case , snake_case : List[str] = self._get_tensors(9 )
self.assertFalse(criteria(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ) )
snake_case , snake_case : int = self._get_tensors(10 )
self.assertTrue(criteria(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ) )
def snake_case_ ( self ):
'''simple docstring'''
snake_case : Union[str, Any] = MaxLengthCriteria(max_length=10 )
snake_case , snake_case : int = self._get_tensors(5 )
self.assertFalse(criteria(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ) )
snake_case , snake_case : List[str] = self._get_tensors(9 )
self.assertFalse(criteria(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ) )
snake_case , snake_case : List[str] = self._get_tensors(10 )
self.assertTrue(criteria(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ) )
def snake_case_ ( self ):
'''simple docstring'''
snake_case : str = MaxNewTokensCriteria(start_length=5 ,max_new_tokens=5 )
snake_case , snake_case : int = self._get_tensors(5 )
self.assertFalse(criteria(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ) )
snake_case , snake_case : Union[str, Any] = self._get_tensors(9 )
self.assertFalse(criteria(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ) )
snake_case , snake_case : Tuple = self._get_tensors(10 )
self.assertTrue(criteria(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ) )
snake_case : Optional[Any] = StoppingCriteriaList([criteria] )
self.assertEqual(criteria_list.max_length ,10 )
def snake_case_ ( self ):
'''simple docstring'''
snake_case , snake_case : Dict = self._get_tensors(5 )
snake_case : Optional[Any] = MaxTimeCriteria(max_time=0.1 )
self.assertFalse(criteria(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ) )
snake_case : Optional[int] = MaxTimeCriteria(max_time=0.1 ,initial_timestamp=time.time() - 0.2 )
self.assertTrue(criteria(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ) )
def snake_case_ ( self ):
'''simple docstring'''
validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10 )] ) ,10 )
with self.assertWarns(SCREAMING_SNAKE_CASE_ ):
validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10 )] ) ,11 )
snake_case : Union[str, Any] = validate_stopping_criteria(StoppingCriteriaList() ,11 )
self.assertEqual(len(SCREAMING_SNAKE_CASE_ ) ,1 )
| 36 |
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
flip_channel_order,
get_resize_output_image_size,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, is_vision_available, logging
if is_vision_available():
import PIL
if is_torch_available():
import torch
__lowercase : Dict = logging.get_logger(__name__)
class _A ( snake_case ):
'''simple docstring'''
__lowerCamelCase : Optional[int] = ['''pixel_values''']
def __init__( self ,SCREAMING_SNAKE_CASE_ = True ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_ = PILImageResampling.BILINEAR ,SCREAMING_SNAKE_CASE_ = True ,SCREAMING_SNAKE_CASE_ = 1 / 255 ,SCREAMING_SNAKE_CASE_ = True ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_ = True ,**SCREAMING_SNAKE_CASE_ ,):
'''simple docstring'''
super().__init__(**SCREAMING_SNAKE_CASE_ )
snake_case : List[Any] = size if size is not None else {"""shortest_edge""": 224}
snake_case : str = get_size_dict(SCREAMING_SNAKE_CASE_ ,default_to_square=SCREAMING_SNAKE_CASE_ )
snake_case : str = crop_size if crop_size is not None else {"""height""": 256, """width""": 256}
snake_case : Optional[Any] = get_size_dict(SCREAMING_SNAKE_CASE_ ,param_name="""crop_size""" )
snake_case : Optional[Any] = do_resize
snake_case : Union[str, Any] = size
snake_case : Dict = resample
snake_case : Dict = do_rescale
snake_case : Dict = rescale_factor
snake_case : List[str] = do_center_crop
snake_case : Dict = crop_size
snake_case : Any = do_flip_channel_order
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ = PIL.Image.BILINEAR ,SCREAMING_SNAKE_CASE_ = None ,**SCREAMING_SNAKE_CASE_ ,):
'''simple docstring'''
snake_case : str = get_size_dict(SCREAMING_SNAKE_CASE_ ,default_to_square=SCREAMING_SNAKE_CASE_ )
if "shortest_edge" not in size:
raise ValueError(F"""The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}""" )
snake_case : List[Any] = get_resize_output_image_size(SCREAMING_SNAKE_CASE_ ,size=size["""shortest_edge"""] ,default_to_square=SCREAMING_SNAKE_CASE_ )
return resize(SCREAMING_SNAKE_CASE_ ,size=SCREAMING_SNAKE_CASE_ ,resample=SCREAMING_SNAKE_CASE_ ,data_format=SCREAMING_SNAKE_CASE_ ,**SCREAMING_SNAKE_CASE_ )
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ = None ,**SCREAMING_SNAKE_CASE_ ,):
'''simple docstring'''
snake_case : Union[str, Any] = get_size_dict(SCREAMING_SNAKE_CASE_ )
if "height" not in size or "width" not in size:
raise ValueError(F"""The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}""" )
return center_crop(SCREAMING_SNAKE_CASE_ ,size=(size["""height"""], size["""width"""]) ,data_format=SCREAMING_SNAKE_CASE_ ,**SCREAMING_SNAKE_CASE_ )
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ = None ,**SCREAMING_SNAKE_CASE_ ,):
'''simple docstring'''
return rescale(SCREAMING_SNAKE_CASE_ ,scale=SCREAMING_SNAKE_CASE_ ,data_format=SCREAMING_SNAKE_CASE_ ,**SCREAMING_SNAKE_CASE_ )
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ = None ):
'''simple docstring'''
return flip_channel_order(SCREAMING_SNAKE_CASE_ ,data_format=SCREAMING_SNAKE_CASE_ )
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_ = ChannelDimension.FIRST ,**SCREAMING_SNAKE_CASE_ ,):
'''simple docstring'''
snake_case : List[Any] = do_resize if do_resize is not None else self.do_resize
snake_case : List[str] = resample if resample is not None else self.resample
snake_case : Union[str, Any] = do_rescale if do_rescale is not None else self.do_rescale
snake_case : Union[str, Any] = rescale_factor if rescale_factor is not None else self.rescale_factor
snake_case : str = do_center_crop if do_center_crop is not None else self.do_center_crop
snake_case : Union[str, Any] = (
do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
)
snake_case : Tuple = size if size is not None else self.size
snake_case : Optional[Any] = get_size_dict(SCREAMING_SNAKE_CASE_ ,default_to_square=SCREAMING_SNAKE_CASE_ )
snake_case : str = crop_size if crop_size is not None else self.crop_size
snake_case : Optional[Any] = get_size_dict(SCREAMING_SNAKE_CASE_ ,param_name="""crop_size""" )
snake_case : List[Any] = make_list_of_images(SCREAMING_SNAKE_CASE_ )
if not valid_images(SCREAMING_SNAKE_CASE_ ):
raise ValueError(
"""Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
"""torch.Tensor, tf.Tensor or jax.ndarray.""" )
if do_resize and size is None:
raise ValueError("""Size must be specified if do_resize is True.""" )
if do_rescale and rescale_factor is None:
raise ValueError("""Rescale factor must be specified if do_rescale is True.""" )
if do_center_crop and crop_size is None:
raise ValueError("""Crop size must be specified if do_center_crop is True.""" )
# All transformations expect numpy arrays.
snake_case : Dict = [to_numpy_array(SCREAMING_SNAKE_CASE_ ) for image in images]
if do_resize:
snake_case : Union[str, Any] = [self.resize(image=SCREAMING_SNAKE_CASE_ ,size=SCREAMING_SNAKE_CASE_ ,resample=SCREAMING_SNAKE_CASE_ ) for image in images]
if do_center_crop:
snake_case : Optional[Any] = [self.center_crop(image=SCREAMING_SNAKE_CASE_ ,size=SCREAMING_SNAKE_CASE_ ) for image in images]
if do_rescale:
snake_case : Dict = [self.rescale(image=SCREAMING_SNAKE_CASE_ ,scale=SCREAMING_SNAKE_CASE_ ) for image in images]
# the pretrained checkpoints assume images are BGR, not RGB
if do_flip_channel_order:
snake_case : Optional[int] = [self.flip_channel_order(image=SCREAMING_SNAKE_CASE_ ) for image in images]
snake_case : List[Any] = [to_channel_dimension_format(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ) for image in images]
snake_case : int = {"""pixel_values""": images}
return BatchFeature(data=SCREAMING_SNAKE_CASE_ ,tensor_type=SCREAMING_SNAKE_CASE_ )
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ = None ):
'''simple docstring'''
snake_case : Dict = outputs.logits
# Resize logits and compute semantic segmentation maps
if target_sizes is not None:
if len(SCREAMING_SNAKE_CASE_ ) != len(SCREAMING_SNAKE_CASE_ ):
raise ValueError(
"""Make sure that you pass in as many target sizes as the batch dimension of the logits""" )
if is_torch_tensor(SCREAMING_SNAKE_CASE_ ):
snake_case : int = target_sizes.numpy()
snake_case : Optional[Any] = []
for idx in range(len(SCREAMING_SNAKE_CASE_ ) ):
snake_case : Optional[int] = torch.nn.functional.interpolate(
logits[idx].unsqueeze(dim=0 ) ,size=target_sizes[idx] ,mode="""bilinear""" ,align_corners=SCREAMING_SNAKE_CASE_ )
snake_case : Optional[int] = resized_logits[0].argmax(dim=0 )
semantic_segmentation.append(SCREAMING_SNAKE_CASE_ )
else:
snake_case : Tuple = logits.argmax(dim=1 )
snake_case : Dict = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0] )]
return semantic_segmentation
| 36 | 1 |
def lowercase ( __A : int ) -> "list[int]":
'''simple docstring'''
if upper_limit < 0:
raise ValueError("""Limit for the Catalan sequence must be ≥ 0""" )
snake_case : Dict = [0] * (upper_limit + 1)
# Base case: C(0) = C(1) = 1
snake_case : Dict = 1
if upper_limit > 0:
snake_case : Optional[Any] = 1
# Recurrence relation: C(i) = sum(C(j).C(i-j-1)), from j = 0 to i
for i in range(2 , upper_limit + 1 ):
for j in range(__A ):
catalan_list[i] += catalan_list[j] * catalan_list[i - j - 1]
return catalan_list
if __name__ == "__main__":
print('''\n********* Catalan Numbers Using Dynamic Programming ************\n''')
print('''\n*** Enter -1 at any time to quit ***''')
print('''\nEnter the upper limit (≥ 0) for the Catalan number sequence: ''', end='''''')
try:
while True:
__lowercase : Union[str, Any] = int(input().strip())
if N < 0:
print('''\n********* Goodbye!! ************''')
break
else:
print(f'''The Catalan numbers from 0 through {N} are:''')
print(catalan_numbers(N))
print('''Try another upper limit for the sequence: ''', end='''''')
except (NameError, ValueError):
print('''\n********* Invalid input, goodbye! ************\n''')
import doctest
doctest.testmod()
| 36 |
import fire
from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer
def lowercase ( __A : str , __A : str , **__A : Optional[int] ) -> Optional[Any]:
'''simple docstring'''
snake_case : int = AutoConfig.from_pretrained(__A , **__A )
snake_case : Tuple = AutoModelForSeqaSeqLM.from_config(__A )
model.save_pretrained(__A )
AutoTokenizer.from_pretrained(__A ).save_pretrained(__A )
return model
if __name__ == "__main__":
fire.Fire(save_randomly_initialized_version)
| 36 | 1 |
from typing import Any, Dict, Optional
import torch
import torch.nn.functional as F
from torch import nn
from ..utils import maybe_allow_in_graph
from .activations import get_activation
from .attention_processor import Attention
from .embeddings import CombinedTimestepLabelEmbeddings
@maybe_allow_in_graph
class _A ( nn.Module ):
'''simple docstring'''
def __init__( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_=0.0 ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_ = "geglu" ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_ = False ,SCREAMING_SNAKE_CASE_ = False ,SCREAMING_SNAKE_CASE_ = False ,SCREAMING_SNAKE_CASE_ = False ,SCREAMING_SNAKE_CASE_ = True ,SCREAMING_SNAKE_CASE_ = "layer_norm" ,SCREAMING_SNAKE_CASE_ = False ,):
'''simple docstring'''
super().__init__()
snake_case : Union[str, Any] = only_cross_attention
snake_case : List[Any] = (num_embeds_ada_norm is not None) and norm_type == """ada_norm_zero"""
snake_case : Optional[int] = (num_embeds_ada_norm is not None) and norm_type == """ada_norm"""
if norm_type in ("ada_norm", "ada_norm_zero") and num_embeds_ada_norm is None:
raise ValueError(
F"""`norm_type` is set to {norm_type}, but `num_embeds_ada_norm` is not defined. Please make sure to"""
F""" define `num_embeds_ada_norm` if setting `norm_type` to {norm_type}.""" )
# Define 3 blocks. Each block has its own normalization layer.
# 1. Self-Attn
if self.use_ada_layer_norm:
snake_case : Optional[int] = AdaLayerNorm(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )
elif self.use_ada_layer_norm_zero:
snake_case : str = AdaLayerNormZero(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )
else:
snake_case : List[Any] = nn.LayerNorm(SCREAMING_SNAKE_CASE_ ,elementwise_affine=SCREAMING_SNAKE_CASE_ )
snake_case : Optional[int] = Attention(
query_dim=SCREAMING_SNAKE_CASE_ ,heads=SCREAMING_SNAKE_CASE_ ,dim_head=SCREAMING_SNAKE_CASE_ ,dropout=SCREAMING_SNAKE_CASE_ ,bias=SCREAMING_SNAKE_CASE_ ,cross_attention_dim=cross_attention_dim if only_cross_attention else None ,upcast_attention=SCREAMING_SNAKE_CASE_ ,)
# 2. Cross-Attn
if cross_attention_dim is not None or double_self_attention:
# We currently only use AdaLayerNormZero for self attention where there will only be one attention block.
# I.e. the number of returned modulation chunks from AdaLayerZero would not make sense if returned during
# the second cross attention block.
snake_case : List[str] = (
AdaLayerNorm(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )
if self.use_ada_layer_norm
else nn.LayerNorm(SCREAMING_SNAKE_CASE_ ,elementwise_affine=SCREAMING_SNAKE_CASE_ )
)
snake_case : Union[str, Any] = Attention(
query_dim=SCREAMING_SNAKE_CASE_ ,cross_attention_dim=cross_attention_dim if not double_self_attention else None ,heads=SCREAMING_SNAKE_CASE_ ,dim_head=SCREAMING_SNAKE_CASE_ ,dropout=SCREAMING_SNAKE_CASE_ ,bias=SCREAMING_SNAKE_CASE_ ,upcast_attention=SCREAMING_SNAKE_CASE_ ,) # is self-attn if encoder_hidden_states is none
else:
snake_case : str = None
snake_case : Any = None
# 3. Feed-forward
snake_case : Tuple = nn.LayerNorm(SCREAMING_SNAKE_CASE_ ,elementwise_affine=SCREAMING_SNAKE_CASE_ )
snake_case : Dict = FeedForward(SCREAMING_SNAKE_CASE_ ,dropout=SCREAMING_SNAKE_CASE_ ,activation_fn=SCREAMING_SNAKE_CASE_ ,final_dropout=SCREAMING_SNAKE_CASE_ )
# let chunk size default to None
snake_case : Optional[int] = None
snake_case : Optional[int] = 0
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
# Sets chunk feed-forward
snake_case : Any = chunk_size
snake_case : List[Any] = dim
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_ = None ,):
'''simple docstring'''
# Notice that normalization is always applied before the real computation in the following blocks.
# 1. Self-Attention
if self.use_ada_layer_norm:
snake_case : List[Any] = self.norma(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )
elif self.use_ada_layer_norm_zero:
snake_case , snake_case , snake_case , snake_case , snake_case : int = self.norma(
SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,hidden_dtype=hidden_states.dtype )
else:
snake_case : str = self.norma(SCREAMING_SNAKE_CASE_ )
snake_case : Dict = cross_attention_kwargs if cross_attention_kwargs is not None else {}
snake_case : Tuple = self.attna(
SCREAMING_SNAKE_CASE_ ,encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None ,attention_mask=SCREAMING_SNAKE_CASE_ ,**SCREAMING_SNAKE_CASE_ ,)
if self.use_ada_layer_norm_zero:
snake_case : Tuple = gate_msa.unsqueeze(1 ) * attn_output
snake_case : Dict = attn_output + hidden_states
# 2. Cross-Attention
if self.attna is not None:
snake_case : int = (
self.norma(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ) if self.use_ada_layer_norm else self.norma(SCREAMING_SNAKE_CASE_ )
)
snake_case : Any = self.attna(
SCREAMING_SNAKE_CASE_ ,encoder_hidden_states=SCREAMING_SNAKE_CASE_ ,attention_mask=SCREAMING_SNAKE_CASE_ ,**SCREAMING_SNAKE_CASE_ ,)
snake_case : Optional[Any] = attn_output + hidden_states
# 3. Feed-forward
snake_case : Dict = self.norma(SCREAMING_SNAKE_CASE_ )
if self.use_ada_layer_norm_zero:
snake_case : int = norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]
if self._chunk_size is not None:
# "feed_forward_chunk_size" can be used to save memory
if norm_hidden_states.shape[self._chunk_dim] % self._chunk_size != 0:
raise ValueError(
F"""`hidden_states` dimension to be chunked: {norm_hidden_states.shape[self._chunk_dim]} has to be divisible by chunk size: {self._chunk_size}. Make sure to set an appropriate `chunk_size` when calling `unet.enable_forward_chunking`.""" )
snake_case : Dict = norm_hidden_states.shape[self._chunk_dim] // self._chunk_size
snake_case : Union[str, Any] = torch.cat(
[self.ff(SCREAMING_SNAKE_CASE_ ) for hid_slice in norm_hidden_states.chunk(SCREAMING_SNAKE_CASE_ ,dim=self._chunk_dim )] ,dim=self._chunk_dim ,)
else:
snake_case : int = self.ff(SCREAMING_SNAKE_CASE_ )
if self.use_ada_layer_norm_zero:
snake_case : List[Any] = gate_mlp.unsqueeze(1 ) * ff_output
snake_case : Tuple = ff_output + hidden_states
return hidden_states
class _A ( nn.Module ):
'''simple docstring'''
def __init__( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_ = 4 ,SCREAMING_SNAKE_CASE_ = 0.0 ,SCREAMING_SNAKE_CASE_ = "geglu" ,SCREAMING_SNAKE_CASE_ = False ,):
'''simple docstring'''
super().__init__()
snake_case : List[Any] = int(dim * mult )
snake_case : int = dim_out if dim_out is not None else dim
if activation_fn == "gelu":
snake_case : Optional[Any] = GELU(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )
if activation_fn == "gelu-approximate":
snake_case : List[str] = GELU(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,approximate="""tanh""" )
elif activation_fn == "geglu":
snake_case : str = GEGLU(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )
elif activation_fn == "geglu-approximate":
snake_case : str = ApproximateGELU(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )
snake_case : List[Any] = nn.ModuleList([] )
# project in
self.net.append(SCREAMING_SNAKE_CASE_ )
# project dropout
self.net.append(nn.Dropout(SCREAMING_SNAKE_CASE_ ) )
# project out
self.net.append(nn.Linear(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ) )
# FF as used in Vision Transformer, MLP-Mixer, etc. have a final dropout
if final_dropout:
self.net.append(nn.Dropout(SCREAMING_SNAKE_CASE_ ) )
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
for module in self.net:
snake_case : str = module(SCREAMING_SNAKE_CASE_ )
return hidden_states
class _A ( nn.Module ):
'''simple docstring'''
def __init__( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ = "none" ):
'''simple docstring'''
super().__init__()
snake_case : Optional[Any] = nn.Linear(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )
snake_case : Optional[int] = approximate
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
if gate.device.type != "mps":
return F.gelu(SCREAMING_SNAKE_CASE_ ,approximate=self.approximate )
# mps: gelu is not implemented for float16
return F.gelu(gate.to(dtype=torch.floataa ) ,approximate=self.approximate ).to(dtype=gate.dtype )
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
snake_case : str = self.proj(SCREAMING_SNAKE_CASE_ )
snake_case : Dict = self.gelu(SCREAMING_SNAKE_CASE_ )
return hidden_states
class _A ( nn.Module ):
'''simple docstring'''
def __init__( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
super().__init__()
snake_case : Dict = nn.Linear(SCREAMING_SNAKE_CASE_ ,dim_out * 2 )
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
if gate.device.type != "mps":
return F.gelu(SCREAMING_SNAKE_CASE_ )
# mps: gelu is not implemented for float16
return F.gelu(gate.to(dtype=torch.floataa ) ).to(dtype=gate.dtype )
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
snake_case , snake_case : Optional[int] = self.proj(SCREAMING_SNAKE_CASE_ ).chunk(2 ,dim=-1 )
return hidden_states * self.gelu(SCREAMING_SNAKE_CASE_ )
class _A ( nn.Module ):
'''simple docstring'''
def __init__( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
super().__init__()
snake_case : str = nn.Linear(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
snake_case : Dict = self.proj(SCREAMING_SNAKE_CASE_ )
return x * torch.sigmoid(1.7_02 * x )
class _A ( nn.Module ):
'''simple docstring'''
def __init__( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
super().__init__()
snake_case : List[Any] = nn.Embedding(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )
snake_case : List[str] = nn.SiLU()
snake_case : str = nn.Linear(SCREAMING_SNAKE_CASE_ ,embedding_dim * 2 )
snake_case : str = nn.LayerNorm(SCREAMING_SNAKE_CASE_ ,elementwise_affine=SCREAMING_SNAKE_CASE_ )
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
snake_case : int = self.linear(self.silu(self.emb(SCREAMING_SNAKE_CASE_ ) ) )
snake_case , snake_case : Tuple = torch.chunk(SCREAMING_SNAKE_CASE_ ,2 )
snake_case : int = self.norm(SCREAMING_SNAKE_CASE_ ) * (1 + scale) + shift
return x
class _A ( nn.Module ):
'''simple docstring'''
def __init__( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
super().__init__()
snake_case : int = CombinedTimestepLabelEmbeddings(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )
snake_case : Dict = nn.SiLU()
snake_case : Dict = nn.Linear(SCREAMING_SNAKE_CASE_ ,6 * embedding_dim ,bias=SCREAMING_SNAKE_CASE_ )
snake_case : Optional[Any] = nn.LayerNorm(SCREAMING_SNAKE_CASE_ ,elementwise_affine=SCREAMING_SNAKE_CASE_ ,eps=1E-6 )
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_=None ):
'''simple docstring'''
snake_case : Any = self.linear(self.silu(self.emb(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,hidden_dtype=SCREAMING_SNAKE_CASE_ ) ) )
snake_case , snake_case , snake_case , snake_case , snake_case , snake_case : Optional[Any] = emb.chunk(6 ,dim=1 )
snake_case : int = self.norm(SCREAMING_SNAKE_CASE_ ) * (1 + scale_msa[:, None]) + shift_msa[:, None]
return x, gate_msa, shift_mlp, scale_mlp, gate_mlp
class _A ( nn.Module ):
'''simple docstring'''
def __init__( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_ = 1E-5 ):
'''simple docstring'''
super().__init__()
snake_case : Optional[Any] = num_groups
snake_case : Dict = eps
if act_fn is None:
snake_case : Tuple = None
else:
snake_case : Tuple = get_activation(SCREAMING_SNAKE_CASE_ )
snake_case : Tuple = nn.Linear(SCREAMING_SNAKE_CASE_ ,out_dim * 2 )
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
if self.act:
snake_case : int = self.act(SCREAMING_SNAKE_CASE_ )
snake_case : List[Any] = self.linear(SCREAMING_SNAKE_CASE_ )
snake_case : Optional[int] = emb[:, :, None, None]
snake_case , snake_case : Optional[int] = emb.chunk(2 ,dim=1 )
snake_case : Union[str, Any] = F.group_norm(SCREAMING_SNAKE_CASE_ ,self.num_groups ,eps=self.eps )
snake_case : List[Any] = x * (1 + scale) + shift
return x
| 36 |
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
__lowercase : Any = logging.get_logger(__name__)
__lowercase : str = {
'''google/mobilenet_v1_1.0_224''': '''https://huggingface.co/google/mobilenet_v1_1.0_224/resolve/main/config.json''',
'''google/mobilenet_v1_0.75_192''': '''https://huggingface.co/google/mobilenet_v1_0.75_192/resolve/main/config.json''',
# See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
}
class _A ( snake_case ):
'''simple docstring'''
__lowerCamelCase : Dict = '''mobilenet_v1'''
def __init__( self ,SCREAMING_SNAKE_CASE_=3 ,SCREAMING_SNAKE_CASE_=224 ,SCREAMING_SNAKE_CASE_=1.0 ,SCREAMING_SNAKE_CASE_=8 ,SCREAMING_SNAKE_CASE_="relu6" ,SCREAMING_SNAKE_CASE_=True ,SCREAMING_SNAKE_CASE_=0.9_99 ,SCREAMING_SNAKE_CASE_=0.02 ,SCREAMING_SNAKE_CASE_=0.0_01 ,**SCREAMING_SNAKE_CASE_ ,):
'''simple docstring'''
super().__init__(**SCREAMING_SNAKE_CASE_ )
if depth_multiplier <= 0:
raise ValueError("""depth_multiplier must be greater than zero.""" )
snake_case : List[Any] = num_channels
snake_case : str = image_size
snake_case : List[Any] = depth_multiplier
snake_case : Optional[int] = min_depth
snake_case : Union[str, Any] = hidden_act
snake_case : int = tf_padding
snake_case : Optional[int] = classifier_dropout_prob
snake_case : Tuple = initializer_range
snake_case : List[str] = layer_norm_eps
class _A ( snake_case ):
'''simple docstring'''
__lowerCamelCase : Optional[Any] = version.parse('''1.11''' )
@property
def snake_case_ ( self ):
'''simple docstring'''
return OrderedDict([("""pixel_values""", {0: """batch"""})] )
@property
def snake_case_ ( self ):
'''simple docstring'''
if self.task == "image-classification":
return OrderedDict([("""logits""", {0: """batch"""})] )
else:
return OrderedDict([("""last_hidden_state""", {0: """batch"""}), ("""pooler_output""", {0: """batch"""})] )
@property
def snake_case_ ( self ):
'''simple docstring'''
return 1E-4
| 36 | 1 |
import baseaa
def lowercase ( __A : str ) -> bytes:
'''simple docstring'''
return baseaa.baaencode(string.encode("""utf-8""" ) )
def lowercase ( __A : bytes ) -> str:
'''simple docstring'''
return baseaa.baadecode(__A ).decode("""utf-8""" )
if __name__ == "__main__":
__lowercase : int = '''Hello World!'''
__lowercase : Union[str, Any] = baseaa_encode(test)
print(encoded)
__lowercase : Any = baseaa_decode(encoded)
print(decoded)
| 36 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowercase : List[str] = logging.get_logger(__name__)
__lowercase : List[str] = {
'''edbeeching/decision-transformer-gym-hopper-medium''': (
'''https://huggingface.co/edbeeching/decision-transformer-gym-hopper-medium/resolve/main/config.json'''
),
# See all DecisionTransformer models at https://huggingface.co/models?filter=decision_transformer
}
class _A ( snake_case ):
'''simple docstring'''
__lowerCamelCase : int = '''decision_transformer'''
__lowerCamelCase : Optional[Any] = ['''past_key_values''']
__lowerCamelCase : Tuple = {
'''max_position_embeddings''': '''n_positions''',
'''num_attention_heads''': '''n_head''',
'''num_hidden_layers''': '''n_layer''',
}
def __init__( self ,SCREAMING_SNAKE_CASE_=17 ,SCREAMING_SNAKE_CASE_=4 ,SCREAMING_SNAKE_CASE_=128 ,SCREAMING_SNAKE_CASE_=4096 ,SCREAMING_SNAKE_CASE_=True ,SCREAMING_SNAKE_CASE_=1 ,SCREAMING_SNAKE_CASE_=1024 ,SCREAMING_SNAKE_CASE_=3 ,SCREAMING_SNAKE_CASE_=1 ,SCREAMING_SNAKE_CASE_=None ,SCREAMING_SNAKE_CASE_="relu" ,SCREAMING_SNAKE_CASE_=0.1 ,SCREAMING_SNAKE_CASE_=0.1 ,SCREAMING_SNAKE_CASE_=0.1 ,SCREAMING_SNAKE_CASE_=1E-5 ,SCREAMING_SNAKE_CASE_=0.02 ,SCREAMING_SNAKE_CASE_=True ,SCREAMING_SNAKE_CASE_=True ,SCREAMING_SNAKE_CASE_=50256 ,SCREAMING_SNAKE_CASE_=50256 ,SCREAMING_SNAKE_CASE_=False ,SCREAMING_SNAKE_CASE_=False ,**SCREAMING_SNAKE_CASE_ ,):
'''simple docstring'''
snake_case : Any = state_dim
snake_case : Optional[Any] = act_dim
snake_case : Union[str, Any] = hidden_size
snake_case : Any = max_ep_len
snake_case : int = action_tanh
snake_case : Any = vocab_size
snake_case : Any = n_positions
snake_case : List[str] = n_layer
snake_case : int = n_head
snake_case : Optional[int] = n_inner
snake_case : List[Any] = activation_function
snake_case : Tuple = resid_pdrop
snake_case : Optional[Any] = embd_pdrop
snake_case : Dict = attn_pdrop
snake_case : List[str] = layer_norm_epsilon
snake_case : Union[str, Any] = initializer_range
snake_case : Optional[Any] = scale_attn_weights
snake_case : str = use_cache
snake_case : int = scale_attn_by_inverse_layer_idx
snake_case : Tuple = reorder_and_upcast_attn
snake_case : Tuple = bos_token_id
snake_case : List[str] = eos_token_id
super().__init__(bos_token_id=SCREAMING_SNAKE_CASE_ ,eos_token_id=SCREAMING_SNAKE_CASE_ ,**SCREAMING_SNAKE_CASE_ )
| 36 | 1 |
from typing import Dict
import numpy as np
import torch
from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map
def lowercase ( __A : Dict[str, torch.Tensor] ) -> Dict[str, torch.Tensor]:
'''simple docstring'''
snake_case : str = []
snake_case : Optional[int] = []
snake_case : str = []
for rt in rc.restypes:
snake_case : List[Any] = rc.restype_name_to_atomaa_names[rc.restype_atoa[rt]]
restype_atomaa_to_atomaa_list.append([(rc.atom_order[name] if name else 0) for name in atom_names] )
snake_case : List[str] = {name: i for i, name in enumerate(__A )}
restype_atomaa_to_atomaa_list.append(
[(atom_name_to_idxaa[name] if name in atom_name_to_idxaa else 0) for name in rc.atom_types] )
restype_atomaa_mask_list.append([(1.0 if name else 0.0) for name in atom_names] )
# Add dummy mapping for restype 'UNK'
restype_atomaa_to_atomaa_list.append([0] * 14 )
restype_atomaa_to_atomaa_list.append([0] * 37 )
restype_atomaa_mask_list.append([0.0] * 14 )
snake_case : List[Any] = torch.tensor(
__A , dtype=torch.intaa , device=protein["""aatype"""].device , )
snake_case : int = torch.tensor(
__A , dtype=torch.intaa , device=protein["""aatype"""].device , )
snake_case : Tuple = torch.tensor(
__A , dtype=torch.floataa , device=protein["""aatype"""].device , )
snake_case : List[str] = protein["""aatype"""].to(torch.long )
# create the mapping for (residx, atom14) --> atom37, i.e. an array
# with shape (num_res, 14) containing the atom37 indices for this protein
snake_case : Any = restype_atomaa_to_atomaa[protein_aatype]
snake_case : Dict = restype_atomaa_mask[protein_aatype]
snake_case : str = residx_atomaa_mask
snake_case : Optional[int] = residx_atomaa_to_atomaa.long()
# create the gather indices for mapping back
snake_case : Tuple = restype_atomaa_to_atomaa[protein_aatype]
snake_case : Optional[Any] = residx_atomaa_to_atomaa.long()
# create the corresponding mask
snake_case : int = torch.zeros([21, 37] , dtype=torch.floataa , device=protein["""aatype"""].device )
for restype, restype_letter in enumerate(rc.restypes ):
snake_case : Optional[Any] = rc.restype_atoa[restype_letter]
snake_case : Union[str, Any] = rc.residue_atoms[restype_name]
for atom_name in atom_names:
snake_case : int = rc.atom_order[atom_name]
snake_case : Union[str, Any] = 1
snake_case : Any = restype_atomaa_mask[protein_aatype]
snake_case : Union[str, Any] = residx_atomaa_mask
return protein
def lowercase ( __A : Dict[str, torch.Tensor] ) -> Dict[str, np.ndarray]:
'''simple docstring'''
snake_case : Tuple = tree_map(lambda __A : torch.tensor(__A , device=batch["""aatype"""].device ) , __A , np.ndarray )
snake_case : int = tensor_tree_map(lambda __A : np.array(__A ) , make_atomaa_masks(__A ) )
return out
| 36 |
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
| 36 | 1 |
def lowercase ( __A : int = 1 , __A : int = 1000 ) -> int:
'''simple docstring'''
snake_case : int = 1
snake_case : str = 0
for divide_by_number in range(__A , digit + 1 ):
snake_case : list[int] = []
snake_case : Dict = numerator
for _ in range(1 , digit + 1 ):
if now_divide in has_been_divided:
if longest_list_length < len(__A ):
snake_case : List[str] = len(__A )
snake_case : str = divide_by_number
else:
has_been_divided.append(__A )
snake_case : Optional[int] = now_divide * 10 % divide_by_number
return the_digit
# Tests
if __name__ == "__main__":
import doctest
doctest.testmod()
| 36 |
# Usage:
# ./gen-card-facebook-wmt19.py
import os
from pathlib import Path
def lowercase ( __A : Dict , __A : Union[str, Any] , __A : List[str] ) -> Any:
'''simple docstring'''
snake_case : Tuple = {
"""en""": """Machine learning is great, isn't it?""",
"""ru""": """Машинное обучение - это здорово, не так ли?""",
"""de""": """Maschinelles Lernen ist großartig, oder?""",
}
# BLUE scores as follows:
# "pair": [fairseq, transformers]
snake_case : Optional[Any] = {
"""ru-en""": ["""[41.3](http://matrix.statmt.org/matrix/output/1907?run_id=6937)""", """39.20"""],
"""en-ru""": ["""[36.4](http://matrix.statmt.org/matrix/output/1914?run_id=6724)""", """33.47"""],
"""en-de""": ["""[43.1](http://matrix.statmt.org/matrix/output/1909?run_id=6862)""", """42.83"""],
"""de-en""": ["""[42.3](http://matrix.statmt.org/matrix/output/1902?run_id=6750)""", """41.35"""],
}
snake_case : Optional[int] = f"""{src_lang}-{tgt_lang}"""
snake_case : Any = f"""
---
language:
- {src_lang}
- {tgt_lang}
thumbnail:
tags:
- translation
- wmt19
- facebook
license: apache-2.0
datasets:
- wmt19
metrics:
- bleu
---
# FSMT
## Model description
This is a ported version of [fairseq wmt19 transformer](https://github.com/pytorch/fairseq/blob/master/examples/wmt19/README.md) for {src_lang}-{tgt_lang}.
For more details, please see, [Facebook FAIR's WMT19 News Translation Task Submission](https://arxiv.org/abs/1907.06616).
The abbreviation FSMT stands for FairSeqMachineTranslation
All four models are available:
* [wmt19-en-ru](https://huggingface.co/facebook/wmt19-en-ru)
* [wmt19-ru-en](https://huggingface.co/facebook/wmt19-ru-en)
* [wmt19-en-de](https://huggingface.co/facebook/wmt19-en-de)
* [wmt19-de-en](https://huggingface.co/facebook/wmt19-de-en)
## Intended uses & limitations
#### How to use
```python
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
mname = \"facebook/wmt19-{src_lang}-{tgt_lang}\"
tokenizer = FSMTTokenizer.from_pretrained(mname)
model = FSMTForConditionalGeneration.from_pretrained(mname)
input = \"{texts[src_lang]}\"
input_ids = tokenizer.encode(input, return_tensors=\"pt\")
outputs = model.generate(input_ids)
decoded = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(decoded) # {texts[tgt_lang]}
```
#### Limitations and bias
- The original (and this ported model) doesn't seem to handle well inputs with repeated sub-phrases, [content gets truncated](https://discuss.huggingface.co/t/issues-with-translating-inputs-containing-repeated-phrases/981)
## Training data
Pretrained weights were left identical to the original model released by fairseq. For more details, please, see the [paper](https://arxiv.org/abs/1907.06616).
## Eval results
pair | fairseq | transformers
-------|---------|----------
{pair} | {scores[pair][0]} | {scores[pair][1]}
The score is slightly below the score reported by `fairseq`, since `transformers`` currently doesn't support:
- model ensemble, therefore the best performing checkpoint was ported (``model4.pt``).
- re-ranking
The score was calculated using this code:
```bash
git clone https://github.com/huggingface/transformers
cd transformers
export PAIR={pair}
export DATA_DIR=data/$PAIR
export SAVE_DIR=data/$PAIR
export BS=8
export NUM_BEAMS=15
mkdir -p $DATA_DIR
sacrebleu -t wmt19 -l $PAIR --echo src > $DATA_DIR/val.source
sacrebleu -t wmt19 -l $PAIR --echo ref > $DATA_DIR/val.target
echo $PAIR
PYTHONPATH=\"src:examples/seq2seq\" python examples/seq2seq/run_eval.py facebook/wmt19-$PAIR $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation --num_beams $NUM_BEAMS
```
note: fairseq reports using a beam of 50, so you should get a slightly higher score if re-run with `--num_beams 50`.
## Data Sources
- [training, etc.](http://www.statmt.org/wmt19/)
- [test set](http://matrix.statmt.org/test_sets/newstest2019.tgz?1556572561)
### BibTeX entry and citation info
```bibtex
@inproceedings{{...,
year={{2020}},
title={{Facebook FAIR's WMT19 News Translation Task Submission}},
author={{Ng, Nathan and Yee, Kyra and Baevski, Alexei and Ott, Myle and Auli, Michael and Edunov, Sergey}},
booktitle={{Proc. of WMT}},
}}
```
## TODO
- port model ensemble (fairseq uses 4 model checkpoints)
"""
os.makedirs(__A , exist_ok=__A )
snake_case : Union[str, Any] = os.path.join(__A , """README.md""" )
print(f"""Generating {path}""" )
with open(__A , """w""" , encoding="""utf-8""" ) as f:
f.write(__A )
# make sure we are under the root of the project
__lowercase : int = Path(__file__).resolve().parent.parent.parent
__lowercase : List[str] = repo_dir / '''model_cards'''
for model_name in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
__lowercase , __lowercase , __lowercase : List[str] = model_name.split('''-''')
__lowercase : str = model_cards_dir / '''facebook''' / model_name
write_model_card(model_card_dir, src_lang=src_lang, tgt_lang=tgt_lang)
| 36 | 1 |
from __future__ import annotations
import math
class _A :
'''simple docstring'''
def __init__( self ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
snake_case : Dict = size
# approximate the overall size of segment tree with given value
snake_case : Dict = [0 for i in range(0 ,4 * size )]
# create array to store lazy update
snake_case : List[Any] = [0 for i in range(0 ,4 * size )]
snake_case : Any = [0 for i in range(0 ,4 * size )] # flag for lazy update
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
return idx * 2
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
return idx * 2 + 1
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
if left_element == right_element:
snake_case : int = a[left_element - 1]
else:
snake_case : List[str] = (left_element + right_element) // 2
self.build(self.left(SCREAMING_SNAKE_CASE_ ) ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )
self.build(self.right(SCREAMING_SNAKE_CASE_ ) ,mid + 1 ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )
snake_case : str = max(
self.segment_tree[self.left(SCREAMING_SNAKE_CASE_ )] ,self.segment_tree[self.right(SCREAMING_SNAKE_CASE_ )] )
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
if self.flag[idx] is True:
snake_case : int = self.lazy[idx]
snake_case : List[str] = False
if left_element != right_element:
snake_case : int = self.lazy[idx]
snake_case : List[str] = self.lazy[idx]
snake_case : List[Any] = True
snake_case : Union[str, Any] = True
if right_element < a or left_element > b:
return True
if left_element >= a and right_element <= b:
snake_case : Optional[Any] = val
if left_element != right_element:
snake_case : str = val
snake_case : Optional[Any] = val
snake_case : Optional[Any] = True
snake_case : List[Any] = True
return True
snake_case : List[str] = (left_element + right_element) // 2
self.update(self.left(SCREAMING_SNAKE_CASE_ ) ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )
self.update(self.right(SCREAMING_SNAKE_CASE_ ) ,mid + 1 ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )
snake_case : Tuple = max(
self.segment_tree[self.left(SCREAMING_SNAKE_CASE_ )] ,self.segment_tree[self.right(SCREAMING_SNAKE_CASE_ )] )
return True
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
if self.flag[idx] is True:
snake_case : List[Any] = self.lazy[idx]
snake_case : List[Any] = False
if left_element != right_element:
snake_case : List[str] = self.lazy[idx]
snake_case : int = self.lazy[idx]
snake_case : int = True
snake_case : str = True
if right_element < a or left_element > b:
return -math.inf
if left_element >= a and right_element <= b:
return self.segment_tree[idx]
snake_case : List[Any] = (left_element + right_element) // 2
snake_case : List[str] = self.query(self.left(SCREAMING_SNAKE_CASE_ ) ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )
snake_case : Union[str, Any] = self.query(self.right(SCREAMING_SNAKE_CASE_ ) ,mid + 1 ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )
return max(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )
def __str__( self ):
'''simple docstring'''
return str([self.query(1 ,1 ,self.size ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ) for i in range(1 ,self.size + 1 )] )
if __name__ == "__main__":
__lowercase : Union[str, Any] = [1, 2, -4, 7, 3, -5, 6, 11, -20, 9, 14, 15, 5, 2, -8]
__lowercase : Union[str, Any] = 15
__lowercase : Union[str, Any] = SegmentTree(size)
segt.build(1, 1, size, A)
print(segt.query(1, 1, size, 4, 6))
print(segt.query(1, 1, size, 7, 11))
print(segt.query(1, 1, size, 7, 12))
segt.update(1, 1, size, 1, 3, 111)
print(segt.query(1, 1, size, 1, 15))
segt.update(1, 1, size, 7, 8, 235)
print(segt)
| 36 |
__lowercase : List[str] = '''
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
'''
__lowercase : str = [{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
__lowercase : List[str] = {
'''{processor_class}''': '''FakeProcessorClass''',
'''{model_class}''': '''FakeModelClass''',
'''{object_class}''': '''FakeObjectClass''',
}
| 36 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__lowercase : Optional[int] = {
'''configuration_tapas''': ['''TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TapasConfig'''],
'''tokenization_tapas''': ['''TapasTokenizer'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowercase : int = [
'''TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TapasForMaskedLM''',
'''TapasForQuestionAnswering''',
'''TapasForSequenceClassification''',
'''TapasModel''',
'''TapasPreTrainedModel''',
'''load_tf_weights_in_tapas''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowercase : List[Any] = [
'''TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFTapasForMaskedLM''',
'''TFTapasForQuestionAnswering''',
'''TFTapasForSequenceClassification''',
'''TFTapasModel''',
'''TFTapasPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
from .tokenization_tapas import TapasTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tapas import (
TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
TapasModel,
TapasPreTrainedModel,
load_tf_weights_in_tapas,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_tapas import (
TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
TFTapasForMaskedLM,
TFTapasForQuestionAnswering,
TFTapasForSequenceClassification,
TFTapasModel,
TFTapasPreTrainedModel,
)
else:
import sys
__lowercase : List[str] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 36 |
import warnings
from ..trainer import Trainer
from ..utils import logging
__lowercase : str = logging.get_logger(__name__)
class _A ( snake_case ):
'''simple docstring'''
def __init__( self ,SCREAMING_SNAKE_CASE_=None ,**SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
warnings.warn(
"""`SageMakerTrainer` is deprecated and will be removed in v5 of Transformers. You can use `Trainer` """
"""instead.""" ,SCREAMING_SNAKE_CASE_ ,)
super().__init__(args=SCREAMING_SNAKE_CASE_ ,**SCREAMING_SNAKE_CASE_ )
| 36 | 1 |
def lowercase ( __A : list ) -> bool:
'''simple docstring'''
if not isinstance(__A , __A ):
raise ValueError("""Input series is not valid, valid series - [2, 4, 6]""" )
if len(__A ) == 0:
raise ValueError("""Input list must be a non empty list""" )
if len(__A ) == 1:
return True
snake_case : int = series[1] - series[0]
for index in range(len(__A ) - 1 ):
if series[index + 1] - series[index] != common_diff:
return False
return True
def lowercase ( __A : list ) -> float:
'''simple docstring'''
if not isinstance(__A , __A ):
raise ValueError("""Input series is not valid, valid series - [2, 4, 6]""" )
if len(__A ) == 0:
raise ValueError("""Input list must be a non empty list""" )
snake_case : Any = 0
for val in series:
answer += val
return answer / len(__A )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 36 |
from pathlib import Path
from typing import List
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
__lowercase : List[str] = ['''text''', '''image''', '''audio''']
def lowercase ( __A : List[str] ) -> List[str]:
'''simple docstring'''
snake_case : Optional[int] = []
for input_type in input_types:
if input_type == "text":
inputs.append("""Text input""" )
elif input_type == "image":
inputs.append(
Image.open(Path(get_tests_dir("""fixtures/tests_samples/COCO""" ) ) / """000000039769.png""" ).resize((512, 512) ) )
elif input_type == "audio":
inputs.append(torch.ones(3000 ) )
elif isinstance(__A , __A ):
inputs.append(create_inputs(__A ) )
else:
raise ValueError(f"""Invalid type requested: {input_type}""" )
return inputs
def lowercase ( __A : List ) -> Union[str, Any]:
'''simple docstring'''
snake_case : Dict = []
for output in outputs:
if isinstance(__A , (str, AgentText) ):
output_types.append("""text""" )
elif isinstance(__A , (Image.Image, AgentImage) ):
output_types.append("""image""" )
elif isinstance(__A , (torch.Tensor, AgentAudio) ):
output_types.append("""audio""" )
else:
raise ValueError(f"""Invalid output: {output}""" )
return output_types
@is_tool_test
class _A :
'''simple docstring'''
def snake_case_ ( self ):
'''simple docstring'''
self.assertTrue(hasattr(self.tool ,"""inputs""" ) )
self.assertTrue(hasattr(self.tool ,"""outputs""" ) )
snake_case : Dict = self.tool.inputs
for _input in inputs:
if isinstance(_input ,SCREAMING_SNAKE_CASE_ ):
for __input in _input:
self.assertTrue(__input in authorized_types )
else:
self.assertTrue(_input in authorized_types )
snake_case : int = self.tool.outputs
for _output in outputs:
self.assertTrue(_output in authorized_types )
def snake_case_ ( self ):
'''simple docstring'''
snake_case : List[str] = create_inputs(self.tool.inputs )
snake_case : str = self.tool(*SCREAMING_SNAKE_CASE_ )
# There is a single output
if len(self.tool.outputs ) == 1:
snake_case : Union[str, Any] = [outputs]
self.assertListEqual(output_types(SCREAMING_SNAKE_CASE_ ) ,self.tool.outputs )
def snake_case_ ( self ):
'''simple docstring'''
self.assertTrue(hasattr(self.tool ,"""description""" ) )
self.assertTrue(hasattr(self.tool ,"""default_checkpoint""" ) )
self.assertTrue(self.tool.description.startswith("""This is a tool that""" ) )
def snake_case_ ( self ):
'''simple docstring'''
snake_case : List[Any] = create_inputs(self.tool.inputs )
snake_case : int = self.tool(*SCREAMING_SNAKE_CASE_ )
if not isinstance(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ):
snake_case : Any = [outputs]
self.assertEqual(len(SCREAMING_SNAKE_CASE_ ) ,len(self.tool.outputs ) )
for output, output_type in zip(SCREAMING_SNAKE_CASE_ ,self.tool.outputs ):
snake_case : List[str] = AGENT_TYPE_MAPPING[output_type]
self.assertTrue(isinstance(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ) )
def snake_case_ ( self ):
'''simple docstring'''
snake_case : Tuple = create_inputs(self.tool.inputs )
snake_case : Any = []
for _input, input_type in zip(SCREAMING_SNAKE_CASE_ ,self.tool.inputs ):
if isinstance(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ):
_inputs.append([AGENT_TYPE_MAPPING[_input_type](_input ) for _input_type in input_type] )
else:
_inputs.append(AGENT_TYPE_MAPPING[input_type](_input ) )
# Should not raise an error
snake_case : Tuple = self.tool(*SCREAMING_SNAKE_CASE_ )
if not isinstance(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ):
snake_case : Union[str, Any] = [outputs]
self.assertEqual(len(SCREAMING_SNAKE_CASE_ ) ,len(self.tool.outputs ) )
| 36 | 1 |
def lowercase ( __A : int = 400_0000 ) -> int:
'''simple docstring'''
snake_case : str = [0, 1]
snake_case : List[str] = 0
while fib[i] <= n:
fib.append(fib[i] + fib[i + 1] )
if fib[i + 2] > n:
break
i += 1
snake_case : Tuple = 0
for j in range(len(__A ) - 1 ):
if fib[j] % 2 == 0:
total += fib[j]
return total
if __name__ == "__main__":
print(f'''{solution() = }''')
| 36 |
import os
import pytest
from datasets import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
)
__lowercase : Optional[Any] = pytest.mark.integration
@pytest.mark.parametrize("""path""" , ["""paws""", """csv"""] )
def lowercase ( __A : Optional[Any] , __A : Optional[Any] ) -> str:
'''simple docstring'''
inspect_dataset(__A , __A )
snake_case : List[str] = path + """.py"""
assert script_name in os.listdir(__A )
assert "__pycache__" not in os.listdir(__A )
@pytest.mark.filterwarnings("""ignore:inspect_metric is deprecated:FutureWarning""" )
@pytest.mark.filterwarnings("""ignore:metric_module_factory is deprecated:FutureWarning""" )
@pytest.mark.parametrize("""path""" , ["""accuracy"""] )
def lowercase ( __A : Optional[int] , __A : Any ) -> Optional[Any]:
'''simple docstring'''
inspect_metric(__A , __A )
snake_case : Any = path + """.py"""
assert script_name in os.listdir(__A )
assert "__pycache__" not in os.listdir(__A )
@pytest.mark.parametrize(
"""path, config_name, expected_splits""" , [
("""squad""", """plain_text""", ["""train""", """validation"""]),
("""dalle-mini/wit""", """dalle-mini--wit""", ["""train"""]),
("""paws""", """labeled_final""", ["""train""", """test""", """validation"""]),
] , )
def lowercase ( __A : Tuple , __A : Dict , __A : Any ) -> Optional[int]:
'''simple docstring'''
snake_case : List[str] = get_dataset_config_info(__A , config_name=__A )
assert info.config_name == config_name
assert list(info.splits.keys() ) == expected_splits
@pytest.mark.parametrize(
"""path, config_name, expected_exception""" , [
("""paws""", None, ValueError),
] , )
def lowercase ( __A : Tuple , __A : Any , __A : List[str] ) -> Optional[int]:
'''simple docstring'''
with pytest.raises(__A ):
get_dataset_config_info(__A , config_name=__A )
@pytest.mark.parametrize(
"""path, expected""" , [
("""squad""", """plain_text"""),
("""acronym_identification""", """default"""),
("""lhoestq/squad""", """plain_text"""),
("""lhoestq/test""", """default"""),
("""lhoestq/demo1""", """lhoestq--demo1"""),
("""dalle-mini/wit""", """dalle-mini--wit"""),
] , )
def lowercase ( __A : Any , __A : Dict ) -> Dict:
'''simple docstring'''
snake_case : int = get_dataset_config_names(__A )
assert expected in config_names
@pytest.mark.parametrize(
"""path, expected_configs, expected_splits_in_first_config""" , [
("""squad""", ["""plain_text"""], ["""train""", """validation"""]),
("""dalle-mini/wit""", ["""dalle-mini--wit"""], ["""train"""]),
("""paws""", ["""labeled_final""", """labeled_swap""", """unlabeled_final"""], ["""train""", """test""", """validation"""]),
] , )
def lowercase ( __A : Optional[Any] , __A : Dict , __A : List[str] ) -> Union[str, Any]:
'''simple docstring'''
snake_case : List[Any] = get_dataset_infos(__A )
assert list(infos.keys() ) == expected_configs
snake_case : Any = expected_configs[0]
assert expected_config in infos
snake_case : Any = infos[expected_config]
assert info.config_name == expected_config
assert list(info.splits.keys() ) == expected_splits_in_first_config
@pytest.mark.parametrize(
"""path, expected_config, expected_splits""" , [
("""squad""", """plain_text""", ["""train""", """validation"""]),
("""dalle-mini/wit""", """dalle-mini--wit""", ["""train"""]),
("""paws""", """labeled_final""", ["""train""", """test""", """validation"""]),
] , )
def lowercase ( __A : Optional[int] , __A : Tuple , __A : Union[str, Any] ) -> Optional[int]:
'''simple docstring'''
snake_case : Dict = get_dataset_infos(__A )
assert expected_config in infos
snake_case : str = infos[expected_config]
assert info.config_name == expected_config
assert list(info.splits.keys() ) == expected_splits
@pytest.mark.parametrize(
"""path, config_name, expected_exception""" , [
("""paws""", None, ValueError),
] , )
def lowercase ( __A : Optional[int] , __A : Any , __A : Dict ) -> int:
'''simple docstring'''
with pytest.raises(__A ):
get_dataset_split_names(__A , config_name=__A )
| 36 | 1 |
import time
import warnings
from abc import ABC
from copy import deepcopy
from typing import Optional
import torch
from ..utils import add_start_docstrings, logging
__lowercase : Tuple = logging.get_logger(__name__)
__lowercase : Union[str, Any] = r'''
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
scores (`torch.FloatTensor` of shape `(batch_size, config.vocab_size)`):
Prediction scores of a language modeling head. These can be scores for each vocabulary token before SoftMax
or scores for each vocabulary token after SoftMax.
kwargs (`Dict[str, Any]`, *optional*):
Additional stopping criteria specific kwargs.
Return:
`bool`. `False` indicates we should continue, `True` indicates we should stop.
'''
class _A ( snake_case ):
'''simple docstring'''
@add_start_docstrings(SCREAMING_SNAKE_CASE_ )
def __call__( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,**SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
raise NotImplementedError("""StoppingCriteria needs to be subclassed""" )
class _A ( snake_case ):
'''simple docstring'''
def __init__( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ = None ):
'''simple docstring'''
snake_case : List[Any] = max_length
snake_case : Union[str, Any] = max_position_embeddings
@add_start_docstrings(SCREAMING_SNAKE_CASE_ )
def __call__( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,**SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
snake_case : List[str] = input_ids.shape[-1]
snake_case : List[Any] = cur_len >= self.max_length
if self.max_position_embeddings is not None and not is_done and cur_len >= self.max_position_embeddings:
logger.warning_once(
"""This is a friendly reminder - the current text generation call will exceed the model's predefined """
F"""maximum length ({self.max_position_embeddings}). Depending on the model, you may observe """
"""exceptions, performance degradation, or nothing at all.""" )
return is_done
class _A ( snake_case ):
'''simple docstring'''
def __init__( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
warnings.warn(
"""The class `MaxNewTokensCriteria` is deprecated. """
F"""Please use `MaxLengthCriteria(max_length={start_length + max_new_tokens})` """
"""with `max_length = start_length + max_new_tokens` instead.""" ,SCREAMING_SNAKE_CASE_ ,)
snake_case : List[str] = start_length
snake_case : str = max_new_tokens
snake_case : Union[str, Any] = start_length + max_new_tokens
@add_start_docstrings(SCREAMING_SNAKE_CASE_ )
def __call__( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,**SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
return input_ids.shape[-1] >= self.max_length
class _A ( snake_case ):
'''simple docstring'''
def __init__( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ = None ):
'''simple docstring'''
snake_case : Any = max_time
snake_case : Dict = time.time() if initial_timestamp is None else initial_timestamp
@add_start_docstrings(SCREAMING_SNAKE_CASE_ )
def __call__( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,**SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
return time.time() - self.initial_timestamp > self.max_time
class _A ( snake_case ):
'''simple docstring'''
@add_start_docstrings(SCREAMING_SNAKE_CASE_ )
def __call__( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,**SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
return any(criteria(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ) for criteria in self )
@property
def snake_case_ ( self ):
'''simple docstring'''
for stopping_criterium in self:
if isinstance(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ):
return stopping_criterium.max_length
elif isinstance(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ):
return stopping_criterium.max_length
return None
def lowercase ( __A : StoppingCriteriaList , __A : int ) -> StoppingCriteriaList:
'''simple docstring'''
snake_case : List[str] = stopping_criteria.max_length
snake_case : int = deepcopy(__A )
if stopping_max_length is not None and stopping_max_length != max_length:
warnings.warn("""You set different `max_length` for stopping criteria and `max_length` parameter""" , __A )
elif stopping_max_length is None:
new_stopping_criteria.append(MaxLengthCriteria(max_length=__A ) )
return new_stopping_criteria
| 36 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
__lowercase : Optional[Any] = {
'''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/config.json''',
'''albert-large-v1''': '''https://huggingface.co/albert-large-v1/resolve/main/config.json''',
'''albert-xlarge-v1''': '''https://huggingface.co/albert-xlarge-v1/resolve/main/config.json''',
'''albert-xxlarge-v1''': '''https://huggingface.co/albert-xxlarge-v1/resolve/main/config.json''',
'''albert-base-v2''': '''https://huggingface.co/albert-base-v2/resolve/main/config.json''',
'''albert-large-v2''': '''https://huggingface.co/albert-large-v2/resolve/main/config.json''',
'''albert-xlarge-v2''': '''https://huggingface.co/albert-xlarge-v2/resolve/main/config.json''',
'''albert-xxlarge-v2''': '''https://huggingface.co/albert-xxlarge-v2/resolve/main/config.json''',
}
class _A ( snake_case ):
'''simple docstring'''
__lowerCamelCase : int = '''albert'''
def __init__( self ,SCREAMING_SNAKE_CASE_=30000 ,SCREAMING_SNAKE_CASE_=128 ,SCREAMING_SNAKE_CASE_=4096 ,SCREAMING_SNAKE_CASE_=12 ,SCREAMING_SNAKE_CASE_=1 ,SCREAMING_SNAKE_CASE_=64 ,SCREAMING_SNAKE_CASE_=16384 ,SCREAMING_SNAKE_CASE_=1 ,SCREAMING_SNAKE_CASE_="gelu_new" ,SCREAMING_SNAKE_CASE_=0 ,SCREAMING_SNAKE_CASE_=0 ,SCREAMING_SNAKE_CASE_=512 ,SCREAMING_SNAKE_CASE_=2 ,SCREAMING_SNAKE_CASE_=0.02 ,SCREAMING_SNAKE_CASE_=1E-12 ,SCREAMING_SNAKE_CASE_=0.1 ,SCREAMING_SNAKE_CASE_="absolute" ,SCREAMING_SNAKE_CASE_=0 ,SCREAMING_SNAKE_CASE_=2 ,SCREAMING_SNAKE_CASE_=3 ,**SCREAMING_SNAKE_CASE_ ,):
'''simple docstring'''
super().__init__(pad_token_id=SCREAMING_SNAKE_CASE_ ,bos_token_id=SCREAMING_SNAKE_CASE_ ,eos_token_id=SCREAMING_SNAKE_CASE_ ,**SCREAMING_SNAKE_CASE_ )
snake_case : List[str] = vocab_size
snake_case : int = embedding_size
snake_case : int = hidden_size
snake_case : List[Any] = num_hidden_layers
snake_case : int = num_hidden_groups
snake_case : List[str] = num_attention_heads
snake_case : List[str] = inner_group_num
snake_case : Any = hidden_act
snake_case : Any = intermediate_size
snake_case : Union[str, Any] = hidden_dropout_prob
snake_case : List[Any] = attention_probs_dropout_prob
snake_case : Tuple = max_position_embeddings
snake_case : Any = type_vocab_size
snake_case : Optional[Any] = initializer_range
snake_case : int = layer_norm_eps
snake_case : Optional[int] = classifier_dropout_prob
snake_case : str = position_embedding_type
class _A ( snake_case ):
'''simple docstring'''
@property
def snake_case_ ( self ):
'''simple docstring'''
if self.task == "multiple-choice":
snake_case : List[Any] = {0: """batch""", 1: """choice""", 2: """sequence"""}
else:
snake_case : int = {0: """batch""", 1: """sequence"""}
return OrderedDict(
[
("""input_ids""", dynamic_axis),
("""attention_mask""", dynamic_axis),
("""token_type_ids""", dynamic_axis),
] )
| 36 | 1 |
import argparse
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt
if __name__ == "__main__":
__lowercase : Dict = argparse.ArgumentParser()
parser.add_argument(
'''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the checkpoint to convert.'''
)
parser.add_argument(
'''--original_config_file''',
type=str,
required=True,
help='''The YAML config file corresponding to the original architecture.''',
)
parser.add_argument(
'''--num_in_channels''',
default=None,
type=int,
help='''The number of input channels. If `None` number of input channels will be automatically inferred.''',
)
parser.add_argument(
'''--image_size''',
default=512,
type=int,
help=(
'''The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2'''
''' Base. Use 768 for Stable Diffusion v2.'''
),
)
parser.add_argument(
'''--extract_ema''',
action='''store_true''',
help=(
'''Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights'''
''' or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield'''
''' higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning.'''
),
)
parser.add_argument(
'''--upcast_attention''',
action='''store_true''',
help=(
'''Whether the attention computation should always be upcasted. This is necessary when running stable'''
''' diffusion 2.1.'''
),
)
parser.add_argument(
'''--from_safetensors''',
action='''store_true''',
help='''If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.''',
)
parser.add_argument(
'''--to_safetensors''',
action='''store_true''',
help='''Whether to store pipeline in safetensors format or not.''',
)
parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')
parser.add_argument('''--device''', type=str, help='''Device to use (e.g. cpu, cuda:0, cuda:1, etc.)''')
def lowercase ( __A : Tuple ) -> Union[str, Any]:
'''simple docstring'''
if string == "True":
return True
elif string == "False":
return False
else:
raise ValueError(f"""could not parse string as bool {string}""" )
parser.add_argument(
'''--use_linear_projection''', help='''Override for use linear projection''', required=False, type=parse_bool
)
parser.add_argument('''--cross_attention_dim''', help='''Override for cross attention_dim''', required=False, type=int)
__lowercase : Union[str, Any] = parser.parse_args()
__lowercase : Optional[int] = download_controlnet_from_original_ckpt(
checkpoint_path=args.checkpoint_path,
original_config_file=args.original_config_file,
image_size=args.image_size,
extract_ema=args.extract_ema,
num_in_channels=args.num_in_channels,
upcast_attention=args.upcast_attention,
from_safetensors=args.from_safetensors,
device=args.device,
use_linear_projection=args.use_linear_projection,
cross_attention_dim=args.cross_attention_dim,
)
controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
| 36 |
from __future__ import annotations
def lowercase ( __A : list ) -> float:
'''simple docstring'''
if not nums:
raise ValueError("""List is empty""" )
return sum(__A ) / len(__A )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 36 | 1 |
from __future__ import annotations
import unittest
from transformers import DebertaVaConfig, 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 import (
TFDebertaVaForMaskedLM,
TFDebertaVaForQuestionAnswering,
TFDebertaVaForSequenceClassification,
TFDebertaVaForTokenClassification,
TFDebertaVaModel,
)
class _A :
'''simple docstring'''
def __init__( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_=13 ,SCREAMING_SNAKE_CASE_=7 ,SCREAMING_SNAKE_CASE_=True ,SCREAMING_SNAKE_CASE_=True ,SCREAMING_SNAKE_CASE_=True ,SCREAMING_SNAKE_CASE_=True ,SCREAMING_SNAKE_CASE_=99 ,SCREAMING_SNAKE_CASE_=32 ,SCREAMING_SNAKE_CASE_=2 ,SCREAMING_SNAKE_CASE_=4 ,SCREAMING_SNAKE_CASE_=37 ,SCREAMING_SNAKE_CASE_="gelu" ,SCREAMING_SNAKE_CASE_=0.1 ,SCREAMING_SNAKE_CASE_=0.1 ,SCREAMING_SNAKE_CASE_=512 ,SCREAMING_SNAKE_CASE_=16 ,SCREAMING_SNAKE_CASE_=2 ,SCREAMING_SNAKE_CASE_=0.02 ,SCREAMING_SNAKE_CASE_=False ,SCREAMING_SNAKE_CASE_=True ,SCREAMING_SNAKE_CASE_="None" ,SCREAMING_SNAKE_CASE_=3 ,SCREAMING_SNAKE_CASE_=4 ,SCREAMING_SNAKE_CASE_=None ,):
'''simple docstring'''
snake_case : List[Any] = parent
snake_case : Dict = batch_size
snake_case : Dict = seq_length
snake_case : Optional[Any] = is_training
snake_case : Optional[Any] = use_input_mask
snake_case : Any = use_token_type_ids
snake_case : str = use_labels
snake_case : int = vocab_size
snake_case : Optional[int] = hidden_size
snake_case : Optional[int] = num_hidden_layers
snake_case : str = num_attention_heads
snake_case : List[Any] = intermediate_size
snake_case : str = hidden_act
snake_case : Optional[Any] = hidden_dropout_prob
snake_case : Any = attention_probs_dropout_prob
snake_case : Union[str, Any] = max_position_embeddings
snake_case : Tuple = type_vocab_size
snake_case : Optional[Any] = type_sequence_label_size
snake_case : List[Any] = initializer_range
snake_case : Tuple = num_labels
snake_case : int = num_choices
snake_case : List[Any] = relative_attention
snake_case : int = position_biased_input
snake_case : Any = pos_att_type
snake_case : Optional[int] = scope
def snake_case_ ( self ):
'''simple docstring'''
snake_case : Optional[Any] = ids_tensor([self.batch_size, self.seq_length] ,self.vocab_size )
snake_case : List[Any] = None
if self.use_input_mask:
snake_case : Any = random_attention_mask([self.batch_size, self.seq_length] )
snake_case : List[str] = None
if self.use_token_type_ids:
snake_case : Tuple = ids_tensor([self.batch_size, self.seq_length] ,self.type_vocab_size )
snake_case : Any = None
snake_case : Optional[Any] = None
snake_case : str = None
if self.use_labels:
snake_case : Optional[Any] = ids_tensor([self.batch_size] ,self.type_sequence_label_size )
snake_case : Union[str, Any] = ids_tensor([self.batch_size, self.seq_length] ,self.num_labels )
snake_case : int = DebertaVaConfig(
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 ,relative_attention=self.relative_attention ,position_biased_input=self.position_biased_input ,initializer_range=self.initializer_range ,return_dict=SCREAMING_SNAKE_CASE_ ,)
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
snake_case : Optional[Any] = TFDebertaVaModel(config=SCREAMING_SNAKE_CASE_ )
snake_case : Optional[int] = {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
snake_case : Dict = [input_ids, input_mask]
snake_case : List[Any] = model(SCREAMING_SNAKE_CASE_ )
snake_case : Optional[Any] = model(SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.last_hidden_state.shape ,(self.batch_size, self.seq_length, self.hidden_size) )
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
snake_case : Any = TFDebertaVaForMaskedLM(config=SCREAMING_SNAKE_CASE_ )
snake_case : int = {
"""input_ids""": input_ids,
"""attention_mask""": input_mask,
"""token_type_ids""": token_type_ids,
}
snake_case : Any = model(SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.seq_length, self.vocab_size) )
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
snake_case : Any = self.num_labels
snake_case : Any = TFDebertaVaForSequenceClassification(config=SCREAMING_SNAKE_CASE_ )
snake_case : List[str] = {
"""input_ids""": input_ids,
"""attention_mask""": input_mask,
"""token_type_ids""": token_type_ids,
}
snake_case : List[str] = model(SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.num_labels) )
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
snake_case : Tuple = self.num_labels
snake_case : Union[str, Any] = TFDebertaVaForTokenClassification(config=SCREAMING_SNAKE_CASE_ )
snake_case : Optional[int] = {
"""input_ids""": input_ids,
"""attention_mask""": input_mask,
"""token_type_ids""": token_type_ids,
}
snake_case : Union[str, Any] = model(SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.seq_length, self.num_labels) )
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
snake_case : Dict = TFDebertaVaForQuestionAnswering(config=SCREAMING_SNAKE_CASE_ )
snake_case : List[Any] = {
"""input_ids""": input_ids,
"""attention_mask""": input_mask,
"""token_type_ids""": token_type_ids,
}
snake_case : Tuple = model(SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.start_logits.shape ,(self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape ,(self.batch_size, self.seq_length) )
def snake_case_ ( self ):
'''simple docstring'''
snake_case : Tuple = self.prepare_config_and_inputs()
(
(
snake_case
) , (
snake_case
) , (
snake_case
) , (
snake_case
) , (
snake_case
) , (
snake_case
) , (
snake_case
) ,
) : Optional[Any] = config_and_inputs
snake_case : List[Any] = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_tf
class _A ( snake_case , snake_case , unittest.TestCase ):
'''simple docstring'''
__lowerCamelCase : Optional[int] = (
(
TFDebertaVaModel,
TFDebertaVaForMaskedLM,
TFDebertaVaForQuestionAnswering,
TFDebertaVaForSequenceClassification,
TFDebertaVaForTokenClassification,
)
if is_tf_available()
else ()
)
__lowerCamelCase : Any = (
{
'''feature-extraction''': TFDebertaVaModel,
'''fill-mask''': TFDebertaVaForMaskedLM,
'''question-answering''': TFDebertaVaForQuestionAnswering,
'''text-classification''': TFDebertaVaForSequenceClassification,
'''token-classification''': TFDebertaVaForTokenClassification,
'''zero-shot''': TFDebertaVaForSequenceClassification,
}
if is_tf_available()
else {}
)
__lowerCamelCase : Optional[int] = False
__lowerCamelCase : Dict = False
def snake_case_ ( self ):
'''simple docstring'''
snake_case : Optional[int] = TFDebertaVaModelTester(self )
snake_case : Dict = ConfigTester(self ,config_class=SCREAMING_SNAKE_CASE_ ,hidden_size=37 )
def snake_case_ ( self ):
'''simple docstring'''
self.config_tester.run_common_tests()
def snake_case_ ( self ):
'''simple docstring'''
snake_case : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_ )
def snake_case_ ( self ):
'''simple docstring'''
snake_case : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*SCREAMING_SNAKE_CASE_ )
def snake_case_ ( self ):
'''simple docstring'''
snake_case : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*SCREAMING_SNAKE_CASE_ )
def snake_case_ ( self ):
'''simple docstring'''
snake_case : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*SCREAMING_SNAKE_CASE_ )
def snake_case_ ( self ):
'''simple docstring'''
snake_case : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*SCREAMING_SNAKE_CASE_ )
@slow
def snake_case_ ( self ):
'''simple docstring'''
snake_case : Any = TFDebertaVaModel.from_pretrained("""kamalkraj/deberta-v2-xlarge""" )
self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )
@require_tf
class _A ( unittest.TestCase ):
'''simple docstring'''
@unittest.skip(reason="""Model not available yet""" )
def snake_case_ ( self ):
'''simple docstring'''
pass
@slow
def snake_case_ ( self ):
'''simple docstring'''
snake_case : Dict = TFDebertaVaModel.from_pretrained("""kamalkraj/deberta-v2-xlarge""" )
snake_case : Dict = tf.constant([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]] )
snake_case : Tuple = tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
snake_case : Optional[Any] = model(SCREAMING_SNAKE_CASE_ ,attention_mask=SCREAMING_SNAKE_CASE_ )[0]
snake_case : Optional[Any] = tf.constant(
[[[0.23_56, 0.19_48, 0.03_69], [-0.10_63, 0.35_86, -0.51_52], [-0.63_99, -0.02_59, -0.25_25]]] )
tf.debugging.assert_near(output[:, 1:4, 1:4] ,SCREAMING_SNAKE_CASE_ ,atol=1E-4 )
| 36 |
import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig
if TYPE_CHECKING:
from ... import PreTrainedTokenizerBase, TensorType
__lowercase : List[str] = logging.get_logger(__name__)
class _A ( snake_case ):
'''simple docstring'''
__lowerCamelCase : Tuple = '''vision-encoder-decoder'''
__lowerCamelCase : List[Any] = True
def __init__( self ,**SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
super().__init__(**SCREAMING_SNAKE_CASE_ )
if "encoder" not in kwargs or "decoder" not in kwargs:
raise ValueError(
F"""A configuraton of type {self.model_type} cannot be instantiated because """
F"""not both `encoder` and `decoder` sub-configurations are passed, but only {kwargs}""" )
snake_case : Union[str, Any] = kwargs.pop("""encoder""" )
snake_case : Any = encoder_config.pop("""model_type""" )
snake_case : Optional[Any] = kwargs.pop("""decoder""" )
snake_case : Union[str, Any] = decoder_config.pop("""model_type""" )
snake_case : Any = AutoConfig.for_model(SCREAMING_SNAKE_CASE_ ,**SCREAMING_SNAKE_CASE_ )
snake_case : Union[str, Any] = AutoConfig.for_model(SCREAMING_SNAKE_CASE_ ,**SCREAMING_SNAKE_CASE_ )
snake_case : int = True
@classmethod
def snake_case_ ( cls ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,**SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
logger.info("""Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config""" )
snake_case : Tuple = True
snake_case : Union[str, Any] = True
return cls(encoder=encoder_config.to_dict() ,decoder=decoder_config.to_dict() ,**SCREAMING_SNAKE_CASE_ )
def snake_case_ ( self ):
'''simple docstring'''
snake_case : Union[str, Any] = copy.deepcopy(self.__dict__ )
snake_case : Union[str, Any] = self.encoder.to_dict()
snake_case : Union[str, Any] = self.decoder.to_dict()
snake_case : Dict = self.__class__.model_type
return output
class _A ( snake_case ):
'''simple docstring'''
__lowerCamelCase : Optional[Any] = version.parse('''1.11''' )
@property
def snake_case_ ( self ):
'''simple docstring'''
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
] )
@property
def snake_case_ ( self ):
'''simple docstring'''
return 1E-4
@property
def snake_case_ ( self ):
'''simple docstring'''
return OrderedDict({"""last_hidden_state""": {0: """batch""", 1: """encoder_sequence"""}} )
class _A ( snake_case ):
'''simple docstring'''
@property
def snake_case_ ( self ):
'''simple docstring'''
snake_case : Tuple = OrderedDict()
snake_case : Optional[int] = {0: """batch""", 1: """past_decoder_sequence + sequence"""}
snake_case : Union[str, Any] = {0: """batch""", 1: """past_decoder_sequence + sequence"""}
snake_case : Optional[Any] = {0: """batch""", 1: """encoder_sequence"""}
return common_inputs
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ = -1 ,SCREAMING_SNAKE_CASE_ = -1 ,SCREAMING_SNAKE_CASE_ = False ,SCREAMING_SNAKE_CASE_ = None ,):
'''simple docstring'''
import torch
snake_case : Optional[Any] = OrderedDict()
snake_case : Tuple = super().generate_dummy_inputs(
SCREAMING_SNAKE_CASE_ ,batch_size=SCREAMING_SNAKE_CASE_ ,seq_length=SCREAMING_SNAKE_CASE_ ,is_pair=SCREAMING_SNAKE_CASE_ ,framework=SCREAMING_SNAKE_CASE_ )
snake_case , snake_case : List[Any] = dummy_input["""input_ids"""].shape
snake_case : Optional[int] = (batch, encoder_sequence, self._config.encoder_hidden_size)
snake_case : List[str] = dummy_input.pop("""input_ids""" )
snake_case : int = dummy_input.pop("""attention_mask""" )
snake_case : Dict = torch.zeros(SCREAMING_SNAKE_CASE_ )
return common_inputs
class _A ( snake_case ):
'''simple docstring'''
@property
def snake_case_ ( self ):
'''simple docstring'''
pass
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
return VisionEncoderDecoderEncoderOnnxConfig(SCREAMING_SNAKE_CASE_ )
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ = "default" ):
'''simple docstring'''
snake_case : int = encoder_config.hidden_size
return VisionEncoderDecoderDecoderOnnxConfig(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )
| 36 | 1 |
import bza
import gzip
import lzma
import os
import shutil
import struct
import tarfile
import warnings
import zipfile
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Dict, List, Optional, Type, Union
from .. import config
from .filelock import FileLock
from .logging import get_logger
__lowercase : Optional[int] = get_logger(__name__)
class _A :
'''simple docstring'''
def __init__( self ,SCREAMING_SNAKE_CASE_ = None ):
'''simple docstring'''
snake_case : Optional[int] = (
os.path.join(SCREAMING_SNAKE_CASE_ ,config.EXTRACTED_DATASETS_DIR ) if cache_dir else config.EXTRACTED_DATASETS_PATH
)
snake_case : Union[str, Any] = Extractor
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
from .file_utils import hash_url_to_filename
# Path where we extract compressed archives
# We extract in the cache dir, and get the extracted path name by hashing the original path"
snake_case : str = os.path.abspath(SCREAMING_SNAKE_CASE_ )
return os.path.join(self.extract_dir ,hash_url_to_filename(SCREAMING_SNAKE_CASE_ ) )
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
return force_extract or (
not os.path.isfile(SCREAMING_SNAKE_CASE_ ) and not (os.path.isdir(SCREAMING_SNAKE_CASE_ ) and os.listdir(SCREAMING_SNAKE_CASE_ ))
)
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ = False ):
'''simple docstring'''
snake_case : Optional[Any] = self.extractor.infer_extractor_format(SCREAMING_SNAKE_CASE_ )
if not extractor_format:
return input_path
snake_case : int = self._get_output_path(SCREAMING_SNAKE_CASE_ )
if self._do_extract(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ):
self.extractor.extract(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )
return output_path
class _A ( snake_case ):
'''simple docstring'''
@classmethod
@abstractmethod
def snake_case_ ( cls ,SCREAMING_SNAKE_CASE_ ,**SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
...
