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'''simple docstring'''
def __UpperCAmelCase ( a_: int ):
_UpperCAmelCase : Union[str, Any] = n ** (1 / 3)
return (val * val * val) == n
if __name__ == "__main__":
print(perfect_cube(27))
print(perfect_cube(4))
| 350
|
'''simple docstring'''
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
__a = (3, 9, -11, 0, 7, 5, 1, -1)
__a = (4, 6, 2, 0, 8, 10, 3, -2)
@dataclass
class A__ :
"""simple docstring"""
UpperCamelCase_ : int
UpperCamelCase_ : Node | None
class A__ :
"""simple docstring"""
def __init__( self : Dict , lowerCAmelCase__ : Iterable[int] ) -> None:
"""simple docstring"""
_UpperCAmelCase : Node | None = None
for i in sorted(lowerCAmelCase__ , reverse=lowerCAmelCase__ ):
_UpperCAmelCase : str = Node(lowerCAmelCase__ , self.head )
def __iter__( self : int ) -> Iterator[int]:
"""simple docstring"""
_UpperCAmelCase : List[Any] = self.head
while node:
yield node.data
_UpperCAmelCase : List[str] = node.next_node
def __len__( self : Any ) -> int:
"""simple docstring"""
return sum(1 for _ in self )
def __str__( self : Union[str, Any] ) -> str:
"""simple docstring"""
return " -> ".join([str(lowerCAmelCase__ ) for node in self] )
def __UpperCAmelCase ( a_: SortedLinkedList, a_: SortedLinkedList ):
return SortedLinkedList(list(a_ ) + list(a_ ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
__a = SortedLinkedList
print(merge_lists(SSL(test_data_odd), SSL(test_data_even)))
| 17
| 0
|
'''simple docstring'''
def __UpperCAmelCase ( a_: Any ):
if collection == []:
return []
# get some information about the collection
_UpperCAmelCase : List[str] = len(lowercase__ )
_UpperCAmelCase : List[str] = max(lowercase__ )
_UpperCAmelCase : int = min(lowercase__ )
# create the counting array
_UpperCAmelCase : Dict = coll_max + 1 - coll_min
_UpperCAmelCase : Optional[Any] = [0] * counting_arr_length
# count how much a number appears in the collection
for number in collection:
counting_arr[number - coll_min] += 1
# sum each position with it's predecessors. now, counting_arr[i] tells
# us how many elements <= i has in the collection
for i in range(1, lowercase__ ):
_UpperCAmelCase : Tuple = counting_arr[i] + counting_arr[i - 1]
# create the output collection
_UpperCAmelCase : Tuple = [0] * coll_len
# place the elements in the output, respecting the original order (stable
# sort) from end to begin, updating counting_arr
for i in reversed(range(0, lowercase__ ) ):
_UpperCAmelCase : List[Any] = collection[i]
counting_arr[collection[i] - coll_min] -= 1
return ordered
def __UpperCAmelCase ( a_: Any ):
return "".join([chr(lowercase__ ) for i in counting_sort([ord(lowercase__ ) for c in string] )] )
if __name__ == "__main__":
# Test string sort
assert counting_sort_string('thisisthestring') == "eghhiiinrsssttt"
__a = input('Enter numbers separated by a comma:\n').strip()
__a = [int(item) for item in user_input.split(',')]
print(counting_sort(unsorted))
| 351
|
'''simple docstring'''
def __UpperCAmelCase ( a_: str ):
if not all(char in "01" for char in bin_string ):
raise ValueError("Non-binary value was passed to the function" )
if not bin_string:
raise ValueError("Empty string was passed to the function" )
_UpperCAmelCase : Optional[Any] = ""
while len(a_ ) % 3 != 0:
_UpperCAmelCase : List[Any] = "0" + bin_string
_UpperCAmelCase : Dict = [
bin_string[index : index + 3]
for index in range(len(a_ ) )
if index % 3 == 0
]
for bin_group in bin_string_in_3_list:
_UpperCAmelCase : Optional[Any] = 0
for index, val in enumerate(a_ ):
oct_val += int(2 ** (2 - index) * int(a_ ) )
oct_string += str(a_ )
return oct_string
if __name__ == "__main__":
from doctest import testmod
testmod()
| 17
| 0
|
'''simple docstring'''
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision import transforms
from transformers import BitImageProcessor, FocalNetConfig, FocalNetForImageClassification
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling
def __UpperCAmelCase ( a_: Tuple ) -> List[str]:
_UpperCAmelCase : int = [2, 2, 6, 2] if '''tiny''' in model_name else [2, 2, 18, 2]
_UpperCAmelCase : Any = True if '''large''' in model_name or '''huge''' in model_name else False
_UpperCAmelCase : Dict = True if '''large''' in model_name or '''huge''' in model_name else False
_UpperCAmelCase : Tuple = True if '''large''' in model_name or '''huge''' in model_name else False
if "large" in model_name or "xlarge" in model_name or "huge" in model_name:
if "fl3" in model_name:
_UpperCAmelCase : Any = [3, 3, 3, 3]
_UpperCAmelCase : Any = [5, 5, 5, 5]
elif "fl4" in model_name:
_UpperCAmelCase : Optional[int] = [4, 4, 4, 4]
_UpperCAmelCase : Dict = [3, 3, 3, 3]
if "tiny" in model_name or "small" in model_name or "base" in model_name:
_UpperCAmelCase : Tuple = [3, 3, 3, 3]
if "lrf" in model_name:
_UpperCAmelCase : Dict = [3, 3, 3, 3]
else:
_UpperCAmelCase : List[Any] = [2, 2, 2, 2]
if "tiny" in model_name:
_UpperCAmelCase : Any = 96
elif "small" in model_name:
_UpperCAmelCase : Optional[int] = 96
elif "base" in model_name:
_UpperCAmelCase : Optional[Any] = 128
elif "large" in model_name:
_UpperCAmelCase : List[str] = 192
elif "xlarge" in model_name:
_UpperCAmelCase : str = 256
elif "huge" in model_name:
_UpperCAmelCase : Dict = 352
# set label information
_UpperCAmelCase : Tuple = '''huggingface/label-files'''
if "large" in model_name or "huge" in model_name:
_UpperCAmelCase : Any = '''imagenet-22k-id2label.json'''
else:
_UpperCAmelCase : Dict = '''imagenet-1k-id2label.json'''
_UpperCAmelCase : Dict = json.load(open(hf_hub_download(__UpperCAmelCase, __UpperCAmelCase, repo_type="dataset" ), "r" ) )
_UpperCAmelCase : int = {int(__UpperCAmelCase ): v for k, v in idalabel.items()}
_UpperCAmelCase : Union[str, Any] = {v: k for k, v in idalabel.items()}
_UpperCAmelCase : Any = FocalNetConfig(
embed_dim=__UpperCAmelCase, depths=__UpperCAmelCase, focal_levels=__UpperCAmelCase, focal_windows=__UpperCAmelCase, use_conv_embed=__UpperCAmelCase, idalabel=__UpperCAmelCase, labelaid=__UpperCAmelCase, use_post_layernorm=__UpperCAmelCase, use_layerscale=__UpperCAmelCase, )
return config
def __UpperCAmelCase ( a_: List[Any] ) -> Optional[Any]:
if "patch_embed.proj" in name:
_UpperCAmelCase : Dict = name.replace("patch_embed.proj", "embeddings.patch_embeddings.projection" )
if "patch_embed.norm" in name:
_UpperCAmelCase : Any = name.replace("patch_embed.norm", "embeddings.norm" )
if "layers" in name:
_UpperCAmelCase : Union[str, Any] = '''encoder.''' + name
if "encoder.layers" in name:
_UpperCAmelCase : List[Any] = name.replace("encoder.layers", "encoder.stages" )
if "downsample.proj" in name:
_UpperCAmelCase : List[str] = name.replace("downsample.proj", "downsample.projection" )
if "blocks" in name:
_UpperCAmelCase : Dict = name.replace("blocks", "layers" )
if "modulation.f.weight" in name or "modulation.f.bias" in name:
_UpperCAmelCase : Any = name.replace("modulation.f", "modulation.projection_in" )
if "modulation.h.weight" in name or "modulation.h.bias" in name:
_UpperCAmelCase : Any = name.replace("modulation.h", "modulation.projection_context" )
if "modulation.proj.weight" in name or "modulation.proj.bias" in name:
_UpperCAmelCase : Optional[Any] = name.replace("modulation.proj", "modulation.projection_out" )
if name == "norm.weight":
_UpperCAmelCase : Dict = '''layernorm.weight'''
if name == "norm.bias":
_UpperCAmelCase : str = '''layernorm.bias'''
if "head" in name:
_UpperCAmelCase : Dict = name.replace("head", "classifier" )
else:
_UpperCAmelCase : int = '''focalnet.''' + name
return name
def __UpperCAmelCase ( a_: Tuple, a_: Dict, a_: Any=False ) -> Optional[int]:
# fmt: off
_UpperCAmelCase : Any = {
'''focalnet-tiny''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_tiny_srf.pth''',
'''focalnet-tiny-lrf''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_tiny_lrf.pth''',
'''focalnet-small''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_small_srf.pth''',
'''focalnet-small-lrf''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_small_lrf.pth''',
'''focalnet-base''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_base_srf.pth''',
'''focalnet-base-lrf''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_base_lrf.pth''',
'''focalnet-large-lrf-fl3''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_large_lrf_384.pth''',
'''focalnet-large-lrf-fl4''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_large_lrf_384_fl4.pth''',
'''focalnet-xlarge-lrf-fl3''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_xlarge_lrf_384.pth''',
'''focalnet-xlarge-lrf-fl4''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_xlarge_lrf_384_fl4.pth''',
}
# fmt: on
_UpperCAmelCase : Union[str, Any] = model_name_to_url[model_name]
print("Checkpoint URL: ", __UpperCAmelCase )
_UpperCAmelCase : Tuple = torch.hub.load_state_dict_from_url(__UpperCAmelCase, map_location="cpu" )['''model''']
# rename keys
for key in state_dict.copy().keys():
_UpperCAmelCase : Optional[Any] = state_dict.pop(__UpperCAmelCase )
_UpperCAmelCase : str = val
_UpperCAmelCase : Optional[int] = get_focalnet_config(__UpperCAmelCase )
_UpperCAmelCase : Union[str, Any] = FocalNetForImageClassification(__UpperCAmelCase )
model.eval()
# load state dict
model.load_state_dict(__UpperCAmelCase )
# verify conversion
_UpperCAmelCase : Any = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
_UpperCAmelCase : Optional[Any] = BitImageProcessor(
do_resize=__UpperCAmelCase, size={"shortest_edge": 256}, resample=PILImageResampling.BILINEAR, do_center_crop=__UpperCAmelCase, crop_size=224, do_normalize=__UpperCAmelCase, image_mean=__UpperCAmelCase, image_std=__UpperCAmelCase, )
_UpperCAmelCase : List[str] = Image.open(requests.get(__UpperCAmelCase, stream=__UpperCAmelCase ).raw )
_UpperCAmelCase : Optional[int] = processor(images=__UpperCAmelCase, return_tensors="pt" )
_UpperCAmelCase : Optional[int] = transforms.Compose(
[
transforms.Resize(256 ),
transforms.CenterCrop(224 ),
transforms.ToTensor(),
transforms.Normalize(mean=[0.4_85, 0.4_56, 0.4_06], std=[0.2_29, 0.2_24, 0.2_25] ),
] )
_UpperCAmelCase : str = image_transforms(__UpperCAmelCase ).unsqueeze(0 )
# verify pixel_values
assert torch.allclose(inputs.pixel_values, __UpperCAmelCase, atol=1e-4 )
_UpperCAmelCase : str = model(**__UpperCAmelCase )
_UpperCAmelCase : Union[str, Any] = outputs.logits.argmax(-1 ).item()
print("Predicted class:", model.config.idalabel[predicted_class_idx] )
print("First values of logits:", outputs.logits[0, :3] )
if model_name == "focalnet-tiny":
_UpperCAmelCase : Union[str, Any] = torch.tensor([0.21_66, -0.43_68, 0.21_91] )
elif model_name == "focalnet-tiny-lrf":
_UpperCAmelCase : Union[str, Any] = torch.tensor([1.16_69, 0.01_25, -0.16_95] )
elif model_name == "focalnet-small":
_UpperCAmelCase : List[Any] = torch.tensor([0.49_17, -0.04_30, 0.13_41] )
elif model_name == "focalnet-small-lrf":
_UpperCAmelCase : List[Any] = torch.tensor([-0.25_88, -0.53_42, -0.23_31] )
elif model_name == "focalnet-base":
_UpperCAmelCase : Union[str, Any] = torch.tensor([-0.16_55, -0.40_90, -0.17_30] )
elif model_name == "focalnet-base-lrf":
_UpperCAmelCase : Any = torch.tensor([0.53_06, -0.04_83, -0.39_28] )
assert torch.allclose(outputs.logits[0, :3], __UpperCAmelCase, atol=1e-4 )
print("Looks ok!" )
if pytorch_dump_folder_path is not None:
print(f"""Saving model and processor of {model_name} to {pytorch_dump_folder_path}""" )
model.save_pretrained(__UpperCAmelCase )
processor.save_pretrained(__UpperCAmelCase )
if push_to_hub:
print(f"""Pushing model and processor of {model_name} to the hub...""" )
model.push_to_hub(f"""{model_name}""" )
processor.push_to_hub(f"""{model_name}""" )
if __name__ == "__main__":
__a = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--model_name',
default='focalnet-tiny',
type=str,
help='Name of the FocalNet 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 and processor to the hub.',
)
__a = parser.parse_args()
convert_focalnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 352
|
'''simple docstring'''
from datetime import datetime
import matplotlib.pyplot as plt
import torch
def __UpperCAmelCase ( a_: str ):
for param in module.parameters():
_UpperCAmelCase : Any = False
def __UpperCAmelCase ( ):
_UpperCAmelCase : Union[str, Any] = "cuda" if torch.cuda.is_available() else "cpu"
if torch.backends.mps.is_available() and torch.backends.mps.is_built():
_UpperCAmelCase : int = "mps"
if device == "mps":
print(
"WARNING: MPS currently doesn't seem to work, and messes up backpropagation without any visible torch"
" errors. I recommend using CUDA on a colab notebook or CPU instead if you're facing inexplicable issues"
" with generations." )
return device
def __UpperCAmelCase ( a_: Optional[Any] ):
_UpperCAmelCase : int = plt.imshow(a_ )
fig.axes.get_xaxis().set_visible(a_ )
fig.axes.get_yaxis().set_visible(a_ )
plt.show()
def __UpperCAmelCase ( ):
_UpperCAmelCase : Dict = datetime.now()
_UpperCAmelCase : List[str] = current_time.strftime("%H:%M:%S" )
return timestamp
| 17
| 0
|
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@dataclass
class A__ ( A__ ):
"""simple docstring"""
UpperCamelCase_ : List[Any] = 42
UpperCamelCase_ : str = 42
UpperCamelCase_ : Optional[int] = None
class A__ ( A__ , A__ ):
"""simple docstring"""
UpperCamelCase_ : Optional[int] = 2
@register_to_config
def __init__( self : Tuple , lowerCAmelCase__ : float = 0.02 , lowerCAmelCase__ : float = 1_0_0 , lowerCAmelCase__ : float = 1.007 , lowerCAmelCase__ : float = 8_0 , lowerCAmelCase__ : float = 0.05 , lowerCAmelCase__ : float = 5_0 , ) -> List[Any]:
"""simple docstring"""
_UpperCAmelCase : Tuple = sigma_max
# setable values
_UpperCAmelCase : Optional[Any] = None
_UpperCAmelCase : Any = None
_UpperCAmelCase : str = None # sigma(t_i)
def _lowerCAmelCase ( self : int , lowerCAmelCase__ : torch.FloatTensor , lowerCAmelCase__ : Optional[int] = None ) -> torch.FloatTensor:
"""simple docstring"""
return sample
def _lowerCAmelCase ( self : Dict , lowerCAmelCase__ : int , lowerCAmelCase__ : Union[str, torch.device] = None ) -> int:
"""simple docstring"""
_UpperCAmelCase : Dict = num_inference_steps
_UpperCAmelCase : int = np.arange(0 , self.num_inference_steps )[::-1].copy()
_UpperCAmelCase : Optional[Any] = torch.from_numpy(__snake_case ).to(__snake_case )
_UpperCAmelCase : List[str] = [
(
self.config.sigma_max**2
* (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
)
for i in self.timesteps
]
_UpperCAmelCase : List[Any] = torch.tensor(__snake_case , dtype=torch.floataa , device=__snake_case )
def _lowerCAmelCase ( self : List[str] , lowerCAmelCase__ : torch.FloatTensor , lowerCAmelCase__ : float , lowerCAmelCase__ : Optional[torch.Generator] = None ) -> Tuple[torch.FloatTensor, float]:
"""simple docstring"""
if self.config.s_min <= sigma <= self.config.s_max:
_UpperCAmelCase : Dict = min(self.config.s_churn / self.num_inference_steps , 2**0.5 - 1 )
else:
_UpperCAmelCase : int = 0
# sample eps ~ N(0, S_noise^2 * I)
_UpperCAmelCase : Dict = self.config.s_noise * randn_tensor(sample.shape , generator=__snake_case ).to(sample.device )
_UpperCAmelCase : Optional[int] = sigma + gamma * sigma
_UpperCAmelCase : Any = sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)
return sample_hat, sigma_hat
def _lowerCAmelCase ( self : Tuple , lowerCAmelCase__ : torch.FloatTensor , lowerCAmelCase__ : float , lowerCAmelCase__ : float , lowerCAmelCase__ : torch.FloatTensor , lowerCAmelCase__ : bool = True , ) -> Union[KarrasVeOutput, Tuple]:
"""simple docstring"""
_UpperCAmelCase : Optional[int] = sample_hat + sigma_hat * model_output
_UpperCAmelCase : Optional[int] = (sample_hat - pred_original_sample) / sigma_hat
_UpperCAmelCase : Dict = sample_hat + (sigma_prev - sigma_hat) * derivative
if not return_dict:
return (sample_prev, derivative)
return KarrasVeOutput(
prev_sample=__snake_case , derivative=__snake_case , pred_original_sample=__snake_case )
def _lowerCAmelCase ( self : Tuple , lowerCAmelCase__ : torch.FloatTensor , lowerCAmelCase__ : float , lowerCAmelCase__ : float , lowerCAmelCase__ : torch.FloatTensor , lowerCAmelCase__ : torch.FloatTensor , lowerCAmelCase__ : torch.FloatTensor , lowerCAmelCase__ : bool = True , ) -> Union[KarrasVeOutput, Tuple]:
"""simple docstring"""
_UpperCAmelCase : Tuple = sample_prev + sigma_prev * model_output
_UpperCAmelCase : List[Any] = (sample_prev - pred_original_sample) / sigma_prev
_UpperCAmelCase : str = sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)
if not return_dict:
return (sample_prev, derivative)
return KarrasVeOutput(
prev_sample=__snake_case , derivative=__snake_case , pred_original_sample=__snake_case )
def _lowerCAmelCase ( self : List[str] , lowerCAmelCase__ : Dict , lowerCAmelCase__ : str , lowerCAmelCase__ : Dict ) -> Tuple:
"""simple docstring"""
raise NotImplementedError()
| 353
|
'''simple docstring'''
import torch
from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class A__ ( UpperCamelCase ):
"""simple docstring"""
UpperCamelCase_ : Optional[int] = (EulerDiscreteScheduler,)
UpperCamelCase_ : Tuple = 10
def _lowerCAmelCase ( self : Dict , **lowerCAmelCase__ : Tuple ) -> Any:
"""simple docstring"""
_UpperCAmelCase : str = {
"num_train_timesteps": 1_1_0_0,
"beta_start": 0.0001,
"beta_end": 0.02,
"beta_schedule": "linear",
}
config.update(**lowerCAmelCase__ )
return config
def _lowerCAmelCase ( self : Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
self.check_over_configs(num_train_timesteps=lowerCAmelCase__ )
def _lowerCAmelCase ( self : Any ) -> List[str]:
"""simple docstring"""
for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001] , [0.0002, 0.002, 0.02] ):
self.check_over_configs(beta_start=lowerCAmelCase__ , beta_end=lowerCAmelCase__ )
def _lowerCAmelCase ( self : List[str] ) -> List[str]:
"""simple docstring"""
for schedule in ["linear", "scaled_linear"]:
self.check_over_configs(beta_schedule=lowerCAmelCase__ )
def _lowerCAmelCase ( self : Union[str, Any] ) -> Optional[Any]:
"""simple docstring"""
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=lowerCAmelCase__ )
def _lowerCAmelCase ( self : List[Any] ) -> List[Any]:
"""simple docstring"""
_UpperCAmelCase : List[str] = self.scheduler_classes[0]
_UpperCAmelCase : int = self.get_scheduler_config()
_UpperCAmelCase : Optional[int] = scheduler_class(**lowerCAmelCase__ )
scheduler.set_timesteps(self.num_inference_steps )
_UpperCAmelCase : int = torch.manual_seed(0 )
_UpperCAmelCase : Any = self.dummy_model()
_UpperCAmelCase : List[str] = self.dummy_sample_deter * scheduler.init_noise_sigma
_UpperCAmelCase : List[Any] = sample.to(lowerCAmelCase__ )
for i, t in enumerate(scheduler.timesteps ):
_UpperCAmelCase : List[str] = scheduler.scale_model_input(lowerCAmelCase__ , lowerCAmelCase__ )
_UpperCAmelCase : int = model(lowerCAmelCase__ , lowerCAmelCase__ )
_UpperCAmelCase : int = scheduler.step(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , generator=lowerCAmelCase__ )
_UpperCAmelCase : Optional[int] = output.prev_sample
_UpperCAmelCase : Optional[Any] = torch.sum(torch.abs(lowerCAmelCase__ ) )
_UpperCAmelCase : Tuple = torch.mean(torch.abs(lowerCAmelCase__ ) )
assert abs(result_sum.item() - 10.0807 ) < 1e-2
assert abs(result_mean.item() - 0.0131 ) < 1e-3
def _lowerCAmelCase ( self : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
_UpperCAmelCase : Any = self.scheduler_classes[0]
_UpperCAmelCase : List[Any] = self.get_scheduler_config(prediction_type="v_prediction" )
_UpperCAmelCase : Any = scheduler_class(**lowerCAmelCase__ )
scheduler.set_timesteps(self.num_inference_steps )
_UpperCAmelCase : str = torch.manual_seed(0 )
_UpperCAmelCase : Optional[Any] = self.dummy_model()
_UpperCAmelCase : Union[str, Any] = self.dummy_sample_deter * scheduler.init_noise_sigma
_UpperCAmelCase : Tuple = sample.to(lowerCAmelCase__ )
for i, t in enumerate(scheduler.timesteps ):
_UpperCAmelCase : Union[str, Any] = scheduler.scale_model_input(lowerCAmelCase__ , lowerCAmelCase__ )
_UpperCAmelCase : int = model(lowerCAmelCase__ , lowerCAmelCase__ )
_UpperCAmelCase : Union[str, Any] = scheduler.step(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , generator=lowerCAmelCase__ )
_UpperCAmelCase : Union[str, Any] = output.prev_sample
_UpperCAmelCase : Tuple = torch.sum(torch.abs(lowerCAmelCase__ ) )
_UpperCAmelCase : Any = torch.mean(torch.abs(lowerCAmelCase__ ) )
assert abs(result_sum.item() - 0.0002 ) < 1e-2
assert abs(result_mean.item() - 2.26_76e-06 ) < 1e-3
def _lowerCAmelCase ( self : Tuple ) -> str:
"""simple docstring"""
_UpperCAmelCase : Optional[int] = self.scheduler_classes[0]
_UpperCAmelCase : List[Any] = self.get_scheduler_config()
_UpperCAmelCase : int = scheduler_class(**lowerCAmelCase__ )
scheduler.set_timesteps(self.num_inference_steps , device=lowerCAmelCase__ )
_UpperCAmelCase : Optional[int] = torch.manual_seed(0 )
_UpperCAmelCase : str = self.dummy_model()
_UpperCAmelCase : Any = self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
_UpperCAmelCase : str = sample.to(lowerCAmelCase__ )
for t in scheduler.timesteps:
_UpperCAmelCase : List[str] = scheduler.scale_model_input(lowerCAmelCase__ , lowerCAmelCase__ )
_UpperCAmelCase : Any = model(lowerCAmelCase__ , lowerCAmelCase__ )
_UpperCAmelCase : Tuple = scheduler.step(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , generator=lowerCAmelCase__ )
_UpperCAmelCase : int = output.prev_sample
_UpperCAmelCase : List[Any] = torch.sum(torch.abs(lowerCAmelCase__ ) )
_UpperCAmelCase : str = torch.mean(torch.abs(lowerCAmelCase__ ) )
assert abs(result_sum.item() - 10.0807 ) < 1e-2
assert abs(result_mean.item() - 0.0131 ) < 1e-3
def _lowerCAmelCase ( self : List[str] ) -> int:
"""simple docstring"""
_UpperCAmelCase : List[Any] = self.scheduler_classes[0]
_UpperCAmelCase : int = self.get_scheduler_config()
_UpperCAmelCase : Union[str, Any] = scheduler_class(**lowerCAmelCase__ , use_karras_sigmas=lowerCAmelCase__ )
scheduler.set_timesteps(self.num_inference_steps , device=lowerCAmelCase__ )
_UpperCAmelCase : Optional[int] = torch.manual_seed(0 )
_UpperCAmelCase : List[str] = self.dummy_model()
_UpperCAmelCase : str = self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
_UpperCAmelCase : Optional[int] = sample.to(lowerCAmelCase__ )
for t in scheduler.timesteps:
_UpperCAmelCase : List[Any] = scheduler.scale_model_input(lowerCAmelCase__ , lowerCAmelCase__ )
_UpperCAmelCase : str = model(lowerCAmelCase__ , lowerCAmelCase__ )
_UpperCAmelCase : Optional[Any] = scheduler.step(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , generator=lowerCAmelCase__ )
_UpperCAmelCase : List[Any] = output.prev_sample
_UpperCAmelCase : List[Any] = torch.sum(torch.abs(lowerCAmelCase__ ) )
_UpperCAmelCase : Optional[Any] = torch.mean(torch.abs(lowerCAmelCase__ ) )
assert abs(result_sum.item() - 124.52_2994_9951_1719 ) < 1e-2
assert abs(result_mean.item() - 0.1_6213_9326_3339_9963 ) < 1e-3
| 17
| 0
|
'''simple docstring'''
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F
from transformers import DetrImageProcessor, TableTransformerConfig, TableTransformerForObjectDetection
from transformers.utils import logging
logging.set_verbosity_info()
__a = logging.get_logger(__name__)
# here we list all keys to be renamed (original name on the left, our name on the right)
__a = []
for i in range(6):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append(
(f'transformer.encoder.layers.{i}.self_attn.out_proj.weight', f'encoder.layers.{i}.self_attn.out_proj.weight')
)
rename_keys.append(
(f'transformer.encoder.layers.{i}.self_attn.out_proj.bias', f'encoder.layers.{i}.self_attn.out_proj.bias')
)
rename_keys.append((f'transformer.encoder.layers.{i}.linear1.weight', f'encoder.layers.{i}.fc1.weight'))
rename_keys.append((f'transformer.encoder.layers.{i}.linear1.bias', f'encoder.layers.{i}.fc1.bias'))
rename_keys.append((f'transformer.encoder.layers.{i}.linear2.weight', f'encoder.layers.{i}.fc2.weight'))
rename_keys.append((f'transformer.encoder.layers.{i}.linear2.bias', f'encoder.layers.{i}.fc2.bias'))
rename_keys.append(
(f'transformer.encoder.layers.{i}.norm1.weight', f'encoder.layers.{i}.self_attn_layer_norm.weight')
)
rename_keys.append((f'transformer.encoder.layers.{i}.norm1.bias', f'encoder.layers.{i}.self_attn_layer_norm.bias'))
rename_keys.append((f'transformer.encoder.layers.{i}.norm2.weight', f'encoder.layers.{i}.final_layer_norm.weight'))
rename_keys.append((f'transformer.encoder.layers.{i}.norm2.bias', f'encoder.layers.{i}.final_layer_norm.bias'))
# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
rename_keys.append(
(f'transformer.decoder.layers.{i}.self_attn.out_proj.weight', f'decoder.layers.{i}.self_attn.out_proj.weight')
)
rename_keys.append(
(f'transformer.decoder.layers.{i}.self_attn.out_proj.bias', f'decoder.layers.{i}.self_attn.out_proj.bias')
)
rename_keys.append(
(
f'transformer.decoder.layers.{i}.multihead_attn.out_proj.weight',
f'decoder.layers.{i}.encoder_attn.out_proj.weight',
)
)
rename_keys.append(
(
f'transformer.decoder.layers.{i}.multihead_attn.out_proj.bias',
f'decoder.layers.{i}.encoder_attn.out_proj.bias',
)
)
rename_keys.append((f'transformer.decoder.layers.{i}.linear1.weight', f'decoder.layers.{i}.fc1.weight'))
rename_keys.append((f'transformer.decoder.layers.{i}.linear1.bias', f'decoder.layers.{i}.fc1.bias'))
rename_keys.append((f'transformer.decoder.layers.{i}.linear2.weight', f'decoder.layers.{i}.fc2.weight'))
rename_keys.append((f'transformer.decoder.layers.{i}.linear2.bias', f'decoder.layers.{i}.fc2.bias'))
rename_keys.append(
(f'transformer.decoder.layers.{i}.norm1.weight', f'decoder.layers.{i}.self_attn_layer_norm.weight')
)
rename_keys.append((f'transformer.decoder.layers.{i}.norm1.bias', f'decoder.layers.{i}.self_attn_layer_norm.bias'))
rename_keys.append(
(f'transformer.decoder.layers.{i}.norm2.weight', f'decoder.layers.{i}.encoder_attn_layer_norm.weight')
)
rename_keys.append(
(f'transformer.decoder.layers.{i}.norm2.bias', f'decoder.layers.{i}.encoder_attn_layer_norm.bias')
)
rename_keys.append((f'transformer.decoder.layers.{i}.norm3.weight', f'decoder.layers.{i}.final_layer_norm.weight'))
rename_keys.append((f'transformer.decoder.layers.{i}.norm3.bias', f'decoder.layers.{i}.final_layer_norm.bias'))
# convolutional projection + query embeddings + layernorm of encoder + layernorm of decoder + class and bounding box heads
rename_keys.extend(
[
('input_proj.weight', 'input_projection.weight'),
('input_proj.bias', 'input_projection.bias'),
('query_embed.weight', 'query_position_embeddings.weight'),
('transformer.encoder.norm.weight', 'encoder.layernorm.weight'),
('transformer.encoder.norm.bias', 'encoder.layernorm.bias'),
('transformer.decoder.norm.weight', 'decoder.layernorm.weight'),
('transformer.decoder.norm.bias', 'decoder.layernorm.bias'),
('class_embed.weight', 'class_labels_classifier.weight'),
('class_embed.bias', 'class_labels_classifier.bias'),
('bbox_embed.layers.0.weight', 'bbox_predictor.layers.0.weight'),
('bbox_embed.layers.0.bias', 'bbox_predictor.layers.0.bias'),
('bbox_embed.layers.1.weight', 'bbox_predictor.layers.1.weight'),
('bbox_embed.layers.1.bias', 'bbox_predictor.layers.1.bias'),
('bbox_embed.layers.2.weight', 'bbox_predictor.layers.2.weight'),
('bbox_embed.layers.2.bias', 'bbox_predictor.layers.2.bias'),
]
)
def __UpperCAmelCase ( a_: List[Any], a_: List[Any], a_: List[Any] ):
_UpperCAmelCase : Tuple = state_dict.pop(lowerCamelCase__ )
_UpperCAmelCase : Optional[Any] = val
def __UpperCAmelCase ( a_: Dict ):
_UpperCAmelCase : int = OrderedDict()
for key, value in state_dict.items():
if "backbone.0.body" in key:
_UpperCAmelCase : int = key.replace("backbone.0.body", "backbone.conv_encoder.model" )
_UpperCAmelCase : List[str] = value
else:
_UpperCAmelCase : Optional[int] = value
return new_state_dict
def __UpperCAmelCase ( a_: Union[str, Any] ):
_UpperCAmelCase : Any = ''''''
# first: transformer encoder
for i in range(6 ):
# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
_UpperCAmelCase : Tuple = state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight""" )
_UpperCAmelCase : Dict = state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias""" )
# next, add query, keys and values (in that order) to the state dict
_UpperCAmelCase : str = in_proj_weight[:256, :]
_UpperCAmelCase : Optional[Any] = in_proj_bias[:256]
_UpperCAmelCase : Dict = in_proj_weight[256:512, :]
_UpperCAmelCase : Tuple = in_proj_bias[256:512]
_UpperCAmelCase : Tuple = in_proj_weight[-256:, :]
_UpperCAmelCase : Optional[int] = in_proj_bias[-256:]
# next: transformer decoder (which is a bit more complex because it also includes cross-attention)
for i in range(6 ):
# read in weights + bias of input projection layer of self-attention
_UpperCAmelCase : Union[str, Any] = state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight""" )
_UpperCAmelCase : Dict = state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias""" )
# next, add query, keys and values (in that order) to the state dict
_UpperCAmelCase : List[str] = in_proj_weight[:256, :]
_UpperCAmelCase : int = in_proj_bias[:256]
_UpperCAmelCase : Any = in_proj_weight[256:512, :]
_UpperCAmelCase : List[str] = in_proj_bias[256:512]
_UpperCAmelCase : Union[str, Any] = in_proj_weight[-256:, :]
_UpperCAmelCase : Optional[Any] = in_proj_bias[-256:]
# read in weights + bias of input projection layer of cross-attention
_UpperCAmelCase : Tuple = state_dict.pop(
f"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight""" )
_UpperCAmelCase : Optional[Any] = state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias""" )
# next, add query, keys and values (in that order) of cross-attention to the state dict
_UpperCAmelCase : Dict = in_proj_weight_cross_attn[:256, :]
_UpperCAmelCase : Tuple = in_proj_bias_cross_attn[:256]
_UpperCAmelCase : int = in_proj_weight_cross_attn[256:512, :]
_UpperCAmelCase : List[str] = in_proj_bias_cross_attn[256:512]
_UpperCAmelCase : Any = in_proj_weight_cross_attn[-256:, :]
_UpperCAmelCase : Any = in_proj_bias_cross_attn[-256:]
def __UpperCAmelCase ( a_: List[str], a_: Tuple ):
_UpperCAmelCase : int = image.size
_UpperCAmelCase : Tuple = max(lowerCamelCase__, lowerCamelCase__ )
_UpperCAmelCase : Optional[Any] = 800 if '''detection''' in checkpoint_url else 1_000
_UpperCAmelCase : Union[str, Any] = target_max_size / current_max_size
_UpperCAmelCase : Any = image.resize((int(round(scale * width ) ), int(round(scale * height ) )) )
return resized_image
def __UpperCAmelCase ( a_: Tuple ):
_UpperCAmelCase : Any = F.to_tensor(lowerCamelCase__ )
_UpperCAmelCase : Optional[Any] = F.normalize(lowerCamelCase__, mean=[0.4_85, 0.4_56, 0.4_06], std=[0.2_29, 0.2_24, 0.2_25] )
return image
@torch.no_grad()
def __UpperCAmelCase ( a_: List[Any], a_: int, a_: int ):
logger.info("Converting model..." )
# load original state dict
_UpperCAmelCase : Tuple = torch.hub.load_state_dict_from_url(lowerCamelCase__, map_location="cpu" )
# rename keys
for src, dest in rename_keys:
rename_key(lowerCamelCase__, lowerCamelCase__, lowerCamelCase__ )
_UpperCAmelCase : str = rename_backbone_keys(lowerCamelCase__ )
# query, key and value matrices need special treatment
read_in_q_k_v(lowerCamelCase__ )
# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
_UpperCAmelCase : List[Any] = '''model.'''
for key in state_dict.copy().keys():
if not key.startswith("class_labels_classifier" ) and not key.startswith("bbox_predictor" ):
_UpperCAmelCase : List[Any] = state_dict.pop(lowerCamelCase__ )
_UpperCAmelCase : str = val
# create HuggingFace model and load state dict
_UpperCAmelCase : Union[str, Any] = TableTransformerConfig(
backbone="resnet18", mask_loss_coefficient=1, dice_loss_coefficient=1, ce_loss_coefficient=1, bbox_loss_coefficient=5, giou_loss_coefficient=2, eos_coefficient=0.4, class_cost=1, bbox_cost=5, giou_cost=2, )
if "detection" in checkpoint_url:
_UpperCAmelCase : Dict = 15
_UpperCAmelCase : Dict = 2
_UpperCAmelCase : int = {0: '''table''', 1: '''table rotated'''}
_UpperCAmelCase : List[str] = idalabel
_UpperCAmelCase : Optional[int] = {v: k for k, v in idalabel.items()}
else:
_UpperCAmelCase : Union[str, Any] = 125
_UpperCAmelCase : Optional[Any] = 6
_UpperCAmelCase : Optional[Any] = {
0: '''table''',
1: '''table column''',
2: '''table row''',
3: '''table column header''',
4: '''table projected row header''',
5: '''table spanning cell''',
}
_UpperCAmelCase : int = idalabel
_UpperCAmelCase : Tuple = {v: k for k, v in idalabel.items()}
_UpperCAmelCase : Optional[Any] = DetrImageProcessor(
format="coco_detection", max_size=800 if "detection" in checkpoint_url else 1_000 )
_UpperCAmelCase : int = TableTransformerForObjectDetection(lowerCamelCase__ )
model.load_state_dict(lowerCamelCase__ )
model.eval()
# verify our conversion
_UpperCAmelCase : Optional[int] = '''example_pdf.png''' if '''detection''' in checkpoint_url else '''example_table.png'''
_UpperCAmelCase : Union[str, Any] = hf_hub_download(repo_id="nielsr/example-pdf", repo_type="dataset", filename=lowerCamelCase__ )
_UpperCAmelCase : Tuple = Image.open(lowerCamelCase__ ).convert("RGB" )
_UpperCAmelCase : int = normalize(resize(lowerCamelCase__, lowerCamelCase__ ) ).unsqueeze(0 )
_UpperCAmelCase : str = model(lowerCamelCase__ )
if "detection" in checkpoint_url:
_UpperCAmelCase : str = (1, 15, 3)
_UpperCAmelCase : int = torch.tensor(
[[-6.78_97, -16.99_85, 6.79_37], [-8.01_86, -22.21_92, 6.96_77], [-7.31_17, -21.07_08, 7.40_55]] )
_UpperCAmelCase : Tuple = torch.tensor([[0.48_67, 0.17_67, 0.67_32], [0.67_18, 0.44_79, 0.38_30], [0.47_16, 0.17_60, 0.63_64]] )
else:
_UpperCAmelCase : Optional[int] = (1, 125, 7)
_UpperCAmelCase : Dict = torch.tensor(
[[-18.14_30, -8.32_14, 4.82_74], [-18.46_85, -7.13_61, -4.26_67], [-26.36_93, -9.34_29, -4.99_62]] )
_UpperCAmelCase : Any = torch.tensor([[0.49_83, 0.55_95, 0.94_40], [0.49_16, 0.63_15, 0.59_54], [0.61_08, 0.86_37, 0.11_35]] )
assert outputs.logits.shape == expected_shape
assert torch.allclose(outputs.logits[0, :3, :3], lowerCamelCase__, atol=1e-4 )
assert torch.allclose(outputs.pred_boxes[0, :3, :3], lowerCamelCase__, atol=1e-4 )
print("Looks ok!" )
if pytorch_dump_folder_path is not None:
# Save model and image processor
logger.info(f"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}...""" )
Path(lowerCamelCase__ ).mkdir(exist_ok=lowerCamelCase__ )
model.save_pretrained(lowerCamelCase__ )
image_processor.save_pretrained(lowerCamelCase__ )
if push_to_hub:
# Push model to HF hub
logger.info("Pushing model to the hub..." )
_UpperCAmelCase : List[Any] = (
'''microsoft/table-transformer-detection'''
if '''detection''' in checkpoint_url
else '''microsoft/table-transformer-structure-recognition'''
)
model.push_to_hub(lowerCamelCase__ )
image_processor.push_to_hub(lowerCamelCase__ )
if __name__ == "__main__":
__a = argparse.ArgumentParser()
parser.add_argument(
'--checkpoint_url',
default='https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth',
type=str,
choices=[
'https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth',
'https://pubtables1m.blob.core.windows.net/model/pubtables1m_structure_detr_r18.pth',
],
help='URL of the Table Transformer checkpoint you\'d like to convert.',
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
)
parser.add_argument(
'--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
)
__a = parser.parse_args()
convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)
| 354
|
'''simple docstring'''
def __UpperCAmelCase ( a_: int, a_: int ):
if a < 0 or b < 0:
raise ValueError("the value of both inputs must be positive" )
_UpperCAmelCase : List[str] = str(bin(a_ ) )[2:] # remove the leading "0b"
_UpperCAmelCase : Any = str(bin(a_ ) )[2:] # remove the leading "0b"
_UpperCAmelCase : Dict = max(len(a_ ), len(a_ ) )
return "0b" + "".join(
str(int(char_a == "1" and char_b == "1" ) )
for char_a, char_b in zip(a_binary.zfill(a_ ), b_binary.zfill(a_ ) ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 17
| 0
|
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__a = logging.get_logger(__name__)
class A__ ( lowercase__ , lowercase__ ):
"""simple docstring"""
UpperCamelCase_ : Dict = '''maskformer-swin'''
UpperCamelCase_ : Tuple = {
'''num_attention_heads''': '''num_heads''',
'''num_hidden_layers''': '''num_layers''',
}
def __init__( self : Dict , lowerCAmelCase__ : Tuple=2_2_4 , lowerCAmelCase__ : Any=4 , lowerCAmelCase__ : List[str]=3 , lowerCAmelCase__ : Any=9_6 , lowerCAmelCase__ : int=[2, 2, 6, 2] , lowerCAmelCase__ : Optional[int]=[3, 6, 1_2, 2_4] , lowerCAmelCase__ : List[Any]=7 , lowerCAmelCase__ : Union[str, Any]=4.0 , lowerCAmelCase__ : str=True , lowerCAmelCase__ : int=0.0 , lowerCAmelCase__ : Optional[Any]=0.0 , lowerCAmelCase__ : int=0.1 , lowerCAmelCase__ : Tuple="gelu" , lowerCAmelCase__ : int=False , lowerCAmelCase__ : Tuple=0.02 , lowerCAmelCase__ : int=1e-5 , lowerCAmelCase__ : Optional[Any]=None , lowerCAmelCase__ : List[str]=None , **lowerCAmelCase__ : Tuple , ) -> Tuple:
"""simple docstring"""
super().__init__(**_a )
_UpperCAmelCase : Optional[Any] = image_size
_UpperCAmelCase : List[Any] = patch_size
_UpperCAmelCase : int = num_channels
_UpperCAmelCase : List[str] = embed_dim
_UpperCAmelCase : int = depths
_UpperCAmelCase : str = len(_a )
_UpperCAmelCase : Dict = num_heads
_UpperCAmelCase : Any = window_size
_UpperCAmelCase : str = mlp_ratio
_UpperCAmelCase : str = qkv_bias
_UpperCAmelCase : Tuple = hidden_dropout_prob
_UpperCAmelCase : Tuple = attention_probs_dropout_prob
_UpperCAmelCase : Tuple = drop_path_rate
_UpperCAmelCase : List[str] = hidden_act
_UpperCAmelCase : int = use_absolute_embeddings
_UpperCAmelCase : Optional[int] = layer_norm_eps
_UpperCAmelCase : Optional[int] = initializer_range
# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
# this indicates the channel dimension after the last stage of the model
_UpperCAmelCase : str = int(embed_dim * 2 ** (len(_a ) - 1) )
_UpperCAmelCase : Tuple = ['stem'] + [F"""stage{idx}""" for idx in range(1 , len(_a ) + 1 )]
_UpperCAmelCase : Optional[Any] = get_aligned_output_features_output_indices(
out_features=_a , out_indices=_a , stage_names=self.stage_names )
| 355
|
'''simple docstring'''
from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def __UpperCAmelCase ( a_: int ):
# A local function to see if a dot lands in the circle.
def is_in_circle(a_: float, a_: float ) -> bool:
_UpperCAmelCase : Optional[Any] = sqrt((x**2) + (y**2) )
# Our circle has a radius of 1, so a distance
# greater than 1 would land outside the circle.
return distance_from_centre <= 1
# The proportion of guesses that landed in the circle
_UpperCAmelCase : str = mean(
int(is_in_circle(uniform(-1.0, 1.0 ), uniform(-1.0, 1.0 ) ) )
for _ in range(a_ ) )