@staticmethod
@abstractmethod
def snake_case_ ( SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
...
class _A ( snake_case , snake_case ):
'''simple docstring'''
__lowerCamelCase : List[bytes] = []
@staticmethod
def snake_case_ ( SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
with open(SCREAMING_SNAKE_CASE_ ,"""rb""" ) as f:
return f.read(SCREAMING_SNAKE_CASE_ )
@classmethod
def snake_case_ ( cls ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ = b"" ):
'''simple docstring'''
if not magic_number:
snake_case : str = max(len(SCREAMING_SNAKE_CASE_ ) for cls_magic_number in cls.magic_numbers )
try:
snake_case : Dict = cls.read_magic_number(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )
except OSError:
return False
return any(magic_number.startswith(SCREAMING_SNAKE_CASE_ ) for cls_magic_number in cls.magic_numbers )
class _A ( snake_case ):
'''simple docstring'''
@classmethod
def snake_case_ ( cls ,SCREAMING_SNAKE_CASE_ ,**SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
return tarfile.is_tarfile(SCREAMING_SNAKE_CASE_ )
@staticmethod
def snake_case_ ( SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
def resolved(SCREAMING_SNAKE_CASE_ ) -> str:
return os.path.realpath(os.path.abspath(SCREAMING_SNAKE_CASE_ ) )
def badpath(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ) -> bool:
# joinpath will ignore base if path is absolute
return not resolved(os.path.join(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ) ).startswith(SCREAMING_SNAKE_CASE_ )
def badlink(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ) -> bool:
# Links are interpreted relative to the directory containing the link
snake_case : Optional[Any] = resolved(os.path.join(SCREAMING_SNAKE_CASE_ ,os.path.dirname(info.name ) ) )
return badpath(info.linkname ,base=SCREAMING_SNAKE_CASE_ )
snake_case : Dict = resolved(SCREAMING_SNAKE_CASE_ )
for finfo in members:
if badpath(finfo.name ,SCREAMING_SNAKE_CASE_ ):
logger.error(F"""Extraction of {finfo.name} is blocked (illegal path)""" )
elif finfo.issym() and badlink(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ):
logger.error(F"""Extraction of {finfo.name} is blocked: Symlink to {finfo.linkname}""" )
elif finfo.islnk() and badlink(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ):
logger.error(F"""Extraction of {finfo.name} is blocked: Hard link to {finfo.linkname}""" )
else:
yield finfo
@staticmethod
def snake_case_ ( SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
os.makedirs(SCREAMING_SNAKE_CASE_ ,exist_ok=SCREAMING_SNAKE_CASE_ )
snake_case : List[str] = tarfile.open(SCREAMING_SNAKE_CASE_ )
tar_file.extractall(SCREAMING_SNAKE_CASE_ ,members=TarExtractor.safemembers(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ) )
tar_file.close()
class _A ( snake_case ):
'''simple docstring'''
__lowerCamelCase : str = [B'''\x1F\x8B''']
@staticmethod
def snake_case_ ( SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
with gzip.open(SCREAMING_SNAKE_CASE_ ,"""rb""" ) as gzip_file:
with open(SCREAMING_SNAKE_CASE_ ,"""wb""" ) as extracted_file:
shutil.copyfileobj(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )
class _A ( snake_case ):
'''simple docstring'''
__lowerCamelCase : List[str] = [
B'''PK\x03\x04''',
B'''PK\x05\x06''', # empty archive
B'''PK\x07\x08''', # spanned archive
]
@classmethod
def snake_case_ ( cls ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ = b"" ):
'''simple docstring'''
if super().is_extractable(SCREAMING_SNAKE_CASE_ ,magic_number=SCREAMING_SNAKE_CASE_ ):
return True
try:
# Alternative version of zipfile.is_zipfile that has less false positives, but misses executable zip archives.
# From: https://github.com/python/cpython/pull/5053
from zipfile import (
_CD_SIGNATURE,
_ECD_DISK_NUMBER,
_ECD_DISK_START,
_ECD_ENTRIES_TOTAL,
_ECD_OFFSET,
_ECD_SIZE,
_EndRecData,
sizeCentralDir,
stringCentralDir,
structCentralDir,
)
with open(SCREAMING_SNAKE_CASE_ ,"""rb""" ) as fp:
snake_case : List[Any] = _EndRecData(SCREAMING_SNAKE_CASE_ )
if endrec:
if endrec[_ECD_ENTRIES_TOTAL] == 0 and endrec[_ECD_SIZE] == 0 and endrec[_ECD_OFFSET] == 0:
return True # Empty zipfiles are still zipfiles
elif endrec[_ECD_DISK_NUMBER] == endrec[_ECD_DISK_START]:
fp.seek(endrec[_ECD_OFFSET] ) # Central directory is on the same disk
if fp.tell() == endrec[_ECD_OFFSET] and endrec[_ECD_SIZE] >= sizeCentralDir:
snake_case : List[Any] = fp.read(SCREAMING_SNAKE_CASE_ ) # CD is where we expect it to be
if len(SCREAMING_SNAKE_CASE_ ) == sizeCentralDir:
snake_case : str = struct.unpack(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ) # CD is the right size
if centdir[_CD_SIGNATURE] == stringCentralDir:
return True # First central directory entry has correct magic number
return False
except Exception: # catch all errors in case future python versions change the zipfile internals
return False
@staticmethod
def snake_case_ ( SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
os.makedirs(SCREAMING_SNAKE_CASE_ ,exist_ok=SCREAMING_SNAKE_CASE_ )
with zipfile.ZipFile(SCREAMING_SNAKE_CASE_ ,"""r""" ) as zip_file:
zip_file.extractall(SCREAMING_SNAKE_CASE_ )
zip_file.close()
class _A ( snake_case ):
'''simple docstring'''
__lowerCamelCase : Union[str, Any] = [B'''\xFD\x37\x7A\x58\x5A\x00''']
@staticmethod
def snake_case_ ( SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
with lzma.open(SCREAMING_SNAKE_CASE_ ) as compressed_file:
with open(SCREAMING_SNAKE_CASE_ ,"""wb""" ) as extracted_file:
shutil.copyfileobj(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )
class _A ( snake_case ):
'''simple docstring'''
__lowerCamelCase : Union[str, Any] = [B'''Rar!\x1a\x07\x00''', B'''Rar!\x1a\x07\x01\x00'''] # RAR_ID # RAR5_ID
@staticmethod
def snake_case_ ( SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
if not config.RARFILE_AVAILABLE:
raise ImportError("""Please pip install rarfile""" )
import rarfile
os.makedirs(SCREAMING_SNAKE_CASE_ ,exist_ok=SCREAMING_SNAKE_CASE_ )
snake_case : Optional[Any] = rarfile.RarFile(SCREAMING_SNAKE_CASE_ )
rf.extractall(SCREAMING_SNAKE_CASE_ )
rf.close()
class _A ( snake_case ):
'''simple docstring'''
__lowerCamelCase : Optional[int] = [B'''\x28\xb5\x2F\xFD''']
@staticmethod
def snake_case_ ( SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
if not config.ZSTANDARD_AVAILABLE:
raise ImportError("""Please pip install zstandard""" )
import zstandard as zstd
snake_case : Any = zstd.ZstdDecompressor()
with open(SCREAMING_SNAKE_CASE_ ,"""rb""" ) as ifh, open(SCREAMING_SNAKE_CASE_ ,"""wb""" ) as ofh:
dctx.copy_stream(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )
class _A ( snake_case ):
'''simple docstring'''
__lowerCamelCase : List[Any] = [B'''\x42\x5A\x68''']
@staticmethod
def snake_case_ ( SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
with bza.open(SCREAMING_SNAKE_CASE_ ,"""rb""" ) as compressed_file:
with open(SCREAMING_SNAKE_CASE_ ,"""wb""" ) as extracted_file:
shutil.copyfileobj(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )
class _A ( snake_case ):
'''simple docstring'''
__lowerCamelCase : Dict = [B'''\x37\x7A\xBC\xAF\x27\x1C''']
@staticmethod
def snake_case_ ( SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
if not config.PY7ZR_AVAILABLE:
raise ImportError("""Please pip install py7zr""" )
import pyazr
os.makedirs(SCREAMING_SNAKE_CASE_ ,exist_ok=SCREAMING_SNAKE_CASE_ )
with pyazr.SevenZipFile(SCREAMING_SNAKE_CASE_ ,"""r""" ) as archive:
archive.extractall(SCREAMING_SNAKE_CASE_ )
class _A ( snake_case ):
'''simple docstring'''
__lowerCamelCase : Any = [B'''\x04\x22\x4D\x18''']
@staticmethod
def snake_case_ ( SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
if not config.LZ4_AVAILABLE:
raise ImportError("""Please pip install lz4""" )
import lza.frame
with lza.frame.open(SCREAMING_SNAKE_CASE_ ,"""rb""" ) as compressed_file:
with open(SCREAMING_SNAKE_CASE_ ,"""wb""" ) as extracted_file:
shutil.copyfileobj(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )
class _A :
'''simple docstring'''
__lowerCamelCase : Dict[str, Type[BaseExtractor]] = {
"tar": TarExtractor,
"gzip": GzipExtractor,
"zip": ZipExtractor,
"xz": XzExtractor,
"rar": RarExtractor,
"zstd": ZstdExtractor,
"bz2": BzipaExtractor,
"7z": SevenZipExtractor, # <Added version="2.4.0"/>
"lz4": LzaExtractor, # <Added version="2.4.0"/>
}
@classmethod
def snake_case_ ( cls ):
'''simple docstring'''
return max(
len(SCREAMING_SNAKE_CASE_ )
for extractor in cls.extractors.values()
if issubclass(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )
for extractor_magic_number in extractor.magic_numbers )
@staticmethod
def snake_case_ ( SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
try:
return MagicNumberBaseExtractor.read_magic_number(SCREAMING_SNAKE_CASE_ ,magic_number_length=SCREAMING_SNAKE_CASE_ )
except OSError:
return b""
@classmethod
def snake_case_ ( cls ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ = False ):
'''simple docstring'''
warnings.warn(
"""Method 'is_extractable' was deprecated in version 2.4.0 and will be removed in 3.0.0. """
"""Use 'infer_extractor_format' instead.""" ,category=SCREAMING_SNAKE_CASE_ ,)
snake_case : Union[str, Any] = cls.infer_extractor_format(SCREAMING_SNAKE_CASE_ )
if extractor_format:
return True if not return_extractor else (True, cls.extractors[extractor_format])
return False if not return_extractor else (False, None)
@classmethod
def snake_case_ ( cls ,SCREAMING_SNAKE_CASE_ ): # <Added version="2.4.0"/>
'''simple docstring'''
snake_case : str = cls._get_magic_number_max_length()
snake_case : Optional[Any] = cls._read_magic_number(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )
for extractor_format, extractor in cls.extractors.items():
if extractor.is_extractable(SCREAMING_SNAKE_CASE_ ,magic_number=SCREAMING_SNAKE_CASE_ ):
return extractor_format
@classmethod
def snake_case_ ( cls ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_ = "deprecated" ,):
'''simple docstring'''
os.makedirs(os.path.dirname(SCREAMING_SNAKE_CASE_ ) ,exist_ok=SCREAMING_SNAKE_CASE_ )
# Prevent parallel extractions
snake_case : str = str(Path(SCREAMING_SNAKE_CASE_ ).with_suffix(""".lock""" ) )
with FileLock(SCREAMING_SNAKE_CASE_ ):
shutil.rmtree(SCREAMING_SNAKE_CASE_ ,ignore_errors=SCREAMING_SNAKE_CASE_ )
if extractor_format or extractor != "deprecated":
if extractor != "deprecated" or not isinstance(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ): # passed as positional arg
warnings.warn(
"""Parameter 'extractor' was deprecated in version 2.4.0 and will be removed in 3.0.0. """
"""Use 'extractor_format' instead.""" ,category=SCREAMING_SNAKE_CASE_ ,)
snake_case : Dict = extractor if extractor != """deprecated""" else extractor_format
else:
snake_case : Optional[Any] = cls.extractors[extractor_format]
return extractor.extract(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )
else:
warnings.warn(
"""Parameter 'extractor_format' was made required in version 2.4.0 and not passing it will raise an """
"""exception in 3.0.0.""" ,category=SCREAMING_SNAKE_CASE_ ,)
for extractor in cls.extractors.values():
if extractor.is_extractable(SCREAMING_SNAKE_CASE_ ):
return extractor.extract(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )
| 36 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers import BitConfig, BitForImageClassification, BitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging
logging.set_verbosity_info()
__lowercase : Any = logging.get_logger(__name__)
def lowercase ( __A : Optional[Any] ) -> Dict:
'''simple docstring'''
snake_case : Dict = """huggingface/label-files"""
snake_case : int = """imagenet-1k-id2label.json"""
snake_case : Tuple = json.load(open(hf_hub_download(__A , __A , repo_type="""dataset""" ) , """r""" ) )
snake_case : Any = {int(__A ): v for k, v in idalabel.items()}
snake_case : Dict = {v: k for k, v in idalabel.items()}
snake_case : Any = """std_conv""" if """bit""" in model_name else False
# note that when using BiT as backbone for ViT-hybrid checkpoints,
# one needs to additionally set config.layer_type = "bottleneck", config.stem_type = "same",
# config.conv_layer = "std_conv_same"
snake_case : List[Any] = BitConfig(
conv_layer=__A , num_labels=1000 , idalabel=__A , labelaid=__A , )
return config
def lowercase ( __A : Optional[int] ) -> Union[str, Any]:
'''simple docstring'''
if "stem.conv" in name:
snake_case : List[str] = name.replace("""stem.conv""" , """bit.embedder.convolution""" )
if "blocks" in name:
snake_case : List[str] = name.replace("""blocks""" , """layers""" )
if "head.fc" in name:
snake_case : Optional[int] = name.replace("""head.fc""" , """classifier.1""" )
if name.startswith("""norm""" ):
snake_case : Optional[Any] = """bit.""" + name
if "bit" not in name and "classifier" not in name:
snake_case : Tuple = """bit.encoder.""" + name
return name
def lowercase ( ) -> Optional[int]:
'''simple docstring'''
snake_case : int = """http://images.cocodataset.org/val2017/000000039769.jpg"""
snake_case : Optional[Any] = Image.open(requests.get(__A , stream=__A ).raw )
return im
@torch.no_grad()
def lowercase ( __A : Any , __A : Union[str, Any] , __A : str=False ) -> Optional[int]:
'''simple docstring'''
snake_case : str = get_config(__A )
# load original model from timm
snake_case : Tuple = create_model(__A , pretrained=__A )
timm_model.eval()
# load state_dict of original model
snake_case : List[str] = timm_model.state_dict()
for key in state_dict.copy().keys():
snake_case : List[Any] = state_dict.pop(__A )
snake_case : Union[str, Any] = val.squeeze() if """head""" in key else val
# load HuggingFace model
snake_case : List[Any] = BitForImageClassification(__A )
model.eval()
model.load_state_dict(__A )
# create image processor
snake_case : Dict = create_transform(**resolve_data_config({} , model=__A ) )
snake_case : Optional[Any] = transform.transforms
snake_case : List[Any] = {
"""bilinear""": PILImageResampling.BILINEAR,
"""bicubic""": PILImageResampling.BICUBIC,
"""nearest""": PILImageResampling.NEAREST,
}
snake_case : Union[str, Any] = BitImageProcessor(
do_resize=__A , size={"""shortest_edge""": timm_transforms[0].size} , resample=pillow_resamplings[timm_transforms[0].interpolation.value] , do_center_crop=__A , crop_size={"""height""": timm_transforms[1].size[0], """width""": timm_transforms[1].size[1]} , do_normalize=__A , image_mean=timm_transforms[-1].mean.tolist() , image_std=timm_transforms[-1].std.tolist() , )
snake_case : Dict = prepare_img()
snake_case : List[str] = transform(__A ).unsqueeze(0 )
snake_case : int = processor(__A , return_tensors="""pt""" ).pixel_values
# verify pixel values
assert torch.allclose(__A , __A )
# verify logits
with torch.no_grad():
snake_case : Optional[int] = model(__A )
snake_case : Dict = outputs.logits
print("""Logits:""" , logits[0, :3] )
print("""Predicted class:""" , model.config.idalabel[logits.argmax(-1 ).item()] )
snake_case : int = timm_model(__A )
assert timm_logits.shape == outputs.logits.shape
assert torch.allclose(__A , outputs.logits , atol=1E-3 )
print("""Looks ok!""" )
if pytorch_dump_folder_path is not None:
Path(__A ).mkdir(exist_ok=__A )
print(f"""Saving model {model_name} and processor to {pytorch_dump_folder_path}""" )
model.save_pretrained(__A )
processor.save_pretrained(__A )
if push_to_hub:
print(f"""Pushing model {model_name} and processor to the hub""" )
model.push_to_hub(f"""ybelkada/{model_name}""" )
processor.push_to_hub(f"""ybelkada/{model_name}""" )
if __name__ == "__main__":
__lowercase : List[str] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--model_name''',
default='''resnetv2_50x1_bitm''',
type=str,
help='''Name of the BiT timm model you\'d like to convert.''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
)
parser.add_argument(
'''--push_to_hub''',
action='''store_true''',
help='''Whether to push the model to the hub.''',
)
__lowercase : Union[str, Any] = parser.parse_args()
convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 36 | 1 |
def lowercase ( __A : str , __A : str ) -> bool:
'''simple docstring'''
snake_case : str = len(__A )
snake_case : Union[str, Any] = len(__A )
snake_case : Union[str, Any] = [[False for _ in range(m + 1 )] for _ in range(n + 1 )]
snake_case : List[Any] = True
for i in range(__A ):
for j in range(m + 1 ):
if dp[i][j]:
if j < m and a[i].upper() == b[j]:
snake_case : Any = True
if a[i].islower():
snake_case : Dict = True
return dp[n][m]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 36 |
import os
import pytest
from attr import dataclass
__lowercase : Optional[int] = '''us-east-1''' # defaults region
@dataclass
class _A :
'''simple docstring'''
__lowerCamelCase : str
__lowerCamelCase : Dict = '''arn:aws:iam::558105141721:role/sagemaker_execution_role'''
__lowerCamelCase : Optional[Any] = {
'''task_name''': '''mnli''',
'''per_device_train_batch_size''': 1_6,
'''per_device_eval_batch_size''': 1_6,
'''do_train''': True,
'''do_eval''': True,
'''do_predict''': True,
'''output_dir''': '''/opt/ml/model''',
'''overwrite_output_dir''': True,
'''max_steps''': 5_0_0,
'''save_steps''': 5_5_0_0,
}
__lowerCamelCase : List[str] = {**hyperparameters, '''max_steps''': 1_0_0_0}
@property
def snake_case_ ( self ):
'''simple docstring'''
if self.framework == "pytorch":
return [
{"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
{"Name": "eval_accuracy", "Regex": r"eval_accuracy.*=\D*(.*?)$"},
{"Name": "eval_loss", "Regex": r"eval_loss.*=\D*(.*?)$"},
]
else:
return [
{"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
{"Name": "eval_accuracy", "Regex": r"loss.*=\D*(.*?)]?$"},
{"Name": "eval_loss", "Regex": r"sparse_categorical_accuracy.*=\D*(.*?)]?$"},
]
@property
def snake_case_ ( self ):
'''simple docstring'''
return F"""{self.framework}-transfromers-test"""
@property
def snake_case_ ( self ):
'''simple docstring'''
return F"""./tests/sagemaker/scripts/{self.framework}"""
@property
def snake_case_ ( self ):
'''simple docstring'''
if self.framework == "pytorch":
return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-pytorch-training:1.7.1-transformers4.6.1-gpu-py36-cu110-ubuntu18.04"
else:
return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-tensorflow-training:2.4.1-transformers4.6.1-gpu-py37-cu110-ubuntu18.04"
@pytest.fixture(scope="""class""" )
def lowercase ( __A : List[str] ) -> List[str]:
'''simple docstring'''
snake_case : Optional[Any] = SageMakerTestEnvironment(framework=request.cls.framework )
| 36 | 1 |
def lowercase ( __A : int = 100 ) -> int:
'''simple docstring'''
snake_case : Tuple = 0
snake_case : Tuple = 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() = }''')
| 36 |
from .imports import is_rich_available
if is_rich_available():
from rich.traceback import install
install(show_locals=False)
else:
raise ModuleNotFoundError('''To use the rich extension, install rich with `pip install rich`''')
| 36 | 1 |
import inspect
import unittest
import warnings
from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import (
MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
MODEL_MAPPING,
DeiTForImageClassification,
DeiTForImageClassificationWithTeacher,
DeiTForMaskedImageModeling,
DeiTModel,
)
from transformers.models.deit.modeling_deit import DEIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import DeiTImageProcessor
class _A :
'''simple docstring'''
def __init__( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_=13 ,SCREAMING_SNAKE_CASE_=30 ,SCREAMING_SNAKE_CASE_=2 ,SCREAMING_SNAKE_CASE_=3 ,SCREAMING_SNAKE_CASE_=True ,SCREAMING_SNAKE_CASE_=True ,SCREAMING_SNAKE_CASE_=32 ,SCREAMING_SNAKE_CASE_=5 ,SCREAMING_SNAKE_CASE_=4 ,SCREAMING_SNAKE_CASE_=37 ,SCREAMING_SNAKE_CASE_="gelu" ,SCREAMING_SNAKE_CASE_=0.1 ,SCREAMING_SNAKE_CASE_=0.1 ,SCREAMING_SNAKE_CASE_=10 ,SCREAMING_SNAKE_CASE_=0.02 ,SCREAMING_SNAKE_CASE_=3 ,SCREAMING_SNAKE_CASE_=None ,SCREAMING_SNAKE_CASE_=2 ,):
'''simple docstring'''
snake_case : List[Any] = parent
snake_case : Optional[Any] = batch_size
snake_case : List[str] = image_size
snake_case : Dict = patch_size
snake_case : Any = num_channels
snake_case : Dict = is_training
snake_case : Dict = use_labels
snake_case : Union[str, Any] = hidden_size
snake_case : int = num_hidden_layers
snake_case : str = num_attention_heads
snake_case : Dict = intermediate_size
snake_case : Tuple = hidden_act
snake_case : List[str] = hidden_dropout_prob
snake_case : Union[str, Any] = attention_probs_dropout_prob
snake_case : Optional[Any] = type_sequence_label_size
snake_case : Tuple = initializer_range
snake_case : Optional[Any] = scope
snake_case : int = encoder_stride
# in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
snake_case : Union[str, Any] = (image_size // patch_size) ** 2
snake_case : str = num_patches + 2
def snake_case_ ( self ):
'''simple docstring'''
snake_case : List[str] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
snake_case : List[Any] = None
if self.use_labels:
snake_case : Optional[int] = ids_tensor([self.batch_size] ,self.type_sequence_label_size )
snake_case : List[Any] = self.get_config()
return config, pixel_values, labels
def snake_case_ ( self ):
'''simple docstring'''
return DeiTConfig(
image_size=self.image_size ,patch_size=self.patch_size ,num_channels=self.num_channels ,hidden_size=self.hidden_size ,num_hidden_layers=self.num_hidden_layers ,num_attention_heads=self.num_attention_heads ,intermediate_size=self.intermediate_size ,hidden_act=self.hidden_act ,hidden_dropout_prob=self.hidden_dropout_prob ,attention_probs_dropout_prob=self.attention_probs_dropout_prob ,is_decoder=SCREAMING_SNAKE_CASE_ ,initializer_range=self.initializer_range ,encoder_stride=self.encoder_stride ,)
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
snake_case : Dict = DeiTModel(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
snake_case : Optional[int] = model(SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.last_hidden_state.shape ,(self.batch_size, self.seq_length, self.hidden_size) )
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
snake_case : Union[str, Any] = DeiTForMaskedImageModeling(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
snake_case : List[Any] = model(SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(
result.reconstruction.shape ,(self.batch_size, self.num_channels, self.image_size, self.image_size) )
# test greyscale images
snake_case : str = 1
snake_case : Union[str, Any] = DeiTForMaskedImageModeling(SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
snake_case : List[Any] = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
snake_case : Optional[int] = model(SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.reconstruction.shape ,(self.batch_size, 1, self.image_size, self.image_size) )
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
snake_case : Any = self.type_sequence_label_size
snake_case : Optional[int] = DeiTForImageClassification(SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
snake_case : Any = model(SCREAMING_SNAKE_CASE_ ,labels=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.type_sequence_label_size) )
# test greyscale images
snake_case : Any = 1
snake_case : List[str] = DeiTForImageClassification(SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
snake_case : Union[str, Any] = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
snake_case : Tuple = model(SCREAMING_SNAKE_CASE_ ,labels=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.type_sequence_label_size) )
def snake_case_ ( self ):
'''simple docstring'''
snake_case : List[Any] = self.prepare_config_and_inputs()
(
(
snake_case
) , (
snake_case
) , (
snake_case
) ,
) : str = config_and_inputs
snake_case : List[Any] = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_torch
class _A ( snake_case , snake_case , unittest.TestCase ):
'''simple docstring'''
__lowerCamelCase : List[str] = (
(
DeiTModel,
DeiTForImageClassification,
DeiTForImageClassificationWithTeacher,
DeiTForMaskedImageModeling,
)
if is_torch_available()
else ()
)
__lowerCamelCase : int = (
{
'''feature-extraction''': DeiTModel,
'''image-classification''': (DeiTForImageClassification, DeiTForImageClassificationWithTeacher),
}
if is_torch_available()
else {}
)
__lowerCamelCase : List[str] = False
__lowerCamelCase : Optional[Any] = False
__lowerCamelCase : List[Any] = False
def snake_case_ ( self ):
'''simple docstring'''
snake_case : Optional[int] = DeiTModelTester(self )
snake_case : Union[str, Any] = ConfigTester(self ,config_class=SCREAMING_SNAKE_CASE_ ,has_text_modality=SCREAMING_SNAKE_CASE_ ,hidden_size=37 )
def snake_case_ ( self ):
'''simple docstring'''
self.config_tester.run_common_tests()
@unittest.skip(reason="""DeiT does not use inputs_embeds""" )
def snake_case_ ( self ):
'''simple docstring'''
pass
def snake_case_ ( self ):
'''simple docstring'''
snake_case , snake_case : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
snake_case : str = model_class(SCREAMING_SNAKE_CASE_ )
self.assertIsInstance(model.get_input_embeddings() ,(nn.Module) )
snake_case : List[Any] = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(SCREAMING_SNAKE_CASE_ ,nn.Linear ) )
def snake_case_ ( self ):
'''simple docstring'''
snake_case , snake_case : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
snake_case : str = model_class(SCREAMING_SNAKE_CASE_ )
snake_case : Optional[int] = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
snake_case : Any = [*signature.parameters.keys()]
snake_case : Optional[int] = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] ,SCREAMING_SNAKE_CASE_ )
def snake_case_ ( self ):
'''simple docstring'''
snake_case : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_ )
def snake_case_ ( self ):
'''simple docstring'''
snake_case : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_image_modeling(*SCREAMING_SNAKE_CASE_ )
def snake_case_ ( self ):
'''simple docstring'''
snake_case : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE_ )
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_=False ):
'''simple docstring'''
snake_case : Any = super()._prepare_for_class(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,return_labels=SCREAMING_SNAKE_CASE_ )
if return_labels:
if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
del inputs_dict["labels"]
return inputs_dict
def snake_case_ ( self ):
'''simple docstring'''
if not self.model_tester.is_training:
return
snake_case , snake_case : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
snake_case : str = True
for model_class in self.all_model_classes:
# DeiTForImageClassificationWithTeacher supports inference-only
if (
model_class in get_values(SCREAMING_SNAKE_CASE_ )
or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
):
continue
snake_case : Tuple = model_class(SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.train()
snake_case : Union[str, Any] = self._prepare_for_class(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,return_labels=SCREAMING_SNAKE_CASE_ )
snake_case : Optional[Any] = model(**SCREAMING_SNAKE_CASE_ ).loss
loss.backward()
def snake_case_ ( self ):
'''simple docstring'''
snake_case , snake_case : int = self.model_tester.prepare_config_and_inputs_for_common()
if not self.model_tester.is_training:
return
snake_case : Tuple = False
snake_case : Tuple = True
for model_class in self.all_model_classes:
if model_class in get_values(SCREAMING_SNAKE_CASE_ ) or not model_class.supports_gradient_checkpointing:
continue
# DeiTForImageClassificationWithTeacher supports inference-only
if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
continue
snake_case : Optional[Any] = model_class(SCREAMING_SNAKE_CASE_ )
model.gradient_checkpointing_enable()
model.to(SCREAMING_SNAKE_CASE_ )
model.train()
snake_case : Union[str, Any] = self._prepare_for_class(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,return_labels=SCREAMING_SNAKE_CASE_ )
snake_case : Optional[Any] = model(**SCREAMING_SNAKE_CASE_ ).loss
loss.backward()
def snake_case_ ( self ):
'''simple docstring'''
snake_case , snake_case : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
snake_case : List[str] = [
{"""title""": """multi_label_classification""", """num_labels""": 2, """dtype""": torch.float},
{"""title""": """single_label_classification""", """num_labels""": 1, """dtype""": torch.long},
{"""title""": """regression""", """num_labels""": 1, """dtype""": torch.float},
]
for model_class in self.all_model_classes:
if (
model_class
not in [
*get_values(SCREAMING_SNAKE_CASE_ ),
*get_values(SCREAMING_SNAKE_CASE_ ),
]
or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
):
continue
for problem_type in problem_types:
with self.subTest(msg=F"""Testing {model_class} with {problem_type["title"]}""" ):
snake_case : Any = problem_type["""title"""]
snake_case : str = problem_type["""num_labels"""]
snake_case : str = model_class(SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.train()
snake_case : Tuple = self._prepare_for_class(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,return_labels=SCREAMING_SNAKE_CASE_ )
if problem_type["num_labels"] > 1:
snake_case : Optional[Any] = inputs["""labels"""].unsqueeze(1 ).repeat(1 ,problem_type["""num_labels"""] )
snake_case : List[Any] = inputs["""labels"""].to(problem_type["""dtype"""] )
# This tests that we do not trigger the warning form PyTorch "Using a target size that is different
# to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
# they have the same size." which is a symptom something in wrong for the regression problem.
# See https://github.com/huggingface/transformers/issues/11780
with warnings.catch_warnings(record=SCREAMING_SNAKE_CASE_ ) as warning_list:
snake_case : Any = model(**SCREAMING_SNAKE_CASE_ ).loss
for w in warning_list:
if "Using a target size that is different to the input size" in str(w.message ):
raise ValueError(
F"""Something is going wrong in the regression problem: intercepted {w.message}""" )
loss.backward()
@slow
def snake_case_ ( self ):
'''simple docstring'''
for model_name in DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
snake_case : int = DeiTModel.from_pretrained(SCREAMING_SNAKE_CASE_ )
self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )
def lowercase ( ) -> List[Any]:
'''simple docstring'''
snake_case : Any = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_torch
@require_vision
class _A ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def snake_case_ ( self ):
'''simple docstring'''
return (
DeiTImageProcessor.from_pretrained("""facebook/deit-base-distilled-patch16-224""" )
if is_vision_available()
else None
)
@slow
def snake_case_ ( self ):
'''simple docstring'''
snake_case : Optional[Any] = DeiTForImageClassificationWithTeacher.from_pretrained("""facebook/deit-base-distilled-patch16-224""" ).to(
SCREAMING_SNAKE_CASE_ )
snake_case : List[Any] = self.default_image_processor
snake_case : List[str] = prepare_img()
snake_case : Tuple = image_processor(images=SCREAMING_SNAKE_CASE_ ,return_tensors="""pt""" ).to(SCREAMING_SNAKE_CASE_ )
# forward pass
with torch.no_grad():
snake_case : int = model(**SCREAMING_SNAKE_CASE_ )
# verify the logits
snake_case : Dict = torch.Size((1, 1000) )
self.assertEqual(outputs.logits.shape ,SCREAMING_SNAKE_CASE_ )
snake_case : Union[str, Any] = torch.tensor([-1.02_66, 0.19_12, -1.28_61] ).to(SCREAMING_SNAKE_CASE_ )
self.assertTrue(torch.allclose(outputs.logits[0, :3] ,SCREAMING_SNAKE_CASE_ ,atol=1E-4 ) )
@slow
@require_accelerate
@require_torch_gpu
def snake_case_ ( self ):
'''simple docstring'''
snake_case : Dict = DeiTModel.from_pretrained(
"""facebook/deit-base-distilled-patch16-224""" ,torch_dtype=torch.floataa ,device_map="""auto""" )
snake_case : List[Any] = self.default_image_processor
snake_case : Tuple = prepare_img()
snake_case : List[str] = image_processor(images=SCREAMING_SNAKE_CASE_ ,return_tensors="""pt""" )
snake_case : List[str] = inputs.pixel_values.to(SCREAMING_SNAKE_CASE_ )
# forward pass to make sure inference works in fp16
with torch.no_grad():
snake_case : Optional[Any] = model(SCREAMING_SNAKE_CASE_ )
| 36 |
import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union
import tqdm
from filelock import FileLock
from transformers import (
BartTokenizer,
BartTokenizerFast,
DataProcessor,
PreTrainedTokenizer,
RobertaTokenizer,
RobertaTokenizerFast,
XLMRobertaTokenizer,
is_tf_available,
is_torch_available,
)
__lowercase : Any = logging.getLogger(__name__)
@dataclass(frozen=snake_case )
class _A :
'''simple docstring'''
__lowerCamelCase : str
__lowerCamelCase : str
__lowerCamelCase : Optional[str] = None
__lowerCamelCase : Optional[str] = None
__lowerCamelCase : Optional[str] = None
@dataclass(frozen=snake_case )
class _A :
'''simple docstring'''
__lowerCamelCase : List[int]
__lowerCamelCase : Optional[List[int]] = None
__lowerCamelCase : Optional[List[int]] = None
__lowerCamelCase : Optional[Union[int, float]] = None
__lowerCamelCase : Optional[int] = None
if is_torch_available():
import torch
from torch.utils.data import Dataset
class _A ( snake_case ):
'''simple docstring'''
__lowerCamelCase : List[InputFeatures]
def __init__( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_=False ,SCREAMING_SNAKE_CASE_ = False ,):
'''simple docstring'''
snake_case : str = hans_processors[task]()
snake_case : str = os.path.join(
SCREAMING_SNAKE_CASE_ ,"""cached_{}_{}_{}_{}""".format(
"""dev""" if evaluate else """train""" ,tokenizer.__class__.__name__ ,str(SCREAMING_SNAKE_CASE_ ) ,SCREAMING_SNAKE_CASE_ ,) ,)
snake_case : Dict = processor.get_labels()
if tokenizer.__class__ in (
RobertaTokenizer,
RobertaTokenizerFast,
XLMRobertaTokenizer,
BartTokenizer,
BartTokenizerFast,
):
# HACK(label indices are swapped in RoBERTa pretrained model)
snake_case , snake_case : List[Any] = label_list[2], label_list[1]
snake_case : List[Any] = label_list
# Make sure only the first process in distributed training processes the dataset,
# and the others will use the cache.
snake_case : Any = cached_features_file + """.lock"""
with FileLock(SCREAMING_SNAKE_CASE_ ):
if os.path.exists(SCREAMING_SNAKE_CASE_ ) and not overwrite_cache:
logger.info(F"""Loading features from cached file {cached_features_file}""" )
snake_case : int = torch.load(SCREAMING_SNAKE_CASE_ )
else:
logger.info(F"""Creating features from dataset file at {data_dir}""" )
snake_case : Union[str, Any] = (
processor.get_dev_examples(SCREAMING_SNAKE_CASE_ ) if evaluate else processor.get_train_examples(SCREAMING_SNAKE_CASE_ )
)
logger.info("""Training examples: %s""" ,len(SCREAMING_SNAKE_CASE_ ) )
snake_case : Dict = hans_convert_examples_to_features(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )
logger.info("""Saving features into cached file %s""" ,SCREAMING_SNAKE_CASE_ )
torch.save(self.features ,SCREAMING_SNAKE_CASE_ )
def __len__( self ):
'''simple docstring'''
return len(self.features )
def __getitem__( self ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
return self.features[i]
def snake_case_ ( self ):
'''simple docstring'''
return self.label_list
if is_tf_available():
import tensorflow as tf
class _A :
'''simple docstring'''
__lowerCamelCase : List[InputFeatures]
def __init__( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ = 128 ,SCREAMING_SNAKE_CASE_=False ,SCREAMING_SNAKE_CASE_ = False ,):
'''simple docstring'''
snake_case : Any = hans_processors[task]()
snake_case : List[str] = processor.get_labels()
if tokenizer.__class__ in (
RobertaTokenizer,
RobertaTokenizerFast,
XLMRobertaTokenizer,
BartTokenizer,
BartTokenizerFast,
):
# HACK(label indices are swapped in RoBERTa pretrained model)
snake_case , snake_case : int = label_list[2], label_list[1]
snake_case : List[str] = label_list
snake_case : int = processor.get_dev_examples(SCREAMING_SNAKE_CASE_ ) if evaluate else processor.get_train_examples(SCREAMING_SNAKE_CASE_ )
snake_case : Any = hans_convert_examples_to_features(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )
def gen():
for ex_index, ex in tqdm.tqdm(enumerate(self.features ) ,desc="""convert examples to features""" ):
if ex_index % 10000 == 0:
logger.info("""Writing example %d of %d""" % (ex_index, len(SCREAMING_SNAKE_CASE_ )) )
yield (
{
"example_id": 0,
"input_ids": ex.input_ids,
"attention_mask": ex.attention_mask,
"token_type_ids": ex.token_type_ids,
},
ex.label,
)
snake_case : Any = tf.data.Dataset.from_generator(
SCREAMING_SNAKE_CASE_ ,(
{
"""example_id""": tf.intaa,
"""input_ids""": tf.intaa,
"""attention_mask""": tf.intaa,
"""token_type_ids""": tf.intaa,
},
tf.intaa,
) ,(
{
"""example_id""": tf.TensorShape([] ),
"""input_ids""": tf.TensorShape([None, None] ),
"""attention_mask""": tf.TensorShape([None, None] ),
"""token_type_ids""": tf.TensorShape([None, None] ),
},
tf.TensorShape([] ),
) ,)
def snake_case_ ( self ):
'''simple docstring'''
return self.dataset
def __len__( self ):
'''simple docstring'''
return len(self.features )
def __getitem__( self ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
return self.features[i]
def snake_case_ ( self ):
'''simple docstring'''
return self.label_list
class _A ( snake_case ):
'''simple docstring'''
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
return self._create_examples(self._read_tsv(os.path.join(SCREAMING_SNAKE_CASE_ ,"""heuristics_train_set.txt""" ) ) ,"""train""" )
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
return self._create_examples(self._read_tsv(os.path.join(SCREAMING_SNAKE_CASE_ ,"""heuristics_evaluation_set.txt""" ) ) ,"""dev""" )
def snake_case_ ( self ):
'''simple docstring'''
return ["contradiction", "entailment", "neutral"]
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
snake_case : List[str] = []
for i, line in enumerate(SCREAMING_SNAKE_CASE_ ):
if i == 0:
continue
snake_case : Any = """%s-%s""" % (set_type, line[0])
snake_case : Optional[int] = line[5]
snake_case : Union[str, Any] = line[6]
snake_case : Optional[Any] = line[7][2:] if line[7].startswith("""ex""" ) else line[7]
snake_case : Dict = line[0]
examples.append(InputExample(guid=SCREAMING_SNAKE_CASE_ ,text_a=SCREAMING_SNAKE_CASE_ ,text_b=SCREAMING_SNAKE_CASE_ ,label=SCREAMING_SNAKE_CASE_ ,pairID=SCREAMING_SNAKE_CASE_ ) )
return examples
def lowercase ( __A : List[InputExample] , __A : List[str] , __A : int , __A : PreTrainedTokenizer , ) -> Tuple:
'''simple docstring'''
snake_case : List[Any] = {label: i for i, label in enumerate(__A )}
snake_case : Union[str, Any] = []
for ex_index, example in tqdm.tqdm(enumerate(__A ) , desc="""convert examples to features""" ):
if ex_index % 1_0000 == 0:
logger.info("""Writing example %d""" % (ex_index) )
snake_case : Union[str, Any] = tokenizer(
example.text_a , example.text_b , add_special_tokens=__A , max_length=__A , padding="""max_length""" , truncation=__A , return_overflowing_tokens=__A , )
snake_case : Tuple = label_map[example.label] if example.label in label_map else 0
snake_case : Tuple = int(example.pairID )
features.append(InputFeatures(**__A , label=__A , pairID=__A ) )
for i, example in enumerate(examples[:5] ):
logger.info("""*** Example ***""" )
logger.info(f"""guid: {example}""" )
logger.info(f"""features: {features[i]}""" )
return features
__lowercase : Dict = {
'''hans''': 3,
}
__lowercase : Union[str, Any] = {
'''hans''': HansProcessor,
}
| 36 | 1 |
import logging
import os
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional
import faiss
import torch
from datasets import Features, Sequence, Value, load_dataset
from transformers import DPRContextEncoder, DPRContextEncoderTokenizerFast, HfArgumentParser
__lowercase : int = logging.getLogger(__name__)
torch.set_grad_enabled(False)
__lowercase : Optional[Any] = '''cuda''' if torch.cuda.is_available() else '''cpu'''
def lowercase ( __A : str , __A : Union[str, Any]=100 , __A : Optional[Any]=" " ) -> List[str]:
'''simple docstring'''
snake_case : Dict = text.split(__A )
return [character.join(text[i : i + n] ).strip() for i in range(0 , len(__A ) , __A )]
def lowercase ( __A : dict ) -> dict:
'''simple docstring'''
snake_case , snake_case : Optional[Any] = [], []
for title, text in zip(documents["""title"""] , documents["""text"""] ):
if text is not None:
for passage in split_text(__A ):
titles.append(title if title is not None else """""" )
texts.append(__A )
return {"title": titles, "text": texts}
def lowercase ( __A : dict , __A : DPRContextEncoder , __A : DPRContextEncoderTokenizerFast ) -> dict:
'''simple docstring'''
snake_case : List[str] = ctx_tokenizer(
documents["""title"""] , documents["""text"""] , truncation=__A , padding="""longest""" , return_tensors="""pt""" )["""input_ids"""]
snake_case : int = ctx_encoder(input_ids.to(device=__A ) , return_dict=__A ).pooler_output
return {"embeddings": embeddings.detach().cpu().numpy()}
def lowercase ( __A : "RagExampleArguments" , __A : "ProcessingArguments" , __A : "IndexHnswArguments" , ) -> int:
'''simple docstring'''
logger.info("""Step 1 - Create the dataset""" )
######################################
# The dataset needed for RAG must have three columns:
# - title (string): title of the document
# - text (string): text of a passage of the document
# - embeddings (array of dimension d): DPR representation of the passage
# Let's say you have documents in tab-separated csv files with columns "title" and "text"
assert os.path.isfile(rag_example_args.csv_path ), "Please provide a valid path to a csv file"
# You can load a Dataset object this way
snake_case : Any = load_dataset(
"""csv""" , data_files=[rag_example_args.csv_path] , split="""train""" , delimiter="""\t""" , column_names=["""title""", """text"""] )
# More info about loading csv files in the documentation: https://huggingface.co/docs/datasets/loading_datasets.html?highlight=csv#csv-files
# Then split the documents into passages of 100 words
snake_case : Optional[Any] = dataset.map(__A , batched=__A , num_proc=processing_args.num_proc )
# And compute the embeddings
snake_case : Optional[Any] = DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name ).to(device=__A )
snake_case : Union[str, Any] = DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name )
snake_case : List[str] = Features(
{"""text""": Value("""string""" ), """title""": Value("""string""" ), """embeddings""": Sequence(Value("""float32""" ) )} ) # optional, save as float32 instead of float64 to save space
snake_case : Optional[int] = dataset.map(
partial(__A , ctx_encoder=__A , ctx_tokenizer=__A ) , batched=__A , batch_size=processing_args.batch_size , features=__A , )
# And finally save your dataset
snake_case : Optional[Any] = os.path.join(rag_example_args.output_dir , """my_knowledge_dataset""" )
dataset.save_to_disk(__A )
# from datasets import load_from_disk
# dataset = load_from_disk(passages_path) # to reload the dataset
######################################
logger.info("""Step 2 - Index the dataset""" )
######################################
# Let's use the Faiss implementation of HNSW for fast approximate nearest neighbor search
snake_case : Union[str, Any] = faiss.IndexHNSWFlat(index_hnsw_args.d , index_hnsw_args.m , faiss.METRIC_INNER_PRODUCT )
dataset.add_faiss_index("""embeddings""" , custom_index=__A )
# And save the index
snake_case : Optional[Any] = os.path.join(rag_example_args.output_dir , """my_knowledge_dataset_hnsw_index.faiss""" )
dataset.get_index("""embeddings""" ).save(__A )
# dataset.load_faiss_index("embeddings", index_path) # to reload the index
@dataclass
class _A :
'''simple docstring'''
__lowerCamelCase : str = field(
default=str(Path(snake_case ).parent / '''test_run''' / '''dummy-kb''' / '''my_knowledge_dataset.csv''' ) , metadata={'''help''': '''Path to a tab-separated csv file with columns \'title\' and \'text\''''} , )
__lowerCamelCase : Optional[str] = field(
default=snake_case , metadata={'''help''': '''Question that is passed as input to RAG. Default is \'What does Moses\' rod turn into ?\'.'''} , )
__lowerCamelCase : str = field(
default='''facebook/rag-sequence-nq''' , metadata={'''help''': '''The RAG model to use. Either \'facebook/rag-sequence-nq\' or \'facebook/rag-token-nq\''''} , )
__lowerCamelCase : str = field(
default='''facebook/dpr-ctx_encoder-multiset-base''' , metadata={
'''help''': (
'''The DPR context encoder model to use. Either \'facebook/dpr-ctx_encoder-single-nq-base\' or'''
''' \'facebook/dpr-ctx_encoder-multiset-base\''''
)
} , )
__lowerCamelCase : Optional[str] = field(
default=str(Path(snake_case ).parent / '''test_run''' / '''dummy-kb''' ) , metadata={'''help''': '''Path to a directory where the dataset passages and the index will be saved'''} , )
@dataclass
class _A :
'''simple docstring'''
__lowerCamelCase : Optional[int] = field(
default=snake_case , metadata={
'''help''': '''The number of processes to use to split the documents into passages. Default is single process.'''
} , )
__lowerCamelCase : int = field(
default=1_6 , metadata={
'''help''': '''The batch size to use when computing the passages embeddings using the DPR context encoder.'''
} , )
@dataclass
class _A :
'''simple docstring'''
__lowerCamelCase : int = field(
default=7_6_8 , metadata={'''help''': '''The dimension of the embeddings to pass to the HNSW Faiss index.'''} , )
__lowerCamelCase : int = field(
default=1_2_8 , metadata={
'''help''': (
'''The number of bi-directional links created for every new element during the HNSW index construction.'''
)
} , )
if __name__ == "__main__":
logging.basicConfig(level=logging.WARNING)
logger.setLevel(logging.INFO)
__lowercase : Dict = HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
__lowercase , __lowercase , __lowercase : Dict = parser.parse_args_into_dataclasses()
with TemporaryDirectory() as tmp_dir:
__lowercase : Union[str, Any] = rag_example_args.output_dir or tmp_dir
main(rag_example_args, processing_args, index_hnsw_args)
| 36 |
from __future__ import annotations
def lowercase ( __A : int ) -> list[int]:
'''simple docstring'''
snake_case : Dict = 2
snake_case : int = []
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.append(__A )
if n > 1:
factors.append(__A )
return factors
if __name__ == "__main__":
import doctest
doctest.testmod()
| 36 | 1 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_vision_available():
import PIL
__lowercase : int = logging.get_logger(__name__)
class _A ( snake_case ):
'''simple docstring'''
__lowerCamelCase : Dict = ['''pixel_values''']
def __init__( self ,SCREAMING_SNAKE_CASE_ = True ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_ = PIL.Image.BICUBIC ,SCREAMING_SNAKE_CASE_ = True ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_ = 1 / 255 ,SCREAMING_SNAKE_CASE_ = True ,SCREAMING_SNAKE_CASE_ = True ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_ = None ,**SCREAMING_SNAKE_CASE_ ,):
'''simple docstring'''
super().__init__(**SCREAMING_SNAKE_CASE_ )
snake_case : Optional[Any] = size if size is not None else {"""height""": 256, """width""": 256}
snake_case : Any = get_size_dict(SCREAMING_SNAKE_CASE_ )
snake_case : List[Any] = crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
snake_case : List[Any] = get_size_dict(SCREAMING_SNAKE_CASE_ ,param_name="""crop_size""" )
snake_case : Union[str, Any] = do_resize
snake_case : Dict = size
snake_case : Optional[int] = resample
snake_case : Union[str, Any] = do_center_crop
snake_case : Union[str, Any] = crop_size
snake_case : Optional[int] = do_rescale
snake_case : Dict = rescale_factor
snake_case : Dict = do_normalize
snake_case : int = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
snake_case : Optional[int] = image_std if image_std is not None else IMAGENET_STANDARD_STD
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ = PIL.Image.BICUBIC ,SCREAMING_SNAKE_CASE_ = None ,**SCREAMING_SNAKE_CASE_ ,):
'''simple docstring'''
snake_case : int = get_size_dict(SCREAMING_SNAKE_CASE_ )
if "height" not in size or "width" not in size:
raise ValueError(F"""The size dictionary must have keys 'height' and 'width'. Got {size.keys()}""" )
return resize(
SCREAMING_SNAKE_CASE_ ,size=(size["""height"""], size["""width"""]) ,resample=SCREAMING_SNAKE_CASE_ ,data_format=SCREAMING_SNAKE_CASE_ ,**SCREAMING_SNAKE_CASE_ )
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ = None ,**SCREAMING_SNAKE_CASE_ ,):
'''simple docstring'''
snake_case : List[Any] = get_size_dict(SCREAMING_SNAKE_CASE_ )
if "height" not in size or "width" not in size:
raise ValueError(F"""The size dictionary must have keys 'height' and 'width'. Got {size.keys()}""" )
return center_crop(SCREAMING_SNAKE_CASE_ ,size=(size["""height"""], size["""width"""]) ,data_format=SCREAMING_SNAKE_CASE_ ,**SCREAMING_SNAKE_CASE_ )
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ = None ,**SCREAMING_SNAKE_CASE_ ,):
'''simple docstring'''
return rescale(SCREAMING_SNAKE_CASE_ ,scale=SCREAMING_SNAKE_CASE_ ,data_format=SCREAMING_SNAKE_CASE_ ,**SCREAMING_SNAKE_CASE_ )
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ = None ,**SCREAMING_SNAKE_CASE_ ,):
'''simple docstring'''
return normalize(SCREAMING_SNAKE_CASE_ ,mean=SCREAMING_SNAKE_CASE_ ,std=SCREAMING_SNAKE_CASE_ ,data_format=SCREAMING_SNAKE_CASE_ ,**SCREAMING_SNAKE_CASE_ )
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_=None ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_ = ChannelDimension.FIRST ,**SCREAMING_SNAKE_CASE_ ,):
'''simple docstring'''
snake_case : Optional[Any] = do_resize if do_resize is not None else self.do_resize
snake_case : Dict = resample if resample is not None else self.resample
snake_case : List[Any] = do_center_crop if do_center_crop is not None else self.do_center_crop
snake_case : List[str] = do_rescale if do_rescale is not None else self.do_rescale
snake_case : Any = rescale_factor if rescale_factor is not None else self.rescale_factor
snake_case : Optional[Any] = do_normalize if do_normalize is not None else self.do_normalize
snake_case : Any = image_mean if image_mean is not None else self.image_mean
snake_case : int = image_std if image_std is not None else self.image_std
snake_case : str = size if size is not None else self.size
snake_case : Tuple = get_size_dict(SCREAMING_SNAKE_CASE_ )
snake_case : Tuple = crop_size if crop_size is not None else self.crop_size
snake_case : Dict = get_size_dict(SCREAMING_SNAKE_CASE_ ,param_name="""crop_size""" )
snake_case : str = make_list_of_images(SCREAMING_SNAKE_CASE_ )
if not valid_images(SCREAMING_SNAKE_CASE_ ):
raise ValueError(
"""Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
"""torch.Tensor, tf.Tensor or jax.ndarray.""" )
if do_resize and size is None or resample is None:
raise ValueError("""Size and resample must be specified if do_resize is True.""" )
if do_center_crop and crop_size is None:
raise ValueError("""Crop size must be specified if do_center_crop is True.""" )
if do_rescale and rescale_factor is None:
raise ValueError("""Rescale factor must be specified if do_rescale is True.""" )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError("""Image mean and std must be specified if do_normalize is True.""" )
# All transformations expect numpy arrays.
snake_case : Dict = [to_numpy_array(SCREAMING_SNAKE_CASE_ ) for image in images]
if do_resize:
snake_case : Tuple = [self.resize(image=SCREAMING_SNAKE_CASE_ ,size=SCREAMING_SNAKE_CASE_ ,resample=SCREAMING_SNAKE_CASE_ ) for image in images]
if do_center_crop:
snake_case : int = [self.center_crop(image=SCREAMING_SNAKE_CASE_ ,size=SCREAMING_SNAKE_CASE_ ) for image in images]
if do_rescale:
snake_case : Dict = [self.rescale(image=SCREAMING_SNAKE_CASE_ ,scale=SCREAMING_SNAKE_CASE_ ) for image in images]
if do_normalize:
snake_case : str = [self.normalize(image=SCREAMING_SNAKE_CASE_ ,mean=SCREAMING_SNAKE_CASE_ ,std=SCREAMING_SNAKE_CASE_ ) for image in images]
snake_case : Dict = [to_channel_dimension_format(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ) for image in images]
snake_case : Union[str, Any] = {"""pixel_values""": images}
return BatchFeature(data=SCREAMING_SNAKE_CASE_ ,tensor_type=SCREAMING_SNAKE_CASE_ )
| 36 |
import numpy as np
def lowercase ( __A : np.array ) -> np.array:
'''simple docstring'''
return (2 / (1 + np.exp(-2 * vector ))) - 1
if __name__ == "__main__":
import doctest
doctest.testmod()
| 36 | 1 |
import string
def lowercase ( __A : str ) -> str:
'''simple docstring'''
snake_case : Union[str, Any] = """"""
for i in sequence:
snake_case : Optional[int] = ord(__A )
if 65 <= extract <= 90:
output += chr(155 - extract )
elif 97 <= extract <= 122:
output += chr(219 - extract )
else:
output += i
return output
def lowercase ( __A : str ) -> str:
'''simple docstring'''
snake_case : Optional[Any] = string.ascii_letters
snake_case : str = string.ascii_lowercase[::-1] + string.ascii_uppercase[::-1]
return "".join(
letters_reversed[letters.index(__A )] if c in letters else c for c in sequence )
def lowercase ( ) -> None:
'''simple docstring'''
from timeit import timeit
print("""Running performance benchmarks...""" )
snake_case : List[Any] = """from string import printable ; from __main__ import atbash, atbash_slow"""
print(f"""> atbash_slow(): {timeit("atbash_slow(printable)" , setup=__A )} seconds""" )
print(f"""> atbash(): {timeit("atbash(printable)" , setup=__A )} seconds""" )
if __name__ == "__main__":
for example in ("ABCDEFGH", "123GGjj", "testStringtest", "with space"):
print(f'''{example} encrypted in atbash: {atbash(example)}''')
benchmark()
| 36 |
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
__lowercase : Optional[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 ( __A : Union[str, Any] ) -> Optional[int]:
'''simple docstring'''
for pegasus_name, hf_name in PATTERNS:
snake_case : Dict = k.replace(__A , __A )
return k
def lowercase ( __A : dict , __A : dict ) -> PegasusForConditionalGeneration:
'''simple docstring'''
snake_case : Dict = DEFAULTS.copy()
cfg_kwargs.update(__A )
snake_case : int = PegasusConfig(**__A )
snake_case : List[Any] = PegasusForConditionalGeneration(__A )
snake_case : Optional[Any] = torch_model.model.state_dict()
snake_case : Optional[int] = {}
for k, v in tf_weights.items():
snake_case : str = rename_state_dict_key(__A )
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:
snake_case : Optional[Any] = v.T
snake_case : List[Any] = torch.tensor(__A , 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
snake_case : List[str] = torch.zeros_like(mapping["""shared.weight"""][cfg.pad_token_id + 1] )
snake_case : Optional[Any] = mapping["""shared.weight"""]
snake_case : Optional[Any] = mapping["""shared.weight"""]
snake_case : Tuple = {k: torch.zeros_like(__A ) for k, v in sd.items() if k.endswith("""bias""" ) and k not in mapping}
mapping.update(**__A )
snake_case , snake_case : Union[str, Any] = torch_model.model.load_state_dict(__A , strict=__A )
snake_case : Union[str, Any] = [
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 ( __A : int="./ckpt/aeslc/model.ckpt-32000" ) -> Dict:
'''simple docstring'''
snake_case : Optional[Any] = tf.train.list_variables(__A )
snake_case : Union[str, Any] = {}
snake_case : List[str] = ["""Adafactor""", """global_step"""]
for name, shape in tqdm(__A , desc="""converting tf checkpoint to dict""" ):
snake_case : str = any(pat in name for pat in ignore_name )
if skip_key:
continue
snake_case : List[str] = tf.train.load_variable(__A , __A )
snake_case : Optional[Any] = array
return tf_weights
def lowercase ( __A : str , __A : str ) -> Optional[int]:
'''simple docstring'''
snake_case : Dict = Path(__A ).parent.name
snake_case : Dict = task_specific_params[f"""summarization_{dataset}"""]["""max_position_embeddings"""]
snake_case : Any = PegasusTokenizer.from_pretrained("""sshleifer/pegasus""" , model_max_length=__A )
assert tok.model_max_length == desired_max_model_length
tok.save_pretrained(__A )
# convert model
snake_case : Dict = get_tf_weights_as_numpy(__A )
snake_case : List[Any] = task_specific_params[f"""summarization_{dataset}"""]
if dataset == "large":
snake_case : Optional[int] = task_specific_params
snake_case : Optional[int] = convert_pegasus(__A , __A )
torch_model.save_pretrained(__A )
snake_case : int = torch_model.state_dict()
sd.pop("""model.decoder.embed_positions.weight""" )
sd.pop("""model.encoder.embed_positions.weight""" )
torch.save(__A , Path(__A ) / """pytorch_model.bin""" )
if __name__ == "__main__":
__lowercase : Tuple = 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.''')
__lowercase : List[Any] = parser.parse_args()
if args.save_dir is None:
__lowercase : Optional[Any] = Path(args.tf_ckpt_path).parent.name
__lowercase : Union[str, Any] = os.path.join('''pegasus''', dataset)
convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)
| 36 | 1 |
from math import pi, sqrt
def lowercase ( __A : float ) -> float:
'''simple docstring'''
if num <= 0:
raise ValueError("""math domain error""" )
if num > 171.5:
raise OverflowError("""math range error""" )
elif num - int(__A ) not in (0, 0.5):
raise NotImplementedError("""num must be an integer or a half-integer""" )
elif num == 0.5:
return sqrt(__A )
else:
return 1.0 if num == 1 else (num - 1) * gamma(num - 1 )
def lowercase ( ) -> None:
'''simple docstring'''
assert gamma(0.5 ) == sqrt(__A )
assert gamma(1 ) == 1.0
assert gamma(2 ) == 1.0
if __name__ == "__main__":
from doctest import testmod
testmod()
__lowercase : Optional[int] = 1.0
while num:
__lowercase : Dict = float(input('''Gamma of: '''))
print(f'''gamma({num}) = {gamma(num)}''')
print('''\nEnter 0 to exit...''')
| 36 |
import argparse
import pytorch_lightning as pl
import torch
from torch import nn
from transformers import LongformerForQuestionAnswering, LongformerModel
class _A ( pl.LightningModule ):
'''simple docstring'''
def __init__( self ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
super().__init__()
snake_case : Dict = model
snake_case : Optional[int] = 2
snake_case : Optional[Any] = nn.Linear(self.model.config.hidden_size ,self.num_labels )
def snake_case_ ( self ):
'''simple docstring'''
pass
def lowercase ( __A : str , __A : str , __A : str ) -> Optional[Any]:
'''simple docstring'''
snake_case : Optional[Any] = LongformerModel.from_pretrained(__A )
snake_case : Tuple = LightningModel(__A )
snake_case : Optional[int] = torch.load(__A , map_location=torch.device("""cpu""" ) )
lightning_model.load_state_dict(ckpt["""state_dict"""] )
# init longformer question answering model
snake_case : Dict = LongformerForQuestionAnswering.from_pretrained(__A )
# transfer weights
longformer_for_qa.longformer.load_state_dict(lightning_model.model.state_dict() )
longformer_for_qa.qa_outputs.load_state_dict(lightning_model.qa_outputs.state_dict() )
longformer_for_qa.eval()
# save model
longformer_for_qa.save_pretrained(__A )
print(f"""Conversion successful. Model saved under {pytorch_dump_folder_path}""" )
if __name__ == "__main__":
__lowercase : str = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--longformer_model''',
default=None,
type=str,
required=True,
help='''model identifier of longformer. Should be either `longformer-base-4096` or `longformer-large-4096`.''',
)
parser.add_argument(
'''--longformer_question_answering_ckpt_path''',
default=None,
type=str,
required=True,
help='''Path the official PyTorch Lightning Checkpoint.''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
__lowercase : List[str] = parser.parse_args()
convert_longformer_qa_checkpoint_to_pytorch(
args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
)
| 36 | 1 |
from copy import deepcopy
import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import DistributedType, is_torch_version, set_seed
def lowercase ( __A : Union[str, Any] , __A : int , __A : Dict , __A : Optional[int] ) -> str:
'''simple docstring'''
for param, grad_param in zip(model_a.parameters() , model_b.parameters() ):
if not param.requires_grad:
continue
if not did_step:
# Grads should not be in sync
assert (
torch.allclose(param.grad , grad_param.grad ) is False
), f"""Gradients in sync when they should not be at iteration {iteration}:\nmodel_a grad ({param.grad}) == model_b grad ({grad_param.grad})"""
else:
# Grads should be in sync
assert (
torch.allclose(param.grad , grad_param.grad ) is True
), f"""Gradients not in sync when they should be at iteration {iteration}:\nmodel_a grad ({param.grad}) != model_b grad ({grad_param.grad})"""
def lowercase ( __A : str , __A : List[str] , __A : Tuple , __A : Dict , __A : Tuple=True ) -> Tuple:
'''simple docstring'''
model.train()
snake_case : List[Any] = model(__A )
snake_case : int = F.mse_loss(__A , target.to(output.device ) )
if not do_backward:
loss /= accelerator.gradient_accumulation_steps
loss.backward()
else:
accelerator.backward(__A )
def lowercase ( __A : List[str] , __A : int=False ) -> Union[str, Any]:
'''simple docstring'''
set_seed(42 )
snake_case : Optional[Any] = RegressionModel()
snake_case : Dict = deepcopy(__A )
snake_case : Optional[Any] = RegressionDataset(length=80 )
snake_case : Optional[Any] = DataLoader(__A , batch_size=16 )
model.to(accelerator.device )
if sched:
snake_case : List[str] = AdamW(params=model.parameters() , lr=1E-3 )
snake_case : List[str] = AdamW(params=ddp_model.parameters() , lr=1E-3 )
snake_case : int = LambdaLR(__A , lr_lambda=lambda __A : epoch**0.65 )
snake_case : Any = LambdaLR(__A , lr_lambda=lambda __A : epoch**0.65 )
# Make a copy of `model`
if sched:
snake_case , snake_case , snake_case , snake_case : Optional[int] = accelerator.prepare(__A , __A , __A , __A )
else:
snake_case , snake_case : Any = accelerator.prepare(__A , __A )
if sched:
return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
return model, ddp_model, dataloader
def lowercase ( __A : List[Any] ) -> Any:
'''simple docstring'''
snake_case , snake_case , snake_case : Union[str, Any] = get_training_setup(__A )
# Use a single batch
snake_case , snake_case : str = next(iter(__A ) ).values()
for iteration in range(3 ):
# Gather the distributed inputs and targs for the base model
snake_case , snake_case : List[Any] = accelerator.gather((ddp_input, ddp_target) )
snake_case , snake_case : int = input.to(accelerator.device ), target.to(accelerator.device )
# Perform our initial ground truth step in non "DDP"
step_model(__A , __A , __A , __A )
# Do "gradient accumulation" (noop)
if iteration % 2 == 0:
# Accumulate grads locally
with accelerator.no_sync(__A ):
step_model(__A , __A , __A , __A )
else:
# Sync grads
step_model(__A , __A , __A , __A )
# Since `no_sync` is a noop, `ddp_model` and `model` grads should always be in sync
check_model_parameters(__A , __A , __A , __A )
for param, ddp_param in zip(model.parameters() , ddp_model.parameters() ):
if not param.requires_grad:
continue
assert torch.allclose(
param.grad , ddp_param.grad ), f"""Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})"""
# Shuffle ddp_input on each iteration
torch.manual_seed(1337 + iteration )
snake_case : Tuple = ddp_input[torch.randperm(len(__A ) )]
def lowercase ( __A : Optional[int] ) -> List[str]:
'''simple docstring'''
snake_case , snake_case , snake_case : str = get_training_setup(__A )
# Use a single batch
snake_case , snake_case : Any = next(iter(__A ) ).values()
for iteration in range(3 ):
# Gather the distributed inputs and targs for the base model
snake_case , snake_case : List[str] = accelerator.gather((ddp_input, ddp_target) )
snake_case , snake_case : Optional[int] = input.to(accelerator.device ), target.to(accelerator.device )
# Perform our initial ground truth step in non "DDP"
step_model(__A , __A , __A , __A )
# Do "gradient accumulation" (noop)
if iteration % 2 == 0:
# Accumulate grads locally
with accelerator.no_sync(__A ):
step_model(__A , __A , __A , __A )
else:
# Sync grads
step_model(__A , __A , __A , __A )
# DDP model and model should only be in sync when not (iteration % 2 == 0)
for param, ddp_param in zip(model.parameters() , ddp_model.parameters() ):
if not param.requires_grad:
continue
if iteration % 2 == 0:
# Grads should not be in sync
assert (
torch.allclose(param.grad , ddp_param.grad ) is False
), f"""Gradients in sync when they should not be:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})"""
else:
# Grads should be in sync
assert (
torch.allclose(param.grad , ddp_param.grad ) is True
), f"""Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})"""
# Shuffle ddp_input on each iteration
torch.manual_seed(1337 + iteration )
snake_case : Optional[int] = ddp_input[torch.randperm(len(__A ) )]
def lowercase ( __A : Union[str, Any]=False , __A : Any=False ) -> Tuple:
'''simple docstring'''
snake_case : Optional[Any] = Accelerator(
split_batches=__A , dispatch_batches=__A , gradient_accumulation_steps=2 )
# Test that context manager behaves properly
snake_case , snake_case , snake_case : Optional[int] = get_training_setup(__A )
for iteration, batch in enumerate(__A ):
snake_case , snake_case : Optional[Any] = batch.values()
# Gather the distributed inputs and targs for the base model
snake_case , snake_case : List[str] = accelerator.gather((ddp_input, ddp_target) )
snake_case , snake_case : Union[str, Any] = input.to(accelerator.device ), target.to(accelerator.device )
# Perform our initial ground truth step in non "DDP"
step_model(__A , __A , __A , __A , __A )
# Do "gradient accumulation" (noop)
with accelerator.accumulate(__A ):
step_model(__A , __A , __A , __A )
# DDP model and model should only be in sync when not (iteration % 2 == 0)
for param, ddp_param in zip(model.parameters() , ddp_model.parameters() ):
if not param.requires_grad:
continue
if ((iteration + 1) % 2 == 0) or (iteration == len(__A ) - 1):
# Grads should be in sync
assert (
torch.allclose(param.grad , ddp_param.grad ) is True
), f"""Gradients not in sync when they should be at iteration {iteration}:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})"""
else:
# Grads should not be in sync
assert (
torch.allclose(param.grad , ddp_param.grad ) is False
), f"""Gradients in sync when they should not be at iteration {iteration}:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})"""
# Shuffle ddp_input on each iteration
torch.manual_seed(1337 + iteration )
snake_case : Tuple = ddp_input[torch.randperm(len(__A ) )]
GradientState._reset_state()
def lowercase ( __A : Any=False , __A : str=False ) -> str:
'''simple docstring'''
snake_case : Any = Accelerator(
split_batches=__A , dispatch_batches=__A , gradient_accumulation_steps=2 )
# Test that context manager behaves properly
snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case : int = get_training_setup(__A , __A )
for iteration, batch in enumerate(__A ):
snake_case , snake_case : Optional[Any] = batch.values()
# Gather the distributed inputs and targs for the base model
snake_case , snake_case : List[Any] = accelerator.gather((ddp_input, ddp_target) )
snake_case , snake_case : str = input.to(accelerator.device ), target.to(accelerator.device )
# Perform our initial ground truth step in non "DDP"
model.train()
ddp_model.train()
step_model(__A , __A , __A , __A , __A )
opt.step()
if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(__A )):
if split_batches:
sched.step()
else:
for _ in range(accelerator.num_processes ):
sched.step()
opt.zero_grad()
# Perform gradient accumulation under wrapper
with accelerator.accumulate(__A ):
step_model(__A , __A , __A , __A )
ddp_opt.step()
ddp_sched.step()
ddp_opt.zero_grad()
# Learning rates should be the same
assert (
opt.param_groups[0]["lr"] == ddp_opt.param_groups[0]["lr"]
), f"""Learning rates found in each optimizer did not align\nopt: {opt.param_groups[0]["lr"]}\nDDP opt: {ddp_opt.param_groups[0]["lr"]}\n"""
snake_case : List[str] = (((iteration + 1) % 2) == 0) or ((iteration + 1) == len(__A ))
if accelerator.num_processes > 1:
check_model_parameters(__A , __A , __A , __A )
# Shuffle ddp_input on each iteration
torch.manual_seed(1337 + iteration )
GradientState._reset_state()
def lowercase ( ) -> List[Any]:
'''simple docstring'''
snake_case : List[str] = Accelerator()
snake_case : Dict = RegressionDataset(length=80 )
snake_case : Tuple = DataLoader(__A , batch_size=16 )
snake_case : Tuple = RegressionDataset(length=96 )
snake_case : Optional[Any] = DataLoader(__A , batch_size=16 )
snake_case , snake_case : Any = accelerator.prepare(__A , __A )
assert accelerator.gradient_state.active_dataloader is None
for iteration, _ in enumerate(__A ):
assert id(accelerator.gradient_state.active_dataloader ) == id(__A )
if iteration < len(__A ) - 1:
assert not accelerator.gradient_state.end_of_dataloader
if iteration == 1:
for batch_num, _ in enumerate(__A ):
assert id(accelerator.gradient_state.active_dataloader ) == id(__A )
if batch_num < len(__A ) - 1:
assert not accelerator.gradient_state.end_of_dataloader
else:
assert accelerator.gradient_state.end_of_dataloader
else:
assert accelerator.gradient_state.end_of_dataloader
assert accelerator.gradient_state.active_dataloader is None
def lowercase ( ) -> List[str]:
'''simple docstring'''
snake_case : str = Accelerator()
snake_case : Dict = accelerator.state
if state.local_process_index == 0:
print("""**Test `accumulate` gradient accumulation with dataloader break**""" )
test_dataloader_break()
if state.distributed_type == DistributedType.NO:
if state.local_process_index == 0:
print("""**Test NOOP `no_sync` context manager**""" )
test_noop_sync(__A )
if state.distributed_type in (DistributedType.MULTI_GPU, DistributedType.MULTI_CPU):
if state.local_process_index == 0:
print("""**Test Distributed `no_sync` context manager**""" )
test_distributed_sync(__A )
if state.distributed_type == DistributedType.MULTI_GPU:
for split_batch in [True, False]:
for dispatch_batches in [True, False]:
if state.local_process_index == 0:
print(
"""**Test `accumulate` gradient accumulation, """ , f"""`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**""" , )
test_gradient_accumulation(__A , __A )
# Currently will break on torch 2.0 +, need to investigate why
if is_torch_version("""<""" , """2.0""" ) or state.distributed_type == DistributedType.NO:
if state.local_process_index == 0:
print(
"""**Test `accumulate` gradient accumulation with optimizer and scheduler, """ , """`split_batches=False`, `dispatch_batches=False`**""" , )
test_gradient_accumulation_with_opt_and_scheduler()
if state.distributed_type == DistributedType.MULTI_GPU:
for split_batch in [True, False]:
for dispatch_batches in [True, False]:
if not split_batch and not dispatch_batches:
continue
if state.local_process_index == 0:
print(
"""**Test `accumulate` gradient accumulation with optimizer and scheduler, """ , f"""`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**""" , )
test_gradient_accumulation_with_opt_and_scheduler(__A , __A )
def lowercase ( __A : Optional[int] ) -> Union[str, Any]:
'''simple docstring'''
main()
if __name__ == "__main__":
main()
| 36 |
import argparse
import collections
import json
import os
import re
import string
import sys
import numpy as np
__lowercase : Optional[Any] = re.compile(r'''\b(a|an|the)\b''', re.UNICODE)
__lowercase : Optional[int] = None
def lowercase ( ) -> Optional[Any]:
'''simple docstring'''
snake_case : int = argparse.ArgumentParser("""Official evaluation script for SQuAD version 2.0.""" )
parser.add_argument("""data_file""" , metavar="""data.json""" , help="""Input data JSON file.""" )
parser.add_argument("""pred_file""" , metavar="""pred.json""" , help="""Model predictions.""" )
parser.add_argument(
"""--out-file""" , """-o""" , metavar="""eval.json""" , help="""Write accuracy metrics to file (default is stdout).""" )
parser.add_argument(
"""--na-prob-file""" , """-n""" , metavar="""na_prob.json""" , help="""Model estimates of probability of no answer.""" )
parser.add_argument(
"""--na-prob-thresh""" , """-t""" , type=__A , default=1.0 , help="""Predict \"\" if no-answer probability exceeds this (default = 1.0).""" , )
parser.add_argument(
"""--out-image-dir""" , """-p""" , metavar="""out_images""" , default=__A , help="""Save precision-recall curves to directory.""" )
parser.add_argument("""--verbose""" , """-v""" , action="""store_true""" )
if len(sys.argv ) == 1:
parser.print_help()
sys.exit(1 )
return parser.parse_args()
def lowercase ( __A : Union[str, Any] ) -> int:
'''simple docstring'''
snake_case : Any = {}
for article in dataset:
for p in article["paragraphs"]:
for qa in p["qas"]:
snake_case : int = bool(qa["""answers"""]["""text"""] )
return qid_to_has_ans
def lowercase ( __A : int ) -> Optional[int]:
'''simple docstring'''
def remove_articles(__A : List[Any] ):
return ARTICLES_REGEX.sub(""" """ , __A )
def white_space_fix(__A : Union[str, Any] ):
return " ".join(text.split() )
def remove_punc(__A : Tuple ):
snake_case : Optional[Any] = set(string.punctuation )
return "".join(ch for ch in text if ch not in exclude )
def lower(__A : Any ):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(__A ) ) ) )
def lowercase ( __A : List[str] ) -> Union[str, Any]:
'''simple docstring'''
if not s:
return []
return normalize_answer(__A ).split()
def lowercase ( __A : Optional[int] , __A : int ) -> List[Any]:
'''simple docstring'''
return int(normalize_answer(__A ) == normalize_answer(__A ) )
def lowercase ( __A : Any , __A : Optional[Any] ) -> List[str]:
'''simple docstring'''
snake_case : Tuple = get_tokens(__A )
snake_case : str = get_tokens(__A )
snake_case : Dict = collections.Counter(__A ) & collections.Counter(__A )
snake_case : Optional[int] = sum(common.values() )
if len(__A ) == 0 or len(__A ) == 0:
# If either is no-answer, then F1 is 1 if they agree, 0 otherwise
return int(gold_toks == pred_toks )
if num_same == 0:
return 0
snake_case : List[Any] = 1.0 * num_same / len(__A )
snake_case : int = 1.0 * num_same / len(__A )
snake_case : Dict = (2 * precision * recall) / (precision + recall)
return fa
def lowercase ( __A : List[Any] , __A : int ) -> str:
'''simple docstring'''
snake_case : Tuple = {}
snake_case : Tuple = {}
for article in dataset:
for p in article["paragraphs"]:
for qa in p["qas"]:
snake_case : str = qa["""id"""]
snake_case : Union[str, Any] = [t for t in qa["""answers"""]["""text"""] if normalize_answer(__A )]
if not gold_answers:
# For unanswerable questions, only correct answer is empty string
snake_case : Optional[Any] = [""""""]
if qid not in preds:
print(f"""Missing prediction for {qid}""" )
continue
snake_case : Dict = preds[qid]
# Take max over all gold answers
snake_case : Union[str, Any] = max(compute_exact(__A , __A ) for a in gold_answers )
snake_case : Optional[int] = max(compute_fa(__A , __A ) for a in gold_answers )
return exact_scores, fa_scores
def lowercase ( __A : str , __A : Any , __A : List[Any] , __A : List[Any] ) -> Dict:
'''simple docstring'''
snake_case : Optional[int] = {}
for qid, s in scores.items():
snake_case : Any = na_probs[qid] > na_prob_thresh
if pred_na:
snake_case : str = float(not qid_to_has_ans[qid] )
else:
snake_case : List[Any] = s
return new_scores
def lowercase ( __A : Dict , __A : Union[str, Any] , __A : List[str]=None ) -> int:
'''simple docstring'''
if not qid_list:
snake_case : List[str] = len(__A )
return collections.OrderedDict(
[
("""exact""", 100.0 * sum(exact_scores.values() ) / total),
("""f1""", 100.0 * sum(fa_scores.values() ) / total),
("""total""", total),
] )
else:
snake_case : Any = len(__A )
return collections.OrderedDict(
[
("""exact""", 100.0 * sum(exact_scores[k] for k in qid_list ) / total),
("""f1""", 100.0 * sum(fa_scores[k] for k in qid_list ) / total),
("""total""", total),
] )
def lowercase ( __A : Optional[Any] , __A : Tuple , __A : List[str] ) -> Optional[Any]:
'''simple docstring'''
for k in new_eval:
snake_case : str = new_eval[k]
def lowercase ( __A : Tuple , __A : int , __A : Dict , __A : Dict ) -> int:
'''simple docstring'''
plt.step(__A , __A , color="""b""" , alpha=0.2 , where="""post""" )
plt.fill_between(__A , __A , step="""post""" , alpha=0.2 , color="""b""" )
plt.xlabel("""Recall""" )
plt.ylabel("""Precision""" )
plt.xlim([0.0, 1.05] )
plt.ylim([0.0, 1.05] )
plt.title(__A )
plt.savefig(__A )
plt.clf()
def lowercase ( __A : Optional[Any] , __A : Union[str, Any] , __A : Dict , __A : Tuple , __A : Optional[Any]=None , __A : List[str]=None ) -> Union[str, Any]:
'''simple docstring'''
snake_case : Optional[int] = sorted(__A , key=lambda __A : na_probs[k] )
snake_case : Any = 0.0
snake_case : str = 1.0
snake_case : Tuple = 0.0
snake_case : str = [1.0]
snake_case : Any = [0.0]
snake_case : Dict = 0.0
for i, qid in enumerate(__A ):
if qid_to_has_ans[qid]:
true_pos += scores[qid]
snake_case : str = true_pos / float(i + 1 )
snake_case : List[str] = true_pos / float(__A )
if i == len(__A ) - 1 or na_probs[qid] != na_probs[qid_list[i + 1]]:
# i.e., if we can put a threshold after this point
avg_prec += cur_p * (cur_r - recalls[-1])
precisions.append(__A )
recalls.append(__A )
if out_image:
plot_pr_curve(__A , __A , __A , __A )
return {"ap": 100.0 * avg_prec}
def lowercase ( __A : Any , __A : Optional[int] , __A : Tuple , __A : Tuple , __A : List[Any] , __A : Optional[Any] ) -> Optional[Any]:
'''simple docstring'''
if out_image_dir and not os.path.exists(__A ):
os.makedirs(__A )
snake_case : Tuple = sum(1 for v in qid_to_has_ans.values() if v )
if num_true_pos == 0:
return
snake_case : str = make_precision_recall_eval(
__A , __A , __A , __A , out_image=os.path.join(__A , """pr_exact.png""" ) , title="""Precision-Recall curve for Exact Match score""" , )
snake_case : int = make_precision_recall_eval(
__A , __A , __A , __A , out_image=os.path.join(__A , """pr_f1.png""" ) , title="""Precision-Recall curve for F1 score""" , )
snake_case : Dict = {k: float(__A ) for k, v in qid_to_has_ans.items()}
snake_case : int = make_precision_recall_eval(
__A , __A , __A , __A , out_image=os.path.join(__A , """pr_oracle.png""" ) , title="""Oracle Precision-Recall curve (binary task of HasAns vs. NoAns)""" , )
merge_eval(__A , __A , """pr_exact""" )
merge_eval(__A , __A , """pr_f1""" )
merge_eval(__A , __A , """pr_oracle""" )
def lowercase ( __A : List[Any] , __A : Union[str, Any] , __A : Union[str, Any] , __A : Optional[int] ) -> Union[str, Any]:
'''simple docstring'''
if not qid_list:
return
snake_case : int = [na_probs[k] for k in qid_list]
snake_case : List[str] = np.ones_like(__A ) / float(len(__A ) )
plt.hist(__A , weights=__A , bins=20 , range=(0.0, 1.0) )
plt.xlabel("""Model probability of no-answer""" )
plt.ylabel("""Proportion of dataset""" )
plt.title(f"""Histogram of no-answer probability: {name}""" )
plt.savefig(os.path.join(__A , f"""na_prob_hist_{name}.png""" ) )
plt.clf()
def lowercase ( __A : List[Any] , __A : Tuple , __A : Tuple , __A : Any ) -> Dict:
'''simple docstring'''
snake_case : Optional[int] = sum(1 for k in qid_to_has_ans if not qid_to_has_ans[k] )
snake_case : str = num_no_ans
snake_case : Optional[Any] = cur_score
snake_case : Optional[Any] = 0.0
snake_case : List[Any] = sorted(__A , key=lambda __A : na_probs[k] )
for i, qid in enumerate(__A ):
if qid not in scores:
continue
if qid_to_has_ans[qid]:
snake_case : Dict = scores[qid]
else:
if preds[qid]:
snake_case : Dict = -1
else:
snake_case : str = 0
cur_score += diff
if cur_score > best_score:
snake_case : Union[str, Any] = cur_score
snake_case : List[Any] = na_probs[qid]
return 100.0 * best_score / len(__A ), best_thresh
def lowercase ( __A : Dict , __A : str , __A : str , __A : int , __A : str , __A : Any ) -> List[str]:
'''simple docstring'''
snake_case , snake_case : Optional[int] = find_best_thresh(__A , __A , __A , __A )
snake_case , snake_case : str = find_best_thresh(__A , __A , __A , __A )
snake_case : List[str] = best_exact
snake_case : List[Any] = exact_thresh
snake_case : Optional[Any] = best_fa
snake_case : Optional[int] = fa_thresh
def lowercase ( ) -> Any:
'''simple docstring'''
with open(OPTS.data_file ) as f:
snake_case : Dict = json.load(__A )
snake_case : Union[str, Any] = dataset_json["""data"""]
with open(OPTS.pred_file ) as f:
snake_case : int = json.load(__A )
if OPTS.na_prob_file:
with open(OPTS.na_prob_file ) as f:
snake_case : Any = json.load(__A )
else:
snake_case : Any = {k: 0.0 for k in preds}
snake_case : Optional[int] = make_qid_to_has_ans(__A ) # maps qid to True/False
snake_case : Dict = [k for k, v in qid_to_has_ans.items() if v]
snake_case : Optional[int] = [k for k, v in qid_to_has_ans.items() if not v]
snake_case , snake_case : Optional[Any] = get_raw_scores(__A , __A )
snake_case : Tuple = apply_no_ans_threshold(__A , __A , __A , OPTS.na_prob_thresh )
snake_case : Optional[Any] = apply_no_ans_threshold(__A , __A , __A , OPTS.na_prob_thresh )
snake_case : Optional[int] = make_eval_dict(__A , __A )
if has_ans_qids:
snake_case : Any = make_eval_dict(__A , __A , qid_list=__A )
merge_eval(__A , __A , """HasAns""" )
if no_ans_qids:
snake_case : str = make_eval_dict(__A , __A , qid_list=__A )
merge_eval(__A , __A , """NoAns""" )
if OPTS.na_prob_file:
find_all_best_thresh(__A , __A , __A , __A , __A , __A )
if OPTS.na_prob_file and OPTS.out_image_dir:
run_precision_recall_analysis(__A , __A , __A , __A , __A , OPTS.out_image_dir )
histogram_na_prob(__A , __A , OPTS.out_image_dir , """hasAns""" )
histogram_na_prob(__A , __A , OPTS.out_image_dir , """noAns""" )
if OPTS.out_file:
with open(OPTS.out_file , """w""" ) as f:
json.dump(__A , __A )
else:
print(json.dumps(__A , indent=2 ) )
if __name__ == "__main__":
__lowercase : Union[str, Any] = parse_args()
if OPTS.out_image_dir:
import matplotlib
matplotlib.use('''Agg''')
import matplotlib.pyplot as plt
main()
| 36 | 1 |
import json
import logging
import math
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from datasets import Dataset, load_dataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_FOR_MASKED_LM_MAPPING,
AutoConfig,
AutoModelForMaskedLM,
AutoTokenizer,
DataCollatorForWholeWordMask,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process
__lowercase : Any = logging.getLogger(__name__)
__lowercase : Any = list(MODEL_FOR_MASKED_LM_MAPPING.keys())
__lowercase : Dict = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class _A :
'''simple docstring'''
__lowerCamelCase : Optional[str] = field(
default=snake_case , metadata={
'''help''': (
'''The model checkpoint for weights initialization.Don\'t set if you want to train a model from scratch.'''
)
} , )
__lowerCamelCase : Optional[str] = field(
default=snake_case , metadata={'''help''': '''If training from scratch, pass a model type from the list: ''' + ''', '''.join(snake_case )} , )
__lowerCamelCase : Optional[str] = field(
default=snake_case , metadata={
'''help''': (
'''Override some existing default config settings when a model is trained from scratch. Example: '''
'''n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index'''
)
} , )
__lowerCamelCase : Optional[str] = field(
default=snake_case , metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''} )
__lowerCamelCase : Optional[str] = field(
default=snake_case , metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''} )
__lowerCamelCase : Optional[str] = field(
default=snake_case , metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''} , )
__lowerCamelCase : bool = field(
default=snake_case , metadata={'''help''': '''Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.'''} , )
__lowerCamelCase : str = field(
default='''main''' , metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''} , )
__lowerCamelCase : bool = field(
default=snake_case , metadata={
'''help''': (
'''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
'''with private models).'''
)
} , )
def snake_case_ ( self ):
'''simple docstring'''
if self.config_overrides is not None and (self.config_name is not None or self.model_name_or_path is not None):
raise ValueError(
"""--config_overrides can't be used in combination with --config_name or --model_name_or_path""" )
@dataclass
class _A :
'''simple docstring'''
__lowerCamelCase : Optional[str] = field(
default=snake_case , metadata={'''help''': '''The name of the dataset to use (via the datasets library).'''} )
__lowerCamelCase : Optional[str] = field(
default=snake_case , metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''} )
__lowerCamelCase : Optional[str] = field(default=snake_case , metadata={'''help''': '''The input training data file (a text file).'''} )
__lowerCamelCase : Optional[str] = field(
default=snake_case , metadata={'''help''': '''An optional input evaluation data file to evaluate the perplexity on (a text file).'''} , )
__lowerCamelCase : Optional[str] = field(
default=snake_case , metadata={'''help''': '''An optional input train ref data file for whole word masking in Chinese.'''} , )
__lowerCamelCase : Optional[str] = field(
default=snake_case , metadata={'''help''': '''An optional input validation ref data file for whole word masking in Chinese.'''} , )
__lowerCamelCase : bool = field(
default=snake_case , metadata={'''help''': '''Overwrite the cached training and evaluation sets'''} )
__lowerCamelCase : Optional[int] = field(
default=5 , metadata={
'''help''': '''The percentage of the train set used as validation set in case there\'s no validation split'''
} , )
__lowerCamelCase : Optional[int] = field(
default=snake_case , metadata={
'''help''': (
'''The maximum total input sequence length after tokenization. Sequences longer '''
'''than this will be truncated. Default to the max input length of the model.'''
)
} , )
__lowerCamelCase : Optional[int] = field(
default=snake_case , metadata={'''help''': '''The number of processes to use for the preprocessing.'''} , )
__lowerCamelCase : float = field(
default=0.15 , metadata={'''help''': '''Ratio of tokens to mask for masked language modeling loss'''} )
__lowerCamelCase : bool = field(
default=snake_case , metadata={
'''help''': (
'''Whether to pad all samples to `max_seq_length`. '''
'''If False, will pad the samples dynamically when batching to the maximum length in the batch.'''
)
} , )
def snake_case_ ( self ):
'''simple docstring'''
if self.train_file is not None:
snake_case : Tuple = self.train_file.split(""".""" )[-1]
assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, a json or a txt file."
if self.validation_file is not None:
snake_case : Optional[Any] = self.validation_file.split(""".""" )[-1]
assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file."
def lowercase ( __A : Union[str, Any] , __A : List[Any] ) -> str:
'''simple docstring'''
with open(__A , """r""" , encoding="""utf-8""" ) as f:
snake_case : int = [json.loads(__A ) for line in f.read().splitlines() if (len(__A ) > 0 and not line.isspace())]
assert len(__A ) == len(__A )
snake_case : Optional[Any] = {c: dataset[c] for c in dataset.column_names}
snake_case : Dict = refs
return Dataset.from_dict(__A )
def lowercase ( ) -> str:
'''simple docstring'''
snake_case : List[str] = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith(""".json""" ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
snake_case , snake_case , snake_case : Any = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
snake_case , snake_case , snake_case : str = parser.parse_args_into_dataclasses()
# Detecting last checkpoint.
snake_case : Optional[int] = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
snake_case : Tuple = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
f"""Output directory ({training_args.output_dir}) already exists and is not empty. """
"""Use --overwrite_output_dir to overcome.""" )
elif last_checkpoint is not None:
logger.info(
f"""Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change """
"""the `--output_dir` or add `--overwrite_output_dir` to train from scratch.""" )
# Setup logging
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""" , datefmt="""%m/%d/%Y %H:%M:%S""" , handlers=[logging.StreamHandler(sys.stdout )] , )
logger.setLevel(logging.INFO if is_main_process(training_args.local_rank ) else logging.WARN )
# Log on each process the small summary:
logger.warning(
f"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"""
+ f"""distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}""" )
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank ):
transformers.utils.logging.set_verbosity_info()
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
logger.info("""Training/evaluation parameters %s""" , __A )
# Set seed before initializing model.
set_seed(training_args.seed )
# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
# (the dataset will be downloaded automatically from the datasets Hub).
#
# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
# 'text' is found. You can easily tweak this behavior (see below).
#
# In distributed training, the load_dataset function guarantee that only one local process can concurrently
# download the dataset.
if data_args.dataset_name is not None:
# Downloading and loading a dataset from the hub.
snake_case : Dict = load_dataset(data_args.dataset_name , data_args.dataset_config_name )
if "validation" not in datasets.keys():
snake_case : Union[str, Any] = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split=f"""train[:{data_args.validation_split_percentage}%]""" , )
snake_case : List[str] = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split=f"""train[{data_args.validation_split_percentage}%:]""" , )
else:
snake_case : int = {}
if data_args.train_file is not None:
snake_case : Optional[int] = data_args.train_file
if data_args.validation_file is not None:
snake_case : Dict = data_args.validation_file
snake_case : str = data_args.train_file.split(""".""" )[-1]
if extension == "txt":
snake_case : str = """text"""
snake_case : List[str] = load_dataset(__A , data_files=__A )
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
# https://huggingface.co/docs/datasets/loading_datasets.html.
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
snake_case : Optional[Any] = {
"""cache_dir""": model_args.cache_dir,
"""revision""": model_args.model_revision,
"""use_auth_token""": True if model_args.use_auth_token else None,
}
if model_args.config_name:
snake_case : Tuple = AutoConfig.from_pretrained(model_args.config_name , **__A )
elif model_args.model_name_or_path:
snake_case : Tuple = AutoConfig.from_pretrained(model_args.model_name_or_path , **__A )
else:
snake_case : Union[str, Any] = CONFIG_MAPPING[model_args.model_type]()
logger.warning("""You are instantiating a new config instance from scratch.""" )
if model_args.config_overrides is not None:
logger.info(f"""Overriding config: {model_args.config_overrides}""" )
config.update_from_string(model_args.config_overrides )
logger.info(f"""New config: {config}""" )
snake_case : int = {
"""cache_dir""": model_args.cache_dir,
"""use_fast""": model_args.use_fast_tokenizer,
"""revision""": model_args.model_revision,
"""use_auth_token""": True if model_args.use_auth_token else None,
}
if model_args.tokenizer_name:
snake_case : Optional[Any] = AutoTokenizer.from_pretrained(model_args.tokenizer_name , **__A )
elif model_args.model_name_or_path:
snake_case : Union[str, Any] = AutoTokenizer.from_pretrained(model_args.model_name_or_path , **__A )
else:
raise ValueError(
"""You are instantiating a new tokenizer from scratch. This is not supported by this script."""
"""You can do it from another script, save it, and load it from here, using --tokenizer_name.""" )
if model_args.model_name_or_path:
snake_case : Optional[Any] = AutoModelForMaskedLM.from_pretrained(
model_args.model_name_or_path , from_tf=bool(""".ckpt""" in model_args.model_name_or_path ) , config=__A , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
else:
logger.info("""Training new model from scratch""" )
snake_case : Any = AutoModelForMaskedLM.from_config(__A )
model.resize_token_embeddings(len(__A ) )
# Preprocessing the datasets.
# First we tokenize all the texts.
if training_args.do_train:
snake_case : str = datasets["""train"""].column_names
else:
snake_case : Dict = datasets["""validation"""].column_names
snake_case : List[str] = """text""" if """text""" in column_names else column_names[0]
snake_case : int = """max_length""" if data_args.pad_to_max_length else False
def tokenize_function(__A : Tuple ):
# Remove empty lines
snake_case : int = [line for line in examples["""text"""] if len(__A ) > 0 and not line.isspace()]
return tokenizer(examples["""text"""] , padding=__A , truncation=__A , max_length=data_args.max_seq_length )
snake_case : Tuple = datasets.map(
__A , batched=__A , num_proc=data_args.preprocessing_num_workers , remove_columns=[text_column_name] , load_from_cache_file=not data_args.overwrite_cache , )
# Add the chinese references if provided
if data_args.train_ref_file is not None:
snake_case : List[str] = add_chinese_references(tokenized_datasets["""train"""] , data_args.train_ref_file )
if data_args.validation_ref_file is not None:
snake_case : Optional[int] = add_chinese_references(
tokenized_datasets["""validation"""] , data_args.validation_ref_file )
# If we have ref files, need to avoid it removed by trainer
snake_case : int = data_args.train_ref_file or data_args.validation_ref_file
if has_ref:
snake_case : List[Any] = False
# Data collator
# This one will take care of randomly masking the tokens.
snake_case : Optional[int] = DataCollatorForWholeWordMask(tokenizer=__A , mlm_probability=data_args.mlm_probability )
# Initialize our Trainer
snake_case : Optional[Any] = Trainer(
model=__A , args=__A , train_dataset=tokenized_datasets["""train"""] if training_args.do_train else None , eval_dataset=tokenized_datasets["""validation"""] if training_args.do_eval else None , tokenizer=__A , data_collator=__A , )
# Training
if training_args.do_train:
if last_checkpoint is not None:
snake_case : Any = last_checkpoint
elif model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path ):
snake_case : Optional[Any] = model_args.model_name_or_path
else:
snake_case : Any = None
snake_case : Dict = trainer.train(resume_from_checkpoint=__A )
trainer.save_model() # Saves the tokenizer too for easy upload
snake_case : Optional[Any] = os.path.join(training_args.output_dir , """train_results.txt""" )
if trainer.is_world_process_zero():
with open(__A , """w""" ) as writer:
logger.info("""***** Train results *****""" )
for key, value in sorted(train_result.metrics.items() ):
logger.info(f""" {key} = {value}""" )
writer.write(f"""{key} = {value}\n""" )
# Need to save the state, since Trainer.save_model saves only the tokenizer with the model
trainer.state.save_to_json(os.path.join(training_args.output_dir , """trainer_state.json""" ) )
# Evaluation
snake_case : List[str] = {}
if training_args.do_eval:
logger.info("""*** Evaluate ***""" )
snake_case : Union[str, Any] = trainer.evaluate()
snake_case : List[Any] = math.exp(eval_output["""eval_loss"""] )
snake_case : Any = perplexity
snake_case : Any = os.path.join(training_args.output_dir , """eval_results_mlm_wwm.txt""" )
if trainer.is_world_process_zero():
with open(__A , """w""" ) as writer:
logger.info("""***** Eval results *****""" )
for key, value in sorted(results.items() ):
logger.info(f""" {key} = {value}""" )
writer.write(f"""{key} = {value}\n""" )
return results
def lowercase ( __A : int ) -> Optional[Any]:
'''simple docstring'''
main()
if __name__ == "__main__":
main()
| 36 |
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
flip_channel_order,
get_resize_output_image_size,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, is_vision_available, logging
if is_vision_available():
import PIL
if is_torch_available():
import torch
__lowercase : Dict = logging.get_logger(__name__)
class _A ( snake_case ):
'''simple docstring'''
__lowerCamelCase : Optional[int] = ['''pixel_values''']
def __init__( self ,SCREAMING_SNAKE_CASE_ = True ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_ = PILImageResampling.BILINEAR ,SCREAMING_SNAKE_CASE_ = True ,SCREAMING_SNAKE_CASE_ = 1 / 255 ,SCREAMING_SNAKE_CASE_ = True ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_ = True ,**SCREAMING_SNAKE_CASE_ ,):
'''simple docstring'''
super().__init__(**SCREAMING_SNAKE_CASE_ )
snake_case : List[Any] = size if size is not None else {"""shortest_edge""": 224}
snake_case : str = get_size_dict(SCREAMING_SNAKE_CASE_ ,default_to_square=SCREAMING_SNAKE_CASE_ )
snake_case : str = crop_size if crop_size is not None else {"""height""": 256, """width""": 256}
snake_case : Optional[Any] = get_size_dict(SCREAMING_SNAKE_CASE_ ,param_name="""crop_size""" )
snake_case : Optional[Any] = do_resize
snake_case : Union[str, Any] = size
snake_case : Dict = resample
snake_case : Dict = do_rescale
snake_case : Dict = rescale_factor
snake_case : List[str] = do_center_crop
snake_case : Dict = crop_size
snake_case : Any = do_flip_channel_order
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ = PIL.Image.BILINEAR ,SCREAMING_SNAKE_CASE_ = None ,**SCREAMING_SNAKE_CASE_ ,):
'''simple docstring'''
snake_case : str = get_size_dict(SCREAMING_SNAKE_CASE_ ,default_to_square=SCREAMING_SNAKE_CASE_ )
if "shortest_edge" not in size:
raise ValueError(F"""The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}""" )
snake_case : List[Any] = get_resize_output_image_size(SCREAMING_SNAKE_CASE_ ,size=size["""shortest_edge"""] ,default_to_square=SCREAMING_SNAKE_CASE_ )
return resize(SCREAMING_SNAKE_CASE_ ,size=SCREAMING_SNAKE_CASE_ ,resample=SCREAMING_SNAKE_CASE_ ,data_format=SCREAMING_SNAKE_CASE_ ,**SCREAMING_SNAKE_CASE_ )
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ = None ,**SCREAMING_SNAKE_CASE_ ,):
'''simple docstring'''
snake_case : Union[str, Any] = get_size_dict(SCREAMING_SNAKE_CASE_ )
if "height" not in size or "width" not in size:
raise ValueError(F"""The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}""" )
return center_crop(SCREAMING_SNAKE_CASE_ ,size=(size["""height"""], size["""width"""]) ,data_format=SCREAMING_SNAKE_CASE_ ,**SCREAMING_SNAKE_CASE_ )
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ = None ,**SCREAMING_SNAKE_CASE_ ,):
'''simple docstring'''
return rescale(SCREAMING_SNAKE_CASE_ ,scale=SCREAMING_SNAKE_CASE_ ,data_format=SCREAMING_SNAKE_CASE_ ,**SCREAMING_SNAKE_CASE_ )
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ = None ):
'''simple docstring'''
return flip_channel_order(SCREAMING_SNAKE_CASE_ ,data_format=SCREAMING_SNAKE_CASE_ )
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_ = ChannelDimension.FIRST ,**SCREAMING_SNAKE_CASE_ ,):
'''simple docstring'''
snake_case : List[Any] = do_resize if do_resize is not None else self.do_resize
snake_case : List[str] = resample if resample is not None else self.resample
snake_case : Union[str, Any] = do_rescale if do_rescale is not None else self.do_rescale
snake_case : Union[str, Any] = rescale_factor if rescale_factor is not None else self.rescale_factor
snake_case : str = do_center_crop if do_center_crop is not None else self.do_center_crop
snake_case : Union[str, Any] = (
do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
)
snake_case : Tuple = size if size is not None else self.size
snake_case : Optional[Any] = get_size_dict(SCREAMING_SNAKE_CASE_ ,default_to_square=SCREAMING_SNAKE_CASE_ )
snake_case : str = crop_size if crop_size is not None else self.crop_size
snake_case : Optional[Any] = get_size_dict(SCREAMING_SNAKE_CASE_ ,param_name="""crop_size""" )
snake_case : List[Any] = make_list_of_images(SCREAMING_SNAKE_CASE_ )
if not valid_images(SCREAMING_SNAKE_CASE_ ):
raise ValueError(
"""Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
"""torch.Tensor, tf.Tensor or jax.ndarray.""" )
if do_resize and size is None:
raise ValueError("""Size must be specified if do_resize is True.""" )
if do_rescale and rescale_factor is None:
raise ValueError("""Rescale factor must be specified if do_rescale is True.""" )
if do_center_crop and crop_size is None:
raise ValueError("""Crop size must be specified if do_center_crop is True.""" )
# All transformations expect numpy arrays.
snake_case : Dict = [to_numpy_array(SCREAMING_SNAKE_CASE_ ) for image in images]
if do_resize:
snake_case : Union[str, Any] = [self.resize(image=SCREAMING_SNAKE_CASE_ ,size=SCREAMING_SNAKE_CASE_ ,resample=SCREAMING_SNAKE_CASE_ ) for image in images]
if do_center_crop:
snake_case : Optional[Any] = [self.center_crop(image=SCREAMING_SNAKE_CASE_ ,size=SCREAMING_SNAKE_CASE_ ) for image in images]
if do_rescale:
snake_case : Dict = [self.rescale(image=SCREAMING_SNAKE_CASE_ ,scale=SCREAMING_SNAKE_CASE_ ) for image in images]
# the pretrained checkpoints assume images are BGR, not RGB
if do_flip_channel_order:
snake_case : Optional[int] = [self.flip_channel_order(image=SCREAMING_SNAKE_CASE_ ) for image in images]
snake_case : List[Any] = [to_channel_dimension_format(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ) for image in images]
snake_case : int = {"""pixel_values""": images}
return BatchFeature(data=SCREAMING_SNAKE_CASE_ ,tensor_type=SCREAMING_SNAKE_CASE_ )
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ = None ):
'''simple docstring'''
snake_case : Dict = outputs.logits
# Resize logits and compute semantic segmentation maps
if target_sizes is not None:
if len(SCREAMING_SNAKE_CASE_ ) != len(SCREAMING_SNAKE_CASE_ ):
raise ValueError(
"""Make sure that you pass in as many target sizes as the batch dimension of the logits""" )
if is_torch_tensor(SCREAMING_SNAKE_CASE_ ):
snake_case : int = target_sizes.numpy()
snake_case : Optional[Any] = []
for idx in range(len(SCREAMING_SNAKE_CASE_ ) ):
snake_case : Optional[int] = torch.nn.functional.interpolate(
logits[idx].unsqueeze(dim=0 ) ,size=target_sizes[idx] ,mode="""bilinear""" ,align_corners=SCREAMING_SNAKE_CASE_ )
snake_case : Optional[int] = resized_logits[0].argmax(dim=0 )
semantic_segmentation.append(SCREAMING_SNAKE_CASE_ )
else:
snake_case : Tuple = logits.argmax(dim=1 )
snake_case : Dict = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0] )]
return semantic_segmentation
| 36 | 1 |
import os
import re
import shutil
from argparse import ArgumentParser, Namespace
from datasets.commands import BaseDatasetsCLICommand
from datasets.utils.logging import get_logger
__lowercase : Union[str, Any] = '''<<<<<<< This should probably be modified because it mentions: '''
__lowercase : Tuple = '''=======
>>>>>>>
'''
__lowercase : Any = [
'''TextEncoderConfig''',
'''ByteTextEncoder''',
'''SubwordTextEncoder''',
'''encoder_config''',
'''maybe_build_from_corpus''',
'''manual_dir''',
]
__lowercase : List[Any] = [
# (pattern, replacement)
# Order is important here for some replacements
(r'''tfds\.core''', r'''datasets'''),
(r'''tf\.io\.gfile\.GFile''', r'''open'''),
(r'''tf\.([\w\d]+)''', r'''datasets.Value(\'\1\')'''),
(r'''tfds\.features\.Text\(\)''', r'''datasets.Value(\'string\')'''),
(r'''tfds\.features\.Text\(''', r'''datasets.Value(\'string\'),'''),
(r'''features\s*=\s*tfds.features.FeaturesDict\(''', r'''features=datasets.Features('''),
(r'''tfds\.features\.FeaturesDict\(''', r'''dict('''),
(r'''The TensorFlow Datasets Authors''', r'''The TensorFlow Datasets Authors and the HuggingFace Datasets Authors'''),
(r'''tfds\.''', r'''datasets.'''),
(r'''dl_manager\.manual_dir''', r'''self.config.data_dir'''),
(r'''self\.builder_config''', r'''self.config'''),
]
def lowercase ( __A : Namespace ) -> int:
'''simple docstring'''
return ConvertCommand(args.tfds_path , args.datasets_directory )
class _A ( snake_case ):
'''simple docstring'''
@staticmethod
def snake_case_ ( SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
snake_case : List[str] = parser.add_parser(
"""convert""" ,help="""Convert a TensorFlow Datasets dataset to a HuggingFace Datasets dataset.""" ,)
train_parser.add_argument(
"""--tfds_path""" ,type=SCREAMING_SNAKE_CASE_ ,required=SCREAMING_SNAKE_CASE_ ,help="""Path to a TensorFlow Datasets folder to convert or a single tfds file to convert.""" ,)
train_parser.add_argument(
"""--datasets_directory""" ,type=SCREAMING_SNAKE_CASE_ ,required=SCREAMING_SNAKE_CASE_ ,help="""Path to the HuggingFace Datasets folder.""" )
train_parser.set_defaults(func=SCREAMING_SNAKE_CASE_ )
def __init__( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,*SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
snake_case : int = get_logger("""datasets-cli/converting""" )
snake_case : List[str] = tfds_path
snake_case : List[Any] = datasets_directory
def snake_case_ ( self ):
'''simple docstring'''
if os.path.isdir(self._tfds_path ):
snake_case : Tuple = os.path.abspath(self._tfds_path )
elif os.path.isfile(self._tfds_path ):
snake_case : Dict = os.path.dirname(self._tfds_path )
else:
raise ValueError("""--tfds_path is neither a directory nor a file. Please check path.""" )
snake_case : Tuple = os.path.abspath(self._datasets_directory )
self._logger.info(F"""Converting datasets from {abs_tfds_path} to {abs_datasets_path}""" )
snake_case : Union[str, Any] = []
snake_case : Optional[int] = []
snake_case : List[str] = {}
if os.path.isdir(self._tfds_path ):
snake_case : int = os.listdir(SCREAMING_SNAKE_CASE_ )
else:
snake_case : List[Any] = [os.path.basename(self._tfds_path )]
for f_name in file_names:
self._logger.info(F"""Looking at file {f_name}""" )
snake_case : List[str] = os.path.join(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )
snake_case : Optional[int] = os.path.join(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )
if not os.path.isfile(SCREAMING_SNAKE_CASE_ ) or "__init__" in f_name or "_test" in f_name or ".py" not in f_name:
self._logger.info("""Skipping file""" )
continue
with open(SCREAMING_SNAKE_CASE_ ,encoding="""utf-8""" ) as f:
snake_case : str = f.readlines()
snake_case : List[str] = []
snake_case : List[str] = False
snake_case : Union[str, Any] = False
snake_case : Optional[int] = []
for line in lines:
snake_case : Union[str, Any] = line
# Convert imports
if "import tensorflow.compat.v2 as tf" in out_line:
continue
elif "@tfds.core" in out_line:
continue
elif "builder=self" in out_line:
continue
elif "import tensorflow_datasets.public_api as tfds" in out_line:
snake_case : str = """import datasets\n"""
elif "import tensorflow" in out_line:
# order is important here
snake_case : Dict = """"""
continue
elif "from absl import logging" in out_line:
snake_case : Any = """from datasets import logging\n"""
elif "getLogger" in out_line:
snake_case : List[str] = out_line.replace("""getLogger""" ,"""get_logger""" )
elif any(expression in out_line for expression in TO_HIGHLIGHT ):
snake_case : int = True
snake_case : List[str] = list(filter(lambda SCREAMING_SNAKE_CASE_ : e in out_line ,SCREAMING_SNAKE_CASE_ ) )
out_lines.append(HIGHLIGHT_MESSAGE_PRE + str(SCREAMING_SNAKE_CASE_ ) + """\n""" )
out_lines.append(SCREAMING_SNAKE_CASE_ )
out_lines.append(SCREAMING_SNAKE_CASE_ )
continue
else:
for pattern, replacement in TO_CONVERT:
snake_case : List[str] = re.sub(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )
# Take care of saving utilities (to later move them together with main script)
if "tensorflow_datasets" in out_line:
snake_case : str = re.match(R"""from\stensorflow_datasets.*import\s([^\.\r\n]+)""" ,SCREAMING_SNAKE_CASE_ )
tfds_imports.extend(imp.strip() for imp in match.group(1 ).split(""",""" ) )
snake_case : List[str] = """from . import """ + match.group(1 )
# Check we have not forget anything
if "tf." in out_line or "tfds." in out_line or "tensorflow_datasets" in out_line:
raise ValueError(F"""Error converting {out_line.strip()}""" )
if "GeneratorBasedBuilder" in out_line or "BeamBasedBuilder" in out_line:
snake_case : str = True
out_lines.append(SCREAMING_SNAKE_CASE_ )
if is_builder or "wmt" in f_name:
# We create a new directory for each dataset
snake_case : List[Any] = f_name.replace(""".py""" ,"""""" )
snake_case : Optional[int] = os.path.join(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )
snake_case : str = os.path.join(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )
os.makedirs(SCREAMING_SNAKE_CASE_ ,exist_ok=SCREAMING_SNAKE_CASE_ )
self._logger.info(F"""Adding directory {output_dir}""" )
imports_to_builder_map.update({imp: output_dir for imp in tfds_imports} )
else:
# Utilities will be moved at the end
utils_files.append(SCREAMING_SNAKE_CASE_ )
if needs_manual_update:
with_manual_update.append(SCREAMING_SNAKE_CASE_ )
with open(SCREAMING_SNAKE_CASE_ ,"""w""" ,encoding="""utf-8""" ) as f:
f.writelines(SCREAMING_SNAKE_CASE_ )
self._logger.info(F"""Converted in {output_file}""" )
for utils_file in utils_files:
try:
snake_case : int = os.path.basename(SCREAMING_SNAKE_CASE_ )
snake_case : List[Any] = imports_to_builder_map[f_name.replace(""".py""" ,"""""" )]
self._logger.info(F"""Moving {dest_folder} to {utils_file}""" )
shutil.copy(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )
except KeyError:
self._logger.error(F"""Cannot find destination folder for {utils_file}. Please copy manually.""" )
if with_manual_update:
for file_path in with_manual_update:
self._logger.warning(
F"""You need to manually update file {file_path} to remove configurations using 'TextEncoderConfig'.""" )
| 36 |
import fire
from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer
def lowercase ( __A : str , __A : str , **__A : Optional[int] ) -> Optional[Any]:
'''simple docstring'''
snake_case : int = AutoConfig.from_pretrained(__A , **__A )
snake_case : Tuple = AutoModelForSeqaSeqLM.from_config(__A )
model.save_pretrained(__A )
AutoTokenizer.from_pretrained(__A ).save_pretrained(__A )
return model
if __name__ == "__main__":
fire.Fire(save_randomly_initialized_version)
| 36 | 1 |
def lowercase ( __A : Optional[int] , __A : Optional[Any] , __A : Tuple=False ) -> List[str]:
'''simple docstring'''
if isinstance(__A , __A ) and isinstance(__A , __A ):
snake_case : List[Any] = len(set_a.intersection(__A ) )
if alternative_union:
snake_case : Any = len(__A ) + len(__A )
else:
snake_case : List[str] = len(set_a.union(__A ) )
return intersection / union
if isinstance(__A , (list, tuple) ) and isinstance(__A , (list, tuple) ):
snake_case : int = [element for element in set_a if element in set_b]
if alternative_union:
snake_case : Any = len(__A ) + len(__A )
return len(__A ) / union
else:
snake_case : Any = set_a + [element for element in set_b if element not in set_a]
return len(__A ) / len(__A )
return len(__A ) / len(__A )
return None
if __name__ == "__main__":
__lowercase : str = {'''a''', '''b''', '''c''', '''d''', '''e'''}
__lowercase : List[str] = {'''c''', '''d''', '''e''', '''f''', '''h''', '''i'''}
print(jaccard_similarity(set_a, set_b))
| 36 |
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
__lowercase : Any = logging.get_logger(__name__)
__lowercase : str = {
'''google/mobilenet_v1_1.0_224''': '''https://huggingface.co/google/mobilenet_v1_1.0_224/resolve/main/config.json''',
'''google/mobilenet_v1_0.75_192''': '''https://huggingface.co/google/mobilenet_v1_0.75_192/resolve/main/config.json''',
# See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
}
class _A ( snake_case ):
'''simple docstring'''
__lowerCamelCase : Dict = '''mobilenet_v1'''
def __init__( self ,SCREAMING_SNAKE_CASE_=3 ,SCREAMING_SNAKE_CASE_=224 ,SCREAMING_SNAKE_CASE_=1.0 ,SCREAMING_SNAKE_CASE_=8 ,SCREAMING_SNAKE_CASE_="relu6" ,SCREAMING_SNAKE_CASE_=True ,SCREAMING_SNAKE_CASE_=0.9_99 ,SCREAMING_SNAKE_CASE_=0.02 ,SCREAMING_SNAKE_CASE_=0.0_01 ,**SCREAMING_SNAKE_CASE_ ,):
'''simple docstring'''
super().__init__(**SCREAMING_SNAKE_CASE_ )
if depth_multiplier <= 0:
raise ValueError("""depth_multiplier must be greater than zero.""" )
snake_case : List[Any] = num_channels
snake_case : str = image_size
snake_case : List[Any] = depth_multiplier
snake_case : Optional[int] = min_depth
snake_case : Union[str, Any] = hidden_act
snake_case : int = tf_padding
snake_case : Optional[int] = classifier_dropout_prob
snake_case : Tuple = initializer_range
snake_case : List[str] = layer_norm_eps
class _A ( snake_case ):
'''simple docstring'''
__lowerCamelCase : Optional[Any] = version.parse('''1.11''' )
@property
def snake_case_ ( self ):
'''simple docstring'''
return OrderedDict([("""pixel_values""", {0: """batch"""})] )
@property
def snake_case_ ( self ):
'''simple docstring'''
if self.task == "image-classification":
return OrderedDict([("""logits""", {0: """batch"""})] )
else:
return OrderedDict([("""last_hidden_state""", {0: """batch"""}), ("""pooler_output""", {0: """batch"""})] )
@property
def snake_case_ ( self ):
'''simple docstring'''
return 1E-4
| 36 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowercase : str = logging.get_logger(__name__)
__lowercase : Union[str, Any] = {
'''abeja/gpt-neox-japanese-2.7b''': '''https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/config.json''',
}
class _A ( snake_case ):
'''simple docstring'''
__lowerCamelCase : int = '''gpt_neox_japanese'''
def __init__( self ,SCREAMING_SNAKE_CASE_=32000 ,SCREAMING_SNAKE_CASE_=2560 ,SCREAMING_SNAKE_CASE_=32 ,SCREAMING_SNAKE_CASE_=32 ,SCREAMING_SNAKE_CASE_=4 ,SCREAMING_SNAKE_CASE_="gelu" ,SCREAMING_SNAKE_CASE_=1.00 ,SCREAMING_SNAKE_CASE_=10000 ,SCREAMING_SNAKE_CASE_=2048 ,SCREAMING_SNAKE_CASE_=0.02 ,SCREAMING_SNAKE_CASE_=1E-5 ,SCREAMING_SNAKE_CASE_=True ,SCREAMING_SNAKE_CASE_=31996 ,SCREAMING_SNAKE_CASE_=31999 ,SCREAMING_SNAKE_CASE_=0.1 ,SCREAMING_SNAKE_CASE_=0.0 ,**SCREAMING_SNAKE_CASE_ ,):
'''simple docstring'''
super().__init__(bos_token_id=SCREAMING_SNAKE_CASE_ ,eos_token_id=SCREAMING_SNAKE_CASE_ ,**SCREAMING_SNAKE_CASE_ )
snake_case : Optional[Any] = vocab_size
snake_case : Optional[int] = max_position_embeddings
snake_case : List[str] = hidden_size
snake_case : Union[str, Any] = num_hidden_layers
snake_case : Optional[Any] = num_attention_heads
snake_case : Tuple = intermediate_multiple_size
snake_case : Optional[int] = hidden_act
snake_case : List[str] = rotary_pct
snake_case : str = rotary_emb_base
snake_case : Any = initializer_range
snake_case : str = layer_norm_eps
snake_case : Optional[Any] = use_cache
snake_case : Dict = attention_dropout
snake_case : List[str] = hidden_dropout
| 36 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowercase : List[str] = logging.get_logger(__name__)
__lowercase : List[str] = {
'''edbeeching/decision-transformer-gym-hopper-medium''': (
'''https://huggingface.co/edbeeching/decision-transformer-gym-hopper-medium/resolve/main/config.json'''
),
# See all DecisionTransformer models at https://huggingface.co/models?filter=decision_transformer
}
class _A ( snake_case ):
'''simple docstring'''
__lowerCamelCase : int = '''decision_transformer'''
__lowerCamelCase : Optional[Any] = ['''past_key_values''']
__lowerCamelCase : Tuple = {
'''max_position_embeddings''': '''n_positions''',
'''num_attention_heads''': '''n_head''',
'''num_hidden_layers''': '''n_layer''',
}
def __init__( self ,SCREAMING_SNAKE_CASE_=17 ,SCREAMING_SNAKE_CASE_=4 ,SCREAMING_SNAKE_CASE_=128 ,SCREAMING_SNAKE_CASE_=4096 ,SCREAMING_SNAKE_CASE_=True ,SCREAMING_SNAKE_CASE_=1 ,SCREAMING_SNAKE_CASE_=1024 ,SCREAMING_SNAKE_CASE_=3 ,SCREAMING_SNAKE_CASE_=1 ,SCREAMING_SNAKE_CASE_=None ,SCREAMING_SNAKE_CASE_="relu" ,SCREAMING_SNAKE_CASE_=0.1 ,SCREAMING_SNAKE_CASE_=0.1 ,SCREAMING_SNAKE_CASE_=0.1 ,SCREAMING_SNAKE_CASE_=1E-5 ,SCREAMING_SNAKE_CASE_=0.02 ,SCREAMING_SNAKE_CASE_=True ,SCREAMING_SNAKE_CASE_=True ,SCREAMING_SNAKE_CASE_=50256 ,SCREAMING_SNAKE_CASE_=50256 ,SCREAMING_SNAKE_CASE_=False ,SCREAMING_SNAKE_CASE_=False ,**SCREAMING_SNAKE_CASE_ ,):
'''simple docstring'''
snake_case : Any = state_dim
snake_case : Optional[Any] = act_dim
snake_case : Union[str, Any] = hidden_size
snake_case : Any = max_ep_len
snake_case : int = action_tanh
snake_case : Any = vocab_size
snake_case : Any = n_positions
snake_case : List[str] = n_layer
snake_case : int = n_head
snake_case : Optional[int] = n_inner
snake_case : List[Any] = activation_function
snake_case : Tuple = resid_pdrop
snake_case : Optional[Any] = embd_pdrop
snake_case : Dict = attn_pdrop
snake_case : List[str] = layer_norm_epsilon
snake_case : Union[str, Any] = initializer_range
snake_case : Optional[Any] = scale_attn_weights
snake_case : str = use_cache
snake_case : int = scale_attn_by_inverse_layer_idx
snake_case : Tuple = reorder_and_upcast_attn
snake_case : Tuple = bos_token_id
snake_case : List[str] = eos_token_id
super().__init__(bos_token_id=SCREAMING_SNAKE_CASE_ ,eos_token_id=SCREAMING_SNAKE_CASE_ ,**SCREAMING_SNAKE_CASE_ )
| 36 | 1 |
from ..utils import DummyObject, requires_backends
class _A ( metaclass=snake_case ):
'''simple docstring'''
__lowerCamelCase : int = ['''note_seq''']
def __init__( self ,*SCREAMING_SNAKE_CASE_ ,**SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
requires_backends(self ,["""note_seq"""] )
@classmethod
def snake_case_ ( cls ,*SCREAMING_SNAKE_CASE_ ,**SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
requires_backends(cls ,["""note_seq"""] )
@classmethod
def snake_case_ ( cls ,*SCREAMING_SNAKE_CASE_ ,**SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
requires_backends(cls ,["""note_seq"""] )
| 36 |
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
| 36 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__lowercase : List[str] = {'''configuration_vit_mae''': ['''VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTMAEConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowercase : List[str] = [
'''VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''ViTMAEForPreTraining''',
'''ViTMAELayer''',
'''ViTMAEModel''',
'''ViTMAEPreTrainedModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowercase : List[str] = [
'''TFViTMAEForPreTraining''',
'''TFViTMAEModel''',
'''TFViTMAEPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_vit_mae import VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMAEConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_vit_mae import (
VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST,
ViTMAEForPreTraining,
ViTMAELayer,
ViTMAEModel,
ViTMAEPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_vit_mae import TFViTMAEForPreTraining, TFViTMAEModel, TFViTMAEPreTrainedModel
else:
import sys
__lowercase : Optional[int] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 36 |
# Usage:
# ./gen-card-facebook-wmt19.py
import os
from pathlib import Path
def lowercase ( __A : Dict , __A : Union[str, Any] , __A : List[str] ) -> Any:
'''simple docstring'''
snake_case : Tuple = {
"""en""": """Machine learning is great, isn't it?""",
"""ru""": """Машинное обучение - это здорово, не так ли?""",
"""de""": """Maschinelles Lernen ist großartig, oder?""",
}
# BLUE scores as follows:
# "pair": [fairseq, transformers]
snake_case : Optional[Any] = {
"""ru-en""": ["""[41.3](http://matrix.statmt.org/matrix/output/1907?run_id=6937)""", """39.20"""],
"""en-ru""": ["""[36.4](http://matrix.statmt.org/matrix/output/1914?run_id=6724)""", """33.47"""],
"""en-de""": ["""[43.1](http://matrix.statmt.org/matrix/output/1909?run_id=6862)""", """42.83"""],
"""de-en""": ["""[42.3](http://matrix.statmt.org/matrix/output/1902?run_id=6750)""", """41.35"""],
}
snake_case : Optional[int] = f"""{src_lang}-{tgt_lang}"""
snake_case : Any = f"""
---
language:
- {src_lang}
- {tgt_lang}
thumbnail:
tags:
- translation
- wmt19
- facebook
license: apache-2.0
datasets:
- wmt19
metrics:
- bleu
---
# FSMT
## Model description
This is a ported version of [fairseq wmt19 transformer](https://github.com/pytorch/fairseq/blob/master/examples/wmt19/README.md) for {src_lang}-{tgt_lang}.
For more details, please see, [Facebook FAIR's WMT19 News Translation Task Submission](https://arxiv.org/abs/1907.06616).
The abbreviation FSMT stands for FairSeqMachineTranslation
All four models are available:
* [wmt19-en-ru](https://huggingface.co/facebook/wmt19-en-ru)
* [wmt19-ru-en](https://huggingface.co/facebook/wmt19-ru-en)
* [wmt19-en-de](https://huggingface.co/facebook/wmt19-en-de)
* [wmt19-de-en](https://huggingface.co/facebook/wmt19-de-en)
## Intended uses & limitations
#### How to use
```python
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
mname = \"facebook/wmt19-{src_lang}-{tgt_lang}\"
tokenizer = FSMTTokenizer.from_pretrained(mname)
model = FSMTForConditionalGeneration.from_pretrained(mname)
input = \"{texts[src_lang]}\"
input_ids = tokenizer.encode(input, return_tensors=\"pt\")
outputs = model.generate(input_ids)
decoded = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(decoded) # {texts[tgt_lang]}
```
#### Limitations and bias
- The original (and this ported model) doesn't seem to handle well inputs with repeated sub-phrases, [content gets truncated](https://discuss.huggingface.co/t/issues-with-translating-inputs-containing-repeated-phrases/981)
## Training data
Pretrained weights were left identical to the original model released by fairseq. For more details, please, see the [paper](https://arxiv.org/abs/1907.06616).
## Eval results
pair | fairseq | transformers
-------|---------|----------
{pair} | {scores[pair][0]} | {scores[pair][1]}
The score is slightly below the score reported by `fairseq`, since `transformers`` currently doesn't support:
- model ensemble, therefore the best performing checkpoint was ported (``model4.pt``).
- re-ranking
The score was calculated using this code:
```bash
git clone https://github.com/huggingface/transformers
cd transformers
export PAIR={pair}
export DATA_DIR=data/$PAIR
export SAVE_DIR=data/$PAIR
export BS=8
export NUM_BEAMS=15
mkdir -p $DATA_DIR
sacrebleu -t wmt19 -l $PAIR --echo src > $DATA_DIR/val.source
sacrebleu -t wmt19 -l $PAIR --echo ref > $DATA_DIR/val.target
echo $PAIR
PYTHONPATH=\"src:examples/seq2seq\" python examples/seq2seq/run_eval.py facebook/wmt19-$PAIR $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation --num_beams $NUM_BEAMS
```
note: fairseq reports using a beam of 50, so you should get a slightly higher score if re-run with `--num_beams 50`.
## Data Sources
- [training, etc.](http://www.statmt.org/wmt19/)
- [test set](http://matrix.statmt.org/test_sets/newstest2019.tgz?1556572561)
### BibTeX entry and citation info
```bibtex
@inproceedings{{...,
year={{2020}},
title={{Facebook FAIR's WMT19 News Translation Task Submission}},
author={{Ng, Nathan and Yee, Kyra and Baevski, Alexei and Ott, Myle and Auli, Michael and Edunov, Sergey}},
booktitle={{Proc. of WMT}},
}}
```
## TODO
- port model ensemble (fairseq uses 4 model checkpoints)
"""
os.makedirs(__A , exist_ok=__A )
snake_case : Union[str, Any] = os.path.join(__A , """README.md""" )
print(f"""Generating {path}""" )
with open(__A , """w""" , encoding="""utf-8""" ) as f:
f.write(__A )
# make sure we are under the root of the project
__lowercase : int = Path(__file__).resolve().parent.parent.parent
__lowercase : List[str] = repo_dir / '''model_cards'''
for model_name in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
__lowercase , __lowercase , __lowercase : List[str] = model_name.split('''-''')
__lowercase : str = model_cards_dir / '''facebook''' / model_name
write_model_card(model_card_dir, src_lang=src_lang, tgt_lang=tgt_lang)
| 36 | 1 |
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import logging
__lowercase : Any = logging.get_logger(__name__) # pylint: disable=invalid-name
class _A ( snake_case ):
'''simple docstring'''
def __init__( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,):
'''simple docstring'''
super().__init__()
self.register_modules(
vae=SCREAMING_SNAKE_CASE_ ,text_encoder=SCREAMING_SNAKE_CASE_ ,tokenizer=SCREAMING_SNAKE_CASE_ ,unet=SCREAMING_SNAKE_CASE_ ,scheduler=SCREAMING_SNAKE_CASE_ ,safety_checker=SCREAMING_SNAKE_CASE_ ,feature_extractor=SCREAMING_SNAKE_CASE_ ,)
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ = "auto" ):
'''simple docstring'''
if slice_size == "auto":
# half the attention head size is usually a good trade-off between
# speed and memory
snake_case : List[str] = self.unet.config.attention_head_dim // 2
self.unet.set_attention_slice(SCREAMING_SNAKE_CASE_ )
def snake_case_ ( self ):
'''simple docstring'''
self.enable_attention_slicing(SCREAMING_SNAKE_CASE_ )
@torch.no_grad()
def __call__( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ = 512 ,SCREAMING_SNAKE_CASE_ = 512 ,SCREAMING_SNAKE_CASE_ = 50 ,SCREAMING_SNAKE_CASE_ = 7.5 ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_ = 1 ,SCREAMING_SNAKE_CASE_ = 0.0 ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_ = "pil" ,SCREAMING_SNAKE_CASE_ = True ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_ = 1 ,SCREAMING_SNAKE_CASE_ = None ,**SCREAMING_SNAKE_CASE_ ,):
'''simple docstring'''
if isinstance(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ):
snake_case : List[Any] = 1
elif isinstance(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ):
snake_case : Any = len(SCREAMING_SNAKE_CASE_ )
else:
raise ValueError(F"""`prompt` has to be of type `str` or `list` but is {type(SCREAMING_SNAKE_CASE_ )}""" )
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(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ) or callback_steps <= 0)
):
raise ValueError(
F"""`callback_steps` has to be a positive integer but is {callback_steps} of type"""
F""" {type(SCREAMING_SNAKE_CASE_ )}.""" )
# get prompt text embeddings
snake_case : Any = self.tokenizer(
SCREAMING_SNAKE_CASE_ ,padding="""max_length""" ,max_length=self.tokenizer.model_max_length ,return_tensors="""pt""" ,)
snake_case : int = text_inputs.input_ids
if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
snake_case : str = 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}""" )
snake_case : Optional[int] = text_input_ids[:, : self.tokenizer.model_max_length]
if text_embeddings is None:
snake_case : List[Any] = self.text_encoder(text_input_ids.to(self.device ) )[0]
# duplicate text embeddings for each generation per prompt, using mps friendly method
snake_case , snake_case , snake_case : Optional[int] = text_embeddings.shape
snake_case : Any = text_embeddings.repeat(1 ,SCREAMING_SNAKE_CASE_ ,1 )
snake_case : List[str] = text_embeddings.view(bs_embed * num_images_per_prompt ,SCREAMING_SNAKE_CASE_ ,-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.
snake_case : int = guidance_scale > 1.0
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance:
snake_case : List[str]
if negative_prompt is None:
snake_case : int = [""""""]
elif type(SCREAMING_SNAKE_CASE_ ) is not type(SCREAMING_SNAKE_CASE_ ):
raise TypeError(
F"""`negative_prompt` should be the same type to `prompt`, but got {type(SCREAMING_SNAKE_CASE_ )} !="""
F""" {type(SCREAMING_SNAKE_CASE_ )}.""" )
elif isinstance(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ):
snake_case : Dict = [negative_prompt]
elif batch_size != len(SCREAMING_SNAKE_CASE_ ):
raise ValueError(
F"""`negative_prompt`: {negative_prompt} has batch size {len(SCREAMING_SNAKE_CASE_ )}, but `prompt`:"""
F""" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"""
""" the batch size of `prompt`.""" )
else:
snake_case : Optional[Any] = negative_prompt
snake_case : List[str] = text_input_ids.shape[-1]
snake_case : List[str] = self.tokenizer(
SCREAMING_SNAKE_CASE_ ,padding="""max_length""" ,max_length=SCREAMING_SNAKE_CASE_ ,truncation=SCREAMING_SNAKE_CASE_ ,return_tensors="""pt""" ,)
snake_case : str = self.text_encoder(uncond_input.input_ids.to(self.device ) )[0]
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
snake_case : Optional[Any] = uncond_embeddings.shape[1]
snake_case : Union[str, Any] = uncond_embeddings.repeat(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,1 )
snake_case : Any = uncond_embeddings.view(batch_size * num_images_per_prompt ,SCREAMING_SNAKE_CASE_ ,-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
snake_case : List[str] = 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`.
snake_case : Union[str, Any] = (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
snake_case : Tuple = (batch_size * num_images_per_prompt, self.unet.config.in_channels, 64, 64)
snake_case : Optional[int] = text_embeddings.dtype
if latents is None:
if self.device.type == "mps":
# randn does not exist on mps
snake_case : Optional[int] = torch.randn(
SCREAMING_SNAKE_CASE_ ,generator=SCREAMING_SNAKE_CASE_ ,device="""cpu""" ,dtype=SCREAMING_SNAKE_CASE_ ).to(self.device )
snake_case : Any = torch.randn(SCREAMING_SNAKE_CASE_ ,generator=SCREAMING_SNAKE_CASE_ ,device="""cpu""" ,dtype=SCREAMING_SNAKE_CASE_ ).to(
self.device )
else:
snake_case : List[Any] = torch.randn(
SCREAMING_SNAKE_CASE_ ,generator=SCREAMING_SNAKE_CASE_ ,device=self.device ,dtype=SCREAMING_SNAKE_CASE_ )
snake_case : Optional[int] = torch.randn(SCREAMING_SNAKE_CASE_ ,generator=SCREAMING_SNAKE_CASE_ ,device=self.device ,dtype=SCREAMING_SNAKE_CASE_ )
else:
if latents_reference.shape != latents_shape:
raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {latents_shape}""" )
snake_case : str = latents_reference.to(self.device )
snake_case : Optional[int] = latents.to(self.device )
# This is the key part of the pipeline where we
# try to ensure that the generated images w/ the same seed
# but different sizes actually result in similar images
snake_case : Optional[Any] = (latents_shape[3] - latents_shape_reference[3]) // 2
snake_case : List[Any] = (latents_shape[2] - latents_shape_reference[2]) // 2
snake_case : List[Any] = latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
snake_case : Dict = latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
snake_case : List[str] = 0 if dx < 0 else dx
snake_case : Any = 0 if dy < 0 else dy
snake_case : Dict = max(-dx ,0 )
snake_case : Optional[Any] = max(-dy ,0 )
# import pdb
# pdb.set_trace()
snake_case : Optional[Any] = latents_reference[:, :, dy : dy + h, dx : dx + w]
# set timesteps
self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE_ )
# Some schedulers like PNDM have timesteps as arrays
# It's more optimized to move all timesteps to correct device beforehand
snake_case : Union[str, Any] = self.scheduler.timesteps.to(self.device )
# scale the initial noise by the standard deviation required by the scheduler
snake_case : List[str] = 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]
snake_case : List[str] = """eta""" in set(inspect.signature(self.scheduler.step ).parameters.keys() )
snake_case : Optional[Any] = {}
if accepts_eta:
snake_case : int = eta
for i, t in enumerate(self.progress_bar(SCREAMING_SNAKE_CASE_ ) ):
# expand the latents if we are doing classifier free guidance
snake_case : Optional[Any] = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
snake_case : List[str] = self.scheduler.scale_model_input(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )
# predict the noise residual
snake_case : str = self.unet(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,encoder_hidden_states=SCREAMING_SNAKE_CASE_ ).sample
# perform guidance
if do_classifier_free_guidance:
snake_case , snake_case : str = noise_pred.chunk(2 )
snake_case : List[Any] = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t-1
snake_case : int = self.scheduler.step(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,**SCREAMING_SNAKE_CASE_ ).prev_sample
# call the callback, if provided
if callback is not None and i % callback_steps == 0:
callback(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )
snake_case : Optional[Any] = 1 / 0.1_82_15 * latents
snake_case : Any = self.vae.decode(SCREAMING_SNAKE_CASE_ ).sample
snake_case : str = (image / 2 + 0.5).clamp(0 ,1 )
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
snake_case : Optional[int] = image.cpu().permute(0 ,2 ,3 ,1 ).float().numpy()
if self.safety_checker is not None:
snake_case : Tuple = self.feature_extractor(self.numpy_to_pil(SCREAMING_SNAKE_CASE_ ) ,return_tensors="""pt""" ).to(
self.device )
snake_case , snake_case : Optional[Any] = self.safety_checker(
images=SCREAMING_SNAKE_CASE_ ,clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype ) )
else:
snake_case : List[str] = None
if output_type == "pil":
snake_case : Tuple = self.numpy_to_pil(SCREAMING_SNAKE_CASE_ )
if not return_dict:
return (image, has_nsfw_concept)
return StableDiffusionPipelineOutput(images=SCREAMING_SNAKE_CASE_ ,nsfw_content_detected=SCREAMING_SNAKE_CASE_ )
| 36 |
__lowercase : List[str] = '''
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
'''
__lowercase : str = [{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
__lowercase : List[str] = {
'''{processor_class}''': '''FakeProcessorClass''',
'''{model_class}''': '''FakeModelClass''',
'''{object_class}''': '''FakeObjectClass''',
}
| 36 | 1 |
from __future__ import annotations
import math
import random
from typing import Any
class _A :
'''simple docstring'''
def __init__( self ):
'''simple docstring'''
snake_case : list[Any] = []
snake_case : int = 0
snake_case : int = 0
def snake_case_ ( self ):
'''simple docstring'''
return self.head == self.tail
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
self.data.append(SCREAMING_SNAKE_CASE_ )
snake_case : int = self.tail + 1
def snake_case_ ( self ):
'''simple docstring'''
snake_case : Any = self.data[self.head]
snake_case : int = self.head + 1
return ret
def snake_case_ ( self ):
'''simple docstring'''
return self.tail - self.head
def snake_case_ ( self ):
'''simple docstring'''
print(self.data )
print("""**************""" )
print(self.data[self.head : self.tail] )
class _A :
'''simple docstring'''
def __init__( self ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
snake_case : Union[str, Any] = data
snake_case : MyNode | None = None
snake_case : MyNode | None = None
snake_case : int = 1
def snake_case_ ( self ):
'''simple docstring'''
return self.data
def snake_case_ ( self ):
'''simple docstring'''
return self.left
def snake_case_ ( self ):
'''simple docstring'''
return self.right
def snake_case_ ( self ):
'''simple docstring'''
return self.height
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
snake_case : List[str] = data
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
snake_case : List[Any] = node
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
snake_case : List[Any] = node
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
snake_case : Dict = height
def lowercase ( __A : MyNode | None ) -> int:
'''simple docstring'''
if node is None:
return 0
return node.get_height()
def lowercase ( __A : int , __A : int ) -> int:
'''simple docstring'''
if a > b:
return a
return b
def lowercase ( __A : MyNode ) -> MyNode:
'''simple docstring'''
print("""left rotation node:""" , node.get_data() )
snake_case : Optional[int] = node.get_left()
assert ret is not None
node.set_left(ret.get_right() )
ret.set_right(__A )
snake_case : List[str] = my_max(get_height(node.get_right() ) , get_height(node.get_left() ) ) + 1
node.set_height(__A )
snake_case : Dict = my_max(get_height(ret.get_right() ) , get_height(ret.get_left() ) ) + 1
ret.set_height(__A )
return ret
def lowercase ( __A : MyNode ) -> MyNode:
'''simple docstring'''
print("""right rotation node:""" , node.get_data() )
snake_case : Tuple = node.get_right()
assert ret is not None
node.set_right(ret.get_left() )
ret.set_left(__A )
snake_case : str = my_max(get_height(node.get_right() ) , get_height(node.get_left() ) ) + 1
node.set_height(__A )
snake_case : str = my_max(get_height(ret.get_right() ) , get_height(ret.get_left() ) ) + 1
ret.set_height(__A )
return ret
def lowercase ( __A : MyNode ) -> MyNode:
'''simple docstring'''
snake_case : Optional[Any] = node.get_left()
assert left_child is not None
node.set_left(left_rotation(__A ) )
return right_rotation(__A )
def lowercase ( __A : MyNode ) -> MyNode:
'''simple docstring'''
snake_case : Dict = node.get_right()
assert right_child is not None
node.set_right(right_rotation(__A ) )
return left_rotation(__A )
def lowercase ( __A : MyNode | None , __A : Any ) -> MyNode | None:
'''simple docstring'''
if node is None:
return MyNode(__A )
if data < node.get_data():
node.set_left(insert_node(node.get_left() , __A ) )
if (
get_height(node.get_left() ) - get_height(node.get_right() ) == 2
): # an unbalance detected
snake_case : Any = node.get_left()
assert left_child is not None
if (
data < left_child.get_data()
): # new node is the left child of the left child
snake_case : Optional[Any] = right_rotation(__A )
else:
snake_case : List[str] = lr_rotation(__A )
else:
node.set_right(insert_node(node.get_right() , __A ) )
if get_height(node.get_right() ) - get_height(node.get_left() ) == 2:
snake_case : Tuple = node.get_right()
assert right_child is not None
if data < right_child.get_data():
snake_case : int = rl_rotation(__A )
else:
snake_case : Tuple = left_rotation(__A )
snake_case : str = my_max(get_height(node.get_right() ) , get_height(node.get_left() ) ) + 1
node.set_height(__A )
return node
def lowercase ( __A : MyNode ) -> Any:
'''simple docstring'''
while True:
snake_case : Dict = root.get_right()
if right_child is None:
break
snake_case : int = right_child
return root.get_data()
def lowercase ( __A : MyNode ) -> Any:
'''simple docstring'''
while True:
snake_case : Dict = root.get_left()
if left_child is None:
break
snake_case : Optional[Any] = left_child
return root.get_data()
def lowercase ( __A : MyNode , __A : Any ) -> MyNode | None:
'''simple docstring'''
snake_case : Union[str, Any] = root.get_left()
snake_case : Optional[Any] = root.get_right()
if root.get_data() == data:
if left_child is not None and right_child is not None:
snake_case : List[Any] = get_left_most(__A )
root.set_data(__A )
root.set_right(del_node(__A , __A ) )
elif left_child is not None:
snake_case : int = left_child
elif right_child is not None:
snake_case : Union[str, Any] = right_child
else:
return None
elif root.get_data() > data:
if left_child is None:
print("""No such data""" )
return root
else:
root.set_left(del_node(__A , __A ) )
else: # root.get_data() < data
if right_child is None:
return root
else:
root.set_right(del_node(__A , __A ) )
if get_height(__A ) - get_height(__A ) == 2:
assert right_child is not None
if get_height(right_child.get_right() ) > get_height(right_child.get_left() ):
snake_case : List[str] = left_rotation(__A )
else:
snake_case : Optional[int] = rl_rotation(__A )
elif get_height(__A ) - get_height(__A ) == -2:
assert left_child is not None
if get_height(left_child.get_left() ) > get_height(left_child.get_right() ):
snake_case : Any = right_rotation(__A )
else:
snake_case : str = lr_rotation(__A )
snake_case : Dict = my_max(get_height(root.get_right() ) , get_height(root.get_left() ) ) + 1
root.set_height(__A )
return root
class _A :
'''simple docstring'''
def __init__( self ):
'''simple docstring'''
snake_case : MyNode | None = None
def snake_case_ ( self ):
'''simple docstring'''
return get_height(self.root )
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
print("""insert:""" + str(SCREAMING_SNAKE_CASE_ ) )
snake_case : int = insert_node(self.root ,SCREAMING_SNAKE_CASE_ )
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
print("""delete:""" + str(SCREAMING_SNAKE_CASE_ ) )
if self.root is None:
print("""Tree is empty!""" )
return
snake_case : List[Any] = del_node(self.root ,SCREAMING_SNAKE_CASE_ )
def __str__( self ,): # a level traversale, gives a more intuitive look on the tree
'''simple docstring'''
snake_case : Optional[Any] = """"""
snake_case : Union[str, Any] = MyQueue()
q.push(self.root )
snake_case : Optional[int] = self.get_height()
if layer == 0:
return output
snake_case : List[str] = 0
while not q.is_empty():
snake_case : Dict = q.pop()
snake_case : Dict = """ """ * int(math.pow(2 ,layer - 1 ) )
output += space
if node is None:
output += "*"
q.push(SCREAMING_SNAKE_CASE_ )
q.push(SCREAMING_SNAKE_CASE_ )
else:
output += str(node.get_data() )
q.push(node.get_left() )
q.push(node.get_right() )
output += space
snake_case : Tuple = cnt + 1
for i in range(100 ):
if cnt == math.pow(2 ,SCREAMING_SNAKE_CASE_ ) - 1:
snake_case : int = layer - 1
if layer == 0:
output += "\n*************************************"
return output
output += "\n"
break
output += "\n*************************************"
return output
def lowercase ( ) -> None:
'''simple docstring'''
import doctest
doctest.testmod()
if __name__ == "__main__":
_test()
__lowercase : str = AVLtree()
__lowercase : Union[str, Any] = list(range(10))
random.shuffle(lst)
for i in lst:
t.insert(i)
print(str(t))
random.shuffle(lst)
for i in lst:
t.del_node(i)
print(str(t))
| 36 |
import warnings
from ..trainer import Trainer
from ..utils import logging
__lowercase : str = logging.get_logger(__name__)
class _A ( snake_case ):
'''simple docstring'''
def __init__( self ,SCREAMING_SNAKE_CASE_=None ,**SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
warnings.warn(
"""`SageMakerTrainer` is deprecated and will be removed in v5 of Transformers. You can use `Trainer` """
"""instead.""" ,SCREAMING_SNAKE_CASE_ ,)
super().__init__(args=SCREAMING_SNAKE_CASE_ ,**SCREAMING_SNAKE_CASE_ )
| 36 | 1 |
def lowercase ( __A : list ) -> list:
'''simple docstring'''
if len(__A ) <= 1:
return lst
snake_case : List[Any] = 1
while i < len(__A ):
if lst[i - 1] <= lst[i]:
i += 1
else:
snake_case , snake_case : Tuple = lst[i], lst[i - 1]
i -= 1
if i == 0:
snake_case : int = 1
return lst
if __name__ == "__main__":
__lowercase : Dict = input('''Enter numbers separated by a comma:\n''').strip()
__lowercase : Dict = [int(item) for item in user_input.split(''',''')]
print(gnome_sort(unsorted))
| 36 |
from pathlib import Path
from typing import List
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
__lowercase : List[str] = ['''text''', '''image''', '''audio''']
def lowercase ( __A : List[str] ) -> List[str]:
'''simple docstring'''
snake_case : Optional[int] = []
for input_type in input_types:
if input_type == "text":
inputs.append("""Text input""" )
elif input_type == "image":
inputs.append(
Image.open(Path(get_tests_dir("""fixtures/tests_samples/COCO""" ) ) / """000000039769.png""" ).resize((512, 512) ) )
elif input_type == "audio":
inputs.append(torch.ones(3000 ) )
elif isinstance(__A , __A ):
inputs.append(create_inputs(__A ) )
else:
raise ValueError(f"""Invalid type requested: {input_type}""" )
return inputs
def lowercase ( __A : List ) -> Union[str, Any]:
'''simple docstring'''
snake_case : Dict = []
for output in outputs:
if isinstance(__A , (str, AgentText) ):
output_types.append("""text""" )
elif isinstance(__A , (Image.Image, AgentImage) ):
output_types.append("""image""" )
elif isinstance(__A , (torch.Tensor, AgentAudio) ):
output_types.append("""audio""" )
else:
raise ValueError(f"""Invalid output: {output}""" )
return output_types
@is_tool_test
class _A :
'''simple docstring'''
def snake_case_ ( self ):
'''simple docstring'''
self.assertTrue(hasattr(self.tool ,"""inputs""" ) )
self.assertTrue(hasattr(self.tool ,"""outputs""" ) )
snake_case : Dict = self.tool.inputs
for _input in inputs:
if isinstance(_input ,SCREAMING_SNAKE_CASE_ ):
for __input in _input:
self.assertTrue(__input in authorized_types )
else:
self.assertTrue(_input in authorized_types )
snake_case : int = self.tool.outputs
for _output in outputs:
self.assertTrue(_output in authorized_types )
def snake_case_ ( self ):
'''simple docstring'''
snake_case : List[str] = create_inputs(self.tool.inputs )
snake_case : str = self.tool(*SCREAMING_SNAKE_CASE_ )
# There is a single output
if len(self.tool.outputs ) == 1:
snake_case : Union[str, Any] = [outputs]
self.assertListEqual(output_types(SCREAMING_SNAKE_CASE_ ) ,self.tool.outputs )
def snake_case_ ( self ):
'''simple docstring'''
self.assertTrue(hasattr(self.tool ,"""description""" ) )
self.assertTrue(hasattr(self.tool ,"""default_checkpoint""" ) )
self.assertTrue(self.tool.description.startswith("""This is a tool that""" ) )
def snake_case_ ( self ):
'''simple docstring'''
snake_case : List[Any] = create_inputs(self.tool.inputs )
snake_case : int = self.tool(*SCREAMING_SNAKE_CASE_ )
if not isinstance(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ):
snake_case : Any = [outputs]
self.assertEqual(len(SCREAMING_SNAKE_CASE_ ) ,len(self.tool.outputs ) )
for output, output_type in zip(SCREAMING_SNAKE_CASE_ ,self.tool.outputs ):
snake_case : List[str] = AGENT_TYPE_MAPPING[output_type]
self.assertTrue(isinstance(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ) )
def snake_case_ ( self ):
'''simple docstring'''
snake_case : Tuple = create_inputs(self.tool.inputs )
snake_case : Any = []
for _input, input_type in zip(SCREAMING_SNAKE_CASE_ ,self.tool.inputs ):
if isinstance(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ):
_inputs.append([AGENT_TYPE_MAPPING[_input_type](_input ) for _input_type in input_type] )
else:
_inputs.append(AGENT_TYPE_MAPPING[input_type](_input ) )
# Should not raise an error
snake_case : Tuple = self.tool(*SCREAMING_SNAKE_CASE_ )
if not isinstance(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ):
snake_case : Union[str, Any] = [outputs]
self.assertEqual(len(SCREAMING_SNAKE_CASE_ ) ,len(self.tool.outputs ) )
| 36 | 1 |
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device
__lowercase : str = False
class _A ( unittest.TestCase ):
'''simple docstring'''
pass
@slow
@require_torch_gpu
class _A ( unittest.TestCase ):
'''simple docstring'''
def snake_case_ ( self ):
'''simple docstring'''
snake_case : List[Any] = VersatileDiffusionImageVariationPipeline.from_pretrained("""shi-labs/versatile-diffusion""" )
pipe.to(SCREAMING_SNAKE_CASE_ )
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
snake_case : List[str] = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg""" )
snake_case : int = torch.manual_seed(0 )
snake_case : List[Any] = pipe(
image=SCREAMING_SNAKE_CASE_ ,generator=SCREAMING_SNAKE_CASE_ ,guidance_scale=7.5 ,num_inference_steps=50 ,output_type="""numpy""" ,).images
snake_case : Optional[Any] = image[0, 253:256, 253:256, -1]
assert image.shape == (1, 512, 512, 3)
snake_case : List[str] = np.array([0.04_41, 0.04_69, 0.05_07, 0.05_75, 0.06_32, 0.06_50, 0.08_65, 0.09_09, 0.09_45] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
| 36 |
import os
import pytest
from datasets import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
)
__lowercase : Optional[Any] = pytest.mark.integration
@pytest.mark.parametrize("""path""" , ["""paws""", """csv"""] )
def lowercase ( __A : Optional[Any] , __A : Optional[Any] ) -> str:
'''simple docstring'''
inspect_dataset(__A , __A )
snake_case : List[str] = path + """.py"""
assert script_name in os.listdir(__A )
assert "__pycache__" not in os.listdir(__A )
@pytest.mark.filterwarnings("""ignore:inspect_metric is deprecated:FutureWarning""" )
@pytest.mark.filterwarnings("""ignore:metric_module_factory is deprecated:FutureWarning""" )
@pytest.mark.parametrize("""path""" , ["""accuracy"""] )
def lowercase ( __A : Optional[int] , __A : Any ) -> Optional[Any]:
'''simple docstring'''
inspect_metric(__A , __A )
snake_case : Any = path + """.py"""
assert script_name in os.listdir(__A )
assert "__pycache__" not in os.listdir(__A )
@pytest.mark.parametrize(
"""path, config_name, expected_splits""" , [
("""squad""", """plain_text""", ["""train""", """validation"""]),
("""dalle-mini/wit""", """dalle-mini--wit""", ["""train"""]),
("""paws""", """labeled_final""", ["""train""", """test""", """validation"""]),
] , )
def lowercase ( __A : Tuple , __A : Dict , __A : Any ) -> Optional[int]:
'''simple docstring'''
snake_case : List[str] = get_dataset_config_info(__A , config_name=__A )
assert info.config_name == config_name
assert list(info.splits.keys() ) == expected_splits
@pytest.mark.parametrize(
"""path, config_name, expected_exception""" , [
("""paws""", None, ValueError),
] , )
def lowercase ( __A : Tuple , __A : Any , __A : List[str] ) -> Optional[int]:
'''simple docstring'''
with pytest.raises(__A ):
get_dataset_config_info(__A , config_name=__A )
@pytest.mark.parametrize(
"""path, expected""" , [
("""squad""", """plain_text"""),
("""acronym_identification""", """default"""),
("""lhoestq/squad""", """plain_text"""),
("""lhoestq/test""", """default"""),
("""lhoestq/demo1""", """lhoestq--demo1"""),
("""dalle-mini/wit""", """dalle-mini--wit"""),
] , )
def lowercase ( __A : Any , __A : Dict ) -> Dict:
'''simple docstring'''
snake_case : int = get_dataset_config_names(__A )
assert expected in config_names
@pytest.mark.parametrize(
"""path, expected_configs, expected_splits_in_first_config""" , [
("""squad""", ["""plain_text"""], ["""train""", """validation"""]),
("""dalle-mini/wit""", ["""dalle-mini--wit"""], ["""train"""]),
("""paws""", ["""labeled_final""", """labeled_swap""", """unlabeled_final"""], ["""train""", """test""", """validation"""]),
] , )
def lowercase ( __A : Optional[Any] , __A : Dict , __A : List[str] ) -> Union[str, Any]:
'''simple docstring'''
snake_case : List[Any] = get_dataset_infos(__A )
assert list(infos.keys() ) == expected_configs
snake_case : Any = expected_configs[0]
assert expected_config in infos
snake_case : Any = infos[expected_config]
assert info.config_name == expected_config
assert list(info.splits.keys() ) == expected_splits_in_first_config
@pytest.mark.parametrize(
"""path, expected_config, expected_splits""" , [
("""squad""", """plain_text""", ["""train""", """validation"""]),
("""dalle-mini/wit""", """dalle-mini--wit""", ["""train"""]),
("""paws""", """labeled_final""", ["""train""", """test""", """validation"""]),
] , )
def lowercase ( __A : Optional[int] , __A : Tuple , __A : Union[str, Any] ) -> Optional[int]:
'''simple docstring'''
snake_case : Dict = get_dataset_infos(__A )
assert expected_config in infos
snake_case : str = infos[expected_config]
assert info.config_name == expected_config
assert list(info.splits.keys() ) == expected_splits
@pytest.mark.parametrize(
"""path, config_name, expected_exception""" , [
("""paws""", None, ValueError),
] , )
def lowercase ( __A : Optional[int] , __A : Any , __A : Dict ) -> int:
'''simple docstring'''
with pytest.raises(__A ):
get_dataset_split_names(__A , config_name=__A )
| 36 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__lowercase : Dict = logging.get_logger(__name__)
__lowercase : Tuple = {
'''shi-labs/nat-mini-in1k-224''': '''https://huggingface.co/shi-labs/nat-mini-in1k-224/resolve/main/config.json''',
# See all Nat models at https://huggingface.co/models?filter=nat
}
class _A ( snake_case , snake_case ):
'''simple docstring'''
__lowerCamelCase : List[str] = '''nat'''
__lowerCamelCase : List[Any] = {
'''num_attention_heads''': '''num_heads''',
'''num_hidden_layers''': '''num_layers''',
}
def __init__( self ,SCREAMING_SNAKE_CASE_=4 ,SCREAMING_SNAKE_CASE_=3 ,SCREAMING_SNAKE_CASE_=64 ,SCREAMING_SNAKE_CASE_=[3, 4, 6, 5] ,SCREAMING_SNAKE_CASE_=[2, 4, 8, 16] ,SCREAMING_SNAKE_CASE_=7 ,SCREAMING_SNAKE_CASE_=3.0 ,SCREAMING_SNAKE_CASE_=True ,SCREAMING_SNAKE_CASE_=0.0 ,SCREAMING_SNAKE_CASE_=0.0 ,SCREAMING_SNAKE_CASE_=0.1 ,SCREAMING_SNAKE_CASE_="gelu" ,SCREAMING_SNAKE_CASE_=0.02 ,SCREAMING_SNAKE_CASE_=1E-5 ,SCREAMING_SNAKE_CASE_=0.0 ,SCREAMING_SNAKE_CASE_=None ,SCREAMING_SNAKE_CASE_=None ,**SCREAMING_SNAKE_CASE_ ,):
'''simple docstring'''
super().__init__(**SCREAMING_SNAKE_CASE_ )
snake_case : Optional[Any] = patch_size
snake_case : Union[str, Any] = num_channels
snake_case : Dict = embed_dim
snake_case : List[str] = depths
snake_case : int = len(SCREAMING_SNAKE_CASE_ )
snake_case : Optional[Any] = num_heads
snake_case : str = kernel_size
snake_case : Dict = mlp_ratio
snake_case : Any = qkv_bias
snake_case : str = hidden_dropout_prob
snake_case : str = attention_probs_dropout_prob
snake_case : str = drop_path_rate
snake_case : Any = hidden_act
snake_case : Tuple = layer_norm_eps
snake_case : List[Any] = initializer_range
# we set the hidden_size attribute in order to make Nat work with VisionEncoderDecoderModel
# this indicates the channel dimension after the last stage of the model
snake_case : List[Any] = int(embed_dim * 2 ** (len(SCREAMING_SNAKE_CASE_ ) - 1) )
snake_case : Any = layer_scale_init_value
snake_case : int = ["""stem"""] + [F"""stage{idx}""" for idx in range(1 ,len(SCREAMING_SNAKE_CASE_ ) + 1 )]
snake_case , snake_case : Union[str, Any] = get_aligned_output_features_output_indices(
out_features=SCREAMING_SNAKE_CASE_ ,out_indices=SCREAMING_SNAKE_CASE_ ,stage_names=self.stage_names )
| 36 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
__lowercase : Optional[Any] = {
'''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/config.json''',
'''albert-large-v1''': '''https://huggingface.co/albert-large-v1/resolve/main/config.json''',
'''albert-xlarge-v1''': '''https://huggingface.co/albert-xlarge-v1/resolve/main/config.json''',
'''albert-xxlarge-v1''': '''https://huggingface.co/albert-xxlarge-v1/resolve/main/config.json''',
'''albert-base-v2''': '''https://huggingface.co/albert-base-v2/resolve/main/config.json''',
'''albert-large-v2''': '''https://huggingface.co/albert-large-v2/resolve/main/config.json''',
'''albert-xlarge-v2''': '''https://huggingface.co/albert-xlarge-v2/resolve/main/config.json''',
'''albert-xxlarge-v2''': '''https://huggingface.co/albert-xxlarge-v2/resolve/main/config.json''',
}
class _A ( snake_case ):
'''simple docstring'''
__lowerCamelCase : int = '''albert'''
def __init__( self ,SCREAMING_SNAKE_CASE_=30000 ,SCREAMING_SNAKE_CASE_=128 ,SCREAMING_SNAKE_CASE_=4096 ,SCREAMING_SNAKE_CASE_=12 ,SCREAMING_SNAKE_CASE_=1 ,SCREAMING_SNAKE_CASE_=64 ,SCREAMING_SNAKE_CASE_=16384 ,SCREAMING_SNAKE_CASE_=1 ,SCREAMING_SNAKE_CASE_="gelu_new" ,SCREAMING_SNAKE_CASE_=0 ,SCREAMING_SNAKE_CASE_=0 ,SCREAMING_SNAKE_CASE_=512 ,SCREAMING_SNAKE_CASE_=2 ,SCREAMING_SNAKE_CASE_=0.02 ,SCREAMING_SNAKE_CASE_=1E-12 ,SCREAMING_SNAKE_CASE_=0.1 ,SCREAMING_SNAKE_CASE_="absolute" ,SCREAMING_SNAKE_CASE_=0 ,SCREAMING_SNAKE_CASE_=2 ,SCREAMING_SNAKE_CASE_=3 ,**SCREAMING_SNAKE_CASE_ ,):
'''simple docstring'''
super().__init__(pad_token_id=SCREAMING_SNAKE_CASE_ ,bos_token_id=SCREAMING_SNAKE_CASE_ ,eos_token_id=SCREAMING_SNAKE_CASE_ ,**SCREAMING_SNAKE_CASE_ )
snake_case : List[str] = vocab_size
snake_case : int = embedding_size
snake_case : int = hidden_size
snake_case : List[Any] = num_hidden_layers
snake_case : int = num_hidden_groups
snake_case : List[str] = num_attention_heads
snake_case : List[str] = inner_group_num
snake_case : Any = hidden_act
snake_case : Any = intermediate_size
snake_case : Union[str, Any] = hidden_dropout_prob
snake_case : List[Any] = attention_probs_dropout_prob
snake_case : Tuple = max_position_embeddings
snake_case : Any = type_vocab_size
snake_case : Optional[Any] = initializer_range
snake_case : int = layer_norm_eps
snake_case : Optional[int] = classifier_dropout_prob
snake_case : str = position_embedding_type
class _A ( snake_case ):
'''simple docstring'''
@property
def snake_case_ ( self ):
'''simple docstring'''
if self.task == "multiple-choice":
snake_case : List[Any] = {0: """batch""", 1: """choice""", 2: """sequence"""}
else:
snake_case : int = {0: """batch""", 1: """sequence"""}
return OrderedDict(
[
("""input_ids""", dynamic_axis),
("""attention_mask""", dynamic_axis),
("""token_type_ids""", dynamic_axis),
] )
| 36 | 1 |
import fire
from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer
def lowercase ( __A : str , __A : str , **__A : Optional[int] ) -> Optional[Any]:
'''simple docstring'''
snake_case : int = AutoConfig.from_pretrained(__A , **__A )
snake_case : Tuple = AutoModelForSeqaSeqLM.from_config(__A )
model.save_pretrained(__A )
AutoTokenizer.from_pretrained(__A ).save_pretrained(__A )
return model
if __name__ == "__main__":
fire.Fire(save_randomly_initialized_version)
| 36 |
from __future__ import annotations
def lowercase ( __A : list ) -> float:
'''simple docstring'''
if not nums:
raise ValueError("""List is empty""" )
return sum(__A ) / len(__A )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 36 | 1 |
import os
import pytest
from transformers.dynamic_module_utils import get_imports
__lowercase : int = '''
import os
'''
__lowercase : Optional[Any] = '''
def foo():
import os
return False
'''
__lowercase : Optional[int] = '''
def foo():
def bar():
if True:
import os
return False
return bar()
'''
__lowercase : List[str] = '''
import os
try:
import bar
except ImportError:
raise ValueError()
'''
__lowercase : Any = '''
import os
def foo():
try:
import bar
except ImportError:
raise ValueError()
'''
__lowercase : str = '''
import os
try:
import bar
except (ImportError, AttributeError):
raise ValueError()
'''
__lowercase : Optional[int] = '''
import os
try:
import bar
except ImportError as e:
raise ValueError()
'''
__lowercase : Optional[int] = '''
import os
try:
import bar
except:
raise ValueError()
'''
__lowercase : List[Any] = '''
import os
try:
import bar
import baz
except ImportError:
raise ValueError()
'''
__lowercase : Optional[Any] = '''
import os
try:
import bar
import baz
except ImportError:
x = 1
raise ValueError()
'''
__lowercase : Tuple = [
TOP_LEVEL_IMPORT,
IMPORT_IN_FUNCTION,
DEEPLY_NESTED_IMPORT,
TOP_LEVEL_TRY_IMPORT,
GENERIC_EXCEPT_IMPORT,
MULTILINE_TRY_IMPORT,
MULTILINE_BOTH_IMPORT,
MULTIPLE_EXCEPTS_IMPORT,
EXCEPT_AS_IMPORT,
TRY_IMPORT_IN_FUNCTION,
]
@pytest.mark.parametrize("""case""" , __A )
def lowercase ( __A : int , __A : int ) -> List[Any]:
'''simple docstring'''
snake_case : List[Any] = os.path.join(__A , """test_file.py""" )
with open(__A , """w""" ) as _tmp_file:
_tmp_file.write(__A )
snake_case : Dict = get_imports(__A )
assert parsed_imports == ["os"]
| 36 |
import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig
if TYPE_CHECKING:
from ... import PreTrainedTokenizerBase, TensorType
__lowercase : List[str] = logging.get_logger(__name__)
class _A ( snake_case ):
'''simple docstring'''
__lowerCamelCase : Tuple = '''vision-encoder-decoder'''
__lowerCamelCase : List[Any] = True
def __init__( self ,**SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
super().__init__(**SCREAMING_SNAKE_CASE_ )
if "encoder" not in kwargs or "decoder" not in kwargs:
raise ValueError(
F"""A configuraton of type {self.model_type} cannot be instantiated because """
F"""not both `encoder` and `decoder` sub-configurations are passed, but only {kwargs}""" )
snake_case : Union[str, Any] = kwargs.pop("""encoder""" )
snake_case : Any = encoder_config.pop("""model_type""" )
snake_case : Optional[Any] = kwargs.pop("""decoder""" )
snake_case : Union[str, Any] = decoder_config.pop("""model_type""" )
snake_case : Any = AutoConfig.for_model(SCREAMING_SNAKE_CASE_ ,**SCREAMING_SNAKE_CASE_ )
snake_case : Union[str, Any] = AutoConfig.for_model(SCREAMING_SNAKE_CASE_ ,**SCREAMING_SNAKE_CASE_ )
snake_case : int = True
@classmethod
def snake_case_ ( cls ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,**SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
logger.info("""Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config""" )
snake_case : Tuple = True
snake_case : Union[str, Any] = True
return cls(encoder=encoder_config.to_dict() ,decoder=decoder_config.to_dict() ,**SCREAMING_SNAKE_CASE_ )
def snake_case_ ( self ):
'''simple docstring'''
snake_case : Union[str, Any] = copy.deepcopy(self.__dict__ )
snake_case : Union[str, Any] = self.encoder.to_dict()
snake_case : Union[str, Any] = self.decoder.to_dict()
snake_case : Dict = self.__class__.model_type
return output
class _A ( snake_case ):
'''simple docstring'''
__lowerCamelCase : Optional[Any] = version.parse('''1.11''' )
@property
def snake_case_ ( self ):
'''simple docstring'''
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
] )
@property
def snake_case_ ( self ):
'''simple docstring'''
return 1E-4
@property
def snake_case_ ( self ):
'''simple docstring'''
return OrderedDict({"""last_hidden_state""": {0: """batch""", 1: """encoder_sequence"""}} )
class _A ( snake_case ):
'''simple docstring'''
@property
def snake_case_ ( self ):
'''simple docstring'''
snake_case : Tuple = OrderedDict()
snake_case : Optional[int] = {0: """batch""", 1: """past_decoder_sequence + sequence"""}
snake_case : Union[str, Any] = {0: """batch""", 1: """past_decoder_sequence + sequence"""}
snake_case : Optional[Any] = {0: """batch""", 1: """encoder_sequence"""}
return common_inputs
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ = -1 ,SCREAMING_SNAKE_CASE_ = -1 ,SCREAMING_SNAKE_CASE_ = False ,SCREAMING_SNAKE_CASE_ = None ,):
'''simple docstring'''
import torch
snake_case : Optional[Any] = OrderedDict()
snake_case : Tuple = super().generate_dummy_inputs(
SCREAMING_SNAKE_CASE_ ,batch_size=SCREAMING_SNAKE_CASE_ ,seq_length=SCREAMING_SNAKE_CASE_ ,is_pair=SCREAMING_SNAKE_CASE_ ,framework=SCREAMING_SNAKE_CASE_ )
snake_case , snake_case : List[Any] = dummy_input["""input_ids"""].shape
snake_case : Optional[int] = (batch, encoder_sequence, self._config.encoder_hidden_size)
snake_case : List[str] = dummy_input.pop("""input_ids""" )
snake_case : int = dummy_input.pop("""attention_mask""" )
snake_case : Dict = torch.zeros(SCREAMING_SNAKE_CASE_ )
return common_inputs
class _A ( snake_case ):
'''simple docstring'''
@property
def snake_case_ ( self ):
'''simple docstring'''
pass
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
return VisionEncoderDecoderEncoderOnnxConfig(SCREAMING_SNAKE_CASE_ )
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ = "default" ):
'''simple docstring'''
snake_case : int = encoder_config.hidden_size
return VisionEncoderDecoderDecoderOnnxConfig(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )
| 36 | 1 |
__lowercase : List[str] = '''
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
'''
__lowercase : str = [{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
__lowercase : List[str] = {
'''{processor_class}''': '''FakeProcessorClass''',
'''{model_class}''': '''FakeModelClass''',
'''{object_class}''': '''FakeObjectClass''',
}
| 36 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers import BitConfig, BitForImageClassification, BitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging
logging.set_verbosity_info()
__lowercase : Any = logging.get_logger(__name__)
def lowercase ( __A : Optional[Any] ) -> Dict:
'''simple docstring'''
snake_case : Dict = """huggingface/label-files"""
snake_case : int = """imagenet-1k-id2label.json"""
snake_case : Tuple = json.load(open(hf_hub_download(__A , __A , repo_type="""dataset""" ) , """r""" ) )
snake_case : Any = {int(__A ): v for k, v in idalabel.items()}
snake_case : Dict = {v: k for k, v in idalabel.items()}
snake_case : Any = """std_conv""" if """bit""" in model_name else False
# note that when using BiT as backbone for ViT-hybrid checkpoints,
# one needs to additionally set config.layer_type = "bottleneck", config.stem_type = "same",
# config.conv_layer = "std_conv_same"
snake_case : List[Any] = BitConfig(
conv_layer=__A , num_labels=1000 , idalabel=__A , labelaid=__A , )
return config
def lowercase ( __A : Optional[int] ) -> Union[str, Any]:
'''simple docstring'''
if "stem.conv" in name:
snake_case : List[str] = name.replace("""stem.conv""" , """bit.embedder.convolution""" )
if "blocks" in name:
snake_case : List[str] = name.replace("""blocks""" , """layers""" )
if "head.fc" in name:
snake_case : Optional[int] = name.replace("""head.fc""" , """classifier.1""" )
if name.startswith("""norm""" ):
snake_case : Optional[Any] = """bit.""" + name
if "bit" not in name and "classifier" not in name:
snake_case : Tuple = """bit.encoder.""" + name
return name
def lowercase ( ) -> Optional[int]:
'''simple docstring'''
snake_case : int = """http://images.cocodataset.org/val2017/000000039769.jpg"""
snake_case : Optional[Any] = Image.open(requests.get(__A , stream=__A ).raw )
return im
@torch.no_grad()
def lowercase ( __A : Any , __A : Union[str, Any] , __A : str=False ) -> Optional[int]:
'''simple docstring'''
snake_case : str = get_config(__A )
# load original model from timm
snake_case : Tuple = create_model(__A , pretrained=__A )
timm_model.eval()
# load state_dict of original model
snake_case : List[str] = timm_model.state_dict()
for key in state_dict.copy().keys():
snake_case : List[Any] = state_dict.pop(__A )
snake_case : Union[str, Any] = val.squeeze() if """head""" in key else val
# load HuggingFace model
snake_case : List[Any] = BitForImageClassification(__A )
model.eval()
model.load_state_dict(__A )
# create image processor
snake_case : Dict = create_transform(**resolve_data_config({} , model=__A ) )
snake_case : Optional[Any] = transform.transforms
snake_case : List[Any] = {
"""bilinear""": PILImageResampling.BILINEAR,
"""bicubic""": PILImageResampling.BICUBIC,
"""nearest""": PILImageResampling.NEAREST,
}
snake_case : Union[str, Any] = BitImageProcessor(
do_resize=__A , size={"""shortest_edge""": timm_transforms[0].size} , resample=pillow_resamplings[timm_transforms[0].interpolation.value] , do_center_crop=__A , crop_size={"""height""": timm_transforms[1].size[0], """width""": timm_transforms[1].size[1]} , do_normalize=__A , image_mean=timm_transforms[-1].mean.tolist() , image_std=timm_transforms[-1].std.tolist() , )
snake_case : Dict = prepare_img()
snake_case : List[str] = transform(__A ).unsqueeze(0 )
snake_case : int = processor(__A , return_tensors="""pt""" ).pixel_values
# verify pixel values
assert torch.allclose(__A , __A )
# verify logits
with torch.no_grad():
snake_case : Optional[int] = model(__A )
snake_case : Dict = outputs.logits
print("""Logits:""" , logits[0, :3] )
print("""Predicted class:""" , model.config.idalabel[logits.argmax(-1 ).item()] )
snake_case : int = timm_model(__A )
assert timm_logits.shape == outputs.logits.shape
assert torch.allclose(__A , outputs.logits , atol=1E-3 )
print("""Looks ok!""" )
if pytorch_dump_folder_path is not None:
Path(__A ).mkdir(exist_ok=__A )
print(f"""Saving model {model_name} and processor to {pytorch_dump_folder_path}""" )
model.save_pretrained(__A )
processor.save_pretrained(__A )
if push_to_hub:
print(f"""Pushing model {model_name} and processor to the hub""" )
model.push_to_hub(f"""ybelkada/{model_name}""" )
processor.push_to_hub(f"""ybelkada/{model_name}""" )
if __name__ == "__main__":
__lowercase : List[str] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--model_name''',
default='''resnetv2_50x1_bitm''',
type=str,
help='''Name of the BiT timm model you\'d like to convert.''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
)
parser.add_argument(
'''--push_to_hub''',
action='''store_true''',
help='''Whether to push the model to the hub.''',
)
__lowercase : Union[str, Any] = parser.parse_args()
convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 36 | 1 |
import argparse
import requests
import torch
from PIL import Image
from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor
def lowercase ( __A : Union[str, Any] ) -> Any:
'''simple docstring'''
if "cls_token" in name:
snake_case : List[str] = name.replace("""cls_token""" , """vit.embeddings.cls_token""" )
if "mask_token" in name:
snake_case : Dict = name.replace("""mask_token""" , """decoder.mask_token""" )
if "decoder_pos_embed" in name:
snake_case : List[str] = name.replace("""decoder_pos_embed""" , """decoder.decoder_pos_embed""" )
if "pos_embed" in name and "decoder" not in name:
snake_case : Optional[Any] = name.replace("""pos_embed""" , """vit.embeddings.position_embeddings""" )
if "patch_embed.proj" in name:
snake_case : Any = name.replace("""patch_embed.proj""" , """vit.embeddings.patch_embeddings.projection""" )
if "patch_embed.norm" in name:
snake_case : Optional[int] = name.replace("""patch_embed.norm""" , """vit.embeddings.norm""" )
if "decoder_blocks" in name:
snake_case : Optional[Any] = name.replace("""decoder_blocks""" , """decoder.decoder_layers""" )
if "blocks" in name:
snake_case : int = name.replace("""blocks""" , """vit.encoder.layer""" )
if "attn.proj" in name:
snake_case : Tuple = name.replace("""attn.proj""" , """attention.output.dense""" )
if "attn" in name:
snake_case : Tuple = name.replace("""attn""" , """attention.self""" )
if "norm1" in name:
snake_case : Tuple = name.replace("""norm1""" , """layernorm_before""" )
if "norm2" in name:
snake_case : List[str] = name.replace("""norm2""" , """layernorm_after""" )
if "mlp.fc1" in name:
snake_case : Dict = name.replace("""mlp.fc1""" , """intermediate.dense""" )
if "mlp.fc2" in name:
snake_case : Dict = name.replace("""mlp.fc2""" , """output.dense""" )
if "decoder_embed" in name:
snake_case : Union[str, Any] = name.replace("""decoder_embed""" , """decoder.decoder_embed""" )
if "decoder_norm" in name:
snake_case : Tuple = name.replace("""decoder_norm""" , """decoder.decoder_norm""" )
if "decoder_pred" in name:
snake_case : Optional[Any] = name.replace("""decoder_pred""" , """decoder.decoder_pred""" )
if "norm.weight" in name and "decoder" not in name:
snake_case : List[Any] = name.replace("""norm.weight""" , """vit.layernorm.weight""" )
if "norm.bias" in name and "decoder" not in name:
snake_case : Optional[Any] = name.replace("""norm.bias""" , """vit.layernorm.bias""" )
return name
def lowercase ( __A : Tuple , __A : Optional[int] ) -> Tuple:
'''simple docstring'''
for key in orig_state_dict.copy().keys():
snake_case : Any = orig_state_dict.pop(__A )
if "qkv" in key:
snake_case : List[Any] = key.split(""".""" )
snake_case : int = int(key_split[1] )
if "decoder_blocks" in key:
snake_case : int = config.decoder_hidden_size
snake_case : Union[str, Any] = """decoder.decoder_layers."""