# The ratio of the area for circle to square is pi/4.
_UpperCAmelCase : Optional[int] = proportion * 4
print(f"""The estimated value of pi is {pi_estimate}""" )
print(f"""The numpy value of pi is {pi}""" )
print(f"""The total error is {abs(pi - pi_estimate )}""" )
def __UpperCAmelCase ( a_: int, a_: Callable[[float], float], a_: float = 0.0, a_: float = 1.0, ):
return mean(
function_to_integrate(uniform(a_, a_ ) ) for _ in range(a_ ) ) * (max_value - min_value)
def __UpperCAmelCase ( a_: int, a_: float = 0.0, a_: float = 1.0 ):
def identity_function(a_: float ) -> float:
return x
_UpperCAmelCase : Union[str, Any] = area_under_curve_estimator(
a_, a_, a_, a_ )
_UpperCAmelCase : List[str] = (max_value * max_value - min_value * min_value) / 2
print("******************" )
print(f"""Estimating area under y=x where x varies from {min_value} to {max_value}""" )
print(f"""Estimated value is {estimated_value}""" )
print(f"""Expected value is {expected_value}""" )
print(f"""Total error is {abs(estimated_value - expected_value )}""" )
print("******************" )
def __UpperCAmelCase ( a_: int ):
def function_to_integrate(a_: float ) -> float:
return sqrt(4.0 - x * x )
_UpperCAmelCase : List[str] = area_under_curve_estimator(
a_, a_, 0.0, 2.0 )
print("******************" )
print("Estimating pi using area_under_curve_estimator" )
print(f"""Estimated value is {estimated_value}""" )
print(f"""Expected value is {pi}""" )
print(f"""Total error is {abs(estimated_value - pi )}""" )
print("******************" )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 17
| 0
|
'''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device
from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class A__ ( lowerCamelCase__ , unittest.TestCase ):
"""simple docstring"""
UpperCamelCase_ : Optional[Any] = DDIMPipeline
UpperCamelCase_ : Tuple = UNCONDITIONAL_IMAGE_GENERATION_PARAMS
UpperCamelCase_ : Dict = PipelineTesterMixin.required_optional_params - {
'num_images_per_prompt',
'latents',
'callback',
'callback_steps',
}
UpperCamelCase_ : str = UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
UpperCamelCase_ : Optional[Any] = False
def _lowerCAmelCase ( self : Dict ) -> Tuple:
"""simple docstring"""
torch.manual_seed(0 )
_UpperCAmelCase : int = UNetaDModel(
block_out_channels=(3_2, 6_4) , layers_per_block=2 , sample_size=3_2 , in_channels=3 , out_channels=3 , down_block_types=("DownBlock2D", "AttnDownBlock2D") , up_block_types=("AttnUpBlock2D", "UpBlock2D") , )
_UpperCAmelCase : Optional[Any] = DDIMScheduler()
_UpperCAmelCase : str = {"unet": unet, "scheduler": scheduler}
return components
def _lowerCAmelCase ( self : Optional[int] , lowerCAmelCase__ : str , lowerCAmelCase__ : Optional[int]=0 ) -> int:
"""simple docstring"""
if str(lowerCAmelCase__ ).startswith("mps" ):
_UpperCAmelCase : List[str] = torch.manual_seed(lowerCAmelCase__ )
else:
_UpperCAmelCase : Tuple = torch.Generator(device=lowerCAmelCase__ ).manual_seed(lowerCAmelCase__ )
_UpperCAmelCase : List[str] = {
"batch_size": 1,
"generator": generator,
"num_inference_steps": 2,
"output_type": "numpy",
}
return inputs
def _lowerCAmelCase ( self : int ) -> str:
"""simple docstring"""
_UpperCAmelCase : Any = "cpu"
_UpperCAmelCase : Tuple = self.get_dummy_components()
_UpperCAmelCase : Tuple = self.pipeline_class(**lowerCAmelCase__ )
pipe.to(lowerCAmelCase__ )
pipe.set_progress_bar_config(disable=lowerCAmelCase__ )
_UpperCAmelCase : Tuple = self.get_dummy_inputs(lowerCAmelCase__ )
_UpperCAmelCase : Tuple = pipe(**lowerCAmelCase__ ).images
_UpperCAmelCase : Dict = image[0, -3:, -3:, -1]
self.assertEqual(image.shape , (1, 3_2, 3_2, 3) )
_UpperCAmelCase : List[Any] = np.array(
[1.0_00e00, 5.7_17e-01, 4.7_17e-01, 1.0_00e00, 0.0_00e00, 1.0_00e00, 3.0_00e-04, 0.0_00e00, 9.0_00e-04] )
_UpperCAmelCase : Optional[int] = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(lowerCAmelCase__ , 1e-3 )
def _lowerCAmelCase ( self : Any ) -> Tuple:
"""simple docstring"""
super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3 )
def _lowerCAmelCase ( self : int ) -> Optional[int]:
"""simple docstring"""
super().test_save_load_local(expected_max_difference=3e-3 )
def _lowerCAmelCase ( self : Optional[int] ) -> str:
"""simple docstring"""
super().test_save_load_optional_components(expected_max_difference=3e-3 )
def _lowerCAmelCase ( self : Optional[Any] ) -> int:
"""simple docstring"""
super().test_inference_batch_single_identical(expected_max_diff=3e-3 )
@slow
@require_torch_gpu
class A__ ( unittest.TestCase ):
"""simple docstring"""
def _lowerCAmelCase ( self : str ) -> Dict:
"""simple docstring"""
_UpperCAmelCase : List[Any] = "google/ddpm-cifar10-32"
_UpperCAmelCase : int = UNetaDModel.from_pretrained(lowerCAmelCase__ )
_UpperCAmelCase : Optional[int] = DDIMScheduler()
_UpperCAmelCase : List[str] = DDIMPipeline(unet=lowerCAmelCase__ , scheduler=lowerCAmelCase__ )
ddim.to(lowerCAmelCase__ )
ddim.set_progress_bar_config(disable=lowerCAmelCase__ )
_UpperCAmelCase : List[str] = torch.manual_seed(0 )
_UpperCAmelCase : int = ddim(generator=lowerCAmelCase__ , eta=0.0 , output_type="numpy" ).images
_UpperCAmelCase : Union[str, Any] = image[0, -3:, -3:, -1]
assert image.shape == (1, 3_2, 3_2, 3)
_UpperCAmelCase : Optional[int] = np.array([0.1723, 0.1617, 0.1600, 0.1626, 0.1497, 0.1513, 0.1505, 0.1442, 0.1453] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def _lowerCAmelCase ( self : Union[str, Any] ) -> Dict:
"""simple docstring"""
_UpperCAmelCase : Tuple = "google/ddpm-ema-bedroom-256"
_UpperCAmelCase : Tuple = UNetaDModel.from_pretrained(lowerCAmelCase__ )
_UpperCAmelCase : List[str] = DDIMScheduler.from_pretrained(lowerCAmelCase__ )
_UpperCAmelCase : Tuple = DDIMPipeline(unet=lowerCAmelCase__ , scheduler=lowerCAmelCase__ )
ddpm.to(lowerCAmelCase__ )
ddpm.set_progress_bar_config(disable=lowerCAmelCase__ )
_UpperCAmelCase : Tuple = torch.manual_seed(0 )
_UpperCAmelCase : Optional[int] = ddpm(generator=lowerCAmelCase__ , output_type="numpy" ).images
_UpperCAmelCase : Dict = image[0, -3:, -3:, -1]
assert image.shape == (1, 2_5_6, 2_5_6, 3)
_UpperCAmelCase : str = np.array([0.0060, 0.0201, 0.0344, 0.0024, 0.0018, 0.0002, 0.0022, 0.0000, 0.0069] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
| 356
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
__a = {
'configuration_layoutlmv2': ['LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LayoutLMv2Config'],
'processing_layoutlmv2': ['LayoutLMv2Processor'],
'tokenization_layoutlmv2': ['LayoutLMv2Tokenizer'],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = ['LayoutLMv2TokenizerFast']
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = ['LayoutLMv2FeatureExtractor']
__a = ['LayoutLMv2ImageProcessor']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = [
'LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST',
'LayoutLMv2ForQuestionAnswering',
'LayoutLMv2ForSequenceClassification',
'LayoutLMv2ForTokenClassification',
'LayoutLMv2Layer',
'LayoutLMv2Model',
'LayoutLMv2PreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_layoutlmva import LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMvaConfig
from .processing_layoutlmva import LayoutLMvaProcessor
from .tokenization_layoutlmva import LayoutLMvaTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor, LayoutLMvaImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_layoutlmva import (
LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST,
LayoutLMvaForQuestionAnswering,
LayoutLMvaForSequenceClassification,
LayoutLMvaForTokenClassification,
LayoutLMvaLayer,
LayoutLMvaModel,
LayoutLMvaPreTrainedModel,
)
else:
import sys
__a = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 17
| 0
|
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a = logging.get_logger(__name__)
__a = {
"google/fnet-base": "https://huggingface.co/google/fnet-base/resolve/main/config.json",
"google/fnet-large": "https://huggingface.co/google/fnet-large/resolve/main/config.json"
# See all FNet models at https://huggingface.co/models?filter=fnet
}
class A__ ( UpperCamelCase ):
"""simple docstring"""
UpperCamelCase_ : Optional[Any] = '''fnet'''
def __init__( self : Optional[int] , lowerCAmelCase__ : Tuple=3_2_0_0_0 , lowerCAmelCase__ : List[Any]=7_6_8 , lowerCAmelCase__ : Dict=1_2 , lowerCAmelCase__ : Any=3_0_7_2 , lowerCAmelCase__ : str="gelu_new" , lowerCAmelCase__ : Dict=0.1 , lowerCAmelCase__ : Optional[int]=5_1_2 , lowerCAmelCase__ : int=4 , lowerCAmelCase__ : int=0.02 , lowerCAmelCase__ : Dict=1e-12 , lowerCAmelCase__ : Optional[Any]=False , lowerCAmelCase__ : Optional[Any]=5_1_2 , lowerCAmelCase__ : Union[str, Any]=3 , lowerCAmelCase__ : str=1 , lowerCAmelCase__ : Optional[int]=2 , **lowerCAmelCase__ : List[str] , ) -> Any:
"""simple docstring"""
super().__init__(pad_token_id=_snake_case , bos_token_id=_snake_case , eos_token_id=_snake_case , **_snake_case )
_UpperCAmelCase : List[Any] = vocab_size
_UpperCAmelCase : int = max_position_embeddings
_UpperCAmelCase : Any = hidden_size
_UpperCAmelCase : Union[str, Any] = num_hidden_layers
_UpperCAmelCase : List[str] = intermediate_size
_UpperCAmelCase : Tuple = hidden_act
_UpperCAmelCase : Optional[Any] = hidden_dropout_prob
_UpperCAmelCase : Union[str, Any] = initializer_range
_UpperCAmelCase : Optional[Any] = type_vocab_size
_UpperCAmelCase : Tuple = layer_norm_eps
_UpperCAmelCase : str = use_tpu_fourier_optimizations
_UpperCAmelCase : List[Any] = tpu_short_seq_length
| 357
|
'''simple docstring'''
def __UpperCAmelCase ( a_: int, a_: int ):
if not isinstance(a_, a_ ):
raise ValueError("iterations must be defined as integers" )
if not isinstance(a_, a_ ) or not number >= 1:
raise ValueError(
"starting number must be\n and integer and be more than 0" )
if not iterations >= 1:
raise ValueError("Iterations must be done more than 0 times to play FizzBuzz" )
_UpperCAmelCase : List[str] = ""
while number <= iterations:
if number % 3 == 0:
out += "Fizz"
if number % 5 == 0:
out += "Buzz"
if 0 not in (number % 3, number % 5):
out += str(a_ )
# print(out)
number += 1
out += " "
return out
if __name__ == "__main__":
import doctest
doctest.testmod()
| 17
| 0
|
'''simple docstring'''
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 : Any , lowerCAmelCase__ : int , lowerCAmelCase__ : int , lowerCAmelCase__ : int , lowerCAmelCase__ : int=0.0 , lowerCAmelCase__ : Optional[int] = None , lowerCAmelCase__ : str = "geglu" , lowerCAmelCase__ : Optional[int] = None , lowerCAmelCase__ : bool = False , lowerCAmelCase__ : bool = False , lowerCAmelCase__ : bool = False , lowerCAmelCase__ : bool = False , lowerCAmelCase__ : bool = True , lowerCAmelCase__ : str = "layer_norm" , lowerCAmelCase__ : bool = False , ) -> Union[str, Any]:
"""simple docstring"""
super().__init__()
_UpperCAmelCase : Any = only_cross_attention
_UpperCAmelCase : Union[str, Any] = (num_embeds_ada_norm is not None) and norm_type == """ada_norm_zero"""
_UpperCAmelCase : Any = (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:
_UpperCAmelCase : Dict = AdaLayerNorm(_UpperCamelCase , _UpperCamelCase )
elif self.use_ada_layer_norm_zero:
_UpperCAmelCase : str = AdaLayerNormZero(_UpperCamelCase , _UpperCamelCase )
else:
_UpperCAmelCase : List[Any] = nn.LayerNorm(_UpperCamelCase , elementwise_affine=_UpperCamelCase )
_UpperCAmelCase : List[str] = Attention(
query_dim=_UpperCamelCase , heads=_UpperCamelCase , dim_head=_UpperCamelCase , dropout=_UpperCamelCase , bias=_UpperCamelCase , cross_attention_dim=cross_attention_dim if only_cross_attention else None , upcast_attention=_UpperCamelCase , )
# 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.
_UpperCAmelCase : str = (
AdaLayerNorm(_UpperCamelCase , _UpperCamelCase )
if self.use_ada_layer_norm
else nn.LayerNorm(_UpperCamelCase , elementwise_affine=_UpperCamelCase )
)
_UpperCAmelCase : List[str] = Attention(
query_dim=_UpperCamelCase , cross_attention_dim=cross_attention_dim if not double_self_attention else None , heads=_UpperCamelCase , dim_head=_UpperCamelCase , dropout=_UpperCamelCase , bias=_UpperCamelCase , upcast_attention=_UpperCamelCase , ) # is self-attn if encoder_hidden_states is none
else:
_UpperCAmelCase : Any = None
_UpperCAmelCase : Optional[Any] = None
# 3. Feed-forward
_UpperCAmelCase : List[str] = nn.LayerNorm(_UpperCamelCase , elementwise_affine=_UpperCamelCase )
_UpperCAmelCase : Union[str, Any] = FeedForward(_UpperCamelCase , dropout=_UpperCamelCase , activation_fn=_UpperCamelCase , final_dropout=_UpperCamelCase )
# let chunk size default to None
_UpperCAmelCase : Optional[int] = None
_UpperCAmelCase : Dict = 0
def _lowerCAmelCase ( self : List[Any] , lowerCAmelCase__ : Optional[int] , lowerCAmelCase__ : int ) -> Optional[int]:
"""simple docstring"""
_UpperCAmelCase : Optional[Any] = chunk_size
_UpperCAmelCase : Optional[Any] = dim
def _lowerCAmelCase ( self : List[str] , lowerCAmelCase__ : torch.FloatTensor , lowerCAmelCase__ : Optional[torch.FloatTensor] = None , lowerCAmelCase__ : Optional[torch.FloatTensor] = None , lowerCAmelCase__ : Optional[torch.FloatTensor] = None , lowerCAmelCase__ : Optional[torch.LongTensor] = None , lowerCAmelCase__ : Dict[str, Any] = None , lowerCAmelCase__ : Optional[torch.LongTensor] = None , ) -> Optional[int]:
"""simple docstring"""
if self.use_ada_layer_norm:
_UpperCAmelCase : Optional[Any] = self.norma(_UpperCamelCase , _UpperCamelCase )
elif self.use_ada_layer_norm_zero:
_UpperCAmelCase : Union[str, Any] = self.norma(
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , hidden_dtype=hidden_states.dtype )
else:
_UpperCAmelCase : Optional[int] = self.norma(_UpperCamelCase )
_UpperCAmelCase : int = cross_attention_kwargs if cross_attention_kwargs is not None else {}
_UpperCAmelCase : Union[str, Any] = self.attna(
_UpperCamelCase , encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None , attention_mask=_UpperCamelCase , **_UpperCamelCase , )
if self.use_ada_layer_norm_zero:
_UpperCAmelCase : Union[str, Any] = gate_msa.unsqueeze(1 ) * attn_output
_UpperCAmelCase : Union[str, Any] = attn_output + hidden_states
# 2. Cross-Attention
if self.attna is not None:
_UpperCAmelCase : Any = (
self.norma(_UpperCamelCase , _UpperCamelCase ) if self.use_ada_layer_norm else self.norma(_UpperCamelCase )
)
_UpperCAmelCase : List[Any] = self.attna(
_UpperCamelCase , encoder_hidden_states=_UpperCamelCase , attention_mask=_UpperCamelCase , **_UpperCamelCase , )
_UpperCAmelCase : Tuple = attn_output + hidden_states
# 3. Feed-forward
_UpperCAmelCase : Optional[Any] = self.norma(_UpperCamelCase )
if self.use_ada_layer_norm_zero:
_UpperCAmelCase : Dict = 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`.""" )
_UpperCAmelCase : Union[str, Any] = norm_hidden_states.shape[self._chunk_dim] // self._chunk_size
_UpperCAmelCase : int = torch.cat(
[self.ff(_UpperCamelCase ) for hid_slice in norm_hidden_states.chunk(_UpperCamelCase , dim=self._chunk_dim )] , dim=self._chunk_dim , )
else:
_UpperCAmelCase : List[str] = self.ff(_UpperCamelCase )
if self.use_ada_layer_norm_zero:
_UpperCAmelCase : Union[str, Any] = gate_mlp.unsqueeze(1 ) * ff_output
_UpperCAmelCase : Any = ff_output + hidden_states
return hidden_states
class A__ ( nn.Module ):
"""simple docstring"""
def __init__( self : Dict , lowerCAmelCase__ : int , lowerCAmelCase__ : Optional[int] = None , lowerCAmelCase__ : int = 4 , lowerCAmelCase__ : float = 0.0 , lowerCAmelCase__ : str = "geglu" , lowerCAmelCase__ : bool = False , ) -> Optional[int]:
"""simple docstring"""
super().__init__()
_UpperCAmelCase : Tuple = int(dim * mult )
_UpperCAmelCase : Optional[int] = dim_out if dim_out is not None else dim
if activation_fn == "gelu":
_UpperCAmelCase : Any = GELU(_UpperCamelCase , _UpperCamelCase )
if activation_fn == "gelu-approximate":
_UpperCAmelCase : Tuple = GELU(_UpperCamelCase , _UpperCamelCase , approximate="tanh" )
elif activation_fn == "geglu":
_UpperCAmelCase : Dict = GEGLU(_UpperCamelCase , _UpperCamelCase )
elif activation_fn == "geglu-approximate":
_UpperCAmelCase : Optional[Any] = ApproximateGELU(_UpperCamelCase , _UpperCamelCase )
_UpperCAmelCase : Dict = nn.ModuleList([] )
# project in
self.net.append(_UpperCamelCase )
# project dropout
self.net.append(nn.Dropout(_UpperCamelCase ) )
# project out
self.net.append(nn.Linear(_UpperCamelCase , _UpperCamelCase ) )
# FF as used in Vision Transformer, MLP-Mixer, etc. have a final dropout
if final_dropout:
self.net.append(nn.Dropout(_UpperCamelCase ) )
def _lowerCAmelCase ( self : Tuple , lowerCAmelCase__ : Union[str, Any] ) -> Optional[Any]:
"""simple docstring"""
for module in self.net:
_UpperCAmelCase : Tuple = module(_UpperCamelCase )
return hidden_states
class A__ ( nn.Module ):
"""simple docstring"""
def __init__( self : Optional[Any] , lowerCAmelCase__ : int , lowerCAmelCase__ : int , lowerCAmelCase__ : str = "none" ) -> Any:
"""simple docstring"""
super().__init__()
_UpperCAmelCase : Union[str, Any] = nn.Linear(_UpperCamelCase , _UpperCamelCase )
_UpperCAmelCase : Optional[Any] = approximate
def _lowerCAmelCase ( self : str , lowerCAmelCase__ : int ) -> Tuple:
"""simple docstring"""
if gate.device.type != "mps":
return F.gelu(_UpperCamelCase , 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 _lowerCAmelCase ( self : Optional[int] , lowerCAmelCase__ : Optional[Any] ) -> Any:
"""simple docstring"""
_UpperCAmelCase : Optional[Any] = self.proj(_UpperCamelCase )
_UpperCAmelCase : int = self.gelu(_UpperCamelCase )
return hidden_states
class A__ ( nn.Module ):
"""simple docstring"""
def __init__( self : List[Any] , lowerCAmelCase__ : int , lowerCAmelCase__ : int ) -> Tuple:
"""simple docstring"""
super().__init__()
_UpperCAmelCase : str = nn.Linear(_UpperCamelCase , dim_out * 2 )
def _lowerCAmelCase ( self : Dict , lowerCAmelCase__ : List[str] ) -> Any:
"""simple docstring"""
if gate.device.type != "mps":
return F.gelu(_UpperCamelCase )
# mps: gelu is not implemented for float16
return F.gelu(gate.to(dtype=torch.floataa ) ).to(dtype=gate.dtype )
def _lowerCAmelCase ( self : Optional[Any] , lowerCAmelCase__ : Optional[int] ) -> str:
"""simple docstring"""
_UpperCAmelCase : Dict = self.proj(_UpperCamelCase ).chunk(2 , dim=-1 )
return hidden_states * self.gelu(_UpperCamelCase )
class A__ ( nn.Module ):
"""simple docstring"""
def __init__( self : List[str] , lowerCAmelCase__ : int , lowerCAmelCase__ : int ) -> Optional[Any]:
"""simple docstring"""
super().__init__()
_UpperCAmelCase : int = nn.Linear(_UpperCamelCase , _UpperCamelCase )
def _lowerCAmelCase ( self : Optional[int] , lowerCAmelCase__ : Optional[int] ) -> Optional[int]:
"""simple docstring"""
_UpperCAmelCase : int = self.proj(_UpperCamelCase )
return x * torch.sigmoid(1.702 * x )
class A__ ( nn.Module ):
"""simple docstring"""
def __init__( self : int , lowerCAmelCase__ : str , lowerCAmelCase__ : List[Any] ) -> Dict:
"""simple docstring"""
super().__init__()
_UpperCAmelCase : int = nn.Embedding(_UpperCamelCase , _UpperCamelCase )
_UpperCAmelCase : Union[str, Any] = nn.SiLU()
_UpperCAmelCase : Any = nn.Linear(_UpperCamelCase , embedding_dim * 2 )
_UpperCAmelCase : Dict = nn.LayerNorm(_UpperCamelCase , elementwise_affine=_UpperCamelCase )
def _lowerCAmelCase ( self : int , lowerCAmelCase__ : List[str] , lowerCAmelCase__ : int ) -> Dict:
"""simple docstring"""
_UpperCAmelCase : Union[str, Any] = self.linear(self.silu(self.emb(_UpperCamelCase ) ) )
_UpperCAmelCase : Tuple = torch.chunk(_UpperCamelCase , 2 )
_UpperCAmelCase : Tuple = self.norm(_UpperCamelCase ) * (1 + scale) + shift
return x
class A__ ( nn.Module ):
"""simple docstring"""
def __init__( self : List[str] , lowerCAmelCase__ : Tuple , lowerCAmelCase__ : int ) -> Tuple:
"""simple docstring"""
super().__init__()
_UpperCAmelCase : int = CombinedTimestepLabelEmbeddings(_UpperCamelCase , _UpperCamelCase )
_UpperCAmelCase : int = nn.SiLU()
_UpperCAmelCase : List[str] = nn.Linear(_UpperCamelCase , 6 * embedding_dim , bias=_UpperCamelCase )
_UpperCAmelCase : str = nn.LayerNorm(_UpperCamelCase , elementwise_affine=_UpperCamelCase , eps=1e-6 )
def _lowerCAmelCase ( self : Union[str, Any] , lowerCAmelCase__ : Any , lowerCAmelCase__ : Tuple , lowerCAmelCase__ : Optional[Any] , lowerCAmelCase__ : str=None ) -> List[str]:
"""simple docstring"""
_UpperCAmelCase : Union[str, Any] = self.linear(self.silu(self.emb(_UpperCamelCase , _UpperCamelCase , hidden_dtype=_UpperCamelCase ) ) )
_UpperCAmelCase : Any = emb.chunk(6 , dim=1 )
_UpperCAmelCase : str = self.norm(_UpperCamelCase ) * (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 : Optional[int] , lowerCAmelCase__ : int , lowerCAmelCase__ : int , lowerCAmelCase__ : int , lowerCAmelCase__ : Optional[str] = None , lowerCAmelCase__ : float = 1e-5 ) -> Optional[int]:
"""simple docstring"""
super().__init__()
_UpperCAmelCase : Optional[int] = num_groups
_UpperCAmelCase : List[Any] = eps
if act_fn is None:
_UpperCAmelCase : int = None
else:
_UpperCAmelCase : Dict = get_activation(_UpperCamelCase )
_UpperCAmelCase : Optional[int] = nn.Linear(_UpperCamelCase , out_dim * 2 )
def _lowerCAmelCase ( self : List[Any] , lowerCAmelCase__ : Optional[Any] , lowerCAmelCase__ : List[str] ) -> List[Any]:
"""simple docstring"""
if self.act:
_UpperCAmelCase : Any = self.act(_UpperCamelCase )
_UpperCAmelCase : Optional[int] = self.linear(_UpperCamelCase )
_UpperCAmelCase : Dict = emb[:, :, None, None]
_UpperCAmelCase : str = emb.chunk(2 , dim=1 )
_UpperCAmelCase : str = F.group_norm(_UpperCamelCase , self.num_groups , eps=self.eps )
_UpperCAmelCase : List[str] = x * (1 + scale) + shift
return x
| 358
|
'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from itertools import chain
from typing import Optional, Union
import datasets
import numpy as np
import torch
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,
AutoTokenizer,
HfArgumentParser,
Trainer,
TrainingArguments,
default_data_collator,
set_seed,
)
from transformers.tokenization_utils_base import PreTrainedTokenizerBase
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import PaddingStrategy, check_min_version, send_example_telemetry
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('4.31.0')
__a = logging.getLogger(__name__)
@dataclass
class A__ :
"""simple docstring"""
UpperCamelCase_ : str = field(
metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''} )
UpperCamelCase_ : Optional[str] = field(
default=UpperCamelCase , metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''} )
UpperCamelCase_ : Optional[str] = field(
default=UpperCamelCase , metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''} )
UpperCamelCase_ : Optional[str] = field(
default=UpperCamelCase , metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''} , )
UpperCamelCase_ : bool = field(
default=UpperCamelCase , metadata={'''help''': '''Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.'''} , )
UpperCamelCase_ : str = field(
default='''main''' , metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''} , )
UpperCamelCase_ : bool = field(
default=UpperCamelCase , metadata={
'''help''': (
'''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
'''with private models).'''
)
} , )
@dataclass
class A__ :
"""simple docstring"""
UpperCamelCase_ : Optional[str] = field(default=UpperCamelCase , metadata={'''help''': '''The input training data file (a text file).'''} )
UpperCamelCase_ : Optional[str] = field(
default=UpperCamelCase , metadata={'''help''': '''An optional input evaluation data file to evaluate the perplexity on (a text file).'''} , )
UpperCamelCase_ : bool = field(
default=UpperCamelCase , metadata={'''help''': '''Overwrite the cached training and evaluation sets'''} )
UpperCamelCase_ : Optional[int] = field(
default=UpperCamelCase , metadata={'''help''': '''The number of processes to use for the preprocessing.'''} , )
UpperCamelCase_ : Optional[int] = field(
default=UpperCamelCase , metadata={
'''help''': (
'''The maximum total input sequence length after tokenization. If passed, sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
)
} , )
UpperCamelCase_ : bool = field(
default=UpperCamelCase , metadata={
'''help''': (
'''Whether to pad all samples to the maximum sentence length. '''
'''If False, will pad the samples dynamically when batching to the maximum length in the batch. More '''
'''efficient on GPU but very bad for TPU.'''
)
} , )
UpperCamelCase_ : Optional[int] = field(
default=UpperCamelCase , metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of training examples to this '''
'''value if set.'''
)
} , )
UpperCamelCase_ : Optional[int] = field(
default=UpperCamelCase , metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
'''value if set.'''