if "weight" in key:
snake_case : Optional[Any] = val[:dim, :]
snake_case : Tuple = val[dim : dim * 2, :]
snake_case : Optional[int] = val[-dim:, :]
elif "bias" in key:
snake_case : Union[str, Any] = val[:dim]
snake_case : int = val[dim : dim * 2]
snake_case : Optional[Any] = val[-dim:]
else:
snake_case : List[str] = config.hidden_size
snake_case : List[str] = """vit.encoder.layer."""
if "weight" in key:
snake_case : Any = val[:dim, :]
snake_case : int = val[dim : dim * 2, :]
snake_case : Union[str, Any] = val[-dim:, :]
elif "bias" in key:
snake_case : Optional[Any] = val[:dim]
snake_case : int = val[dim : dim * 2]
snake_case : Optional[int] = val[-dim:]
else:
snake_case : Optional[Any] = val
return orig_state_dict
def lowercase ( __A : Tuple , __A : str ) -> List[Any]:
'''simple docstring'''
snake_case : Optional[int] = ViTMAEConfig()
if "large" in checkpoint_url:
snake_case : List[str] = 1024
snake_case : Optional[int] = 4096
snake_case : Optional[int] = 24
snake_case : Tuple = 16
elif "huge" in checkpoint_url:
snake_case : Dict = 14
snake_case : int = 1280
snake_case : Dict = 5120
snake_case : List[str] = 32
snake_case : Optional[Any] = 16
snake_case : str = ViTMAEForPreTraining(__A )
snake_case : Optional[int] = torch.hub.load_state_dict_from_url(__A , map_location="""cpu""" )["""model"""]
snake_case : Any = ViTMAEImageProcessor(size=config.image_size )
snake_case : Tuple = convert_state_dict(__A , __A )
model.load_state_dict(__A )
model.eval()
snake_case : Tuple = """https://user-images.githubusercontent.com/11435359/147738734-196fd92f-9260-48d5-ba7e-bf103d29364d.jpg"""
snake_case : Union[str, Any] = Image.open(requests.get(__A , stream=__A ).raw )
snake_case : Dict = ViTMAEImageProcessor(size=config.image_size )
snake_case : str = image_processor(images=__A , return_tensors="""pt""" )
# forward pass
torch.manual_seed(2 )
snake_case : List[str] = model(**__A )
snake_case : str = outputs.logits
if "large" in checkpoint_url:
snake_case : str = torch.tensor(
[[-0.7_309, -0.7_128, -1.0_169], [-1.0_161, -0.9_058, -1.1_878], [-1.0_478, -0.9_411, -1.1_911]] )
elif "huge" in checkpoint_url:
snake_case : List[Any] = torch.tensor(
[[-1.1_599, -0.9_199, -1.2_221], [-1.1_952, -0.9_269, -1.2_307], [-1.2_143, -0.9_337, -1.2_262]] )
else:
snake_case : Optional[int] = torch.tensor(
[[-0.9_192, -0.8_481, -1.1_259], [-1.1_349, -1.0_034, -1.2_599], [-1.1_757, -1.0_429, -1.2_726]] )
# verify logits
assert torch.allclose(logits[0, :3, :3] , __A , atol=1E-4 )
print(f"""Saving model to {pytorch_dump_folder_path}""" )
model.save_pretrained(__A )
print(f"""Saving image processor to {pytorch_dump_folder_path}""" )
image_processor.save_pretrained(__A )
if __name__ == "__main__":
__lowercase : Optional[int] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--checkpoint_url''',
default='''https://dl.fbaipublicfiles.com/mae/visualize/mae_visualize_vit_base.pth''',
type=str,
help='''URL of the checkpoint you\'d like to convert.''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
)
__lowercase : Optional[Any] = parser.parse_args()
convert_vit_mae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)
| 36 |
import os
import pytest
from attr import dataclass
__lowercase : Optional[int] = '''us-east-1''' # defaults region
@dataclass
class _A :
'''simple docstring'''
__lowerCamelCase : str
__lowerCamelCase : Dict = '''arn:aws:iam::558105141721:role/sagemaker_execution_role'''
__lowerCamelCase : Optional[Any] = {
'''task_name''': '''mnli''',
'''per_device_train_batch_size''': 1_6,
'''per_device_eval_batch_size''': 1_6,
'''do_train''': True,
'''do_eval''': True,
'''do_predict''': True,
'''output_dir''': '''/opt/ml/model''',
'''overwrite_output_dir''': True,
'''max_steps''': 5_0_0,
'''save_steps''': 5_5_0_0,
}
__lowerCamelCase : List[str] = {**hyperparameters, '''max_steps''': 1_0_0_0}
@property
def snake_case_ ( self ):
'''simple docstring'''
if self.framework == "pytorch":
return [
{"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
{"Name": "eval_accuracy", "Regex": r"eval_accuracy.*=\D*(.*?)$"},
{"Name": "eval_loss", "Regex": r"eval_loss.*=\D*(.*?)$"},
]
else:
return [
{"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
{"Name": "eval_accuracy", "Regex": r"loss.*=\D*(.*?)]?$"},
{"Name": "eval_loss", "Regex": r"sparse_categorical_accuracy.*=\D*(.*?)]?$"},
]
@property
def snake_case_ ( self ):
'''simple docstring'''
return F"""{self.framework}-transfromers-test"""
@property
def snake_case_ ( self ):
'''simple docstring'''
return F"""./tests/sagemaker/scripts/{self.framework}"""
@property
def snake_case_ ( self ):
'''simple docstring'''
if self.framework == "pytorch":
return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-pytorch-training:1.7.1-transformers4.6.1-gpu-py36-cu110-ubuntu18.04"
else:
return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-tensorflow-training:2.4.1-transformers4.6.1-gpu-py37-cu110-ubuntu18.04"
@pytest.fixture(scope="""class""" )
def lowercase ( __A : List[str] ) -> List[str]:
'''simple docstring'''
snake_case : Optional[Any] = SageMakerTestEnvironment(framework=request.cls.framework )
| 36 | 1 |
import os
import pytest
from datasets import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
)
__lowercase : Optional[Any] = pytest.mark.integration
@pytest.mark.parametrize("""path""" , ["""paws""", """csv"""] )
def lowercase ( __A : Optional[Any] , __A : Optional[Any] ) -> str:
'''simple docstring'''
inspect_dataset(__A , __A )
snake_case : List[str] = path + """.py"""
assert script_name in os.listdir(__A )
assert "__pycache__" not in os.listdir(__A )
@pytest.mark.filterwarnings("""ignore:inspect_metric is deprecated:FutureWarning""" )
@pytest.mark.filterwarnings("""ignore:metric_module_factory is deprecated:FutureWarning""" )
@pytest.mark.parametrize("""path""" , ["""accuracy"""] )
def lowercase ( __A : Optional[int] , __A : Any ) -> Optional[Any]:
'''simple docstring'''
inspect_metric(__A , __A )
snake_case : Any = path + """.py"""
assert script_name in os.listdir(__A )
assert "__pycache__" not in os.listdir(__A )
@pytest.mark.parametrize(
"""path, config_name, expected_splits""" , [
("""squad""", """plain_text""", ["""train""", """validation"""]),
("""dalle-mini/wit""", """dalle-mini--wit""", ["""train"""]),
("""paws""", """labeled_final""", ["""train""", """test""", """validation"""]),
] , )
def lowercase ( __A : Tuple , __A : Dict , __A : Any ) -> Optional[int]:
'''simple docstring'''
snake_case : List[str] = get_dataset_config_info(__A , config_name=__A )
assert info.config_name == config_name
assert list(info.splits.keys() ) == expected_splits
@pytest.mark.parametrize(
"""path, config_name, expected_exception""" , [
("""paws""", None, ValueError),
] , )
def lowercase ( __A : Tuple , __A : Any , __A : List[str] ) -> Optional[int]:
'''simple docstring'''
with pytest.raises(__A ):
get_dataset_config_info(__A , config_name=__A )
@pytest.mark.parametrize(
"""path, expected""" , [
("""squad""", """plain_text"""),
("""acronym_identification""", """default"""),
("""lhoestq/squad""", """plain_text"""),
("""lhoestq/test""", """default"""),
("""lhoestq/demo1""", """lhoestq--demo1"""),
("""dalle-mini/wit""", """dalle-mini--wit"""),
] , )
def lowercase ( __A : Any , __A : Dict ) -> Dict:
'''simple docstring'''
snake_case : int = get_dataset_config_names(__A )
assert expected in config_names
@pytest.mark.parametrize(
"""path, expected_configs, expected_splits_in_first_config""" , [
("""squad""", ["""plain_text"""], ["""train""", """validation"""]),
("""dalle-mini/wit""", ["""dalle-mini--wit"""], ["""train"""]),
("""paws""", ["""labeled_final""", """labeled_swap""", """unlabeled_final"""], ["""train""", """test""", """validation"""]),
] , )
def lowercase ( __A : Optional[Any] , __A : Dict , __A : List[str] ) -> Union[str, Any]:
'''simple docstring'''
snake_case : List[Any] = get_dataset_infos(__A )
assert list(infos.keys() ) == expected_configs
snake_case : Any = expected_configs[0]
assert expected_config in infos
snake_case : Any = infos[expected_config]
assert info.config_name == expected_config
assert list(info.splits.keys() ) == expected_splits_in_first_config
@pytest.mark.parametrize(
"""path, expected_config, expected_splits""" , [
("""squad""", """plain_text""", ["""train""", """validation"""]),
("""dalle-mini/wit""", """dalle-mini--wit""", ["""train"""]),
("""paws""", """labeled_final""", ["""train""", """test""", """validation"""]),
] , )
def lowercase ( __A : Optional[int] , __A : Tuple , __A : Union[str, Any] ) -> Optional[int]:
'''simple docstring'''
snake_case : Dict = get_dataset_infos(__A )
assert expected_config in infos
snake_case : str = infos[expected_config]
assert info.config_name == expected_config
assert list(info.splits.keys() ) == expected_splits
@pytest.mark.parametrize(
"""path, config_name, expected_exception""" , [
("""paws""", None, ValueError),
] , )
def lowercase ( __A : Optional[int] , __A : Any , __A : Dict ) -> int:
'''simple docstring'''
with pytest.raises(__A ):
get_dataset_split_names(__A , config_name=__A )
| 36 |
from .imports import is_rich_available
if is_rich_available():
from rich.traceback import install
install(show_locals=False)
else:
raise ModuleNotFoundError('''To use the rich extension, install rich with `pip install rich`''')
| 36 | 1 |
def lowercase ( __A : float , __A : float , __A : float , __A : float , __A : float , ) -> float:
'''simple docstring'''
snake_case : Any = [redshift, radiation_density, matter_density, dark_energy]
if any(p < 0 for p in parameters ):
raise ValueError("""All input parameters must be positive""" )
if any(p > 1 for p in parameters[1:4] ):
raise ValueError("""Relative densities cannot be greater than one""" )
else:
snake_case : List[Any] = 1 - (matter_density + radiation_density + dark_energy)
snake_case : Tuple = (
radiation_density * (redshift + 1) ** 4
+ matter_density * (redshift + 1) ** 3
+ curvature * (redshift + 1) ** 2
+ dark_energy
)
snake_case : Tuple = hubble_constant * e_a ** (1 / 2)
return hubble
if __name__ == "__main__":
import doctest
# run doctest
doctest.testmod()
# demo LCDM approximation
__lowercase : Tuple = 0.3
print(
hubble_parameter(
hubble_constant=68.3,
radiation_density=1E-4,
matter_density=matter_density,
dark_energy=1 - matter_density,
redshift=0,
)
)
| 36 |
import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union
import tqdm
from filelock import FileLock
from transformers import (
BartTokenizer,
BartTokenizerFast,
DataProcessor,
PreTrainedTokenizer,
RobertaTokenizer,
RobertaTokenizerFast,
XLMRobertaTokenizer,
is_tf_available,
is_torch_available,
)
__lowercase : Any = logging.getLogger(__name__)
@dataclass(frozen=snake_case )
class _A :
'''simple docstring'''
__lowerCamelCase : str
__lowerCamelCase : str
__lowerCamelCase : Optional[str] = None
__lowerCamelCase : Optional[str] = None
__lowerCamelCase : Optional[str] = None
@dataclass(frozen=snake_case )
class _A :
'''simple docstring'''
__lowerCamelCase : List[int]
__lowerCamelCase : Optional[List[int]] = None
__lowerCamelCase : Optional[List[int]] = None
__lowerCamelCase : Optional[Union[int, float]] = None
__lowerCamelCase : Optional[int] = None
if is_torch_available():
import torch
from torch.utils.data import Dataset
class _A ( snake_case ):
'''simple docstring'''
__lowerCamelCase : List[InputFeatures]
def __init__( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_=False ,SCREAMING_SNAKE_CASE_ = False ,):
'''simple docstring'''
snake_case : str = hans_processors[task]()
snake_case : str = os.path.join(
SCREAMING_SNAKE_CASE_ ,"""cached_{}_{}_{}_{}""".format(
"""dev""" if evaluate else """train""" ,tokenizer.__class__.__name__ ,str(SCREAMING_SNAKE_CASE_ ) ,SCREAMING_SNAKE_CASE_ ,) ,)
snake_case : Dict = processor.get_labels()
if tokenizer.__class__ in (
RobertaTokenizer,
RobertaTokenizerFast,
XLMRobertaTokenizer,
BartTokenizer,
BartTokenizerFast,
):
# HACK(label indices are swapped in RoBERTa pretrained model)
snake_case , snake_case : List[Any] = label_list[2], label_list[1]
snake_case : List[Any] = label_list
# Make sure only the first process in distributed training processes the dataset,
# and the others will use the cache.
snake_case : Any = cached_features_file + """.lock"""
with FileLock(SCREAMING_SNAKE_CASE_ ):
if os.path.exists(SCREAMING_SNAKE_CASE_ ) and not overwrite_cache:
logger.info(F"""Loading features from cached file {cached_features_file}""" )
snake_case : int = torch.load(SCREAMING_SNAKE_CASE_ )
else:
logger.info(F"""Creating features from dataset file at {data_dir}""" )
snake_case : Union[str, Any] = (
processor.get_dev_examples(SCREAMING_SNAKE_CASE_ ) if evaluate else processor.get_train_examples(SCREAMING_SNAKE_CASE_ )
)
logger.info("""Training examples: %s""" ,len(SCREAMING_SNAKE_CASE_ ) )
snake_case : Dict = hans_convert_examples_to_features(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )
logger.info("""Saving features into cached file %s""" ,SCREAMING_SNAKE_CASE_ )
torch.save(self.features ,SCREAMING_SNAKE_CASE_ )
def __len__( self ):
'''simple docstring'''
return len(self.features )
def __getitem__( self ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
return self.features[i]
def snake_case_ ( self ):
'''simple docstring'''
return self.label_list
if is_tf_available():
import tensorflow as tf
class _A :
'''simple docstring'''
__lowerCamelCase : List[InputFeatures]
def __init__( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ = 128 ,SCREAMING_SNAKE_CASE_=False ,SCREAMING_SNAKE_CASE_ = False ,):
'''simple docstring'''
snake_case : Any = hans_processors[task]()
snake_case : List[str] = processor.get_labels()
if tokenizer.__class__ in (
RobertaTokenizer,
RobertaTokenizerFast,
XLMRobertaTokenizer,
BartTokenizer,
BartTokenizerFast,
):
# HACK(label indices are swapped in RoBERTa pretrained model)
snake_case , snake_case : int = label_list[2], label_list[1]
snake_case : List[str] = label_list
snake_case : int = processor.get_dev_examples(SCREAMING_SNAKE_CASE_ ) if evaluate else processor.get_train_examples(SCREAMING_SNAKE_CASE_ )
snake_case : Any = hans_convert_examples_to_features(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )
def gen():
for ex_index, ex in tqdm.tqdm(enumerate(self.features ) ,desc="""convert examples to features""" ):
if ex_index % 10000 == 0:
logger.info("""Writing example %d of %d""" % (ex_index, len(SCREAMING_SNAKE_CASE_ )) )
yield (
{
"example_id": 0,
"input_ids": ex.input_ids,
"attention_mask": ex.attention_mask,
"token_type_ids": ex.token_type_ids,
},
ex.label,
)
snake_case : Any = tf.data.Dataset.from_generator(
SCREAMING_SNAKE_CASE_ ,(
{
"""example_id""": tf.intaa,
"""input_ids""": tf.intaa,
"""attention_mask""": tf.intaa,
"""token_type_ids""": tf.intaa,
},
tf.intaa,
) ,(
{
"""example_id""": tf.TensorShape([] ),
"""input_ids""": tf.TensorShape([None, None] ),
"""attention_mask""": tf.TensorShape([None, None] ),
"""token_type_ids""": tf.TensorShape([None, None] ),
},
tf.TensorShape([] ),
) ,)
def snake_case_ ( self ):
'''simple docstring'''
return self.dataset
def __len__( self ):
'''simple docstring'''
return len(self.features )
def __getitem__( self ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
return self.features[i]
def snake_case_ ( self ):
'''simple docstring'''
return self.label_list
class _A ( snake_case ):
'''simple docstring'''
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
return self._create_examples(self._read_tsv(os.path.join(SCREAMING_SNAKE_CASE_ ,"""heuristics_train_set.txt""" ) ) ,"""train""" )
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
return self._create_examples(self._read_tsv(os.path.join(SCREAMING_SNAKE_CASE_ ,"""heuristics_evaluation_set.txt""" ) ) ,"""dev""" )
def snake_case_ ( self ):
'''simple docstring'''
return ["contradiction", "entailment", "neutral"]
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
snake_case : List[str] = []
for i, line in enumerate(SCREAMING_SNAKE_CASE_ ):
if i == 0:
continue
snake_case : Any = """%s-%s""" % (set_type, line[0])
snake_case : Optional[int] = line[5]
snake_case : Union[str, Any] = line[6]
snake_case : Optional[Any] = line[7][2:] if line[7].startswith("""ex""" ) else line[7]
snake_case : Dict = line[0]
examples.append(InputExample(guid=SCREAMING_SNAKE_CASE_ ,text_a=SCREAMING_SNAKE_CASE_ ,text_b=SCREAMING_SNAKE_CASE_ ,label=SCREAMING_SNAKE_CASE_ ,pairID=SCREAMING_SNAKE_CASE_ ) )
return examples
def lowercase ( __A : List[InputExample] , __A : List[str] , __A : int , __A : PreTrainedTokenizer , ) -> Tuple:
'''simple docstring'''
snake_case : List[Any] = {label: i for i, label in enumerate(__A )}
snake_case : Union[str, Any] = []
for ex_index, example in tqdm.tqdm(enumerate(__A ) , desc="""convert examples to features""" ):
if ex_index % 1_0000 == 0:
logger.info("""Writing example %d""" % (ex_index) )
snake_case : Union[str, Any] = tokenizer(
example.text_a , example.text_b , add_special_tokens=__A , max_length=__A , padding="""max_length""" , truncation=__A , return_overflowing_tokens=__A , )
snake_case : Tuple = label_map[example.label] if example.label in label_map else 0
snake_case : Tuple = int(example.pairID )
features.append(InputFeatures(**__A , label=__A , pairID=__A ) )
for i, example in enumerate(examples[:5] ):
logger.info("""*** Example ***""" )
logger.info(f"""guid: {example}""" )
logger.info(f"""features: {features[i]}""" )
return features
__lowercase : Dict = {
'''hans''': 3,
}
__lowercase : Union[str, Any] = {
'''hans''': HansProcessor,
}
| 36 | 1 |
import tempfile
import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel
from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device
from ..test_pipelines_common import to_np
class _A :
'''simple docstring'''
def snake_case_ ( self ):
'''simple docstring'''
torch.manual_seed(0 )
snake_case : Any = TaEncoderModel.from_pretrained("""hf-internal-testing/tiny-random-t5""" )
torch.manual_seed(0 )
snake_case : Optional[int] = AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-t5""" )
torch.manual_seed(0 )
snake_case : Any = UNetaDConditionModel(
sample_size=32 ,layers_per_block=1 ,block_out_channels=[32, 64] ,down_block_types=[
"""ResnetDownsampleBlock2D""",
"""SimpleCrossAttnDownBlock2D""",
] ,mid_block_type="""UNetMidBlock2DSimpleCrossAttn""" ,up_block_types=["""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""] ,in_channels=3 ,out_channels=6 ,cross_attention_dim=32 ,encoder_hid_dim=32 ,attention_head_dim=8 ,addition_embed_type="""text""" ,addition_embed_type_num_heads=2 ,cross_attention_norm="""group_norm""" ,resnet_time_scale_shift="""scale_shift""" ,act_fn="""gelu""" ,)
unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
torch.manual_seed(0 )
snake_case : Dict = DDPMScheduler(
num_train_timesteps=1000 ,beta_schedule="""squaredcos_cap_v2""" ,beta_start=0.00_01 ,beta_end=0.02 ,thresholding=SCREAMING_SNAKE_CASE_ ,dynamic_thresholding_ratio=0.95 ,sample_max_value=1.0 ,prediction_type="""epsilon""" ,variance_type="""learned_range""" ,)
torch.manual_seed(0 )
snake_case : Tuple = IFWatermarker()
return {
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"unet": unet,
"scheduler": scheduler,
"watermarker": watermarker,
"safety_checker": None,
"feature_extractor": None,
}
def snake_case_ ( self ):
'''simple docstring'''
torch.manual_seed(0 )
snake_case : Optional[int] = TaEncoderModel.from_pretrained("""hf-internal-testing/tiny-random-t5""" )
torch.manual_seed(0 )
snake_case : Union[str, Any] = AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-t5""" )
torch.manual_seed(0 )
snake_case : int = UNetaDConditionModel(
sample_size=32 ,layers_per_block=[1, 2] ,block_out_channels=[32, 64] ,down_block_types=[
"""ResnetDownsampleBlock2D""",
"""SimpleCrossAttnDownBlock2D""",
] ,mid_block_type="""UNetMidBlock2DSimpleCrossAttn""" ,up_block_types=["""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""] ,in_channels=6 ,out_channels=6 ,cross_attention_dim=32 ,encoder_hid_dim=32 ,attention_head_dim=8 ,addition_embed_type="""text""" ,addition_embed_type_num_heads=2 ,cross_attention_norm="""group_norm""" ,resnet_time_scale_shift="""scale_shift""" ,act_fn="""gelu""" ,class_embed_type="""timestep""" ,mid_block_scale_factor=1.4_14 ,time_embedding_act_fn="""gelu""" ,time_embedding_dim=32 ,)
unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
torch.manual_seed(0 )
snake_case : List[Any] = DDPMScheduler(
num_train_timesteps=1000 ,beta_schedule="""squaredcos_cap_v2""" ,beta_start=0.00_01 ,beta_end=0.02 ,thresholding=SCREAMING_SNAKE_CASE_ ,dynamic_thresholding_ratio=0.95 ,sample_max_value=1.0 ,prediction_type="""epsilon""" ,variance_type="""learned_range""" ,)
torch.manual_seed(0 )
snake_case : List[Any] = DDPMScheduler(
num_train_timesteps=1000 ,beta_schedule="""squaredcos_cap_v2""" ,beta_start=0.00_01 ,beta_end=0.02 ,)
torch.manual_seed(0 )
snake_case : int = IFWatermarker()
return {
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"unet": unet,
"scheduler": scheduler,
"image_noising_scheduler": image_noising_scheduler,
"watermarker": watermarker,
"safety_checker": None,
"feature_extractor": None,
}
def snake_case_ ( self ):
'''simple docstring'''
snake_case : Any = self.get_dummy_components()
snake_case : Any = self.pipeline_class(**SCREAMING_SNAKE_CASE_ )
pipe.to(SCREAMING_SNAKE_CASE_ )
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
snake_case : List[Any] = self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ )
snake_case : int = inputs["""prompt"""]
snake_case : Dict = inputs["""generator"""]
snake_case : Optional[int] = inputs["""num_inference_steps"""]
snake_case : Any = inputs["""output_type"""]
if "image" in inputs:
snake_case : Any = inputs["""image"""]
else:
snake_case : Tuple = None
if "mask_image" in inputs:
snake_case : Any = inputs["""mask_image"""]
else:
snake_case : List[Any] = None
if "original_image" in inputs:
snake_case : str = inputs["""original_image"""]
else:
snake_case : int = None
snake_case , snake_case : List[str] = pipe.encode_prompt(SCREAMING_SNAKE_CASE_ )
# inputs with prompt converted to embeddings
snake_case : Union[str, Any] = {
"""prompt_embeds""": prompt_embeds,
"""negative_prompt_embeds""": negative_prompt_embeds,
"""generator""": generator,
"""num_inference_steps""": num_inference_steps,
"""output_type""": output_type,
}
if image is not None:
snake_case : Any = image
if mask_image is not None:
snake_case : List[str] = mask_image
if original_image is not None:
snake_case : List[Any] = original_image
# set all optional components to None
for optional_component in pipe._optional_components:
setattr(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )
snake_case : Optional[int] = pipe(**SCREAMING_SNAKE_CASE_ )[0]
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(SCREAMING_SNAKE_CASE_ )
snake_case : str = self.pipeline_class.from_pretrained(SCREAMING_SNAKE_CASE_ )
pipe_loaded.to(SCREAMING_SNAKE_CASE_ )
pipe_loaded.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
for optional_component in pipe._optional_components:
self.assertTrue(
getattr(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ) is None ,F"""`{optional_component}` did not stay set to None after loading.""" ,)
snake_case : Dict = self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ )
snake_case : Optional[int] = inputs["""generator"""]
snake_case : Tuple = inputs["""num_inference_steps"""]
snake_case : Optional[Any] = inputs["""output_type"""]
# inputs with prompt converted to embeddings
snake_case : List[Any] = {
"""prompt_embeds""": prompt_embeds,
"""negative_prompt_embeds""": negative_prompt_embeds,
"""generator""": generator,
"""num_inference_steps""": num_inference_steps,
"""output_type""": output_type,
}
if image is not None:
snake_case : Dict = image
if mask_image is not None:
snake_case : List[str] = mask_image
if original_image is not None:
snake_case : List[Any] = original_image
snake_case : List[str] = pipe_loaded(**SCREAMING_SNAKE_CASE_ )[0]
snake_case : Optional[int] = np.abs(to_np(SCREAMING_SNAKE_CASE_ ) - to_np(SCREAMING_SNAKE_CASE_ ) ).max()
self.assertLess(SCREAMING_SNAKE_CASE_ ,1E-4 )
def snake_case_ ( self ):
'''simple docstring'''
snake_case : Tuple = self.get_dummy_components()
snake_case : Optional[Any] = self.pipeline_class(**SCREAMING_SNAKE_CASE_ )
pipe.to(SCREAMING_SNAKE_CASE_ )
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
snake_case : int = self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ )
snake_case : int = pipe(**SCREAMING_SNAKE_CASE_ )[0]
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(SCREAMING_SNAKE_CASE_ )
snake_case : Tuple = self.pipeline_class.from_pretrained(SCREAMING_SNAKE_CASE_ )
pipe_loaded.to(SCREAMING_SNAKE_CASE_ )
pipe_loaded.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
snake_case : List[str] = self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ )
snake_case : Union[str, Any] = pipe_loaded(**SCREAMING_SNAKE_CASE_ )[0]
snake_case : Optional[Any] = np.abs(to_np(SCREAMING_SNAKE_CASE_ ) - to_np(SCREAMING_SNAKE_CASE_ ) ).max()
self.assertLess(SCREAMING_SNAKE_CASE_ ,1E-4 )
| 36 |
from __future__ import annotations
def lowercase ( __A : int ) -> list[int]:
'''simple docstring'''
snake_case : Dict = 2
snake_case : int = []
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.append(__A )
if n > 1:
factors.append(__A )
return factors
if __name__ == "__main__":
import doctest
doctest.testmod()
| 36 | 1 |
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
import math
from typing import Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin
class _A ( snake_case , snake_case ):
'''simple docstring'''
__lowerCamelCase : Union[str, Any] = 1
@register_to_config
def __init__( self ,SCREAMING_SNAKE_CASE_=2000 ,SCREAMING_SNAKE_CASE_=0.1 ,SCREAMING_SNAKE_CASE_=20 ,SCREAMING_SNAKE_CASE_=1E-3 ):
'''simple docstring'''
snake_case : Tuple = None
snake_case : List[Any] = None
snake_case : List[str] = None
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ = None ):
'''simple docstring'''
snake_case : int = torch.linspace(1 ,self.config.sampling_eps ,SCREAMING_SNAKE_CASE_ ,device=SCREAMING_SNAKE_CASE_ )
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_=None ):
'''simple docstring'''
if self.timesteps is None:
raise ValueError(
"""`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler""" )
# TODO(Patrick) better comments + non-PyTorch
# postprocess model score
snake_case : List[str] = (
-0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
)
snake_case : Any = torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff ) )
snake_case : Tuple = std.flatten()
while len(std.shape ) < len(score.shape ):
snake_case : Tuple = std.unsqueeze(-1 )
snake_case : Union[str, Any] = -score / std
# compute
snake_case : List[str] = -1.0 / len(self.timesteps )
snake_case : Tuple = self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
snake_case : Any = beta_t.flatten()
while len(beta_t.shape ) < len(x.shape ):
snake_case : Optional[Any] = beta_t.unsqueeze(-1 )
snake_case : Optional[int] = -0.5 * beta_t * x
snake_case : str = torch.sqrt(SCREAMING_SNAKE_CASE_ )
snake_case : Tuple = drift - diffusion**2 * score
snake_case : int = x + drift * dt
# add noise
snake_case : Tuple = randn_tensor(x.shape ,layout=x.layout ,generator=SCREAMING_SNAKE_CASE_ ,device=x.device ,dtype=x.dtype )
snake_case : str = x_mean + diffusion * math.sqrt(-dt ) * noise
return x, x_mean
def __len__( self ):
'''simple docstring'''
return self.config.num_train_timesteps
| 36 |
import numpy as np
def lowercase ( __A : np.array ) -> np.array:
'''simple docstring'''
return (2 / (1 + np.exp(-2 * vector ))) - 1
if __name__ == "__main__":
import doctest
doctest.testmod()
| 36 | 1 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_lxmert import LxmertTokenizer
__lowercase : Dict = {'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}
__lowercase : Dict = {
'''vocab_file''': {
'''unc-nlp/lxmert-base-uncased''': '''https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/vocab.txt''',
},
'''tokenizer_file''': {
'''unc-nlp/lxmert-base-uncased''': (
'''https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/tokenizer.json'''
),
},
}
__lowercase : List[str] = {
'''unc-nlp/lxmert-base-uncased''': 512,
}
__lowercase : List[Any] = {
'''unc-nlp/lxmert-base-uncased''': {'''do_lower_case''': True},
}
class _A ( snake_case ):
'''simple docstring'''
__lowerCamelCase : List[str] = VOCAB_FILES_NAMES
__lowerCamelCase : Union[str, Any] = PRETRAINED_VOCAB_FILES_MAP
__lowerCamelCase : List[str] = PRETRAINED_INIT_CONFIGURATION
__lowerCamelCase : int = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__lowerCamelCase : str = LxmertTokenizer
def __init__( self ,SCREAMING_SNAKE_CASE_=None ,SCREAMING_SNAKE_CASE_=None ,SCREAMING_SNAKE_CASE_=True ,SCREAMING_SNAKE_CASE_="[UNK]" ,SCREAMING_SNAKE_CASE_="[SEP]" ,SCREAMING_SNAKE_CASE_="[PAD]" ,SCREAMING_SNAKE_CASE_="[CLS]" ,SCREAMING_SNAKE_CASE_="[MASK]" ,SCREAMING_SNAKE_CASE_=True ,SCREAMING_SNAKE_CASE_=None ,**SCREAMING_SNAKE_CASE_ ,):
'''simple docstring'''
super().__init__(
SCREAMING_SNAKE_CASE_ ,tokenizer_file=SCREAMING_SNAKE_CASE_ ,do_lower_case=SCREAMING_SNAKE_CASE_ ,unk_token=SCREAMING_SNAKE_CASE_ ,sep_token=SCREAMING_SNAKE_CASE_ ,pad_token=SCREAMING_SNAKE_CASE_ ,cls_token=SCREAMING_SNAKE_CASE_ ,mask_token=SCREAMING_SNAKE_CASE_ ,tokenize_chinese_chars=SCREAMING_SNAKE_CASE_ ,strip_accents=SCREAMING_SNAKE_CASE_ ,**SCREAMING_SNAKE_CASE_ ,)
snake_case : List[Any] = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get("""lowercase""" ,SCREAMING_SNAKE_CASE_ ) != do_lower_case
or normalizer_state.get("""strip_accents""" ,SCREAMING_SNAKE_CASE_ ) != strip_accents
or normalizer_state.get("""handle_chinese_chars""" ,SCREAMING_SNAKE_CASE_ ) != tokenize_chinese_chars
):
snake_case : Union[str, Any] = getattr(SCREAMING_SNAKE_CASE_ ,normalizer_state.pop("""type""" ) )
snake_case : List[Any] = do_lower_case
snake_case : List[Any] = strip_accents
snake_case : str = tokenize_chinese_chars
snake_case : int = normalizer_class(**SCREAMING_SNAKE_CASE_ )
snake_case : List[Any] = do_lower_case
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_=None ):
'''simple docstring'''
snake_case : 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 snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ = None ):
'''simple docstring'''
snake_case : Optional[Any] = [self.sep_token_id]
snake_case : 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 ) * [0] + len(token_ids_a + sep ) * [1]
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ = None ):
'''simple docstring'''
snake_case : Dict = self._tokenizer.model.save(SCREAMING_SNAKE_CASE_ ,name=SCREAMING_SNAKE_CASE_ )
return tuple(SCREAMING_SNAKE_CASE_ )
| 36 |
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
__lowercase : Optional[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 ( __A : Union[str, Any] ) -> Optional[int]:
'''simple docstring'''
for pegasus_name, hf_name in PATTERNS:
snake_case : Dict = k.replace(__A , __A )
return k
def lowercase ( __A : dict , __A : dict ) -> PegasusForConditionalGeneration:
'''simple docstring'''
snake_case : Dict = DEFAULTS.copy()
cfg_kwargs.update(__A )
snake_case : int = PegasusConfig(**__A )
snake_case : List[Any] = PegasusForConditionalGeneration(__A )
snake_case : Optional[Any] = torch_model.model.state_dict()
snake_case : Optional[int] = {}
for k, v in tf_weights.items():
snake_case : str = rename_state_dict_key(__A )
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:
snake_case : Optional[Any] = v.T
snake_case : List[Any] = torch.tensor(__A , 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
snake_case : List[str] = torch.zeros_like(mapping["""shared.weight"""][cfg.pad_token_id + 1] )
snake_case : Optional[Any] = mapping["""shared.weight"""]
snake_case : Optional[Any] = mapping["""shared.weight"""]
snake_case : Tuple = {k: torch.zeros_like(__A ) for k, v in sd.items() if k.endswith("""bias""" ) and k not in mapping}
mapping.update(**__A )
snake_case , snake_case : Union[str, Any] = torch_model.model.load_state_dict(__A , strict=__A )
snake_case : Union[str, Any] = [
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 ( __A : int="./ckpt/aeslc/model.ckpt-32000" ) -> Dict:
'''simple docstring'''
snake_case : Optional[Any] = tf.train.list_variables(__A )
snake_case : Union[str, Any] = {}
snake_case : List[str] = ["""Adafactor""", """global_step"""]
for name, shape in tqdm(__A , desc="""converting tf checkpoint to dict""" ):
snake_case : str = any(pat in name for pat in ignore_name )
if skip_key:
continue
snake_case : List[str] = tf.train.load_variable(__A , __A )
snake_case : Optional[Any] = array
return tf_weights
def lowercase ( __A : str , __A : str ) -> Optional[int]:
'''simple docstring'''
snake_case : Dict = Path(__A ).parent.name
snake_case : Dict = task_specific_params[f"""summarization_{dataset}"""]["""max_position_embeddings"""]
snake_case : Any = PegasusTokenizer.from_pretrained("""sshleifer/pegasus""" , model_max_length=__A )
assert tok.model_max_length == desired_max_model_length
tok.save_pretrained(__A )
# convert model
snake_case : Dict = get_tf_weights_as_numpy(__A )
snake_case : List[Any] = task_specific_params[f"""summarization_{dataset}"""]
if dataset == "large":
snake_case : Optional[int] = task_specific_params
snake_case : Optional[int] = convert_pegasus(__A , __A )
torch_model.save_pretrained(__A )
snake_case : int = torch_model.state_dict()
sd.pop("""model.decoder.embed_positions.weight""" )
sd.pop("""model.encoder.embed_positions.weight""" )
torch.save(__A , Path(__A ) / """pytorch_model.bin""" )
if __name__ == "__main__":
__lowercase : Tuple = 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.''')
__lowercase : List[Any] = parser.parse_args()
if args.save_dir is None:
__lowercase : Optional[Any] = Path(args.tf_ckpt_path).parent.name
__lowercase : Union[str, Any] = os.path.join('''pegasus''', dataset)
convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)
| 36 | 1 |
import unittest
import numpy as np
from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_pytesseract_available():
from PIL import Image
from transformers import LayoutLMvaImageProcessor
class _A ( unittest.TestCase ):
'''simple docstring'''
def __init__( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_=7 ,SCREAMING_SNAKE_CASE_=3 ,SCREAMING_SNAKE_CASE_=18 ,SCREAMING_SNAKE_CASE_=30 ,SCREAMING_SNAKE_CASE_=400 ,SCREAMING_SNAKE_CASE_=True ,SCREAMING_SNAKE_CASE_=None ,SCREAMING_SNAKE_CASE_=True ,):
'''simple docstring'''
snake_case : List[str] = size if size is not None else {"""height""": 18, """width""": 18}
snake_case : int = parent
snake_case : str = batch_size
snake_case : Optional[int] = num_channels
snake_case : Optional[Any] = image_size
snake_case : Union[str, Any] = min_resolution
snake_case : Optional[int] = max_resolution
snake_case : int = do_resize
snake_case : Optional[int] = size
snake_case : Tuple = apply_ocr
def snake_case_ ( self ):
'''simple docstring'''
return {"do_resize": self.do_resize, "size": self.size, "apply_ocr": self.apply_ocr}
@require_torch
@require_pytesseract
class _A ( snake_case , unittest.TestCase ):
'''simple docstring'''
__lowerCamelCase : Union[str, Any] = LayoutLMvaImageProcessor if is_pytesseract_available() else None
def snake_case_ ( self ):
'''simple docstring'''
snake_case : Optional[int] = LayoutLMvaImageProcessingTester(self )
@property
def snake_case_ ( self ):
'''simple docstring'''
return self.image_processor_tester.prepare_image_processor_dict()
def snake_case_ ( self ):
'''simple docstring'''
snake_case : Optional[Any] = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ ,"""do_resize""" ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ ,"""size""" ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ ,"""apply_ocr""" ) )
def snake_case_ ( self ):
'''simple docstring'''
snake_case : int = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size ,{"""height""": 18, """width""": 18} )
snake_case : List[Any] = self.image_processing_class.from_dict(self.image_processor_dict ,size=42 )
self.assertEqual(image_processor.size ,{"""height""": 42, """width""": 42} )
def snake_case_ ( self ):
'''simple docstring'''
pass
def snake_case_ ( self ):
'''simple docstring'''
# Initialize image_processing
snake_case : Optional[int] = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
snake_case : Tuple = prepare_image_inputs(self.image_processor_tester ,equal_resolution=SCREAMING_SNAKE_CASE_ )
for image in image_inputs:
self.assertIsInstance(SCREAMING_SNAKE_CASE_ ,Image.Image )
# Test not batched input
snake_case : Tuple = image_processing(image_inputs[0] ,return_tensors="""pt""" )
self.assertEqual(
encoding.pixel_values.shape ,(
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["""height"""],
self.image_processor_tester.size["""width"""],
) ,)
self.assertIsInstance(encoding.words ,SCREAMING_SNAKE_CASE_ )
self.assertIsInstance(encoding.boxes ,SCREAMING_SNAKE_CASE_ )
# Test batched
snake_case : Union[str, Any] = image_processing(SCREAMING_SNAKE_CASE_ ,return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape ,(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["""height"""],
self.image_processor_tester.size["""width"""],
) ,)
def snake_case_ ( self ):
'''simple docstring'''
# Initialize image_processing
snake_case : Tuple = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
snake_case : Union[str, Any] = prepare_image_inputs(self.image_processor_tester ,equal_resolution=SCREAMING_SNAKE_CASE_ ,numpify=SCREAMING_SNAKE_CASE_ )
for image in image_inputs:
self.assertIsInstance(SCREAMING_SNAKE_CASE_ ,np.ndarray )
# Test not batched input
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.size["""height"""],
self.image_processor_tester.size["""width"""],
) ,)
# Test batched
snake_case : List[Any] = image_processing(SCREAMING_SNAKE_CASE_ ,return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape ,(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["""height"""],
self.image_processor_tester.size["""width"""],
) ,)
def snake_case_ ( self ):
'''simple docstring'''
# Initialize image_processing
snake_case : Optional[Any] = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
snake_case : Optional[Any] = prepare_image_inputs(self.image_processor_tester ,equal_resolution=SCREAMING_SNAKE_CASE_ ,torchify=SCREAMING_SNAKE_CASE_ )
for image in image_inputs:
self.assertIsInstance(SCREAMING_SNAKE_CASE_ ,torch.Tensor )
# Test not batched input
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.size["""height"""],
self.image_processor_tester.size["""width"""],
) ,)
# Test batched
snake_case : int = image_processing(SCREAMING_SNAKE_CASE_ ,return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape ,(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["""height"""],
self.image_processor_tester.size["""width"""],
) ,)
def snake_case_ ( self ):
'''simple docstring'''
# with apply_OCR = True
snake_case : Union[str, Any] = LayoutLMvaImageProcessor()
from datasets import load_dataset
snake_case : int = load_dataset("""hf-internal-testing/fixtures_docvqa""" ,split="""test""" )
snake_case : Any = Image.open(ds[0]["""file"""] ).convert("""RGB""" )
snake_case : Optional[int] = image_processing(SCREAMING_SNAKE_CASE_ ,return_tensors="""pt""" )
self.assertEqual(encoding.pixel_values.shape ,(1, 3, 224, 224) )
self.assertEqual(len(encoding.words ) ,len(encoding.boxes ) )
# fmt: off
# the words and boxes were obtained with Tesseract 4.1.1
snake_case : Optional[Any] = [["""11:14""", """to""", """11:39""", """a.m""", """11:39""", """to""", """11:44""", """a.m.""", """11:44""", """a.m.""", """to""", """12:25""", """p.m.""", """12:25""", """to""", """12:58""", """p.m.""", """12:58""", """to""", """4:00""", """p.m.""", """2:00""", """to""", """5:00""", """p.m.""", """Coffee""", """Break""", """Coffee""", """will""", """be""", """served""", """for""", """men""", """and""", """women""", """in""", """the""", """lobby""", """adjacent""", """to""", """exhibit""", """area.""", """Please""", """move""", """into""", """exhibit""", """area.""", """(Exhibits""", """Open)""", """TRRF""", """GENERAL""", """SESSION""", """(PART""", """|)""", """Presiding:""", """Lee""", """A.""", """Waller""", """TRRF""", """Vice""", """President""", """“Introductory""", """Remarks”""", """Lee""", """A.""", """Waller,""", """TRRF""", """Vice""", """Presi-""", """dent""", """Individual""", """Interviews""", """with""", """TRRF""", """Public""", """Board""", """Members""", """and""", """Sci-""", """entific""", """Advisory""", """Council""", """Mem-""", """bers""", """Conducted""", """by""", """TRRF""", """Treasurer""", """Philip""", """G.""", """Kuehn""", """to""", """get""", """answers""", """which""", """the""", """public""", """refrigerated""", """warehousing""", """industry""", """is""", """looking""", """for.""", """Plus""", """questions""", """from""", """the""", """floor.""", """Dr.""", """Emil""", """M.""", """Mrak,""", """University""", """of""", """Cal-""", """ifornia,""", """Chairman,""", """TRRF""", """Board;""", """Sam""", """R.""", """Cecil,""", """University""", """of""", """Georgia""", """College""", """of""", """Agriculture;""", """Dr.""", """Stanley""", """Charm,""", """Tufts""", """University""", """School""", """of""", """Medicine;""", """Dr.""", """Robert""", """H.""", """Cotton,""", """ITT""", """Continental""", """Baking""", """Company;""", """Dr.""", """Owen""", """Fennema,""", """University""", """of""", """Wis-""", """consin;""", """Dr.""", """Robert""", """E.""", """Hardenburg,""", """USDA.""", """Questions""", """and""", """Answers""", """Exhibits""", """Open""", """Capt.""", """Jack""", """Stoney""", """Room""", """TRRF""", """Scientific""", """Advisory""", """Council""", """Meeting""", """Ballroom""", """Foyer"""]] # noqa: E231
snake_case : List[Any] = [[[141, 57, 214, 69], [228, 58, 252, 69], [141, 75, 216, 88], [230, 79, 280, 88], [142, 260, 218, 273], [230, 261, 255, 273], [143, 279, 218, 290], [231, 282, 290, 291], [143, 342, 218, 354], [231, 345, 289, 355], [202, 362, 227, 373], [143, 379, 220, 392], [231, 382, 291, 394], [144, 714, 220, 726], [231, 715, 256, 726], [144, 732, 220, 745], [232, 736, 291, 747], [144, 769, 218, 782], [231, 770, 256, 782], [141, 788, 202, 801], [215, 791, 274, 804], [143, 826, 204, 838], [215, 826, 240, 838], [142, 844, 202, 857], [215, 847, 274, 859], [334, 57, 427, 69], [440, 57, 522, 69], [369, 75, 461, 88], [469, 75, 516, 88], [528, 76, 562, 88], [570, 76, 667, 88], [675, 75, 711, 87], [721, 79, 778, 88], [789, 75, 840, 88], [369, 97, 470, 107], [484, 94, 507, 106], [518, 94, 562, 107], [576, 94, 655, 110], [668, 94, 792, 109], [804, 95, 829, 107], [369, 113, 465, 125], [477, 116, 547, 125], [562, 113, 658, 125], [671, 116, 748, 125], [761, 113, 811, 125], [369, 131, 465, 143], [477, 133, 548, 143], [563, 130, 698, 145], [710, 130, 802, 146], [336, 171, 412, 183], [423, 171, 572, 183], [582, 170, 716, 184], [728, 171, 817, 187], [829, 171, 844, 186], [338, 197, 482, 212], [507, 196, 557, 209], [569, 196, 595, 208], [610, 196, 702, 209], [505, 214, 583, 226], [595, 214, 656, 227], [670, 215, 807, 227], [335, 259, 543, 274], [556, 259, 708, 272], [372, 279, 422, 291], [435, 279, 460, 291], [474, 279, 574, 292], [587, 278, 664, 291], [676, 278, 738, 291], [751, 279, 834, 291], [372, 298, 434, 310], [335, 341, 483, 354], [497, 341, 655, 354], [667, 341, 728, 354], [740, 341, 825, 354], [335, 360, 430, 372], [442, 360, 534, 372], [545, 359, 687, 372], [697, 360, 754, 372], [765, 360, 823, 373], [334, 378, 428, 391], [440, 378, 577, 394], [590, 378, 705, 391], [720, 378, 801, 391], [334, 397, 400, 409], [370, 416, 529, 429], [544, 416, 576, 432], [587, 416, 665, 428], [677, 416, 814, 429], [372, 435, 452, 450], [465, 434, 495, 447], [511, 434, 600, 447], [611, 436, 637, 447], [649, 436, 694, 451], [705, 438, 824, 447], [369, 453, 452, 466], [464, 454, 509, 466], [522, 453, 611, 469], [625, 453, 792, 469], [370, 472, 556, 488], [570, 472, 684, 487], [697, 472, 718, 485], [732, 472, 835, 488], [369, 490, 411, 503], [425, 490, 484, 503], [496, 490, 635, 506], [645, 490, 707, 503], [718, 491, 761, 503], [771, 490, 840, 503], [336, 510, 374, 521], [388, 510, 447, 522], [460, 510, 489, 521], [503, 510, 580, 522], [592, 509, 736, 525], [745, 509, 770, 522], [781, 509, 840, 522], [338, 528, 434, 541], [448, 528, 596, 541], [609, 527, 687, 540], [700, 528, 792, 541], [336, 546, 397, 559], [407, 546, 431, 559], [443, 546, 525, 560], [537, 546, 680, 562], [688, 546, 714, 559], [722, 546, 837, 562], [336, 565, 449, 581], [461, 565, 485, 577], [497, 565, 665, 581], [681, 565, 718, 577], [732, 565, 837, 580], [337, 584, 438, 597], [452, 583, 521, 596], [535, 584, 677, 599], [690, 583, 787, 596], [801, 583, 825, 596], [338, 602, 478, 615], [492, 602, 530, 614], [543, 602, 638, 615], [650, 602, 676, 614], [688, 602, 788, 615], [802, 602, 843, 614], [337, 621, 502, 633], [516, 621, 615, 637], [629, 621, 774, 636], [789, 621, 827, 633], [337, 639, 418, 652], [432, 640, 571, 653], [587, 639, 731, 655], [743, 639, 769, 652], [780, 639, 841, 652], [338, 658, 440, 673], [455, 658, 491, 670], [508, 658, 602, 671], [616, 658, 638, 670], [654, 658, 835, 674], [337, 677, 429, 689], [337, 714, 482, 726], [495, 714, 548, 726], [561, 714, 683, 726], [338, 770, 461, 782], [474, 769, 554, 785], [489, 788, 562, 803], [576, 788, 643, 801], [656, 787, 751, 804], [764, 788, 844, 801], [334, 825, 421, 838], [430, 824, 574, 838], [584, 824, 723, 841], [335, 844, 450, 857], [464, 843, 583, 860], [628, 862, 755, 875], [769, 861, 848, 878]]] # noqa: E231
# fmt: on
self.assertListEqual(encoding.words ,SCREAMING_SNAKE_CASE_ )
self.assertListEqual(encoding.boxes ,SCREAMING_SNAKE_CASE_ )
# with apply_OCR = False
snake_case : Union[str, Any] = LayoutLMvaImageProcessor(apply_ocr=SCREAMING_SNAKE_CASE_ )
snake_case : Union[str, Any] = image_processing(SCREAMING_SNAKE_CASE_ ,return_tensors="""pt""" )
self.assertEqual(encoding.pixel_values.shape ,(1, 3, 224, 224) )
| 36 |
import argparse
import pytorch_lightning as pl
import torch
from torch import nn
from transformers import LongformerForQuestionAnswering, LongformerModel
class _A ( pl.LightningModule ):
'''simple docstring'''
def __init__( self ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
super().__init__()
snake_case : Dict = model
snake_case : Optional[int] = 2
snake_case : Optional[Any] = nn.Linear(self.model.config.hidden_size ,self.num_labels )
def snake_case_ ( self ):
'''simple docstring'''
pass
def lowercase ( __A : str , __A : str , __A : str ) -> Optional[Any]:
'''simple docstring'''
snake_case : Optional[Any] = LongformerModel.from_pretrained(__A )
snake_case : Tuple = LightningModel(__A )
snake_case : Optional[int] = torch.load(__A , map_location=torch.device("""cpu""" ) )
lightning_model.load_state_dict(ckpt["""state_dict"""] )
# init longformer question answering model
snake_case : Dict = LongformerForQuestionAnswering.from_pretrained(__A )
# transfer weights
longformer_for_qa.longformer.load_state_dict(lightning_model.model.state_dict() )
longformer_for_qa.qa_outputs.load_state_dict(lightning_model.qa_outputs.state_dict() )
longformer_for_qa.eval()
# save model
longformer_for_qa.save_pretrained(__A )
print(f"""Conversion successful. Model saved under {pytorch_dump_folder_path}""" )
if __name__ == "__main__":
__lowercase : str = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--longformer_model''',
default=None,
type=str,
required=True,
help='''model identifier of longformer. Should be either `longformer-base-4096` or `longformer-large-4096`.''',
)
parser.add_argument(
'''--longformer_question_answering_ckpt_path''',
default=None,
type=str,
required=True,
help='''Path the official PyTorch Lightning Checkpoint.''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
__lowercase : List[str] = parser.parse_args()
convert_longformer_qa_checkpoint_to_pytorch(
args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
)
| 36 | 1 |
from __future__ import annotations
def lowercase ( __A : list[list[int]] ) -> bool:
'''simple docstring'''
snake_case : Dict = len(__A )
# We need to create solution object to save path.
snake_case : Union[str, Any] = [[0 for _ in range(__A )] for _ in range(__A )]
snake_case : str = run_maze(__A , 0 , 0 , __A )
if solved:
print("""\n""".join(str(__A ) for row in solutions ) )
else:
print("""No solution exists!""" )
return solved
def lowercase ( __A : list[list[int]] , __A : int , __A : int , __A : list[list[int]] ) -> bool:
'''simple docstring'''
snake_case : Any = len(__A )
# Final check point.
if i == j == (size - 1):
snake_case : str = 1
return True
snake_case : str = (not i < 0) and (not j < 0) # Check lower bounds
snake_case : Union[str, Any] = (i < size) and (j < size) # Check upper bounds
if lower_flag and upper_flag:
# check for already visited and block points.
snake_case : Optional[int] = (not solutions[i][j]) and (not maze[i][j])
if block_flag:
# check visited
snake_case : List[str] = 1
# check for directions
if (
run_maze(__A , i + 1 , __A , __A )
or run_maze(__A , __A , j + 1 , __A )
or run_maze(__A , i - 1 , __A , __A )
or run_maze(__A , __A , j - 1 , __A )
):
return True
snake_case : Optional[int] = 0
return False
return False
if __name__ == "__main__":
import doctest
doctest.testmod()
| 36 |
import argparse
import collections
import json
import os
import re
import string
import sys
import numpy as np
__lowercase : Optional[Any] = re.compile(r'''\b(a|an|the)\b''', re.UNICODE)
__lowercase : Optional[int] = None
def lowercase ( ) -> Optional[Any]:
'''simple docstring'''
snake_case : int = argparse.ArgumentParser("""Official evaluation script for SQuAD version 2.0.""" )
parser.add_argument("""data_file""" , metavar="""data.json""" , help="""Input data JSON file.""" )
parser.add_argument("""pred_file""" , metavar="""pred.json""" , help="""Model predictions.""" )
parser.add_argument(
"""--out-file""" , """-o""" , metavar="""eval.json""" , help="""Write accuracy metrics to file (default is stdout).""" )
parser.add_argument(
"""--na-prob-file""" , """-n""" , metavar="""na_prob.json""" , help="""Model estimates of probability of no answer.""" )
parser.add_argument(
"""--na-prob-thresh""" , """-t""" , type=__A , default=1.0 , help="""Predict \"\" if no-answer probability exceeds this (default = 1.0).""" , )
parser.add_argument(
"""--out-image-dir""" , """-p""" , metavar="""out_images""" , default=__A , help="""Save precision-recall curves to directory.""" )
parser.add_argument("""--verbose""" , """-v""" , action="""store_true""" )
if len(sys.argv ) == 1:
parser.print_help()
sys.exit(1 )
return parser.parse_args()
def lowercase ( __A : Union[str, Any] ) -> int:
'''simple docstring'''
snake_case : Any = {}
for article in dataset:
for p in article["paragraphs"]:
for qa in p["qas"]:
snake_case : int = bool(qa["""answers"""]["""text"""] )
return qid_to_has_ans
def lowercase ( __A : int ) -> Optional[int]:
'''simple docstring'''
def remove_articles(__A : List[Any] ):
return ARTICLES_REGEX.sub(""" """ , __A )
def white_space_fix(__A : Union[str, Any] ):
return " ".join(text.split() )
def remove_punc(__A : Tuple ):
snake_case : Optional[Any] = set(string.punctuation )
return "".join(ch for ch in text if ch not in exclude )
def lower(__A : Any ):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(__A ) ) ) )
def lowercase ( __A : List[str] ) -> Union[str, Any]:
'''simple docstring'''
if not s:
return []
return normalize_answer(__A ).split()
def lowercase ( __A : Optional[int] , __A : int ) -> List[Any]:
'''simple docstring'''
return int(normalize_answer(__A ) == normalize_answer(__A ) )
def lowercase ( __A : Any , __A : Optional[Any] ) -> List[str]:
'''simple docstring'''
snake_case : Tuple = get_tokens(__A )
snake_case : str = get_tokens(__A )
snake_case : Dict = collections.Counter(__A ) & collections.Counter(__A )
snake_case : Optional[int] = sum(common.values() )
if len(__A ) == 0 or len(__A ) == 0:
# If either is no-answer, then F1 is 1 if they agree, 0 otherwise
return int(gold_toks == pred_toks )
if num_same == 0:
return 0
snake_case : List[Any] = 1.0 * num_same / len(__A )
snake_case : int = 1.0 * num_same / len(__A )
snake_case : Dict = (2 * precision * recall) / (precision + recall)
return fa
def lowercase ( __A : List[Any] , __A : int ) -> str:
'''simple docstring'''
snake_case : Tuple = {}
snake_case : Tuple = {}
for article in dataset:
for p in article["paragraphs"]:
for qa in p["qas"]:
snake_case : str = qa["""id"""]
snake_case : Union[str, Any] = [t for t in qa["""answers"""]["""text"""] if normalize_answer(__A )]
if not gold_answers:
# For unanswerable questions, only correct answer is empty string
snake_case : Optional[Any] = [""""""]
if qid not in preds:
print(f"""Missing prediction for {qid}""" )
continue
snake_case : Dict = preds[qid]
# Take max over all gold answers
snake_case : Union[str, Any] = max(compute_exact(__A , __A ) for a in gold_answers )
snake_case : Optional[int] = max(compute_fa(__A , __A ) for a in gold_answers )
return exact_scores, fa_scores
def lowercase ( __A : str , __A : Any , __A : List[Any] , __A : List[Any] ) -> Dict:
'''simple docstring'''
snake_case : Optional[int] = {}
for qid, s in scores.items():
snake_case : Any = na_probs[qid] > na_prob_thresh
if pred_na:
snake_case : str = float(not qid_to_has_ans[qid] )
else:
snake_case : List[Any] = s
return new_scores
def lowercase ( __A : Dict , __A : Union[str, Any] , __A : List[str]=None ) -> int:
'''simple docstring'''
if not qid_list:
snake_case : List[str] = len(__A )
return collections.OrderedDict(
[
("""exact""", 100.0 * sum(exact_scores.values() ) / total),
("""f1""", 100.0 * sum(fa_scores.values() ) / total),
("""total""", total),
] )
else:
snake_case : Any = len(__A )
return collections.OrderedDict(
[
("""exact""", 100.0 * sum(exact_scores[k] for k in qid_list ) / total),
("""f1""", 100.0 * sum(fa_scores[k] for k in qid_list ) / total),
("""total""", total),
] )
def lowercase ( __A : Optional[Any] , __A : Tuple , __A : List[str] ) -> Optional[Any]:
'''simple docstring'''
for k in new_eval:
snake_case : str = new_eval[k]
def lowercase ( __A : Tuple , __A : int , __A : Dict , __A : Dict ) -> int:
'''simple docstring'''
plt.step(__A , __A , color="""b""" , alpha=0.2 , where="""post""" )
plt.fill_between(__A , __A , step="""post""" , alpha=0.2 , color="""b""" )
plt.xlabel("""Recall""" )
plt.ylabel("""Precision""" )
plt.xlim([0.0, 1.05] )
plt.ylim([0.0, 1.05] )
plt.title(__A )
plt.savefig(__A )
plt.clf()
def lowercase ( __A : Optional[Any] , __A : Union[str, Any] , __A : Dict , __A : Tuple , __A : Optional[Any]=None , __A : List[str]=None ) -> Union[str, Any]:
'''simple docstring'''
snake_case : Optional[int] = sorted(__A , key=lambda __A : na_probs[k] )
snake_case : Any = 0.0
snake_case : str = 1.0
snake_case : Tuple = 0.0
snake_case : str = [1.0]
snake_case : Any = [0.0]
snake_case : Dict = 0.0
for i, qid in enumerate(__A ):
if qid_to_has_ans[qid]:
true_pos += scores[qid]
snake_case : str = true_pos / float(i + 1 )
snake_case : List[str] = true_pos / float(__A )
if i == len(__A ) - 1 or na_probs[qid] != na_probs[qid_list[i + 1]]:
# i.e., if we can put a threshold after this point
avg_prec += cur_p * (cur_r - recalls[-1])
precisions.append(__A )
recalls.append(__A )
if out_image:
plot_pr_curve(__A , __A , __A , __A )
return {"ap": 100.0 * avg_prec}
def lowercase ( __A : Any , __A : Optional[int] , __A : Tuple , __A : Tuple , __A : List[Any] , __A : Optional[Any] ) -> Optional[Any]:
'''simple docstring'''
if out_image_dir and not os.path.exists(__A ):
os.makedirs(__A )
snake_case : Tuple = sum(1 for v in qid_to_has_ans.values() if v )
if num_true_pos == 0:
return
snake_case : str = make_precision_recall_eval(
__A , __A , __A , __A , out_image=os.path.join(__A , """pr_exact.png""" ) , title="""Precision-Recall curve for Exact Match score""" , )
snake_case : int = make_precision_recall_eval(
__A , __A , __A , __A , out_image=os.path.join(__A , """pr_f1.png""" ) , title="""Precision-Recall curve for F1 score""" , )
snake_case : Dict = {k: float(__A ) for k, v in qid_to_has_ans.items()}
snake_case : int = make_precision_recall_eval(
__A , __A , __A , __A , out_image=os.path.join(__A , """pr_oracle.png""" ) , title="""Oracle Precision-Recall curve (binary task of HasAns vs. NoAns)""" , )
merge_eval(__A , __A , """pr_exact""" )
merge_eval(__A , __A , """pr_f1""" )
merge_eval(__A , __A , """pr_oracle""" )
def lowercase ( __A : List[Any] , __A : Union[str, Any] , __A : Union[str, Any] , __A : Optional[int] ) -> Union[str, Any]:
'''simple docstring'''
if not qid_list:
return
snake_case : int = [na_probs[k] for k in qid_list]
snake_case : List[str] = np.ones_like(__A ) / float(len(__A ) )
plt.hist(__A , weights=__A , bins=20 , range=(0.0, 1.0) )
plt.xlabel("""Model probability of no-answer""" )
plt.ylabel("""Proportion of dataset""" )
plt.title(f"""Histogram of no-answer probability: {name}""" )
plt.savefig(os.path.join(__A , f"""na_prob_hist_{name}.png""" ) )
plt.clf()
def lowercase ( __A : List[Any] , __A : Tuple , __A : Tuple , __A : Any ) -> Dict:
'''simple docstring'''
snake_case : Optional[int] = sum(1 for k in qid_to_has_ans if not qid_to_has_ans[k] )
snake_case : str = num_no_ans
snake_case : Optional[Any] = cur_score
snake_case : Optional[Any] = 0.0
snake_case : List[Any] = sorted(__A , key=lambda __A : na_probs[k] )
for i, qid in enumerate(__A ):
if qid not in scores:
continue
if qid_to_has_ans[qid]:
snake_case : Dict = scores[qid]
else:
if preds[qid]:
snake_case : Dict = -1
else:
snake_case : str = 0
cur_score += diff
if cur_score > best_score:
snake_case : Union[str, Any] = cur_score
snake_case : List[Any] = na_probs[qid]
return 100.0 * best_score / len(__A ), best_thresh
def lowercase ( __A : Dict , __A : str , __A : str , __A : int , __A : str , __A : Any ) -> List[str]:
'''simple docstring'''
snake_case , snake_case : Optional[int] = find_best_thresh(__A , __A , __A , __A )
snake_case , snake_case : str = find_best_thresh(__A , __A , __A , __A )
snake_case : List[str] = best_exact
snake_case : List[Any] = exact_thresh
snake_case : Optional[Any] = best_fa
snake_case : Optional[int] = fa_thresh
def lowercase ( ) -> Any:
'''simple docstring'''
with open(OPTS.data_file ) as f:
snake_case : Dict = json.load(__A )
snake_case : Union[str, Any] = dataset_json["""data"""]
with open(OPTS.pred_file ) as f:
snake_case : int = json.load(__A )
if OPTS.na_prob_file:
with open(OPTS.na_prob_file ) as f:
snake_case : Any = json.load(__A )
else:
snake_case : Any = {k: 0.0 for k in preds}
snake_case : Optional[int] = make_qid_to_has_ans(__A ) # maps qid to True/False
snake_case : Dict = [k for k, v in qid_to_has_ans.items() if v]
snake_case : Optional[int] = [k for k, v in qid_to_has_ans.items() if not v]
snake_case , snake_case : Optional[Any] = get_raw_scores(__A , __A )
snake_case : Tuple = apply_no_ans_threshold(__A , __A , __A , OPTS.na_prob_thresh )
snake_case : Optional[Any] = apply_no_ans_threshold(__A , __A , __A , OPTS.na_prob_thresh )
snake_case : Optional[int] = make_eval_dict(__A , __A )
if has_ans_qids:
snake_case : Any = make_eval_dict(__A , __A , qid_list=__A )
merge_eval(__A , __A , """HasAns""" )
if no_ans_qids:
snake_case : str = make_eval_dict(__A , __A , qid_list=__A )
merge_eval(__A , __A , """NoAns""" )
if OPTS.na_prob_file:
find_all_best_thresh(__A , __A , __A , __A , __A , __A )
if OPTS.na_prob_file and OPTS.out_image_dir:
run_precision_recall_analysis(__A , __A , __A , __A , __A , OPTS.out_image_dir )
histogram_na_prob(__A , __A , OPTS.out_image_dir , """hasAns""" )
histogram_na_prob(__A , __A , OPTS.out_image_dir , """noAns""" )
if OPTS.out_file:
with open(OPTS.out_file , """w""" ) as f:
json.dump(__A , __A )
else:
print(json.dumps(__A , indent=2 ) )
if __name__ == "__main__":
__lowercase : Union[str, Any] = parse_args()
if OPTS.out_image_dir:
import matplotlib
matplotlib.use('''Agg''')
import matplotlib.pyplot as plt
main()
| 36 | 1 |
import unittest
from transformers import (
MODEL_FOR_OBJECT_DETECTION_MAPPING,
AutoFeatureExtractor,
AutoModelForObjectDetection,
ObjectDetectionPipeline,
is_vision_available,
pipeline,
)
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_pytesseract,
require_tf,
require_timm,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_available():
from PIL import Image
else:
class _A :
'''simple docstring'''
@staticmethod
def snake_case_ ( *SCREAMING_SNAKE_CASE_ ,**SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
pass
@is_pipeline_test
@require_vision
@require_timm
@require_torch
class _A ( unittest.TestCase ):
'''simple docstring'''
__lowerCamelCase : Tuple = MODEL_FOR_OBJECT_DETECTION_MAPPING
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
snake_case : Any = ObjectDetectionPipeline(model=SCREAMING_SNAKE_CASE_ ,image_processor=SCREAMING_SNAKE_CASE_ )
return object_detector, ["./tests/fixtures/tests_samples/COCO/000000039769.png"]
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
snake_case : Dict = object_detector("""./tests/fixtures/tests_samples/COCO/000000039769.png""" ,threshold=0.0 )
self.assertGreater(len(SCREAMING_SNAKE_CASE_ ) ,0 )
for detected_object in outputs:
self.assertEqual(
SCREAMING_SNAKE_CASE_ ,{
"""score""": ANY(SCREAMING_SNAKE_CASE_ ),
"""label""": ANY(SCREAMING_SNAKE_CASE_ ),
"""box""": {"""xmin""": ANY(SCREAMING_SNAKE_CASE_ ), """ymin""": ANY(SCREAMING_SNAKE_CASE_ ), """xmax""": ANY(SCREAMING_SNAKE_CASE_ ), """ymax""": ANY(SCREAMING_SNAKE_CASE_ )},
} ,)
import datasets
snake_case : Any = datasets.load_dataset("""hf-internal-testing/fixtures_image_utils""" ,"""image""" ,split="""test""" )
snake_case : Any = [
Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" ),
"""http://images.cocodataset.org/val2017/000000039769.jpg""",
# RGBA
dataset[0]["""file"""],
# LA
dataset[1]["""file"""],
# L
dataset[2]["""file"""],
]
snake_case : Union[str, Any] = object_detector(SCREAMING_SNAKE_CASE_ ,threshold=0.0 )
self.assertEqual(len(SCREAMING_SNAKE_CASE_ ) ,len(SCREAMING_SNAKE_CASE_ ) )
for outputs in batch_outputs:
self.assertGreater(len(SCREAMING_SNAKE_CASE_ ) ,0 )
for detected_object in outputs:
self.assertEqual(
SCREAMING_SNAKE_CASE_ ,{
"""score""": ANY(SCREAMING_SNAKE_CASE_ ),
"""label""": ANY(SCREAMING_SNAKE_CASE_ ),
"""box""": {"""xmin""": ANY(SCREAMING_SNAKE_CASE_ ), """ymin""": ANY(SCREAMING_SNAKE_CASE_ ), """xmax""": ANY(SCREAMING_SNAKE_CASE_ ), """ymax""": ANY(SCREAMING_SNAKE_CASE_ )},
} ,)
@require_tf
@unittest.skip("""Object detection not implemented in TF""" )
def snake_case_ ( self ):
'''simple docstring'''
pass
@require_torch
def snake_case_ ( self ):
'''simple docstring'''
snake_case : Optional[int] = """hf-internal-testing/tiny-detr-mobilenetsv3"""
snake_case : Optional[int] = AutoModelForObjectDetection.from_pretrained(SCREAMING_SNAKE_CASE_ )
snake_case : Optional[Any] = AutoFeatureExtractor.from_pretrained(SCREAMING_SNAKE_CASE_ )
snake_case : str = ObjectDetectionPipeline(model=SCREAMING_SNAKE_CASE_ ,feature_extractor=SCREAMING_SNAKE_CASE_ )
snake_case : List[str] = object_detector("""http://images.cocodataset.org/val2017/000000039769.jpg""" ,threshold=0.0 )
self.assertEqual(
nested_simplify(SCREAMING_SNAKE_CASE_ ,decimals=4 ) ,[
{"""score""": 0.33_76, """label""": """LABEL_0""", """box""": {"""xmin""": 159, """ymin""": 120, """xmax""": 480, """ymax""": 359}},
{"""score""": 0.33_76, """label""": """LABEL_0""", """box""": {"""xmin""": 159, """ymin""": 120, """xmax""": 480, """ymax""": 359}},
] ,)
snake_case : Optional[Any] = object_detector(
[
"""http://images.cocodataset.org/val2017/000000039769.jpg""",
"""http://images.cocodataset.org/val2017/000000039769.jpg""",
] ,threshold=0.0 ,)
self.assertEqual(
nested_simplify(SCREAMING_SNAKE_CASE_ ,decimals=4 ) ,[
[
{"""score""": 0.33_76, """label""": """LABEL_0""", """box""": {"""xmin""": 159, """ymin""": 120, """xmax""": 480, """ymax""": 359}},
{"""score""": 0.33_76, """label""": """LABEL_0""", """box""": {"""xmin""": 159, """ymin""": 120, """xmax""": 480, """ymax""": 359}},
],
[
{"""score""": 0.33_76, """label""": """LABEL_0""", """box""": {"""xmin""": 159, """ymin""": 120, """xmax""": 480, """ymax""": 359}},
{"""score""": 0.33_76, """label""": """LABEL_0""", """box""": {"""xmin""": 159, """ymin""": 120, """xmax""": 480, """ymax""": 359}},
],
] ,)
@require_torch
@slow
def snake_case_ ( self ):
'''simple docstring'''
snake_case : Optional[int] = """facebook/detr-resnet-50"""
snake_case : Union[str, Any] = AutoModelForObjectDetection.from_pretrained(SCREAMING_SNAKE_CASE_ )
snake_case : Any = AutoFeatureExtractor.from_pretrained(SCREAMING_SNAKE_CASE_ )
snake_case : Union[str, Any] = ObjectDetectionPipeline(model=SCREAMING_SNAKE_CASE_ ,feature_extractor=SCREAMING_SNAKE_CASE_ )
snake_case : Optional[Any] = object_detector("""http://images.cocodataset.org/val2017/000000039769.jpg""" )
self.assertEqual(
nested_simplify(SCREAMING_SNAKE_CASE_ ,decimals=4 ) ,[
{"""score""": 0.99_82, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 70, """xmax""": 175, """ymax""": 117}},
{"""score""": 0.99_60, """label""": """remote""", """box""": {"""xmin""": 333, """ymin""": 72, """xmax""": 368, """ymax""": 187}},
{"""score""": 0.99_55, """label""": """couch""", """box""": {"""xmin""": 0, """ymin""": 1, """xmax""": 639, """ymax""": 473}},
{"""score""": 0.99_88, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 314, """ymax""": 470}},
{"""score""": 0.99_87, """label""": """cat""", """box""": {"""xmin""": 345, """ymin""": 23, """xmax""": 640, """ymax""": 368}},
] ,)
snake_case : str = object_detector(
[
"""http://images.cocodataset.org/val2017/000000039769.jpg""",
"""http://images.cocodataset.org/val2017/000000039769.jpg""",
] )
self.assertEqual(
nested_simplify(SCREAMING_SNAKE_CASE_ ,decimals=4 ) ,[
[
{"""score""": 0.99_82, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 70, """xmax""": 175, """ymax""": 117}},
{"""score""": 0.99_60, """label""": """remote""", """box""": {"""xmin""": 333, """ymin""": 72, """xmax""": 368, """ymax""": 187}},
{"""score""": 0.99_55, """label""": """couch""", """box""": {"""xmin""": 0, """ymin""": 1, """xmax""": 639, """ymax""": 473}},
{"""score""": 0.99_88, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 314, """ymax""": 470}},
{"""score""": 0.99_87, """label""": """cat""", """box""": {"""xmin""": 345, """ymin""": 23, """xmax""": 640, """ymax""": 368}},
],
[
{"""score""": 0.99_82, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 70, """xmax""": 175, """ymax""": 117}},
{"""score""": 0.99_60, """label""": """remote""", """box""": {"""xmin""": 333, """ymin""": 72, """xmax""": 368, """ymax""": 187}},
{"""score""": 0.99_55, """label""": """couch""", """box""": {"""xmin""": 0, """ymin""": 1, """xmax""": 639, """ymax""": 473}},
{"""score""": 0.99_88, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 314, """ymax""": 470}},
{"""score""": 0.99_87, """label""": """cat""", """box""": {"""xmin""": 345, """ymin""": 23, """xmax""": 640, """ymax""": 368}},
],
] ,)
@require_torch
@slow
def snake_case_ ( self ):
'''simple docstring'''
snake_case : str = """facebook/detr-resnet-50"""
snake_case : str = pipeline("""object-detection""" ,model=SCREAMING_SNAKE_CASE_ )
snake_case : Any = object_detector("""http://images.cocodataset.org/val2017/000000039769.jpg""" )
self.assertEqual(
nested_simplify(SCREAMING_SNAKE_CASE_ ,decimals=4 ) ,[
{"""score""": 0.99_82, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 70, """xmax""": 175, """ymax""": 117}},
{"""score""": 0.99_60, """label""": """remote""", """box""": {"""xmin""": 333, """ymin""": 72, """xmax""": 368, """ymax""": 187}},
{"""score""": 0.99_55, """label""": """couch""", """box""": {"""xmin""": 0, """ymin""": 1, """xmax""": 639, """ymax""": 473}},
{"""score""": 0.99_88, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 314, """ymax""": 470}},
{"""score""": 0.99_87, """label""": """cat""", """box""": {"""xmin""": 345, """ymin""": 23, """xmax""": 640, """ymax""": 368}},
] ,)
snake_case : List[Any] = object_detector(
[
"""http://images.cocodataset.org/val2017/000000039769.jpg""",
"""http://images.cocodataset.org/val2017/000000039769.jpg""",
] )
self.assertEqual(
nested_simplify(SCREAMING_SNAKE_CASE_ ,decimals=4 ) ,[
[
{"""score""": 0.99_82, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 70, """xmax""": 175, """ymax""": 117}},
{"""score""": 0.99_60, """label""": """remote""", """box""": {"""xmin""": 333, """ymin""": 72, """xmax""": 368, """ymax""": 187}},
{"""score""": 0.99_55, """label""": """couch""", """box""": {"""xmin""": 0, """ymin""": 1, """xmax""": 639, """ymax""": 473}},
{"""score""": 0.99_88, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 314, """ymax""": 470}},
{"""score""": 0.99_87, """label""": """cat""", """box""": {"""xmin""": 345, """ymin""": 23, """xmax""": 640, """ymax""": 368}},
],
[
{"""score""": 0.99_82, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 70, """xmax""": 175, """ymax""": 117}},
{"""score""": 0.99_60, """label""": """remote""", """box""": {"""xmin""": 333, """ymin""": 72, """xmax""": 368, """ymax""": 187}},
{"""score""": 0.99_55, """label""": """couch""", """box""": {"""xmin""": 0, """ymin""": 1, """xmax""": 639, """ymax""": 473}},
{"""score""": 0.99_88, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 314, """ymax""": 470}},
{"""score""": 0.99_87, """label""": """cat""", """box""": {"""xmin""": 345, """ymin""": 23, """xmax""": 640, """ymax""": 368}},
],
] ,)
@require_torch
@slow
def snake_case_ ( self ):
'''simple docstring'''
snake_case : Optional[Any] = 0.99_85
snake_case : List[Any] = """facebook/detr-resnet-50"""
snake_case : Any = pipeline("""object-detection""" ,model=SCREAMING_SNAKE_CASE_ )
snake_case : Tuple = object_detector("""http://images.cocodataset.org/val2017/000000039769.jpg""" ,threshold=SCREAMING_SNAKE_CASE_ )
self.assertEqual(
nested_simplify(SCREAMING_SNAKE_CASE_ ,decimals=4 ) ,[
{"""score""": 0.99_88, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 314, """ymax""": 470}},
{"""score""": 0.99_87, """label""": """cat""", """box""": {"""xmin""": 345, """ymin""": 23, """xmax""": 640, """ymax""": 368}},
] ,)
@require_torch
@require_pytesseract
@slow
def snake_case_ ( self ):
'''simple docstring'''
snake_case : int = """Narsil/layoutlmv3-finetuned-funsd"""
snake_case : Dict = 0.99_93
snake_case : Any = pipeline("""object-detection""" ,model=SCREAMING_SNAKE_CASE_ ,threshold=SCREAMING_SNAKE_CASE_ )
snake_case : Dict = object_detector(
"""https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png""" )
self.assertEqual(
nested_simplify(SCREAMING_SNAKE_CASE_ ,decimals=4 ) ,[
{"""score""": 0.99_93, """label""": """I-ANSWER""", """box""": {"""xmin""": 294, """ymin""": 254, """xmax""": 343, """ymax""": 264}},
{"""score""": 0.99_93, """label""": """I-ANSWER""", """box""": {"""xmin""": 294, """ymin""": 254, """xmax""": 343, """ymax""": 264}},
] ,)
| 36 |
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
flip_channel_order,
get_resize_output_image_size,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, is_vision_available, logging
if is_vision_available():
import PIL
if is_torch_available():
import torch
__lowercase : Dict = logging.get_logger(__name__)
class _A ( snake_case ):
'''simple docstring'''
__lowerCamelCase : Optional[int] = ['''pixel_values''']
def __init__( self ,SCREAMING_SNAKE_CASE_ = True ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_ = PILImageResampling.BILINEAR ,SCREAMING_SNAKE_CASE_ = True ,SCREAMING_SNAKE_CASE_ = 1 / 255 ,SCREAMING_SNAKE_CASE_ = True ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_ = True ,**SCREAMING_SNAKE_CASE_ ,):
'''simple docstring'''
super().__init__(**SCREAMING_SNAKE_CASE_ )
snake_case : List[Any] = size if size is not None else {"""shortest_edge""": 224}
snake_case : str = get_size_dict(SCREAMING_SNAKE_CASE_ ,default_to_square=SCREAMING_SNAKE_CASE_ )
snake_case : str = crop_size if crop_size is not None else {"""height""": 256, """width""": 256}
snake_case : Optional[Any] = get_size_dict(SCREAMING_SNAKE_CASE_ ,param_name="""crop_size""" )
snake_case : Optional[Any] = do_resize
snake_case : Union[str, Any] = size
snake_case : Dict = resample
snake_case : Dict = do_rescale
snake_case : Dict = rescale_factor
snake_case : List[str] = do_center_crop
snake_case : Dict = crop_size
snake_case : Any = do_flip_channel_order
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ = PIL.Image.BILINEAR ,SCREAMING_SNAKE_CASE_ = None ,**SCREAMING_SNAKE_CASE_ ,):
'''simple docstring'''
snake_case : str = get_size_dict(SCREAMING_SNAKE_CASE_ ,default_to_square=SCREAMING_SNAKE_CASE_ )
if "shortest_edge" not in size:
raise ValueError(F"""The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}""" )
snake_case : List[Any] = get_resize_output_image_size(SCREAMING_SNAKE_CASE_ ,size=size["""shortest_edge"""] ,default_to_square=SCREAMING_SNAKE_CASE_ )
return resize(SCREAMING_SNAKE_CASE_ ,size=SCREAMING_SNAKE_CASE_ ,resample=SCREAMING_SNAKE_CASE_ ,data_format=SCREAMING_SNAKE_CASE_ ,**SCREAMING_SNAKE_CASE_ )
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ = None ,**SCREAMING_SNAKE_CASE_ ,):
'''simple docstring'''
snake_case : Union[str, Any] = get_size_dict(SCREAMING_SNAKE_CASE_ )
if "height" not in size or "width" not in size:
raise ValueError(F"""The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}""" )
return center_crop(SCREAMING_SNAKE_CASE_ ,size=(size["""height"""], size["""width"""]) ,data_format=SCREAMING_SNAKE_CASE_ ,**SCREAMING_SNAKE_CASE_ )
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ = None ,**SCREAMING_SNAKE_CASE_ ,):
'''simple docstring'''
return rescale(SCREAMING_SNAKE_CASE_ ,scale=SCREAMING_SNAKE_CASE_ ,data_format=SCREAMING_SNAKE_CASE_ ,**SCREAMING_SNAKE_CASE_ )
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ = None ):
'''simple docstring'''
return flip_channel_order(SCREAMING_SNAKE_CASE_ ,data_format=SCREAMING_SNAKE_CASE_ )
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_ = ChannelDimension.FIRST ,**SCREAMING_SNAKE_CASE_ ,):
'''simple docstring'''
snake_case : List[Any] = do_resize if do_resize is not None else self.do_resize
snake_case : List[str] = resample if resample is not None else self.resample
snake_case : Union[str, Any] = do_rescale if do_rescale is not None else self.do_rescale
snake_case : Union[str, Any] = rescale_factor if rescale_factor is not None else self.rescale_factor
snake_case : str = do_center_crop if do_center_crop is not None else self.do_center_crop
snake_case : Union[str, Any] = (
do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
)
snake_case : Tuple = size if size is not None else self.size
snake_case : Optional[Any] = get_size_dict(SCREAMING_SNAKE_CASE_ ,default_to_square=SCREAMING_SNAKE_CASE_ )
snake_case : str = crop_size if crop_size is not None else self.crop_size
snake_case : Optional[Any] = get_size_dict(SCREAMING_SNAKE_CASE_ ,param_name="""crop_size""" )
snake_case : List[Any] = make_list_of_images(SCREAMING_SNAKE_CASE_ )
if not valid_images(SCREAMING_SNAKE_CASE_ ):
raise ValueError(
"""Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
"""torch.Tensor, tf.Tensor or jax.ndarray.""" )
if do_resize and size is None:
raise ValueError("""Size must be specified if do_resize is True.""" )
if do_rescale and rescale_factor is None:
raise ValueError("""Rescale factor must be specified if do_rescale is True.""" )
if do_center_crop and crop_size is None:
raise ValueError("""Crop size must be specified if do_center_crop is True.""" )