)
} , )
def _lowerCAmelCase ( self : Any ) -> Any:
"""simple docstring"""
if self.train_file is not None:
_UpperCAmelCase : List[Any] = self.train_file.split("." )[-1]
assert extension in ["csv", "json"], "`train_file` should be a csv or a json file."
if self.validation_file is not None:
_UpperCAmelCase : List[str] = self.validation_file.split("." )[-1]
assert extension in ["csv", "json"], "`validation_file` should be a csv or a json file."
@dataclass
class A__ :
"""simple docstring"""
UpperCamelCase_ : PreTrainedTokenizerBase
UpperCamelCase_ : Union[bool, str, PaddingStrategy] = True
UpperCamelCase_ : Optional[int] = None
UpperCamelCase_ : Optional[int] = None
def __call__( self : List[Any] , lowerCAmelCase__ : List[str] ) -> List[str]:
"""simple docstring"""
_UpperCAmelCase : int = "label" if "label" in features[0].keys() else "labels"
_UpperCAmelCase : Dict = [feature.pop(lowerCAmelCase__ ) for feature in features]
_UpperCAmelCase : str = len(lowerCAmelCase__ )
_UpperCAmelCase : int = len(features[0]["input_ids"] )
_UpperCAmelCase : str = [
[{k: v[i] for k, v in feature.items()} for i in range(lowerCAmelCase__ )] for feature in features
]
_UpperCAmelCase : List[str] = list(chain(*lowerCAmelCase__ ) )
_UpperCAmelCase : Any = self.tokenizer.pad(
lowerCAmelCase__ , padding=self.padding , max_length=self.max_length , pad_to_multiple_of=self.pad_to_multiple_of , return_tensors="pt" , )
# Un-flatten
_UpperCAmelCase : Any = {k: v.view(lowerCAmelCase__ , lowerCAmelCase__ , -1 ) for k, v in batch.items()}
# Add back labels
_UpperCAmelCase : List[str] = torch.tensor(lowerCAmelCase__ , dtype=torch.intaa )
return batch
def __UpperCAmelCase ( ):
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
_UpperCAmelCase : Union[str, Any] = 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.
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase : str = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase : Dict = parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry("run_swag", a_, a_ )
# 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 )], )
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
_UpperCAmelCase : Optional[int] = training_args.get_process_log_level()
logger.setLevel(a_ )
datasets.utils.logging.set_verbosity(a_ )
transformers.utils.logging.set_verbosity(a_ )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# 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}""" )
logger.info(f"""Training/evaluation parameters {training_args}""" )
# Detecting last checkpoint.
_UpperCAmelCase : Any = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
_UpperCAmelCase : Any = 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 and training_args.resume_from_checkpoint is 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." )
# 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.train_file is not None or data_args.validation_file is not None:
_UpperCAmelCase : Union[str, Any] = {}
if data_args.train_file is not None:
_UpperCAmelCase : str = data_args.train_file
if data_args.validation_file is not None:
_UpperCAmelCase : Optional[Any] = data_args.validation_file
_UpperCAmelCase : Dict = data_args.train_file.split("." )[-1]
_UpperCAmelCase : Optional[int] = load_dataset(
a_, data_files=a_, cache_dir=model_args.cache_dir, use_auth_token=True if model_args.use_auth_token else None, )
else:
# Downloading and loading the swag dataset from the hub.
_UpperCAmelCase : Dict = load_dataset(
"swag", "regular", cache_dir=model_args.cache_dir, use_auth_token=True if model_args.use_auth_token else None, )
# 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.
_UpperCAmelCase : Any = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path, cache_dir=model_args.cache_dir, revision=model_args.model_revision, use_auth_token=True if model_args.use_auth_token else None, )
_UpperCAmelCase : Any = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path, 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, )
_UpperCAmelCase : str = AutoModelForMultipleChoice.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, )
# When using your own dataset or a different dataset from swag, you will probably need to change this.
_UpperCAmelCase : Optional[Any] = [f"""ending{i}""" for i in range(4 )]
_UpperCAmelCase : List[Any] = "sent1"
_UpperCAmelCase : Optional[int] = "sent2"
if data_args.max_seq_length is None:
_UpperCAmelCase : List[str] = tokenizer.model_max_length
if max_seq_length > 1_024:
logger.warning(
"The chosen tokenizer supports a `model_max_length` that is longer than the default `block_size` value"
" of 1024. If you would like to use a longer `block_size` up to `tokenizer.model_max_length` you can"
" override this default with `--block_size xxx`." )
_UpperCAmelCase : Dict = 1_024
else:
if data_args.max_seq_length > tokenizer.model_max_length:
logger.warning(
f"""The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the"""
f"""model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}.""" )
_UpperCAmelCase : Dict = min(data_args.max_seq_length, tokenizer.model_max_length )
# Preprocessing the datasets.
def preprocess_function(a_: Union[str, Any] ):
_UpperCAmelCase : Optional[int] = [[context] * 4 for context in examples[context_name]]
_UpperCAmelCase : Tuple = examples[question_header_name]
_UpperCAmelCase : Optional[Any] = [
[f"""{header} {examples[end][i]}""" for end in ending_names] for i, header in enumerate(a_ )
]
# Flatten out
_UpperCAmelCase : List[str] = list(chain(*a_ ) )
_UpperCAmelCase : Dict = list(chain(*a_ ) )
# Tokenize
_UpperCAmelCase : List[Any] = tokenizer(
a_, a_, truncation=a_, max_length=a_, padding="max_length" if data_args.pad_to_max_length else False, )
# Un-flatten
return {k: [v[i : i + 4] for i in range(0, len(a_ ), 4 )] for k, v in tokenized_examples.items()}
if training_args.do_train:
if "train" not in raw_datasets:
raise ValueError("--do_train requires a train dataset" )
_UpperCAmelCase : int = raw_datasets["train"]
if data_args.max_train_samples is not None:
_UpperCAmelCase : Optional[Any] = min(len(a_ ), data_args.max_train_samples )
_UpperCAmelCase : List[Any] = train_dataset.select(range(a_ ) )
with training_args.main_process_first(desc="train dataset map pre-processing" ):
_UpperCAmelCase : Union[str, Any] = train_dataset.map(
a_, batched=a_, num_proc=data_args.preprocessing_num_workers, load_from_cache_file=not data_args.overwrite_cache, )
if training_args.do_eval:
if "validation" not in raw_datasets:
raise ValueError("--do_eval requires a validation dataset" )
_UpperCAmelCase : Dict = raw_datasets["validation"]
if data_args.max_eval_samples is not None:
_UpperCAmelCase : int = min(len(a_ ), data_args.max_eval_samples )
_UpperCAmelCase : List[str] = eval_dataset.select(range(a_ ) )
with training_args.main_process_first(desc="validation dataset map pre-processing" ):
_UpperCAmelCase : Optional[int] = eval_dataset.map(
a_, batched=a_, num_proc=data_args.preprocessing_num_workers, load_from_cache_file=not data_args.overwrite_cache, )
# Data collator
_UpperCAmelCase : Tuple = (
default_data_collator
if data_args.pad_to_max_length
else DataCollatorForMultipleChoice(tokenizer=a_, pad_to_multiple_of=8 if training_args.fpaa else None )
)
# Metric
def compute_metrics(a_: Tuple ):
_UpperCAmelCase , _UpperCAmelCase : Tuple = eval_predictions
_UpperCAmelCase : Union[str, Any] = np.argmax(a_, axis=1 )
return {"accuracy": (preds == label_ids).astype(np.floataa ).mean().item()}
# Initialize our Trainer
_UpperCAmelCase : Any = Trainer(
model=a_, args=a_, train_dataset=train_dataset if training_args.do_train else None, eval_dataset=eval_dataset if training_args.do_eval else None, tokenizer=a_, data_collator=a_, compute_metrics=a_, )
# Training
if training_args.do_train:
_UpperCAmelCase : Optional[Any] = None
if training_args.resume_from_checkpoint is not None:
_UpperCAmelCase : List[Any] = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
_UpperCAmelCase : List[str] = last_checkpoint
_UpperCAmelCase : Any = trainer.train(resume_from_checkpoint=a_ )
trainer.save_model() # Saves the tokenizer too for easy upload
_UpperCAmelCase : str = train_result.metrics
_UpperCAmelCase : List[str] = (
data_args.max_train_samples if data_args.max_train_samples is not None else len(a_ )
)
_UpperCAmelCase : Union[str, Any] = min(a_, len(a_ ) )
trainer.log_metrics("train", a_ )
trainer.save_metrics("train", a_ )
trainer.save_state()
# Evaluation
if training_args.do_eval:
logger.info("*** Evaluate ***" )
_UpperCAmelCase : List[Any] = trainer.evaluate()
_UpperCAmelCase : int = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(a_ )
_UpperCAmelCase : Tuple = min(a_, len(a_ ) )
trainer.log_metrics("eval", a_ )
trainer.save_metrics("eval", a_ )
_UpperCAmelCase : int = {
"finetuned_from": model_args.model_name_or_path,
"tasks": "multiple-choice",
"dataset_tags": "swag",
"dataset_args": "regular",
"dataset": "SWAG",
"language": "en",
}
if training_args.push_to_hub:
trainer.push_to_hub(**a_ )
else:
trainer.create_model_card(**a_ )
def __UpperCAmelCase ( a_: int ):
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 17
| 0
|
'''simple docstring'''
from argparse import ArgumentParser, Namespace
from ..utils import logging
from . import BaseTransformersCLICommand
def __UpperCAmelCase ( a_: List[str] ):
return ConvertCommand(
args.model_type, args.tf_checkpoint, args.pytorch_dump_output, args.config, args.finetuning_task_name )
__a = """
transformers can only be used from the commandline to convert TensorFlow models in PyTorch, In that case, it requires
TensorFlow to be installed. Please see https://www.tensorflow.org/install/ for installation instructions.
"""
class A__ ( __A ):
"""simple docstring"""
@staticmethod
def _lowerCAmelCase ( lowerCAmelCase__ : Any ) -> Union[str, Any]:
"""simple docstring"""
_UpperCAmelCase : List[str] = parser.add_parser(
"convert" , help="CLI tool to run convert model from original author checkpoints to Transformers PyTorch checkpoints." , )
train_parser.add_argument("--model_type" , type=__lowercase , required=__lowercase , help="Model\'s type." )
train_parser.add_argument(
"--tf_checkpoint" , type=__lowercase , required=__lowercase , help="TensorFlow checkpoint path or folder." )
train_parser.add_argument(
"--pytorch_dump_output" , type=__lowercase , required=__lowercase , help="Path to the PyTorch saved model output." )
train_parser.add_argument("--config" , type=__lowercase , default="" , help="Configuration file path or folder." )
train_parser.add_argument(
"--finetuning_task_name" , type=__lowercase , default=__lowercase , help="Optional fine-tuning task name if the TF model was a finetuned model." , )
train_parser.set_defaults(func=__lowercase )
def __init__( self : List[Any] , lowerCAmelCase__ : Optional[int] , lowerCAmelCase__ : List[Any] , lowerCAmelCase__ : int , lowerCAmelCase__ : int , lowerCAmelCase__ : Optional[int] , *lowerCAmelCase__ : Any , ) -> Dict:
"""simple docstring"""
_UpperCAmelCase : int = logging.get_logger("transformers-cli/converting" )
self._logger.info(F"""Loading model {model_type}""" )
_UpperCAmelCase : Dict = model_type
_UpperCAmelCase : str = tf_checkpoint
_UpperCAmelCase : Dict = pytorch_dump_output
_UpperCAmelCase : Dict = config
_UpperCAmelCase : List[str] = finetuning_task_name
def _lowerCAmelCase ( self : Dict ) -> Dict:
"""simple docstring"""
if self._model_type == "albert":
try:
from ..models.albert.convert_albert_original_tf_checkpoint_to_pytorch import (
convert_tf_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(__lowercase )
convert_tf_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
elif self._model_type == "bert":
try:
from ..models.bert.convert_bert_original_tf_checkpoint_to_pytorch import (
convert_tf_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(__lowercase )
convert_tf_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
elif self._model_type == "funnel":
try:
from ..models.funnel.convert_funnel_original_tf_checkpoint_to_pytorch import (
convert_tf_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(__lowercase )
convert_tf_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
elif self._model_type == "t5":
try:
from ..models.ta.convert_ta_original_tf_checkpoint_to_pytorch import convert_tf_checkpoint_to_pytorch
except ImportError:
raise ImportError(__lowercase )
convert_tf_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
elif self._model_type == "gpt":
from ..models.openai.convert_openai_original_tf_checkpoint_to_pytorch import (
convert_openai_checkpoint_to_pytorch,
)
convert_openai_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
elif self._model_type == "transfo_xl":
try:
from ..models.transfo_xl.convert_transfo_xl_original_tf_checkpoint_to_pytorch import (
convert_transfo_xl_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(__lowercase )
if "ckpt" in self._tf_checkpoint.lower():
_UpperCAmelCase : Any = self._tf_checkpoint
_UpperCAmelCase : Optional[Any] = ""
else:
_UpperCAmelCase : Any = self._tf_checkpoint
_UpperCAmelCase : str = ""
convert_transfo_xl_checkpoint_to_pytorch(
__lowercase , self._config , self._pytorch_dump_output , __lowercase )
elif self._model_type == "gpt2":
try:
from ..models.gpta.convert_gpta_original_tf_checkpoint_to_pytorch import (
convert_gpta_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(__lowercase )
convert_gpta_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
elif self._model_type == "xlnet":
try:
from ..models.xlnet.convert_xlnet_original_tf_checkpoint_to_pytorch import (
convert_xlnet_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(__lowercase )
convert_xlnet_checkpoint_to_pytorch(
self._tf_checkpoint , self._config , self._pytorch_dump_output , self._finetuning_task_name )
elif self._model_type == "xlm":
from ..models.xlm.convert_xlm_original_pytorch_checkpoint_to_pytorch import (
convert_xlm_checkpoint_to_pytorch,
)
convert_xlm_checkpoint_to_pytorch(self._tf_checkpoint , self._pytorch_dump_output )
elif self._model_type == "lxmert":
from ..models.lxmert.convert_lxmert_original_tf_checkpoint_to_pytorch import (
convert_lxmert_checkpoint_to_pytorch,
)
convert_lxmert_checkpoint_to_pytorch(self._tf_checkpoint , self._pytorch_dump_output )
elif self._model_type == "rembert":
from ..models.rembert.convert_rembert_tf_checkpoint_to_pytorch import (
convert_rembert_tf_checkpoint_to_pytorch,
)
convert_rembert_tf_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
else:
raise ValueError(
"--model_type should be selected in the list [bert, gpt, gpt2, t5, transfo_xl, xlnet, xlm, lxmert]" )
| 359
|
'''simple docstring'''
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 : Any , lowerCAmelCase__ : Optional[Any] ) -> str:
"""simple docstring"""
super().__init__()
_UpperCAmelCase : List[str] = model
_UpperCAmelCase : Dict = 2
_UpperCAmelCase : Tuple = nn.Linear(self.model.config.hidden_size , self.num_labels )
def _lowerCAmelCase ( self : Tuple ) -> int:
"""simple docstring"""
pass
def __UpperCAmelCase ( a_: str, a_: str, a_: str ):
# load longformer model from model identifier
_UpperCAmelCase : int = LongformerModel.from_pretrained(a_ )
_UpperCAmelCase : Any = LightningModel(a_ )
_UpperCAmelCase : int = torch.load(a_, map_location=torch.device("cpu" ) )
lightning_model.load_state_dict(ckpt["state_dict"] )
# init longformer question answering model
_UpperCAmelCase : List[str] = 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__":
__a = 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.'
)
__a = parser.parse_args()
convert_longformer_qa_checkpoint_to_pytorch(
args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
)
| 17
| 0
|
'''simple docstring'''
from __future__ import annotations
import os
import tempfile
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import is_tensorflow_text_available, is_tf_available
from transformers.testing_utils import require_tensorflow_text, require_tf, slow
from ..test_modeling_tf_common import floats_tensor
from .test_framework_agnostic import GenerationIntegrationTestsMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
AutoTokenizer,
TFAutoModelForCausalLM,
TFAutoModelForSeqaSeqLM,
TFAutoModelForSpeechSeqaSeq,
TFAutoModelForVisionaSeq,
TFBartForConditionalGeneration,
TFLogitsProcessorList,
TFMinLengthLogitsProcessor,
tf_top_k_top_p_filtering,
)
if is_tensorflow_text_available():
import tensorflow_text as text
@require_tf
class A__ ( unittest.TestCase ):
"""simple docstring"""
def _lowerCAmelCase ( self : int ) -> str:
"""simple docstring"""
_UpperCAmelCase : str = tf.convert_to_tensor(
[
[
8.222_0991, # 3rd highest value; idx. 0
-0.562_0044,
5.2322_9752,
4.038_6393,
-6.879_8378,
-0.5478_5802,
-3.201_2153,
2.9277_7176,
1.8817_1953,
7.3534_1276, # 5th highest value; idx. 9
8.4320_7833, # 2nd highest value; idx. 10
-9.8571_1836,
-5.9620_9236,
-1.1303_9161,
-7.111_5294,
-0.836_9633,
-5.318_6408,
7.0642_7407,
0.8136_9344,
-0.8202_3817,
-5.917_9796,
0.5881_3443,
-6.9977_8438,
4.7155_1189,
-0.1877_1637,
7.4402_0759, # 4th highest value; idx. 25
9.3845_0987, # 1st highest value; idx. 26
2.1266_2941,
-9.3256_2038,
2.3565_2522,
], # cummulative prob of 5 highest values <= 0.6
[
0.5842_5518,
4.5313_9238,
-5.5751_0464,
-6.2803_0699,
-7.1952_9503,
-4.0212_2551,
1.3933_7037,
-6.0670_7057,
1.5948_0517,
-9.64_3119,
0.0390_7799,
0.6723_1762,
-8.8820_6726,
6.2711_5922, # 4th highest value; idx. 13
2.2852_0723,
4.8276_7506,
4.3042_1368,
8.827_5313, # 2nd highest value; idx. 17
5.4402_9958, # 5th highest value; idx. 18
-4.473_5794,
7.3857_9536, # 3rd highest value; idx. 20
-2.9105_1663,
2.6194_6077,
-2.567_4762,
-9.4895_9302,
-4.0292_2645,
-1.3541_6918,
9.6770_2323, # 1st highest value; idx. 27
-5.8947_8553,
1.8537_0467,
], # cummulative prob of 5 highest values <= 0.6
] , dtype=tf.floataa , )
_UpperCAmelCase : int = tf.convert_to_tensor(
[[0, 0], [0, 9], [0, 1_0], [0, 2_5], [0, 2_6], [1, 1_3], [1, 1_7], [1, 1_8], [1, 2_0], [1, 2_7]] , dtype=tf.intaa , ) # expected non filtered idx as noted above
_UpperCAmelCase : Optional[Any] = tf.convert_to_tensor(
[8.22_2099, 7.353_4126, 8.43_2078, 7.440_2075, 9.3_8451, 6.27_1159, 8.82_7531, 5.440_2995, 7.385_7956, 9.67_7023] , dtype=tf.floataa , ) # expected non filtered values as noted above
_UpperCAmelCase : Union[str, Any] = tf_top_k_top_p_filtering(snake_case__ , top_k=1_0 , top_p=0.6 , min_tokens_to_keep=4 )
_UpperCAmelCase : Optional[Any] = output[output != -float("inf" )]
_UpperCAmelCase : Any = tf.cast(
tf.where(tf.not_equal(snake_case__ , tf.constant(-float("inf" ) , dtype=tf.floataa ) ) ) , dtype=tf.intaa , )
tf.debugging.assert_near(snake_case__ , snake_case__ , rtol=1e-12 )
tf.debugging.assert_equal(snake_case__ , snake_case__ )
@require_tf
class A__ ( unittest.TestCase , A_ ):
"""simple docstring"""
if is_tf_available():
UpperCamelCase_ : str = {
"AutoModelForCausalLM": TFAutoModelForCausalLM,
"AutoModelForSpeechSeq2Seq": TFAutoModelForSpeechSeqaSeq,
"AutoModelForSeq2SeqLM": TFAutoModelForSeqaSeqLM,
"AutoModelForVision2Seq": TFAutoModelForVisionaSeq,
"LogitsProcessorList": TFLogitsProcessorList,
"MinLengthLogitsProcessor": TFMinLengthLogitsProcessor,
"create_tensor_fn": tf.convert_to_tensor,
"floats_tensor": floats_tensor,
"return_tensors": "tf",
}
@slow
def _lowerCAmelCase ( self : Optional[Any] ) -> List[str]:
"""simple docstring"""
_UpperCAmelCase : List[Any] = TFAutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2" )
_UpperCAmelCase : List[Any] = 2
_UpperCAmelCase : List[str] = 2
class A__ ( tf.Module ):
"""simple docstring"""
def __init__( self : Optional[Any] , lowerCAmelCase__ : Tuple ) -> Dict:
"""simple docstring"""
super(snake_case__ , self ).__init__()
_UpperCAmelCase : str = model
@tf.function(
input_signature=(
tf.TensorSpec((None, input_length) , tf.intaa , name="input_ids" ),
tf.TensorSpec((None, input_length) , tf.intaa , name="attention_mask" ),
) , jit_compile=snake_case__ , )
def _lowerCAmelCase ( self : Union[str, Any] , lowerCAmelCase__ : Tuple , lowerCAmelCase__ : Dict ) -> Optional[int]:
"""simple docstring"""
_UpperCAmelCase : Optional[int] = self.model.generate(
input_ids=snake_case__ , attention_mask=snake_case__ , max_new_tokens=snake_case__ , return_dict_in_generate=snake_case__ , )
return {"sequences": outputs["sequences"]}
_UpperCAmelCase : List[Any] = [[2, 0], [1_0_2, 1_0_3]]
_UpperCAmelCase : Any = [[1, 0], [1, 1]]
_UpperCAmelCase : str = DummyModel(model=snake_case__ )
with tempfile.TemporaryDirectory() as tmp_dir:
tf.saved_model.save(snake_case__ , snake_case__ , signatures={"serving_default": dummy_model.serving} )
_UpperCAmelCase : List[str] = tf.saved_model.load(snake_case__ ).signatures["serving_default"]
for batch_size in range(1 , len(snake_case__ ) + 1 ):
_UpperCAmelCase : int = {
"input_ids": tf.constant(dummy_input_ids[:batch_size] ),
"attention_mask": tf.constant(dummy_attention_masks[:batch_size] ),
}
_UpperCAmelCase : Dict = serving_func(**snake_case__ )["sequences"]
_UpperCAmelCase : Optional[int] = test_model.generate(**snake_case__ , max_new_tokens=snake_case__ )
tf.debugging.assert_equal(snake_case__ , snake_case__ )
@slow
def _lowerCAmelCase ( self : Dict ) -> List[str]:
"""simple docstring"""
_UpperCAmelCase : str = TFAutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2" )
_UpperCAmelCase : Tuple = 1
_UpperCAmelCase : Any = 2
class A__ ( tf.Module ):
"""simple docstring"""
def __init__( self : List[Any] , lowerCAmelCase__ : Optional[int] ) -> Any:
"""simple docstring"""
super(snake_case__ , self ).__init__()
_UpperCAmelCase : Dict = model
@tf.function(
input_signature=(
tf.TensorSpec((batch_size, None) , tf.intaa , name="input_ids" ),
tf.TensorSpec((batch_size, None) , tf.intaa , name="attention_mask" ),
) , jit_compile=snake_case__ , )
def _lowerCAmelCase ( self : Any , lowerCAmelCase__ : int , lowerCAmelCase__ : List[str] ) -> int:
"""simple docstring"""
_UpperCAmelCase : Union[str, Any] = self.model.generate(
input_ids=snake_case__ , attention_mask=snake_case__ , max_new_tokens=snake_case__ , return_dict_in_generate=snake_case__ , )
return {"sequences": outputs["sequences"]}
_UpperCAmelCase : List[Any] = [[2], [1_0_2, 1_0_3]]
_UpperCAmelCase : Union[str, Any] = [[1], [1, 1]]
_UpperCAmelCase : Optional[Any] = DummyModel(model=snake_case__ )
with tempfile.TemporaryDirectory() as tmp_dir:
tf.saved_model.save(snake_case__ , snake_case__ , signatures={"serving_default": dummy_model.serving} )
_UpperCAmelCase : int = tf.saved_model.load(snake_case__ ).signatures["serving_default"]
for input_row in range(len(snake_case__ ) ):
_UpperCAmelCase : List[str] = {
"input_ids": tf.constant([dummy_input_ids[input_row]] ),
"attention_mask": tf.constant([dummy_attention_masks[input_row]] ),
}
_UpperCAmelCase : str = serving_func(**snake_case__ )["sequences"]
_UpperCAmelCase : Optional[int] = test_model.generate(**snake_case__ , max_new_tokens=snake_case__ )
tf.debugging.assert_equal(snake_case__ , snake_case__ )
@slow
@require_tensorflow_text
def _lowerCAmelCase ( self : List[Any] ) -> Union[str, Any]:
"""simple docstring"""
with tempfile.TemporaryDirectory() as tmp_dir:
# file needed to load the TF tokenizer
hf_hub_download(repo_id="google/flan-t5-small" , filename="spiece.model" , local_dir=snake_case__ )
class A__ ( tf.keras.layers.Layer ):
"""simple docstring"""
def __init__( self : str ) -> Any:
"""simple docstring"""
super().__init__()
_UpperCAmelCase : List[str] = text.SentencepieceTokenizer(
model=tf.io.gfile.GFile(os.path.join(snake_case__ , "spiece.model" ) , "rb" ).read() )
_UpperCAmelCase : Optional[int] = TFAutoModelForSeqaSeqLM.from_pretrained("hf-internal-testing/tiny-random-t5" )
def _lowerCAmelCase ( self : List[str] , lowerCAmelCase__ : str , *lowerCAmelCase__ : Any , **lowerCAmelCase__ : List[str] ) -> Dict:
"""simple docstring"""
_UpperCAmelCase : str = self.tokenizer.tokenize(snake_case__ )
_UpperCAmelCase : str = text.pad_model_inputs(
snake_case__ , max_seq_length=6_4 , pad_value=self.model.config.pad_token_id )
_UpperCAmelCase : str = self.model.generate(input_ids=snake_case__ , attention_mask=snake_case__ )
return self.tokenizer.detokenize(snake_case__ )
_UpperCAmelCase : Union[str, Any] = CompleteSentenceTransformer()
_UpperCAmelCase : Any = tf.keras.layers.Input(shape=(1,) , dtype=tf.string , name="inputs" )
_UpperCAmelCase : List[Any] = complete_model(snake_case__ )
_UpperCAmelCase : Optional[Any] = tf.keras.Model(snake_case__ , snake_case__ )
keras_model.save(snake_case__ )
def _lowerCAmelCase ( self : List[Any] ) -> int:
"""simple docstring"""
_UpperCAmelCase : int = {
"do_sample": True,
"num_beams": 1,
"top_p": 0.7,
"top_k": 1_0,
"temperature": 0.7,
}
_UpperCAmelCase : List[Any] = 1_4
_UpperCAmelCase : List[Any] = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2" )
_UpperCAmelCase : str = "Hello, my dog is cute and"
_UpperCAmelCase : Union[str, Any] = tokenizer(snake_case__ , return_tensors="tf" )
_UpperCAmelCase : List[Any] = TFAutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2" )
_UpperCAmelCase : List[Any] = 6_3_8
# forces the generation to happen on CPU, to avoid GPU-related quirks
with tf.device(":/CPU:0" ):
tf.random.set_seed(0 )
_UpperCAmelCase : int = model.generate(**snake_case__ , eos_token_id=snake_case__ , **snake_case__ )
self.assertTrue(expectation == len(generated_tokens[0] ) )
_UpperCAmelCase : Tuple = [6_3_8, 1_9_8]
with tf.device(":/CPU:0" ):
tf.random.set_seed(0 )
_UpperCAmelCase : Union[str, Any] = model.generate(**snake_case__ , eos_token_id=snake_case__ , **snake_case__ )
self.assertTrue(expectation == len(generated_tokens[0] ) )
def _lowerCAmelCase ( self : List[str] ) -> List[Any]:
"""simple docstring"""
_UpperCAmelCase : Dict = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-bart" )
_UpperCAmelCase : Tuple = "Hugging Face is a technology company based in New York and Paris."
_UpperCAmelCase : int = bart_tokenizer(snake_case__ , return_tensors="tf" ).input_ids
_UpperCAmelCase : Optional[int] = TFBartForConditionalGeneration.from_pretrained("hf-internal-testing/tiny-random-bart" )
_UpperCAmelCase : str = bart_model.generate(snake_case__ ).numpy()
class A__ ( A_ ):
"""simple docstring"""
def _lowerCAmelCase ( self : Dict , lowerCAmelCase__ : Optional[Any] , lowerCAmelCase__ : int=None , **lowerCAmelCase__ : int ) -> Dict:
"""simple docstring"""
return super().call(snake_case__ , **snake_case__ )
_UpperCAmelCase : str = FakeBart.from_pretrained("hf-internal-testing/tiny-random-bart" )
_UpperCAmelCase : Tuple = bart_model.generate(snake_case__ , foo="bar" ).numpy()
self.assertTrue(np.array_equal(snake_case__ , snake_case__ ) )
class A__ ( bart_model.model.encoder.__class__ ):
"""simple docstring"""
def _lowerCAmelCase ( self : Optional[int] , lowerCAmelCase__ : Tuple , **lowerCAmelCase__ : Optional[Any] ) -> List[Any]:
"""simple docstring"""
return super().call(snake_case__ , **snake_case__ )
_UpperCAmelCase : Tuple = FakeEncoder(bart_model.config , bart_model.model.shared )
_UpperCAmelCase : Tuple = fake_encoder
# Normal generation still works (the output will be different because the encoder weights are different)
_UpperCAmelCase : Tuple = bart_model.generate(snake_case__ ).numpy()
with self.assertRaises(snake_case__ ):
# FakeEncoder.call() accepts **kwargs -> no filtering -> value error due to unexpected input "foo"
bart_model.generate(snake_case__ , foo="bar" )
| 360
|
'''simple docstring'''
from importlib import import_module
from .logging import get_logger
__a = get_logger(__name__)
class A__ :
"""simple docstring"""
def __init__( self : List[str] , lowerCAmelCase__ : List[Any] , lowerCAmelCase__ : Optional[Any]=None ) -> Optional[int]:
"""simple docstring"""
_UpperCAmelCase : Any = attrs or []
if module is not None:
for key in module.__dict__:
if key in attrs or not key.startswith("__" ):
setattr(self , lowerCAmelCase__ , getattr(lowerCAmelCase__ , lowerCAmelCase__ ) )
_UpperCAmelCase : int = module._original_module if isinstance(lowerCAmelCase__ , _PatchedModuleObj ) else module
class A__ :
"""simple docstring"""
UpperCamelCase_ : Union[str, Any] = []
def __init__( self : int , lowerCAmelCase__ : List[str] , lowerCAmelCase__ : str , lowerCAmelCase__ : List[str] , lowerCAmelCase__ : Optional[int]=None ) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase : List[Any] = obj
_UpperCAmelCase : int = target
_UpperCAmelCase : Optional[int] = new
_UpperCAmelCase : Any = target.split("." )[0]
_UpperCAmelCase : Optional[int] = {}
_UpperCAmelCase : Dict = attrs or []
def __enter__( self : List[str] ) -> int:
"""simple docstring"""
*_UpperCAmelCase , _UpperCAmelCase : List[str] = self.target.split("." )
# Patch modules:
# it's used to patch attributes of submodules like "os.path.join";
# in this case we need to patch "os" and "os.path"
for i in range(len(lowerCAmelCase__ ) ):
try:
_UpperCAmelCase : int = import_module(".".join(submodules[: i + 1] ) )
except ModuleNotFoundError:
continue
# We iterate over all the globals in self.obj in case we find "os" or "os.path"
for attr in self.obj.__dir__():
_UpperCAmelCase : List[Any] = getattr(self.obj , lowerCAmelCase__ )
# We don't check for the name of the global, but rather if its value *is* "os" or "os.path".
# This allows to patch renamed modules like "from os import path as ospath".
if obj_attr is submodule or (
(isinstance(lowerCAmelCase__ , _PatchedModuleObj ) and obj_attr._original_module is submodule)
):
_UpperCAmelCase : Tuple = obj_attr
# patch at top level
setattr(self.obj , lowerCAmelCase__ , _PatchedModuleObj(lowerCAmelCase__ , attrs=self.attrs ) )
_UpperCAmelCase : List[Any] = getattr(self.obj , lowerCAmelCase__ )
# construct lower levels patches
for key in submodules[i + 1 :]:
setattr(lowerCAmelCase__ , lowerCAmelCase__ , _PatchedModuleObj(getattr(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ) , attrs=self.attrs ) )
_UpperCAmelCase : Any = getattr(lowerCAmelCase__ , lowerCAmelCase__ )
# finally set the target attribute
setattr(lowerCAmelCase__ , lowerCAmelCase__ , self.new )
# Patch attribute itself:
# it's used for builtins like "open",
# and also to patch "os.path.join" we may also need to patch "join"
# itself if it was imported as "from os.path import join".
if submodules: # if it's an attribute of a submodule like "os.path.join"
try:
_UpperCAmelCase : Dict = getattr(import_module(".".join(lowerCAmelCase__ ) ) , lowerCAmelCase__ )
except (AttributeError, ModuleNotFoundError):
return
# We iterate over all the globals in self.obj in case we find "os.path.join"
for attr in self.obj.__dir__():
# We don't check for the name of the global, but rather if its value *is* "os.path.join".
# This allows to patch renamed attributes like "from os.path import join as pjoin".
if getattr(self.obj , lowerCAmelCase__ ) is attr_value:
_UpperCAmelCase : Optional[Any] = getattr(self.obj , lowerCAmelCase__ )
setattr(self.obj , lowerCAmelCase__ , self.new )
elif target_attr in globals()["__builtins__"]: # if it'a s builtin like "open"
_UpperCAmelCase : Dict = globals()["__builtins__"][target_attr]
setattr(self.obj , lowerCAmelCase__ , self.new )
else:
raise RuntimeError(F"""Tried to patch attribute {target_attr} instead of a submodule.""" )
def __exit__( self : Optional[int] , *lowerCAmelCase__ : List[str] ) -> Union[str, Any]:
"""simple docstring"""
for attr in list(self.original ):
setattr(self.obj , lowerCAmelCase__ , self.original.pop(lowerCAmelCase__ ) )
def _lowerCAmelCase ( self : Union[str, Any] ) -> Optional[Any]:
"""simple docstring"""
self.__enter__()
self._active_patches.append(self )
def _lowerCAmelCase ( self : Optional[int] ) -> Tuple:
"""simple docstring"""
try:
self._active_patches.remove(self )
except ValueError:
# If the patch hasn't been started this will fail
return None
return self.__exit__()
| 17
| 0
|
'''simple docstring'''
import argparse
import json
import math
import os
import time
import traceback
import zipfile
from collections import Counter
import requests
def __UpperCAmelCase ( a_: int, a_: Dict=None ):
_UpperCAmelCase : List[Any] = None
if token is not None:
_UpperCAmelCase : Dict = {"Accept": "application/vnd.github+json", "Authorization": f"""Bearer {token}"""}
_UpperCAmelCase : Dict = f"""https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100"""
_UpperCAmelCase : Tuple = requests.get(lowerCAmelCase__, headers=lowerCAmelCase__ ).json()
_UpperCAmelCase : Union[str, Any] = {}
try:
job_links.update({job["name"]: job["html_url"] for job in result["jobs"]} )
_UpperCAmelCase : Optional[Any] = math.ceil((result["total_count"] - 100) / 100 )
for i in range(lowerCAmelCase__ ):
_UpperCAmelCase : Optional[Any] = requests.get(url + f"""&page={i + 2}""", headers=lowerCAmelCase__ ).json()
job_links.update({job["name"]: job["html_url"] for job in result["jobs"]} )
return job_links
except Exception:
print(f"""Unknown error, could not fetch links:\n{traceback.format_exc()}""" )
return {}
def __UpperCAmelCase ( a_: List[str], a_: Optional[int]=None ):
_UpperCAmelCase : List[Any] = None
if token is not None:
_UpperCAmelCase : Dict = {"Accept": "application/vnd.github+json", "Authorization": f"""Bearer {token}"""}
_UpperCAmelCase : int = f"""https://api.github.com/repos/huggingface/transformers/actions/runs/{worflow_run_id}/artifacts?per_page=100"""
_UpperCAmelCase : List[str] = requests.get(lowerCAmelCase__, headers=lowerCAmelCase__ ).json()
_UpperCAmelCase : Dict = {}
try:
artifacts.update({artifact["name"]: artifact["archive_download_url"] for artifact in result["artifacts"]} )
_UpperCAmelCase : Any = math.ceil((result["total_count"] - 100) / 100 )
for i in range(lowerCAmelCase__ ):
_UpperCAmelCase : List[Any] = requests.get(url + f"""&page={i + 2}""", headers=lowerCAmelCase__ ).json()
artifacts.update({artifact["name"]: artifact["archive_download_url"] for artifact in result["artifacts"]} )
return artifacts
except Exception:
print(f"""Unknown error, could not fetch links:\n{traceback.format_exc()}""" )
return {}
def __UpperCAmelCase ( a_: Dict, a_: List[Any], a_: Any, a_: Optional[int] ):
_UpperCAmelCase : Tuple = None
if token is not None:
_UpperCAmelCase : List[str] = {"Accept": "application/vnd.github+json", "Authorization": f"""Bearer {token}"""}
_UpperCAmelCase : Tuple = requests.get(lowerCAmelCase__, headers=lowerCAmelCase__, allow_redirects=lowerCAmelCase__ )
_UpperCAmelCase : Union[str, Any] = result.headers["Location"]
_UpperCAmelCase : Any = requests.get(lowerCAmelCase__, allow_redirects=lowerCAmelCase__ )
_UpperCAmelCase : Dict = os.path.join(lowerCAmelCase__, f"""{artifact_name}.zip""" )
with open(lowerCAmelCase__, "wb" ) as fp:
fp.write(response.content )
def __UpperCAmelCase ( a_: Union[str, Any], a_: Dict=None ):
_UpperCAmelCase : Optional[int] = []
_UpperCAmelCase : Dict = []
_UpperCAmelCase : Dict = None
with zipfile.ZipFile(lowerCAmelCase__ ) as z:
for filename in z.namelist():
if not os.path.isdir(lowerCAmelCase__ ):
# read the file
if filename in ["failures_line.txt", "summary_short.txt", "job_name.txt"]:
with z.open(lowerCAmelCase__ ) as f:
for line in f:
_UpperCAmelCase : Tuple = line.decode("UTF-8" ).strip()
if filename == "failures_line.txt":
try:
# `error_line` is the place where `error` occurs
_UpperCAmelCase : str = line[: line.index(": " )]
_UpperCAmelCase : Tuple = line[line.index(": " ) + len(": " ) :]
errors.append([error_line, error] )
except Exception:
# skip un-related lines
pass
elif filename == "summary_short.txt" and line.startswith("FAILED " ):
# `test` is the test method that failed
_UpperCAmelCase : Optional[int] = line[len("FAILED " ) :]
failed_tests.append(lowerCAmelCase__ )
elif filename == "job_name.txt":
_UpperCAmelCase : Union[str, Any] = line
if len(lowerCAmelCase__ ) != len(lowerCAmelCase__ ):
raise ValueError(
f"""`errors` and `failed_tests` should have the same number of elements. Got {len(lowerCAmelCase__ )} for `errors` """
f"""and {len(lowerCAmelCase__ )} for `failed_tests` instead. The test reports in {artifact_zip_path} have some"""
" problem." )
_UpperCAmelCase : List[Any] = None
if job_name and job_links:
_UpperCAmelCase : Union[str, Any] = job_links.get(lowerCAmelCase__, lowerCAmelCase__ )
# A list with elements of the form (line of error, error, failed test)
_UpperCAmelCase : List[Any] = [x + [y] + [job_link] for x, y in zip(lowerCAmelCase__, lowerCAmelCase__ )]
return result
def __UpperCAmelCase ( a_: str, a_: Optional[int]=None ):
_UpperCAmelCase : List[str] = []
_UpperCAmelCase : str = [os.path.join(lowerCAmelCase__, lowerCAmelCase__ ) for p in os.listdir(lowerCAmelCase__ ) if p.endswith(".zip" )]
for p in paths:
errors.extend(get_errors_from_single_artifact(lowerCAmelCase__, job_links=lowerCAmelCase__ ) )
return errors
def __UpperCAmelCase ( a_: Any, a_: Union[str, Any]=None ):
_UpperCAmelCase : str = Counter()
counter.update([x[1] for x in logs] )
_UpperCAmelCase : Dict = counter.most_common()
_UpperCAmelCase : str = {}
for error, count in counts:
if error_filter is None or error not in error_filter:
_UpperCAmelCase : Any = {"count": count, "failed_tests": [(x[2], x[0]) for x in logs if x[1] == error]}
_UpperCAmelCase : Optional[Any] = dict(sorted(r.items(), key=lambda a_ : item[1]["count"], reverse=lowerCAmelCase__ ) )
return r
def __UpperCAmelCase ( a_: Optional[Any] ):
_UpperCAmelCase : List[Any] = test.split("::" )[0]
if test.startswith("tests/models/" ):
_UpperCAmelCase : Any = test.split("/" )[2]
else:
_UpperCAmelCase : int = None
return test
def __UpperCAmelCase ( a_: int, a_: List[Any]=None ):
_UpperCAmelCase : Union[str, Any] = [(x[0], x[1], get_model(x[2] )) for x in logs]
_UpperCAmelCase : int = [x for x in logs if x[2] is not None]
_UpperCAmelCase : Union[str, Any] = {x[2] for x in logs}
_UpperCAmelCase : List[str] = {}
for test in tests:
_UpperCAmelCase : Dict = Counter()
# count by errors in `test`
counter.update([x[1] for x in logs if x[2] == test] )
_UpperCAmelCase : int = counter.most_common()
_UpperCAmelCase : Optional[int] = {error: count for error, count in counts if (error_filter is None or error not in error_filter)}
_UpperCAmelCase : int = sum(error_counts.values() )
if n_errors > 0:
_UpperCAmelCase : Optional[Any] = {"count": n_errors, "errors": error_counts}
_UpperCAmelCase : Optional[int] = dict(sorted(r.items(), key=lambda a_ : item[1]["count"], reverse=lowerCAmelCase__ ) )
return r
def __UpperCAmelCase ( a_: Union[str, Any] ):
_UpperCAmelCase : Optional[Any] = "| no. | error | status |"
_UpperCAmelCase : Optional[Any] = "|-:|:-|:-|"
_UpperCAmelCase : Optional[int] = [header, sep]
for error in reduced_by_error:
_UpperCAmelCase : Any = reduced_by_error[error]["count"]
_UpperCAmelCase : int = f"""| {count} | {error[:100]} | |"""
lines.append(lowerCAmelCase__ )
return "\n".join(lowerCAmelCase__ )
def __UpperCAmelCase ( a_: int ):
_UpperCAmelCase : Dict = "| model | no. of errors | major error | count |"
_UpperCAmelCase : Optional[int] = "|-:|-:|-:|-:|"
_UpperCAmelCase : List[str] = [header, sep]
for model in reduced_by_model:
_UpperCAmelCase : List[Any] = reduced_by_model[model]["count"]
_UpperCAmelCase , _UpperCAmelCase : Union[str, Any] = list(reduced_by_model[model]["errors"].items() )[0]
_UpperCAmelCase : List[str] = f"""| {model} | {count} | {error[:60]} | {_count} |"""
lines.append(lowerCAmelCase__ )
return "\n".join(lowerCAmelCase__ )
if __name__ == "__main__":
__a = argparse.ArgumentParser()
# Required parameters
parser.add_argument('--workflow_run_id', type=str, required=True, help='A GitHub Actions workflow run id.')
parser.add_argument(
'--output_dir',
type=str,
required=True,
help='Where to store the downloaded artifacts and other result files.',
)
parser.add_argument('--token', default=None, type=str, help='A token that has actions:read permission.')
__a = parser.parse_args()
os.makedirs(args.output_dir, exist_ok=True)
__a = get_job_links(args.workflow_run_id, token=args.token)
__a = {}
# To deal with `workflow_call` event, where a job name is the combination of the job names in the caller and callee.
# For example, `PyTorch 1.11 / Model tests (models/albert, single-gpu)`.
if _job_links:
for k, v in _job_links.items():
# This is how GitHub actions combine job names.
if " / " in k:
__a = k.find(' / ')
__a = k[index + len(' / ') :]
__a = v
with open(os.path.join(args.output_dir, 'job_links.json'), 'w', encoding='UTF-8') as fp:
json.dump(job_links, fp, ensure_ascii=False, indent=4)
__a = get_artifacts_links(args.workflow_run_id, token=args.token)
with open(os.path.join(args.output_dir, 'artifacts.json'), 'w', encoding='UTF-8') as fp:
json.dump(artifacts, fp, ensure_ascii=False, indent=4)
for idx, (name, url) in enumerate(artifacts.items()):
download_artifact(name, url, args.output_dir, args.token)
# Be gentle to GitHub
time.sleep(1)
__a = get_all_errors(args.output_dir, job_links=job_links)
# `e[1]` is the error
__a = Counter()
counter.update([e[1] for e in errors])
# print the top 30 most common test errors
__a = counter.most_common(30)
for item in most_common:
print(item)
with open(os.path.join(args.output_dir, 'errors.json'), 'w', encoding='UTF-8') as fp:
json.dump(errors, fp, ensure_ascii=False, indent=4)
__a = reduce_by_error(errors)
__a = reduce_by_model(errors)
__a = make_github_table(reduced_by_error)
__a = make_github_table_per_model(reduced_by_model)
with open(os.path.join(args.output_dir, 'reduced_by_error.txt'), 'w', encoding='UTF-8') as fp:
fp.write(sa)
with open(os.path.join(args.output_dir, 'reduced_by_model.txt'), 'w', encoding='UTF-8') as fp:
fp.write(sa)
| 361
|
'''simple docstring'''
import itertools
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union
import pandas as pd
import pyarrow as pa
import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast
from datasets.utils.py_utils import Literal
__a = datasets.utils.logging.get_logger(__name__)
__a = ['names', 'prefix']
__a = ['warn_bad_lines', 'error_bad_lines', 'mangle_dupe_cols']
__a = ['encoding_errors', 'on_bad_lines']
__a = ['date_format']
@dataclass
class A__ ( datasets.BuilderConfig ):
"""simple docstring"""
UpperCamelCase_ : str = ","
UpperCamelCase_ : Optional[str] = None
UpperCamelCase_ : Optional[Union[int, List[int], str]] = "infer"
UpperCamelCase_ : Optional[List[str]] = None
UpperCamelCase_ : Optional[List[str]] = None
UpperCamelCase_ : Optional[Union[int, str, List[int], List[str]]] = None
UpperCamelCase_ : Optional[Union[List[int], List[str]]] = None
UpperCamelCase_ : Optional[str] = None
UpperCamelCase_ : bool = True
UpperCamelCase_ : Optional[Literal["c", "python", "pyarrow"]] = None
UpperCamelCase_ : Dict[Union[int, str], Callable[[Any], Any]] = None
UpperCamelCase_ : Optional[list] = None
UpperCamelCase_ : Optional[list] = None
UpperCamelCase_ : bool = False
UpperCamelCase_ : Optional[Union[int, List[int]]] = None
UpperCamelCase_ : Optional[int] = None
UpperCamelCase_ : Optional[Union[str, List[str]]] = None
UpperCamelCase_ : bool = True
UpperCamelCase_ : bool = True
UpperCamelCase_ : bool = False
UpperCamelCase_ : bool = True
UpperCamelCase_ : Optional[str] = None
UpperCamelCase_ : str = "."