# All transformations expect numpy arrays.
snake_case : Dict = [to_numpy_array(SCREAMING_SNAKE_CASE_ ) for image in images]
if do_resize:
snake_case : Union[str, Any] = [self.resize(image=SCREAMING_SNAKE_CASE_ ,size=SCREAMING_SNAKE_CASE_ ,resample=SCREAMING_SNAKE_CASE_ ) for image in images]
if do_center_crop:
snake_case : Optional[Any] = [self.center_crop(image=SCREAMING_SNAKE_CASE_ ,size=SCREAMING_SNAKE_CASE_ ) for image in images]
if do_rescale:
snake_case : Dict = [self.rescale(image=SCREAMING_SNAKE_CASE_ ,scale=SCREAMING_SNAKE_CASE_ ) for image in images]
# the pretrained checkpoints assume images are BGR, not RGB
if do_flip_channel_order:
snake_case : Optional[int] = [self.flip_channel_order(image=SCREAMING_SNAKE_CASE_ ) for image in images]
snake_case : List[Any] = [to_channel_dimension_format(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ) for image in images]
snake_case : int = {"""pixel_values""": images}
return BatchFeature(data=SCREAMING_SNAKE_CASE_ ,tensor_type=SCREAMING_SNAKE_CASE_ )
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ = None ):
'''simple docstring'''
snake_case : Dict = outputs.logits
# Resize logits and compute semantic segmentation maps
if target_sizes is not None:
if len(SCREAMING_SNAKE_CASE_ ) != len(SCREAMING_SNAKE_CASE_ ):
raise ValueError(
"""Make sure that you pass in as many target sizes as the batch dimension of the logits""" )
if is_torch_tensor(SCREAMING_SNAKE_CASE_ ):
snake_case : int = target_sizes.numpy()
snake_case : Optional[Any] = []
for idx in range(len(SCREAMING_SNAKE_CASE_ ) ):
snake_case : Optional[int] = torch.nn.functional.interpolate(
logits[idx].unsqueeze(dim=0 ) ,size=target_sizes[idx] ,mode="""bilinear""" ,align_corners=SCREAMING_SNAKE_CASE_ )
snake_case : Optional[int] = resized_logits[0].argmax(dim=0 )
semantic_segmentation.append(SCREAMING_SNAKE_CASE_ )
else:
snake_case : Tuple = logits.argmax(dim=1 )
snake_case : Dict = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0] )]
return semantic_segmentation
| 36 | 1 |
from collections import defaultdict
def lowercase ( __A : int ) -> int:
'''simple docstring'''
snake_case : Union[str, Any] = 1
snake_case : str = True
for v in tree[start]:
if v not in visited:
ret += dfs(__A )
if ret % 2 == 0:
cuts.append(__A )
return ret
def lowercase ( ) -> Union[str, Any]:
'''simple docstring'''
dfs(1 )
if __name__ == "__main__":
__lowercase , __lowercase : Union[str, Any] = 10, 9
__lowercase : Dict = defaultdict(list)
__lowercase : dict[int, bool] = {}
__lowercase : list[int] = []
__lowercase : Optional[Any] = 0
__lowercase : Optional[Any] = [(2, 1), (3, 1), (4, 3), (5, 2), (6, 1), (7, 2), (8, 6), (9, 8), (10, 8)]
for u, v in edges:
tree[u].append(v)
tree[v].append(u)
even_tree()
print(len(cuts) - 1)
| 36 |
import fire
from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer
def lowercase ( __A : str , __A : str , **__A : Optional[int] ) -> Optional[Any]:
'''simple docstring'''
snake_case : int = AutoConfig.from_pretrained(__A , **__A )
snake_case : Tuple = AutoModelForSeqaSeqLM.from_config(__A )
model.save_pretrained(__A )
AutoTokenizer.from_pretrained(__A ).save_pretrained(__A )
return model
if __name__ == "__main__":
fire.Fire(save_randomly_initialized_version)
| 36 | 1 |
from __future__ import annotations
__lowercase : Tuple = 1.6021E-19 # units = C
def lowercase ( __A : float , __A : float , __A : float , ) -> tuple[str, float]:
'''simple docstring'''
if (conductivity, electron_conc, mobility).count(0 ) != 1:
raise ValueError("""You cannot supply more or less than 2 values""" )
elif conductivity < 0:
raise ValueError("""Conductivity cannot be negative""" )
elif electron_conc < 0:
raise ValueError("""Electron concentration cannot be negative""" )
elif mobility < 0:
raise ValueError("""mobility cannot be negative""" )
elif conductivity == 0:
return (
"conductivity",
mobility * electron_conc * ELECTRON_CHARGE,
)
elif electron_conc == 0:
return (
"electron_conc",
conductivity / (mobility * ELECTRON_CHARGE),
)
else:
return (
"mobility",
conductivity / (electron_conc * ELECTRON_CHARGE),
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 36 |
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
__lowercase : Any = logging.get_logger(__name__)
__lowercase : str = {
'''google/mobilenet_v1_1.0_224''': '''https://huggingface.co/google/mobilenet_v1_1.0_224/resolve/main/config.json''',
'''google/mobilenet_v1_0.75_192''': '''https://huggingface.co/google/mobilenet_v1_0.75_192/resolve/main/config.json''',
# See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
}
class _A ( snake_case ):
'''simple docstring'''
__lowerCamelCase : Dict = '''mobilenet_v1'''
def __init__( self ,SCREAMING_SNAKE_CASE_=3 ,SCREAMING_SNAKE_CASE_=224 ,SCREAMING_SNAKE_CASE_=1.0 ,SCREAMING_SNAKE_CASE_=8 ,SCREAMING_SNAKE_CASE_="relu6" ,SCREAMING_SNAKE_CASE_=True ,SCREAMING_SNAKE_CASE_=0.9_99 ,SCREAMING_SNAKE_CASE_=0.02 ,SCREAMING_SNAKE_CASE_=0.0_01 ,**SCREAMING_SNAKE_CASE_ ,):
'''simple docstring'''
super().__init__(**SCREAMING_SNAKE_CASE_ )
if depth_multiplier <= 0:
raise ValueError("""depth_multiplier must be greater than zero.""" )
snake_case : List[Any] = num_channels
snake_case : str = image_size
snake_case : List[Any] = depth_multiplier
snake_case : Optional[int] = min_depth
snake_case : Union[str, Any] = hidden_act
snake_case : int = tf_padding
snake_case : Optional[int] = classifier_dropout_prob
snake_case : Tuple = initializer_range
snake_case : List[str] = layer_norm_eps
class _A ( snake_case ):
'''simple docstring'''
__lowerCamelCase : Optional[Any] = version.parse('''1.11''' )
@property
def snake_case_ ( self ):
'''simple docstring'''
return OrderedDict([("""pixel_values""", {0: """batch"""})] )
@property
def snake_case_ ( self ):
'''simple docstring'''
if self.task == "image-classification":
return OrderedDict([("""logits""", {0: """batch"""})] )
else:
return OrderedDict([("""last_hidden_state""", {0: """batch"""}), ("""pooler_output""", {0: """batch"""})] )
@property
def snake_case_ ( self ):
'''simple docstring'''
return 1E-4
| 36 | 1 |
def lowercase ( __A : int = 10 ) -> str:
'''simple docstring'''
if not isinstance(__A , __A ) or n < 0:
raise ValueError("""Invalid input""" )
snake_case : Dict = 10**n
snake_case : Optional[int] = 2_8433 * (pow(2 , 783_0457 , __A )) + 1
return str(number % modulus )
if __name__ == "__main__":
from doctest import testmod
testmod()
print(f'''{solution(10) = }''')
| 36 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowercase : List[str] = logging.get_logger(__name__)
__lowercase : List[str] = {
'''edbeeching/decision-transformer-gym-hopper-medium''': (
'''https://huggingface.co/edbeeching/decision-transformer-gym-hopper-medium/resolve/main/config.json'''
),
# See all DecisionTransformer models at https://huggingface.co/models?filter=decision_transformer
}
class _A ( snake_case ):
'''simple docstring'''
__lowerCamelCase : int = '''decision_transformer'''
__lowerCamelCase : Optional[Any] = ['''past_key_values''']
__lowerCamelCase : Tuple = {
'''max_position_embeddings''': '''n_positions''',
'''num_attention_heads''': '''n_head''',
'''num_hidden_layers''': '''n_layer''',
}
def __init__( self ,SCREAMING_SNAKE_CASE_=17 ,SCREAMING_SNAKE_CASE_=4 ,SCREAMING_SNAKE_CASE_=128 ,SCREAMING_SNAKE_CASE_=4096 ,SCREAMING_SNAKE_CASE_=True ,SCREAMING_SNAKE_CASE_=1 ,SCREAMING_SNAKE_CASE_=1024 ,SCREAMING_SNAKE_CASE_=3 ,SCREAMING_SNAKE_CASE_=1 ,SCREAMING_SNAKE_CASE_=None ,SCREAMING_SNAKE_CASE_="relu" ,SCREAMING_SNAKE_CASE_=0.1 ,SCREAMING_SNAKE_CASE_=0.1 ,SCREAMING_SNAKE_CASE_=0.1 ,SCREAMING_SNAKE_CASE_=1E-5 ,SCREAMING_SNAKE_CASE_=0.02 ,SCREAMING_SNAKE_CASE_=True ,SCREAMING_SNAKE_CASE_=True ,SCREAMING_SNAKE_CASE_=50256 ,SCREAMING_SNAKE_CASE_=50256 ,SCREAMING_SNAKE_CASE_=False ,SCREAMING_SNAKE_CASE_=False ,**SCREAMING_SNAKE_CASE_ ,):
'''simple docstring'''
snake_case : Any = state_dim
snake_case : Optional[Any] = act_dim
snake_case : Union[str, Any] = hidden_size
snake_case : Any = max_ep_len
snake_case : int = action_tanh
snake_case : Any = vocab_size
snake_case : Any = n_positions
snake_case : List[str] = n_layer
snake_case : int = n_head
snake_case : Optional[int] = n_inner
snake_case : List[Any] = activation_function
snake_case : Tuple = resid_pdrop
snake_case : Optional[Any] = embd_pdrop
snake_case : Dict = attn_pdrop
snake_case : List[str] = layer_norm_epsilon
snake_case : Union[str, Any] = initializer_range
snake_case : Optional[Any] = scale_attn_weights
snake_case : str = use_cache
snake_case : int = scale_attn_by_inverse_layer_idx
snake_case : Tuple = reorder_and_upcast_attn
snake_case : Tuple = bos_token_id
snake_case : List[str] = eos_token_id
super().__init__(bos_token_id=SCREAMING_SNAKE_CASE_ ,eos_token_id=SCREAMING_SNAKE_CASE_ ,**SCREAMING_SNAKE_CASE_ )
| 36 | 1 |
import os
import numpy
import onnx
def lowercase ( __A : str , __A : List[Any] ) -> Union[str, Any]:
'''simple docstring'''
snake_case : Any = a.name
snake_case : int = b.name
snake_case : Tuple = """"""
snake_case : Any = """"""
snake_case : Optional[int] = a == b
snake_case : Any = name_a
snake_case : str = name_b
return res
def lowercase ( __A : Optional[int] , __A : List[Any] , __A : List[Any] ) -> int:
'''simple docstring'''
for i, input_name in enumerate(node_proto.input ):
if input_name == name:
node_proto.input.insert(__A , __A )
node_proto.input.pop(i + 1 )
if node_proto.op_type == "If":
_graph_replace_input_with(node_proto.attribute[0].g , __A , __A )
_graph_replace_input_with(node_proto.attribute[1].g , __A , __A )
if node_proto.op_type == "Loop":
_graph_replace_input_with(node_proto.attribute[0].g , __A , __A )
def lowercase ( __A : Tuple , __A : Tuple , __A : List[str] ) -> Optional[Any]:
'''simple docstring'''
for n in graph_proto.node:
_node_replace_input_with(__A , __A , __A )
def lowercase ( __A : Dict , __A : Any , __A : Union[str, Any] ) -> Dict:
'''simple docstring'''
snake_case : Dict = list(model.graph.initializer )
snake_case : Optional[Any] = 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
snake_case : Optional[int] = inits[i].name
snake_case : str = 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 , __A , __A )
def lowercase ( __A : Tuple ) -> List[Any]:
'''simple docstring'''
snake_case : List[Any] = os.path.dirname(__A )
snake_case : Union[str, Any] = os.path.basename(__A )
snake_case : Dict = onnx.load(os.path.join(__A , __A ) )
snake_case : Optional[Any] = list(model.graph.initializer )
snake_case : Optional[Any] = set()
snake_case : Optional[int] = {}
snake_case : Optional[int] = []
snake_case : List[str] = 0
for i in range(len(__A ) ):
if i in dup_set:
continue
for j in range(i + 1 , len(__A ) ):
if j in dup_set:
continue
if _is_equal_tensor_proto(inits[i] , inits[j] ):
dup_set.add(__A )
dup_set.add(__A )
snake_case : Optional[Any] = inits[j].data_type
snake_case : Any = numpy.prod(inits[j].dims )
if dtype == 1:
mem_size *= 4
elif dtype == 6:
mem_size *= 4
elif dtype == 7 or dtype == 11:
mem_size *= 8
else:
print("""unexpected data type: """ , __A )
total_reduced_size += mem_size
snake_case : Tuple = inits[i].name
snake_case : Optional[Any] = inits[j].name
if name_i in dup_map:
dup_map[name_i].append(__A )
else:
snake_case : int = [name_j]
ind_to_replace.append((j, i) )
print("""total reduced size: """ , total_reduced_size / 1024 / 1024 / 1024 , """GB""" )
snake_case : Union[str, Any] = sorted(__A )
_remove_dup_initializers_from_model(__A , __A , __A )
snake_case : List[str] = """optimized_""" + model_file_name
snake_case : List[Any] = os.path.join(__A , __A )
onnx.save(__A , __A )
return new_model
| 36 |
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
| 36 | 1 |
def lowercase ( __A : int = 100 ) -> int:
'''simple docstring'''
snake_case : Dict = set()
snake_case : Optional[Any] = 0
snake_case : List[str] = n + 1 # maximum limit
for a in range(2 , __A ):
for b in range(2 , __A ):
snake_case : List[Any] = a**b # calculates the current power
collect_powers.add(__A ) # adds the result to the set
return len(__A )
if __name__ == "__main__":
print('''Number of terms ''', solution(int(str(input()).strip())))
| 36 |
# Usage:
# ./gen-card-facebook-wmt19.py
import os
from pathlib import Path
def lowercase ( __A : Dict , __A : Union[str, Any] , __A : List[str] ) -> Any:
'''simple docstring'''
snake_case : Tuple = {
"""en""": """Machine learning is great, isn't it?""",
"""ru""": """Машинное обучение - это здорово, не так ли?""",
"""de""": """Maschinelles Lernen ist großartig, oder?""",
}
# BLUE scores as follows:
# "pair": [fairseq, transformers]
snake_case : Optional[Any] = {
"""ru-en""": ["""[41.3](http://matrix.statmt.org/matrix/output/1907?run_id=6937)""", """39.20"""],
"""en-ru""": ["""[36.4](http://matrix.statmt.org/matrix/output/1914?run_id=6724)""", """33.47"""],
"""en-de""": ["""[43.1](http://matrix.statmt.org/matrix/output/1909?run_id=6862)""", """42.83"""],
"""de-en""": ["""[42.3](http://matrix.statmt.org/matrix/output/1902?run_id=6750)""", """41.35"""],
}
snake_case : Optional[int] = f"""{src_lang}-{tgt_lang}"""
snake_case : Any = f"""
---
language:
- {src_lang}
- {tgt_lang}
thumbnail:
tags:
- translation
- wmt19
- facebook
license: apache-2.0
datasets:
- wmt19
metrics:
- bleu
---
# FSMT
## Model description
This is a ported version of [fairseq wmt19 transformer](https://github.com/pytorch/fairseq/blob/master/examples/wmt19/README.md) for {src_lang}-{tgt_lang}.
For more details, please see, [Facebook FAIR's WMT19 News Translation Task Submission](https://arxiv.org/abs/1907.06616).
The abbreviation FSMT stands for FairSeqMachineTranslation
All four models are available:
* [wmt19-en-ru](https://huggingface.co/facebook/wmt19-en-ru)
* [wmt19-ru-en](https://huggingface.co/facebook/wmt19-ru-en)
* [wmt19-en-de](https://huggingface.co/facebook/wmt19-en-de)
* [wmt19-de-en](https://huggingface.co/facebook/wmt19-de-en)
## Intended uses & limitations
#### How to use
```python
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
mname = \"facebook/wmt19-{src_lang}-{tgt_lang}\"
tokenizer = FSMTTokenizer.from_pretrained(mname)
model = FSMTForConditionalGeneration.from_pretrained(mname)
input = \"{texts[src_lang]}\"
input_ids = tokenizer.encode(input, return_tensors=\"pt\")
outputs = model.generate(input_ids)
decoded = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(decoded) # {texts[tgt_lang]}
```
#### Limitations and bias
- The original (and this ported model) doesn't seem to handle well inputs with repeated sub-phrases, [content gets truncated](https://discuss.huggingface.co/t/issues-with-translating-inputs-containing-repeated-phrases/981)
## Training data
Pretrained weights were left identical to the original model released by fairseq. For more details, please, see the [paper](https://arxiv.org/abs/1907.06616).
## Eval results
pair | fairseq | transformers
-------|---------|----------
{pair} | {scores[pair][0]} | {scores[pair][1]}
The score is slightly below the score reported by `fairseq`, since `transformers`` currently doesn't support:
- model ensemble, therefore the best performing checkpoint was ported (``model4.pt``).
- re-ranking
The score was calculated using this code:
```bash
git clone https://github.com/huggingface/transformers
cd transformers
export PAIR={pair}
export DATA_DIR=data/$PAIR
export SAVE_DIR=data/$PAIR
export BS=8
export NUM_BEAMS=15
mkdir -p $DATA_DIR
sacrebleu -t wmt19 -l $PAIR --echo src > $DATA_DIR/val.source
sacrebleu -t wmt19 -l $PAIR --echo ref > $DATA_DIR/val.target
echo $PAIR
PYTHONPATH=\"src:examples/seq2seq\" python examples/seq2seq/run_eval.py facebook/wmt19-$PAIR $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation --num_beams $NUM_BEAMS
```
note: fairseq reports using a beam of 50, so you should get a slightly higher score if re-run with `--num_beams 50`.
## Data Sources
- [training, etc.](http://www.statmt.org/wmt19/)
- [test set](http://matrix.statmt.org/test_sets/newstest2019.tgz?1556572561)
### BibTeX entry and citation info
```bibtex
@inproceedings{{...,
year={{2020}},
title={{Facebook FAIR's WMT19 News Translation Task Submission}},
author={{Ng, Nathan and Yee, Kyra and Baevski, Alexei and Ott, Myle and Auli, Michael and Edunov, Sergey}},
booktitle={{Proc. of WMT}},
}}
```
## TODO
- port model ensemble (fairseq uses 4 model checkpoints)
"""
os.makedirs(__A , exist_ok=__A )
snake_case : Union[str, Any] = os.path.join(__A , """README.md""" )
print(f"""Generating {path}""" )
with open(__A , """w""" , encoding="""utf-8""" ) as f:
f.write(__A )
# make sure we are under the root of the project
__lowercase : int = Path(__file__).resolve().parent.parent.parent
__lowercase : List[str] = repo_dir / '''model_cards'''
for model_name in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
__lowercase , __lowercase , __lowercase : List[str] = model_name.split('''-''')
__lowercase : str = model_cards_dir / '''facebook''' / model_name
write_model_card(model_card_dir, src_lang=src_lang, tgt_lang=tgt_lang)
| 36 | 1 |
__lowercase : Optional[Any] = [
999,
800,
799,
600,
599,
500,
400,
399,
377,
355,
333,
311,
288,
266,
244,
222,
200,
199,
177,
155,
133,
111,
88,
66,
44,
22,
0,
]
__lowercase : Union[str, Any] = [
999,
976,
952,
928,
905,
882,
858,
857,
810,
762,
715,
714,
572,
429,
428,
286,
285,
238,
190,
143,
142,
118,
95,
71,
47,
24,
0,
]
__lowercase : Any = [
999,
988,
977,
966,
955,
944,
933,
922,
911,
900,
899,
879,
859,
840,
820,
800,
799,
766,
733,
700,
699,
650,
600,
599,
500,
499,
400,
399,
350,
300,
299,
266,
233,
200,
199,
179,
159,
140,
120,
100,
99,
88,
77,
66,
55,
44,
33,
22,
11,
0,
]
__lowercase : Union[str, Any] = [
999,
995,
992,
989,
985,
981,
978,
975,
971,
967,
964,
961,
957,
956,
951,
947,
942,
937,
933,
928,
923,
919,
914,
913,
908,
903,
897,
892,
887,
881,
876,
871,
870,
864,
858,
852,
846,
840,
834,
828,
827,
820,
813,
806,
799,
792,
785,
784,
777,
770,
763,
756,
749,
742,
741,
733,
724,
716,
707,
699,
698,
688,
677,
666,
656,
655,
645,
634,
623,
613,
612,
598,
584,
570,
569,
555,
541,
527,
526,
505,
484,
483,
462,
440,
439,
396,
395,
352,
351,
308,
307,
264,
263,
220,
219,
176,
132,
88,
44,
0,
]
__lowercase : Optional[int] = [
999,
997,
995,
992,
990,
988,
986,
984,
981,
979,
977,
975,
972,
970,
968,
966,
964,
961,
959,
957,
956,
954,
951,
949,
946,
944,
941,
939,
936,
934,
931,
929,
926,
924,
921,
919,
916,
914,
913,
910,
907,
905,
902,
899,
896,
893,
891,
888,
885,
882,
879,
877,
874,
871,
870,
867,
864,
861,
858,
855,
852,
849,
846,
843,
840,
837,
834,
831,
828,
827,
824,
821,
817,
814,
811,
808,
804,
801,
798,
795,
791,
788,
785,
784,
780,
777,
774,
770,
766,
763,
760,
756,
752,
749,
746,
742,
741,
737,
733,
730,
726,
722,
718,
714,
710,
707,
703,
699,
698,
694,
690,
685,
681,
677,
673,
669,
664,
660,
656,
655,
650,
646,
641,
636,
632,
627,
622,
618,
613,
612,
607,
602,
596,
591,
586,
580,
575,
570,
569,
563,
557,
551,
545,
539,
533,
527,
526,
519,
512,
505,
498,
491,
484,
483,
474,
466,
457,
449,
440,
439,
428,
418,
407,
396,
395,
381,
366,
352,
351,
330,
308,
307,
286,
264,
263,
242,
220,
219,
176,
175,
132,
131,
88,
44,
0,
]
__lowercase : Union[str, Any] = [
999,
991,
982,
974,
966,
958,
950,
941,
933,
925,
916,
908,
900,
899,
874,
850,
825,
800,
799,
700,
600,
500,
400,
300,
200,
100,
0,
]
__lowercase : List[Any] = [
999,
992,
985,
978,
971,
964,
957,
949,
942,
935,
928,
921,
914,
907,
900,
899,
879,
859,
840,
820,
800,
799,
766,
733,
700,
699,
650,
600,
599,
500,
499,
400,
399,
300,
299,
200,
199,
100,
99,
0,
]
__lowercase : Optional[int] = [
999,
996,
992,
989,
985,
982,
979,
975,
972,
968,
965,
961,
958,
955,
951,
948,
944,
941,
938,
934,
931,
927,
924,
920,
917,
914,
910,
907,
903,
900,
899,
891,
884,
876,
869,
861,
853,
846,
838,
830,
823,
815,
808,
800,
799,
788,
777,
766,
755,
744,
733,
722,
711,
700,
699,
688,
677,
666,
655,
644,
633,
622,
611,
600,
599,
585,
571,
557,
542,
528,
514,
500,
499,
485,
471,
457,
442,
428,
414,
400,
399,
379,
359,
340,
320,
300,
299,
279,
259,
240,
220,
200,
199,
166,
133,
100,
99,
66,
33,
0,
]
| 36 |
__lowercase : List[str] = '''
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
'''
__lowercase : str = [{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
__lowercase : List[str] = {
'''{processor_class}''': '''FakeProcessorClass''',
'''{model_class}''': '''FakeModelClass''',
'''{object_class}''': '''FakeObjectClass''',
}
| 36 | 1 |
import numpy as np
def lowercase ( __A : np.array ) -> np.array:
'''simple docstring'''
return (2 / (1 + np.exp(-2 * vector ))) - 1
if __name__ == "__main__":
import doctest
doctest.testmod()
| 36 |
import warnings
from ..trainer import Trainer
from ..utils import logging
__lowercase : str = logging.get_logger(__name__)
class _A ( snake_case ):
'''simple docstring'''
def __init__( self ,SCREAMING_SNAKE_CASE_=None ,**SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
warnings.warn(
"""`SageMakerTrainer` is deprecated and will be removed in v5 of Transformers. You can use `Trainer` """
"""instead.""" ,SCREAMING_SNAKE_CASE_ ,)
super().__init__(args=SCREAMING_SNAKE_CASE_ ,**SCREAMING_SNAKE_CASE_ )
| 36 | 1 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
is_valid_image,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_vision_available():
import PIL
__lowercase : Optional[Any] = logging.get_logger(__name__)
def lowercase ( __A : List[str] ) -> List[List[ImageInput]]:
'''simple docstring'''
if isinstance(__A , (list, tuple) ) and isinstance(videos[0] , (list, tuple) ) and is_valid_image(videos[0][0] ):
return videos
elif isinstance(__A , (list, tuple) ) and is_valid_image(videos[0] ):
return [videos]
elif is_valid_image(__A ):
return [[videos]]
raise ValueError(f"""Could not make batched video from {videos}""" )
class _A ( snake_case ):
'''simple docstring'''
__lowerCamelCase : List[str] = ['''pixel_values''']
def __init__( self ,SCREAMING_SNAKE_CASE_ = True ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_ = PILImageResampling.BILINEAR ,SCREAMING_SNAKE_CASE_ = True ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_ = True ,SCREAMING_SNAKE_CASE_ = 1 / 255 ,SCREAMING_SNAKE_CASE_ = True ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_ = None ,**SCREAMING_SNAKE_CASE_ ,):
'''simple docstring'''
super().__init__(**SCREAMING_SNAKE_CASE_ )
snake_case : List[str] = size if size is not None else {"""shortest_edge""": 224}
snake_case : str = get_size_dict(SCREAMING_SNAKE_CASE_ ,default_to_square=SCREAMING_SNAKE_CASE_ )
snake_case : Union[str, Any] = crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
snake_case : Optional[int] = get_size_dict(SCREAMING_SNAKE_CASE_ ,param_name="""crop_size""" )
snake_case : Optional[int] = do_resize
snake_case : Optional[Any] = size
snake_case : Optional[Any] = do_center_crop
snake_case : int = crop_size
snake_case : str = resample
snake_case : Optional[int] = do_rescale
snake_case : List[Any] = rescale_factor
snake_case : Tuple = do_normalize
snake_case : Dict = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
snake_case : Dict = image_std if image_std is not None else IMAGENET_STANDARD_STD
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ = PILImageResampling.BILINEAR ,SCREAMING_SNAKE_CASE_ = None ,**SCREAMING_SNAKE_CASE_ ,):
'''simple docstring'''
snake_case : int = get_size_dict(SCREAMING_SNAKE_CASE_ ,default_to_square=SCREAMING_SNAKE_CASE_ )
if "shortest_edge" in size:
snake_case : List[str] = get_resize_output_image_size(SCREAMING_SNAKE_CASE_ ,size["""shortest_edge"""] ,default_to_square=SCREAMING_SNAKE_CASE_ )
elif "height" in size and "width" in size:
snake_case : Optional[int] = (size["""height"""], size["""width"""])
else:
raise ValueError(F"""Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}""" )
return resize(SCREAMING_SNAKE_CASE_ ,size=SCREAMING_SNAKE_CASE_ ,resample=SCREAMING_SNAKE_CASE_ ,data_format=SCREAMING_SNAKE_CASE_ ,**SCREAMING_SNAKE_CASE_ )
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ = None ,**SCREAMING_SNAKE_CASE_ ,):
'''simple docstring'''
snake_case : Dict = get_size_dict(SCREAMING_SNAKE_CASE_ )
if "height" not in size or "width" not in size:
raise ValueError(F"""Size must have 'height' and 'width' as keys. Got {size.keys()}""" )
return center_crop(SCREAMING_SNAKE_CASE_ ,size=(size["""height"""], size["""width"""]) ,data_format=SCREAMING_SNAKE_CASE_ ,**SCREAMING_SNAKE_CASE_ )
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ = None ,**SCREAMING_SNAKE_CASE_ ,):
'''simple docstring'''
return rescale(SCREAMING_SNAKE_CASE_ ,scale=SCREAMING_SNAKE_CASE_ ,data_format=SCREAMING_SNAKE_CASE_ ,**SCREAMING_SNAKE_CASE_ )
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ = None ,**SCREAMING_SNAKE_CASE_ ,):
'''simple docstring'''
return normalize(SCREAMING_SNAKE_CASE_ ,mean=SCREAMING_SNAKE_CASE_ ,std=SCREAMING_SNAKE_CASE_ ,data_format=SCREAMING_SNAKE_CASE_ ,**SCREAMING_SNAKE_CASE_ )
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_ = ChannelDimension.FIRST ,):
'''simple docstring'''
if do_resize and size is None or resample is None:
raise ValueError("""Size and resample must be specified if do_resize is True.""" )
if do_center_crop and crop_size is None:
raise ValueError("""Crop size must be specified if do_center_crop is True.""" )
if do_rescale and rescale_factor is None:
raise ValueError("""Rescale factor must be specified if do_rescale is True.""" )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError("""Image mean and std must be specified if do_normalize is True.""" )
# All transformations expect numpy arrays.
snake_case : int = to_numpy_array(SCREAMING_SNAKE_CASE_ )
if do_resize:
snake_case : Any = self.resize(image=SCREAMING_SNAKE_CASE_ ,size=SCREAMING_SNAKE_CASE_ ,resample=SCREAMING_SNAKE_CASE_ )
if do_center_crop:
snake_case : List[Any] = self.center_crop(SCREAMING_SNAKE_CASE_ ,size=SCREAMING_SNAKE_CASE_ )
if do_rescale:
snake_case : int = self.rescale(image=SCREAMING_SNAKE_CASE_ ,scale=SCREAMING_SNAKE_CASE_ )
if do_normalize:
snake_case : Tuple = self.normalize(image=SCREAMING_SNAKE_CASE_ ,mean=SCREAMING_SNAKE_CASE_ ,std=SCREAMING_SNAKE_CASE_ )
snake_case : int = to_channel_dimension_format(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )
return image
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_ = ChannelDimension.FIRST ,**SCREAMING_SNAKE_CASE_ ,):
'''simple docstring'''
snake_case : int = do_resize if do_resize is not None else self.do_resize
snake_case : Union[str, Any] = resample if resample is not None else self.resample
snake_case : Tuple = do_center_crop if do_center_crop is not None else self.do_center_crop
snake_case : List[Any] = do_rescale if do_rescale is not None else self.do_rescale
snake_case : Dict = rescale_factor if rescale_factor is not None else self.rescale_factor
snake_case : List[Any] = do_normalize if do_normalize is not None else self.do_normalize
snake_case : Any = image_mean if image_mean is not None else self.image_mean
snake_case : List[Any] = image_std if image_std is not None else self.image_std
snake_case : Dict = size if size is not None else self.size
snake_case : List[Any] = get_size_dict(SCREAMING_SNAKE_CASE_ ,default_to_square=SCREAMING_SNAKE_CASE_ )
snake_case : str = crop_size if crop_size is not None else self.crop_size
snake_case : Any = get_size_dict(SCREAMING_SNAKE_CASE_ ,param_name="""crop_size""" )
if not valid_images(SCREAMING_SNAKE_CASE_ ):
raise ValueError(
"""Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
"""torch.Tensor, tf.Tensor or jax.ndarray.""" )
snake_case : List[str] = make_batched(SCREAMING_SNAKE_CASE_ )
snake_case : Any = [
[
self._preprocess_image(
image=SCREAMING_SNAKE_CASE_ ,do_resize=SCREAMING_SNAKE_CASE_ ,size=SCREAMING_SNAKE_CASE_ ,resample=SCREAMING_SNAKE_CASE_ ,do_center_crop=SCREAMING_SNAKE_CASE_ ,crop_size=SCREAMING_SNAKE_CASE_ ,do_rescale=SCREAMING_SNAKE_CASE_ ,rescale_factor=SCREAMING_SNAKE_CASE_ ,do_normalize=SCREAMING_SNAKE_CASE_ ,image_mean=SCREAMING_SNAKE_CASE_ ,image_std=SCREAMING_SNAKE_CASE_ ,data_format=SCREAMING_SNAKE_CASE_ ,)
for img in video
]
for video in videos
]
snake_case : Tuple = {"""pixel_values""": videos}
return BatchFeature(data=SCREAMING_SNAKE_CASE_ ,tensor_type=SCREAMING_SNAKE_CASE_ )
| 36 |
from pathlib import Path
from typing import List
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
__lowercase : List[str] = ['''text''', '''image''', '''audio''']
def lowercase ( __A : List[str] ) -> List[str]:
'''simple docstring'''
snake_case : Optional[int] = []
for input_type in input_types:
if input_type == "text":
inputs.append("""Text input""" )
elif input_type == "image":
inputs.append(
Image.open(Path(get_tests_dir("""fixtures/tests_samples/COCO""" ) ) / """000000039769.png""" ).resize((512, 512) ) )
elif input_type == "audio":
inputs.append(torch.ones(3000 ) )
elif isinstance(__A , __A ):
inputs.append(create_inputs(__A ) )
else:
raise ValueError(f"""Invalid type requested: {input_type}""" )
return inputs
def lowercase ( __A : List ) -> Union[str, Any]:
'''simple docstring'''
snake_case : Dict = []
for output in outputs:
if isinstance(__A , (str, AgentText) ):
output_types.append("""text""" )
elif isinstance(__A , (Image.Image, AgentImage) ):
output_types.append("""image""" )
elif isinstance(__A , (torch.Tensor, AgentAudio) ):
output_types.append("""audio""" )
else:
raise ValueError(f"""Invalid output: {output}""" )
return output_types
@is_tool_test
class _A :
'''simple docstring'''
def snake_case_ ( self ):
'''simple docstring'''
self.assertTrue(hasattr(self.tool ,"""inputs""" ) )
self.assertTrue(hasattr(self.tool ,"""outputs""" ) )
snake_case : Dict = self.tool.inputs
for _input in inputs:
if isinstance(_input ,SCREAMING_SNAKE_CASE_ ):
for __input in _input:
self.assertTrue(__input in authorized_types )
else:
self.assertTrue(_input in authorized_types )
snake_case : int = self.tool.outputs
for _output in outputs:
self.assertTrue(_output in authorized_types )
def snake_case_ ( self ):
'''simple docstring'''
snake_case : List[str] = create_inputs(self.tool.inputs )
snake_case : str = self.tool(*SCREAMING_SNAKE_CASE_ )
# There is a single output
if len(self.tool.outputs ) == 1:
snake_case : Union[str, Any] = [outputs]
self.assertListEqual(output_types(SCREAMING_SNAKE_CASE_ ) ,self.tool.outputs )
def snake_case_ ( self ):
'''simple docstring'''
self.assertTrue(hasattr(self.tool ,"""description""" ) )
self.assertTrue(hasattr(self.tool ,"""default_checkpoint""" ) )
self.assertTrue(self.tool.description.startswith("""This is a tool that""" ) )
def snake_case_ ( self ):
'''simple docstring'''
snake_case : List[Any] = create_inputs(self.tool.inputs )
snake_case : int = self.tool(*SCREAMING_SNAKE_CASE_ )
if not isinstance(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ):
snake_case : Any = [outputs]
self.assertEqual(len(SCREAMING_SNAKE_CASE_ ) ,len(self.tool.outputs ) )
for output, output_type in zip(SCREAMING_SNAKE_CASE_ ,self.tool.outputs ):
snake_case : List[str] = AGENT_TYPE_MAPPING[output_type]
self.assertTrue(isinstance(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ) )
def snake_case_ ( self ):
'''simple docstring'''
snake_case : Tuple = create_inputs(self.tool.inputs )
snake_case : Any = []
for _input, input_type in zip(SCREAMING_SNAKE_CASE_ ,self.tool.inputs ):
if isinstance(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ):
_inputs.append([AGENT_TYPE_MAPPING[_input_type](_input ) for _input_type in input_type] )
else:
_inputs.append(AGENT_TYPE_MAPPING[input_type](_input ) )
# Should not raise an error
snake_case : Tuple = self.tool(*SCREAMING_SNAKE_CASE_ )
if not isinstance(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ):
snake_case : Union[str, Any] = [outputs]
self.assertEqual(len(SCREAMING_SNAKE_CASE_ ) ,len(self.tool.outputs ) )
| 36 | 1 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image
from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
__lowercase : List[Any] = logging.get_logger(__name__)
def lowercase ( __A : Any ) -> Dict:
'''simple docstring'''
snake_case : int = DPTConfig()
if "large" in checkpoint_url:
snake_case : Optional[Any] = 1024
snake_case : List[str] = 4096
snake_case : Optional[int] = 24
snake_case : Dict = 16
snake_case : Optional[Any] = [5, 11, 17, 23]
snake_case : int = [256, 512, 1024, 1024]
snake_case : int = (1, 384, 384)
if "ade" in checkpoint_url:
snake_case : str = True
snake_case : Optional[int] = 150
snake_case : List[str] = """huggingface/label-files"""
snake_case : Optional[int] = """ade20k-id2label.json"""
snake_case : Optional[int] = json.load(open(cached_download(hf_hub_url(__A , __A , repo_type="""dataset""" ) ) , """r""" ) )
snake_case : Tuple = {int(__A ): v for k, v in idalabel.items()}
snake_case : Any = idalabel
snake_case : Any = {v: k for k, v in idalabel.items()}
snake_case : Dict = [1, 150, 480, 480]
return config, expected_shape
def lowercase ( __A : int ) -> int:
'''simple docstring'''
snake_case : List[Any] = ["""pretrained.model.head.weight""", """pretrained.model.head.bias"""]
for k in ignore_keys:
state_dict.pop(__A , __A )
def lowercase ( __A : Tuple ) -> str:
'''simple docstring'''
if (
"pretrained.model" in name
and "cls_token" not in name
and "pos_embed" not in name
and "patch_embed" not in name
):
snake_case : List[str] = name.replace("""pretrained.model""" , """dpt.encoder""" )
if "pretrained.model" in name:
snake_case : int = name.replace("""pretrained.model""" , """dpt.embeddings""" )
if "patch_embed" in name:
snake_case : Tuple = name.replace("""patch_embed""" , """patch_embeddings""" )
if "pos_embed" in name:
snake_case : List[str] = name.replace("""pos_embed""" , """position_embeddings""" )
if "attn.proj" in name:
snake_case : int = name.replace("""attn.proj""" , """attention.output.dense""" )
if "proj" in name and "project" not in name:
snake_case : int = name.replace("""proj""" , """projection""" )
if "blocks" in name:
snake_case : Union[str, Any] = name.replace("""blocks""" , """layer""" )
if "mlp.fc1" in name:
snake_case : Optional[int] = name.replace("""mlp.fc1""" , """intermediate.dense""" )
if "mlp.fc2" in name:
snake_case : List[Any] = name.replace("""mlp.fc2""" , """output.dense""" )
if "norm1" in name:
snake_case : List[Any] = name.replace("""norm1""" , """layernorm_before""" )
if "norm2" in name:
snake_case : List[str] = name.replace("""norm2""" , """layernorm_after""" )
if "scratch.output_conv" in name:
snake_case : str = name.replace("""scratch.output_conv""" , """head""" )
if "scratch" in name:
snake_case : Dict = name.replace("""scratch""" , """neck""" )
if "layer1_rn" in name:
snake_case : Optional[int] = name.replace("""layer1_rn""" , """convs.0""" )
if "layer2_rn" in name:
snake_case : Tuple = name.replace("""layer2_rn""" , """convs.1""" )
if "layer3_rn" in name:
snake_case : Any = name.replace("""layer3_rn""" , """convs.2""" )
if "layer4_rn" in name:
snake_case : Optional[Any] = name.replace("""layer4_rn""" , """convs.3""" )
if "refinenet" in name:
snake_case : int = int(name[len("""neck.refinenet""" ) : len("""neck.refinenet""" ) + 1] )
# tricky here: we need to map 4 to 0, 3 to 1, 2 to 2 and 1 to 3
snake_case : Tuple = name.replace(f"""refinenet{layer_idx}""" , f"""fusion_stage.layers.{abs(layer_idx-4 )}""" )
if "out_conv" in name:
snake_case : Tuple = name.replace("""out_conv""" , """projection""" )
if "resConfUnit1" in name:
snake_case : Optional[Any] = name.replace("""resConfUnit1""" , """residual_layer1""" )
if "resConfUnit2" in name:
snake_case : Optional[Any] = name.replace("""resConfUnit2""" , """residual_layer2""" )
if "conv1" in name:
snake_case : Dict = name.replace("""conv1""" , """convolution1""" )
if "conv2" in name:
snake_case : List[Any] = name.replace("""conv2""" , """convolution2""" )
# readout blocks
if "pretrained.act_postprocess1.0.project.0" in name:
snake_case : List[Any] = name.replace("""pretrained.act_postprocess1.0.project.0""" , """neck.reassemble_stage.readout_projects.0.0""" )
if "pretrained.act_postprocess2.0.project.0" in name:
snake_case : List[str] = name.replace("""pretrained.act_postprocess2.0.project.0""" , """neck.reassemble_stage.readout_projects.1.0""" )
if "pretrained.act_postprocess3.0.project.0" in name:
snake_case : Optional[Any] = name.replace("""pretrained.act_postprocess3.0.project.0""" , """neck.reassemble_stage.readout_projects.2.0""" )
if "pretrained.act_postprocess4.0.project.0" in name:
snake_case : List[str] = name.replace("""pretrained.act_postprocess4.0.project.0""" , """neck.reassemble_stage.readout_projects.3.0""" )
# resize blocks
if "pretrained.act_postprocess1.3" in name:
snake_case : Any = name.replace("""pretrained.act_postprocess1.3""" , """neck.reassemble_stage.layers.0.projection""" )
if "pretrained.act_postprocess1.4" in name:
snake_case : Tuple = name.replace("""pretrained.act_postprocess1.4""" , """neck.reassemble_stage.layers.0.resize""" )
if "pretrained.act_postprocess2.3" in name:
snake_case : Optional[int] = name.replace("""pretrained.act_postprocess2.3""" , """neck.reassemble_stage.layers.1.projection""" )
if "pretrained.act_postprocess2.4" in name:
snake_case : Any = name.replace("""pretrained.act_postprocess2.4""" , """neck.reassemble_stage.layers.1.resize""" )
if "pretrained.act_postprocess3.3" in name:
snake_case : List[Any] = name.replace("""pretrained.act_postprocess3.3""" , """neck.reassemble_stage.layers.2.projection""" )
if "pretrained.act_postprocess4.3" in name:
snake_case : List[str] = name.replace("""pretrained.act_postprocess4.3""" , """neck.reassemble_stage.layers.3.projection""" )
if "pretrained.act_postprocess4.4" in name:
snake_case : str = name.replace("""pretrained.act_postprocess4.4""" , """neck.reassemble_stage.layers.3.resize""" )
if "pretrained" in name:
snake_case : Any = name.replace("""pretrained""" , """dpt""" )
if "bn" in name:
snake_case : Optional[Any] = name.replace("""bn""" , """batch_norm""" )
if "head" in name:
snake_case : Optional[int] = name.replace("""head""" , """head.head""" )
if "encoder.norm" in name:
snake_case : Optional[int] = name.replace("""encoder.norm""" , """layernorm""" )
if "auxlayer" in name:
snake_case : Union[str, Any] = name.replace("""auxlayer""" , """auxiliary_head.head""" )
return name
def lowercase ( __A : Dict , __A : Tuple ) -> Dict:
'''simple docstring'''
for i in range(config.num_hidden_layers ):
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
snake_case : Any = state_dict.pop(f"""dpt.encoder.layer.{i}.attn.qkv.weight""" )
snake_case : List[str] = state_dict.pop(f"""dpt.encoder.layer.{i}.attn.qkv.bias""" )
# next, add query, keys and values (in that order) to the state dict
snake_case : Optional[int] = in_proj_weight[: config.hidden_size, :]
snake_case : Optional[Any] = in_proj_bias[: config.hidden_size]
snake_case : Union[str, Any] = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
snake_case : str = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
snake_case : List[str] = in_proj_weight[
-config.hidden_size :, :
]
snake_case : Tuple = in_proj_bias[-config.hidden_size :]
def lowercase ( ) -> Any:
'''simple docstring'''
snake_case : Optional[int] = """http://images.cocodataset.org/val2017/000000039769.jpg"""
snake_case : List[Any] = Image.open(requests.get(__A , stream=__A ).raw )
return im
@torch.no_grad()
def lowercase ( __A : Tuple , __A : Optional[int] , __A : Optional[Any] , __A : Optional[Any] ) -> Union[str, Any]:
'''simple docstring'''
snake_case , snake_case : Union[str, Any] = get_dpt_config(__A )
# load original state_dict from URL
snake_case : Union[str, Any] = torch.hub.load_state_dict_from_url(__A , map_location="""cpu""" )
# remove certain keys
remove_ignore_keys_(__A )
# rename keys
for key in state_dict.copy().keys():
snake_case : Optional[Any] = state_dict.pop(__A )
snake_case : List[str] = val
# read in qkv matrices
read_in_q_k_v(__A , __A )
# load HuggingFace model
snake_case : int = DPTForSemanticSegmentation(__A ) if """ade""" in checkpoint_url else DPTForDepthEstimation(__A )
model.load_state_dict(__A )
model.eval()
# Check outputs on an image
snake_case : int = 480 if """ade""" in checkpoint_url else 384
snake_case : Optional[int] = DPTImageProcessor(size=__A )
snake_case : List[Any] = prepare_img()
snake_case : Optional[Any] = image_processor(__A , return_tensors="""pt""" )
# forward pass
snake_case : Any = model(**__A ).logits if """ade""" in checkpoint_url else model(**__A ).predicted_depth
# Assert logits
snake_case : int = torch.tensor([[6.3_199, 6.3_629, 6.4_148], [6.3_850, 6.3_615, 6.4_166], [6.3_519, 6.3_176, 6.3_575]] )
if "ade" in checkpoint_url:
snake_case : List[str] = torch.tensor([[4.0_480, 4.2_420, 4.4_360], [4.3_124, 4.5_693, 4.8_261], [4.5_768, 4.8_965, 5.2_163]] )
assert outputs.shape == torch.Size(__A )
assert (
torch.allclose(outputs[0, 0, :3, :3] , __A , atol=1E-4 )
if "ade" in checkpoint_url
else torch.allclose(outputs[0, :3, :3] , __A )
)
Path(__A ).mkdir(exist_ok=__A )
print(f"""Saving model to {pytorch_dump_folder_path}""" )
model.save_pretrained(__A )
print(f"""Saving image processor to {pytorch_dump_folder_path}""" )
image_processor.save_pretrained(__A )
if push_to_hub:
print("""Pushing model to hub...""" )
model.push_to_hub(
repo_path_or_name=Path(__A , __A ) , organization="""nielsr""" , commit_message="""Add model""" , use_temp_dir=__A , )
image_processor.push_to_hub(
repo_path_or_name=Path(__A , __A ) , organization="""nielsr""" , commit_message="""Add image processor""" , use_temp_dir=__A , )
if __name__ == "__main__":
__lowercase : Tuple = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--checkpoint_url''',
default='''https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt''',
type=str,
help='''URL of the original DPT checkpoint you\'d like to convert.''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''',
default=None,
type=str,
required=True,
help='''Path to the output PyTorch model directory.''',
)
parser.add_argument(
'''--push_to_hub''',
action='''store_true''',
)
parser.add_argument(
'''--model_name''',
default='''dpt-large''',
type=str,
help='''Name of the model, in case you\'re pushing to the hub.''',
)
__lowercase : List[Any] = parser.parse_args()
convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)
| 36 |
import os
import pytest
from datasets import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
)
__lowercase : Optional[Any] = pytest.mark.integration
@pytest.mark.parametrize("""path""" , ["""paws""", """csv"""] )
def lowercase ( __A : Optional[Any] , __A : Optional[Any] ) -> str:
'''simple docstring'''
inspect_dataset(__A , __A )
snake_case : List[str] = path + """.py"""
assert script_name in os.listdir(__A )
assert "__pycache__" not in os.listdir(__A )
@pytest.mark.filterwarnings("""ignore:inspect_metric is deprecated:FutureWarning""" )
@pytest.mark.filterwarnings("""ignore:metric_module_factory is deprecated:FutureWarning""" )
@pytest.mark.parametrize("""path""" , ["""accuracy"""] )
def lowercase ( __A : Optional[int] , __A : Any ) -> Optional[Any]:
'''simple docstring'''
inspect_metric(__A , __A )
snake_case : Any = path + """.py"""
assert script_name in os.listdir(__A )
assert "__pycache__" not in os.listdir(__A )
@pytest.mark.parametrize(
"""path, config_name, expected_splits""" , [
("""squad""", """plain_text""", ["""train""", """validation"""]),
("""dalle-mini/wit""", """dalle-mini--wit""", ["""train"""]),
("""paws""", """labeled_final""", ["""train""", """test""", """validation"""]),
] , )
def lowercase ( __A : Tuple , __A : Dict , __A : Any ) -> Optional[int]:
'''simple docstring'''
snake_case : List[str] = get_dataset_config_info(__A , config_name=__A )
assert info.config_name == config_name
assert list(info.splits.keys() ) == expected_splits
@pytest.mark.parametrize(
"""path, config_name, expected_exception""" , [
("""paws""", None, ValueError),
] , )
def lowercase ( __A : Tuple , __A : Any , __A : List[str] ) -> Optional[int]:
'''simple docstring'''
with pytest.raises(__A ):
get_dataset_config_info(__A , config_name=__A )
@pytest.mark.parametrize(
"""path, expected""" , [
("""squad""", """plain_text"""),
("""acronym_identification""", """default"""),
("""lhoestq/squad""", """plain_text"""),
("""lhoestq/test""", """default"""),
("""lhoestq/demo1""", """lhoestq--demo1"""),
("""dalle-mini/wit""", """dalle-mini--wit"""),
] , )
def lowercase ( __A : Any , __A : Dict ) -> Dict:
'''simple docstring'''
snake_case : int = get_dataset_config_names(__A )
assert expected in config_names
@pytest.mark.parametrize(
"""path, expected_configs, expected_splits_in_first_config""" , [
("""squad""", ["""plain_text"""], ["""train""", """validation"""]),
("""dalle-mini/wit""", ["""dalle-mini--wit"""], ["""train"""]),
("""paws""", ["""labeled_final""", """labeled_swap""", """unlabeled_final"""], ["""train""", """test""", """validation"""]),
] , )
def lowercase ( __A : Optional[Any] , __A : Dict , __A : List[str] ) -> Union[str, Any]:
'''simple docstring'''
snake_case : List[Any] = get_dataset_infos(__A )
assert list(infos.keys() ) == expected_configs
snake_case : Any = expected_configs[0]
assert expected_config in infos
snake_case : Any = infos[expected_config]
assert info.config_name == expected_config
assert list(info.splits.keys() ) == expected_splits_in_first_config
@pytest.mark.parametrize(
"""path, expected_config, expected_splits""" , [
("""squad""", """plain_text""", ["""train""", """validation"""]),
("""dalle-mini/wit""", """dalle-mini--wit""", ["""train"""]),
("""paws""", """labeled_final""", ["""train""", """test""", """validation"""]),
] , )
def lowercase ( __A : Optional[int] , __A : Tuple , __A : Union[str, Any] ) -> Optional[int]:
'''simple docstring'''
snake_case : Dict = get_dataset_infos(__A )
assert expected_config in infos
snake_case : str = infos[expected_config]
assert info.config_name == expected_config
assert list(info.splits.keys() ) == expected_splits
@pytest.mark.parametrize(
"""path, config_name, expected_exception""" , [
("""paws""", None, ValueError),
] , )
def lowercase ( __A : Optional[int] , __A : Any , __A : Dict ) -> int:
'''simple docstring'''
with pytest.raises(__A ):
get_dataset_split_names(__A , config_name=__A )
| 36 | 1 |
from typing import Dict, List, Optional
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__lowercase : int = logging.get_logger(__name__)
__lowercase : List[Any] = {
'''nielsr/canine-s''': 2_048,
}
# Unicode defines 1,114,112 total “codepoints”
__lowercase : List[str] = 1_114_112
# Below: Constants defining canonical codepoints for special, pseudo-characters.
# Copied from https://github.com/google-research/language/blob/master/language/canine/special_codepoints.py
__lowercase : int = 0
__lowercase : Union[str, Any] = 0Xe000
__lowercase : Tuple = 0Xe001
__lowercase : List[Any] = 0Xe002
__lowercase : Optional[Any] = 0Xe003
__lowercase : Union[str, Any] = 0Xe004
# Maps special codepoints to human-readable names.
__lowercase : Dict[int, str] = {
# Special symbols are represented using codepoints values that are valid,
# but designated as "Private Use", meaning that they will never be assigned
# characters by the Unicode Consortium, and are thus safe for use here.
#
# NOTE: Do *NOT* add any sort of [UNK_CHAR] here. They are explicitly
# excluded and should fail with a hard error.
CLS: "[CLS]",
SEP: "[SEP]",
BOS: "[BOS]",
MASK: "[MASK]",
PAD: "[PAD]",
RESERVED: "[RESERVED]",
}
# Maps special codepoint human-readable names to their codepoint values.
__lowercase : Dict[str, int] = {name: codepoint for codepoint, name in SPECIAL_CODEPOINTS.items()}
class _A ( snake_case ):
'''simple docstring'''
__lowerCamelCase : List[str] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
def __init__( self ,SCREAMING_SNAKE_CASE_=chr(SCREAMING_SNAKE_CASE_ ) ,SCREAMING_SNAKE_CASE_=chr(SCREAMING_SNAKE_CASE_ ) ,SCREAMING_SNAKE_CASE_=chr(SCREAMING_SNAKE_CASE_ ) ,SCREAMING_SNAKE_CASE_=chr(SCREAMING_SNAKE_CASE_ ) ,SCREAMING_SNAKE_CASE_=chr(SCREAMING_SNAKE_CASE_ ) ,SCREAMING_SNAKE_CASE_=chr(SCREAMING_SNAKE_CASE_ ) ,SCREAMING_SNAKE_CASE_=False ,SCREAMING_SNAKE_CASE_=2048 ,**SCREAMING_SNAKE_CASE_ ,):
'''simple docstring'''
snake_case : Optional[Any] = AddedToken(SCREAMING_SNAKE_CASE_ ,lstrip=SCREAMING_SNAKE_CASE_ ,rstrip=SCREAMING_SNAKE_CASE_ ) if isinstance(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ) else bos_token
snake_case : Tuple = AddedToken(SCREAMING_SNAKE_CASE_ ,lstrip=SCREAMING_SNAKE_CASE_ ,rstrip=SCREAMING_SNAKE_CASE_ ) if isinstance(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ) else eos_token
snake_case : Optional[Any] = AddedToken(SCREAMING_SNAKE_CASE_ ,lstrip=SCREAMING_SNAKE_CASE_ ,rstrip=SCREAMING_SNAKE_CASE_ ) if isinstance(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ) else sep_token
snake_case : str = AddedToken(SCREAMING_SNAKE_CASE_ ,lstrip=SCREAMING_SNAKE_CASE_ ,rstrip=SCREAMING_SNAKE_CASE_ ) if isinstance(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ) else cls_token
snake_case : Tuple = AddedToken(SCREAMING_SNAKE_CASE_ ,lstrip=SCREAMING_SNAKE_CASE_ ,rstrip=SCREAMING_SNAKE_CASE_ ) if isinstance(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ) else pad_token
# Mask token behave like a normal word, i.e. include the space before it
snake_case : List[Any] = AddedToken(SCREAMING_SNAKE_CASE_ ,lstrip=SCREAMING_SNAKE_CASE_ ,rstrip=SCREAMING_SNAKE_CASE_ ) if isinstance(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ) else mask_token
super().__init__(
bos_token=SCREAMING_SNAKE_CASE_ ,eos_token=SCREAMING_SNAKE_CASE_ ,sep_token=SCREAMING_SNAKE_CASE_ ,cls_token=SCREAMING_SNAKE_CASE_ ,pad_token=SCREAMING_SNAKE_CASE_ ,mask_token=SCREAMING_SNAKE_CASE_ ,add_prefix_space=SCREAMING_SNAKE_CASE_ ,model_max_length=SCREAMING_SNAKE_CASE_ ,**SCREAMING_SNAKE_CASE_ ,)
# Creates a mapping for looking up the IDs of special symbols.
snake_case : Dict[str, int] = {}
for codepoint, name in SPECIAL_CODEPOINTS.items():
snake_case : Optional[Any] = codepoint
# Creates a mapping for looking up the string forms of special symbol IDs.
snake_case : Dict[int, str] = {
codepoint: name for name, codepoint in self._special_codepoints.items()
}
snake_case : Tuple = UNICODE_VOCAB_SIZE
snake_case : Union[str, Any] = len(self._special_codepoints )
@property
def snake_case_ ( self ):
'''simple docstring'''
return self._unicode_vocab_size
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
return list(SCREAMING_SNAKE_CASE_ )
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
try:
return ord(SCREAMING_SNAKE_CASE_ )
except TypeError:
raise ValueError(F"""invalid token: '{token}'""" )
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
try:
if index in SPECIAL_CODEPOINTS:
return SPECIAL_CODEPOINTS[index]
return chr(SCREAMING_SNAKE_CASE_ )
except TypeError:
raise ValueError(F"""invalid id: {index}""" )
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
return "".join(SCREAMING_SNAKE_CASE_ )
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ = None ):
'''simple docstring'''
snake_case : List[str] = [self.sep_token_id]
snake_case : Tuple = [self.cls_token_id]
snake_case : List[Any] = cls + token_ids_a + sep
if token_ids_a is not None:
result += token_ids_a + sep
return result
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_ = False ):
'''simple docstring'''
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=SCREAMING_SNAKE_CASE_ ,token_ids_a=SCREAMING_SNAKE_CASE_ ,already_has_special_tokens=SCREAMING_SNAKE_CASE_ )
snake_case : Dict = [1] + ([0] * len(SCREAMING_SNAKE_CASE_ )) + [1]
if token_ids_a is not None:
result += ([0] * len(SCREAMING_SNAKE_CASE_ )) + [1]
return result
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ = None ):
'''simple docstring'''
snake_case : Optional[int] = [self.sep_token_id]
snake_case : Optional[Any] = [self.cls_token_id]
snake_case : Union[str, Any] = len(cls + token_ids_a + sep ) * [0]
if token_ids_a is not None:
result += len(token_ids_a + sep ) * [1]
return result
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ = None ):
'''simple docstring'''
return ()
| 36 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
__lowercase : Optional[Any] = {
'''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/config.json''',
'''albert-large-v1''': '''https://huggingface.co/albert-large-v1/resolve/main/config.json''',
'''albert-xlarge-v1''': '''https://huggingface.co/albert-xlarge-v1/resolve/main/config.json''',
'''albert-xxlarge-v1''': '''https://huggingface.co/albert-xxlarge-v1/resolve/main/config.json''',
'''albert-base-v2''': '''https://huggingface.co/albert-base-v2/resolve/main/config.json''',
'''albert-large-v2''': '''https://huggingface.co/albert-large-v2/resolve/main/config.json''',
'''albert-xlarge-v2''': '''https://huggingface.co/albert-xlarge-v2/resolve/main/config.json''',
'''albert-xxlarge-v2''': '''https://huggingface.co/albert-xxlarge-v2/resolve/main/config.json''',
}
class _A ( snake_case ):
'''simple docstring'''
__lowerCamelCase : int = '''albert'''
def __init__( self ,SCREAMING_SNAKE_CASE_=30000 ,SCREAMING_SNAKE_CASE_=128 ,SCREAMING_SNAKE_CASE_=4096 ,SCREAMING_SNAKE_CASE_=12 ,SCREAMING_SNAKE_CASE_=1 ,SCREAMING_SNAKE_CASE_=64 ,SCREAMING_SNAKE_CASE_=16384 ,SCREAMING_SNAKE_CASE_=1 ,SCREAMING_SNAKE_CASE_="gelu_new" ,SCREAMING_SNAKE_CASE_=0 ,SCREAMING_SNAKE_CASE_=0 ,SCREAMING_SNAKE_CASE_=512 ,SCREAMING_SNAKE_CASE_=2 ,SCREAMING_SNAKE_CASE_=0.02 ,SCREAMING_SNAKE_CASE_=1E-12 ,SCREAMING_SNAKE_CASE_=0.1 ,SCREAMING_SNAKE_CASE_="absolute" ,SCREAMING_SNAKE_CASE_=0 ,SCREAMING_SNAKE_CASE_=2 ,SCREAMING_SNAKE_CASE_=3 ,**SCREAMING_SNAKE_CASE_ ,):
'''simple docstring'''
super().__init__(pad_token_id=SCREAMING_SNAKE_CASE_ ,bos_token_id=SCREAMING_SNAKE_CASE_ ,eos_token_id=SCREAMING_SNAKE_CASE_ ,**SCREAMING_SNAKE_CASE_ )
snake_case : List[str] = vocab_size
snake_case : int = embedding_size
snake_case : int = hidden_size
snake_case : List[Any] = num_hidden_layers
snake_case : int = num_hidden_groups
snake_case : List[str] = num_attention_heads
snake_case : List[str] = inner_group_num
snake_case : Any = hidden_act
snake_case : Any = intermediate_size
snake_case : Union[str, Any] = hidden_dropout_prob
snake_case : List[Any] = attention_probs_dropout_prob
snake_case : Tuple = max_position_embeddings
snake_case : Any = type_vocab_size
snake_case : Optional[Any] = initializer_range
snake_case : int = layer_norm_eps
snake_case : Optional[int] = classifier_dropout_prob
snake_case : str = position_embedding_type
class _A ( snake_case ):
'''simple docstring'''
@property
def snake_case_ ( self ):
'''simple docstring'''
if self.task == "multiple-choice":
snake_case : List[Any] = {0: """batch""", 1: """choice""", 2: """sequence"""}
else:
snake_case : int = {0: """batch""", 1: """sequence"""}
return OrderedDict(
[
("""input_ids""", dynamic_axis),
("""attention_mask""", dynamic_axis),
("""token_type_ids""", dynamic_axis),
] )
| 36 | 1 |
def lowercase ( __A : str ) -> str:
'''simple docstring'''
snake_case : Optional[int] = 0
# if input_string is "aba" than new_input_string become "a|b|a"
snake_case : Optional[int] = """"""
snake_case : Tuple = """"""
# append each character + "|" in new_string for range(0, length-1)
for i in input_string[: len(__A ) - 1]:
new_input_string += i + "|"
# append last character
new_input_string += input_string[-1]
# we will store the starting and ending of previous furthest ending palindromic
# substring
snake_case , snake_case : Any = 0, 0
# length[i] shows the length of palindromic substring with center i
snake_case : Union[str, Any] = [1 for i in range(len(__A ) )]
# for each character in new_string find corresponding palindromic string
snake_case : Tuple = 0
for j in range(len(__A ) ):
snake_case : List[str] = 1 if j > r else min(length[l + r - j] // 2 , r - j + 1 )
while (
j - k >= 0
and j + k < len(__A )
and new_input_string[k + j] == new_input_string[j - k]
):