UpperCamelCase_ : Optional[str] = None
UpperCamelCase_ : str = '"'
UpperCamelCase_ : int = 0
UpperCamelCase_ : Optional[str] = None
UpperCamelCase_ : Optional[str] = None
UpperCamelCase_ : Optional[str] = None
UpperCamelCase_ : Optional[str] = None
UpperCamelCase_ : bool = True
UpperCamelCase_ : bool = True
UpperCamelCase_ : int = 0
UpperCamelCase_ : bool = True
UpperCamelCase_ : bool = False
UpperCamelCase_ : Optional[str] = None
UpperCamelCase_ : int = 1_00_00
UpperCamelCase_ : Optional[datasets.Features] = None
UpperCamelCase_ : Optional[str] = "strict"
UpperCamelCase_ : Literal["error", "warn", "skip"] = "error"
UpperCamelCase_ : Optional[str] = None
def _lowerCAmelCase ( self : str ) -> Tuple:
"""simple docstring"""
if self.delimiter is not None:
_UpperCAmelCase : Any = self.delimiter
if self.column_names is not None:
_UpperCAmelCase : List[Any] = self.column_names
@property
def _lowerCAmelCase ( self : Optional[int] ) -> Optional[int]:
"""simple docstring"""
_UpperCAmelCase : Dict = {
"sep": self.sep,
"header": self.header,
"names": self.names,
"index_col": self.index_col,
"usecols": self.usecols,
"prefix": self.prefix,
"mangle_dupe_cols": self.mangle_dupe_cols,
"engine": self.engine,
"converters": self.converters,
"true_values": self.true_values,
"false_values": self.false_values,
"skipinitialspace": self.skipinitialspace,
"skiprows": self.skiprows,
"nrows": self.nrows,
"na_values": self.na_values,
"keep_default_na": self.keep_default_na,
"na_filter": self.na_filter,
"verbose": self.verbose,
"skip_blank_lines": self.skip_blank_lines,
"thousands": self.thousands,
"decimal": self.decimal,
"lineterminator": self.lineterminator,
"quotechar": self.quotechar,
"quoting": self.quoting,
"escapechar": self.escapechar,
"comment": self.comment,
"encoding": self.encoding,
"dialect": self.dialect,
"error_bad_lines": self.error_bad_lines,
"warn_bad_lines": self.warn_bad_lines,
"skipfooter": self.skipfooter,
"doublequote": self.doublequote,
"memory_map": self.memory_map,
"float_precision": self.float_precision,
"chunksize": self.chunksize,
"encoding_errors": self.encoding_errors,
"on_bad_lines": self.on_bad_lines,
"date_format": self.date_format,
}
# some kwargs must not be passed if they don't have a default value
# some others are deprecated and we can also not pass them if they are the default value
for pd_read_csv_parameter in _PANDAS_READ_CSV_NO_DEFAULT_PARAMETERS + _PANDAS_READ_CSV_DEPRECATED_PARAMETERS:
if pd_read_csv_kwargs[pd_read_csv_parameter] == getattr(CsvConfig() , lowerCAmelCase__ ):
del pd_read_csv_kwargs[pd_read_csv_parameter]
# Remove 2.0 new arguments
if not (datasets.config.PANDAS_VERSION.major >= 2):
for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_2_0_0_PARAMETERS:
del pd_read_csv_kwargs[pd_read_csv_parameter]
# Remove 1.3 new arguments
if not (datasets.config.PANDAS_VERSION.major >= 1 and datasets.config.PANDAS_VERSION.minor >= 3):
for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_1_3_0_PARAMETERS:
del pd_read_csv_kwargs[pd_read_csv_parameter]
return pd_read_csv_kwargs
class A__ ( datasets.ArrowBasedBuilder ):
"""simple docstring"""
UpperCamelCase_ : int = CsvConfig
def _lowerCAmelCase ( self : Optional[Any] ) -> Optional[int]:
"""simple docstring"""
return datasets.DatasetInfo(features=self.config.features )
def _lowerCAmelCase ( self : Tuple , lowerCAmelCase__ : str ) -> List[str]:
"""simple docstring"""
if not self.config.data_files:
raise ValueError(F"""At least one data file must be specified, but got data_files={self.config.data_files}""" )
_UpperCAmelCase : List[str] = dl_manager.download_and_extract(self.config.data_files )
if isinstance(lowerCAmelCase__ , (str, list, tuple) ):
_UpperCAmelCase : int = data_files
if isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
_UpperCAmelCase : Any = [files]
_UpperCAmelCase : List[Any] = [dl_manager.iter_files(lowerCAmelCase__ ) for file in files]
return [datasets.SplitGenerator(name=datasets.Split.TRAIN , gen_kwargs={"files": files} )]
_UpperCAmelCase : Optional[Any] = []
for split_name, files in data_files.items():
if isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
_UpperCAmelCase : str = [files]
_UpperCAmelCase : Any = [dl_manager.iter_files(lowerCAmelCase__ ) for file in files]
splits.append(datasets.SplitGenerator(name=lowerCAmelCase__ , gen_kwargs={"files": files} ) )
return splits
def _lowerCAmelCase ( self : List[Any] , lowerCAmelCase__ : pa.Table ) -> pa.Table:
"""simple docstring"""
if self.config.features is not None:
_UpperCAmelCase : Tuple = self.config.features.arrow_schema
if all(not require_storage_cast(lowerCAmelCase__ ) for feature in self.config.features.values() ):
# cheaper cast
_UpperCAmelCase : Any = pa.Table.from_arrays([pa_table[field.name] for field in schema] , schema=lowerCAmelCase__ )
else:
# more expensive cast; allows str <-> int/float or str to Audio for example
_UpperCAmelCase : int = table_cast(lowerCAmelCase__ , lowerCAmelCase__ )
return pa_table
def _lowerCAmelCase ( self : Dict , lowerCAmelCase__ : Dict ) -> Dict:
"""simple docstring"""
_UpperCAmelCase : int = self.config.features.arrow_schema if self.config.features else None
# dtype allows reading an int column as str
_UpperCAmelCase : Optional[Any] = (
{
name: dtype.to_pandas_dtype() if not require_storage_cast(lowerCAmelCase__ ) else object
for name, dtype, feature in zip(schema.names , schema.types , self.config.features.values() )
}
if schema is not None
else None
)
for file_idx, file in enumerate(itertools.chain.from_iterable(lowerCAmelCase__ ) ):
_UpperCAmelCase : Optional[Any] = pd.read_csv(lowerCAmelCase__ , iterator=lowerCAmelCase__ , dtype=lowerCAmelCase__ , **self.config.pd_read_csv_kwargs )
try:
for batch_idx, df in enumerate(lowerCAmelCase__ ):
_UpperCAmelCase : Optional[int] = pa.Table.from_pandas(lowerCAmelCase__ )
# Uncomment for debugging (will print the Arrow table size and elements)
# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
yield (file_idx, batch_idx), self._cast_table(lowerCAmelCase__ )
except ValueError as e:
logger.error(F"""Failed to read file '{file}' with error {type(lowerCAmelCase__ )}: {e}""" )
raise
| 17
| 0
|
'''simple docstring'''
import os
import socket
from contextlib import contextmanager
import torch
from ..commands.config.default import write_basic_config # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
from .transformer_engine import convert_model
from .versions import is_torch_version
if is_deepspeed_available():
from deepspeed import DeepSpeedEngine
if is_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
def __UpperCAmelCase ( a_: str ):
if is_torch_version("<", "2.0.0" ) or not hasattr(lowercase__, "_dynamo" ):
return False
return isinstance(lowercase__, torch._dynamo.eval_frame.OptimizedModule )
def __UpperCAmelCase ( a_: Optional[Any], a_: Tuple = True ):
_UpperCAmelCase : Optional[int] = (torch.nn.parallel.DistributedDataParallel, torch.nn.DataParallel)
_UpperCAmelCase : Optional[int] = is_compiled_module(lowercase__ )
if is_compiled:
_UpperCAmelCase : Optional[int] = model
_UpperCAmelCase : int = model._orig_mod
if is_deepspeed_available():
options += (DeepSpeedEngine,)
while isinstance(lowercase__, lowercase__ ):
_UpperCAmelCase : Union[str, Any] = model.module
if not keep_fpaa_wrapper:
_UpperCAmelCase : Any = getattr(lowercase__, "forward" )
_UpperCAmelCase : Tuple = model.__dict__.pop("_original_forward", lowercase__ )
if original_forward is not None:
while hasattr(lowercase__, "__wrapped__" ):
_UpperCAmelCase : List[Any] = forward.__wrapped__
if forward == original_forward:
break
_UpperCAmelCase : Optional[Any] = forward
if getattr(lowercase__, "_converted_to_transformer_engine", lowercase__ ):
convert_model(lowercase__, to_transformer_engine=lowercase__ )
if is_compiled:
_UpperCAmelCase : List[Any] = model
_UpperCAmelCase : str = compiled_model
return model
def __UpperCAmelCase ( ):
PartialState().wait_for_everyone()
def __UpperCAmelCase ( a_: List[Any], a_: Optional[Any] ):
if PartialState().distributed_type == DistributedType.TPU:
xm.save(lowercase__, lowercase__ )
elif PartialState().local_process_index == 0:
torch.save(lowercase__, lowercase__ )
@contextmanager
def __UpperCAmelCase ( **a_: str ):
for key, value in kwargs.items():
_UpperCAmelCase : List[str] = str(lowercase__ )
yield
for key in kwargs:
if key.upper() in os.environ:
del os.environ[key.upper()]
def __UpperCAmelCase ( a_: Union[str, Any] ):
if not hasattr(lowercase__, "__qualname__" ) and not hasattr(lowercase__, "__name__" ):
_UpperCAmelCase : List[Any] = getattr(lowercase__, "__class__", lowercase__ )
if hasattr(lowercase__, "__qualname__" ):
return obj.__qualname__
if hasattr(lowercase__, "__name__" ):
return obj.__name__
return str(lowercase__ )
def __UpperCAmelCase ( a_: Tuple, a_: Union[str, Any] ):
for key, value in source.items():
if isinstance(lowercase__, lowercase__ ):
_UpperCAmelCase : List[Any] = destination.setdefault(lowercase__, {} )
merge_dicts(lowercase__, lowercase__ )
else:
_UpperCAmelCase : Union[str, Any] = value
return destination
def __UpperCAmelCase ( a_: Union[str, Any] = None ):
if port is None:
_UpperCAmelCase : List[str] = 29_500
with socket.socket(socket.AF_INET, socket.SOCK_STREAM ) as s:
return s.connect_ex(("localhost", port) ) == 0
| 362
|
'''simple docstring'''
from __future__ import annotations
def __UpperCAmelCase ( a_: list[int] ):
if not nums:
return 0
_UpperCAmelCase : int = nums[0]
_UpperCAmelCase : Dict = 0
for num in nums[1:]:
_UpperCAmelCase , _UpperCAmelCase : Any = (
max_excluding + num,
max(a_, a_ ),
)
return max(a_, a_ )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 17
| 0
|
'''simple docstring'''
import shutil
import tempfile
import unittest
from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio
from .test_feature_extraction_clap import floats_list
@require_torchaudio
@require_sentencepiece
class A__ ( unittest.TestCase ):
"""simple docstring"""
def _lowerCAmelCase ( self : Dict ) -> List[Any]:
"""simple docstring"""
_UpperCAmelCase : Union[str, Any] = """laion/clap-htsat-unfused"""
_UpperCAmelCase : Dict = tempfile.mkdtemp()
def _lowerCAmelCase ( self : Union[str, Any] , **lowerCAmelCase__ : List[str] ) -> str:
"""simple docstring"""
return RobertaTokenizer.from_pretrained(self.checkpoint , **lowerCAmelCase__ )
def _lowerCAmelCase ( self : List[str] , **lowerCAmelCase__ : List[Any] ) -> Optional[int]:
"""simple docstring"""
return ClapFeatureExtractor.from_pretrained(self.checkpoint , **lowerCAmelCase__ )
def _lowerCAmelCase ( self : Optional[Any] ) -> int:
"""simple docstring"""
shutil.rmtree(self.tmpdirname )
def _lowerCAmelCase ( self : Tuple ) -> Dict:
"""simple docstring"""
_UpperCAmelCase : List[str] = self.get_tokenizer()
_UpperCAmelCase : Tuple = self.get_feature_extractor()
_UpperCAmelCase : Any = ClapProcessor(tokenizer=lowerCAmelCase__ , feature_extractor=lowerCAmelCase__ )
processor.save_pretrained(self.tmpdirname )
_UpperCAmelCase : Optional[Any] = ClapProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer.get_vocab() )
self.assertIsInstance(processor.tokenizer , lowerCAmelCase__ )
self.assertEqual(processor.feature_extractor.to_json_string() , feature_extractor.to_json_string() )
self.assertIsInstance(processor.feature_extractor , lowerCAmelCase__ )
def _lowerCAmelCase ( self : Optional[Any] ) -> Dict:
"""simple docstring"""
_UpperCAmelCase : Optional[int] = ClapProcessor(tokenizer=self.get_tokenizer() , feature_extractor=self.get_feature_extractor() )
processor.save_pretrained(self.tmpdirname )
_UpperCAmelCase : List[Any] = self.get_tokenizer(bos_token="(BOS)" , eos_token="(EOS)" )
_UpperCAmelCase : List[Any] = self.get_feature_extractor(do_normalize=lowerCAmelCase__ , padding_value=1.0 )
_UpperCAmelCase : Any = ClapProcessor.from_pretrained(
self.tmpdirname , bos_token="(BOS)" , eos_token="(EOS)" , do_normalize=lowerCAmelCase__ , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , lowerCAmelCase__ )
self.assertEqual(processor.feature_extractor.to_json_string() , feature_extractor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.feature_extractor , lowerCAmelCase__ )
def _lowerCAmelCase ( self : Union[str, Any] ) -> Union[str, Any]:
"""simple docstring"""
_UpperCAmelCase : Any = self.get_feature_extractor()
_UpperCAmelCase : int = self.get_tokenizer()
_UpperCAmelCase : List[Any] = ClapProcessor(tokenizer=lowerCAmelCase__ , feature_extractor=lowerCAmelCase__ )
_UpperCAmelCase : Tuple = floats_list((3, 1_0_0_0) )
_UpperCAmelCase : Union[str, Any] = feature_extractor(lowerCAmelCase__ , return_tensors="np" )
_UpperCAmelCase : str = processor(audios=lowerCAmelCase__ , return_tensors="np" )
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1e-2 )
def _lowerCAmelCase ( self : Union[str, Any] ) -> Union[str, Any]:
"""simple docstring"""
_UpperCAmelCase : str = self.get_feature_extractor()
_UpperCAmelCase : Any = self.get_tokenizer()
_UpperCAmelCase : List[Any] = ClapProcessor(tokenizer=lowerCAmelCase__ , feature_extractor=lowerCAmelCase__ )
_UpperCAmelCase : int = """This is a test string"""
_UpperCAmelCase : List[Any] = processor(text=lowerCAmelCase__ )
_UpperCAmelCase : List[Any] = tokenizer(lowerCAmelCase__ )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def _lowerCAmelCase ( self : List[Any] ) -> Tuple:
"""simple docstring"""
_UpperCAmelCase : str = self.get_feature_extractor()
_UpperCAmelCase : Optional[Any] = self.get_tokenizer()
_UpperCAmelCase : List[Any] = ClapProcessor(tokenizer=lowerCAmelCase__ , feature_extractor=lowerCAmelCase__ )
_UpperCAmelCase : str = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
_UpperCAmelCase : Optional[int] = processor.batch_decode(lowerCAmelCase__ )
_UpperCAmelCase : int = tokenizer.batch_decode(lowerCAmelCase__ )
self.assertListEqual(lowerCAmelCase__ , lowerCAmelCase__ )
def _lowerCAmelCase ( self : Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
_UpperCAmelCase : Dict = self.get_feature_extractor()
_UpperCAmelCase : List[Any] = self.get_tokenizer()
_UpperCAmelCase : Dict = ClapProcessor(tokenizer=lowerCAmelCase__ , feature_extractor=lowerCAmelCase__ )
self.assertListEqual(
processor.model_input_names[2:] , feature_extractor.model_input_names , msg="`processor` and `feature_extractor` model input names do not match" , )
| 363
|
'''simple docstring'''
import argparse
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
__a = logging.get_logger(__name__)
def __UpperCAmelCase ( a_: List[str] ):
_UpperCAmelCase : Union[str, Any] = OrderedDict()
for key, value in state_dict.items():
if key.startswith("module.encoder" ):
_UpperCAmelCase : Optional[int] = key.replace("module.encoder", "glpn.encoder" )
if key.startswith("module.decoder" ):
_UpperCAmelCase : List[Any] = key.replace("module.decoder", "decoder.stages" )
if "patch_embed" in key:
# replace for example patch_embed1 by patch_embeddings.0
_UpperCAmelCase : int = key[key.find("patch_embed" ) + len("patch_embed" )]
_UpperCAmelCase : Union[str, Any] = key.replace(f"""patch_embed{idx}""", f"""patch_embeddings.{int(a_ )-1}""" )
if "norm" in key:
_UpperCAmelCase : Union[str, Any] = key.replace("norm", "layer_norm" )
if "glpn.encoder.layer_norm" in key:
# replace for example layer_norm1 by layer_norm.0
_UpperCAmelCase : str = key[key.find("glpn.encoder.layer_norm" ) + len("glpn.encoder.layer_norm" )]
_UpperCAmelCase : Optional[Any] = key.replace(f"""layer_norm{idx}""", f"""layer_norm.{int(a_ )-1}""" )
if "layer_norm1" in key:
_UpperCAmelCase : Union[str, Any] = key.replace("layer_norm1", "layer_norm_1" )
if "layer_norm2" in key:
_UpperCAmelCase : List[Any] = key.replace("layer_norm2", "layer_norm_2" )
if "block" in key:
# replace for example block1 by block.0
_UpperCAmelCase : Optional[Any] = key[key.find("block" ) + len("block" )]
_UpperCAmelCase : List[str] = key.replace(f"""block{idx}""", f"""block.{int(a_ )-1}""" )
if "attn.q" in key:
_UpperCAmelCase : Optional[int] = key.replace("attn.q", "attention.self.query" )
if "attn.proj" in key:
_UpperCAmelCase : List[str] = key.replace("attn.proj", "attention.output.dense" )
if "attn" in key:
_UpperCAmelCase : Dict = key.replace("attn", "attention.self" )
if "fc1" in key:
_UpperCAmelCase : List[Any] = key.replace("fc1", "dense1" )
if "fc2" in key:
_UpperCAmelCase : List[Any] = key.replace("fc2", "dense2" )
if "linear_pred" in key:
_UpperCAmelCase : Any = key.replace("linear_pred", "classifier" )
if "linear_fuse" in key:
_UpperCAmelCase : Dict = key.replace("linear_fuse.conv", "linear_fuse" )
_UpperCAmelCase : List[str] = key.replace("linear_fuse.bn", "batch_norm" )
if "linear_c" in key:
# replace for example linear_c4 by linear_c.3
_UpperCAmelCase : List[Any] = key[key.find("linear_c" ) + len("linear_c" )]
_UpperCAmelCase : Tuple = key.replace(f"""linear_c{idx}""", f"""linear_c.{int(a_ )-1}""" )
if "bot_conv" in key:
_UpperCAmelCase : Union[str, Any] = key.replace("bot_conv", "0.convolution" )
if "skip_conv1" in key:
_UpperCAmelCase : Optional[int] = key.replace("skip_conv1", "1.convolution" )
if "skip_conv2" in key:
_UpperCAmelCase : Optional[int] = key.replace("skip_conv2", "2.convolution" )
if "fusion1" in key:
_UpperCAmelCase : List[str] = key.replace("fusion1", "1.fusion" )
if "fusion2" in key:
_UpperCAmelCase : List[str] = key.replace("fusion2", "2.fusion" )
if "fusion3" in key:
_UpperCAmelCase : Optional[Any] = key.replace("fusion3", "3.fusion" )
if "fusion" in key and "conv" in key:
_UpperCAmelCase : List[Any] = key.replace("conv", "convolutional_layer" )
if key.startswith("module.last_layer_depth" ):
_UpperCAmelCase : Optional[int] = key.replace("module.last_layer_depth", "head.head" )
_UpperCAmelCase : int = value
return new_state_dict
def __UpperCAmelCase ( a_: str, a_: List[Any] ):
# for each of the encoder blocks:
for i in range(config.num_encoder_blocks ):
for j in range(config.depths[i] ):
# read in weights + bias of keys and values (which is a single matrix in the original implementation)
_UpperCAmelCase : Tuple = state_dict.pop(f"""glpn.encoder.block.{i}.{j}.attention.self.kv.weight""" )
_UpperCAmelCase : Union[str, Any] = state_dict.pop(f"""glpn.encoder.block.{i}.{j}.attention.self.kv.bias""" )
# next, add keys and values (in that order) to the state dict
_UpperCAmelCase : Optional[int] = kv_weight[
: config.hidden_sizes[i], :
]
_UpperCAmelCase : Dict = kv_bias[: config.hidden_sizes[i]]
_UpperCAmelCase : Optional[int] = kv_weight[
config.hidden_sizes[i] :, :
]
_UpperCAmelCase : Optional[Any] = kv_bias[config.hidden_sizes[i] :]
def __UpperCAmelCase ( ):
_UpperCAmelCase : Optional[int] = "http://images.cocodataset.org/val2017/000000039769.jpg"
_UpperCAmelCase : List[Any] = Image.open(requests.get(a_, stream=a_ ).raw )
return image
@torch.no_grad()
def __UpperCAmelCase ( a_: Tuple, a_: Any, a_: Optional[Any]=False, a_: List[Any]=None ):
_UpperCAmelCase : Optional[Any] = GLPNConfig(hidden_sizes=[64, 128, 320, 512], decoder_hidden_size=64, depths=[3, 8, 27, 3] )
# load image processor (only resize + rescale)
_UpperCAmelCase : Dict = GLPNImageProcessor()
# prepare image
_UpperCAmelCase : List[Any] = prepare_img()
_UpperCAmelCase : Optional[int] = image_processor(images=a_, return_tensors="pt" ).pixel_values
logger.info("Converting model..." )
# load original state dict
_UpperCAmelCase : Union[str, Any] = torch.load(a_, map_location=torch.device("cpu" ) )
# rename keys
_UpperCAmelCase : List[str] = rename_keys(a_ )
# key and value matrices need special treatment
read_in_k_v(a_, a_ )
# create HuggingFace model and load state dict
_UpperCAmelCase : List[str] = GLPNForDepthEstimation(a_ )
model.load_state_dict(a_ )
model.eval()
# forward pass
_UpperCAmelCase : Dict = model(a_ )
_UpperCAmelCase : List[str] = outputs.predicted_depth
# verify output
if model_name is not None:
if "nyu" in model_name:
_UpperCAmelCase : Optional[Any] = torch.tensor(
[[4.41_47, 4.08_73, 4.06_73], [3.78_90, 3.28_81, 3.15_25], [3.76_74, 3.54_23, 3.49_13]] )
elif "kitti" in model_name:
_UpperCAmelCase : Tuple = torch.tensor(
[[3.42_91, 2.78_65, 2.51_51], [3.28_41, 2.70_21, 2.35_02], [3.11_47, 2.46_25, 2.24_81]] )
else:
raise ValueError(f"""Unknown model name: {model_name}""" )
_UpperCAmelCase : Dict = torch.Size([1, 480, 640] )
assert predicted_depth.shape == expected_shape
assert torch.allclose(predicted_depth[0, :3, :3], a_, atol=1e-4 )
print("Looks ok!" )
# finally, push to hub if required
if push_to_hub:
logger.info("Pushing model and image processor to the 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__":
__a = argparse.ArgumentParser()
parser.add_argument(
'--checkpoint_path',
default=None,
type=str,
help='Path to the original PyTorch checkpoint (.pth file).',
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
)
parser.add_argument(
'--push_to_hub', action='store_true', help='Whether to upload the model to the HuggingFace hub.'
)
parser.add_argument(
'--model_name',
default='glpn-kitti',
type=str,
help='Name of the model in case you\'re pushing to the hub.',
)
__a = parser.parse_args()
convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)
| 17
| 0
|
'''simple docstring'''
def __UpperCAmelCase ( a_: List[Any], a_: str ):
_UpperCAmelCase : str = 0
while b > 0:
if b & 1:
res += a
a += a
b >>= 1
return res
def __UpperCAmelCase ( a_: Optional[int], a_: Tuple, a_: Any ):
_UpperCAmelCase : Any = 0
while b > 0:
if b & 1:
_UpperCAmelCase : int = ((res % c) + (a % c)) % c
a += a
b >>= 1
return res
| 364
|
'''simple docstring'''
import contextlib
import csv
import json
import os
import sqlitea
import tarfile
import textwrap
import zipfile
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
import datasets
import datasets.config
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( ):
_UpperCAmelCase : Optional[Any] = 10
_UpperCAmelCase : int = datasets.Features(
{
"tokens": datasets.Sequence(datasets.Value("string" ) ),
"labels": datasets.Sequence(datasets.ClassLabel(names=["negative", "positive"] ) ),
"answers": datasets.Sequence(
{
"text": datasets.Value("string" ),
"answer_start": datasets.Value("int32" ),
} ),
"id": datasets.Value("int64" ),
} )
_UpperCAmelCase : List[str] = datasets.Dataset.from_dict(
{
"tokens": [["foo"] * 5] * n,
"labels": [[1] * 5] * n,
"answers": [{"answer_start": [97], "text": ["1976"]}] * 10,
"id": list(range(a_ ) ),
}, features=a_, )
return dataset
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: Optional[int], a_: Dict ):
_UpperCAmelCase : Any = str(tmp_path_factory.mktemp("data" ) / "file.arrow" )
dataset.map(cache_file_name=a_ )
return filename
# FILE_CONTENT + files
__a = '\\n Text data.\n Second line of data.'
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: Dict ):
_UpperCAmelCase : Dict = tmp_path_factory.mktemp("data" ) / "file.txt"
_UpperCAmelCase : Tuple = FILE_CONTENT
with open(a_, "w" ) as f:
f.write(a_ )
return filename
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: Union[str, Any] ):
import bza
_UpperCAmelCase : str = tmp_path_factory.mktemp("data" ) / "file.txt.bz2"
_UpperCAmelCase : Optional[int] = bytes(a_, "utf-8" )
with bza.open(a_, "wb" ) as f:
f.write(a_ )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: Union[str, Any] ):
import gzip
_UpperCAmelCase : str = str(tmp_path_factory.mktemp("data" ) / "file.txt.gz" )
_UpperCAmelCase : Any = bytes(a_, "utf-8" )
with gzip.open(a_, "wb" ) as f:
f.write(a_ )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: str ):
if datasets.config.LZ4_AVAILABLE:
import lza.frame
_UpperCAmelCase : Optional[int] = tmp_path_factory.mktemp("data" ) / "file.txt.lz4"
_UpperCAmelCase : str = bytes(a_, "utf-8" )
with lza.frame.open(a_, "wb" ) as f:
f.write(a_ )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: int, a_: Any ):
if datasets.config.PY7ZR_AVAILABLE:
import pyazr
_UpperCAmelCase : Any = tmp_path_factory.mktemp("data" ) / "file.txt.7z"
with pyazr.SevenZipFile(a_, "w" ) as archive:
archive.write(a_, arcname=os.path.basename(a_ ) )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: Any, a_: List[str] ):
import tarfile
_UpperCAmelCase : Union[str, Any] = tmp_path_factory.mktemp("data" ) / "file.txt.tar"
with tarfile.TarFile(a_, "w" ) as f:
f.add(a_, arcname=os.path.basename(a_ ) )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: int ):
import lzma
_UpperCAmelCase : List[Any] = tmp_path_factory.mktemp("data" ) / "file.txt.xz"
_UpperCAmelCase : List[str] = bytes(a_, "utf-8" )
with lzma.open(a_, "wb" ) as f:
f.write(a_ )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: Dict, a_: Tuple ):
import zipfile
_UpperCAmelCase : Tuple = tmp_path_factory.mktemp("data" ) / "file.txt.zip"
with zipfile.ZipFile(a_, "w" ) as f:
f.write(a_, arcname=os.path.basename(a_ ) )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: Optional[int] ):
if datasets.config.ZSTANDARD_AVAILABLE:
import zstandard as zstd
_UpperCAmelCase : Optional[int] = tmp_path_factory.mktemp("data" ) / "file.txt.zst"
_UpperCAmelCase : int = bytes(a_, "utf-8" )
with zstd.open(a_, "wb" ) as f:
f.write(a_ )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: Optional[int] ):
_UpperCAmelCase : List[str] = tmp_path_factory.mktemp("data" ) / "file.xml"
_UpperCAmelCase : Tuple = textwrap.dedent(
"\\n <?xml version=\"1.0\" encoding=\"UTF-8\" ?>\n <tmx version=\"1.4\">\n <header segtype=\"sentence\" srclang=\"ca\" />\n <body>\n <tu>\n <tuv xml:lang=\"ca\"><seg>Contingut 1</seg></tuv>\n <tuv xml:lang=\"en\"><seg>Content 1</seg></tuv>\n </tu>\n <tu>\n <tuv xml:lang=\"ca\"><seg>Contingut 2</seg></tuv>\n <tuv xml:lang=\"en\"><seg>Content 2</seg></tuv>\n </tu>\n <tu>\n <tuv xml:lang=\"ca\"><seg>Contingut 3</seg></tuv>\n <tuv xml:lang=\"en\"><seg>Content 3</seg></tuv>\n </tu>\n <tu>\n <tuv xml:lang=\"ca\"><seg>Contingut 4</seg></tuv>\n <tuv xml:lang=\"en\"><seg>Content 4</seg></tuv>\n </tu>\n <tu>\n <tuv xml:lang=\"ca\"><seg>Contingut 5</seg></tuv>\n <tuv xml:lang=\"en\"><seg>Content 5</seg></tuv>\n </tu>\n </body>\n </tmx>" )
with open(a_, "w" ) as f:
f.write(a_ )
return filename
__a = [
{'col_1': '0', 'col_2': 0, 'col_3': 0.0},
{'col_1': '1', 'col_2': 1, 'col_3': 1.0},
{'col_1': '2', 'col_2': 2, 'col_3': 2.0},
{'col_1': '3', 'col_2': 3, 'col_3': 3.0},
]
__a = [
{'col_1': '4', 'col_2': 4, 'col_3': 4.0},
{'col_1': '5', 'col_2': 5, 'col_3': 5.0},
]
__a = {
'col_1': ['0', '1', '2', '3'],
'col_2': [0, 1, 2, 3],
'col_3': [0.0, 1.0, 2.0, 3.0],
}
__a = [
{'col_3': 0.0, 'col_1': '0', 'col_2': 0},
{'col_3': 1.0, 'col_1': '1', 'col_2': 1},
]
__a = [
{'col_1': 's0', 'col_2': 0, 'col_3': 0.0},
{'col_1': 's1', 'col_2': 1, 'col_3': 1.0},
{'col_1': 's2', 'col_2': 2, 'col_3': 2.0},
{'col_1': 's3', 'col_2': 3, 'col_3': 3.0},
]
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( ):
return DATA_DICT_OF_LISTS
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: Union[str, Any] ):
_UpperCAmelCase : str = datasets.Dataset.from_dict(a_ )
_UpperCAmelCase : Optional[int] = str(tmp_path_factory.mktemp("data" ) / "dataset.arrow" )
dataset.map(cache_file_name=a_ )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: str ):
_UpperCAmelCase : int = str(tmp_path_factory.mktemp("data" ) / "dataset.sqlite" )
with contextlib.closing(sqlitea.connect(a_ ) ) as con:
_UpperCAmelCase : List[Any] = con.cursor()
cur.execute("CREATE TABLE dataset(col_1 text, col_2 int, col_3 real)" )
for item in DATA:
cur.execute("INSERT INTO dataset(col_1, col_2, col_3) VALUES (?, ?, ?)", tuple(item.values() ) )
con.commit()
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: Any ):
_UpperCAmelCase : Dict = str(tmp_path_factory.mktemp("data" ) / "dataset.csv" )
with open(a_, "w", newline="" ) as f:
_UpperCAmelCase : Dict = csv.DictWriter(a_, fieldnames=["col_1", "col_2", "col_3"] )
writer.writeheader()
for item in DATA:
writer.writerow(a_ )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: Union[str, Any] ):
_UpperCAmelCase : Union[str, Any] = str(tmp_path_factory.mktemp("data" ) / "dataset2.csv" )
with open(a_, "w", newline="" ) as f:
_UpperCAmelCase : Optional[int] = csv.DictWriter(a_, fieldnames=["col_1", "col_2", "col_3"] )
writer.writeheader()
for item in DATA:
writer.writerow(a_ )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: str, a_: str ):
import bza
_UpperCAmelCase : str = tmp_path_factory.mktemp("data" ) / "dataset.csv.bz2"
with open(a_, "rb" ) as f:
_UpperCAmelCase : Any = f.read()
# data = bytes(FILE_CONTENT, "utf-8")
with bza.open(a_, "wb" ) as f:
f.write(a_ )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: Optional[int], a_: Dict, a_: Optional[int] ):
_UpperCAmelCase : List[Any] = tmp_path_factory.mktemp("data" ) / "dataset.csv.zip"
with zipfile.ZipFile(a_, "w" ) as f:
f.write(a_, arcname=os.path.basename(a_ ) )
f.write(a_, arcname=os.path.basename(a_ ) )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: List[str], a_: Union[str, Any], a_: int ):
_UpperCAmelCase : int = tmp_path_factory.mktemp("data" ) / "dataset.csv.zip"
with zipfile.ZipFile(a_, "w" ) as f:
f.write(a_, arcname=os.path.basename(csv_path.replace(".csv", ".CSV" ) ) )
f.write(a_, arcname=os.path.basename(csva_path.replace(".csv", ".CSV" ) ) )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: Any, a_: Union[str, Any], a_: Tuple ):
_UpperCAmelCase : Any = tmp_path_factory.mktemp("data" ) / "dataset_with_dir.csv.zip"
with zipfile.ZipFile(a_, "w" ) as f:
f.write(a_, arcname=os.path.join("main_dir", os.path.basename(a_ ) ) )
f.write(a_, arcname=os.path.join("main_dir", os.path.basename(a_ ) ) )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: Tuple ):
_UpperCAmelCase : Optional[Any] = str(tmp_path_factory.mktemp("data" ) / "dataset.parquet" )
_UpperCAmelCase : Dict = pa.schema(
{
"col_1": pa.string(),
"col_2": pa.intaa(),
"col_3": pa.floataa(),
} )
with open(a_, "wb" ) as f:
_UpperCAmelCase : Tuple = pq.ParquetWriter(a_, schema=a_ )
_UpperCAmelCase : Tuple = pa.Table.from_pydict({k: [DATA[i][k] for i in range(len(a_ ) )] for k in DATA[0]}, schema=a_ )
writer.write_table(a_ )
writer.close()
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: Any ):
_UpperCAmelCase : Union[str, Any] = str(tmp_path_factory.mktemp("data" ) / "dataset.json" )
_UpperCAmelCase : str = {"data": DATA}
with open(a_, "w" ) as f:
json.dump(a_, a_ )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: Union[str, Any] ):
_UpperCAmelCase : Optional[int] = str(tmp_path_factory.mktemp("data" ) / "dataset.json" )
_UpperCAmelCase : Dict = {"data": DATA_DICT_OF_LISTS}
with open(a_, "w" ) as f:
json.dump(a_, a_ )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: int ):
_UpperCAmelCase : Optional[Any] = str(tmp_path_factory.mktemp("data" ) / "dataset.jsonl" )
with open(a_, "w" ) as f:
for item in DATA:
f.write(json.dumps(a_ ) + "\n" )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: Tuple ):
_UpperCAmelCase : Any = str(tmp_path_factory.mktemp("data" ) / "dataset2.jsonl" )
with open(a_, "w" ) as f:
for item in DATA:
f.write(json.dumps(a_ ) + "\n" )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: Any ):
_UpperCAmelCase : int = str(tmp_path_factory.mktemp("data" ) / "dataset_312.jsonl" )
with open(a_, "w" ) as f:
for item in DATA_312:
f.write(json.dumps(a_ ) + "\n" )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: Optional[Any] ):
_UpperCAmelCase : Optional[int] = str(tmp_path_factory.mktemp("data" ) / "dataset-str.jsonl" )
with open(a_, "w" ) as f:
for item in DATA_STR:
f.write(json.dumps(a_ ) + "\n" )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: Union[str, Any], a_: Any ):
import gzip
_UpperCAmelCase : Optional[Any] = str(tmp_path_factory.mktemp("data" ) / "dataset.txt.gz" )
with open(a_, "rb" ) as orig_file:
with gzip.open(a_, "wb" ) as zipped_file:
zipped_file.writelines(a_ )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: Optional[Any], a_: Tuple ):
import gzip
_UpperCAmelCase : List[Any] = str(tmp_path_factory.mktemp("data" ) / "dataset.jsonl.gz" )
with open(a_, "rb" ) as orig_file:
with gzip.open(a_, "wb" ) as zipped_file:
zipped_file.writelines(a_ )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: Dict, a_: List[Any], a_: Union[str, Any] ):
_UpperCAmelCase : Tuple = tmp_path_factory.mktemp("data" ) / "dataset.jsonl.zip"
with zipfile.ZipFile(a_, "w" ) as f:
f.write(a_, arcname=os.path.basename(a_ ) )
f.write(a_, arcname=os.path.basename(a_ ) )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: Union[str, Any], a_: Optional[int], a_: Optional[Any], a_: Dict ):
_UpperCAmelCase : Dict = tmp_path_factory.mktemp("data" ) / "dataset_nested.jsonl.zip"
with zipfile.ZipFile(a_, "w" ) as f:
f.write(a_, arcname=os.path.join("nested", os.path.basename(a_ ) ) )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: List[Any], a_: Optional[int], a_: List[str] ):
_UpperCAmelCase : Dict = tmp_path_factory.mktemp("data" ) / "dataset_with_dir.jsonl.zip"
with zipfile.ZipFile(a_, "w" ) as f:
f.write(a_, arcname=os.path.join("main_dir", os.path.basename(a_ ) ) )
f.write(a_, arcname=os.path.join("main_dir", os.path.basename(a_ ) ) )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: List[Any], a_: List[Any], a_: str ):
_UpperCAmelCase : Optional[Any] = tmp_path_factory.mktemp("data" ) / "dataset.jsonl.tar"
with tarfile.TarFile(a_, "w" ) as f:
f.add(a_, arcname=os.path.basename(a_ ) )
f.add(a_, arcname=os.path.basename(a_ ) )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: List[str], a_: List[Any], a_: Tuple, a_: Dict ):
_UpperCAmelCase : List[Any] = tmp_path_factory.mktemp("data" ) / "dataset_nested.jsonl.tar"
with tarfile.TarFile(a_, "w" ) as f:
f.add(a_, arcname=os.path.join("nested", os.path.basename(a_ ) ) )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: List[str] ):
_UpperCAmelCase : List[str] = ["0", "1", "2", "3"]
_UpperCAmelCase : Tuple = str(tmp_path_factory.mktemp("data" ) / "dataset.txt" )
with open(a_, "w" ) as f:
for item in data:
f.write(item + "\n" )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: Union[str, Any] ):
_UpperCAmelCase : Dict = ["0", "1", "2", "3"]
_UpperCAmelCase : Optional[Any] = str(tmp_path_factory.mktemp("data" ) / "dataset2.txt" )
with open(a_, "w" ) as f:
for item in data:
f.write(item + "\n" )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: Any ):
_UpperCAmelCase : int = ["0", "1", "2", "3"]
_UpperCAmelCase : str = tmp_path_factory.mktemp("data" ) / "dataset.abc"
with open(a_, "w" ) as f:
for item in data:
f.write(item + "\n" )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: Optional[Any], a_: Any, a_: Union[str, Any] ):
_UpperCAmelCase : Union[str, Any] = tmp_path_factory.mktemp("data" ) / "dataset.text.zip"
with zipfile.ZipFile(a_, "w" ) as f:
f.write(a_, arcname=os.path.basename(a_ ) )
f.write(a_, arcname=os.path.basename(a_ ) )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: Optional[int], a_: List[Any], a_: List[Any] ):
_UpperCAmelCase : List[Any] = tmp_path_factory.mktemp("data" ) / "dataset_with_dir.text.zip"
with zipfile.ZipFile(a_, "w" ) as f:
f.write(a_, arcname=os.path.join("main_dir", os.path.basename(a_ ) ) )
f.write(a_, arcname=os.path.join("main_dir", os.path.basename(a_ ) ) )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: Any, a_: str, a_: Tuple ):
_UpperCAmelCase : List[Any] = tmp_path_factory.mktemp("data" ) / "dataset.ext.zip"
with zipfile.ZipFile(a_, "w" ) as f:
f.write(a_, arcname=os.path.basename("unsupported.ext" ) )
f.write(a_, arcname=os.path.basename("unsupported_2.ext" ) )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: Optional[Any] ):
_UpperCAmelCase : List[str] = "\n".join(["First", "Second\u2029with Unicode new line", "Third"] )
_UpperCAmelCase : str = str(tmp_path_factory.mktemp("data" ) / "dataset_with_unicode_new_lines.txt" )
with open(a_, "w", encoding="utf-8" ) as f:
f.write(a_ )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( ):
return os.path.join("tests", "features", "data", "test_image_rgb.jpg" )
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( ):
return os.path.join("tests", "features", "data", "test_audio_44100.wav" )
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: int, a_: Optional[Any] ):
_UpperCAmelCase : Union[str, Any] = tmp_path_factory.mktemp("data" ) / "dataset.img.zip"
with zipfile.ZipFile(a_, "w" ) as f:
f.write(a_, arcname=os.path.basename(a_ ) )
f.write(a_, arcname=os.path.basename(a_ ).replace(".jpg", "2.jpg" ) )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: Tuple ):
_UpperCAmelCase : Optional[Any] = tmp_path_factory.mktemp("data_dir" )
(data_dir / "subdir").mkdir()
with open(data_dir / "subdir" / "train.txt", "w" ) as f:
f.write("foo\n" * 10 )
with open(data_dir / "subdir" / "test.txt", "w" ) as f:
f.write("bar\n" * 10 )
# hidden file
with open(data_dir / "subdir" / ".test.txt", "w" ) as f:
f.write("bar\n" * 10 )
# hidden directory
(data_dir / ".subdir").mkdir()
with open(data_dir / ".subdir" / "train.txt", "w" ) as f:
f.write("foo\n" * 10 )
with open(data_dir / ".subdir" / "test.txt", "w" ) as f:
f.write("bar\n" * 10 )
return data_dir
| 17
| 0
|
'''simple docstring'''
from math import factorial
def __UpperCAmelCase ( a_: int, a_: int ):
if n < k or k < 0:
raise ValueError("Please enter positive integers for n and k where n >= k" )
return factorial(__a ) // (factorial(__a ) * factorial(n - k ))
if __name__ == "__main__":
print(
'The number of five-card hands possible from a standard',
f'fifty-two card deck is: {combinations(52, 5)}\n',
)
print(
'If a class of 40 students must be arranged into groups of',
f'4 for group projects, there are {combinations(40, 4)} ways',
'to arrange them.\n',
)
print(
'If 10 teams are competing in a Formula One race, there',
f'are {combinations(10, 3)} ways that first, second and',
'third place can be awarded.',
)
| 365
|
'''simple docstring'''
import unittest
from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
@require_sentencepiece
@slow # see https://github.com/huggingface/transformers/issues/11457
class A__ ( UpperCamelCase , unittest.TestCase ):
"""simple docstring"""
UpperCamelCase_ : str = BarthezTokenizer
UpperCamelCase_ : List[Any] = BarthezTokenizerFast
UpperCamelCase_ : Optional[int] = True
UpperCamelCase_ : Optional[int] = True
def _lowerCAmelCase ( self : Optional[int] ) -> Dict:
"""simple docstring"""
super().setUp()
_UpperCAmelCase : Tuple = BarthezTokenizerFast.from_pretrained("moussaKam/mbarthez" )
tokenizer.save_pretrained(self.tmpdirname )
tokenizer.save_pretrained(self.tmpdirname , legacy_format=lowerCAmelCase__ )
_UpperCAmelCase : List[str] = tokenizer
def _lowerCAmelCase ( self : List[str] ) -> Optional[int]:
"""simple docstring"""
_UpperCAmelCase : Tuple = "<pad>"
_UpperCAmelCase : Dict = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCAmelCase__ ) , lowerCAmelCase__ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCAmelCase__ ) , lowerCAmelCase__ )
def _lowerCAmelCase ( self : Tuple ) -> Optional[int]:
"""simple docstring"""
_UpperCAmelCase : List[str] = 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(lowerCAmelCase__ ) , 1_0_1_1_2_2 )
def _lowerCAmelCase ( self : Union[str, Any] ) -> Dict:
"""simple docstring"""
self.assertEqual(self.get_tokenizer().vocab_size , 1_0_1_1_2_2 )
@require_torch
def _lowerCAmelCase ( self : Any ) -> int:
"""simple docstring"""
_UpperCAmelCase : int = ["A long paragraph for summarization.", "Another paragraph for summarization."]
_UpperCAmelCase : Optional[int] = [0, 5_7, 3_0_1_8, 7_0_3_0_7, 9_1, 2]
_UpperCAmelCase : int = self.tokenizer(
lowerCAmelCase__ , max_length=len(lowerCAmelCase__ ) , padding=lowerCAmelCase__ , truncation=lowerCAmelCase__ , return_tensors="pt" )
self.assertIsInstance(lowerCAmelCase__ , lowerCAmelCase__ )
self.assertEqual((2, 6) , batch.input_ids.shape )
self.assertEqual((2, 6) , batch.attention_mask.shape )
_UpperCAmelCase : str = batch.input_ids.tolist()[0]
self.assertListEqual(lowerCAmelCase__ , lowerCAmelCase__ )
def _lowerCAmelCase ( self : str ) -> Optional[Any]:
"""simple docstring"""
if not self.test_rust_tokenizer:
return
_UpperCAmelCase : Optional[int] = self.get_tokenizer()
_UpperCAmelCase : Optional[int] = self.get_rust_tokenizer()
_UpperCAmelCase : Tuple = "I was born in 92000, and this is falsé."
_UpperCAmelCase : Dict = tokenizer.tokenize(lowerCAmelCase__ )
_UpperCAmelCase : Union[str, Any] = rust_tokenizer.tokenize(lowerCAmelCase__ )
self.assertListEqual(lowerCAmelCase__ , lowerCAmelCase__ )
_UpperCAmelCase : Dict = tokenizer.encode(lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ )
_UpperCAmelCase : Union[str, Any] = rust_tokenizer.encode(lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ )
self.assertListEqual(lowerCAmelCase__ , lowerCAmelCase__ )
_UpperCAmelCase : Optional[Any] = self.get_rust_tokenizer()
_UpperCAmelCase : Optional[Any] = tokenizer.encode(lowerCAmelCase__ )
_UpperCAmelCase : Optional[int] = rust_tokenizer.encode(lowerCAmelCase__ )
self.assertListEqual(lowerCAmelCase__ , lowerCAmelCase__ )
@slow
def _lowerCAmelCase ( self : int ) -> int:
"""simple docstring"""
_UpperCAmelCase : Optional[Any] = {"input_ids": [[0, 4_9_0, 1_4_3_2_8, 4_5_0_7, 3_5_4, 4_7, 4_3_6_6_9, 9_5, 2_5, 7_8_1_1_7, 2_0_2_1_5, 1_9_7_7_9, 1_9_0, 2_2, 4_0_0, 4, 3_5_3_4_3, 8_0_3_1_0, 6_0_3, 8_6, 2_4_9_3_7, 1_0_5, 3_3_4_3_8, 9_4_7_6_2, 1_9_6, 3_9_6_4_2, 7, 1_5, 1_5_9_3_3, 1_7_3, 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], [0, 1_0_5_3_4, 8_7, 2_5, 6_6, 3_3_5_8, 1_9_6, 5_5_2_8_9, 8, 8_2_9_6_1, 8_1, 2_2_0_4, 7_5_2_0_3, 7, 1_5, 7_6_3, 1_2_9_5_6, 2_1_6, 1_7_8, 1_4_3_2_8, 9_5_9_5, 1_3_7_7, 6_9_6_9_3, 7, 4_4_8, 7_1_0_2_1, 1_9_6, 1_8_1_0_6, 1_4_3_7, 1_3_9_7_4, 1_0_8, 9_0_8_3, 4, 4_9_3_1_5, 7, 3_9, 8_6, 1_3_2_6, 2_7_9_3, 4_6_3_3_3, 4, 4_4_8, 1_9_6, 7_4_5_8_8, 7, 4_9_3_1_5, 7, 3_9, 2_1, 8_2_2, 3_8_4_7_0, 7_4, 2_1, 6_6_7_2_3, 6_2_4_8_0, 8, 2_2_0_5_0, 5, 2]], "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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]} # noqa: E501
# fmt: on
# moussaKam/mbarthez is a french model. So we also use french texts.
_UpperCAmelCase : Tuple = [
"Le transformeur est un modèle d'apprentissage profond introduit en 2017, "
"utilisé principalement dans le domaine du traitement automatique des langues (TAL).",
"À l'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus "
"pour gérer des données séquentielles, telles que le langage naturel, pour des tâches "
"telles que la traduction et la synthèse de texte.",
]
self.tokenizer_integration_test_util(
expected_encoding=lowerCAmelCase__ , model_name="moussaKam/mbarthez" , revision="c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6" , sequences=lowerCAmelCase__ , )
| 17
| 0
|
'''simple docstring'''
import sys
import webbrowser
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
print('Googling.....')