k += 1
snake_case : List[str] = 2 * k - 1
# does this string is ending after the previously explored end (that is r) ?
# if yes the update the new r to the last index of this
if j + k - 1 > r:
snake_case : str = j - k + 1 # noqa: E741
snake_case : int = j + k - 1
# update max_length and start position
if max_length < length[j]:
snake_case : str = length[j]
snake_case : Optional[Any] = j
# create that string
snake_case : int = new_input_string[start - max_length // 2 : start + max_length // 2 + 1]
for i in s:
if i != "|":
output_string += i
return output_string
if __name__ == "__main__":
import doctest
doctest.testmod()
| 36 |
from __future__ import annotations
def lowercase ( __A : list ) -> float:
'''simple docstring'''
if not nums:
raise ValueError("""List is empty""" )
return sum(__A ) / len(__A )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 36 | 1 |
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
__lowercase : Any = logging.get_logger(__name__)
__lowercase : str = {
'''google/mobilenet_v1_1.0_224''': '''https://huggingface.co/google/mobilenet_v1_1.0_224/resolve/main/config.json''',
'''google/mobilenet_v1_0.75_192''': '''https://huggingface.co/google/mobilenet_v1_0.75_192/resolve/main/config.json''',
# See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
}
class _A ( snake_case ):
'''simple docstring'''
__lowerCamelCase : Dict = '''mobilenet_v1'''
def __init__( self ,SCREAMING_SNAKE_CASE_=3 ,SCREAMING_SNAKE_CASE_=224 ,SCREAMING_SNAKE_CASE_=1.0 ,SCREAMING_SNAKE_CASE_=8 ,SCREAMING_SNAKE_CASE_="relu6" ,SCREAMING_SNAKE_CASE_=True ,SCREAMING_SNAKE_CASE_=0.9_99 ,SCREAMING_SNAKE_CASE_=0.02 ,SCREAMING_SNAKE_CASE_=0.0_01 ,**SCREAMING_SNAKE_CASE_ ,):
'''simple docstring'''
super().__init__(**SCREAMING_SNAKE_CASE_ )
if depth_multiplier <= 0:
raise ValueError("""depth_multiplier must be greater than zero.""" )
snake_case : List[Any] = num_channels
snake_case : str = image_size
snake_case : List[Any] = depth_multiplier
snake_case : Optional[int] = min_depth
snake_case : Union[str, Any] = hidden_act
snake_case : int = tf_padding
snake_case : Optional[int] = classifier_dropout_prob
snake_case : Tuple = initializer_range
snake_case : List[str] = layer_norm_eps
class _A ( snake_case ):
'''simple docstring'''
__lowerCamelCase : Optional[Any] = version.parse('''1.11''' )
@property
def snake_case_ ( self ):
'''simple docstring'''
return OrderedDict([("""pixel_values""", {0: """batch"""})] )
@property
def snake_case_ ( self ):
'''simple docstring'''
if self.task == "image-classification":
return OrderedDict([("""logits""", {0: """batch"""})] )
else:
return OrderedDict([("""last_hidden_state""", {0: """batch"""}), ("""pooler_output""", {0: """batch"""})] )
@property
def snake_case_ ( self ):
'''simple docstring'''
return 1E-4
| 36 |
import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig
if TYPE_CHECKING:
from ... import PreTrainedTokenizerBase, TensorType
__lowercase : List[str] = logging.get_logger(__name__)
class _A ( snake_case ):
'''simple docstring'''
__lowerCamelCase : Tuple = '''vision-encoder-decoder'''
__lowerCamelCase : List[Any] = True
def __init__( self ,**SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
super().__init__(**SCREAMING_SNAKE_CASE_ )
if "encoder" not in kwargs or "decoder" not in kwargs:
raise ValueError(
F"""A configuraton of type {self.model_type} cannot be instantiated because """
F"""not both `encoder` and `decoder` sub-configurations are passed, but only {kwargs}""" )
snake_case : Union[str, Any] = kwargs.pop("""encoder""" )
snake_case : Any = encoder_config.pop("""model_type""" )
snake_case : Optional[Any] = kwargs.pop("""decoder""" )
snake_case : Union[str, Any] = decoder_config.pop("""model_type""" )
snake_case : Any = AutoConfig.for_model(SCREAMING_SNAKE_CASE_ ,**SCREAMING_SNAKE_CASE_ )
snake_case : Union[str, Any] = AutoConfig.for_model(SCREAMING_SNAKE_CASE_ ,**SCREAMING_SNAKE_CASE_ )
snake_case : int = True
@classmethod
def snake_case_ ( cls ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,**SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
logger.info("""Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config""" )
snake_case : Tuple = True
snake_case : Union[str, Any] = True
return cls(encoder=encoder_config.to_dict() ,decoder=decoder_config.to_dict() ,**SCREAMING_SNAKE_CASE_ )
def snake_case_ ( self ):
'''simple docstring'''
snake_case : Union[str, Any] = copy.deepcopy(self.__dict__ )
snake_case : Union[str, Any] = self.encoder.to_dict()
snake_case : Union[str, Any] = self.decoder.to_dict()
snake_case : Dict = self.__class__.model_type
return output
class _A ( snake_case ):
'''simple docstring'''
__lowerCamelCase : Optional[Any] = version.parse('''1.11''' )
@property
def snake_case_ ( self ):
'''simple docstring'''
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
] )
@property
def snake_case_ ( self ):
'''simple docstring'''
return 1E-4
@property
def snake_case_ ( self ):
'''simple docstring'''
return OrderedDict({"""last_hidden_state""": {0: """batch""", 1: """encoder_sequence"""}} )
class _A ( snake_case ):
'''simple docstring'''
@property
def snake_case_ ( self ):
'''simple docstring'''
snake_case : Tuple = OrderedDict()
snake_case : Optional[int] = {0: """batch""", 1: """past_decoder_sequence + sequence"""}
snake_case : Union[str, Any] = {0: """batch""", 1: """past_decoder_sequence + sequence"""}
snake_case : Optional[Any] = {0: """batch""", 1: """encoder_sequence"""}
return common_inputs
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ = -1 ,SCREAMING_SNAKE_CASE_ = -1 ,SCREAMING_SNAKE_CASE_ = False ,SCREAMING_SNAKE_CASE_ = None ,):
'''simple docstring'''
import torch
snake_case : Optional[Any] = OrderedDict()
snake_case : Tuple = super().generate_dummy_inputs(
SCREAMING_SNAKE_CASE_ ,batch_size=SCREAMING_SNAKE_CASE_ ,seq_length=SCREAMING_SNAKE_CASE_ ,is_pair=SCREAMING_SNAKE_CASE_ ,framework=SCREAMING_SNAKE_CASE_ )
snake_case , snake_case : List[Any] = dummy_input["""input_ids"""].shape
snake_case : Optional[int] = (batch, encoder_sequence, self._config.encoder_hidden_size)
snake_case : List[str] = dummy_input.pop("""input_ids""" )
snake_case : int = dummy_input.pop("""attention_mask""" )
snake_case : Dict = torch.zeros(SCREAMING_SNAKE_CASE_ )
return common_inputs
class _A ( snake_case ):
'''simple docstring'''
@property
def snake_case_ ( self ):
'''simple docstring'''
pass
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
return VisionEncoderDecoderEncoderOnnxConfig(SCREAMING_SNAKE_CASE_ )
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ = "default" ):
'''simple docstring'''
snake_case : int = encoder_config.hidden_size
return VisionEncoderDecoderDecoderOnnxConfig(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )
| 36 | 1 |
import argparse
import json
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler
__lowercase : List[str] = 16
__lowercase : Union[str, Any] = 32
def lowercase ( __A : Accelerator , __A : int = 16 , __A : str = "bert-base-cased" ) -> Optional[Any]:
'''simple docstring'''
snake_case : str = AutoTokenizer.from_pretrained(__A )
snake_case : Optional[int] = load_dataset("""glue""" , """mrpc""" )
def tokenize_function(__A : Optional[int] ):
# max_length=None => use the model max length (it's actually the default)
snake_case : Optional[int] = tokenizer(examples["""sentence1"""] , examples["""sentence2"""] , truncation=__A , max_length=__A )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
snake_case : str = datasets.map(
__A , batched=__A , remove_columns=["""idx""", """sentence1""", """sentence2"""] , load_from_cache_file=__A )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
snake_case : Dict = tokenized_datasets.rename_column("""label""" , """labels""" )
def collate_fn(__A : str ):
# On TPU it's best to pad everything to the same length or training will be very slow.
if accelerator.distributed_type == DistributedType.TPU:
return tokenizer.pad(__A , padding="""max_length""" , max_length=128 , return_tensors="""pt""" )
return tokenizer.pad(__A , padding="""longest""" , return_tensors="""pt""" )
# Instantiate dataloaders.
snake_case : str = DataLoader(
tokenized_datasets["""train"""] , shuffle=__A , collate_fn=__A , batch_size=__A )
snake_case : Any = DataLoader(
tokenized_datasets["""validation"""] , shuffle=__A , collate_fn=__A , batch_size=__A )
return train_dataloader, eval_dataloader
def lowercase ( __A : int , __A : Union[str, Any] , __A : Any , __A : Tuple ) -> str:
'''simple docstring'''
model.eval()
snake_case : Optional[Any] = 0
for step, batch in enumerate(__A ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
snake_case : Tuple = model(**__A )
snake_case : Any = outputs.logits.argmax(dim=-1 )
# It is slightly faster to call this once, than multiple times
snake_case , snake_case : str = accelerator.gather(
(predictions, batch["""labels"""]) ) # If we are in a multiprocess environment, the last batch has duplicates
if accelerator.use_distributed:
if step == len(__A ) - 1:
snake_case : str = predictions[: len(eval_dataloader.dataset ) - samples_seen]
snake_case : Dict = references[: len(eval_dataloader.dataset ) - samples_seen]
else:
samples_seen += references.shape[0]
metric.add_batch(
predictions=__A , references=__A , )
snake_case : Union[str, Any] = metric.compute()
return eval_metric["accuracy"]
def lowercase ( __A : Optional[Any] , __A : int ) -> Union[str, Any]:
'''simple docstring'''
snake_case : List[Any] = Accelerator()
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
snake_case : Any = config["""lr"""]
snake_case : Dict = int(config["""num_epochs"""] )
snake_case : Union[str, Any] = int(config["""seed"""] )
snake_case : Union[str, Any] = int(config["""batch_size"""] )
snake_case : List[Any] = args.model_name_or_path
set_seed(__A )
snake_case , snake_case : str = get_dataloaders(__A , __A , __A )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
snake_case : Union[str, Any] = AutoModelForSequenceClassification.from_pretrained(__A , return_dict=__A )
# Instantiate optimizer
snake_case : str = (
AdamW
if accelerator.state.deepspeed_plugin is None
or """optimizer""" not in accelerator.state.deepspeed_plugin.deepspeed_config
else DummyOptim
)
snake_case : Dict = optimizer_cls(params=model.parameters() , lr=__A )
if accelerator.state.deepspeed_plugin is not None:
snake_case : Union[str, Any] = accelerator.state.deepspeed_plugin.deepspeed_config[
"""gradient_accumulation_steps"""
]
else:
snake_case : Dict = 1
snake_case : Tuple = (len(__A ) * num_epochs) // gradient_accumulation_steps
# Instantiate scheduler
if (
accelerator.state.deepspeed_plugin is None
or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
):
snake_case : Optional[int] = get_linear_schedule_with_warmup(
optimizer=__A , num_warmup_steps=0 , num_training_steps=__A , )
else:
snake_case : Dict = DummyScheduler(__A , total_num_steps=__A , warmup_num_steps=0 )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
snake_case , snake_case , snake_case , snake_case , snake_case : Tuple = accelerator.prepare(
__A , __A , __A , __A , __A )
# We need to keep track of how many total steps we have iterated over
snake_case : Optional[int] = 0
# We also need to keep track of the stating epoch so files are named properly
snake_case : str = 0
snake_case : Dict = evaluate.load("""glue""" , """mrpc""" )
snake_case : int = num_epochs
if args.partial_train_epoch is not None:
snake_case : Dict = args.partial_train_epoch
if args.resume_from_checkpoint:
accelerator.load_state(args.resume_from_checkpoint )
snake_case : List[str] = args.resume_from_checkpoint.split("""epoch_""" )[1]
snake_case : List[str] = """"""
for char in epoch_string:
if char.isdigit():
state_epoch_num += char
else:
break
snake_case : Optional[Any] = int(__A ) + 1
snake_case : Optional[int] = evaluation_loop(__A , __A , __A , __A )
accelerator.print("""resumed checkpoint performance:""" , __A )
accelerator.print("""resumed checkpoint's scheduler's lr:""" , lr_scheduler.get_lr()[0] )
accelerator.print("""resumed optimizers's lr:""" , optimizer.param_groups[0]["""lr"""] )
with open(os.path.join(args.output_dir , f"""state_{starting_epoch-1}.json""" ) , """r""" ) as f:
snake_case : List[str] = json.load(__A )
assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
assert (
resumed_state["lr"] == lr_scheduler.get_lr()[0]
), "Scheduler learning rate mismatch, loading from checkpoint failed"
assert (
resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
), "Optimizer learning rate mismatch, loading from checkpoint failed"
assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
return
# Now we train the model
snake_case : int = {}
for epoch in range(__A , __A ):
model.train()
for step, batch in enumerate(__A ):
snake_case : Dict = model(**__A )
snake_case : Union[str, Any] = outputs.loss
snake_case : int = loss / gradient_accumulation_steps
accelerator.backward(__A )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
overall_step += 1
snake_case : Any = f"""epoch_{epoch}"""
snake_case : Optional[int] = os.path.join(args.output_dir , __A )
accelerator.save_state(__A )
snake_case : Optional[int] = evaluation_loop(__A , __A , __A , __A )
snake_case : str = accuracy
snake_case : Optional[int] = lr_scheduler.get_lr()[0]
snake_case : str = optimizer.param_groups[0]["""lr"""]
snake_case : Tuple = epoch
snake_case : Optional[int] = overall_step
accelerator.print(f"""epoch {epoch}:""" , __A )
accelerator.wait_for_everyone()
if accelerator.is_main_process:
with open(os.path.join(args.output_dir , f"""state_{epoch}.json""" ) , """w""" ) as f:
json.dump(__A , __A )
def lowercase ( ) -> Dict:
'''simple docstring'''
snake_case : int = argparse.ArgumentParser(description="""Simple example of training script tracking peak GPU memory usage.""" )
parser.add_argument(
"""--model_name_or_path""" , type=__A , default="""bert-base-cased""" , help="""Path to pretrained model or model identifier from huggingface.co/models.""" , required=__A , )
parser.add_argument(
"""--output_dir""" , type=__A , default=""".""" , help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory.""" , )
parser.add_argument(
"""--resume_from_checkpoint""" , type=__A , default=__A , help="""If the training should continue from a checkpoint folder.""" , )
parser.add_argument(
"""--partial_train_epoch""" , type=__A , default=__A , help="""If passed, the training will stop after this number of epochs.""" , )
parser.add_argument(
"""--num_epochs""" , type=__A , default=2 , help="""Number of train epochs.""" , )
snake_case : int = parser.parse_args()
snake_case : List[Any] = {"""lr""": 2E-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}
training_function(__A , __A )
if __name__ == "__main__":
main()
| 36 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers import BitConfig, BitForImageClassification, BitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging
logging.set_verbosity_info()
__lowercase : Any = logging.get_logger(__name__)
def lowercase ( __A : Optional[Any] ) -> Dict:
'''simple docstring'''
snake_case : Dict = """huggingface/label-files"""
snake_case : int = """imagenet-1k-id2label.json"""
snake_case : Tuple = json.load(open(hf_hub_download(__A , __A , repo_type="""dataset""" ) , """r""" ) )
snake_case : Any = {int(__A ): v for k, v in idalabel.items()}
snake_case : Dict = {v: k for k, v in idalabel.items()}
snake_case : Any = """std_conv""" if """bit""" in model_name else False
# note that when using BiT as backbone for ViT-hybrid checkpoints,
# one needs to additionally set config.layer_type = "bottleneck", config.stem_type = "same",
# config.conv_layer = "std_conv_same"
snake_case : List[Any] = BitConfig(
conv_layer=__A , num_labels=1000 , idalabel=__A , labelaid=__A , )
return config
def lowercase ( __A : Optional[int] ) -> Union[str, Any]:
'''simple docstring'''
if "stem.conv" in name:
snake_case : List[str] = name.replace("""stem.conv""" , """bit.embedder.convolution""" )
if "blocks" in name:
snake_case : List[str] = name.replace("""blocks""" , """layers""" )
if "head.fc" in name:
snake_case : Optional[int] = name.replace("""head.fc""" , """classifier.1""" )
if name.startswith("""norm""" ):
snake_case : Optional[Any] = """bit.""" + name
if "bit" not in name and "classifier" not in name:
snake_case : Tuple = """bit.encoder.""" + name
return name
def lowercase ( ) -> Optional[int]:
'''simple docstring'''
snake_case : int = """http://images.cocodataset.org/val2017/000000039769.jpg"""
snake_case : Optional[Any] = Image.open(requests.get(__A , stream=__A ).raw )
return im
@torch.no_grad()
def lowercase ( __A : Any , __A : Union[str, Any] , __A : str=False ) -> Optional[int]:
'''simple docstring'''
snake_case : str = get_config(__A )
# load original model from timm
snake_case : Tuple = create_model(__A , pretrained=__A )
timm_model.eval()
# load state_dict of original model
snake_case : List[str] = timm_model.state_dict()
for key in state_dict.copy().keys():
snake_case : List[Any] = state_dict.pop(__A )
snake_case : Union[str, Any] = val.squeeze() if """head""" in key else val
# load HuggingFace model
snake_case : List[Any] = BitForImageClassification(__A )
model.eval()
model.load_state_dict(__A )
# create image processor
snake_case : Dict = create_transform(**resolve_data_config({} , model=__A ) )
snake_case : Optional[Any] = transform.transforms
snake_case : List[Any] = {
"""bilinear""": PILImageResampling.BILINEAR,
"""bicubic""": PILImageResampling.BICUBIC,
"""nearest""": PILImageResampling.NEAREST,
}
snake_case : Union[str, Any] = BitImageProcessor(
do_resize=__A , size={"""shortest_edge""": timm_transforms[0].size} , resample=pillow_resamplings[timm_transforms[0].interpolation.value] , do_center_crop=__A , crop_size={"""height""": timm_transforms[1].size[0], """width""": timm_transforms[1].size[1]} , do_normalize=__A , image_mean=timm_transforms[-1].mean.tolist() , image_std=timm_transforms[-1].std.tolist() , )
snake_case : Dict = prepare_img()
snake_case : List[str] = transform(__A ).unsqueeze(0 )
snake_case : int = processor(__A , return_tensors="""pt""" ).pixel_values
# verify pixel values
assert torch.allclose(__A , __A )
# verify logits
with torch.no_grad():
snake_case : Optional[int] = model(__A )
snake_case : Dict = outputs.logits
print("""Logits:""" , logits[0, :3] )
print("""Predicted class:""" , model.config.idalabel[logits.argmax(-1 ).item()] )
snake_case : int = timm_model(__A )
assert timm_logits.shape == outputs.logits.shape
assert torch.allclose(__A , outputs.logits , atol=1E-3 )
print("""Looks ok!""" )
if pytorch_dump_folder_path is not None:
Path(__A ).mkdir(exist_ok=__A )
print(f"""Saving model {model_name} and processor to {pytorch_dump_folder_path}""" )
model.save_pretrained(__A )
processor.save_pretrained(__A )
if push_to_hub:
print(f"""Pushing model {model_name} and processor to the hub""" )
model.push_to_hub(f"""ybelkada/{model_name}""" )
processor.push_to_hub(f"""ybelkada/{model_name}""" )
if __name__ == "__main__":
__lowercase : List[str] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--model_name''',
default='''resnetv2_50x1_bitm''',
type=str,
help='''Name of the BiT timm model you\'d like to convert.''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
)
parser.add_argument(
'''--push_to_hub''',
action='''store_true''',
help='''Whether to push the model to the hub.''',
)
__lowercase : Union[str, Any] = parser.parse_args()
convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 36 | 1 |
from collections import Counter
from pathlib import Path
from typing import Optional, Tuple
import yaml
class _A ( yaml.SafeLoader ):
'''simple docstring'''
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
snake_case : Tuple = [self.constructed_objects[key_node] for key_node, _ in node.value]
snake_case : Optional[Any] = [tuple(SCREAMING_SNAKE_CASE_ ) if isinstance(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ) else key for key in keys]
snake_case : Optional[int] = Counter(SCREAMING_SNAKE_CASE_ )
snake_case : List[Any] = [key for key in counter if counter[key] > 1]
if duplicate_keys:
raise TypeError(F"""Got duplicate yaml keys: {duplicate_keys}""" )
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_=False ):
'''simple docstring'''
snake_case : Union[str, Any] = super().construct_mapping(SCREAMING_SNAKE_CASE_ ,deep=SCREAMING_SNAKE_CASE_ )
self._check_no_duplicates_on_constructed_node(SCREAMING_SNAKE_CASE_ )
return mapping
def lowercase ( __A : str ) -> Tuple[Optional[str], str]:
'''simple docstring'''
snake_case : Any = list(readme_content.splitlines() )
if full_content and full_content[0] == "---" and "---" in full_content[1:]:
snake_case : Tuple = full_content[1:].index("""---""" ) + 1
snake_case : str = """\n""".join(full_content[1:sep_idx] )
return yamlblock, "\n".join(full_content[sep_idx + 1 :] )
return None, "\n".join(__A )
class _A ( snake_case ):
'''simple docstring'''
__lowerCamelCase : Any = {'''train_eval_index'''} # train-eval-index in the YAML metadata
@classmethod
def snake_case_ ( cls ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
with open(SCREAMING_SNAKE_CASE_ ,encoding="""utf-8""" ) as readme_file:
snake_case , snake_case : int = _split_yaml_from_readme(readme_file.read() )
if yaml_string is not None:
return cls.from_yaml_string(SCREAMING_SNAKE_CASE_ )
else:
return cls()
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
if path.exists():
with open(SCREAMING_SNAKE_CASE_ ,encoding="""utf-8""" ) as readme_file:
snake_case : Union[str, Any] = readme_file.read()
else:
snake_case : Dict = None
snake_case : int = self._to_readme(SCREAMING_SNAKE_CASE_ )
with open(SCREAMING_SNAKE_CASE_ ,"""w""" ,encoding="""utf-8""" ) as readme_file:
readme_file.write(SCREAMING_SNAKE_CASE_ )
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ = None ):
'''simple docstring'''
if readme_content is not None:
snake_case , snake_case : List[str] = _split_yaml_from_readme(SCREAMING_SNAKE_CASE_ )
snake_case : Union[str, Any] = """---\n""" + self.to_yaml_string() + """---\n""" + content
else:
snake_case : Optional[int] = """---\n""" + self.to_yaml_string() + """---\n"""
return full_content
@classmethod
def snake_case_ ( cls ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
snake_case : List[Any] = yaml.load(SCREAMING_SNAKE_CASE_ ,Loader=_NoDuplicateSafeLoader ) or {}
# Convert the YAML keys to DatasetMetadata fields
snake_case : Dict = {
(key.replace("""-""" ,"""_""" ) if key.replace("""-""" ,"""_""" ) in cls._FIELDS_WITH_DASHES else key): value
for key, value in metadata_dict.items()
}
return cls(**SCREAMING_SNAKE_CASE_ )
def snake_case_ ( self ):
'''simple docstring'''
return yaml.safe_dump(
{
(key.replace("""_""" ,"""-""" ) if key in self._FIELDS_WITH_DASHES else key): value
for key, value in self.items()
} ,sort_keys=SCREAMING_SNAKE_CASE_ ,allow_unicode=SCREAMING_SNAKE_CASE_ ,encoding="""utf-8""" ,).decode("""utf-8""" )
__lowercase : Union[str, Any] = {
'''image-classification''': [],
'''translation''': [],
'''image-segmentation''': [],
'''fill-mask''': [],
'''automatic-speech-recognition''': [],
'''token-classification''': [],
'''sentence-similarity''': [],
'''audio-classification''': [],
'''question-answering''': [],
'''summarization''': [],
'''zero-shot-classification''': [],
'''table-to-text''': [],
'''feature-extraction''': [],
'''other''': [],
'''multiple-choice''': [],
'''text-classification''': [],
'''text-to-image''': [],
'''text2text-generation''': [],
'''zero-shot-image-classification''': [],
'''tabular-classification''': [],
'''tabular-regression''': [],
'''image-to-image''': [],
'''tabular-to-text''': [],
'''unconditional-image-generation''': [],
'''text-retrieval''': [],
'''text-to-speech''': [],
'''object-detection''': [],
'''audio-to-audio''': [],
'''text-generation''': [],
'''conversational''': [],
'''table-question-answering''': [],
'''visual-question-answering''': [],
'''image-to-text''': [],
'''reinforcement-learning''': [],
'''voice-activity-detection''': [],
'''time-series-forecasting''': [],
'''document-question-answering''': [],
}
if __name__ == "__main__":
from argparse import ArgumentParser
__lowercase : List[str] = ArgumentParser(usage='''Validate the yaml metadata block of a README.md file.''')
ap.add_argument('''readme_filepath''')
__lowercase : Any = ap.parse_args()
__lowercase : List[Any] = Path(args.readme_filepath)
__lowercase : str = DatasetMetadata.from_readme(readme_filepath)
print(dataset_metadata)
dataset_metadata.to_readme(readme_filepath)
| 36 |
import os
import pytest
from attr import dataclass
__lowercase : Optional[int] = '''us-east-1''' # defaults region
@dataclass
class _A :
'''simple docstring'''
__lowerCamelCase : str
__lowerCamelCase : Dict = '''arn:aws:iam::558105141721:role/sagemaker_execution_role'''
__lowerCamelCase : Optional[Any] = {
'''task_name''': '''mnli''',
'''per_device_train_batch_size''': 1_6,
'''per_device_eval_batch_size''': 1_6,
'''do_train''': True,
'''do_eval''': True,
'''do_predict''': True,
'''output_dir''': '''/opt/ml/model''',
'''overwrite_output_dir''': True,
'''max_steps''': 5_0_0,
'''save_steps''': 5_5_0_0,
}
__lowerCamelCase : List[str] = {**hyperparameters, '''max_steps''': 1_0_0_0}
@property
def snake_case_ ( self ):
'''simple docstring'''
if self.framework == "pytorch":
return [
{"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
{"Name": "eval_accuracy", "Regex": r"eval_accuracy.*=\D*(.*?)$"},
{"Name": "eval_loss", "Regex": r"eval_loss.*=\D*(.*?)$"},
]
else:
return [
{"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
{"Name": "eval_accuracy", "Regex": r"loss.*=\D*(.*?)]?$"},
{"Name": "eval_loss", "Regex": r"sparse_categorical_accuracy.*=\D*(.*?)]?$"},
]
@property
def snake_case_ ( self ):
'''simple docstring'''
return F"""{self.framework}-transfromers-test"""
@property
def snake_case_ ( self ):
'''simple docstring'''
return F"""./tests/sagemaker/scripts/{self.framework}"""
@property
def snake_case_ ( self ):
'''simple docstring'''
if self.framework == "pytorch":
return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-pytorch-training:1.7.1-transformers4.6.1-gpu-py36-cu110-ubuntu18.04"
else:
return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-tensorflow-training:2.4.1-transformers4.6.1-gpu-py37-cu110-ubuntu18.04"
@pytest.fixture(scope="""class""" )
def lowercase ( __A : List[str] ) -> List[str]:
'''simple docstring'''
snake_case : Optional[Any] = SageMakerTestEnvironment(framework=request.cls.framework )
| 36 | 1 |
from __future__ import annotations
def lowercase ( __A : str , __A : list[str] | None = None ) -> list[list[str]]:
'''simple docstring'''
snake_case : Optional[int] = word_bank or []
# create a table
snake_case : int = len(__A ) + 1
snake_case : list[list[list[str]]] = []
for _ in range(__A ):
table.append([] )
# seed value
snake_case : Optional[Any] = [[]] # because empty string has empty combination
# iterate through the indices
for i in range(__A ):
# condition
if table[i] != []:
for word in word_bank:
# slice condition
if target[i : i + len(__A )] == word:
snake_case : list[list[str]] = [
[word, *way] for way in table[i]
]
# adds the word to every combination the current position holds
# now,push that combination to the table[i+len(word)]
table[i + len(__A )] += new_combinations
# combinations are in reverse order so reverse for better output
for combination in table[len(__A )]:
combination.reverse()
return table[len(__A )]
if __name__ == "__main__":
print(all_construct('''jwajalapa''', ['''jwa''', '''j''', '''w''', '''a''', '''la''', '''lapa''']))
print(all_construct('''rajamati''', ['''s''', '''raj''', '''amat''', '''raja''', '''ma''', '''i''', '''t''']))
print(
all_construct(
'''hexagonosaurus''',
['''h''', '''ex''', '''hex''', '''ag''', '''ago''', '''ru''', '''auru''', '''rus''', '''go''', '''no''', '''o''', '''s'''],
)
)
| 36 |
from .imports import is_rich_available
if is_rich_available():
from rich.traceback import install
install(show_locals=False)
else:
raise ModuleNotFoundError('''To use the rich extension, install rich with `pip install rich`''')
| 36 | 1 |
def lowercase ( __A : list ) -> list:
'''simple docstring'''
snake_case : Optional[Any] = False
while is_sorted is False: # Until all the indices are traversed keep looping
snake_case : Tuple = True
for i in range(0 , len(__A ) - 1 , 2 ): # iterating over all even indices
if input_list[i] > input_list[i + 1]:
snake_case , snake_case : List[Any] = input_list[i + 1], input_list[i]
# swapping if elements not in order
snake_case : Tuple = False
for i in range(1 , len(__A ) - 1 , 2 ): # iterating over all odd indices
if input_list[i] > input_list[i + 1]:
snake_case , snake_case : Union[str, Any] = input_list[i + 1], input_list[i]
# swapping if elements not in order
snake_case : Union[str, Any] = False
return input_list
if __name__ == "__main__":
print('''Enter list to be sorted''')
__lowercase : int = [int(x) for x in input().split()]
# inputing elements of the list in one line
__lowercase : List[str] = odd_even_sort(input_list)
print('''The sorted list is''')
print(sorted_list)
| 36 |
import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union
import tqdm
from filelock import FileLock
from transformers import (
BartTokenizer,
BartTokenizerFast,
DataProcessor,
PreTrainedTokenizer,
RobertaTokenizer,
RobertaTokenizerFast,
XLMRobertaTokenizer,
is_tf_available,
is_torch_available,
)
__lowercase : Any = logging.getLogger(__name__)
@dataclass(frozen=snake_case )
class _A :
'''simple docstring'''
__lowerCamelCase : str
__lowerCamelCase : str
__lowerCamelCase : Optional[str] = None
__lowerCamelCase : Optional[str] = None
__lowerCamelCase : Optional[str] = None
@dataclass(frozen=snake_case )
class _A :
'''simple docstring'''
__lowerCamelCase : List[int]
__lowerCamelCase : Optional[List[int]] = None
__lowerCamelCase : Optional[List[int]] = None
__lowerCamelCase : Optional[Union[int, float]] = None
__lowerCamelCase : Optional[int] = None
if is_torch_available():
import torch
from torch.utils.data import Dataset
class _A ( snake_case ):
'''simple docstring'''
__lowerCamelCase : List[InputFeatures]
def __init__( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_=False ,SCREAMING_SNAKE_CASE_ = False ,):
'''simple docstring'''
snake_case : str = hans_processors[task]()
snake_case : str = os.path.join(
SCREAMING_SNAKE_CASE_ ,"""cached_{}_{}_{}_{}""".format(
"""dev""" if evaluate else """train""" ,tokenizer.__class__.__name__ ,str(SCREAMING_SNAKE_CASE_ ) ,SCREAMING_SNAKE_CASE_ ,) ,)
snake_case : Dict = processor.get_labels()
if tokenizer.__class__ in (
RobertaTokenizer,
RobertaTokenizerFast,
XLMRobertaTokenizer,
BartTokenizer,
BartTokenizerFast,
):
# HACK(label indices are swapped in RoBERTa pretrained model)
snake_case , snake_case : List[Any] = label_list[2], label_list[1]
snake_case : List[Any] = label_list
# Make sure only the first process in distributed training processes the dataset,
# and the others will use the cache.
snake_case : Any = cached_features_file + """.lock"""
with FileLock(SCREAMING_SNAKE_CASE_ ):
if os.path.exists(SCREAMING_SNAKE_CASE_ ) and not overwrite_cache:
logger.info(F"""Loading features from cached file {cached_features_file}""" )
snake_case : int = torch.load(SCREAMING_SNAKE_CASE_ )
else:
logger.info(F"""Creating features from dataset file at {data_dir}""" )
snake_case : Union[str, Any] = (
processor.get_dev_examples(SCREAMING_SNAKE_CASE_ ) if evaluate else processor.get_train_examples(SCREAMING_SNAKE_CASE_ )
)
logger.info("""Training examples: %s""" ,len(SCREAMING_SNAKE_CASE_ ) )
snake_case : Dict = hans_convert_examples_to_features(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )
logger.info("""Saving features into cached file %s""" ,SCREAMING_SNAKE_CASE_ )
torch.save(self.features ,SCREAMING_SNAKE_CASE_ )
def __len__( self ):
'''simple docstring'''
return len(self.features )
def __getitem__( self ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
return self.features[i]
def snake_case_ ( self ):
'''simple docstring'''
return self.label_list
if is_tf_available():
import tensorflow as tf
class _A :
'''simple docstring'''
__lowerCamelCase : List[InputFeatures]
def __init__( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ = 128 ,SCREAMING_SNAKE_CASE_=False ,SCREAMING_SNAKE_CASE_ = False ,):
'''simple docstring'''
snake_case : Any = hans_processors[task]()
snake_case : List[str] = processor.get_labels()
if tokenizer.__class__ in (
RobertaTokenizer,
RobertaTokenizerFast,
XLMRobertaTokenizer,
BartTokenizer,
BartTokenizerFast,
):
# HACK(label indices are swapped in RoBERTa pretrained model)
snake_case , snake_case : int = label_list[2], label_list[1]
snake_case : List[str] = label_list
snake_case : int = processor.get_dev_examples(SCREAMING_SNAKE_CASE_ ) if evaluate else processor.get_train_examples(SCREAMING_SNAKE_CASE_ )
snake_case : Any = hans_convert_examples_to_features(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )
def gen():
for ex_index, ex in tqdm.tqdm(enumerate(self.features ) ,desc="""convert examples to features""" ):
if ex_index % 10000 == 0:
logger.info("""Writing example %d of %d""" % (ex_index, len(SCREAMING_SNAKE_CASE_ )) )
yield (
{
"example_id": 0,
"input_ids": ex.input_ids,
"attention_mask": ex.attention_mask,
"token_type_ids": ex.token_type_ids,
},
ex.label,
)
snake_case : Any = tf.data.Dataset.from_generator(
SCREAMING_SNAKE_CASE_ ,(
{
"""example_id""": tf.intaa,
"""input_ids""": tf.intaa,
"""attention_mask""": tf.intaa,
"""token_type_ids""": tf.intaa,
},
tf.intaa,
) ,(
{
"""example_id""": tf.TensorShape([] ),
"""input_ids""": tf.TensorShape([None, None] ),
"""attention_mask""": tf.TensorShape([None, None] ),
"""token_type_ids""": tf.TensorShape([None, None] ),
},
tf.TensorShape([] ),
) ,)
def snake_case_ ( self ):
'''simple docstring'''
return self.dataset
def __len__( self ):
'''simple docstring'''
return len(self.features )
def __getitem__( self ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
return self.features[i]
def snake_case_ ( self ):
'''simple docstring'''
return self.label_list
class _A ( snake_case ):
'''simple docstring'''
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
return self._create_examples(self._read_tsv(os.path.join(SCREAMING_SNAKE_CASE_ ,"""heuristics_train_set.txt""" ) ) ,"""train""" )
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
return self._create_examples(self._read_tsv(os.path.join(SCREAMING_SNAKE_CASE_ ,"""heuristics_evaluation_set.txt""" ) ) ,"""dev""" )
def snake_case_ ( self ):
'''simple docstring'''
return ["contradiction", "entailment", "neutral"]
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
snake_case : List[str] = []
for i, line in enumerate(SCREAMING_SNAKE_CASE_ ):
if i == 0:
continue
snake_case : Any = """%s-%s""" % (set_type, line[0])
snake_case : Optional[int] = line[5]
snake_case : Union[str, Any] = line[6]
snake_case : Optional[Any] = line[7][2:] if line[7].startswith("""ex""" ) else line[7]
snake_case : Dict = line[0]
examples.append(InputExample(guid=SCREAMING_SNAKE_CASE_ ,text_a=SCREAMING_SNAKE_CASE_ ,text_b=SCREAMING_SNAKE_CASE_ ,label=SCREAMING_SNAKE_CASE_ ,pairID=SCREAMING_SNAKE_CASE_ ) )
return examples
def lowercase ( __A : List[InputExample] , __A : List[str] , __A : int , __A : PreTrainedTokenizer , ) -> Tuple:
'''simple docstring'''
snake_case : List[Any] = {label: i for i, label in enumerate(__A )}
snake_case : Union[str, Any] = []
for ex_index, example in tqdm.tqdm(enumerate(__A ) , desc="""convert examples to features""" ):
if ex_index % 1_0000 == 0:
logger.info("""Writing example %d""" % (ex_index) )
snake_case : Union[str, Any] = tokenizer(
example.text_a , example.text_b , add_special_tokens=__A , max_length=__A , padding="""max_length""" , truncation=__A , return_overflowing_tokens=__A , )
snake_case : Tuple = label_map[example.label] if example.label in label_map else 0
snake_case : Tuple = int(example.pairID )
features.append(InputFeatures(**__A , label=__A , pairID=__A ) )
for i, example in enumerate(examples[:5] ):
logger.info("""*** Example ***""" )
logger.info(f"""guid: {example}""" )
logger.info(f"""features: {features[i]}""" )
return features
__lowercase : Dict = {
'''hans''': 3,
}
__lowercase : Union[str, Any] = {
'''hans''': HansProcessor,
}
| 36 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusionInstructPixaPixPipeline,
UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import (
IMAGE_TO_IMAGE_IMAGE_PARAMS,
TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class _A ( snake_case , snake_case , snake_case , unittest.TestCase ):
'''simple docstring'''
__lowerCamelCase : int = StableDiffusionInstructPixaPixPipeline
__lowerCamelCase : Optional[int] = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''height''', '''width''', '''cross_attention_kwargs'''}
__lowerCamelCase : Any = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
__lowerCamelCase : Any = IMAGE_TO_IMAGE_IMAGE_PARAMS
__lowerCamelCase : int = IMAGE_TO_IMAGE_IMAGE_PARAMS
def snake_case_ ( self ):
'''simple docstring'''
torch.manual_seed(0 )
snake_case : Optional[int] = UNetaDConditionModel(
block_out_channels=(32, 64) ,layers_per_block=2 ,sample_size=32 ,in_channels=8 ,out_channels=4 ,down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") ,up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""") ,cross_attention_dim=32 ,)
snake_case : str = PNDMScheduler(skip_prk_steps=SCREAMING_SNAKE_CASE_ )
torch.manual_seed(0 )
snake_case : Optional[int] = 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 ,)
torch.manual_seed(0 )
snake_case : List[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=1000 ,)
snake_case : List[Any] = CLIPTextModel(SCREAMING_SNAKE_CASE_ )
snake_case : Optional[int] = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
snake_case : Optional[Any] = {
"""unet""": unet,
"""scheduler""": scheduler,
"""vae""": vae,
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
"""safety_checker""": None,
"""feature_extractor""": None,
}
return components
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_=0 ):
'''simple docstring'''
snake_case : int = floats_tensor((1, 3, 32, 32) ,rng=random.Random(SCREAMING_SNAKE_CASE_ ) ).to(SCREAMING_SNAKE_CASE_ )
snake_case : List[Any] = image.cpu().permute(0 ,2 ,3 ,1 )[0]
snake_case : Any = Image.fromarray(np.uinta(SCREAMING_SNAKE_CASE_ ) ).convert("""RGB""" )
if str(SCREAMING_SNAKE_CASE_ ).startswith("""mps""" ):
snake_case : Optional[int] = torch.manual_seed(SCREAMING_SNAKE_CASE_ )
else:
snake_case : Optional[Any] = torch.Generator(device=SCREAMING_SNAKE_CASE_ ).manual_seed(SCREAMING_SNAKE_CASE_ )
snake_case : int = {
"""prompt""": """A painting of a squirrel eating a burger""",
"""image""": image,
"""generator""": generator,
"""num_inference_steps""": 2,
"""guidance_scale""": 6.0,
"""image_guidance_scale""": 1,
"""output_type""": """numpy""",
}
return inputs
def snake_case_ ( self ):
'''simple docstring'''
snake_case : Tuple = """cpu""" # ensure determinism for the device-dependent torch.Generator
snake_case : Dict = self.get_dummy_components()
snake_case : List[Any] = StableDiffusionInstructPixaPixPipeline(**SCREAMING_SNAKE_CASE_ )
snake_case : List[Any] = sd_pipe.to(SCREAMING_SNAKE_CASE_ )
sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
snake_case : List[Any] = self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ )
snake_case : Union[str, Any] = sd_pipe(**SCREAMING_SNAKE_CASE_ ).images
snake_case : int = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
snake_case : Optional[int] = np.array([0.75_26, 0.37_50, 0.45_47, 0.61_17, 0.58_66, 0.50_16, 0.43_27, 0.56_42, 0.48_15] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3
def snake_case_ ( self ):
'''simple docstring'''
snake_case : Any = """cpu""" # ensure determinism for the device-dependent torch.Generator
snake_case : Tuple = self.get_dummy_components()
snake_case : List[Any] = StableDiffusionInstructPixaPixPipeline(**SCREAMING_SNAKE_CASE_ )
snake_case : List[Any] = sd_pipe.to(SCREAMING_SNAKE_CASE_ )
sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
snake_case : Tuple = self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ )
snake_case : Tuple = """french fries"""
snake_case : Tuple = sd_pipe(**SCREAMING_SNAKE_CASE_ ,negative_prompt=SCREAMING_SNAKE_CASE_ )
snake_case : int = output.images
snake_case : List[str] = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
snake_case : List[str] = np.array([0.75_11, 0.36_42, 0.45_53, 0.62_36, 0.57_97, 0.50_13, 0.43_43, 0.56_11, 0.48_31] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3
def snake_case_ ( self ):
'''simple docstring'''
snake_case : int = """cpu""" # ensure determinism for the device-dependent torch.Generator
snake_case : str = self.get_dummy_components()
snake_case : Optional[Any] = StableDiffusionInstructPixaPixPipeline(**SCREAMING_SNAKE_CASE_ )
snake_case : List[Any] = sd_pipe.to(SCREAMING_SNAKE_CASE_ )
sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
snake_case : Optional[int] = self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ )
snake_case : Tuple = [inputs["""prompt"""]] * 2
snake_case : Optional[Any] = np.array(inputs["""image"""] ).astype(np.floataa ) / 2_55.0
snake_case : Any = torch.from_numpy(SCREAMING_SNAKE_CASE_ ).unsqueeze(0 ).to(SCREAMING_SNAKE_CASE_ )
snake_case : List[str] = image / 2 + 0.5
snake_case : Any = image.permute(0 ,3 ,1 ,2 )
snake_case : Optional[int] = image.repeat(2 ,1 ,1 ,1 )
snake_case : Tuple = sd_pipe(**SCREAMING_SNAKE_CASE_ ).images
snake_case : int = image[-1, -3:, -3:, -1]
assert image.shape == (2, 32, 32, 3)
snake_case : Optional[int] = np.array([0.58_12, 0.57_48, 0.52_22, 0.59_08, 0.56_95, 0.71_74, 0.68_04, 0.55_23, 0.55_79] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3
def snake_case_ ( self ):
'''simple docstring'''
snake_case : List[str] = """cpu""" # ensure determinism for the device-dependent torch.Generator
snake_case : Union[str, Any] = self.get_dummy_components()
snake_case : Optional[int] = EulerAncestralDiscreteScheduler(
beta_start=0.0_00_85 ,beta_end=0.0_12 ,beta_schedule="""scaled_linear""" )
snake_case : Optional[Any] = StableDiffusionInstructPixaPixPipeline(**SCREAMING_SNAKE_CASE_ )
snake_case : Any = sd_pipe.to(SCREAMING_SNAKE_CASE_ )
sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
snake_case : Optional[int] = self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ )
snake_case : List[str] = sd_pipe(**SCREAMING_SNAKE_CASE_ ).images
snake_case : Optional[int] = image[0, -3:, -3:, -1]
snake_case : Union[str, Any] = [round(SCREAMING_SNAKE_CASE_ ,4 ) for x in image_slice.flatten().tolist()]
print(""",""".join([str(SCREAMING_SNAKE_CASE_ ) for x in slice] ) )
assert image.shape == (1, 32, 32, 3)
snake_case : Union[str, Any] = np.array([0.74_17, 0.38_42, 0.47_32, 0.57_76, 0.58_91, 0.51_39, 0.40_52, 0.56_73, 0.49_86] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3
def snake_case_ ( self ):
'''simple docstring'''
super().test_inference_batch_single_identical(expected_max_diff=3E-3 )
def snake_case_ ( self ):
'''simple docstring'''
snake_case : Any = self.get_dummy_components()
snake_case : int = StableDiffusionInstructPixaPixPipeline(**SCREAMING_SNAKE_CASE_ )
snake_case : Optional[int] = VaeImageProcessor(do_resize=SCREAMING_SNAKE_CASE_ ,do_normalize=SCREAMING_SNAKE_CASE_ )
snake_case : int = pipe.to(SCREAMING_SNAKE_CASE_ )
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
snake_case : int = pipe(**self.get_dummy_inputs_by_type(SCREAMING_SNAKE_CASE_ ,input_image_type="""pt""" ) )[0]
snake_case : List[str] = components["""vae"""]
snake_case : Optional[int] = self.get_dummy_inputs_by_type(SCREAMING_SNAKE_CASE_ ,input_image_type="""pt""" )
for image_param in self.image_latents_params:
if image_param in inputs.keys():
snake_case : List[Any] = vae.encode(inputs[image_param] ).latent_dist.mode()
snake_case : Any = pipe(**SCREAMING_SNAKE_CASE_ )[0]
snake_case : Tuple = np.abs(out - out_latents_inputs ).max()
self.assertLess(SCREAMING_SNAKE_CASE_ ,1E-4 ,"""passing latents as image input generate different result from passing image""" )
@slow
@require_torch_gpu
class _A ( unittest.TestCase ):
'''simple docstring'''
def snake_case_ ( self ):
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_=0 ):
'''simple docstring'''
snake_case : List[str] = torch.manual_seed(SCREAMING_SNAKE_CASE_ )
snake_case : int = load_image(
"""https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg""" )
snake_case : Union[str, Any] = {
"""prompt""": """turn him into a cyborg""",
"""image""": image,
"""generator""": generator,
"""num_inference_steps""": 3,
"""guidance_scale""": 7.5,
"""image_guidance_scale""": 1.0,
"""output_type""": """numpy""",
}
return inputs
def snake_case_ ( self ):
'''simple docstring'''
snake_case : Optional[int] = StableDiffusionInstructPixaPixPipeline.from_pretrained(
"""timbrooks/instruct-pix2pix""" ,safety_checker=SCREAMING_SNAKE_CASE_ )
pipe.to(SCREAMING_SNAKE_CASE_ )
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
pipe.enable_attention_slicing()
snake_case : Optional[int] = self.get_inputs()
snake_case : List[Any] = pipe(**SCREAMING_SNAKE_CASE_ ).images
snake_case : str = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 512, 3)
snake_case : Dict = np.array([0.59_02, 0.60_15, 0.60_27, 0.59_83, 0.60_92, 0.60_61, 0.57_65, 0.57_85, 0.55_55] )
assert np.abs(expected_slice - image_slice ).max() < 1E-3
def snake_case_ ( self ):
'''simple docstring'''
snake_case : Dict = StableDiffusionInstructPixaPixPipeline.from_pretrained(
"""timbrooks/instruct-pix2pix""" ,safety_checker=SCREAMING_SNAKE_CASE_ )
snake_case : Union[str, Any] = LMSDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.to(SCREAMING_SNAKE_CASE_ )
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
pipe.enable_attention_slicing()
snake_case : Union[str, Any] = self.get_inputs()
snake_case : Tuple = pipe(**SCREAMING_SNAKE_CASE_ ).images
snake_case : Any = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 512, 3)
snake_case : int = np.array([0.65_78, 0.68_17, 0.69_72, 0.67_61, 0.68_56, 0.69_16, 0.64_28, 0.65_16, 0.63_01] )
assert np.abs(expected_slice - image_slice ).max() < 1E-3
def snake_case_ ( self ):
'''simple docstring'''
snake_case : List[Any] = StableDiffusionInstructPixaPixPipeline.from_pretrained(
"""timbrooks/instruct-pix2pix""" ,safety_checker=SCREAMING_SNAKE_CASE_ )
snake_case : int = DDIMScheduler.from_config(pipe.scheduler.config )
pipe.to(SCREAMING_SNAKE_CASE_ )
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
pipe.enable_attention_slicing()
snake_case : Optional[Any] = self.get_inputs()
snake_case : Union[str, Any] = pipe(**SCREAMING_SNAKE_CASE_ ).images
snake_case : Optional[int] = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 512, 3)
snake_case : Any = np.array([0.38_28, 0.38_34, 0.38_18, 0.37_92, 0.38_65, 0.37_52, 0.37_92, 0.38_47, 0.37_53] )
assert np.abs(expected_slice - image_slice ).max() < 1E-3
def snake_case_ ( self ):
'''simple docstring'''
snake_case : Optional[Any] = 0
def callback_fn(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ) -> None:
snake_case : str = True
nonlocal number_of_steps
number_of_steps += 1
if step == 1:
snake_case : List[str] = latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 64, 64)
snake_case : Union[str, Any] = latents[0, -3:, -3:, -1]
snake_case : Dict = np.array([-0.24_63, -0.46_44, -0.97_56, 1.51_76, 1.44_14, 0.78_66, 0.98_97, 0.85_21, 0.79_83] )
assert np.abs(latents_slice.flatten() - expected_slice ).max() < 5E-2
elif step == 2:
snake_case : Optional[Any] = latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 64, 64)
snake_case : Optional[int] = latents[0, -3:, -3:, -1]
snake_case : List[str] = np.array([-0.26_44, -0.46_26, -0.96_53, 1.51_76, 1.45_51, 0.76_86, 0.98_05, 0.84_52, 0.81_15] )
assert np.abs(latents_slice.flatten() - expected_slice ).max() < 5E-2
snake_case : List[Any] = False
snake_case : Tuple = StableDiffusionInstructPixaPixPipeline.from_pretrained(
"""timbrooks/instruct-pix2pix""" ,safety_checker=SCREAMING_SNAKE_CASE_ ,torch_dtype=torch.floataa )
snake_case : List[str] = pipe.to(SCREAMING_SNAKE_CASE_ )
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
pipe.enable_attention_slicing()
snake_case : List[Any] = self.get_inputs()
pipe(**SCREAMING_SNAKE_CASE_ ,callback=SCREAMING_SNAKE_CASE_ ,callback_steps=1 )
assert callback_fn.has_been_called
assert number_of_steps == 3
def snake_case_ ( self ):
'''simple docstring'''
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
snake_case : Dict = StableDiffusionInstructPixaPixPipeline.from_pretrained(
"""timbrooks/instruct-pix2pix""" ,safety_checker=SCREAMING_SNAKE_CASE_ ,torch_dtype=torch.floataa )
snake_case : Tuple = pipe.to(SCREAMING_SNAKE_CASE_ )
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
pipe.enable_attention_slicing(1 )
pipe.enable_sequential_cpu_offload()
snake_case : Dict = self.get_inputs()
snake_case : int = pipe(**SCREAMING_SNAKE_CASE_ )
snake_case : List[Any] = torch.cuda.max_memory_allocated()
# make sure that less than 2.2 GB is allocated
assert mem_bytes < 2.2 * 10**9
def snake_case_ ( self ):
'''simple docstring'''
snake_case : Any = self.get_inputs()
# resize to resolution that is divisible by 8 but not 16 or 32
snake_case : Any = inputs["""image"""].resize((504, 504) )
snake_case : Union[str, Any] = """timbrooks/instruct-pix2pix"""
snake_case : Optional[Any] = StableDiffusionInstructPixaPixPipeline.from_pretrained(
SCREAMING_SNAKE_CASE_ ,safety_checker=SCREAMING_SNAKE_CASE_ ,)
pipe.to(SCREAMING_SNAKE_CASE_ )
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
pipe.enable_attention_slicing()
snake_case : Union[str, Any] = pipe(**SCREAMING_SNAKE_CASE_ )
snake_case : Optional[int] = output.images[0]
snake_case : str = image[255:258, 383:386, -1]
assert image.shape == (504, 504, 3)
snake_case : Optional[Any] = np.array([0.27_26, 0.25_29, 0.26_64, 0.26_55, 0.26_41, 0.26_42, 0.25_91, 0.26_49, 0.25_90] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 5E-3
| 36 |
from __future__ import annotations
def lowercase ( __A : int ) -> list[int]:
'''simple docstring'''
snake_case : Dict = 2
snake_case : int = []
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.append(__A )
if n > 1:
factors.append(__A )
return factors
if __name__ == "__main__":
import doctest
doctest.testmod()
| 36 | 1 |
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class _A ( snake_case ):
'''simple docstring'''
__lowerCamelCase : Dict = ''''''
__lowerCamelCase : Union[str, Any] = '''hf-legacy''' # "hf://"" is reserved for hffs
def __init__( self ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_ = None ,**SCREAMING_SNAKE_CASE_ ,):
'''simple docstring'''
super().__init__(self ,**SCREAMING_SNAKE_CASE_ )
snake_case : List[Any] = repo_info
snake_case : Dict = token
snake_case : Any = None
def snake_case_ ( self ):
'''simple docstring'''
if self.dir_cache is None:
snake_case : str = {}
for hf_file in self.repo_info.siblings:
# TODO(QL): add sizes
snake_case : Union[str, Any] = {
"""name""": hf_file.rfilename,
"""size""": None,
"""type""": """file""",
}
self.dir_cache.update(
{
str(SCREAMING_SNAKE_CASE_ ): {"""name""": str(SCREAMING_SNAKE_CASE_ ), """size""": None, """type""": """directory"""}
for d in list(PurePosixPath(hf_file.rfilename ).parents )[:-1]
} )
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ = "rb" ,**SCREAMING_SNAKE_CASE_ ,):
'''simple docstring'''
if not isinstance(self.repo_info ,SCREAMING_SNAKE_CASE_ ):
raise NotImplementedError(F"""Open is only implemented for dataset repositories, but got {self.repo_info}""" )
snake_case : Tuple = hf_hub_url(self.repo_info.id ,SCREAMING_SNAKE_CASE_ ,revision=self.repo_info.sha )
return fsspec.open(
SCREAMING_SNAKE_CASE_ ,mode=SCREAMING_SNAKE_CASE_ ,headers=get_authentication_headers_for_url(SCREAMING_SNAKE_CASE_ ,use_auth_token=self.token ) ,client_kwargs={"""trust_env""": True} ,).open()
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,**SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
self._get_dirs()
snake_case : List[Any] = self._strip_protocol(SCREAMING_SNAKE_CASE_ )
if path in self.dir_cache:
return self.dir_cache[path]
else:
raise FileNotFoundError(SCREAMING_SNAKE_CASE_ )
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_=False ,**SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
self._get_dirs()
snake_case : List[str] = PurePosixPath(path.strip("""/""" ) )
snake_case : Optional[int] = {}
for p, f in self.dir_cache.items():
snake_case : List[str] = PurePosixPath(p.strip("""/""" ) )
snake_case : int = p.parent
if root == path:
snake_case : Any = f
snake_case : Optional[Any] = list(paths.values() )
if detail:
return out
else:
return sorted(f["""name"""] for f in out )
| 36 |
import numpy as np
def lowercase ( __A : np.array ) -> np.array:
'''simple docstring'''
return (2 / (1 + np.exp(-2 * vector ))) - 1
if __name__ == "__main__":
import doctest
doctest.testmod()
| 36 | 1 |
import unittest
from transformers import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING, is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_available():
from PIL import Image
else:
class _A :
'''simple docstring'''
@staticmethod
def snake_case_ ( *SCREAMING_SNAKE_CASE_ ,**SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
pass
@is_pipeline_test
@require_torch
@require_vision
class _A ( unittest.TestCase ):
'''simple docstring'''
__lowerCamelCase : Optional[Any] = MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
snake_case : List[Any] = pipeline("""visual-question-answering""" ,model="""hf-internal-testing/tiny-vilt-random-vqa""" )
snake_case : str = [
{
"""image""": Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" ),
"""question""": """How many cats are there?""",
},
{
"""image""": """./tests/fixtures/tests_samples/COCO/000000039769.png""",
"""question""": """How many cats are there?""",
},
]
return vqa_pipeline, examples
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
snake_case : Optional[int] = vqa_pipeline(SCREAMING_SNAKE_CASE_ ,top_k=1 )
self.assertEqual(
SCREAMING_SNAKE_CASE_ ,[
[{"""score""": ANY(SCREAMING_SNAKE_CASE_ ), """answer""": ANY(SCREAMING_SNAKE_CASE_ )}],
[{"""score""": ANY(SCREAMING_SNAKE_CASE_ ), """answer""": ANY(SCREAMING_SNAKE_CASE_ )}],
] ,)
@require_torch
def snake_case_ ( self ):
'''simple docstring'''
snake_case : List[str] = pipeline("""visual-question-answering""" ,model="""hf-internal-testing/tiny-vilt-random-vqa""" )
snake_case : str = """./tests/fixtures/tests_samples/COCO/000000039769.png"""
snake_case : Any = """How many cats are there?"""
snake_case : Any = vqa_pipeline(image=SCREAMING_SNAKE_CASE_ ,question="""How many cats are there?""" ,top_k=2 )
self.assertEqual(
SCREAMING_SNAKE_CASE_ ,[{"""score""": ANY(SCREAMING_SNAKE_CASE_ ), """answer""": ANY(SCREAMING_SNAKE_CASE_ )}, {"""score""": ANY(SCREAMING_SNAKE_CASE_ ), """answer""": ANY(SCREAMING_SNAKE_CASE_ )}] )
snake_case : Dict = vqa_pipeline({"""image""": image, """question""": question} ,top_k=2 )
self.assertEqual(
SCREAMING_SNAKE_CASE_ ,[{"""score""": ANY(SCREAMING_SNAKE_CASE_ ), """answer""": ANY(SCREAMING_SNAKE_CASE_ )}, {"""score""": ANY(SCREAMING_SNAKE_CASE_ ), """answer""": ANY(SCREAMING_SNAKE_CASE_ )}] )
@slow
@require_torch
def snake_case_ ( self ):
'''simple docstring'''
snake_case : int = pipeline("""visual-question-answering""" ,model="""dandelin/vilt-b32-finetuned-vqa""" )
snake_case : Union[str, Any] = """./tests/fixtures/tests_samples/COCO/000000039769.png"""
snake_case : Union[str, Any] = """How many cats are there?"""
snake_case : str = vqa_pipeline(image=SCREAMING_SNAKE_CASE_ ,question=SCREAMING_SNAKE_CASE_ ,top_k=2 )
self.assertEqual(
nested_simplify(SCREAMING_SNAKE_CASE_ ,decimals=4 ) ,[{"""score""": 0.87_99, """answer""": """2"""}, {"""score""": 0.2_96, """answer""": """1"""}] )
snake_case : Union[str, Any] = vqa_pipeline({"""image""": image, """question""": question} ,top_k=2 )
self.assertEqual(
nested_simplify(SCREAMING_SNAKE_CASE_ ,decimals=4 ) ,[{"""score""": 0.87_99, """answer""": """2"""}, {"""score""": 0.2_96, """answer""": """1"""}] )
snake_case : List[str] = vqa_pipeline(
[{"""image""": image, """question""": question}, {"""image""": image, """question""": question}] ,top_k=2 )
self.assertEqual(
nested_simplify(SCREAMING_SNAKE_CASE_ ,decimals=4 ) ,[[{"""score""": 0.87_99, """answer""": """2"""}, {"""score""": 0.2_96, """answer""": """1"""}]] * 2 ,)
@require_tf
@unittest.skip("""Visual question answering not implemented in TF""" )
def snake_case_ ( self ):
'''simple docstring'''
pass
| 36 |
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
__lowercase : Optional[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 ( __A : Union[str, Any] ) -> Optional[int]:
'''simple docstring'''
for pegasus_name, hf_name in PATTERNS:
snake_case : Dict = k.replace(__A , __A )
return k
def lowercase ( __A : dict , __A : dict ) -> PegasusForConditionalGeneration:
'''simple docstring'''
snake_case : Dict = DEFAULTS.copy()
cfg_kwargs.update(__A )
snake_case : int = PegasusConfig(**__A )
snake_case : List[Any] = PegasusForConditionalGeneration(__A )
snake_case : Optional[Any] = torch_model.model.state_dict()
snake_case : Optional[int] = {}
for k, v in tf_weights.items():
snake_case : str = rename_state_dict_key(__A )
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:
snake_case : Optional[Any] = v.T
snake_case : List[Any] = torch.tensor(__A , 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
snake_case : List[str] = torch.zeros_like(mapping["""shared.weight"""][cfg.pad_token_id + 1] )
snake_case : Optional[Any] = mapping["""shared.weight"""]
snake_case : Optional[Any] = mapping["""shared.weight"""]
snake_case : Tuple = {k: torch.zeros_like(__A ) for k, v in sd.items() if k.endswith("""bias""" ) and k not in mapping}
mapping.update(**__A )
snake_case , snake_case : Union[str, Any] = torch_model.model.load_state_dict(__A , strict=__A )
snake_case : Union[str, Any] = [
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 ( __A : int="./ckpt/aeslc/model.ckpt-32000" ) -> Dict:
'''simple docstring'''
snake_case : Optional[Any] = tf.train.list_variables(__A )
snake_case : Union[str, Any] = {}
snake_case : List[str] = ["""Adafactor""", """global_step"""]
for name, shape in tqdm(__A , desc="""converting tf checkpoint to dict""" ):
snake_case : str = any(pat in name for pat in ignore_name )
if skip_key:
continue
snake_case : List[str] = tf.train.load_variable(__A , __A )
snake_case : Optional[Any] = array
return tf_weights
def lowercase ( __A : str , __A : str ) -> Optional[int]:
'''simple docstring'''
snake_case : Dict = Path(__A ).parent.name
snake_case : Dict = task_specific_params[f"""summarization_{dataset}"""]["""max_position_embeddings"""]
snake_case : Any = PegasusTokenizer.from_pretrained("""sshleifer/pegasus""" , model_max_length=__A )
assert tok.model_max_length == desired_max_model_length
tok.save_pretrained(__A )
# convert model
snake_case : Dict = get_tf_weights_as_numpy(__A )
snake_case : List[Any] = task_specific_params[f"""summarization_{dataset}"""]
if dataset == "large":
snake_case : Optional[int] = task_specific_params
snake_case : Optional[int] = convert_pegasus(__A , __A )
torch_model.save_pretrained(__A )
snake_case : int = torch_model.state_dict()
sd.pop("""model.decoder.embed_positions.weight""" )
sd.pop("""model.encoder.embed_positions.weight""" )
torch.save(__A , Path(__A ) / """pytorch_model.bin""" )
if __name__ == "__main__":
__lowercase : Tuple = 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.''')
__lowercase : List[Any] = parser.parse_args()
if args.save_dir is None:
__lowercase : Optional[Any] = Path(args.tf_ckpt_path).parent.name
__lowercase : Union[str, Any] = os.path.join('''pegasus''', dataset)
convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)
| 36 | 1 |
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# 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.
# this script dumps information about the environment
import os
import platform
import sys
__lowercase : Optional[int] = '''3'''
print('''Python version:''', sys.version)
print('''OS platform:''', platform.platform())
print('''OS architecture:''', platform.machine())
try:
import torch
print('''Torch version:''', torch.__version__)
print('''Cuda available:''', torch.cuda.is_available())
print('''Cuda version:''', torch.version.cuda)
print('''CuDNN version:''', torch.backends.cudnn.version())
print('''Number of GPUs available:''', torch.cuda.device_count())
except ImportError:
print('''Torch version:''', None)
try:
import transformers
print('''transformers version:''', transformers.__version__)
except ImportError:
print('''transformers version:''', None)
| 36 |
import argparse
import pytorch_lightning as pl
import torch
from torch import nn
from transformers import LongformerForQuestionAnswering, LongformerModel
class _A ( pl.LightningModule ):
'''simple docstring'''
def __init__( self ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
super().__init__()
snake_case : Dict = model
snake_case : Optional[int] = 2
snake_case : Optional[Any] = nn.Linear(self.model.config.hidden_size ,self.num_labels )
def snake_case_ ( self ):
'''simple docstring'''
pass
def lowercase ( __A : str , __A : str , __A : str ) -> Optional[Any]:
'''simple docstring'''
snake_case : Optional[Any] = LongformerModel.from_pretrained(__A )
snake_case : Tuple = LightningModel(__A )
snake_case : Optional[int] = torch.load(__A , map_location=torch.device("""cpu""" ) )
lightning_model.load_state_dict(ckpt["""state_dict"""] )
# init longformer question answering model
snake_case : Dict = LongformerForQuestionAnswering.from_pretrained(__A )
# transfer weights
longformer_for_qa.longformer.load_state_dict(lightning_model.model.state_dict() )
longformer_for_qa.qa_outputs.load_state_dict(lightning_model.qa_outputs.state_dict() )
longformer_for_qa.eval()
# save model
longformer_for_qa.save_pretrained(__A )
print(f"""Conversion successful. Model saved under {pytorch_dump_folder_path}""" )
if __name__ == "__main__":
__lowercase : str = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--longformer_model''',
default=None,
type=str,
required=True,
help='''model identifier of longformer. Should be either `longformer-base-4096` or `longformer-large-4096`.''',
)
parser.add_argument(
'''--longformer_question_answering_ckpt_path''',
default=None,
type=str,
required=True,
help='''Path the official PyTorch Lightning Checkpoint.''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
__lowercase : List[str] = parser.parse_args()
convert_longformer_qa_checkpoint_to_pytorch(
args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
)
| 36 | 1 |
from __future__ import annotations
import time
__lowercase : Optional[Any] = list[tuple[int, int]]
__lowercase : Tuple = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0],
[1, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0],
]
__lowercase : Optional[Any] = [[-1, 0], [0, -1], [1, 0], [0, 1]] # up, left, down, right
class _A :
'''simple docstring'''
def __init__( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
snake_case : Tuple = pos_x
snake_case : List[str] = pos_y
snake_case : Optional[int] = (pos_y, pos_x)
snake_case : List[str] = goal_x
snake_case : str = goal_y
snake_case : int = parent
class _A :
'''simple docstring'''
def __init__( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
snake_case : Union[str, Any] = Node(start[1] ,start[0] ,goal[1] ,goal[0] ,SCREAMING_SNAKE_CASE_ )
snake_case : List[Any] = Node(goal[1] ,goal[0] ,goal[1] ,goal[0] ,SCREAMING_SNAKE_CASE_ )
snake_case : Optional[int] = [self.start]
snake_case : int = False
def snake_case_ ( self ):
'''simple docstring'''
while self.node_queue:
snake_case : Any = self.node_queue.pop(0 )
if current_node.pos == self.target.pos:
snake_case : List[Any] = True
return self.retrace_path(SCREAMING_SNAKE_CASE_ )
snake_case : Dict = self.get_successors(SCREAMING_SNAKE_CASE_ )
for node in successors:
self.node_queue.append(SCREAMING_SNAKE_CASE_ )
if not self.reached:
return [self.start.pos]
return None
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
snake_case : int = []
for action in delta:
snake_case : Optional[Any] = parent.pos_x + action[1]
snake_case : Optional[Any] = parent.pos_y + action[0]
if not (0 <= pos_x <= len(grid[0] ) - 1 and 0 <= pos_y <= len(SCREAMING_SNAKE_CASE_ ) - 1):
continue
if grid[pos_y][pos_x] != 0:
continue
successors.append(
Node(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,self.target.pos_y ,self.target.pos_x ,SCREAMING_SNAKE_CASE_ ) )
return successors
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
snake_case : List[Any] = node
snake_case : List[str] = []
while current_node is not None:
path.append((current_node.pos_y, current_node.pos_x) )
snake_case : Any = current_node.parent
path.reverse()
return path
class _A :
'''simple docstring'''
def __init__( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
snake_case : int = BreadthFirstSearch(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )
snake_case : Any = BreadthFirstSearch(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )
snake_case : int = False
def snake_case_ ( self ):
'''simple docstring'''
while self.fwd_bfs.node_queue or self.bwd_bfs.node_queue:
snake_case : List[str] = self.fwd_bfs.node_queue.pop(0 )
snake_case : Union[str, Any] = self.bwd_bfs.node_queue.pop(0 )
if current_bwd_node.pos == current_fwd_node.pos:
snake_case : Optional[int] = True
return self.retrace_bidirectional_path(
SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )
snake_case : Tuple = current_bwd_node
snake_case : Any = current_fwd_node
snake_case : Optional[Any] = {
self.fwd_bfs: self.fwd_bfs.get_successors(SCREAMING_SNAKE_CASE_ ),
self.bwd_bfs: self.bwd_bfs.get_successors(SCREAMING_SNAKE_CASE_ ),
}
for bfs in [self.fwd_bfs, self.bwd_bfs]:
for node in successors[bfs]:
bfs.node_queue.append(SCREAMING_SNAKE_CASE_ )
if not self.reached:
return [self.fwd_bfs.start.pos]
return None
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
snake_case : Dict = self.fwd_bfs.retrace_path(SCREAMING_SNAKE_CASE_ )
snake_case : List[Any] = self.bwd_bfs.retrace_path(SCREAMING_SNAKE_CASE_ )
bwd_path.pop()
bwd_path.reverse()
snake_case : Optional[Any] = fwd_path + bwd_path
return path
if __name__ == "__main__":
# all coordinates are given in format [y,x]
import doctest
doctest.testmod()
__lowercase : List[Any] = (0, 0)
__lowercase : Optional[int] = (len(grid) - 1, len(grid[0]) - 1)
for elem in grid:
print(elem)
__lowercase : Optional[int] = time.time()
__lowercase : Optional[int] = BreadthFirstSearch(init, goal)
__lowercase : Any = bfs.search()
__lowercase : List[str] = time.time() - start_bfs_time
print('''Unidirectional BFS computation time : ''', bfs_time)
__lowercase : List[str] = time.time()
__lowercase : Dict = BidirectionalBreadthFirstSearch(init, goal)
__lowercase : Union[str, Any] = bd_bfs.search()
__lowercase : Optional[Any] = time.time() - start_bd_bfs_time
print('''Bidirectional BFS computation time : ''', bd_bfs_time)
| 36 |
import argparse
import collections
import json
import os
import re
import string
import sys
import numpy as np
__lowercase : Optional[Any] = re.compile(r'''\b(a|an|the)\b''', re.UNICODE)
__lowercase : Optional[int] = None
def lowercase ( ) -> Optional[Any]:
'''simple docstring'''
snake_case : int = argparse.ArgumentParser("""Official evaluation script for SQuAD version 2.0.""" )
parser.add_argument("""data_file""" , metavar="""data.json""" , help="""Input data JSON file.""" )
parser.add_argument("""pred_file""" , metavar="""pred.json""" , help="""Model predictions.""" )
parser.add_argument(
"""--out-file""" , """-o""" , metavar="""eval.json""" , help="""Write accuracy metrics to file (default is stdout).""" )
parser.add_argument(
"""--na-prob-file""" , """-n""" , metavar="""na_prob.json""" , help="""Model estimates of probability of no answer.""" )
parser.add_argument(
"""--na-prob-thresh""" , """-t""" , type=__A , default=1.0 , help="""Predict \"\" if no-answer probability exceeds this (default = 1.0).""" , )
parser.add_argument(
"""--out-image-dir""" , """-p""" , metavar="""out_images""" , default=__A , help="""Save precision-recall curves to directory.""" )
parser.add_argument("""--verbose""" , """-v""" , action="""store_true""" )
if len(sys.argv ) == 1:
parser.print_help()
sys.exit(1 )
return parser.parse_args()
def lowercase ( __A : Union[str, Any] ) -> int:
'''simple docstring'''
snake_case : Any = {}
for article in dataset:
for p in article["paragraphs"]:
for qa in p["qas"]:
snake_case : int = bool(qa["""answers"""]["""text"""] )
return qid_to_has_ans
def lowercase ( __A : int ) -> Optional[int]:
'''simple docstring'''
def remove_articles(__A : List[Any] ):
return ARTICLES_REGEX.sub(""" """ , __A )
def white_space_fix(__A : Union[str, Any] ):
return " ".join(text.split() )
def remove_punc(__A : Tuple ):
snake_case : Optional[Any] = set(string.punctuation )
return "".join(ch for ch in text if ch not in exclude )
def lower(__A : Any ):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(__A ) ) ) )
def lowercase ( __A : List[str] ) -> Union[str, Any]:
'''simple docstring'''
if not s:
return []
return normalize_answer(__A ).split()
def lowercase ( __A : Optional[int] , __A : int ) -> List[Any]:
'''simple docstring'''
return int(normalize_answer(__A ) == normalize_answer(__A ) )
def lowercase ( __A : Any , __A : Optional[Any] ) -> List[str]:
'''simple docstring'''
snake_case : Tuple = get_tokens(__A )
snake_case : str = get_tokens(__A )
snake_case : Dict = collections.Counter(__A ) & collections.Counter(__A )
snake_case : Optional[int] = sum(common.values() )
if len(__A ) == 0 or len(__A ) == 0:
# If either is no-answer, then F1 is 1 if they agree, 0 otherwise
return int(gold_toks == pred_toks )
if num_same == 0:
return 0
snake_case : List[Any] = 1.0 * num_same / len(__A )
snake_case : int = 1.0 * num_same / len(__A )
snake_case : Dict = (2 * precision * recall) / (precision + recall)
return fa
def lowercase ( __A : List[Any] , __A : int ) -> str:
'''simple docstring'''
snake_case : Tuple = {}
snake_case : Tuple = {}
for article in dataset:
for p in article["paragraphs"]:
for qa in p["qas"]:
snake_case : str = qa["""id"""]
snake_case : Union[str, Any] = [t for t in qa["""answers"""]["""text"""] if normalize_answer(__A )]
if not gold_answers:
# For unanswerable questions, only correct answer is empty string
snake_case : Optional[Any] = [""""""]
if qid not in preds:
print(f"""Missing prediction for {qid}""" )
continue
snake_case : Dict = preds[qid]
# Take max over all gold answers
snake_case : Union[str, Any] = max(compute_exact(__A , __A ) for a in gold_answers )
snake_case : Optional[int] = max(compute_fa(__A , __A ) for a in gold_answers )
return exact_scores, fa_scores
def lowercase ( __A : str , __A : Any , __A : List[Any] , __A : List[Any] ) -> Dict:
'''simple docstring'''
snake_case : Optional[int] = {}
for qid, s in scores.items():
snake_case : Any = na_probs[qid] > na_prob_thresh
if pred_na:
snake_case : str = float(not qid_to_has_ans[qid] )
else:
snake_case : List[Any] = s
return new_scores
def lowercase ( __A : Dict , __A : Union[str, Any] , __A : List[str]=None ) -> int:
'''simple docstring'''
if not qid_list:
snake_case : List[str] = len(__A )
return collections.OrderedDict(
[
("""exact""", 100.0 * sum(exact_scores.values() ) / total),
("""f1""", 100.0 * sum(fa_scores.values() ) / total),
("""total""", total),
] )
else:
snake_case : Any = len(__A )
return collections.OrderedDict(
[
("""exact""", 100.0 * sum(exact_scores[k] for k in qid_list ) / total),
("""f1""", 100.0 * sum(fa_scores[k] for k in qid_list ) / total),
("""total""", total),
] )
def lowercase ( __A : Optional[Any] , __A : Tuple , __A : List[str] ) -> Optional[Any]:
'''simple docstring'''
for k in new_eval:
snake_case : str = new_eval[k]
def lowercase ( __A : Tuple , __A : int , __A : Dict , __A : Dict ) -> int:
'''simple docstring'''
plt.step(__A , __A , color="""b""" , alpha=0.2 , where="""post""" )
plt.fill_between(__A , __A , step="""post""" , alpha=0.2 , color="""b""" )
plt.xlabel("""Recall""" )
plt.ylabel("""Precision""" )
plt.xlim([0.0, 1.05] )
plt.ylim([0.0, 1.05] )
plt.title(__A )
plt.savefig(__A )
plt.clf()
def lowercase ( __A : Optional[Any] , __A : Union[str, Any] , __A : Dict , __A : Tuple , __A : Optional[Any]=None , __A : List[str]=None ) -> Union[str, Any]:
'''simple docstring'''
snake_case : Optional[int] = sorted(__A , key=lambda __A : na_probs[k] )
snake_case : Any = 0.0
snake_case : str = 1.0
snake_case : Tuple = 0.0
snake_case : str = [1.0]
snake_case : Any = [0.0]
snake_case : Dict = 0.0
for i, qid in enumerate(__A ):
if qid_to_has_ans[qid]:
true_pos += scores[qid]
snake_case : str = true_pos / float(i + 1 )
snake_case : List[str] = true_pos / float(__A )
if i == len(__A ) - 1 or na_probs[qid] != na_probs[qid_list[i + 1]]:
# i.e., if we can put a threshold after this point
avg_prec += cur_p * (cur_r - recalls[-1])
precisions.append(__A )
recalls.append(__A )
if out_image:
plot_pr_curve(__A , __A , __A , __A )
return {"ap": 100.0 * avg_prec}
def lowercase ( __A : Any , __A : Optional[int] , __A : Tuple , __A : Tuple , __A : List[Any] , __A : Optional[Any] ) -> Optional[Any]:
'''simple docstring'''
if out_image_dir and not os.path.exists(__A ):
os.makedirs(__A )
snake_case : Tuple = sum(1 for v in qid_to_has_ans.values() if v )
if num_true_pos == 0:
return
snake_case : str = make_precision_recall_eval(
__A , __A , __A , __A , out_image=os.path.join(__A , """pr_exact.png""" ) , title="""Precision-Recall curve for Exact Match score""" , )
snake_case : int = make_precision_recall_eval(
__A , __A , __A , __A , out_image=os.path.join(__A , """pr_f1.png""" ) , title="""Precision-Recall curve for F1 score""" , )
snake_case : Dict = {k: float(__A ) for k, v in qid_to_has_ans.items()}
snake_case : int = make_precision_recall_eval(
__A , __A , __A , __A , out_image=os.path.join(__A , """pr_oracle.png""" ) , title="""Oracle Precision-Recall curve (binary task of HasAns vs. NoAns)""" , )
merge_eval(__A , __A , """pr_exact""" )
merge_eval(__A , __A , """pr_f1""" )
merge_eval(__A , __A , """pr_oracle""" )
def lowercase ( __A : List[Any] , __A : Union[str, Any] , __A : Union[str, Any] , __A : Optional[int] ) -> Union[str, Any]:
'''simple docstring'''
if not qid_list:
return
snake_case : int = [na_probs[k] for k in qid_list]
snake_case : List[str] = np.ones_like(__A ) / float(len(__A ) )
plt.hist(__A , weights=__A , bins=20 , range=(0.0, 1.0) )
plt.xlabel("""Model probability of no-answer""" )
plt.ylabel("""Proportion of dataset""" )
plt.title(f"""Histogram of no-answer probability: {name}""" )
plt.savefig(os.path.join(__A , f"""na_prob_hist_{name}.png""" ) )
plt.clf()
def lowercase ( __A : List[Any] , __A : Tuple , __A : Tuple , __A : Any ) -> Dict:
'''simple docstring'''
snake_case : Optional[int] = sum(1 for k in qid_to_has_ans if not qid_to_has_ans[k] )
snake_case : str = num_no_ans
snake_case : Optional[Any] = cur_score
snake_case : Optional[Any] = 0.0
snake_case : List[Any] = sorted(__A , key=lambda __A : na_probs[k] )
for i, qid in enumerate(__A ):
if qid not in scores:
continue
if qid_to_has_ans[qid]:
snake_case : Dict = scores[qid]
else:
if preds[qid]:
snake_case : Dict = -1
else:
snake_case : str = 0
cur_score += diff
if cur_score > best_score:
snake_case : Union[str, Any] = cur_score
snake_case : List[Any] = na_probs[qid]
return 100.0 * best_score / len(__A ), best_thresh
def lowercase ( __A : Dict , __A : str , __A : str , __A : int , __A : str , __A : Any ) -> List[str]:
'''simple docstring'''
snake_case , snake_case : Optional[int] = find_best_thresh(__A , __A , __A , __A )
snake_case , snake_case : str = find_best_thresh(__A , __A , __A , __A )
snake_case : List[str] = best_exact
snake_case : List[Any] = exact_thresh
snake_case : Optional[Any] = best_fa
snake_case : Optional[int] = fa_thresh
def lowercase ( ) -> Any:
'''simple docstring'''
with open(OPTS.data_file ) as f:
snake_case : Dict = json.load(__A )
snake_case : Union[str, Any] = dataset_json["""data"""]
with open(OPTS.pred_file ) as f:
snake_case : int = json.load(__A )
if OPTS.na_prob_file:
with open(OPTS.na_prob_file ) as f:
snake_case : Any = json.load(__A )
else:
snake_case : Any = {k: 0.0 for k in preds}
snake_case : Optional[int] = make_qid_to_has_ans(__A ) # maps qid to True/False
snake_case : Dict = [k for k, v in qid_to_has_ans.items() if v]
snake_case : Optional[int] = [k for k, v in qid_to_has_ans.items() if not v]
snake_case , snake_case : Optional[Any] = get_raw_scores(__A , __A )
snake_case : Tuple = apply_no_ans_threshold(__A , __A , __A , OPTS.na_prob_thresh )
snake_case : Optional[Any] = apply_no_ans_threshold(__A , __A , __A , OPTS.na_prob_thresh )
snake_case : Optional[int] = make_eval_dict(__A , __A )
if has_ans_qids:
snake_case : Any = make_eval_dict(__A , __A , qid_list=__A )
merge_eval(__A , __A , """HasAns""" )
if no_ans_qids:
snake_case : str = make_eval_dict(__A , __A , qid_list=__A )
merge_eval(__A , __A , """NoAns""" )
if OPTS.na_prob_file:
find_all_best_thresh(__A , __A , __A , __A , __A , __A )
if OPTS.na_prob_file and OPTS.out_image_dir:
run_precision_recall_analysis(__A , __A , __A , __A , __A , OPTS.out_image_dir )
histogram_na_prob(__A , __A , OPTS.out_image_dir , """hasAns""" )
histogram_na_prob(__A , __A , OPTS.out_image_dir , """noAns""" )
if OPTS.out_file:
with open(OPTS.out_file , """w""" ) as f:
json.dump(__A , __A )
else:
print(json.dumps(__A , indent=2 ) )
if __name__ == "__main__":
__lowercase : Union[str, Any] = parse_args()
if OPTS.out_image_dir:
import matplotlib
matplotlib.use('''Agg''')
import matplotlib.pyplot as plt
main()
| 36 | 1 |
class _A :
'''simple docstring'''
def __init__( self ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
snake_case : Any = set_counts
snake_case : Union[str, Any] = max(SCREAMING_SNAKE_CASE_ )
snake_case : Optional[int] = len(SCREAMING_SNAKE_CASE_ )
snake_case : Optional[Any] = [1] * num_sets
snake_case : Optional[Any] = list(range(SCREAMING_SNAKE_CASE_ ) )
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
snake_case : Any = self.get_parent(SCREAMING_SNAKE_CASE_ )
snake_case : Any = self.get_parent(SCREAMING_SNAKE_CASE_ )
if src_parent == dst_parent:
return False
if self.ranks[dst_parent] >= self.ranks[src_parent]:
self.set_counts[dst_parent] += self.set_counts[src_parent]
snake_case : Any = 0
snake_case : List[str] = dst_parent
if self.ranks[dst_parent] == self.ranks[src_parent]:
self.ranks[dst_parent] += 1
snake_case : str = self.set_counts[dst_parent]
else:
self.set_counts[src_parent] += self.set_counts[dst_parent]
snake_case : List[Any] = 0
snake_case : List[str] = src_parent
snake_case : Any = self.set_counts[src_parent]
snake_case : List[str] = max(self.max_set ,SCREAMING_SNAKE_CASE_ )
return True
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
if self.parents[disj_set] == disj_set:
return disj_set
snake_case : str = self.get_parent(self.parents[disj_set] )
return self.parents[disj_set]
| 36 |
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
flip_channel_order,
get_resize_output_image_size,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, is_vision_available, logging
if is_vision_available():
import PIL
if is_torch_available():
import torch
__lowercase : Dict = logging.get_logger(__name__)
class _A ( snake_case ):
'''simple docstring'''
__lowerCamelCase : Optional[int] = ['''pixel_values''']
def __init__( self ,SCREAMING_SNAKE_CASE_ = True ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_ = PILImageResampling.BILINEAR ,SCREAMING_SNAKE_CASE_ = True ,SCREAMING_SNAKE_CASE_ = 1 / 255 ,SCREAMING_SNAKE_CASE_ = True ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_ = True ,**SCREAMING_SNAKE_CASE_ ,):
'''simple docstring'''
super().__init__(**SCREAMING_SNAKE_CASE_ )
snake_case : List[Any] = size if size is not None else {"""shortest_edge""": 224}
snake_case : str = get_size_dict(SCREAMING_SNAKE_CASE_ ,default_to_square=SCREAMING_SNAKE_CASE_ )
snake_case : str = crop_size if crop_size is not None else {"""height""": 256, """width""": 256}
snake_case : Optional[Any] = get_size_dict(SCREAMING_SNAKE_CASE_ ,param_name="""crop_size""" )
snake_case : Optional[Any] = do_resize
snake_case : Union[str, Any] = size
snake_case : Dict = resample
snake_case : Dict = do_rescale
snake_case : Dict = rescale_factor
snake_case : List[str] = do_center_crop
snake_case : Dict = crop_size
snake_case : Any = do_flip_channel_order
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ = PIL.Image.BILINEAR ,SCREAMING_SNAKE_CASE_ = None ,**SCREAMING_SNAKE_CASE_ ,):
'''simple docstring'''
snake_case : str = get_size_dict(SCREAMING_SNAKE_CASE_ ,default_to_square=SCREAMING_SNAKE_CASE_ )
if "shortest_edge" not in size:
raise ValueError(F"""The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}""" )
snake_case : List[Any] = get_resize_output_image_size(SCREAMING_SNAKE_CASE_ ,size=size["""shortest_edge"""] ,default_to_square=SCREAMING_SNAKE_CASE_ )
return resize(SCREAMING_SNAKE_CASE_ ,size=SCREAMING_SNAKE_CASE_ ,resample=SCREAMING_SNAKE_CASE_ ,data_format=SCREAMING_SNAKE_CASE_ ,**SCREAMING_SNAKE_CASE_ )
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ = None ,**SCREAMING_SNAKE_CASE_ ,):
'''simple docstring'''
snake_case : Union[str, Any] = get_size_dict(SCREAMING_SNAKE_CASE_ )
if "height" not in size or "width" not in size:
raise ValueError(F"""The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}""" )
return center_crop(SCREAMING_SNAKE_CASE_ ,size=(size["""height"""], size["""width"""]) ,data_format=SCREAMING_SNAKE_CASE_ ,**SCREAMING_SNAKE_CASE_ )
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ = None ,**SCREAMING_SNAKE_CASE_ ,):
'''simple docstring'''
return rescale(SCREAMING_SNAKE_CASE_ ,scale=SCREAMING_SNAKE_CASE_ ,data_format=SCREAMING_SNAKE_CASE_ ,**SCREAMING_SNAKE_CASE_ )
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ = None ):
'''simple docstring'''
return flip_channel_order(SCREAMING_SNAKE_CASE_ ,data_format=SCREAMING_SNAKE_CASE_ )
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_ = ChannelDimension.FIRST ,**SCREAMING_SNAKE_CASE_ ,):
'''simple docstring'''
snake_case : List[Any] = do_resize if do_resize is not None else self.do_resize
snake_case : List[str] = resample if resample is not None else self.resample
snake_case : Union[str, Any] = do_rescale if do_rescale is not None else self.do_rescale
snake_case : Union[str, Any] = rescale_factor if rescale_factor is not None else self.rescale_factor
snake_case : str = do_center_crop if do_center_crop is not None else self.do_center_crop
snake_case : Union[str, Any] = (
do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
)
snake_case : Tuple = size if size is not None else self.size
snake_case : Optional[Any] = get_size_dict(SCREAMING_SNAKE_CASE_ ,default_to_square=SCREAMING_SNAKE_CASE_ )
snake_case : str = crop_size if crop_size is not None else self.crop_size
snake_case : Optional[Any] = get_size_dict(SCREAMING_SNAKE_CASE_ ,param_name="""crop_size""" )
snake_case : List[Any] = make_list_of_images(SCREAMING_SNAKE_CASE_ )
if not valid_images(SCREAMING_SNAKE_CASE_ ):
raise ValueError(
"""Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
"""torch.Tensor, tf.Tensor or jax.ndarray.""" )
if do_resize and size is None:
raise ValueError("""Size must be specified if do_resize is True.""" )
if do_rescale and rescale_factor is None:
raise ValueError("""Rescale factor must be specified if do_rescale is True.""" )
if do_center_crop and crop_size is None:
raise ValueError("""Crop size must be specified if do_center_crop is True.""" )
# All transformations expect numpy arrays.
snake_case : Dict = [to_numpy_array(SCREAMING_SNAKE_CASE_ ) for image in images]
if do_resize:
snake_case : Union[str, Any] = [self.resize(image=SCREAMING_SNAKE_CASE_ ,size=SCREAMING_SNAKE_CASE_ ,resample=SCREAMING_SNAKE_CASE_ ) for image in images]
if do_center_crop:
snake_case : Optional[Any] = [self.center_crop(image=SCREAMING_SNAKE_CASE_ ,size=SCREAMING_SNAKE_CASE_ ) for image in images]
if do_rescale:
snake_case : Dict = [self.rescale(image=SCREAMING_SNAKE_CASE_ ,scale=SCREAMING_SNAKE_CASE_ ) for image in images]
# the pretrained checkpoints assume images are BGR, not RGB
if do_flip_channel_order:
snake_case : Optional[int] = [self.flip_channel_order(image=SCREAMING_SNAKE_CASE_ ) for image in images]
snake_case : List[Any] = [to_channel_dimension_format(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ) for image in images]
snake_case : int = {"""pixel_values""": images}
return BatchFeature(data=SCREAMING_SNAKE_CASE_ ,tensor_type=SCREAMING_SNAKE_CASE_ )
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ = None ):
'''simple docstring'''
snake_case : Dict = outputs.logits
# Resize logits and compute semantic segmentation maps
if target_sizes is not None:
if len(SCREAMING_SNAKE_CASE_ ) != len(SCREAMING_SNAKE_CASE_ ):
raise ValueError(
"""Make sure that you pass in as many target sizes as the batch dimension of the logits""" )
if is_torch_tensor(SCREAMING_SNAKE_CASE_ ):
snake_case : int = target_sizes.numpy()
snake_case : Optional[Any] = []
for idx in range(len(SCREAMING_SNAKE_CASE_ ) ):
snake_case : Optional[int] = torch.nn.functional.interpolate(
logits[idx].unsqueeze(dim=0 ) ,size=target_sizes[idx] ,mode="""bilinear""" ,align_corners=SCREAMING_SNAKE_CASE_ )
snake_case : Optional[int] = resized_logits[0].argmax(dim=0 )
semantic_segmentation.append(SCREAMING_SNAKE_CASE_ )
else:
snake_case : Tuple = logits.argmax(dim=1 )
snake_case : Dict = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0] )]
return semantic_segmentation
| 36 | 1 |
import unittest
from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_available():
from PIL import Image
else:
class _A :
'''simple docstring'''
@staticmethod
def snake_case_ ( *SCREAMING_SNAKE_CASE_ ,**SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
pass
@is_pipeline_test
@require_vision
@require_torch
class _A ( unittest.TestCase ):
'''simple docstring'''
__lowerCamelCase : Dict = MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
snake_case : str = pipeline(
"""zero-shot-object-detection""" ,model="""hf-internal-testing/tiny-random-owlvit-object-detection""" )
snake_case : List[Any] = [
{
"""image""": """./tests/fixtures/tests_samples/COCO/000000039769.png""",
"""candidate_labels""": ["""cat""", """remote""", """couch"""],
}
]
return object_detector, examples
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
snake_case : List[str] = object_detector(examples[0] ,threshold=0.0 )
snake_case : Dict = len(SCREAMING_SNAKE_CASE_ )
self.assertGreater(SCREAMING_SNAKE_CASE_ ,0 )
self.assertEqual(
SCREAMING_SNAKE_CASE_ ,[
{
"""score""": ANY(SCREAMING_SNAKE_CASE_ ),
"""label""": ANY(SCREAMING_SNAKE_CASE_ ),
"""box""": {"""xmin""": ANY(SCREAMING_SNAKE_CASE_ ), """ymin""": ANY(SCREAMING_SNAKE_CASE_ ), """xmax""": ANY(SCREAMING_SNAKE_CASE_ ), """ymax""": ANY(SCREAMING_SNAKE_CASE_ )},
}
for i in range(SCREAMING_SNAKE_CASE_ )
] ,)
@require_tf
@unittest.skip("""Zero Shot Object Detection not implemented in TF""" )
def snake_case_ ( self ):
'''simple docstring'''
pass
@require_torch
def snake_case_ ( self ):
'''simple docstring'''
snake_case : str = pipeline(
"""zero-shot-object-detection""" ,model="""hf-internal-testing/tiny-random-owlvit-object-detection""" )
snake_case : Union[str, Any] = object_detector(
"""./tests/fixtures/tests_samples/COCO/000000039769.png""" ,candidate_labels=["""cat""", """remote""", """couch"""] ,threshold=0.64 ,)
self.assertEqual(
nested_simplify(SCREAMING_SNAKE_CASE_ ,decimals=4 ) ,[
{"""score""": 0.72_35, """label""": """cat""", """box""": {"""xmin""": 204, """ymin""": 167, """xmax""": 232, """ymax""": 190}},
{"""score""": 0.72_18, """label""": """remote""", """box""": {"""xmin""": 204, """ymin""": 167, """xmax""": 232, """ymax""": 190}},
{"""score""": 0.71_84, """label""": """couch""", """box""": {"""xmin""": 204, """ymin""": 167, """xmax""": 232, """ymax""": 190}},
{"""score""": 0.67_48, """label""": """remote""", """box""": {"""xmin""": 571, """ymin""": 83, """xmax""": 598, """ymax""": 103}},
{"""score""": 0.66_56, """label""": """cat""", """box""": {"""xmin""": 571, """ymin""": 83, """xmax""": 598, """ymax""": 103}},
{"""score""": 0.66_14, """label""": """couch""", """box""": {"""xmin""": 571, """ymin""": 83, """xmax""": 598, """ymax""": 103}},
{"""score""": 0.64_56, """label""": """remote""", """box""": {"""xmin""": 494, """ymin""": 105, """xmax""": 521, """ymax""": 127}},
{"""score""": 0.6_42, """label""": """remote""", """box""": {"""xmin""": 67, """ymin""": 274, """xmax""": 93, """ymax""": 297}},
{"""score""": 0.64_19, """label""": """cat""", """box""": {"""xmin""": 494, """ymin""": 105, """xmax""": 521, """ymax""": 127}},
] ,)
snake_case : Optional[int] = object_detector(
[
{
"""image""": """./tests/fixtures/tests_samples/COCO/000000039769.png""",
"""candidate_labels""": ["""cat""", """remote""", """couch"""],
}
] ,threshold=0.64 ,)
self.assertEqual(
nested_simplify(SCREAMING_SNAKE_CASE_ ,decimals=4 ) ,[
[
{"""score""": 0.72_35, """label""": """cat""", """box""": {"""xmin""": 204, """ymin""": 167, """xmax""": 232, """ymax""": 190}},
{"""score""": 0.72_18, """label""": """remote""", """box""": {"""xmin""": 204, """ymin""": 167, """xmax""": 232, """ymax""": 190}},
{"""score""": 0.71_84, """label""": """couch""", """box""": {"""xmin""": 204, """ymin""": 167, """xmax""": 232, """ymax""": 190}},
{"""score""": 0.67_48, """label""": """remote""", """box""": {"""xmin""": 571, """ymin""": 83, """xmax""": 598, """ymax""": 103}},
{"""score""": 0.66_56, """label""": """cat""", """box""": {"""xmin""": 571, """ymin""": 83, """xmax""": 598, """ymax""": 103}},
{"""score""": 0.66_14, """label""": """couch""", """box""": {"""xmin""": 571, """ymin""": 83, """xmax""": 598, """ymax""": 103}},
{"""score""": 0.64_56, """label""": """remote""", """box""": {"""xmin""": 494, """ymin""": 105, """xmax""": 521, """ymax""": 127}},
{"""score""": 0.6_42, """label""": """remote""", """box""": {"""xmin""": 67, """ymin""": 274, """xmax""": 93, """ymax""": 297}},
{"""score""": 0.64_19, """label""": """cat""", """box""": {"""xmin""": 494, """ymin""": 105, """xmax""": 521, """ymax""": 127}},
]
] ,)
@require_torch
@slow
def snake_case_ ( self ):
'''simple docstring'''
snake_case : List[str] = pipeline("""zero-shot-object-detection""" )
snake_case : Dict = object_detector(
"""http://images.cocodataset.org/val2017/000000039769.jpg""" ,candidate_labels=["""cat""", """remote""", """couch"""] ,)
self.assertEqual(
nested_simplify(SCREAMING_SNAKE_CASE_ ,decimals=4 ) ,[
{"""score""": 0.28_68, """label""": """cat""", """box""": {"""xmin""": 324, """ymin""": 20, """xmax""": 640, """ymax""": 373}},
{"""score""": 0.2_77, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 72, """xmax""": 177, """ymax""": 115}},
{"""score""": 0.25_37, """label""": """cat""", """box""": {"""xmin""": 1, """ymin""": 55, """xmax""": 315, """ymax""": 472}},
{"""score""": 0.14_74, """label""": """remote""", """box""": {"""xmin""": 335, """ymin""": 74, """xmax""": 371, """ymax""": 187}},
{"""score""": 0.12_08, """label""": """couch""", """box""": {"""xmin""": 4, """ymin""": 0, """xmax""": 642, """ymax""": 476}},
] ,)
snake_case : int = object_detector(
[
{
"""image""": """http://images.cocodataset.org/val2017/000000039769.jpg""",
"""candidate_labels""": ["""cat""", """remote""", """couch"""],
},
{
"""image""": """http://images.cocodataset.org/val2017/000000039769.jpg""",
"""candidate_labels""": ["""cat""", """remote""", """couch"""],
},
] ,)
self.assertEqual(
nested_simplify(SCREAMING_SNAKE_CASE_ ,decimals=4 ) ,[
[
{"""score""": 0.28_68, """label""": """cat""", """box""": {"""xmin""": 324, """ymin""": 20, """xmax""": 640, """ymax""": 373}},
{"""score""": 0.2_77, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 72, """xmax""": 177, """ymax""": 115}},
{"""score""": 0.25_37, """label""": """cat""", """box""": {"""xmin""": 1, """ymin""": 55, """xmax""": 315, """ymax""": 472}},
{"""score""": 0.14_74, """label""": """remote""", """box""": {"""xmin""": 335, """ymin""": 74, """xmax""": 371, """ymax""": 187}},
{"""score""": 0.12_08, """label""": """couch""", """box""": {"""xmin""": 4, """ymin""": 0, """xmax""": 642, """ymax""": 476}},
],
[
{"""score""": 0.28_68, """label""": """cat""", """box""": {"""xmin""": 324, """ymin""": 20, """xmax""": 640, """ymax""": 373}},
{"""score""": 0.2_77, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 72, """xmax""": 177, """ymax""": 115}},
{"""score""": 0.25_37, """label""": """cat""", """box""": {"""xmin""": 1, """ymin""": 55, """xmax""": 315, """ymax""": 472}},
{"""score""": 0.14_74, """label""": """remote""", """box""": {"""xmin""": 335, """ymin""": 74, """xmax""": 371, """ymax""": 187}},
{"""score""": 0.12_08, """label""": """couch""", """box""": {"""xmin""": 4, """ymin""": 0, """xmax""": 642, """ymax""": 476}},
],
] ,)
@require_tf
@unittest.skip("""Zero Shot Object Detection not implemented in TF""" )
def snake_case_ ( self ):
'''simple docstring'''
pass
@require_torch
@slow
def snake_case_ ( self ):
'''simple docstring'''
snake_case : int = 0.2
snake_case : Dict = pipeline("""zero-shot-object-detection""" )
snake_case : int = object_detector(
"""http://images.cocodataset.org/val2017/000000039769.jpg""" ,candidate_labels=["""cat""", """remote""", """couch"""] ,threshold=SCREAMING_SNAKE_CASE_ ,)
self.assertEqual(
nested_simplify(SCREAMING_SNAKE_CASE_ ,decimals=4 ) ,[
{"""score""": 0.28_68, """label""": """cat""", """box""": {"""xmin""": 324, """ymin""": 20, """xmax""": 640, """ymax""": 373}},
{"""score""": 0.2_77, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 72, """xmax""": 177, """ymax""": 115}},
{"""score""": 0.25_37, """label""": """cat""", """box""": {"""xmin""": 1, """ymin""": 55, """xmax""": 315, """ymax""": 472}},
] ,)
@require_torch
@slow
def snake_case_ ( self ):
'''simple docstring'''
snake_case : Union[str, Any] = 2
snake_case : Dict = pipeline("""zero-shot-object-detection""" )
snake_case : str = object_detector(
"""http://images.cocodataset.org/val2017/000000039769.jpg""" ,candidate_labels=["""cat""", """remote""", """couch"""] ,top_k=SCREAMING_SNAKE_CASE_ ,)
self.assertEqual(
nested_simplify(SCREAMING_SNAKE_CASE_ ,decimals=4 ) ,[
{"""score""": 0.28_68, """label""": """cat""", """box""": {"""xmin""": 324, """ymin""": 20, """xmax""": 640, """ymax""": 373}},
{"""score""": 0.2_77, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 72, """xmax""": 177, """ymax""": 115}},
] ,)
| 36 |
import fire
from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer
def lowercase ( __A : str , __A : str , **__A : Optional[int] ) -> Optional[Any]:
'''simple docstring'''
snake_case : int = AutoConfig.from_pretrained(__A , **__A )
snake_case : Tuple = AutoModelForSeqaSeqLM.from_config(__A )
model.save_pretrained(__A )
AutoTokenizer.from_pretrained(__A ).save_pretrained(__A )
return model
if __name__ == "__main__":
fire.Fire(save_randomly_initialized_version)
| 36 | 1 |
import argparse
import os
import torch
from transformers.utils import WEIGHTS_NAME
__lowercase : str = ['''small''', '''medium''', '''large''']
__lowercase : Any = '''lm_head.decoder.weight'''
__lowercase : str = '''lm_head.weight'''
def lowercase ( __A : str , __A : str ) -> List[Any]:
'''simple docstring'''
snake_case : Optional[int] = torch.load(__A )
snake_case : Union[str, Any] = d.pop(__A )
os.makedirs(__A , exist_ok=__A )
torch.save(__A , os.path.join(__A , __A ) )
if __name__ == "__main__":
__lowercase : Any = argparse.ArgumentParser()
parser.add_argument('''--dialogpt_path''', default='''.''', type=str)
__lowercase : Any = parser.parse_args()
for MODEL in DIALOGPT_MODELS:
__lowercase : List[Any] = os.path.join(args.dialogpt_path, f'''{MODEL}_ft.pkl''')
__lowercase : List[str] = f'''./DialoGPT-{MODEL}'''
convert_dialogpt_checkpoint(
checkpoint_path,
pytorch_dump_folder_path,
)
| 36 |
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
__lowercase : Any = logging.get_logger(__name__)
__lowercase : str = {
'''google/mobilenet_v1_1.0_224''': '''https://huggingface.co/google/mobilenet_v1_1.0_224/resolve/main/config.json''',
'''google/mobilenet_v1_0.75_192''': '''https://huggingface.co/google/mobilenet_v1_0.75_192/resolve/main/config.json''',
# See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
}
class _A ( snake_case ):
'''simple docstring'''
__lowerCamelCase : Dict = '''mobilenet_v1'''
def __init__( self ,SCREAMING_SNAKE_CASE_=3 ,SCREAMING_SNAKE_CASE_=224 ,SCREAMING_SNAKE_CASE_=1.0 ,SCREAMING_SNAKE_CASE_=8 ,SCREAMING_SNAKE_CASE_="relu6" ,SCREAMING_SNAKE_CASE_=True ,SCREAMING_SNAKE_CASE_=0.9_99 ,SCREAMING_SNAKE_CASE_=0.02 ,SCREAMING_SNAKE_CASE_=0.0_01 ,**SCREAMING_SNAKE_CASE_ ,):
'''simple docstring'''
super().__init__(**SCREAMING_SNAKE_CASE_ )
if depth_multiplier <= 0:
raise ValueError("""depth_multiplier must be greater than zero.""" )
snake_case : List[Any] = num_channels
snake_case : str = image_size
snake_case : List[Any] = depth_multiplier
snake_case : Optional[int] = min_depth
snake_case : Union[str, Any] = hidden_act
snake_case : int = tf_padding
snake_case : Optional[int] = classifier_dropout_prob
snake_case : Tuple = initializer_range
snake_case : List[str] = layer_norm_eps
class _A ( snake_case ):
'''simple docstring'''
__lowerCamelCase : Optional[Any] = version.parse('''1.11''' )
@property
def snake_case_ ( self ):
'''simple docstring'''
return OrderedDict([("""pixel_values""", {0: """batch"""})] )
@property
def snake_case_ ( self ):
'''simple docstring'''
if self.task == "image-classification":
return OrderedDict([("""logits""", {0: """batch"""})] )
else:
return OrderedDict([("""last_hidden_state""", {0: """batch"""}), ("""pooler_output""", {0: """batch"""})] )
@property
def snake_case_ ( self ):
'''simple docstring'''
return 1E-4
| 36 | 1 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _A ( snake_case ):
'''simple docstring'''
__lowerCamelCase : Optional[Any] = ['''image_processor''', '''tokenizer''']
__lowerCamelCase : int = '''CLIPImageProcessor'''
__lowerCamelCase : Optional[Any] = ('''XLMRobertaTokenizer''', '''XLMRobertaTokenizerFast''')
def __init__( self ,SCREAMING_SNAKE_CASE_=None ,SCREAMING_SNAKE_CASE_=None ,**SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
snake_case : Optional[int] = None
if "feature_extractor" in kwargs:
warnings.warn(
"""The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
""" instead.""" ,SCREAMING_SNAKE_CASE_ ,)
snake_case : List[Any] = kwargs.pop("""feature_extractor""" )
snake_case : str = 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__(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )
def __call__( self ,SCREAMING_SNAKE_CASE_=None ,SCREAMING_SNAKE_CASE_=None ,SCREAMING_SNAKE_CASE_=None ,**SCREAMING_SNAKE_CASE_ ):
'''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:
snake_case : List[Any] = self.tokenizer(SCREAMING_SNAKE_CASE_ ,return_tensors=SCREAMING_SNAKE_CASE_ ,**SCREAMING_SNAKE_CASE_ )
if images is not None:
snake_case : int = self.image_processor(SCREAMING_SNAKE_CASE_ ,return_tensors=SCREAMING_SNAKE_CASE_ ,**SCREAMING_SNAKE_CASE_ )
if text is not None and images is not None:
snake_case : Dict = image_features.pixel_values
return encoding
elif text is not None:
return encoding
else:
return BatchEncoding(data=dict(**SCREAMING_SNAKE_CASE_ ) ,tensor_type=SCREAMING_SNAKE_CASE_ )
def snake_case_ ( self ,*SCREAMING_SNAKE_CASE_ ,**SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE_ ,**SCREAMING_SNAKE_CASE_ )
def snake_case_ ( self ,*SCREAMING_SNAKE_CASE_ ,**SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
return self.tokenizer.decode(*SCREAMING_SNAKE_CASE_ ,**SCREAMING_SNAKE_CASE_ )
@property
def snake_case_ ( self ):
'''simple docstring'''
snake_case : Optional[Any] = self.tokenizer.model_input_names
snake_case : List[str] = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
| 36 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowercase : List[str] = logging.get_logger(__name__)
__lowercase : List[str] = {
'''edbeeching/decision-transformer-gym-hopper-medium''': (
'''https://huggingface.co/edbeeching/decision-transformer-gym-hopper-medium/resolve/main/config.json'''
),
# See all DecisionTransformer models at https://huggingface.co/models?filter=decision_transformer
}
class _A ( snake_case ):
'''simple docstring'''
__lowerCamelCase : int = '''decision_transformer'''
__lowerCamelCase : Optional[Any] = ['''past_key_values''']
__lowerCamelCase : Tuple = {
'''max_position_embeddings''': '''n_positions''',
'''num_attention_heads''': '''n_head''',
'''num_hidden_layers''': '''n_layer''',
}
def __init__( self ,SCREAMING_SNAKE_CASE_=17 ,SCREAMING_SNAKE_CASE_=4 ,SCREAMING_SNAKE_CASE_=128 ,SCREAMING_SNAKE_CASE_=4096 ,SCREAMING_SNAKE_CASE_=True ,SCREAMING_SNAKE_CASE_=1 ,SCREAMING_SNAKE_CASE_=1024 ,SCREAMING_SNAKE_CASE_=3 ,SCREAMING_SNAKE_CASE_=1 ,SCREAMING_SNAKE_CASE_=None ,SCREAMING_SNAKE_CASE_="relu" ,SCREAMING_SNAKE_CASE_=0.1 ,SCREAMING_SNAKE_CASE_=0.1 ,SCREAMING_SNAKE_CASE_=0.1 ,SCREAMING_SNAKE_CASE_=1E-5 ,SCREAMING_SNAKE_CASE_=0.02 ,SCREAMING_SNAKE_CASE_=True ,SCREAMING_SNAKE_CASE_=True ,SCREAMING_SNAKE_CASE_=50256 ,SCREAMING_SNAKE_CASE_=50256 ,SCREAMING_SNAKE_CASE_=False ,SCREAMING_SNAKE_CASE_=False ,**SCREAMING_SNAKE_CASE_ ,):
'''simple docstring'''
snake_case : Any = state_dim
snake_case : Optional[Any] = act_dim
snake_case : Union[str, Any] = hidden_size
snake_case : Any = max_ep_len
snake_case : int = action_tanh
snake_case : Any = vocab_size
snake_case : Any = n_positions
snake_case : List[str] = n_layer
snake_case : int = n_head
snake_case : Optional[int] = n_inner
snake_case : List[Any] = activation_function
snake_case : Tuple = resid_pdrop
snake_case : Optional[Any] = embd_pdrop
snake_case : Dict = attn_pdrop
snake_case : List[str] = layer_norm_epsilon
snake_case : Union[str, Any] = initializer_range
snake_case : Optional[Any] = scale_attn_weights
snake_case : str = use_cache
snake_case : int = scale_attn_by_inverse_layer_idx
snake_case : Tuple = reorder_and_upcast_attn
snake_case : Tuple = bos_token_id
snake_case : List[str] = eos_token_id
super().__init__(bos_token_id=SCREAMING_SNAKE_CASE_ ,eos_token_id=SCREAMING_SNAKE_CASE_ ,**SCREAMING_SNAKE_CASE_ )
| 36 | 1 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__lowercase : Optional[int] = logging.get_logger(__name__)
__lowercase : Optional[Any] = '''▁'''
__lowercase : Union[str, Any] = {'''vocab_file''': '''sentencepiece.bpe.model'''}
__lowercase : Optional[Any] = {
'''vocab_file''': {
'''xlm-roberta-base''': '''https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model''',
'''xlm-roberta-large''': '''https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model''',
'''xlm-roberta-large-finetuned-conll02-dutch''': (
'''https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model'''
),
'''xlm-roberta-large-finetuned-conll02-spanish''': (
'''https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model'''
),
'''xlm-roberta-large-finetuned-conll03-english''': (
'''https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model'''
),
'''xlm-roberta-large-finetuned-conll03-german''': (
'''https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model'''
),
}
}
__lowercase : Optional[Any] = {
'''xlm-roberta-base''': 512,
'''xlm-roberta-large''': 512,
'''xlm-roberta-large-finetuned-conll02-dutch''': 512,
'''xlm-roberta-large-finetuned-conll02-spanish''': 512,
'''xlm-roberta-large-finetuned-conll03-english''': 512,
'''xlm-roberta-large-finetuned-conll03-german''': 512,
}
class _A ( snake_case ):
'''simple docstring'''
__lowerCamelCase : str = VOCAB_FILES_NAMES
__lowerCamelCase : int = PRETRAINED_VOCAB_FILES_MAP
__lowerCamelCase : Optional[int] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__lowerCamelCase : Optional[Any] = ['''input_ids''', '''attention_mask''']
def __init__( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_="<s>" ,SCREAMING_SNAKE_CASE_="</s>" ,SCREAMING_SNAKE_CASE_="</s>" ,SCREAMING_SNAKE_CASE_="<s>" ,SCREAMING_SNAKE_CASE_="<unk>" ,SCREAMING_SNAKE_CASE_="<pad>" ,SCREAMING_SNAKE_CASE_="<mask>" ,SCREAMING_SNAKE_CASE_ = None ,**SCREAMING_SNAKE_CASE_ ,):
'''simple docstring'''
# Mask token behave like a normal word, i.e. include the space before it
snake_case : Tuple = AddedToken(SCREAMING_SNAKE_CASE_ ,lstrip=SCREAMING_SNAKE_CASE_ ,rstrip=SCREAMING_SNAKE_CASE_ ) if isinstance(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ) else mask_token
snake_case : List[str] = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=SCREAMING_SNAKE_CASE_ ,eos_token=SCREAMING_SNAKE_CASE_ ,unk_token=SCREAMING_SNAKE_CASE_ ,sep_token=SCREAMING_SNAKE_CASE_ ,cls_token=SCREAMING_SNAKE_CASE_ ,pad_token=SCREAMING_SNAKE_CASE_ ,mask_token=SCREAMING_SNAKE_CASE_ ,sp_model_kwargs=self.sp_model_kwargs ,**SCREAMING_SNAKE_CASE_ ,)
snake_case : Dict = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(str(SCREAMING_SNAKE_CASE_ ) )
snake_case : List[Any] = vocab_file
# Original fairseq vocab and spm vocab must be "aligned":
# Vocab | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9
# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
# fairseq | '<s>' | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's' | '▁de' | '-'
# spm | '<unk>' | '<s>' | '</s>' | ',' | '.' | '▁' | 's' | '▁de' | '-' | '▁a'
# Mimic fairseq token-to-id alignment for the first 4 token
snake_case : str = {"""<s>""": 0, """<pad>""": 1, """</s>""": 2, """<unk>""": 3}
# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
snake_case : str = 1
snake_case : List[Any] = len(self.sp_model ) + self.fairseq_offset
snake_case : List[str] = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
def __getstate__( self ):
'''simple docstring'''
snake_case : Union[str, Any] = self.__dict__.copy()
snake_case : List[Any] = None
snake_case : int = self.sp_model.serialized_model_proto()
return state
def __setstate__( self ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
snake_case : Any = d
# for backward compatibility
if not hasattr(self ,"""sp_model_kwargs""" ):
snake_case : Optional[Any] = {}
snake_case : Optional[Any] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.LoadFromSerializedProto(self.sp_model_proto )
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ = None ):
'''simple docstring'''
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
snake_case : Any = [self.cls_token_id]
snake_case : Union[str, Any] = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_ = False ):
'''simple docstring'''
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=SCREAMING_SNAKE_CASE_ ,token_ids_a=SCREAMING_SNAKE_CASE_ ,already_has_special_tokens=SCREAMING_SNAKE_CASE_ )
if token_ids_a is None:
return [1] + ([0] * len(SCREAMING_SNAKE_CASE_ )) + [1]
return [1] + ([0] * len(SCREAMING_SNAKE_CASE_ )) + [1, 1] + ([0] * len(SCREAMING_SNAKE_CASE_ )) + [1]
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ = None ):
'''simple docstring'''
snake_case : Union[str, Any] = [self.sep_token_id]
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]
@property
def snake_case_ ( self ):
'''simple docstring'''
return len(self.sp_model ) + self.fairseq_offset + 1 # Add the <mask> token
def snake_case_ ( self ):
'''simple docstring'''
snake_case : Union[str, Any] = {self.convert_ids_to_tokens(SCREAMING_SNAKE_CASE_ ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
return self.sp_model.encode(SCREAMING_SNAKE_CASE_ ,out_type=SCREAMING_SNAKE_CASE_ )
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
snake_case : str = self.sp_model.PieceToId(SCREAMING_SNAKE_CASE_ )
# Need to return unknown token if the SP model returned 0
return spm_id + self.fairseq_offset if spm_id else self.unk_token_id
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
if index in self.fairseq_ids_to_tokens:
return self.fairseq_ids_to_tokens[index]
return self.sp_model.IdToPiece(index - self.fairseq_offset )
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
snake_case : List[str] = """""".join(SCREAMING_SNAKE_CASE_ ).replace(SCREAMING_SNAKE_CASE_ ,""" """ ).strip()
return out_string
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ = None ):
'''simple docstring'''
if not os.path.isdir(SCREAMING_SNAKE_CASE_ ):
logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
return
snake_case : Optional[Any] = os.path.join(
SCREAMING_SNAKE_CASE_ ,(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(SCREAMING_SNAKE_CASE_ ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file ,SCREAMING_SNAKE_CASE_ )
elif not os.path.isfile(self.vocab_file ):
with open(SCREAMING_SNAKE_CASE_ ,"""wb""" ) as fi:
snake_case : List[str] = self.sp_model.serialized_model_proto()
fi.write(SCREAMING_SNAKE_CASE_ )
return (out_vocab_file,)
| 36 |
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
| 36 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__lowercase : Union[str, Any] = {
'''configuration_graphormer''': ['''GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GraphormerConfig'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowercase : Tuple = [
'''GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''GraphormerForGraphClassification''',
'''GraphormerModel''',
'''GraphormerPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_graphormer import GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, GraphormerConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_graphormer import (
GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
GraphormerForGraphClassification,
GraphormerModel,
GraphormerPreTrainedModel,
)
else:
import sys
__lowercase : Tuple = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 36 |
# Usage:
# ./gen-card-facebook-wmt19.py
import os
from pathlib import Path
def lowercase ( __A : Dict , __A : Union[str, Any] , __A : List[str] ) -> Any:
'''simple docstring'''
snake_case : Tuple = {
"""en""": """Machine learning is great, isn't it?""",
"""ru""": """Машинное обучение - это здорово, не так ли?""",
"""de""": """Maschinelles Lernen ist großartig, oder?""",
}
# BLUE scores as follows:
# "pair": [fairseq, transformers]
snake_case : Optional[Any] = {
"""ru-en""": ["""[41.3](http://matrix.statmt.org/matrix/output/1907?run_id=6937)""", """39.20"""],
"""en-ru""": ["""[36.4](http://matrix.statmt.org/matrix/output/1914?run_id=6724)""", """33.47"""],
"""en-de""": ["""[43.1](http://matrix.statmt.org/matrix/output/1909?run_id=6862)""", """42.83"""],
"""de-en""": ["""[42.3](http://matrix.statmt.org/matrix/output/1902?run_id=6750)""", """41.35"""],
}
snake_case : Optional[int] = f"""{src_lang}-{tgt_lang}"""
snake_case : Any = f"""
---
language:
- {src_lang}
- {tgt_lang}
thumbnail:
tags:
- translation
- wmt19
- facebook
license: apache-2.0
datasets:
- wmt19
metrics:
- bleu
---
# FSMT
## Model description
This is a ported version of [fairseq wmt19 transformer](https://github.com/pytorch/fairseq/blob/master/examples/wmt19/README.md) for {src_lang}-{tgt_lang}.
For more details, please see, [Facebook FAIR's WMT19 News Translation Task Submission](https://arxiv.org/abs/1907.06616).
The abbreviation FSMT stands for FairSeqMachineTranslation
All four models are available:
* [wmt19-en-ru](https://huggingface.co/facebook/wmt19-en-ru)
* [wmt19-ru-en](https://huggingface.co/facebook/wmt19-ru-en)
* [wmt19-en-de](https://huggingface.co/facebook/wmt19-en-de)
* [wmt19-de-en](https://huggingface.co/facebook/wmt19-de-en)
## Intended uses & limitations
#### How to use
```python
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
mname = \"facebook/wmt19-{src_lang}-{tgt_lang}\"
tokenizer = FSMTTokenizer.from_pretrained(mname)
model = FSMTForConditionalGeneration.from_pretrained(mname)
input = \"{texts[src_lang]}\"
input_ids = tokenizer.encode(input, return_tensors=\"pt\")
outputs = model.generate(input_ids)
decoded = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(decoded) # {texts[tgt_lang]}
```
#### Limitations and bias
- The original (and this ported model) doesn't seem to handle well inputs with repeated sub-phrases, [content gets truncated](https://discuss.huggingface.co/t/issues-with-translating-inputs-containing-repeated-phrases/981)
## Training data
Pretrained weights were left identical to the original model released by fairseq. For more details, please, see the [paper](https://arxiv.org/abs/1907.06616).
## Eval results
pair | fairseq | transformers
-------|---------|----------
{pair} | {scores[pair][0]} | {scores[pair][1]}
The score is slightly below the score reported by `fairseq`, since `transformers`` currently doesn't support:
- model ensemble, therefore the best performing checkpoint was ported (``model4.pt``).
- re-ranking
The score was calculated using this code:
```bash
git clone https://github.com/huggingface/transformers
cd transformers
export PAIR={pair}
export DATA_DIR=data/$PAIR
export SAVE_DIR=data/$PAIR
export BS=8
export NUM_BEAMS=15
mkdir -p $DATA_DIR
sacrebleu -t wmt19 -l $PAIR --echo src > $DATA_DIR/val.source
sacrebleu -t wmt19 -l $PAIR --echo ref > $DATA_DIR/val.target
echo $PAIR
PYTHONPATH=\"src:examples/seq2seq\" python examples/seq2seq/run_eval.py facebook/wmt19-$PAIR $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation --num_beams $NUM_BEAMS
```
note: fairseq reports using a beam of 50, so you should get a slightly higher score if re-run with `--num_beams 50`.
## Data Sources
- [training, etc.](http://www.statmt.org/wmt19/)
- [test set](http://matrix.statmt.org/test_sets/newstest2019.tgz?1556572561)
### BibTeX entry and citation info
```bibtex
@inproceedings{{...,
year={{2020}},
title={{Facebook FAIR's WMT19 News Translation Task Submission}},
author={{Ng, Nathan and Yee, Kyra and Baevski, Alexei and Ott, Myle and Auli, Michael and Edunov, Sergey}},
booktitle={{Proc. of WMT}},
}}
```
## TODO
- port model ensemble (fairseq uses 4 model checkpoints)
"""
os.makedirs(__A , exist_ok=__A )
snake_case : Union[str, Any] = os.path.join(__A , """README.md""" )
print(f"""Generating {path}""" )
with open(__A , """w""" , encoding="""utf-8""" ) as f:
f.write(__A )
# make sure we are under the root of the project
__lowercase : int = Path(__file__).resolve().parent.parent.parent
__lowercase : List[str] = repo_dir / '''model_cards'''
for model_name in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
__lowercase , __lowercase , __lowercase : List[str] = model_name.split('''-''')
__lowercase : str = model_cards_dir / '''facebook''' / model_name
write_model_card(model_card_dir, src_lang=src_lang, tgt_lang=tgt_lang)
| 36 | 1 |
from __future__ import annotations
def lowercase ( __A : list ) -> float:
'''simple docstring'''
if not nums:
raise ValueError("""List is empty""" )
return sum(__A ) / len(__A )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 36 |
__lowercase : List[str] = '''
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
'''
__lowercase : str = [{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
__lowercase : List[str] = {
'''{processor_class}''': '''FakeProcessorClass''',
'''{model_class}''': '''FakeModelClass''',
'''{object_class}''': '''FakeObjectClass''',
}
| 36 | 1 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowercase : Dict = logging.get_logger(__name__)
__lowercase : Dict = {
'''asapp/sew-tiny-100k''': '''https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json''',
# See all SEW models at https://huggingface.co/models?filter=sew
}
class _A ( snake_case ):
'''simple docstring'''
__lowerCamelCase : Optional[Any] = '''sew'''
def __init__( self ,SCREAMING_SNAKE_CASE_=32 ,SCREAMING_SNAKE_CASE_=768 ,SCREAMING_SNAKE_CASE_=12 ,SCREAMING_SNAKE_CASE_=12 ,SCREAMING_SNAKE_CASE_=3072 ,SCREAMING_SNAKE_CASE_=2 ,SCREAMING_SNAKE_CASE_="gelu" ,SCREAMING_SNAKE_CASE_=0.1 ,SCREAMING_SNAKE_CASE_=0.1 ,SCREAMING_SNAKE_CASE_=0.1 ,SCREAMING_SNAKE_CASE_=0.0 ,SCREAMING_SNAKE_CASE_=0.1 ,SCREAMING_SNAKE_CASE_=0.1 ,SCREAMING_SNAKE_CASE_=0.02 ,SCREAMING_SNAKE_CASE_=1E-5 ,SCREAMING_SNAKE_CASE_="group" ,SCREAMING_SNAKE_CASE_="gelu" ,SCREAMING_SNAKE_CASE_=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512) ,SCREAMING_SNAKE_CASE_=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1) ,SCREAMING_SNAKE_CASE_=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1) ,SCREAMING_SNAKE_CASE_=False ,SCREAMING_SNAKE_CASE_=128 ,SCREAMING_SNAKE_CASE_=16 ,SCREAMING_SNAKE_CASE_=True ,SCREAMING_SNAKE_CASE_=0.05 ,SCREAMING_SNAKE_CASE_=10 ,SCREAMING_SNAKE_CASE_=2 ,SCREAMING_SNAKE_CASE_=0.0 ,SCREAMING_SNAKE_CASE_=10 ,SCREAMING_SNAKE_CASE_=0 ,SCREAMING_SNAKE_CASE_="mean" ,SCREAMING_SNAKE_CASE_=False ,SCREAMING_SNAKE_CASE_=False ,SCREAMING_SNAKE_CASE_=256 ,SCREAMING_SNAKE_CASE_=0 ,SCREAMING_SNAKE_CASE_=1 ,SCREAMING_SNAKE_CASE_=2 ,**SCREAMING_SNAKE_CASE_ ,):
'''simple docstring'''
super().__init__(**SCREAMING_SNAKE_CASE_ ,pad_token_id=SCREAMING_SNAKE_CASE_ ,bos_token_id=SCREAMING_SNAKE_CASE_ ,eos_token_id=SCREAMING_SNAKE_CASE_ )
snake_case : Optional[Any] = hidden_size
snake_case : List[Any] = feat_extract_norm
snake_case : List[str] = feat_extract_activation
snake_case : int = list(SCREAMING_SNAKE_CASE_ )
snake_case : Tuple = list(SCREAMING_SNAKE_CASE_ )
snake_case : List[str] = list(SCREAMING_SNAKE_CASE_ )
snake_case : List[Any] = conv_bias
snake_case : Any = num_conv_pos_embeddings
snake_case : List[str] = num_conv_pos_embedding_groups
snake_case : Union[str, Any] = len(self.conv_dim )
snake_case : Optional[Any] = num_hidden_layers
snake_case : List[str] = intermediate_size
snake_case : List[Any] = squeeze_factor
snake_case : Dict = hidden_act
snake_case : Tuple = num_attention_heads
snake_case : int = hidden_dropout
snake_case : Tuple = attention_dropout
snake_case : Tuple = activation_dropout
snake_case : List[str] = feat_proj_dropout
snake_case : Tuple = final_dropout
snake_case : Tuple = layerdrop
snake_case : Any = layer_norm_eps
snake_case : Union[str, Any] = initializer_range
snake_case : Optional[int] = vocab_size
if (
(len(self.conv_stride ) != self.num_feat_extract_layers)
or (len(self.conv_kernel ) != self.num_feat_extract_layers)
or (len(self.conv_dim ) != self.num_feat_extract_layers)
):
raise ValueError(
"""Configuration for convolutional layers is incorrect."""
"""It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,"""
F"""but is `len(config.conv_dim) = {len(self.conv_dim )}`, `len(config.conv_stride)"""
F"""= {len(self.conv_stride )}`, `len(config.conv_kernel) = {len(self.conv_kernel )}`.""" )
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
snake_case : int = apply_spec_augment
snake_case : Any = mask_time_prob
snake_case : int = mask_time_length
snake_case : Any = mask_time_min_masks
snake_case : List[Any] = mask_feature_prob
snake_case : Dict = mask_feature_length
snake_case : Any = mask_feature_min_masks
# ctc loss
snake_case : List[str] = ctc_loss_reduction
snake_case : Union[str, Any] = ctc_zero_infinity
# sequence classification
snake_case : int = use_weighted_layer_sum
snake_case : List[str] = classifier_proj_size
@property
def snake_case_ ( self ):
'''simple docstring'''
return functools.reduce(operator.mul ,self.conv_stride ,1 )
| 36 |
import warnings
from ..trainer import Trainer
from ..utils import logging
__lowercase : str = logging.get_logger(__name__)
class _A ( snake_case ):
'''simple docstring'''
def __init__( self ,SCREAMING_SNAKE_CASE_=None ,**SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
warnings.warn(
"""`SageMakerTrainer` is deprecated and will be removed in v5 of Transformers. You can use `Trainer` """
"""instead.""" ,SCREAMING_SNAKE_CASE_ ,)
super().__init__(args=SCREAMING_SNAKE_CASE_ ,**SCREAMING_SNAKE_CASE_ )
| 36 | 1 |
def lowercase ( __A : int = 200_0000 ) -> int:
'''simple docstring'''
snake_case : List[str] = [0 for i in range(n + 1 )]
snake_case : Optional[Any] = 1
snake_case : Tuple = 1
for i in range(2 , int(n**0.5 ) + 1 ):
if primality_list[i] == 0:
for j in range(i * i , n + 1 , __A ):
snake_case : Optional[int] = 1
snake_case : List[str] = 0
for i in range(__A ):
if primality_list[i] == 0:
sum_of_primes += i
return sum_of_primes
if __name__ == "__main__":
print(f'''{solution() = }''')
| 36 |
from pathlib import Path
from typing import List
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
__lowercase : List[str] = ['''text''', '''image''', '''audio''']
def lowercase ( __A : List[str] ) -> List[str]:
'''simple docstring'''
snake_case : Optional[int] = []
for input_type in input_types:
if input_type == "text":
inputs.append("""Text input""" )
elif input_type == "image":
inputs.append(
Image.open(Path(get_tests_dir("""fixtures/tests_samples/COCO""" ) ) / """000000039769.png""" ).resize((512, 512) ) )
elif input_type == "audio":
inputs.append(torch.ones(3000 ) )
elif isinstance(__A , __A ):
inputs.append(create_inputs(__A ) )
else:
raise ValueError(f"""Invalid type requested: {input_type}""" )
return inputs
def lowercase ( __A : List ) -> Union[str, Any]:
'''simple docstring'''
snake_case : Dict = []
for output in outputs:
if isinstance(__A , (str, AgentText) ):
output_types.append("""text""" )
elif isinstance(__A , (Image.Image, AgentImage) ):
output_types.append("""image""" )
elif isinstance(__A , (torch.Tensor, AgentAudio) ):
output_types.append("""audio""" )
else:
raise ValueError(f"""Invalid output: {output}""" )
return output_types
@is_tool_test
class _A :
'''simple docstring'''
def snake_case_ ( self ):
'''simple docstring'''
self.assertTrue(hasattr(self.tool ,"""inputs""" ) )
self.assertTrue(hasattr(self.tool ,"""outputs""" ) )
snake_case : Dict = self.tool.inputs
for _input in inputs:
if isinstance(_input ,SCREAMING_SNAKE_CASE_ ):
for __input in _input:
self.assertTrue(__input in authorized_types )
else:
self.assertTrue(_input in authorized_types )
snake_case : int = self.tool.outputs
for _output in outputs:
self.assertTrue(_output in authorized_types )
def snake_case_ ( self ):
'''simple docstring'''
snake_case : List[str] = create_inputs(self.tool.inputs )
snake_case : str = self.tool(*SCREAMING_SNAKE_CASE_ )
# There is a single output
if len(self.tool.outputs ) == 1:
snake_case : Union[str, Any] = [outputs]
self.assertListEqual(output_types(SCREAMING_SNAKE_CASE_ ) ,self.tool.outputs )
def snake_case_ ( self ):
'''simple docstring'''
self.assertTrue(hasattr(self.tool ,"""description""" ) )
self.assertTrue(hasattr(self.tool ,"""default_checkpoint""" ) )
self.assertTrue(self.tool.description.startswith("""This is a tool that""" ) )
def snake_case_ ( self ):
'''simple docstring'''
snake_case : List[Any] = create_inputs(self.tool.inputs )
snake_case : int = self.tool(*SCREAMING_SNAKE_CASE_ )
if not isinstance(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ):
snake_case : Any = [outputs]
self.assertEqual(len(SCREAMING_SNAKE_CASE_ ) ,len(self.tool.outputs ) )
for output, output_type in zip(SCREAMING_SNAKE_CASE_ ,self.tool.outputs ):
snake_case : List[str] = AGENT_TYPE_MAPPING[output_type]
self.assertTrue(isinstance(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ) )
def snake_case_ ( self ):
'''simple docstring'''
snake_case : Tuple = create_inputs(self.tool.inputs )
snake_case : Any = []
for _input, input_type in zip(SCREAMING_SNAKE_CASE_ ,self.tool.inputs ):
if isinstance(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ):
_inputs.append([AGENT_TYPE_MAPPING[_input_type](_input ) for _input_type in input_type] )
else:
_inputs.append(AGENT_TYPE_MAPPING[input_type](_input ) )
# Should not raise an error
snake_case : Tuple = self.tool(*SCREAMING_SNAKE_CASE_ )
if not isinstance(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ):
snake_case : Union[str, Any] = [outputs]
self.assertEqual(len(SCREAMING_SNAKE_CASE_ ) ,len(self.tool.outputs ) )
| 36 | 1 |
def lowercase ( __A : Any ) -> str: # noqa: E741
'''simple docstring'''
snake_case : Any = len(__A )
snake_case : Dict = 0
snake_case : Optional[Any] = [0] * n
snake_case : str = [False] * n
snake_case : Tuple = [False] * n
def dfs(__A : Dict , __A : Optional[Any] , __A : int , __A : List[str] ):
if parent == root:
out_edge_count += 1
snake_case : List[str] = True
snake_case : List[str] = at
for to in l[at]:
if to == parent:
pass
elif not visited[to]:
snake_case : List[str] = dfs(__A , __A , __A , __A )
snake_case : Optional[Any] = min(low[at] , low[to] )
# AP found via bridge
if at < low[to]:
snake_case : List[str] = True
# AP found via cycle
if at == low[to]:
snake_case : Any = True
else:
snake_case : Optional[Any] = min(low[at] , __A )
return out_edge_count
for i in range(__A ):
if not visited[i]:
snake_case : List[Any] = 0
snake_case : Union[str, Any] = dfs(__A , __A , -1 , __A )
snake_case : Optional[int] = out_edge_count > 1
for x in range(len(__A ) ):
if is_art[x] is True:
print(__A )
# Adjacency list of graph
__lowercase : Dict = {
0: [1, 2],
1: [0, 2],
2: [0, 1, 3, 5],
3: [2, 4],
4: [3],
5: [2, 6, 8],
6: [5, 7],
7: [6, 8],
8: [5, 7],
}
compute_ap(data)
| 36 |
import os
import pytest
from datasets import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
)
__lowercase : Optional[Any] = pytest.mark.integration
@pytest.mark.parametrize("""path""" , ["""paws""", """csv"""] )
def lowercase ( __A : Optional[Any] , __A : Optional[Any] ) -> str:
'''simple docstring'''
inspect_dataset(__A , __A )
snake_case : List[str] = path + """.py"""
assert script_name in os.listdir(__A )
assert "__pycache__" not in os.listdir(__A )
@pytest.mark.filterwarnings("""ignore:inspect_metric is deprecated:FutureWarning""" )
@pytest.mark.filterwarnings("""ignore:metric_module_factory is deprecated:FutureWarning""" )
@pytest.mark.parametrize("""path""" , ["""accuracy"""] )
def lowercase ( __A : Optional[int] , __A : Any ) -> Optional[Any]:
'''simple docstring'''
inspect_metric(__A , __A )
snake_case : Any = path + """.py"""
assert script_name in os.listdir(__A )
assert "__pycache__" not in os.listdir(__A )
@pytest.mark.parametrize(
"""path, config_name, expected_splits""" , [
("""squad""", """plain_text""", ["""train""", """validation"""]),
("""dalle-mini/wit""", """dalle-mini--wit""", ["""train"""]),
("""paws""", """labeled_final""", ["""train""", """test""", """validation"""]),
] , )
def lowercase ( __A : Tuple , __A : Dict , __A : Any ) -> Optional[int]:
'''simple docstring'''
snake_case : List[str] = get_dataset_config_info(__A , config_name=__A )
assert info.config_name == config_name
assert list(info.splits.keys() ) == expected_splits
@pytest.mark.parametrize(
"""path, config_name, expected_exception""" , [
("""paws""", None, ValueError),
] , )
def lowercase ( __A : Tuple , __A : Any , __A : List[str] ) -> Optional[int]:
'''simple docstring'''
with pytest.raises(__A ):
get_dataset_config_info(__A , config_name=__A )
@pytest.mark.parametrize(
"""path, expected""" , [
("""squad""", """plain_text"""),
("""acronym_identification""", """default"""),
("""lhoestq/squad""", """plain_text"""),
("""lhoestq/test""", """default"""),
("""lhoestq/demo1""", """lhoestq--demo1"""),
("""dalle-mini/wit""", """dalle-mini--wit"""),
] , )
def lowercase ( __A : Any , __A : Dict ) -> Dict:
'''simple docstring'''
snake_case : int = get_dataset_config_names(__A )
assert expected in config_names
@pytest.mark.parametrize(
"""path, expected_configs, expected_splits_in_first_config""" , [
("""squad""", ["""plain_text"""], ["""train""", """validation"""]),
("""dalle-mini/wit""", ["""dalle-mini--wit"""], ["""train"""]),
("""paws""", ["""labeled_final""", """labeled_swap""", """unlabeled_final"""], ["""train""", """test""", """validation"""]),
] , )
def lowercase ( __A : Optional[Any] , __A : Dict , __A : List[str] ) -> Union[str, Any]:
'''simple docstring'''
snake_case : List[Any] = get_dataset_infos(__A )
assert list(infos.keys() ) == expected_configs
snake_case : Any = expected_configs[0]
assert expected_config in infos
snake_case : Any = infos[expected_config]
assert info.config_name == expected_config
assert list(info.splits.keys() ) == expected_splits_in_first_config
@pytest.mark.parametrize(
"""path, expected_config, expected_splits""" , [
("""squad""", """plain_text""", ["""train""", """validation"""]),
("""dalle-mini/wit""", """dalle-mini--wit""", ["""train"""]),
("""paws""", """labeled_final""", ["""train""", """test""", """validation"""]),
] , )
def lowercase ( __A : Optional[int] , __A : Tuple , __A : Union[str, Any] ) -> Optional[int]:
'''simple docstring'''
snake_case : Dict = get_dataset_infos(__A )
assert expected_config in infos
snake_case : str = infos[expected_config]
assert info.config_name == expected_config
assert list(info.splits.keys() ) == expected_splits
@pytest.mark.parametrize(
"""path, config_name, expected_exception""" , [
("""paws""", None, ValueError),
] , )
def lowercase ( __A : Optional[int] , __A : Any , __A : Dict ) -> int:
'''simple docstring'''
with pytest.raises(__A ):
get_dataset_split_names(__A , config_name=__A )
| 36 | 1 |
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, ByTaTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...test_tokenization_common import TokenizerTesterMixin
if is_torch_available():
__lowercase : str = '''pt'''
elif is_tf_available():
__lowercase : str = '''tf'''
else:
__lowercase : int = '''jax'''
class _A ( snake_case , unittest.TestCase ):
'''simple docstring'''
__lowerCamelCase : Tuple = ByTaTokenizer
__lowerCamelCase : Union[str, Any] = False
def snake_case_ ( self ):
'''simple docstring'''
super().setUp()
snake_case : List[Any] = ByTaTokenizer()
tokenizer.save_pretrained(self.tmpdirname )
@cached_property
def snake_case_ ( self ):
'''simple docstring'''
return ByTaTokenizer.from_pretrained("""google/byt5-small""" )
def snake_case_ ( self ,**SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
return self.tokenizer_class.from_pretrained(self.tmpdirname ,**SCREAMING_SNAKE_CASE_ )
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_=False ,SCREAMING_SNAKE_CASE_=20 ,SCREAMING_SNAKE_CASE_=5 ):
'''simple docstring'''
# XXX The default common tokenizer tests assume that every ID is decodable on its own.
# This assumption is invalid for ByT5 because single bytes might not be
# valid utf-8 (byte 128 for instance).
# Here we're overriding the smallest possible method to provide
# a clean sequence without making the same assumption.
snake_case : str = []
for i in range(len(SCREAMING_SNAKE_CASE_ ) ):
try:
snake_case : Optional[Any] = tokenizer.decode([i] ,clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE_ )
except UnicodeDecodeError:
pass
toks.append((i, tok) )
snake_case : List[str] = list(filter(lambda SCREAMING_SNAKE_CASE_ : re.match(R"""^[ a-zA-Z]+$""" ,t[1] ) ,SCREAMING_SNAKE_CASE_ ) )
snake_case : Tuple = list(filter(lambda SCREAMING_SNAKE_CASE_ : [t[0]] == tokenizer.encode(t[1] ,add_special_tokens=SCREAMING_SNAKE_CASE_ ) ,SCREAMING_SNAKE_CASE_ ) )
if max_length is not None and len(SCREAMING_SNAKE_CASE_ ) > max_length:
snake_case : List[str] = toks[:max_length]
if min_length is not None and len(SCREAMING_SNAKE_CASE_ ) < min_length and len(SCREAMING_SNAKE_CASE_ ) > 0:
while len(SCREAMING_SNAKE_CASE_ ) < min_length:
snake_case : Tuple = toks + toks
# toks_str = [t[1] for t in toks]
snake_case : Optional[Any] = [t[0] for t in toks]
# Ensure consistency
snake_case : Optional[Any] = tokenizer.decode(SCREAMING_SNAKE_CASE_ ,clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE_ )
if " " not in output_txt and len(SCREAMING_SNAKE_CASE_ ) > 1:
snake_case : List[Any] = (
tokenizer.decode([toks_ids[0]] ,clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE_ )
+ """ """
+ tokenizer.decode(toks_ids[1:] ,clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE_ )
)
if with_prefix_space:
snake_case : Tuple = """ """ + output_txt
snake_case : List[str] = tokenizer.encode(SCREAMING_SNAKE_CASE_ ,add_special_tokens=SCREAMING_SNAKE_CASE_ )
return output_txt, output_ids
def snake_case_ ( self ):
'''simple docstring'''
snake_case : int = self.ta_base_tokenizer
snake_case : Any = tokenizer(["""hi</s>""", """I went to the gym</s>""", """</s>"""] )
snake_case : Tuple = tokenizer(["""hi""", """I went to the gym""", """"""] )
self.assertListEqual(batch_with_eos_added["""input_ids"""] ,batch_without_eos_added["""input_ids"""] )
def snake_case_ ( self ):
'''simple docstring'''
snake_case : Any = self.ta_base_tokenizer
snake_case : Union[str, Any] = """Unicode €."""
snake_case : Any = tokenizer(SCREAMING_SNAKE_CASE_ )
snake_case : Optional[Any] = [88, 113, 108, 102, 114, 103, 104, 35, 229, 133, 175, 49, 1]
self.assertEqual(encoded["""input_ids"""] ,SCREAMING_SNAKE_CASE_ )
# decoding
snake_case : Optional[Any] = tokenizer.decode(SCREAMING_SNAKE_CASE_ )
self.assertEqual(SCREAMING_SNAKE_CASE_ ,"""Unicode €.</s>""" )
snake_case : str = tokenizer("""e è é ê ë""" )
snake_case : Dict = [104, 35, 198, 171, 35, 198, 172, 35, 198, 173, 35, 198, 174, 1]
self.assertEqual(encoded["""input_ids"""] ,SCREAMING_SNAKE_CASE_ )
# decoding
snake_case : Dict = tokenizer.decode(SCREAMING_SNAKE_CASE_ )
self.assertEqual(SCREAMING_SNAKE_CASE_ ,"""e è é ê ë</s>""" )
# encode/decode, but with `encode` instead of `__call__`
self.assertEqual(tokenizer.decode(tokenizer.encode("""e è é ê ë""" ) ) ,"""e è é ê ë</s>""" )
def snake_case_ ( self ):
'''simple docstring'''
snake_case : Tuple = self.ta_base_tokenizer
snake_case : List[str] = ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
# fmt: off
snake_case : Tuple = [68, 35, 111, 114, 113, 106, 35, 115, 100, 117, 100, 106, 117, 100, 115, 107, 35, 105, 114, 117, 35, 118, 120, 112, 112, 100, 117, 108, 125, 100, 119, 108, 114, 113, 49, 1, 0]
# fmt: on
snake_case : Optional[Any] = tokenizer(SCREAMING_SNAKE_CASE_ ,padding=SCREAMING_SNAKE_CASE_ ,return_tensors=SCREAMING_SNAKE_CASE_ )
self.assertIsInstance(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )
if FRAMEWORK != "jax":
snake_case : int = list(batch.input_ids.numpy()[0] )
else:
snake_case : Any = list(batch.input_ids.tolist()[0] )
self.assertListEqual(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )
self.assertEqual((2, 37) ,batch.input_ids.shape )
self.assertEqual((2, 37) ,batch.attention_mask.shape )
def snake_case_ ( self ):
'''simple docstring'''
snake_case : Optional[Any] = self.ta_base_tokenizer
snake_case : Optional[Any] = ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
snake_case : Dict = tokenizer(SCREAMING_SNAKE_CASE_ ,padding=SCREAMING_SNAKE_CASE_ ,return_tensors=SCREAMING_SNAKE_CASE_ )
# check if input_ids are returned and no decoder_input_ids
self.assertIn("""input_ids""" ,SCREAMING_SNAKE_CASE_ )
self.assertIn("""attention_mask""" ,SCREAMING_SNAKE_CASE_ )
self.assertNotIn("""decoder_input_ids""" ,SCREAMING_SNAKE_CASE_ )
self.assertNotIn("""decoder_attention_mask""" ,SCREAMING_SNAKE_CASE_ )
def snake_case_ ( self ):
'''simple docstring'''
snake_case : Union[str, Any] = self.ta_base_tokenizer
snake_case : Dict = [
"""Summary of the text.""",
"""Another summary.""",
]
snake_case : Optional[Any] = tokenizer(
text_target=SCREAMING_SNAKE_CASE_ ,max_length=32 ,padding="""max_length""" ,truncation=SCREAMING_SNAKE_CASE_ ,return_tensors=SCREAMING_SNAKE_CASE_ )
self.assertEqual(32 ,targets["""input_ids"""].shape[1] )
def snake_case_ ( self ):
'''simple docstring'''
snake_case : List[Any] = self.ta_base_tokenizer
snake_case : Dict = ["""A long paragraph for summarization. </s>"""]
snake_case : int = ["""Summary of the text. </s>"""]
# fmt: off
snake_case : Optional[int] = [68, 35, 111, 114, 113, 106, 35, 115, 100, 117, 100, 106, 117, 100, 115, 107, 35, 105, 114, 117, 35, 118, 120, 112, 112, 100, 117, 108, 125, 100, 119, 108, 114, 113, 49, 35, 1]
snake_case : int = [86, 120, 112, 112, 100, 117, 124, 35, 114, 105, 35, 119, 107, 104, 35, 119, 104, 123, 119, 49, 35, 1]
# fmt: on
snake_case : Dict = tokenizer(SCREAMING_SNAKE_CASE_ ,text_target=SCREAMING_SNAKE_CASE_ )
self.assertEqual(SCREAMING_SNAKE_CASE_ ,batch["""input_ids"""][0] )
self.assertEqual(SCREAMING_SNAKE_CASE_ ,batch["""labels"""][0] )
def snake_case_ ( self ):
'''simple docstring'''
# safety check on max_len default value so we are sure the test works
snake_case : List[str] = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(F"""{tokenizer.__class__.__name__}""" ):
self.assertNotEqual(tokenizer.model_max_length ,42 )
# Now let's start the test
snake_case : str = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(F"""{tokenizer.__class__.__name__}""" ):
# Isolate this from the other tests because we save additional tokens/etc
snake_case : Union[str, Any] = tempfile.mkdtemp()
snake_case : Optional[Any] = """ He is very happy, UNwant\u00E9d,running"""
snake_case : Any = tokenizer.encode(SCREAMING_SNAKE_CASE_ ,add_special_tokens=SCREAMING_SNAKE_CASE_ )
tokenizer.save_pretrained(SCREAMING_SNAKE_CASE_ )
snake_case : int = tokenizer.__class__.from_pretrained(SCREAMING_SNAKE_CASE_ )
snake_case : int = after_tokenizer.encode(SCREAMING_SNAKE_CASE_ ,add_special_tokens=SCREAMING_SNAKE_CASE_ )
self.assertListEqual(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )
shutil.rmtree(SCREAMING_SNAKE_CASE_ )
snake_case : str = self.get_tokenizers(model_max_length=42 )
for tokenizer in tokenizers:
with self.subTest(F"""{tokenizer.__class__.__name__}""" ):
# Isolate this from the other tests because we save additional tokens/etc
snake_case : Any = tempfile.mkdtemp()
snake_case : Optional[Any] = """ He is very happy, UNwant\u00E9d,running"""
tokenizer.add_tokens(["""bim""", """bambam"""] )
snake_case : List[Any] = tokenizer.additional_special_tokens
additional_special_tokens.append("""new_additional_special_token""" )
tokenizer.add_special_tokens({"""additional_special_tokens""": additional_special_tokens} )
snake_case : Optional[int] = tokenizer.encode(SCREAMING_SNAKE_CASE_ ,add_special_tokens=SCREAMING_SNAKE_CASE_ )
tokenizer.save_pretrained(SCREAMING_SNAKE_CASE_ )
snake_case : List[Any] = tokenizer.__class__.from_pretrained(SCREAMING_SNAKE_CASE_ )
snake_case : Union[str, Any] = after_tokenizer.encode(SCREAMING_SNAKE_CASE_ ,add_special_tokens=SCREAMING_SNAKE_CASE_ )
self.assertListEqual(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )
self.assertIn("""new_additional_special_token""" ,after_tokenizer.additional_special_tokens )
self.assertEqual(after_tokenizer.model_max_length ,42 )
snake_case : Dict = tokenizer.__class__.from_pretrained(SCREAMING_SNAKE_CASE_ ,model_max_length=43 )
self.assertEqual(tokenizer.model_max_length ,43 )
shutil.rmtree(SCREAMING_SNAKE_CASE_ )
def snake_case_ ( self ):
'''simple docstring'''
snake_case : Optional[int] = []
if self.test_slow_tokenizer:
tokenizer_list.append((self.tokenizer_class, self.get_tokenizer()) )
if self.test_rust_tokenizer:
tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer()) )
for tokenizer_class, tokenizer_utils in tokenizer_list:
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer_utils.save_pretrained(SCREAMING_SNAKE_CASE_ )
with open(os.path.join(SCREAMING_SNAKE_CASE_ ,"""special_tokens_map.json""" ) ,encoding="""utf-8""" ) as json_file:
snake_case : int = json.load(SCREAMING_SNAKE_CASE_ )
with open(os.path.join(SCREAMING_SNAKE_CASE_ ,"""tokenizer_config.json""" ) ,encoding="""utf-8""" ) as json_file:
snake_case : int = json.load(SCREAMING_SNAKE_CASE_ )
snake_case : Union[str, Any] = [F"""<extra_id_{i}>""" for i in range(125 )]
snake_case : List[Any] = added_tokens_extra_ids + [
"""an_additional_special_token"""
]
snake_case : Optional[int] = added_tokens_extra_ids + [
"""an_additional_special_token"""
]
with open(os.path.join(SCREAMING_SNAKE_CASE_ ,"""special_tokens_map.json""" ) ,"""w""" ,encoding="""utf-8""" ) as outfile:
json.dump(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )
with open(os.path.join(SCREAMING_SNAKE_CASE_ ,"""tokenizer_config.json""" ) ,"""w""" ,encoding="""utf-8""" ) as outfile:
json.dump(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )
# the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
# into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
# "special_tokens_map.json" files
snake_case : List[Any] = tokenizer_class.from_pretrained(
SCREAMING_SNAKE_CASE_ ,)
self.assertIn(
"""an_additional_special_token""" ,tokenizer_without_change_in_init.additional_special_tokens )
# self.assertIn("an_additional_special_token",tokenizer_without_change_in_init.get_vocab()) # ByT5Tokenization no vocab
self.assertEqual(
["""an_additional_special_token"""] ,tokenizer_without_change_in_init.convert_ids_to_tokens(
tokenizer_without_change_in_init.convert_tokens_to_ids(["""an_additional_special_token"""] ) ) ,)
# Now we test that we can change the value of additional_special_tokens in the from_pretrained
snake_case : Any = added_tokens_extra_ids + [AddedToken("""a_new_additional_special_token""" ,lstrip=SCREAMING_SNAKE_CASE_ )]
snake_case : int = tokenizer_class.from_pretrained(
SCREAMING_SNAKE_CASE_ ,additional_special_tokens=SCREAMING_SNAKE_CASE_ ,)
self.assertIn("""a_new_additional_special_token""" ,tokenizer.additional_special_tokens )
self.assertEqual(
["""a_new_additional_special_token"""] ,tokenizer.convert_ids_to_tokens(
tokenizer.convert_tokens_to_ids(["""a_new_additional_special_token"""] ) ) ,)
def snake_case_ ( self ):
'''simple docstring'''
snake_case : Optional[int] = []
if self.test_slow_tokenizer:
tokenizer_list.append((self.tokenizer_class, self.get_tokenizer()) )
if self.test_rust_tokenizer:
tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer()) )
for tokenizer_class, tokenizer_utils in tokenizer_list:
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer_utils.save_pretrained(SCREAMING_SNAKE_CASE_ )
snake_case : Any = tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE_ )
self.assertTrue(tokenizer.decode([255] ) == """""" )
def snake_case_ ( self ):
'''simple docstring'''
pass
def snake_case_ ( self ):
'''simple docstring'''
pass
def snake_case_ ( self ):
'''simple docstring'''
pass
def snake_case_ ( self ):
'''simple docstring'''
pass
def snake_case_ ( self ):
'''simple docstring'''
# The default common tokenizer tests uses invalid tokens for ByT5 that can only accept one-character strings
# and special added tokens as tokens
snake_case : Any = self.get_tokenizers(fast=SCREAMING_SNAKE_CASE_ ,do_lower_case=SCREAMING_SNAKE_CASE_ )
for tokenizer in tokenizers:
with self.subTest(F"""{tokenizer.__class__.__name__}""" ):
snake_case : Tuple = ["""t""", """h""", """i""", """s""", """ """, """i""", """s""", """ """, """a""", """ """, """t""", """e""", """x""", """t""", """</s>"""]
snake_case : Union[str, Any] = tokenizer.convert_tokens_to_string(SCREAMING_SNAKE_CASE_ )
self.assertIsInstance(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )
def snake_case_ ( self ):
'''simple docstring'''
snake_case : Optional[int] = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(F"""{tokenizer.__class__.__name__}""" ):
snake_case : List[str] = [
"""bos_token""",
"""eos_token""",
"""unk_token""",
"""sep_token""",
"""pad_token""",
"""cls_token""",
"""mask_token""",
]
snake_case : Dict = 0
snake_case : Optional[int] = tokenizer.convert_ids_to_tokens(
SCREAMING_SNAKE_CASE_ ,skip_special_tokens=SCREAMING_SNAKE_CASE_ )
for attr in attributes_list:
setattr(SCREAMING_SNAKE_CASE_ ,attr + """_id""" ,SCREAMING_SNAKE_CASE_ )
self.assertEqual(getattr(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ) ,SCREAMING_SNAKE_CASE_ )
self.assertEqual(getattr(SCREAMING_SNAKE_CASE_ ,attr + """_id""" ) ,SCREAMING_SNAKE_CASE_ )
setattr(SCREAMING_SNAKE_CASE_ ,attr + """_id""" ,SCREAMING_SNAKE_CASE_ )
self.assertEqual(getattr(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ) ,SCREAMING_SNAKE_CASE_ )
self.assertEqual(getattr(SCREAMING_SNAKE_CASE_ ,attr + """_id""" ) ,SCREAMING_SNAKE_CASE_ )
setattr(SCREAMING_SNAKE_CASE_ ,"""additional_special_tokens_ids""" ,[] )
self.assertListEqual(getattr(SCREAMING_SNAKE_CASE_ ,"""additional_special_tokens""" ) ,[] )
self.assertListEqual(getattr(SCREAMING_SNAKE_CASE_ ,"""additional_special_tokens_ids""" ) ,[] )
setattr(SCREAMING_SNAKE_CASE_ ,"""additional_special_tokens_ids""" ,[token_id_to_test_setters] )
self.assertListEqual(getattr(SCREAMING_SNAKE_CASE_ ,"""additional_special_tokens""" ) ,[token_to_test_setters] )
self.assertListEqual(getattr(SCREAMING_SNAKE_CASE_ ,"""additional_special_tokens_ids""" ) ,[token_id_to_test_setters] )
| 36 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
__lowercase : Optional[Any] = {
'''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/config.json''',
'''albert-large-v1''': '''https://huggingface.co/albert-large-v1/resolve/main/config.json''',
'''albert-xlarge-v1''': '''https://huggingface.co/albert-xlarge-v1/resolve/main/config.json''',
'''albert-xxlarge-v1''': '''https://huggingface.co/albert-xxlarge-v1/resolve/main/config.json''',
'''albert-base-v2''': '''https://huggingface.co/albert-base-v2/resolve/main/config.json''',
'''albert-large-v2''': '''https://huggingface.co/albert-large-v2/resolve/main/config.json''',
'''albert-xlarge-v2''': '''https://huggingface.co/albert-xlarge-v2/resolve/main/config.json''',
'''albert-xxlarge-v2''': '''https://huggingface.co/albert-xxlarge-v2/resolve/main/config.json''',
}
class _A ( snake_case ):
'''simple docstring'''
__lowerCamelCase : int = '''albert'''
def __init__( self ,SCREAMING_SNAKE_CASE_=30000 ,SCREAMING_SNAKE_CASE_=128 ,SCREAMING_SNAKE_CASE_=4096 ,SCREAMING_SNAKE_CASE_=12 ,SCREAMING_SNAKE_CASE_=1 ,SCREAMING_SNAKE_CASE_=64 ,SCREAMING_SNAKE_CASE_=16384 ,SCREAMING_SNAKE_CASE_=1 ,SCREAMING_SNAKE_CASE_="gelu_new" ,SCREAMING_SNAKE_CASE_=0 ,SCREAMING_SNAKE_CASE_=0 ,SCREAMING_SNAKE_CASE_=512 ,SCREAMING_SNAKE_CASE_=2 ,SCREAMING_SNAKE_CASE_=0.02 ,SCREAMING_SNAKE_CASE_=1E-12 ,SCREAMING_SNAKE_CASE_=0.1 ,SCREAMING_SNAKE_CASE_="absolute" ,SCREAMING_SNAKE_CASE_=0 ,SCREAMING_SNAKE_CASE_=2 ,SCREAMING_SNAKE_CASE_=3 ,**SCREAMING_SNAKE_CASE_ ,):
'''simple docstring'''
super().__init__(pad_token_id=SCREAMING_SNAKE_CASE_ ,bos_token_id=SCREAMING_SNAKE_CASE_ ,eos_token_id=SCREAMING_SNAKE_CASE_ ,**SCREAMING_SNAKE_CASE_ )
snake_case : List[str] = vocab_size
snake_case : int = embedding_size
snake_case : int = hidden_size
snake_case : List[Any] = num_hidden_layers
snake_case : int = num_hidden_groups
snake_case : List[str] = num_attention_heads
snake_case : List[str] = inner_group_num
snake_case : Any = hidden_act
snake_case : Any = intermediate_size
snake_case : Union[str, Any] = hidden_dropout_prob
snake_case : List[Any] = attention_probs_dropout_prob
snake_case : Tuple = max_position_embeddings
snake_case : Any = type_vocab_size
snake_case : Optional[Any] = initializer_range
snake_case : int = layer_norm_eps
snake_case : Optional[int] = classifier_dropout_prob
snake_case : str = position_embedding_type
class _A ( snake_case ):
'''simple docstring'''
@property
def snake_case_ ( self ):
'''simple docstring'''
if self.task == "multiple-choice":
snake_case : List[Any] = {0: """batch""", 1: """choice""", 2: """sequence"""}
else:
snake_case : int = {0: """batch""", 1: """sequence"""}
return OrderedDict(
[
("""input_ids""", dynamic_axis),
("""attention_mask""", dynamic_axis),
("""token_type_ids""", dynamic_axis),
] )
| 36 | 1 |
from transformers import BertTokenizerFast
from .custom_tokenization import CustomTokenizer
class _A ( snake_case ):
'''simple docstring'''
__lowerCamelCase : List[str] = CustomTokenizer
pass
| 36 |
from __future__ import annotations
def lowercase ( __A : list ) -> float:
'''simple docstring'''
if not nums:
raise ValueError("""List is empty""" )
return sum(__A ) / len(__A )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 36 | 1 |
from __future__ import annotations
import copy
import inspect
import unittest
import numpy as np
from transformers import is_tf_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
LayoutLMvaConfig,
TFLayoutLMvaForQuestionAnswering,
TFLayoutLMvaForSequenceClassification,
TFLayoutLMvaForTokenClassification,
TFLayoutLMvaModel,
)
if is_vision_available():
from PIL import Image
from transformers import LayoutLMvaImageProcessor
class _A :
'''simple docstring'''
def __init__( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_=2 ,SCREAMING_SNAKE_CASE_=3 ,SCREAMING_SNAKE_CASE_=4 ,SCREAMING_SNAKE_CASE_=2 ,SCREAMING_SNAKE_CASE_=7 ,SCREAMING_SNAKE_CASE_=True ,SCREAMING_SNAKE_CASE_=True ,SCREAMING_SNAKE_CASE_=True ,SCREAMING_SNAKE_CASE_=True ,SCREAMING_SNAKE_CASE_=99 ,SCREAMING_SNAKE_CASE_=36 ,SCREAMING_SNAKE_CASE_=2 ,SCREAMING_SNAKE_CASE_=4 ,SCREAMING_SNAKE_CASE_=37 ,SCREAMING_SNAKE_CASE_="gelu" ,SCREAMING_SNAKE_CASE_=0.1 ,SCREAMING_SNAKE_CASE_=0.1 ,SCREAMING_SNAKE_CASE_=512 ,SCREAMING_SNAKE_CASE_=16 ,SCREAMING_SNAKE_CASE_=2 ,SCREAMING_SNAKE_CASE_=0.02 ,SCREAMING_SNAKE_CASE_=6 ,SCREAMING_SNAKE_CASE_=6 ,SCREAMING_SNAKE_CASE_=3 ,SCREAMING_SNAKE_CASE_=4 ,SCREAMING_SNAKE_CASE_=None ,SCREAMING_SNAKE_CASE_=1000 ,):
'''simple docstring'''
snake_case : int = parent
snake_case : Optional[Any] = batch_size
snake_case : List[Any] = num_channels
snake_case : Optional[Any] = image_size
snake_case : List[Any] = patch_size
snake_case : List[str] = is_training
snake_case : Union[str, Any] = use_input_mask
snake_case : Tuple = use_token_type_ids
snake_case : str = use_labels
snake_case : int = vocab_size
snake_case : Optional[Any] = hidden_size
snake_case : int = num_hidden_layers
snake_case : Dict = num_attention_heads
snake_case : Any = intermediate_size
snake_case : int = hidden_act
snake_case : Any = hidden_dropout_prob
snake_case : int = attention_probs_dropout_prob
snake_case : List[str] = max_position_embeddings
snake_case : List[str] = type_vocab_size
snake_case : List[Any] = type_sequence_label_size
snake_case : Tuple = initializer_range
snake_case : Optional[Any] = coordinate_size
snake_case : Optional[Any] = shape_size
snake_case : Dict = num_labels
snake_case : Tuple = num_choices
snake_case : str = scope
snake_case : Optional[Any] = range_bbox
# LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
snake_case : Dict = text_seq_length
snake_case : Optional[Any] = (image_size // patch_size) ** 2 + 1
snake_case : Union[str, Any] = self.text_seq_length + self.image_seq_length
def snake_case_ ( self ):
'''simple docstring'''
snake_case : Dict = ids_tensor([self.batch_size, self.text_seq_length] ,self.vocab_size )
snake_case : Tuple = ids_tensor([self.batch_size, self.text_seq_length, 4] ,self.range_bbox )
snake_case : Optional[int] = 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]:
snake_case : Dict = bbox[i, j, 3]
snake_case : Dict = bbox[i, j, 1]
snake_case : Optional[int] = tmp_coordinate
if bbox[i, j, 2] < bbox[i, j, 0]:
snake_case : Dict = bbox[i, j, 2]
snake_case : Optional[Any] = bbox[i, j, 0]
snake_case : Optional[int] = tmp_coordinate
snake_case : Optional[Any] = tf.constant(SCREAMING_SNAKE_CASE_ )
snake_case : Tuple = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
snake_case : List[str] = None
if self.use_input_mask:
snake_case : str = random_attention_mask([self.batch_size, self.text_seq_length] )
snake_case : Optional[int] = None
if self.use_token_type_ids:
snake_case : Optional[int] = ids_tensor([self.batch_size, self.text_seq_length] ,self.type_vocab_size )
snake_case : Tuple = None
snake_case : Union[str, Any] = None
if self.use_labels:
snake_case : List[Any] = ids_tensor([self.batch_size] ,self.type_sequence_label_size )
snake_case : Any = ids_tensor([self.batch_size, self.text_seq_length] ,self.num_labels )
snake_case : Any = LayoutLMvaConfig(
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 ,coordinate_size=self.coordinate_size ,shape_size=self.shape_size ,input_size=self.image_size ,patch_size=self.patch_size ,)
return config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels, token_labels
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
snake_case : str = TFLayoutLMvaModel(config=SCREAMING_SNAKE_CASE_ )
# text + image
snake_case : Optional[int] = model(SCREAMING_SNAKE_CASE_ ,pixel_values=SCREAMING_SNAKE_CASE_ ,training=SCREAMING_SNAKE_CASE_ )
snake_case : Optional[int] = model(
SCREAMING_SNAKE_CASE_ ,bbox=SCREAMING_SNAKE_CASE_ ,pixel_values=SCREAMING_SNAKE_CASE_ ,attention_mask=SCREAMING_SNAKE_CASE_ ,token_type_ids=SCREAMING_SNAKE_CASE_ ,training=SCREAMING_SNAKE_CASE_ ,)
snake_case : int = model(SCREAMING_SNAKE_CASE_ ,bbox=SCREAMING_SNAKE_CASE_ ,pixel_values=SCREAMING_SNAKE_CASE_ ,training=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.last_hidden_state.shape ,(self.batch_size, self.seq_length, self.hidden_size) )
# text only
snake_case : str = model(SCREAMING_SNAKE_CASE_ ,training=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(
result.last_hidden_state.shape ,(self.batch_size, self.text_seq_length, self.hidden_size) )
# image only
snake_case : Optional[int] = model({"""pixel_values""": pixel_values} ,training=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(
result.last_hidden_state.shape ,(self.batch_size, self.image_seq_length, self.hidden_size) )
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
snake_case : Dict = self.num_labels
snake_case : Optional[int] = TFLayoutLMvaForSequenceClassification(config=SCREAMING_SNAKE_CASE_ )
snake_case : int = model(
SCREAMING_SNAKE_CASE_ ,bbox=SCREAMING_SNAKE_CASE_ ,pixel_values=SCREAMING_SNAKE_CASE_ ,attention_mask=SCREAMING_SNAKE_CASE_ ,token_type_ids=SCREAMING_SNAKE_CASE_ ,labels=SCREAMING_SNAKE_CASE_ ,training=SCREAMING_SNAKE_CASE_ ,)
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.num_labels) )
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
snake_case : List[str] = self.num_labels
snake_case : Optional[int] = TFLayoutLMvaForTokenClassification(config=SCREAMING_SNAKE_CASE_ )
snake_case : Optional[Any] = model(
SCREAMING_SNAKE_CASE_ ,bbox=SCREAMING_SNAKE_CASE_ ,pixel_values=SCREAMING_SNAKE_CASE_ ,attention_mask=SCREAMING_SNAKE_CASE_ ,token_type_ids=SCREAMING_SNAKE_CASE_ ,labels=SCREAMING_SNAKE_CASE_ ,training=SCREAMING_SNAKE_CASE_ ,)
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.text_seq_length, self.num_labels) )
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
snake_case : Optional[int] = 2
snake_case : Dict = TFLayoutLMvaForQuestionAnswering(config=SCREAMING_SNAKE_CASE_ )
snake_case : Tuple = model(
SCREAMING_SNAKE_CASE_ ,bbox=SCREAMING_SNAKE_CASE_ ,pixel_values=SCREAMING_SNAKE_CASE_ ,attention_mask=SCREAMING_SNAKE_CASE_ ,token_type_ids=SCREAMING_SNAKE_CASE_ ,start_positions=SCREAMING_SNAKE_CASE_ ,end_positions=SCREAMING_SNAKE_CASE_ ,training=SCREAMING_SNAKE_CASE_ ,)
self.parent.assertEqual(result.start_logits.shape ,(self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape ,(self.batch_size, self.seq_length) )
def snake_case_ ( self ):
'''simple docstring'''
snake_case : Optional[Any] = self.prepare_config_and_inputs()
((snake_case) , (snake_case) , (snake_case) , (snake_case) , (snake_case) , (snake_case) , (snake_case) , (snake_case)) : Dict = config_and_inputs
snake_case : Union[str, Any] = {
"""input_ids""": input_ids,
"""bbox""": bbox,
"""pixel_values""": pixel_values,
"""token_type_ids""": token_type_ids,
"""attention_mask""": input_mask,
}
return config, inputs_dict
@require_tf
class _A ( snake_case , snake_case , unittest.TestCase ):
'''simple docstring'''
__lowerCamelCase : Union[str, Any] = (
(
TFLayoutLMvaModel,
TFLayoutLMvaForQuestionAnswering,
TFLayoutLMvaForSequenceClassification,
TFLayoutLMvaForTokenClassification,
)
if is_tf_available()
else ()
)
__lowerCamelCase : List[str] = (
{'''document-question-answering''': TFLayoutLMvaForQuestionAnswering, '''feature-extraction''': TFLayoutLMvaModel}
if is_tf_available()
else {}
)
__lowerCamelCase : Optional[Any] = False
__lowerCamelCase : List[Any] = False
__lowerCamelCase : Any = False
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
return True
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_=False ):
'''simple docstring'''
snake_case : str = copy.deepcopy(SCREAMING_SNAKE_CASE_ )
if model_class in get_values(SCREAMING_SNAKE_CASE_ ):
snake_case : Any = {
k: tf.tile(tf.expand_dims(SCREAMING_SNAKE_CASE_ ,1 ) ,(1, self.model_tester.num_choices) + (1,) * (v.ndim - 1) )
if isinstance(SCREAMING_SNAKE_CASE_ ,tf.Tensor ) and v.ndim > 0
else v
for k, v in inputs_dict.items()
}
if return_labels:
if model_class in get_values(SCREAMING_SNAKE_CASE_ ):
snake_case : Optional[int] = tf.ones(self.model_tester.batch_size ,dtype=tf.intaa )
elif model_class in get_values(SCREAMING_SNAKE_CASE_ ):
snake_case : Dict = tf.zeros(self.model_tester.batch_size ,dtype=tf.intaa )
snake_case : Tuple = tf.zeros(self.model_tester.batch_size ,dtype=tf.intaa )
elif model_class in get_values(SCREAMING_SNAKE_CASE_ ):
snake_case : List[Any] = tf.zeros(self.model_tester.batch_size ,dtype=tf.intaa )
elif model_class in get_values(SCREAMING_SNAKE_CASE_ ):
snake_case : Dict = tf.zeros(
(self.model_tester.batch_size, self.model_tester.text_seq_length) ,dtype=tf.intaa )
return inputs_dict
def snake_case_ ( self ):
'''simple docstring'''
snake_case : str = TFLayoutLMvaModelTester(self )
snake_case : str = ConfigTester(self ,config_class=SCREAMING_SNAKE_CASE_ ,hidden_size=37 )
def snake_case_ ( self ):
'''simple docstring'''
self.config_tester.run_common_tests()
def snake_case_ ( self ):
'''simple docstring'''
snake_case , snake_case : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
snake_case : List[str] = model_class(SCREAMING_SNAKE_CASE_ )
if getattr(SCREAMING_SNAKE_CASE_ ,"""hf_compute_loss""" ,SCREAMING_SNAKE_CASE_ ):
# The number of elements in the loss should be the same as the number of elements in the label
snake_case : Optional[Any] = self._prepare_for_class(inputs_dict.copy() ,SCREAMING_SNAKE_CASE_ ,return_labels=SCREAMING_SNAKE_CASE_ )
snake_case : List[str] = prepared_for_class[
sorted(prepared_for_class.keys() - inputs_dict.keys() ,reverse=SCREAMING_SNAKE_CASE_ )[0]
]
snake_case : List[Any] = added_label.shape.as_list()[:1]
# Test that model correctly compute the loss with kwargs
snake_case : Tuple = self._prepare_for_class(inputs_dict.copy() ,SCREAMING_SNAKE_CASE_ ,return_labels=SCREAMING_SNAKE_CASE_ )
snake_case : Optional[int] = prepared_for_class.pop("""input_ids""" )
snake_case : Dict = model(SCREAMING_SNAKE_CASE_ ,**SCREAMING_SNAKE_CASE_ )[0]
self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1] )
# Test that model correctly compute the loss when we mask some positions
snake_case : Union[str, Any] = self._prepare_for_class(inputs_dict.copy() ,SCREAMING_SNAKE_CASE_ ,return_labels=SCREAMING_SNAKE_CASE_ )
snake_case : Any = prepared_for_class.pop("""input_ids""" )
if "labels" in prepared_for_class:
snake_case : Union[str, Any] = prepared_for_class["""labels"""].numpy()
if len(labels.shape ) > 1 and labels.shape[1] != 1:
snake_case : Dict = -100
snake_case : Optional[Any] = tf.convert_to_tensor(SCREAMING_SNAKE_CASE_ )
snake_case : int = model(SCREAMING_SNAKE_CASE_ ,**SCREAMING_SNAKE_CASE_ )[0]
self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1] )
self.assertTrue(not np.any(np.isnan(loss.numpy() ) ) )
# Test that model correctly compute the loss with a dict
snake_case : str = self._prepare_for_class(inputs_dict.copy() ,SCREAMING_SNAKE_CASE_ ,return_labels=SCREAMING_SNAKE_CASE_ )
snake_case : Dict = model(SCREAMING_SNAKE_CASE_ )[0]
self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1] )
# Test that model correctly compute the loss with a tuple
snake_case : Any = self._prepare_for_class(inputs_dict.copy() ,SCREAMING_SNAKE_CASE_ ,return_labels=SCREAMING_SNAKE_CASE_ )
# Get keys that were added with the _prepare_for_class function
snake_case : Optional[Any] = prepared_for_class.keys() - inputs_dict.keys()
snake_case : Optional[int] = inspect.signature(model.call ).parameters
snake_case : Tuple = list(signature.keys() )
# Create a dictionary holding the location of the tensors in the tuple
snake_case : Optional[Any] = {0: """input_ids"""}
for label_key in label_keys:
snake_case : Dict = signature_names.index(SCREAMING_SNAKE_CASE_ )
snake_case : Union[str, Any] = label_key
snake_case : List[str] = sorted(tuple_index_mapping.items() )
# Initialize a list with their default values, update the values and convert to a tuple
snake_case : Dict = []
for name in signature_names:
if name != "kwargs":
list_input.append(signature[name].default )
for index, value in sorted_tuple_index_mapping:
snake_case : Optional[Any] = prepared_for_class[value]
snake_case : Optional[int] = tuple(SCREAMING_SNAKE_CASE_ )
# Send to model
snake_case : Union[str, Any] = model(tuple_input[:-1] )[0]
self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1] )
def snake_case_ ( self ):
'''simple docstring'''
(
(
snake_case
) , (
snake_case
) , (
snake_case
) , (
snake_case
) , (
snake_case
) , (
snake_case
) , (
snake_case
) , (
snake_case
) ,
) : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )
def snake_case_ ( self ):
'''simple docstring'''
(
(
snake_case
) , (
snake_case
) , (
snake_case
) , (
snake_case
) , (
snake_case
) , (
snake_case
) , (
snake_case
) , (
snake_case
) ,
) : int = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
snake_case : int = type
self.model_tester.create_and_check_model(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )
def snake_case_ ( self ):
'''simple docstring'''
(
(
snake_case
) , (
snake_case
) , (
snake_case
) , (
snake_case
) , (
snake_case
) , (
snake_case
) , (
snake_case
) , (
snake_case
) ,
) : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(
SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )
def snake_case_ ( self ):
'''simple docstring'''
(
(
snake_case
) , (
snake_case
) , (
snake_case
) , (
snake_case
) , (
snake_case
) , (
snake_case
) , (
snake_case
) , (
snake_case
) ,
) : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(
SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )
def snake_case_ ( self ):
'''simple docstring'''
(
(
snake_case
) , (
snake_case
) , (
snake_case
) , (
snake_case
) , (
snake_case
) , (
snake_case
) , (
snake_case
) , (
snake_case
) ,
) : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(
SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )
@slow
def snake_case_ ( self ):
'''simple docstring'''
for model_name in TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
snake_case : str = TFLayoutLMvaModel.from_pretrained(SCREAMING_SNAKE_CASE_ )
self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )
def lowercase ( ) -> Dict:
'''simple docstring'''
snake_case : List[Any] = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_tf
class _A ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def snake_case_ ( self ):
'''simple docstring'''
return LayoutLMvaImageProcessor(apply_ocr=SCREAMING_SNAKE_CASE_ ) if is_vision_available() else None
@slow
def snake_case_ ( self ):
'''simple docstring'''
snake_case : Any = TFLayoutLMvaModel.from_pretrained("""microsoft/layoutlmv3-base""" )
snake_case : int = self.default_image_processor
snake_case : Optional[Any] = prepare_img()
snake_case : str = image_processor(images=SCREAMING_SNAKE_CASE_ ,return_tensors="""tf""" ).pixel_values
snake_case : Optional[int] = tf.constant([[1, 2]] )
snake_case : List[str] = tf.expand_dims(tf.constant([[1, 2, 3, 4], [5, 6, 7, 8]] ) ,axis=0 )
# forward pass
snake_case : List[str] = model(input_ids=SCREAMING_SNAKE_CASE_ ,bbox=SCREAMING_SNAKE_CASE_ ,pixel_values=SCREAMING_SNAKE_CASE_ ,training=SCREAMING_SNAKE_CASE_ )
# verify the logits
snake_case : List[Any] = (1, 199, 768)
self.assertEqual(outputs.last_hidden_state.shape ,SCREAMING_SNAKE_CASE_ )
snake_case : str = tf.constant(
[[-0.05_29, 0.36_18, 0.16_32], [-0.15_87, -0.16_67, -0.04_00], [-0.15_57, -0.16_71, -0.05_05]] )
self.assertTrue(np.allclose(outputs.last_hidden_state[0, :3, :3] ,SCREAMING_SNAKE_CASE_ ,atol=1E-4 ) )
| 36 |
import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig
if TYPE_CHECKING:
from ... import PreTrainedTokenizerBase, TensorType
__lowercase : List[str] = logging.get_logger(__name__)
class _A ( snake_case ):
'''simple docstring'''
__lowerCamelCase : Tuple = '''vision-encoder-decoder'''
__lowerCamelCase : List[Any] = True
def __init__( self ,**SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
super().__init__(**SCREAMING_SNAKE_CASE_ )
if "encoder" not in kwargs or "decoder" not in kwargs:
raise ValueError(
F"""A configuraton of type {self.model_type} cannot be instantiated because """
F"""not both `encoder` and `decoder` sub-configurations are passed, but only {kwargs}""" )
snake_case : Union[str, Any] = kwargs.pop("""encoder""" )
snake_case : Any = encoder_config.pop("""model_type""" )
snake_case : Optional[Any] = kwargs.pop("""decoder""" )
snake_case : Union[str, Any] = decoder_config.pop("""model_type""" )
snake_case : Any = AutoConfig.for_model(SCREAMING_SNAKE_CASE_ ,**SCREAMING_SNAKE_CASE_ )
snake_case : Union[str, Any] = AutoConfig.for_model(SCREAMING_SNAKE_CASE_ ,**SCREAMING_SNAKE_CASE_ )
snake_case : int = True
@classmethod
def snake_case_ ( cls ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,**SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
logger.info("""Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config""" )
snake_case : Tuple = True
snake_case : Union[str, Any] = True
return cls(encoder=encoder_config.to_dict() ,decoder=decoder_config.to_dict() ,**SCREAMING_SNAKE_CASE_ )
def snake_case_ ( self ):
'''simple docstring'''
snake_case : Union[str, Any] = copy.deepcopy(self.__dict__ )
snake_case : Union[str, Any] = self.encoder.to_dict()
snake_case : Union[str, Any] = self.decoder.to_dict()
snake_case : Dict = self.__class__.model_type
return output
class _A ( snake_case ):
'''simple docstring'''
__lowerCamelCase : Optional[Any] = version.parse('''1.11''' )
@property
def snake_case_ ( self ):
'''simple docstring'''
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
] )
@property
def snake_case_ ( self ):
'''simple docstring'''
return 1E-4
@property
def snake_case_ ( self ):
'''simple docstring'''
return OrderedDict({"""last_hidden_state""": {0: """batch""", 1: """encoder_sequence"""}} )
class _A ( snake_case ):
'''simple docstring'''
@property
def snake_case_ ( self ):
'''simple docstring'''
snake_case : Tuple = OrderedDict()
snake_case : Optional[int] = {0: """batch""", 1: """past_decoder_sequence + sequence"""}
snake_case : Union[str, Any] = {0: """batch""", 1: """past_decoder_sequence + sequence"""}
snake_case : Optional[Any] = {0: """batch""", 1: """encoder_sequence"""}
return common_inputs
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ = -1 ,SCREAMING_SNAKE_CASE_ = -1 ,SCREAMING_SNAKE_CASE_ = False ,SCREAMING_SNAKE_CASE_ = None ,):
'''simple docstring'''
import torch
snake_case : Optional[Any] = OrderedDict()
snake_case : Tuple = super().generate_dummy_inputs(
SCREAMING_SNAKE_CASE_ ,batch_size=SCREAMING_SNAKE_CASE_ ,seq_length=SCREAMING_SNAKE_CASE_ ,is_pair=SCREAMING_SNAKE_CASE_ ,framework=SCREAMING_SNAKE_CASE_ )
snake_case , snake_case : List[Any] = dummy_input["""input_ids"""].shape
snake_case : Optional[int] = (batch, encoder_sequence, self._config.encoder_hidden_size)
snake_case : List[str] = dummy_input.pop("""input_ids""" )
snake_case : int = dummy_input.pop("""attention_mask""" )
snake_case : Dict = torch.zeros(SCREAMING_SNAKE_CASE_ )
return common_inputs
class _A ( snake_case ):
'''simple docstring'''
@property
def snake_case_ ( self ):
'''simple docstring'''
pass
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
return VisionEncoderDecoderEncoderOnnxConfig(SCREAMING_SNAKE_CASE_ )
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ = "default" ):
'''simple docstring'''
snake_case : int = encoder_config.hidden_size
return VisionEncoderDecoderDecoderOnnxConfig(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )
| 36 | 1 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__lowercase : str = {'''configuration_focalnet''': ['''FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FocalNetConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowercase : List[Any] = [
'''FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''FocalNetForImageClassification''',
'''FocalNetForMaskedImageModeling''',
'''FocalNetBackbone''',
'''FocalNetModel''',
'''FocalNetPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_focalnet import FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FocalNetConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_focalnet import (
FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST,
FocalNetBackbone,
FocalNetForImageClassification,
FocalNetForMaskedImageModeling,
FocalNetModel,
FocalNetPreTrainedModel,
)
else:
import sys
__lowercase : Optional[int] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 36 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers import BitConfig, BitForImageClassification, BitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging
logging.set_verbosity_info()
__lowercase : Any = logging.get_logger(__name__)
def lowercase ( __A : Optional[Any] ) -> Dict:
'''simple docstring'''
snake_case : Dict = """huggingface/label-files"""
snake_case : int = """imagenet-1k-id2label.json"""
snake_case : Tuple = json.load(open(hf_hub_download(__A , __A , repo_type="""dataset""" ) , """r""" ) )
snake_case : Any = {int(__A ): v for k, v in idalabel.items()}
snake_case : Dict = {v: k for k, v in idalabel.items()}
snake_case : Any = """std_conv""" if """bit""" in model_name else False
# note that when using BiT as backbone for ViT-hybrid checkpoints,
# one needs to additionally set config.layer_type = "bottleneck", config.stem_type = "same",
# config.conv_layer = "std_conv_same"
snake_case : List[Any] = BitConfig(
conv_layer=__A , num_labels=1000 , idalabel=__A , labelaid=__A , )
return config
def lowercase ( __A : Optional[int] ) -> Union[str, Any]:
'''simple docstring'''
if "stem.conv" in name:
snake_case : List[str] = name.replace("""stem.conv""" , """bit.embedder.convolution""" )
if "blocks" in name:
snake_case : List[str] = name.replace("""blocks""" , """layers""" )
if "head.fc" in name:
snake_case : Optional[int] = name.replace("""head.fc""" , """classifier.1""" )
if name.startswith("""norm""" ):
snake_case : Optional[Any] = """bit.""" + name
if "bit" not in name and "classifier" not in name:
snake_case : Tuple = """bit.encoder.""" + name
return name
def lowercase ( ) -> Optional[int]:
'''simple docstring'''
snake_case : int = """http://images.cocodataset.org/val2017/000000039769.jpg"""
snake_case : Optional[Any] = Image.open(requests.get(__A , stream=__A ).raw )
return im
@torch.no_grad()
def lowercase ( __A : Any , __A : Union[str, Any] , __A : str=False ) -> Optional[int]:
'''simple docstring'''
snake_case : str = get_config(__A )
# load original model from timm
snake_case : Tuple = create_model(__A , pretrained=__A )
timm_model.eval()
# load state_dict of original model
snake_case : List[str] = timm_model.state_dict()
for key in state_dict.copy().keys():
snake_case : List[Any] = state_dict.pop(__A )
snake_case : Union[str, Any] = val.squeeze() if """head""" in key else val
# load HuggingFace model
snake_case : List[Any] = BitForImageClassification(__A )
model.eval()
model.load_state_dict(__A )
# create image processor
snake_case : Dict = create_transform(**resolve_data_config({} , model=__A ) )
snake_case : Optional[Any] = transform.transforms
snake_case : List[Any] = {
"""bilinear""": PILImageResampling.BILINEAR,
"""bicubic""": PILImageResampling.BICUBIC,
"""nearest""": PILImageResampling.NEAREST,
}
snake_case : Union[str, Any] = BitImageProcessor(
do_resize=__A , size={"""shortest_edge""": timm_transforms[0].size} , resample=pillow_resamplings[timm_transforms[0].interpolation.value] , do_center_crop=__A , crop_size={"""height""": timm_transforms[1].size[0], """width""": timm_transforms[1].size[1]} , do_normalize=__A , image_mean=timm_transforms[-1].mean.tolist() , image_std=timm_transforms[-1].std.tolist() , )
snake_case : Dict = prepare_img()
snake_case : List[str] = transform(__A ).unsqueeze(0 )
snake_case : int = processor(__A , return_tensors="""pt""" ).pixel_values
# verify pixel values
assert torch.allclose(__A , __A )
# verify logits
with torch.no_grad():
snake_case : Optional[int] = model(__A )
snake_case : Dict = outputs.logits
print("""Logits:""" , logits[0, :3] )
print("""Predicted class:""" , model.config.idalabel[logits.argmax(-1 ).item()] )
snake_case : int = timm_model(__A )
assert timm_logits.shape == outputs.logits.shape
assert torch.allclose(__A , outputs.logits , atol=1E-3 )
print("""Looks ok!""" )
if pytorch_dump_folder_path is not None:
Path(__A ).mkdir(exist_ok=__A )
print(f"""Saving model {model_name} and processor to {pytorch_dump_folder_path}""" )
model.save_pretrained(__A )
processor.save_pretrained(__A )
if push_to_hub:
print(f"""Pushing model {model_name} and processor to the hub""" )
model.push_to_hub(f"""ybelkada/{model_name}""" )
processor.push_to_hub(f"""ybelkada/{model_name}""" )
if __name__ == "__main__":
__lowercase : List[str] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--model_name''',
default='''resnetv2_50x1_bitm''',
type=str,
help='''Name of the BiT timm model you\'d like to convert.''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
)
parser.add_argument(
'''--push_to_hub''',
action='''store_true''',
help='''Whether to push the model to the hub.''',
)
__lowercase : Union[str, Any] = parser.parse_args()
convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 36 | 1 |
import gc
import unittest
from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.training.common_utils import shard
@slow
@require_flax
class _A ( unittest.TestCase ):
'''simple docstring'''
def snake_case_ ( self ):
'''simple docstring'''
# clean up the VRAM after each test
super().tearDown()
gc.collect()
def snake_case_ ( self ):
'''simple docstring'''
snake_case , snake_case : Tuple = FlaxStableDiffusionPipeline.from_pretrained(
"""stabilityai/stable-diffusion-2""" ,revision="""bf16""" ,dtype=jnp.bfloataa ,)
snake_case : int = """A painting of a squirrel eating a burger"""
snake_case : Dict = jax.device_count()
snake_case : Optional[Any] = num_samples * [prompt]
snake_case : Any = sd_pipe.prepare_inputs(SCREAMING_SNAKE_CASE_ )
snake_case : List[str] = replicate(SCREAMING_SNAKE_CASE_ )
snake_case : List[Any] = shard(SCREAMING_SNAKE_CASE_ )
snake_case : Any = jax.random.PRNGKey(0 )
snake_case : List[Any] = jax.random.split(SCREAMING_SNAKE_CASE_ ,jax.device_count() )
snake_case : Dict = sd_pipe(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,num_inference_steps=25 ,jit=SCREAMING_SNAKE_CASE_ )[0]
assert images.shape == (jax.device_count(), 1, 768, 768, 3)
snake_case : Optional[int] = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:] )
snake_case : Union[str, Any] = images[0, 253:256, 253:256, -1]
snake_case : List[Any] = jnp.asarray(jax.device_get(image_slice.flatten() ) )
snake_case : Tuple = jnp.array([0.42_38, 0.44_14, 0.43_95, 0.44_53, 0.46_29, 0.45_90, 0.45_31, 0.4_55_08, 0.45_12] )
print(F"""output_slice: {output_slice}""" )
assert jnp.abs(output_slice - expected_slice ).max() < 1E-2
def snake_case_ ( self ):
'''simple docstring'''
snake_case : Dict = """stabilityai/stable-diffusion-2"""
snake_case , snake_case : str = FlaxDPMSolverMultistepScheduler.from_pretrained(SCREAMING_SNAKE_CASE_ ,subfolder="""scheduler""" )
snake_case , snake_case : List[Any] = FlaxStableDiffusionPipeline.from_pretrained(
SCREAMING_SNAKE_CASE_ ,scheduler=SCREAMING_SNAKE_CASE_ ,revision="""bf16""" ,dtype=jnp.bfloataa ,)
snake_case : int = scheduler_params
snake_case : int = """A painting of a squirrel eating a burger"""
snake_case : int = jax.device_count()
snake_case : List[str] = num_samples * [prompt]
snake_case : Union[str, Any] = sd_pipe.prepare_inputs(SCREAMING_SNAKE_CASE_ )
snake_case : Optional[Any] = replicate(SCREAMING_SNAKE_CASE_ )
snake_case : Any = shard(SCREAMING_SNAKE_CASE_ )
snake_case : Optional[int] = jax.random.PRNGKey(0 )
snake_case : List[Any] = jax.random.split(SCREAMING_SNAKE_CASE_ ,jax.device_count() )
snake_case : Tuple = sd_pipe(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,num_inference_steps=25 ,jit=SCREAMING_SNAKE_CASE_ )[0]
assert images.shape == (jax.device_count(), 1, 768, 768, 3)
snake_case : List[Any] = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:] )
snake_case : str = images[0, 253:256, 253:256, -1]
snake_case : List[Any] = jnp.asarray(jax.device_get(image_slice.flatten() ) )
snake_case : Union[str, Any] = jnp.array([0.43_36, 0.4_29_69, 0.44_53, 0.41_99, 0.42_97, 0.45_31, 0.44_34, 0.44_34, 0.42_97] )
print(F"""output_slice: {output_slice}""" )
assert jnp.abs(output_slice - expected_slice ).max() < 1E-2
| 36 |
import os
import pytest
from attr import dataclass
__lowercase : Optional[int] = '''us-east-1''' # defaults region
@dataclass
class _A :
'''simple docstring'''
__lowerCamelCase : str
__lowerCamelCase : Dict = '''arn:aws:iam::558105141721:role/sagemaker_execution_role'''
__lowerCamelCase : Optional[Any] = {
'''task_name''': '''mnli''',
'''per_device_train_batch_size''': 1_6,
'''per_device_eval_batch_size''': 1_6,
'''do_train''': True,
'''do_eval''': True,
'''do_predict''': True,
'''output_dir''': '''/opt/ml/model''',
'''overwrite_output_dir''': True,
'''max_steps''': 5_0_0,
'''save_steps''': 5_5_0_0,
}
__lowerCamelCase : List[str] = {**hyperparameters, '''max_steps''': 1_0_0_0}
@property
def snake_case_ ( self ):
'''simple docstring'''
if self.framework == "pytorch":
return [
{"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
{"Name": "eval_accuracy", "Regex": r"eval_accuracy.*=\D*(.*?)$"},
{"Name": "eval_loss", "Regex": r"eval_loss.*=\D*(.*?)$"},
]
else:
return [
{"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
{"Name": "eval_accuracy", "Regex": r"loss.*=\D*(.*?)]?$"},
{"Name": "eval_loss", "Regex": r"sparse_categorical_accuracy.*=\D*(.*?)]?$"},
]
@property
def snake_case_ ( self ):
'''simple docstring'''
return F"""{self.framework}-transfromers-test"""
@property
def snake_case_ ( self ):
'''simple docstring'''
return F"""./tests/sagemaker/scripts/{self.framework}"""
@property
def snake_case_ ( self ):
'''simple docstring'''
if self.framework == "pytorch":
return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-pytorch-training:1.7.1-transformers4.6.1-gpu-py36-cu110-ubuntu18.04"
else:
return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-tensorflow-training:2.4.1-transformers4.6.1-gpu-py37-cu110-ubuntu18.04"
@pytest.fixture(scope="""class""" )
def lowercase ( __A : List[str] ) -> List[str]:
'''simple docstring'''
snake_case : Optional[Any] = SageMakerTestEnvironment(framework=request.cls.framework )
| 36 | 1 |
import argparse
from pathlib import Path
import torch
from transformers import OPTConfig, OPTModel
from transformers.utils import logging
logging.set_verbosity_info()
__lowercase : Optional[int] = logging.get_logger(__name__)
def lowercase ( __A : Union[str, Any] ) -> Tuple:
'''simple docstring'''
snake_case : Optional[Any] = torch.load(__A , map_location="""cpu""" )
if "model" in sd.keys():
snake_case : str = torch.load(__A , map_location="""cpu""" )["""model"""]
# pop unnecessary weights
snake_case : Optional[Any] = [
"""decoder.version""",
"""decoder.output_projection.weight""",
]
for key in keys_to_delete:
if key in sd:
sd.pop(__A )
snake_case : Union[str, Any] = {
"""decoder.project_in_dim.weight""": """decoder.project_in.weight""",
"""decoder.project_out_dim.weight""": """decoder.project_out.weight""",
"""decoder.layer_norm.weight""": """decoder.final_layer_norm.weight""",
"""decoder.layer_norm.bias""": """decoder.final_layer_norm.bias""",
}
for old_key, new_key in keys_to_rename.items():
if old_key in sd:
snake_case : Dict = sd.pop(__A )
snake_case : List[str] = list(sd.keys() )
for key in keys:
if ".qkv_proj." in key:
snake_case : Dict = sd[key]
# We split QKV in separate Q,K,V
snake_case : str = key.replace(""".qkv_proj.""" , """.q_proj.""" )
snake_case : List[str] = key.replace(""".qkv_proj.""" , """.k_proj.""" )
snake_case : Union[str, Any] = key.replace(""".qkv_proj.""" , """.v_proj.""" )
snake_case : Optional[Any] = value.shape[0]
assert depth % 3 == 0
# `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
# https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
snake_case , snake_case , snake_case : Union[str, Any] = torch.split(__A , depth // 3 , dim=0 )
snake_case : Optional[int] = q
snake_case : Optional[Any] = k
snake_case : Any = v
del sd[key]
return sd
@torch.no_grad()
def lowercase ( __A : Tuple , __A : Dict , __A : List[str]=None ) -> Tuple:
'''simple docstring'''
snake_case : Optional[int] = load_checkpoint(__A )
if config is not None:
snake_case : List[Any] = OPTConfig.from_pretrained(__A )
else:
snake_case : Union[str, Any] = OPTConfig()
snake_case : str = OPTModel(__A ).half().eval()
model.load_state_dict(__A )
# Check results
Path(__A ).mkdir(exist_ok=__A )
model.save_pretrained(__A )
if __name__ == "__main__":
__lowercase : List[str] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--fairseq_path''',
type=str,
help=(
'''path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:'''
''' https://huggingface.co/models?other=opt_metasq'''
),
)
parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
parser.add_argument('''--hf_config''', default=None, type=str, help='''Define HF config.''')
__lowercase : Union[str, Any] = parser.parse_args()
convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)
| 36 |
from .imports import is_rich_available
if is_rich_available():
from rich.traceback import install
install(show_locals=False)
else:
raise ModuleNotFoundError('''To use the rich extension, install rich with `pip install rich`''')
| 36 | 1 |
import unittest
import numpy as np
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
from transformers.models.bert.modeling_flax_bert import (
FlaxBertForMaskedLM,
FlaxBertForMultipleChoice,
FlaxBertForNextSentencePrediction,
FlaxBertForPreTraining,
FlaxBertForQuestionAnswering,
FlaxBertForSequenceClassification,
FlaxBertForTokenClassification,
FlaxBertModel,
)
class _A ( unittest.TestCase ):
'''simple docstring'''
def __init__( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_=13 ,SCREAMING_SNAKE_CASE_=7 ,SCREAMING_SNAKE_CASE_=True ,SCREAMING_SNAKE_CASE_=True ,SCREAMING_SNAKE_CASE_=True ,SCREAMING_SNAKE_CASE_=True ,SCREAMING_SNAKE_CASE_=99 ,SCREAMING_SNAKE_CASE_=32 ,SCREAMING_SNAKE_CASE_=5 ,SCREAMING_SNAKE_CASE_=4 ,SCREAMING_SNAKE_CASE_=37 ,SCREAMING_SNAKE_CASE_="gelu" ,SCREAMING_SNAKE_CASE_=0.1 ,SCREAMING_SNAKE_CASE_=0.1 ,SCREAMING_SNAKE_CASE_=512 ,SCREAMING_SNAKE_CASE_=16 ,SCREAMING_SNAKE_CASE_=2 ,SCREAMING_SNAKE_CASE_=0.02 ,SCREAMING_SNAKE_CASE_=4 ,):
'''simple docstring'''
snake_case : Tuple = parent
snake_case : Union[str, Any] = batch_size
snake_case : Union[str, Any] = seq_length
snake_case : List[str] = is_training
snake_case : Optional[int] = use_attention_mask
snake_case : List[Any] = use_token_type_ids
snake_case : Tuple = use_labels
snake_case : List[str] = vocab_size
snake_case : Optional[Any] = hidden_size
snake_case : str = num_hidden_layers
snake_case : Optional[Any] = num_attention_heads
snake_case : List[str] = intermediate_size
snake_case : Tuple = hidden_act
snake_case : Optional[Any] = hidden_dropout_prob
snake_case : Union[str, Any] = attention_probs_dropout_prob
snake_case : List[str] = max_position_embeddings
snake_case : List[Any] = type_vocab_size
snake_case : Tuple = type_sequence_label_size
snake_case : int = initializer_range
snake_case : List[Any] = num_choices
def snake_case_ ( self ):
'''simple docstring'''
snake_case : Any = ids_tensor([self.batch_size, self.seq_length] ,self.vocab_size )
snake_case : List[str] = None
if self.use_attention_mask:
snake_case : str = random_attention_mask([self.batch_size, self.seq_length] )
snake_case : Dict = None
if self.use_token_type_ids:
snake_case : List[str] = ids_tensor([self.batch_size, self.seq_length] ,self.type_vocab_size )
snake_case : List[str] = BertConfig(
vocab_size=self.vocab_size ,hidden_size=self.hidden_size ,num_hidden_layers=self.num_hidden_layers ,num_attention_heads=self.num_attention_heads ,intermediate_size=self.intermediate_size ,hidden_act=self.hidden_act ,hidden_dropout_prob=self.hidden_dropout_prob ,attention_probs_dropout_prob=self.attention_probs_dropout_prob ,max_position_embeddings=self.max_position_embeddings ,type_vocab_size=self.type_vocab_size ,is_decoder=SCREAMING_SNAKE_CASE_ ,initializer_range=self.initializer_range ,)
return config, input_ids, token_type_ids, attention_mask
def snake_case_ ( self ):
'''simple docstring'''
snake_case : int = self.prepare_config_and_inputs()
snake_case , snake_case , snake_case , snake_case : List[Any] = config_and_inputs
snake_case : List[Any] = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
return config, inputs_dict
def snake_case_ ( self ):
'''simple docstring'''
snake_case : Tuple = self.prepare_config_and_inputs()
snake_case , snake_case , snake_case , snake_case : List[Any] = config_and_inputs
snake_case : Tuple = True
snake_case : Optional[Any] = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
snake_case : int = ids_tensor([self.batch_size, self.seq_length] ,vocab_size=2 )
return (
config,
input_ids,
attention_mask,
encoder_hidden_states,
encoder_attention_mask,
)
@require_flax
class _A ( snake_case , unittest.TestCase ):
'''simple docstring'''
__lowerCamelCase : List[str] = True
__lowerCamelCase : Optional[Any] = (
(
FlaxBertModel,
FlaxBertForPreTraining,
FlaxBertForMaskedLM,
FlaxBertForMultipleChoice,
FlaxBertForQuestionAnswering,
FlaxBertForNextSentencePrediction,
FlaxBertForSequenceClassification,
FlaxBertForTokenClassification,
FlaxBertForQuestionAnswering,
)
if is_flax_available()
else ()
)
def snake_case_ ( self ):
'''simple docstring'''
snake_case : int = FlaxBertModelTester(self )
@slow
def snake_case_ ( self ):
'''simple docstring'''
# Only check this for base model, not necessary for all model classes.
# This will also help speed-up tests.
snake_case : str = FlaxBertModel.from_pretrained("""bert-base-cased""" )
snake_case : Tuple = model(np.ones((1, 1) ) )
self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )
| 36 |
import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union
import tqdm
from filelock import FileLock
from transformers import (
BartTokenizer,
BartTokenizerFast,
DataProcessor,
PreTrainedTokenizer,
RobertaTokenizer,
RobertaTokenizerFast,
XLMRobertaTokenizer,
is_tf_available,
is_torch_available,
)
__lowercase : Any = logging.getLogger(__name__)
@dataclass(frozen=snake_case )
class _A :
'''simple docstring'''
__lowerCamelCase : str
__lowerCamelCase : str
__lowerCamelCase : Optional[str] = None
__lowerCamelCase : Optional[str] = None
__lowerCamelCase : Optional[str] = None
@dataclass(frozen=snake_case )
class _A :
'''simple docstring'''
__lowerCamelCase : List[int]
__lowerCamelCase : Optional[List[int]] = None
__lowerCamelCase : Optional[List[int]] = None
__lowerCamelCase : Optional[Union[int, float]] = None
__lowerCamelCase : Optional[int] = None
if is_torch_available():
import torch
from torch.utils.data import Dataset
class _A ( snake_case ):
'''simple docstring'''
__lowerCamelCase : List[InputFeatures]
def __init__( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_=False ,SCREAMING_SNAKE_CASE_ = False ,):
'''simple docstring'''
snake_case : str = hans_processors[task]()
snake_case : str = os.path.join(
SCREAMING_SNAKE_CASE_ ,"""cached_{}_{}_{}_{}""".format(
"""dev""" if evaluate else """train""" ,tokenizer.__class__.__name__ ,str(SCREAMING_SNAKE_CASE_ ) ,SCREAMING_SNAKE_CASE_ ,) ,)
snake_case : Dict = processor.get_labels()
if tokenizer.__class__ in (
RobertaTokenizer,
RobertaTokenizerFast,
XLMRobertaTokenizer,
BartTokenizer,
BartTokenizerFast,
):
# HACK(label indices are swapped in RoBERTa pretrained model)
snake_case , snake_case : List[Any] = label_list[2], label_list[1]
snake_case : List[Any] = label_list
# Make sure only the first process in distributed training processes the dataset,
# and the others will use the cache.
snake_case : Any = cached_features_file + """.lock"""
with FileLock(SCREAMING_SNAKE_CASE_ ):
if os.path.exists(SCREAMING_SNAKE_CASE_ ) and not overwrite_cache:
logger.info(F"""Loading features from cached file {cached_features_file}""" )
snake_case : int = torch.load(SCREAMING_SNAKE_CASE_ )
else:
logger.info(F"""Creating features from dataset file at {data_dir}""" )
snake_case : Union[str, Any] = (
processor.get_dev_examples(SCREAMING_SNAKE_CASE_ ) if evaluate else processor.get_train_examples(SCREAMING_SNAKE_CASE_ )
)
logger.info("""Training examples: %s""" ,len(SCREAMING_SNAKE_CASE_ ) )
snake_case : Dict = hans_convert_examples_to_features(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )
logger.info("""Saving features into cached file %s""" ,SCREAMING_SNAKE_CASE_ )
torch.save(self.features ,SCREAMING_SNAKE_CASE_ )
def __len__( self ):
'''simple docstring'''
return len(self.features )
def __getitem__( self ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
return self.features[i]
def snake_case_ ( self ):
'''simple docstring'''
return self.label_list
if is_tf_available():
import tensorflow as tf
class _A :
'''simple docstring'''
__lowerCamelCase : List[InputFeatures]
def __init__( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ = 128 ,SCREAMING_SNAKE_CASE_=False ,SCREAMING_SNAKE_CASE_ = False ,):
'''simple docstring'''
snake_case : Any = hans_processors[task]()
snake_case : List[str] = processor.get_labels()
if tokenizer.__class__ in (
RobertaTokenizer,
RobertaTokenizerFast,
XLMRobertaTokenizer,
BartTokenizer,
BartTokenizerFast,
):
# HACK(label indices are swapped in RoBERTa pretrained model)
snake_case , snake_case : int = label_list[2], label_list[1]
snake_case : List[str] = label_list
snake_case : int = processor.get_dev_examples(SCREAMING_SNAKE_CASE_ ) if evaluate else processor.get_train_examples(SCREAMING_SNAKE_CASE_ )
snake_case : Any = hans_convert_examples_to_features(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )
def gen():
for ex_index, ex in tqdm.tqdm(enumerate(self.features ) ,desc="""convert examples to features""" ):
if ex_index % 10000 == 0:
logger.info("""Writing example %d of %d""" % (ex_index, len(SCREAMING_SNAKE_CASE_ )) )
yield (
{
"example_id": 0,
"input_ids": ex.input_ids,
"attention_mask": ex.attention_mask,
"token_type_ids": ex.token_type_ids,
},
ex.label,
)
snake_case : Any = tf.data.Dataset.from_generator(
SCREAMING_SNAKE_CASE_ ,(
{
"""example_id""": tf.intaa,
"""input_ids""": tf.intaa,
"""attention_mask""": tf.intaa,
"""token_type_ids""": tf.intaa,
},
tf.intaa,
) ,(
{
"""example_id""": tf.TensorShape([] ),
"""input_ids""": tf.TensorShape([None, None] ),
"""attention_mask""": tf.TensorShape([None, None] ),
"""token_type_ids""": tf.TensorShape([None, None] ),
},
tf.TensorShape([] ),
) ,)
def snake_case_ ( self ):
'''simple docstring'''
return self.dataset
def __len__( self ):
'''simple docstring'''
return len(self.features )
def __getitem__( self ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
return self.features[i]
def snake_case_ ( self ):
'''simple docstring'''
return self.label_list
class _A ( snake_case ):
'''simple docstring'''
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
return self._create_examples(self._read_tsv(os.path.join(SCREAMING_SNAKE_CASE_ ,"""heuristics_train_set.txt""" ) ) ,"""train""" )
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
return self._create_examples(self._read_tsv(os.path.join(SCREAMING_SNAKE_CASE_ ,"""heuristics_evaluation_set.txt""" ) ) ,"""dev""" )
def snake_case_ ( self ):
'''simple docstring'''
return ["contradiction", "entailment", "neutral"]
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
snake_case : List[str] = []
for i, line in enumerate(SCREAMING_SNAKE_CASE_ ):
if i == 0:
continue
snake_case : Any = """%s-%s""" % (set_type, line[0])
snake_case : Optional[int] = line[5]
snake_case : Union[str, Any] = line[6]
snake_case : Optional[Any] = line[7][2:] if line[7].startswith("""ex""" ) else line[7]
snake_case : Dict = line[0]
examples.append(InputExample(guid=SCREAMING_SNAKE_CASE_ ,text_a=SCREAMING_SNAKE_CASE_ ,text_b=SCREAMING_SNAKE_CASE_ ,label=SCREAMING_SNAKE_CASE_ ,pairID=SCREAMING_SNAKE_CASE_ ) )
return examples
def lowercase ( __A : List[InputExample] , __A : List[str] , __A : int , __A : PreTrainedTokenizer , ) -> Tuple:
'''simple docstring'''
snake_case : List[Any] = {label: i for i, label in enumerate(__A )}
snake_case : Union[str, Any] = []
for ex_index, example in tqdm.tqdm(enumerate(__A ) , desc="""convert examples to features""" ):
if ex_index % 1_0000 == 0:
logger.info("""Writing example %d""" % (ex_index) )
snake_case : Union[str, Any] = tokenizer(
example.text_a , example.text_b , add_special_tokens=__A , max_length=__A , padding="""max_length""" , truncation=__A , return_overflowing_tokens=__A , )
snake_case : Tuple = label_map[example.label] if example.label in label_map else 0
snake_case : Tuple = int(example.pairID )
features.append(InputFeatures(**__A , label=__A , pairID=__A ) )
for i, example in enumerate(examples[:5] ):
logger.info("""*** Example ***""" )
logger.info(f"""guid: {example}""" )
logger.info(f"""features: {features[i]}""" )
return features
__lowercase : Dict = {
'''hans''': 3,
}
__lowercase : Union[str, Any] = {
'''hans''': HansProcessor,
}
| 36 | 1 |
from scipy.stats import spearmanr
import datasets
__lowercase : Any = '''
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no correlation.
Positive correlations imply that as data in dataset x increases, so
does data in dataset y. Negative correlations imply that as x increases,
y decreases. Correlations of -1 or +1 imply an exact monotonic relationship.
Unlike the Pearson correlation, the Spearman correlation does not
assume that both datasets are normally distributed.
The p-value roughly indicates the probability of an uncorrelated system
producing datasets that have a Spearman correlation at least as extreme
as the one computed from these datasets. The p-values are not entirely
reliable but are probably reasonable for datasets larger than 500 or so.
'''
__lowercase : Optional[Any] = '''
Args:
predictions (`List[float]`): Predicted labels, as returned by a model.
references (`List[float]`): Ground truth labels.
return_pvalue (`bool`): If `True`, returns the p-value. If `False`, returns
only the spearmanr score. Defaults to `False`.
Returns:
spearmanr (`float`): Spearman correlation coefficient.
p-value (`float`): p-value. **Note**: is only returned if `return_pvalue=True` is input.
Examples:
Example 1:
>>> spearmanr_metric = datasets.load_metric("spearmanr")
>>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5], predictions=[10, 9, 2.5, 6, 4])
>>> print(results)
{\'spearmanr\': -0.7}
Example 2:
>>> spearmanr_metric = datasets.load_metric("spearmanr")
>>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5],
... predictions=[10, 9, 2.5, 6, 4],
... return_pvalue=True)
>>> print(results[\'spearmanr\'])
-0.7
>>> print(round(results[\'spearmanr_pvalue\'], 2))
0.19
'''
__lowercase : Dict = r'''\
@book{kokoska2000crc,
title={CRC standard probability and statistics tables and formulae},
author={Kokoska, Stephen and Zwillinger, Daniel},
year={2000},
publisher={Crc Press}
}
@article{2020SciPy-NMeth,
author = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and
Haberland, Matt and Reddy, Tyler and Cournapeau, David and
Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and
Bright, Jonathan and {van der Walt}, St{\'e}fan J. and
Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and
Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and
Kern, Robert and Larson, Eric and Carey, C J and
Polat, {\.I}lhan and Feng, Yu and Moore, Eric W. and
{VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and
Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and
Harris, Charles R. and Archibald, Anne M. and
Ribeiro, Ant{\^o}nio H. and Pedregosa, Fabian and
{van Mulbregt}, Paul and {SciPy 1.0 Contributors}},
title = {{{SciPy} 1.0: Fundamental Algorithms for Scientific
Computing in Python}},
journal = {Nature Methods},
year = {2020},
volume = {17},
pages = {261--272},
adsurl = {https://rdcu.be/b08Wh},
doi = {10.1038/s41592-019-0686-2},
}
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class _A ( datasets.Metric ):
'''simple docstring'''
def snake_case_ ( self ):
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION ,citation=_CITATION ,inputs_description=_KWARGS_DESCRIPTION ,features=datasets.Features(
{
"""predictions""": datasets.Value("""float""" ),
"""references""": datasets.Value("""float""" ),
} ) ,reference_urls=["""https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.spearmanr.html"""] ,)
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_=False ):
'''simple docstring'''
snake_case : Optional[Any] = spearmanr(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )
if return_pvalue:
return {"spearmanr": results[0], "spearmanr_pvalue": results[1]}
else:
return {"spearmanr": results[0]}
| 36 |
from __future__ import annotations
def lowercase ( __A : int ) -> list[int]:
'''simple docstring'''
snake_case : Dict = 2
snake_case : int = []
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.append(__A )
if n > 1:
factors.append(__A )
return factors
if __name__ == "__main__":
import doctest
doctest.testmod()
| 36 | 1 |
import webbrowser
from sys import argv
from urllib.parse import parse_qs, quote
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
__lowercase : Tuple = '''%20'''.join(argv[1:]) if len(argv) > 1 else quote(str(input('''Search: ''')))
print('''Googling.....''')
__lowercase : int = f'''https://www.google.com/search?q={query}&num=100'''
__lowercase : int = requests.get(
url,
headers={'''User-Agent''': str(UserAgent().random)},
)
try:
__lowercase : Tuple = (
BeautifulSoup(res.text, '''html.parser''')
.find('''div''', attrs={'''class''': '''yuRUbf'''})
.find('''a''')
.get('''href''')
)
except AttributeError:
__lowercase : Union[str, Any] = parse_qs(
BeautifulSoup(res.text, '''html.parser''')
.find('''div''', attrs={'''class''': '''kCrYT'''})
.find('''a''')
.get('''href''')
)['''url'''][0]
webbrowser.open(link)
| 36 |
import numpy as np
def lowercase ( __A : np.array ) -> np.array:
'''simple docstring'''
return (2 / (1 + np.exp(-2 * vector ))) - 1
if __name__ == "__main__":
import doctest
doctest.testmod()
| 36 | 1 |
def lowercase ( __A : str , __A : str ) -> int:
'''simple docstring'''
if len(__A ) != len(__A ):
raise ValueError("""String lengths must match!""" )
snake_case : List[Any] = 0
for chara, chara in zip(__A , __A ):
if chara != chara:
count += 1
return count
if __name__ == "__main__":
import doctest
doctest.testmod()
| 36 |
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
__lowercase : Optional[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 ( __A : Union[str, Any] ) -> Optional[int]:
'''simple docstring'''
for pegasus_name, hf_name in PATTERNS:
snake_case : Dict = k.replace(__A , __A )
return k
def lowercase ( __A : dict , __A : dict ) -> PegasusForConditionalGeneration:
'''simple docstring'''
snake_case : Dict = DEFAULTS.copy()
cfg_kwargs.update(__A )
snake_case : int = PegasusConfig(**__A )
snake_case : List[Any] = PegasusForConditionalGeneration(__A )
snake_case : Optional[Any] = torch_model.model.state_dict()
snake_case : Optional[int] = {}
for k, v in tf_weights.items():
snake_case : str = rename_state_dict_key(__A )
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:
snake_case : Optional[Any] = v.T
snake_case : List[Any] = torch.tensor(__A , 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
snake_case : List[str] = torch.zeros_like(mapping["""shared.weight"""][cfg.pad_token_id + 1] )
snake_case : Optional[Any] = mapping["""shared.weight"""]
snake_case : Optional[Any] = mapping["""shared.weight"""]
snake_case : Tuple = {k: torch.zeros_like(__A ) for k, v in sd.items() if k.endswith("""bias""" ) and k not in mapping}
mapping.update(**__A )
snake_case , snake_case : Union[str, Any] = torch_model.model.load_state_dict(__A , strict=__A )
snake_case : Union[str, Any] = [
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 ( __A : int="./ckpt/aeslc/model.ckpt-32000" ) -> Dict:
'''simple docstring'''
snake_case : Optional[Any] = tf.train.list_variables(__A )
snake_case : Union[str, Any] = {}
snake_case : List[str] = ["""Adafactor""", """global_step"""]
for name, shape in tqdm(__A , desc="""converting tf checkpoint to dict""" ):
snake_case : str = any(pat in name for pat in ignore_name )
if skip_key:
continue
snake_case : List[str] = tf.train.load_variable(__A , __A )
snake_case : Optional[Any] = array
return tf_weights
def lowercase ( __A : str , __A : str ) -> Optional[int]:
'''simple docstring'''
snake_case : Dict = Path(__A ).parent.name
snake_case : Dict = task_specific_params[f"""summarization_{dataset}"""]["""max_position_embeddings"""]
snake_case : Any = PegasusTokenizer.from_pretrained("""sshleifer/pegasus""" , model_max_length=__A )
assert tok.model_max_length == desired_max_model_length
tok.save_pretrained(__A )
# convert model
snake_case : Dict = get_tf_weights_as_numpy(__A )
snake_case : List[Any] = task_specific_params[f"""summarization_{dataset}"""]
if dataset == "large":
snake_case : Optional[int] = task_specific_params
snake_case : Optional[int] = convert_pegasus(__A , __A )
torch_model.save_pretrained(__A )
snake_case : int = torch_model.state_dict()
sd.pop("""model.decoder.embed_positions.weight""" )
sd.pop("""model.encoder.embed_positions.weight""" )
torch.save(__A , Path(__A ) / """pytorch_model.bin""" )
if __name__ == "__main__":
__lowercase : Tuple = 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.''')
__lowercase : List[Any] = parser.parse_args()
if args.save_dir is None:
__lowercase : Optional[Any] = Path(args.tf_ckpt_path).parent.name
__lowercase : Union[str, Any] = os.path.join('''pegasus''', dataset)
convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)
| 36 | 1 |
import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union
import tqdm
from filelock import FileLock
from transformers import (
BartTokenizer,
BartTokenizerFast,
DataProcessor,
PreTrainedTokenizer,
RobertaTokenizer,
RobertaTokenizerFast,
XLMRobertaTokenizer,
is_tf_available,
is_torch_available,
)
__lowercase : Any = logging.getLogger(__name__)
@dataclass(frozen=snake_case )
class _A :
'''simple docstring'''
__lowerCamelCase : str
__lowerCamelCase : str
__lowerCamelCase : Optional[str] = None
__lowerCamelCase : Optional[str] = None
__lowerCamelCase : Optional[str] = None
@dataclass(frozen=snake_case )
class _A :
'''simple docstring'''
__lowerCamelCase : List[int]
__lowerCamelCase : Optional[List[int]] = None
__lowerCamelCase : Optional[List[int]] = None
__lowerCamelCase : Optional[Union[int, float]] = None
__lowerCamelCase : Optional[int] = None
if is_torch_available():
import torch
from torch.utils.data import Dataset
class _A ( snake_case ):
'''simple docstring'''
__lowerCamelCase : List[InputFeatures]
def __init__( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_=False ,SCREAMING_SNAKE_CASE_ = False ,):
'''simple docstring'''
snake_case : str = hans_processors[task]()
snake_case : str = os.path.join(
SCREAMING_SNAKE_CASE_ ,"""cached_{}_{}_{}_{}""".format(
"""dev""" if evaluate else """train""" ,tokenizer.__class__.__name__ ,str(SCREAMING_SNAKE_CASE_ ) ,SCREAMING_SNAKE_CASE_ ,) ,)
snake_case : Dict = processor.get_labels()
if tokenizer.__class__ in (
RobertaTokenizer,
RobertaTokenizerFast,
XLMRobertaTokenizer,
BartTokenizer,
BartTokenizerFast,
):
# HACK(label indices are swapped in RoBERTa pretrained model)
snake_case , snake_case : List[Any] = label_list[2], label_list[1]
snake_case : List[Any] = label_list
# Make sure only the first process in distributed training processes the dataset,
# and the others will use the cache.
snake_case : Any = cached_features_file + """.lock"""
with FileLock(SCREAMING_SNAKE_CASE_ ):
if os.path.exists(SCREAMING_SNAKE_CASE_ ) and not overwrite_cache:
logger.info(F"""Loading features from cached file {cached_features_file}""" )
snake_case : int = torch.load(SCREAMING_SNAKE_CASE_ )
else:
logger.info(F"""Creating features from dataset file at {data_dir}""" )
snake_case : Union[str, Any] = (
processor.get_dev_examples(SCREAMING_SNAKE_CASE_ ) if evaluate else processor.get_train_examples(SCREAMING_SNAKE_CASE_ )
)
logger.info("""Training examples: %s""" ,len(SCREAMING_SNAKE_CASE_ ) )
snake_case : Dict = hans_convert_examples_to_features(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )
logger.info("""Saving features into cached file %s""" ,SCREAMING_SNAKE_CASE_ )
torch.save(self.features ,SCREAMING_SNAKE_CASE_ )
def __len__( self ):
'''simple docstring'''
return len(self.features )
def __getitem__( self ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
return self.features[i]
def snake_case_ ( self ):
'''simple docstring'''
return self.label_list
if is_tf_available():
import tensorflow as tf
class _A :
'''simple docstring'''
__lowerCamelCase : List[InputFeatures]
def __init__( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ = 128 ,SCREAMING_SNAKE_CASE_=False ,SCREAMING_SNAKE_CASE_ = False ,):
'''simple docstring'''
snake_case : Any = hans_processors[task]()
snake_case : List[str] = processor.get_labels()
if tokenizer.__class__ in (
RobertaTokenizer,
RobertaTokenizerFast,
XLMRobertaTokenizer,
BartTokenizer,
BartTokenizerFast,
):
# HACK(label indices are swapped in RoBERTa pretrained model)
snake_case , snake_case : int = label_list[2], label_list[1]
snake_case : List[str] = label_list
snake_case : int = processor.get_dev_examples(SCREAMING_SNAKE_CASE_ ) if evaluate else processor.get_train_examples(SCREAMING_SNAKE_CASE_ )
snake_case : Any = hans_convert_examples_to_features(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )
def gen():
for ex_index, ex in tqdm.tqdm(enumerate(self.features ) ,desc="""convert examples to features""" ):
if ex_index % 10000 == 0:
logger.info("""Writing example %d of %d""" % (ex_index, len(SCREAMING_SNAKE_CASE_ )) )
yield (
{
"example_id": 0,
"input_ids": ex.input_ids,
"attention_mask": ex.attention_mask,
"token_type_ids": ex.token_type_ids,
},
ex.label,
)
snake_case : Any = tf.data.Dataset.from_generator(
SCREAMING_SNAKE_CASE_ ,(
{
"""example_id""": tf.intaa,
"""input_ids""": tf.intaa,
"""attention_mask""": tf.intaa,
"""token_type_ids""": tf.intaa,
},
tf.intaa,
) ,(
{
"""example_id""": tf.TensorShape([] ),
"""input_ids""": tf.TensorShape([None, None] ),
"""attention_mask""": tf.TensorShape([None, None] ),
"""token_type_ids""": tf.TensorShape([None, None] ),
},
tf.TensorShape([] ),
) ,)
def snake_case_ ( self ):
'''simple docstring'''
return self.dataset
def __len__( self ):
'''simple docstring'''
return len(self.features )
def __getitem__( self ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
return self.features[i]
def snake_case_ ( self ):
'''simple docstring'''
return self.label_list
class _A ( snake_case ):
'''simple docstring'''
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
return self._create_examples(self._read_tsv(os.path.join(SCREAMING_SNAKE_CASE_ ,"""heuristics_train_set.txt""" ) ) ,"""train""" )
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
return self._create_examples(self._read_tsv(os.path.join(SCREAMING_SNAKE_CASE_ ,"""heuristics_evaluation_set.txt""" ) ) ,"""dev""" )
def snake_case_ ( self ):
'''simple docstring'''
return ["contradiction", "entailment", "neutral"]
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
snake_case : List[str] = []
for i, line in enumerate(SCREAMING_SNAKE_CASE_ ):
if i == 0:
continue
snake_case : Any = """%s-%s""" % (set_type, line[0])
snake_case : Optional[int] = line[5]
snake_case : Union[str, Any] = line[6]
snake_case : Optional[Any] = line[7][2:] if line[7].startswith("""ex""" ) else line[7]
snake_case : Dict = line[0]
examples.append(InputExample(guid=SCREAMING_SNAKE_CASE_ ,text_a=SCREAMING_SNAKE_CASE_ ,text_b=SCREAMING_SNAKE_CASE_ ,label=SCREAMING_SNAKE_CASE_ ,pairID=SCREAMING_SNAKE_CASE_ ) )
return examples
def lowercase ( __A : List[InputExample] , __A : List[str] , __A : int , __A : PreTrainedTokenizer , ) -> Tuple:
'''simple docstring'''
snake_case : List[Any] = {label: i for i, label in enumerate(__A )}
snake_case : Union[str, Any] = []
for ex_index, example in tqdm.tqdm(enumerate(__A ) , desc="""convert examples to features""" ):
if ex_index % 1_0000 == 0:
logger.info("""Writing example %d""" % (ex_index) )
snake_case : Union[str, Any] = tokenizer(
example.text_a , example.text_b , add_special_tokens=__A , max_length=__A , padding="""max_length""" , truncation=__A , return_overflowing_tokens=__A , )
snake_case : Tuple = label_map[example.label] if example.label in label_map else 0
snake_case : Tuple = int(example.pairID )
features.append(InputFeatures(**__A , label=__A , pairID=__A ) )
for i, example in enumerate(examples[:5] ):
logger.info("""*** Example ***""" )
logger.info(f"""guid: {example}""" )
logger.info(f"""features: {features[i]}""" )
return features
__lowercase : Dict = {
'''hans''': 3,
}
__lowercase : Union[str, Any] = {
'''hans''': HansProcessor,
}
| 36 |
import argparse
import pytorch_lightning as pl
import torch
from torch import nn
from transformers import LongformerForQuestionAnswering, LongformerModel
class _A ( pl.LightningModule ):
'''simple docstring'''
def __init__( self ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
super().__init__()
snake_case : Dict = model
snake_case : Optional[int] = 2
snake_case : Optional[Any] = nn.Linear(self.model.config.hidden_size ,self.num_labels )
def snake_case_ ( self ):
'''simple docstring'''
pass
def lowercase ( __A : str , __A : str , __A : str ) -> Optional[Any]:
'''simple docstring'''
snake_case : Optional[Any] = LongformerModel.from_pretrained(__A )
snake_case : Tuple = LightningModel(__A )
snake_case : Optional[int] = torch.load(__A , map_location=torch.device("""cpu""" ) )
lightning_model.load_state_dict(ckpt["""state_dict"""] )
# init longformer question answering model
snake_case : Dict = LongformerForQuestionAnswering.from_pretrained(__A )
# transfer weights
longformer_for_qa.longformer.load_state_dict(lightning_model.model.state_dict() )
longformer_for_qa.qa_outputs.load_state_dict(lightning_model.qa_outputs.state_dict() )
longformer_for_qa.eval()
# save model
longformer_for_qa.save_pretrained(__A )
print(f"""Conversion successful. Model saved under {pytorch_dump_folder_path}""" )
if __name__ == "__main__":
__lowercase : str = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--longformer_model''',
default=None,
type=str,
required=True,
help='''model identifier of longformer. Should be either `longformer-base-4096` or `longformer-large-4096`.''',
)
parser.add_argument(
'''--longformer_question_answering_ckpt_path''',
default=None,
type=str,
required=True,
help='''Path the official PyTorch Lightning Checkpoint.''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
__lowercase : List[str] = parser.parse_args()
convert_longformer_qa_checkpoint_to_pytorch(
args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
)
| 36 | 1 |
from math import sqrt
def lowercase ( __A : int = 100_0000 ) -> int:
'''simple docstring'''
snake_case : int = 0
snake_case : int = 0
snake_case : int
while num_cuboids <= limit:
max_cuboid_size += 1
for sum_shortest_sides in range(2 , 2 * max_cuboid_size + 1 ):
if sqrt(sum_shortest_sides**2 + max_cuboid_size**2 ).is_integer():
num_cuboids += (
min(__A , sum_shortest_sides // 2 )
- max(1 , sum_shortest_sides - max_cuboid_size )
+ 1
)
return max_cuboid_size
if __name__ == "__main__":
print(f'''{solution() = }''')
| 36 |
import argparse
import collections
import json
import os
import re
import string
import sys
import numpy as np
__lowercase : Optional[Any] = re.compile(r'''\b(a|an|the)\b''', re.UNICODE)
__lowercase : Optional[int] = None
def lowercase ( ) -> Optional[Any]:
'''simple docstring'''
snake_case : int = argparse.ArgumentParser("""Official evaluation script for SQuAD version 2.0.""" )
parser.add_argument("""data_file""" , metavar="""data.json""" , help="""Input data JSON file.""" )
parser.add_argument("""pred_file""" , metavar="""pred.json""" , help="""Model predictions.""" )
parser.add_argument(
"""--out-file""" , """-o""" , metavar="""eval.json""" , help="""Write accuracy metrics to file (default is stdout).""" )
parser.add_argument(
"""--na-prob-file""" , """-n""" , metavar="""na_prob.json""" , help="""Model estimates of probability of no answer.""" )
parser.add_argument(
"""--na-prob-thresh""" , """-t""" , type=__A , default=1.0 , help="""Predict \"\" if no-answer probability exceeds this (default = 1.0).""" , )
parser.add_argument(
"""--out-image-dir""" , """-p""" , metavar="""out_images""" , default=__A , help="""Save precision-recall curves to directory.""" )
parser.add_argument("""--verbose""" , """-v""" , action="""store_true""" )
if len(sys.argv ) == 1:
parser.print_help()
sys.exit(1 )
return parser.parse_args()
def lowercase ( __A : Union[str, Any] ) -> int:
'''simple docstring'''
snake_case : Any = {}
for article in dataset:
for p in article["paragraphs"]:
for qa in p["qas"]:
snake_case : int = bool(qa["""answers"""]["""text"""] )
return qid_to_has_ans
def lowercase ( __A : int ) -> Optional[int]:
'''simple docstring'''
def remove_articles(__A : List[Any] ):
return ARTICLES_REGEX.sub(""" """ , __A )
def white_space_fix(__A : Union[str, Any] ):
return " ".join(text.split() )
def remove_punc(__A : Tuple ):
snake_case : Optional[Any] = set(string.punctuation )
return "".join(ch for ch in text if ch not in exclude )
def lower(__A : Any ):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(__A ) ) ) )
def lowercase ( __A : List[str] ) -> Union[str, Any]:
'''simple docstring'''
if not s:
return []
return normalize_answer(__A ).split()
def lowercase ( __A : Optional[int] , __A : int ) -> List[Any]:
'''simple docstring'''
return int(normalize_answer(__A ) == normalize_answer(__A ) )
def lowercase ( __A : Any , __A : Optional[Any] ) -> List[str]:
'''simple docstring'''
snake_case : Tuple = get_tokens(__A )
snake_case : str = get_tokens(__A )
snake_case : Dict = collections.Counter(__A ) & collections.Counter(__A )
snake_case : Optional[int] = sum(common.values() )
if len(__A ) == 0 or len(__A ) == 0:
# If either is no-answer, then F1 is 1 if they agree, 0 otherwise
return int(gold_toks == pred_toks )
if num_same == 0:
return 0
snake_case : List[Any] = 1.0 * num_same / len(__A )
snake_case : int = 1.0 * num_same / len(__A )
snake_case : Dict = (2 * precision * recall) / (precision + recall)
return fa
def lowercase ( __A : List[Any] , __A : int ) -> str:
'''simple docstring'''
snake_case : Tuple = {}
snake_case : Tuple = {}
for article in dataset:
for p in article["paragraphs"]:
for qa in p["qas"]:
snake_case : str = qa["""id"""]
snake_case : Union[str, Any] = [t for t in qa["""answers"""]["""text"""] if normalize_answer(__A )]
if not gold_answers:
# For unanswerable questions, only correct answer is empty string
snake_case : Optional[Any] = [""""""]
if qid not in preds:
print(f"""Missing prediction for {qid}""" )
continue
snake_case : Dict = preds[qid]
# Take max over all gold answers
snake_case : Union[str, Any] = max(compute_exact(__A , __A ) for a in gold_answers )
snake_case : Optional[int] = max(compute_fa(__A , __A ) for a in gold_answers )
return exact_scores, fa_scores
def lowercase ( __A : str , __A : Any , __A : List[Any] , __A : List[Any] ) -> Dict:
'''simple docstring'''
snake_case : Optional[int] = {}
for qid, s in scores.items():
snake_case : Any = na_probs[qid] > na_prob_thresh
if pred_na:
snake_case : str = float(not qid_to_has_ans[qid] )
else:
snake_case : List[Any] = s
return new_scores
def lowercase ( __A : Dict , __A : Union[str, Any] , __A : List[str]=None ) -> int:
'''simple docstring'''
if not qid_list:
snake_case : List[str] = len(__A )
return collections.OrderedDict(
[
("""exact""", 100.0 * sum(exact_scores.values() ) / total),
("""f1""", 100.0 * sum(fa_scores.values() ) / total),
("""total""", total),
] )
else:
snake_case : Any = len(__A )
return collections.OrderedDict(
[
("""exact""", 100.0 * sum(exact_scores[k] for k in qid_list ) / total),
("""f1""", 100.0 * sum(fa_scores[k] for k in qid_list ) / total),
("""total""", total),
] )
def lowercase ( __A : Optional[Any] , __A : Tuple , __A : List[str] ) -> Optional[Any]:
'''simple docstring'''
for k in new_eval:
snake_case : str = new_eval[k]
def lowercase ( __A : Tuple , __A : int , __A : Dict , __A : Dict ) -> int:
'''simple docstring'''
plt.step(__A , __A , color="""b""" , alpha=0.2 , where="""post""" )
plt.fill_between(__A , __A , step="""post""" , alpha=0.2 , color="""b""" )
plt.xlabel("""Recall""" )
plt.ylabel("""Precision""" )
plt.xlim([0.0, 1.05] )
plt.ylim([0.0, 1.05] )
plt.title(__A )
plt.savefig(__A )
plt.clf()
def lowercase ( __A : Optional[Any] , __A : Union[str, Any] , __A : Dict , __A : Tuple , __A : Optional[Any]=None , __A : List[str]=None ) -> Union[str, Any]:
'''simple docstring'''
snake_case : Optional[int] = sorted(__A , key=lambda __A : na_probs[k] )
snake_case : Any = 0.0
snake_case : str = 1.0
snake_case : Tuple = 0.0
snake_case : str = [1.0]
snake_case : Any = [0.0]
snake_case : Dict = 0.0
for i, qid in enumerate(__A ):
if qid_to_has_ans[qid]:
true_pos += scores[qid]
snake_case : str = true_pos / float(i + 1 )
snake_case : List[str] = true_pos / float(__A )
if i == len(__A ) - 1 or na_probs[qid] != na_probs[qid_list[i + 1]]:
# i.e., if we can put a threshold after this point
avg_prec += cur_p * (cur_r - recalls[-1])
precisions.append(__A )
recalls.append(__A )
if out_image:
plot_pr_curve(__A , __A , __A , __A )
return {"ap": 100.0 * avg_prec}
def lowercase ( __A : Any , __A : Optional[int] , __A : Tuple , __A : Tuple , __A : List[Any] , __A : Optional[Any] ) -> Optional[Any]:
'''simple docstring'''
if out_image_dir and not os.path.exists(__A ):
os.makedirs(__A )
snake_case : Tuple = sum(1 for v in qid_to_has_ans.values() if v )
if num_true_pos == 0:
return
snake_case : str = make_precision_recall_eval(
__A , __A , __A , __A , out_image=os.path.join(__A , """pr_exact.png""" ) , title="""Precision-Recall curve for Exact Match score""" , )
snake_case : int = make_precision_recall_eval(
__A , __A , __A , __A , out_image=os.path.join(__A , """pr_f1.png""" ) , title="""Precision-Recall curve for F1 score""" , )
snake_case : Dict = {k: float(__A ) for k, v in qid_to_has_ans.items()}
snake_case : int = make_precision_recall_eval(
__A , __A , __A , __A , out_image=os.path.join(__A , """pr_oracle.png""" ) , title="""Oracle Precision-Recall curve (binary task of HasAns vs. NoAns)""" , )
merge_eval(__A , __A , """pr_exact""" )
merge_eval(__A , __A , """pr_f1""" )
merge_eval(__A , __A , """pr_oracle""" )
def lowercase ( __A : List[Any] , __A : Union[str, Any] , __A : Union[str, Any] , __A : Optional[int] ) -> Union[str, Any]:
'''simple docstring'''
if not qid_list:
return
snake_case : int = [na_probs[k] for k in qid_list]
snake_case : List[str] = np.ones_like(__A ) / float(len(__A ) )
plt.hist(__A , weights=__A , bins=20 , range=(0.0, 1.0) )
plt.xlabel("""Model probability of no-answer""" )
plt.ylabel("""Proportion of dataset""" )
plt.title(f"""Histogram of no-answer probability: {name}""" )
plt.savefig(os.path.join(__A , f"""na_prob_hist_{name}.png""" ) )
plt.clf()
def lowercase ( __A : List[Any] , __A : Tuple , __A : Tuple , __A : Any ) -> Dict:
'''simple docstring'''
snake_case : Optional[int] = sum(1 for k in qid_to_has_ans if not qid_to_has_ans[k] )
snake_case : str = num_no_ans
snake_case : Optional[Any] = cur_score
snake_case : Optional[Any] = 0.0
snake_case : List[Any] = sorted(__A , key=lambda __A : na_probs[k] )
for i, qid in enumerate(__A ):
if qid not in scores:
continue
if qid_to_has_ans[qid]:
snake_case : Dict = scores[qid]
else:
if preds[qid]:
snake_case : Dict = -1
else:
snake_case : str = 0
cur_score += diff
if cur_score > best_score:
snake_case : Union[str, Any] = cur_score
snake_case : List[Any] = na_probs[qid]
return 100.0 * best_score / len(__A ), best_thresh
def lowercase ( __A : Dict , __A : str , __A : str , __A : int , __A : str , __A : Any ) -> List[str]:
'''simple docstring'''
snake_case , snake_case : Optional[int] = find_best_thresh(__A , __A , __A , __A )
snake_case , snake_case : str = find_best_thresh(__A , __A , __A , __A )
snake_case : List[str] = best_exact
snake_case : List[Any] = exact_thresh
snake_case : Optional[Any] = best_fa
snake_case : Optional[int] = fa_thresh
def lowercase ( ) -> Any:
'''simple docstring'''
with open(OPTS.data_file ) as f:
snake_case : Dict = json.load(__A )
snake_case : Union[str, Any] = dataset_json["""data"""]
with open(OPTS.pred_file ) as f:
snake_case : int = json.load(__A )
if OPTS.na_prob_file:
with open(OPTS.na_prob_file ) as f:
snake_case : Any = json.load(__A )
else:
snake_case : Any = {k: 0.0 for k in preds}
snake_case : Optional[int] = make_qid_to_has_ans(__A ) # maps qid to True/False
snake_case : Dict = [k for k, v in qid_to_has_ans.items() if v]
snake_case : Optional[int] = [k for k, v in qid_to_has_ans.items() if not v]
snake_case , snake_case : Optional[Any] = get_raw_scores(__A , __A )
snake_case : Tuple = apply_no_ans_threshold(__A , __A , __A , OPTS.na_prob_thresh )
snake_case : Optional[Any] = apply_no_ans_threshold(__A , __A , __A , OPTS.na_prob_thresh )
snake_case : Optional[int] = make_eval_dict(__A , __A )
if has_ans_qids:
snake_case : Any = make_eval_dict(__A , __A , qid_list=__A )
merge_eval(__A , __A , """HasAns""" )
if no_ans_qids:
snake_case : str = make_eval_dict(__A , __A , qid_list=__A )
merge_eval(__A , __A , """NoAns""" )
if OPTS.na_prob_file:
find_all_best_thresh(__A , __A , __A , __A , __A , __A )
if OPTS.na_prob_file and OPTS.out_image_dir:
run_precision_recall_analysis(__A , __A , __A , __A , __A , OPTS.out_image_dir )
histogram_na_prob(__A , __A , OPTS.out_image_dir , """hasAns""" )
histogram_na_prob(__A , __A , OPTS.out_image_dir , """noAns""" )
if OPTS.out_file:
with open(OPTS.out_file , """w""" ) as f:
json.dump(__A , __A )
else:
print(json.dumps(__A , indent=2 ) )
if __name__ == "__main__":
__lowercase : Union[str, Any] = parse_args()
if OPTS.out_image_dir:
import matplotlib
matplotlib.use('''Agg''')
import matplotlib.pyplot as plt
main()
| 36 | 1 |
import os
import sys
from contextlib import contextmanager
# Windows only
if os.name == "nt":
import ctypes
import msvcrt # noqa
class _A ( ctypes.Structure ):
'''simple docstring'''
__lowerCamelCase : Union[str, Any] = [('''size''', ctypes.c_int), ('''visible''', ctypes.c_byte)]
def lowercase ( ) -> Optional[Any]:
'''simple docstring'''
if os.name == "nt":
snake_case : int = CursorInfo()
snake_case : Union[str, Any] = ctypes.windll.kernelaa.GetStdHandle(-11 )
ctypes.windll.kernelaa.GetConsoleCursorInfo(__A , ctypes.byref(__A ) )
snake_case : Union[str, Any] = False
ctypes.windll.kernelaa.SetConsoleCursorInfo(__A , ctypes.byref(__A ) )
elif os.name == "posix":
sys.stdout.write("""\033[?25l""" )
sys.stdout.flush()
def lowercase ( ) -> Tuple:
'''simple docstring'''
if os.name == "nt":
snake_case : Optional[Any] = CursorInfo()
snake_case : Dict = ctypes.windll.kernelaa.GetStdHandle(-11 )
ctypes.windll.kernelaa.GetConsoleCursorInfo(__A , ctypes.byref(__A ) )
snake_case : Optional[int] = True
ctypes.windll.kernelaa.SetConsoleCursorInfo(__A , ctypes.byref(__A ) )
elif os.name == "posix":
sys.stdout.write("""\033[?25h""" )
sys.stdout.flush()
@contextmanager
def lowercase ( ) -> Union[str, Any]:
'''simple docstring'''
try:
hide_cursor()
yield
finally:
show_cursor()
| 36 |
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
flip_channel_order,
get_resize_output_image_size,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, is_vision_available, logging
if is_vision_available():
import PIL
if is_torch_available():
import torch
__lowercase : Dict = logging.get_logger(__name__)
class _A ( snake_case ):
'''simple docstring'''
__lowerCamelCase : Optional[int] = ['''pixel_values''']
def __init__( self ,SCREAMING_SNAKE_CASE_ = True ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_ = PILImageResampling.BILINEAR ,SCREAMING_SNAKE_CASE_ = True ,SCREAMING_SNAKE_CASE_ = 1 / 255 ,SCREAMING_SNAKE_CASE_ = True ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_ = True ,**SCREAMING_SNAKE_CASE_ ,):
'''simple docstring'''
super().__init__(**SCREAMING_SNAKE_CASE_ )
snake_case : List[Any] = size if size is not None else {"""shortest_edge""": 224}
snake_case : str = get_size_dict(SCREAMING_SNAKE_CASE_ ,default_to_square=SCREAMING_SNAKE_CASE_ )
snake_case : str = crop_size if crop_size is not None else {"""height""": 256, """width""": 256}
snake_case : Optional[Any] = get_size_dict(SCREAMING_SNAKE_CASE_ ,param_name="""crop_size""" )
snake_case : Optional[Any] = do_resize
snake_case : Union[str, Any] = size
snake_case : Dict = resample
snake_case : Dict = do_rescale
snake_case : Dict = rescale_factor
snake_case : List[str] = do_center_crop
snake_case : Dict = crop_size
snake_case : Any = do_flip_channel_order
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ = PIL.Image.BILINEAR ,SCREAMING_SNAKE_CASE_ = None ,**SCREAMING_SNAKE_CASE_ ,):
'''simple docstring'''
snake_case : str = get_size_dict(SCREAMING_SNAKE_CASE_ ,default_to_square=SCREAMING_SNAKE_CASE_ )
if "shortest_edge" not in size:
raise ValueError(F"""The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}""" )
snake_case : List[Any] = get_resize_output_image_size(SCREAMING_SNAKE_CASE_ ,size=size["""shortest_edge"""] ,default_to_square=SCREAMING_SNAKE_CASE_ )
return resize(SCREAMING_SNAKE_CASE_ ,size=SCREAMING_SNAKE_CASE_ ,resample=SCREAMING_SNAKE_CASE_ ,data_format=SCREAMING_SNAKE_CASE_ ,**SCREAMING_SNAKE_CASE_ )
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ = None ,**SCREAMING_SNAKE_CASE_ ,):
'''simple docstring'''
snake_case : Union[str, Any] = get_size_dict(SCREAMING_SNAKE_CASE_ )
if "height" not in size or "width" not in size:
raise ValueError(F"""The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}""" )
return center_crop(SCREAMING_SNAKE_CASE_ ,size=(size["""height"""], size["""width"""]) ,data_format=SCREAMING_SNAKE_CASE_ ,**SCREAMING_SNAKE_CASE_ )
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ = None ,**SCREAMING_SNAKE_CASE_ ,):
'''simple docstring'''
return rescale(SCREAMING_SNAKE_CASE_ ,scale=SCREAMING_SNAKE_CASE_ ,data_format=SCREAMING_SNAKE_CASE_ ,**SCREAMING_SNAKE_CASE_ )
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ = None ):
'''simple docstring'''
return flip_channel_order(SCREAMING_SNAKE_CASE_ ,data_format=SCREAMING_SNAKE_CASE_ )
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_ = ChannelDimension.FIRST ,**SCREAMING_SNAKE_CASE_ ,):
'''simple docstring'''
snake_case : List[Any] = do_resize if do_resize is not None else self.do_resize
snake_case : List[str] = resample if resample is not None else self.resample
snake_case : Union[str, Any] = do_rescale if do_rescale is not None else self.do_rescale
snake_case : Union[str, Any] = rescale_factor if rescale_factor is not None else self.rescale_factor
snake_case : str = do_center_crop if do_center_crop is not None else self.do_center_crop
snake_case : Union[str, Any] = (
do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
)
snake_case : Tuple = size if size is not None else self.size
snake_case : Optional[Any] = get_size_dict(SCREAMING_SNAKE_CASE_ ,default_to_square=SCREAMING_SNAKE_CASE_ )
snake_case : str = crop_size if crop_size is not None else self.crop_size
snake_case : Optional[Any] = get_size_dict(SCREAMING_SNAKE_CASE_ ,param_name="""crop_size""" )
snake_case : List[Any] = make_list_of_images(SCREAMING_SNAKE_CASE_ )
if not valid_images(SCREAMING_SNAKE_CASE_ ):
raise ValueError(
"""Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
"""torch.Tensor, tf.Tensor or jax.ndarray.""" )
if do_resize and size is None:
raise ValueError("""Size must be specified if do_resize is True.""" )
if do_rescale and rescale_factor is None:
raise ValueError("""Rescale factor must be specified if do_rescale is True.""" )
if do_center_crop and crop_size is None:
raise ValueError("""Crop size must be specified if do_center_crop is True.""" )
# All transformations expect numpy arrays.
snake_case : Dict = [to_numpy_array(SCREAMING_SNAKE_CASE_ ) for image in images]
if do_resize:
snake_case : Union[str, Any] = [self.resize(image=SCREAMING_SNAKE_CASE_ ,size=SCREAMING_SNAKE_CASE_ ,resample=SCREAMING_SNAKE_CASE_ ) for image in images]
if do_center_crop:
snake_case : Optional[Any] = [self.center_crop(image=SCREAMING_SNAKE_CASE_ ,size=SCREAMING_SNAKE_CASE_ ) for image in images]
if do_rescale:
snake_case : Dict = [self.rescale(image=SCREAMING_SNAKE_CASE_ ,scale=SCREAMING_SNAKE_CASE_ ) for image in images]
# the pretrained checkpoints assume images are BGR, not RGB
if do_flip_channel_order:
snake_case : Optional[int] = [self.flip_channel_order(image=SCREAMING_SNAKE_CASE_ ) for image in images]
snake_case : List[Any] = [to_channel_dimension_format(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ) for image in images]
snake_case : int = {"""pixel_values""": images}
return BatchFeature(data=SCREAMING_SNAKE_CASE_ ,tensor_type=SCREAMING_SNAKE_CASE_ )
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ = None ):
'''simple docstring'''
snake_case : Dict = outputs.logits
# Resize logits and compute semantic segmentation maps
if target_sizes is not None:
if len(SCREAMING_SNAKE_CASE_ ) != len(SCREAMING_SNAKE_CASE_ ):
raise ValueError(
"""Make sure that you pass in as many target sizes as the batch dimension of the logits""" )
if is_torch_tensor(SCREAMING_SNAKE_CASE_ ):
snake_case : int = target_sizes.numpy()
snake_case : Optional[Any] = []
for idx in range(len(SCREAMING_SNAKE_CASE_ ) ):
snake_case : Optional[int] = torch.nn.functional.interpolate(
logits[idx].unsqueeze(dim=0 ) ,size=target_sizes[idx] ,mode="""bilinear""" ,align_corners=SCREAMING_SNAKE_CASE_ )
snake_case : Optional[int] = resized_logits[0].argmax(dim=0 )
semantic_segmentation.append(SCREAMING_SNAKE_CASE_ )
else:
snake_case : Tuple = logits.argmax(dim=1 )
snake_case : Dict = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0] )]
return semantic_segmentation
| 36 | 1 |
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