__a = 'https://www.google.com/search?q=' + ' '.join(sys.argv[1:])
__a = requests.get(url, headers={'UserAgent': UserAgent().random})
# res.raise_for_status()
with open('project1a.html', 'wb') as out_file: # only for knowing the class
for data in res.iter_content(10_000):
out_file.write(data)
__a = BeautifulSoup(res.text, 'html.parser')
__a = list(soup.select('.eZt8xd'))[:5]
print(len(links))
for link in links:
if link.text == "Maps":
webbrowser.open(link.get('href'))
else:
webbrowser.open(f'https://google.com{link.get("href")}')
| 366
|
'''simple docstring'''
import unittest
import numpy as np
import requests
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
__a = False
if is_vision_available():
from PIL import Image
from transformers import PixaStructImageProcessor
class A__ ( unittest.TestCase ):
"""simple docstring"""
def __init__( self : int , lowerCAmelCase__ : Optional[int] , lowerCAmelCase__ : Optional[Any]=7 , lowerCAmelCase__ : int=3 , lowerCAmelCase__ : List[Any]=1_8 , lowerCAmelCase__ : str=3_0 , lowerCAmelCase__ : str=4_0_0 , lowerCAmelCase__ : Union[str, Any]=None , lowerCAmelCase__ : Optional[Any]=True , lowerCAmelCase__ : str=True , lowerCAmelCase__ : List[Any]=None , ) -> List[Any]:
"""simple docstring"""
_UpperCAmelCase : List[Any] = size if size is not None else {"height": 2_0, "width": 2_0}
_UpperCAmelCase : Optional[Any] = parent
_UpperCAmelCase : Tuple = batch_size
_UpperCAmelCase : str = num_channels
_UpperCAmelCase : Optional[Any] = image_size
_UpperCAmelCase : Dict = min_resolution
_UpperCAmelCase : str = max_resolution
_UpperCAmelCase : List[Any] = size
_UpperCAmelCase : Union[str, Any] = do_normalize
_UpperCAmelCase : Optional[Any] = do_convert_rgb
_UpperCAmelCase : str = [5_1_2, 1_0_2_4, 2_0_4_8, 4_0_9_6]
_UpperCAmelCase : str = patch_size if patch_size is not None else {"height": 1_6, "width": 1_6}
def _lowerCAmelCase ( self : List[str] ) -> int:
"""simple docstring"""
return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}
def _lowerCAmelCase ( self : Any ) -> str:
"""simple docstring"""
_UpperCAmelCase : Dict = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg"
_UpperCAmelCase : Optional[Any] = Image.open(requests.get(lowerCAmelCase__ , stream=lowerCAmelCase__ ).raw ).convert("RGB" )
return raw_image
@unittest.skipIf(
not is_torch_greater_or_equal_than_1_11 , reason='''`Pix2StructImageProcessor` requires `torch>=1.11.0`.''' , )
@require_torch
@require_vision
class A__ ( UpperCamelCase , unittest.TestCase ):
"""simple docstring"""
UpperCamelCase_ : Any = PixaStructImageProcessor if is_vision_available() else None
def _lowerCAmelCase ( self : Any ) -> Optional[int]:
"""simple docstring"""
_UpperCAmelCase : Tuple = PixaStructImageProcessingTester(self )
@property
def _lowerCAmelCase ( self : Tuple ) -> int:
"""simple docstring"""
return self.image_processor_tester.prepare_image_processor_dict()
def _lowerCAmelCase ( self : Any ) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase : str = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(lowerCAmelCase__ , "do_normalize" ) )
self.assertTrue(hasattr(lowerCAmelCase__ , "do_convert_rgb" ) )
def _lowerCAmelCase ( self : Optional[Any] ) -> Dict:
"""simple docstring"""
_UpperCAmelCase : Optional[Any] = self.image_processor_tester.prepare_dummy_image()
_UpperCAmelCase : Optional[int] = self.image_processing_class(**self.image_processor_dict )
_UpperCAmelCase : str = 2_0_4_8
_UpperCAmelCase : Any = image_processor(lowerCAmelCase__ , return_tensors="pt" , max_patches=lowerCAmelCase__ )
self.assertTrue(torch.allclose(inputs.flattened_patches.mean() , torch.tensor(0.0606 ) , atol=1e-3 , rtol=1e-3 ) )
def _lowerCAmelCase ( self : Dict ) -> int:
"""simple docstring"""
_UpperCAmelCase : Optional[Any] = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
_UpperCAmelCase : Tuple = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCAmelCase__ )
for image in image_inputs:
self.assertIsInstance(lowerCAmelCase__ , Image.Image )
# Test not batched input
_UpperCAmelCase : List[str] = (
(self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
* self.image_processor_tester.num_channels
) + 2
for max_patch in self.image_processor_tester.max_patches:
# Test not batched input
_UpperCAmelCase : Union[str, Any] = image_processor(
image_inputs[0] , return_tensors="pt" , max_patches=lowerCAmelCase__ ).flattened_patches
self.assertEqual(
encoded_images.shape , (1, max_patch, expected_hidden_dim) , )
# Test batched
_UpperCAmelCase : str = image_processor(
lowerCAmelCase__ , return_tensors="pt" , max_patches=lowerCAmelCase__ ).flattened_patches
self.assertEqual(
encoded_images.shape , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) , )
def _lowerCAmelCase ( self : Optional[int] ) -> List[str]:
"""simple docstring"""
_UpperCAmelCase : Any = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
_UpperCAmelCase : List[Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCAmelCase__ )
for image in image_inputs:
self.assertIsInstance(lowerCAmelCase__ , Image.Image )
# Test not batched input
_UpperCAmelCase : Union[str, Any] = (
(self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
* self.image_processor_tester.num_channels
) + 2
_UpperCAmelCase : str = True
for max_patch in self.image_processor_tester.max_patches:
# Test not batched input
with self.assertRaises(lowerCAmelCase__ ):
_UpperCAmelCase : str = image_processor(
image_inputs[0] , return_tensors="pt" , max_patches=lowerCAmelCase__ ).flattened_patches
_UpperCAmelCase : Any = "Hello"
_UpperCAmelCase : Optional[int] = image_processor(
image_inputs[0] , return_tensors="pt" , max_patches=lowerCAmelCase__ , header_text=lowerCAmelCase__ ).flattened_patches
self.assertEqual(
encoded_images.shape , (1, max_patch, expected_hidden_dim) , )
# Test batched
_UpperCAmelCase : List[Any] = image_processor(
lowerCAmelCase__ , return_tensors="pt" , max_patches=lowerCAmelCase__ , header_text=lowerCAmelCase__ ).flattened_patches
self.assertEqual(
encoded_images.shape , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) , )
def _lowerCAmelCase ( self : List[str] ) -> List[Any]:
"""simple docstring"""
_UpperCAmelCase : Union[str, Any] = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
_UpperCAmelCase : List[str] = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCAmelCase__ , numpify=lowerCAmelCase__ )
for image in image_inputs:
self.assertIsInstance(lowerCAmelCase__ , np.ndarray )
_UpperCAmelCase : Any = (
(self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
* self.image_processor_tester.num_channels
) + 2
for max_patch in self.image_processor_tester.max_patches:
# Test not batched input
_UpperCAmelCase : int = image_processor(
image_inputs[0] , return_tensors="pt" , max_patches=lowerCAmelCase__ ).flattened_patches
self.assertEqual(
encoded_images.shape , (1, max_patch, expected_hidden_dim) , )
# Test batched
_UpperCAmelCase : Union[str, Any] = image_processor(
lowerCAmelCase__ , return_tensors="pt" , max_patches=lowerCAmelCase__ ).flattened_patches
self.assertEqual(
encoded_images.shape , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) , )
def _lowerCAmelCase ( self : int ) -> str:
"""simple docstring"""
_UpperCAmelCase : List[str] = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
_UpperCAmelCase : Any = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCAmelCase__ , torchify=lowerCAmelCase__ )
for image in image_inputs:
self.assertIsInstance(lowerCAmelCase__ , torch.Tensor )
# Test not batched input
_UpperCAmelCase : List[str] = (
(self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
* self.image_processor_tester.num_channels
) + 2
for max_patch in self.image_processor_tester.max_patches:
# Test not batched input
_UpperCAmelCase : Union[str, Any] = image_processor(
image_inputs[0] , return_tensors="pt" , max_patches=lowerCAmelCase__ ).flattened_patches
self.assertEqual(
encoded_images.shape , (1, max_patch, expected_hidden_dim) , )
# Test batched
_UpperCAmelCase : str = image_processor(
lowerCAmelCase__ , return_tensors="pt" , max_patches=lowerCAmelCase__ ).flattened_patches
self.assertEqual(
encoded_images.shape , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) , )
@unittest.skipIf(
not is_torch_greater_or_equal_than_1_11 , reason='''`Pix2StructImageProcessor` requires `torch>=1.11.0`.''' , )
@require_torch
@require_vision
class A__ ( UpperCamelCase , unittest.TestCase ):
"""simple docstring"""
UpperCamelCase_ : List[Any] = PixaStructImageProcessor if is_vision_available() else None
def _lowerCAmelCase ( self : Union[str, Any] ) -> Union[str, Any]:
"""simple docstring"""
_UpperCAmelCase : Any = PixaStructImageProcessingTester(self , num_channels=4 )
_UpperCAmelCase : List[Any] = 3
@property
def _lowerCAmelCase ( self : Union[str, Any] ) -> Tuple:
"""simple docstring"""
return self.image_processor_tester.prepare_image_processor_dict()
def _lowerCAmelCase ( self : Dict ) -> Any:
"""simple docstring"""
_UpperCAmelCase : str = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(lowerCAmelCase__ , "do_normalize" ) )
self.assertTrue(hasattr(lowerCAmelCase__ , "do_convert_rgb" ) )
def _lowerCAmelCase ( self : int ) -> List[str]:
"""simple docstring"""
_UpperCAmelCase : Optional[int] = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
_UpperCAmelCase : List[str] = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCAmelCase__ )
for image in image_inputs:
self.assertIsInstance(lowerCAmelCase__ , Image.Image )
# Test not batched input
_UpperCAmelCase : str = (
(self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
* (self.image_processor_tester.num_channels - 1)
) + 2
for max_patch in self.image_processor_tester.max_patches:
# Test not batched input
_UpperCAmelCase : Any = image_processor(
image_inputs[0] , return_tensors="pt" , max_patches=lowerCAmelCase__ ).flattened_patches
self.assertEqual(
encoded_images.shape , (1, max_patch, expected_hidden_dim) , )
# Test batched
_UpperCAmelCase : Tuple = image_processor(
lowerCAmelCase__ , return_tensors="pt" , max_patches=lowerCAmelCase__ ).flattened_patches
self.assertEqual(
encoded_images.shape , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) , )
| 17
| 0
|
'''simple docstring'''
from __future__ import annotations
def __UpperCAmelCase ( a_: list[list[int]] ):
# preprocessing the first row
for i in range(1, len(matrix[0] ) ):
matrix[0][i] += matrix[0][i - 1]
# preprocessing the first column
for i in range(1, len(_UpperCamelCase ) ):
matrix[i][0] += matrix[i - 1][0]
# updating the path cost for current position
for i in range(1, len(_UpperCamelCase ) ):
for j in range(1, len(matrix[0] ) ):
matrix[i][j] += min(matrix[i - 1][j], matrix[i][j - 1] )
return matrix[-1][-1]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 367
|
'''simple docstring'''
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a = logging.get_logger(__name__)
__a = {
'huggingface/time-series-transformer-tourism-monthly': (
'https://huggingface.co/huggingface/time-series-transformer-tourism-monthly/resolve/main/config.json'
),
# See all TimeSeriesTransformer models at https://huggingface.co/models?filter=time_series_transformer
}
class A__ ( UpperCamelCase ):
"""simple docstring"""
UpperCamelCase_ : Tuple = '''time_series_transformer'''
UpperCamelCase_ : Optional[Any] = {
'''hidden_size''': '''d_model''',
'''num_attention_heads''': '''encoder_attention_heads''',
'''num_hidden_layers''': '''encoder_layers''',
}
def __init__( self : Optional[int] , lowerCAmelCase__ : Optional[int] = None , lowerCAmelCase__ : Optional[int] = None , lowerCAmelCase__ : str = "student_t" , lowerCAmelCase__ : str = "nll" , lowerCAmelCase__ : int = 1 , lowerCAmelCase__ : List[int] = [1, 2, 3, 4, 5, 6, 7] , lowerCAmelCase__ : Optional[Union[str, bool]] = "mean" , lowerCAmelCase__ : int = 0 , lowerCAmelCase__ : int = 0 , lowerCAmelCase__ : int = 0 , lowerCAmelCase__ : int = 0 , lowerCAmelCase__ : Optional[List[int]] = None , lowerCAmelCase__ : Optional[List[int]] = None , lowerCAmelCase__ : int = 3_2 , lowerCAmelCase__ : int = 3_2 , lowerCAmelCase__ : int = 2 , lowerCAmelCase__ : int = 2 , lowerCAmelCase__ : int = 2 , lowerCAmelCase__ : int = 2 , lowerCAmelCase__ : bool = True , lowerCAmelCase__ : str = "gelu" , lowerCAmelCase__ : int = 6_4 , lowerCAmelCase__ : float = 0.1 , lowerCAmelCase__ : float = 0.1 , lowerCAmelCase__ : float = 0.1 , lowerCAmelCase__ : float = 0.1 , lowerCAmelCase__ : float = 0.1 , lowerCAmelCase__ : int = 1_0_0 , lowerCAmelCase__ : float = 0.02 , lowerCAmelCase__ : Dict=True , **lowerCAmelCase__ : Tuple , ) -> Tuple:
"""simple docstring"""
_UpperCAmelCase : Optional[int] = prediction_length
_UpperCAmelCase : Optional[Any] = context_length or prediction_length
_UpperCAmelCase : Optional[Any] = distribution_output
_UpperCAmelCase : Union[str, Any] = loss
_UpperCAmelCase : Dict = input_size
_UpperCAmelCase : int = num_time_features
_UpperCAmelCase : Any = lags_sequence
_UpperCAmelCase : Dict = scaling
_UpperCAmelCase : Tuple = num_dynamic_real_features
_UpperCAmelCase : Dict = num_static_real_features
_UpperCAmelCase : Union[str, Any] = num_static_categorical_features
if cardinality and num_static_categorical_features > 0:
if len(lowerCAmelCase__ ) != num_static_categorical_features:
raise ValueError(
"The cardinality should be a list of the same length as `num_static_categorical_features`" )
_UpperCAmelCase : Optional[int] = cardinality
else:
_UpperCAmelCase : Optional[Any] = [0]
if embedding_dimension and num_static_categorical_features > 0:
if len(lowerCAmelCase__ ) != num_static_categorical_features:
raise ValueError(
"The embedding dimension should be a list of the same length as `num_static_categorical_features`" )
_UpperCAmelCase : List[Any] = embedding_dimension
else:
_UpperCAmelCase : Optional[Any] = [min(5_0 , (cat + 1) // 2 ) for cat in self.cardinality]
_UpperCAmelCase : str = num_parallel_samples
# Transformer architecture configuration
_UpperCAmelCase : Union[str, Any] = input_size * len(lowerCAmelCase__ ) + self._number_of_features
_UpperCAmelCase : str = d_model
_UpperCAmelCase : Optional[Any] = encoder_attention_heads
_UpperCAmelCase : Dict = decoder_attention_heads
_UpperCAmelCase : List[Any] = encoder_ffn_dim
_UpperCAmelCase : str = decoder_ffn_dim
_UpperCAmelCase : Dict = encoder_layers
_UpperCAmelCase : str = decoder_layers
_UpperCAmelCase : Any = dropout
_UpperCAmelCase : str = attention_dropout
_UpperCAmelCase : List[Any] = activation_dropout
_UpperCAmelCase : Dict = encoder_layerdrop
_UpperCAmelCase : Any = decoder_layerdrop
_UpperCAmelCase : Optional[Any] = activation_function
_UpperCAmelCase : Tuple = init_std
_UpperCAmelCase : List[str] = use_cache
super().__init__(is_encoder_decoder=lowerCAmelCase__ , **lowerCAmelCase__ )
@property
def _lowerCAmelCase ( self : str ) -> int:
"""simple docstring"""
return (
sum(self.embedding_dimension )
+ self.num_dynamic_real_features
+ self.num_time_features
+ self.num_static_real_features
+ self.input_size * 2 # the log1p(abs(loc)) and log(scale) features
)
| 17
| 0
|
'''simple docstring'''
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool
class A__ ( a__ ):
"""simple docstring"""
UpperCamelCase_ : Union[str, Any] = 'philschmid/bart-large-cnn-samsum'
UpperCamelCase_ : Optional[Any] = (
'This is a tool that summarizes an English text. It takes an input `text` containing the text to summarize, '
'and returns a summary of the text.'
)
UpperCamelCase_ : Tuple = 'summarizer'
UpperCamelCase_ : int = AutoTokenizer
UpperCamelCase_ : int = AutoModelForSeqaSeqLM
UpperCamelCase_ : List[Any] = ['text']
UpperCamelCase_ : Optional[Any] = ['text']
def _lowerCAmelCase ( self : Tuple , lowerCAmelCase__ : Any ) -> Tuple:
"""simple docstring"""
return self.pre_processor(_lowerCamelCase , return_tensors="pt" , truncation=_lowerCamelCase )
def _lowerCAmelCase ( self : Union[str, Any] , lowerCAmelCase__ : Tuple ) -> List[Any]:
"""simple docstring"""
return self.model.generate(**_lowerCamelCase )[0]
def _lowerCAmelCase ( self : int , lowerCAmelCase__ : Tuple ) -> Tuple:
"""simple docstring"""
return self.pre_processor.decode(_lowerCamelCase , skip_special_tokens=_lowerCamelCase , clean_up_tokenization_spaces=_lowerCamelCase )
| 368
|
'''simple docstring'''
import baseaa
def __UpperCAmelCase ( a_: str ):
return baseaa.baaencode(string.encode("utf-8" ) )
def __UpperCAmelCase ( a_: bytes ):
return baseaa.baadecode(a_ ).decode("utf-8" )
if __name__ == "__main__":
__a = 'Hello World!'
__a = baseaa_encode(test)
print(encoded)
__a = baseaa_decode(encoded)
print(decoded)
| 17
| 0
|
'''simple docstring'''
def __UpperCAmelCase ( a_: Union[str, Any], a_: Optional[Any], a_: List[str], a_: int, a_: Tuple, a_: Any ):
if index == r:
for j in range(__SCREAMING_SNAKE_CASE ):
print(data[j], end=" " )
print(" " )
return
# When no more elements are there to put in data[]
if i >= n:
return
# current is included, put next at next location
_UpperCAmelCase : List[Any] = arr[i]
combination_util(__SCREAMING_SNAKE_CASE, __SCREAMING_SNAKE_CASE, __SCREAMING_SNAKE_CASE, index + 1, __SCREAMING_SNAKE_CASE, i + 1 )
# current is excluded, replace it with
# next (Note that i+1 is passed, but
# index is not changed)
combination_util(__SCREAMING_SNAKE_CASE, __SCREAMING_SNAKE_CASE, __SCREAMING_SNAKE_CASE, __SCREAMING_SNAKE_CASE, __SCREAMING_SNAKE_CASE, i + 1 )
# The main function that prints all combinations
# of size r in arr[] of size n. This function
# mainly uses combinationUtil()
def __UpperCAmelCase ( a_: List[Any], a_: List[str], a_: Optional[Any] ):
_UpperCAmelCase : str = [0] * r
# Print all combination using temporary array 'data[]'
combination_util(__SCREAMING_SNAKE_CASE, __SCREAMING_SNAKE_CASE, __SCREAMING_SNAKE_CASE, 0, __SCREAMING_SNAKE_CASE, 0 )
if __name__ == "__main__":
# Driver code to check the function above
__a = [10, 20, 30, 40, 50]
print_combination(arr, len(arr), 3)
# This code is contributed by Ambuj sahu
| 369
|
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow
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.models.xglm.modeling_tf_xglm import (
TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFXGLMForCausalLM,
TFXGLMModel,
)
@require_tf
class A__ :
"""simple docstring"""
UpperCamelCase_ : Any = XGLMConfig
UpperCamelCase_ : Union[str, Any] = {}
UpperCamelCase_ : Dict = '''gelu'''
def __init__( self : Optional[int] , lowerCAmelCase__ : Union[str, Any] , lowerCAmelCase__ : Optional[Any]=1_4 , lowerCAmelCase__ : Any=7 , lowerCAmelCase__ : Optional[Any]=True , lowerCAmelCase__ : List[str]=True , lowerCAmelCase__ : Any=True , lowerCAmelCase__ : List[str]=9_9 , lowerCAmelCase__ : Any=3_2 , lowerCAmelCase__ : Optional[int]=2 , lowerCAmelCase__ : List[Any]=4 , lowerCAmelCase__ : Any=3_7 , lowerCAmelCase__ : List[Any]="gelu" , lowerCAmelCase__ : List[Any]=0.1 , lowerCAmelCase__ : Dict=0.1 , lowerCAmelCase__ : Optional[int]=5_1_2 , lowerCAmelCase__ : Optional[Any]=0.02 , ) -> int:
"""simple docstring"""
_UpperCAmelCase : Optional[Any] = parent
_UpperCAmelCase : str = batch_size
_UpperCAmelCase : str = seq_length
_UpperCAmelCase : int = is_training
_UpperCAmelCase : List[Any] = use_input_mask
_UpperCAmelCase : Optional[int] = use_labels
_UpperCAmelCase : str = vocab_size
_UpperCAmelCase : int = d_model
_UpperCAmelCase : Tuple = num_hidden_layers
_UpperCAmelCase : Tuple = num_attention_heads
_UpperCAmelCase : Tuple = ffn_dim
_UpperCAmelCase : Any = activation_function
_UpperCAmelCase : Union[str, Any] = activation_dropout
_UpperCAmelCase : Union[str, Any] = attention_dropout
_UpperCAmelCase : Any = max_position_embeddings
_UpperCAmelCase : int = initializer_range
_UpperCAmelCase : Any = None
_UpperCAmelCase : int = 0
_UpperCAmelCase : Union[str, Any] = 2
_UpperCAmelCase : Tuple = 1
def _lowerCAmelCase ( self : Optional[Any] ) -> List[Any]:
"""simple docstring"""
return XGLMConfig.from_pretrained("facebook/xglm-564M" )
def _lowerCAmelCase ( self : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
_UpperCAmelCase : int = tf.clip_by_value(
ids_tensor([self.batch_size, self.seq_length] , self.vocab_size ) , clip_value_min=0 , clip_value_max=3 )
_UpperCAmelCase : Any = None
if self.use_input_mask:
_UpperCAmelCase : Union[str, Any] = random_attention_mask([self.batch_size, self.seq_length] )
_UpperCAmelCase : Optional[Any] = self.get_config()
_UpperCAmelCase : Dict = floats_tensor([self.num_hidden_layers, self.num_attention_heads] , 2 )
return (
config,
input_ids,
input_mask,
head_mask,
)
def _lowerCAmelCase ( self : int ) -> Any:
"""simple docstring"""
return XGLMConfig(
vocab_size=self.vocab_size , d_model=self.hidden_size , num_layers=self.num_hidden_layers , attention_heads=self.num_attention_heads , ffn_dim=self.ffn_dim , activation_function=self.activation_function , activation_dropout=self.activation_dropout , attention_dropout=self.attention_dropout , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , use_cache=lowerCAmelCase__ , bos_token_id=self.bos_token_id , eos_token_id=self.eos_token_id , pad_token_id=self.pad_token_id , return_dict=lowerCAmelCase__ , )
def _lowerCAmelCase ( self : Tuple ) -> Union[str, Any]:
"""simple docstring"""
_UpperCAmelCase : Union[str, Any] = self.prepare_config_and_inputs()
(
(
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) ,
) : List[Any] = config_and_inputs
_UpperCAmelCase : Optional[int] = {
"input_ids": input_ids,
"head_mask": head_mask,
}
return config, inputs_dict
@require_tf
class A__ ( UpperCamelCase , UpperCamelCase , unittest.TestCase ):
"""simple docstring"""
UpperCamelCase_ : str = (TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
UpperCamelCase_ : Any = (TFXGLMForCausalLM,) if is_tf_available() else ()
UpperCamelCase_ : Tuple = (
{'''feature-extraction''': TFXGLMModel, '''text-generation''': TFXGLMForCausalLM} if is_tf_available() else {}
)
UpperCamelCase_ : Dict = False
UpperCamelCase_ : List[Any] = False
UpperCamelCase_ : Tuple = False
def _lowerCAmelCase ( self : List[str] ) -> int:
"""simple docstring"""
_UpperCAmelCase : Dict = TFXGLMModelTester(self )
_UpperCAmelCase : Dict = ConfigTester(self , config_class=lowerCAmelCase__ , n_embd=3_7 )
def _lowerCAmelCase ( self : List[str] ) -> Dict:
"""simple docstring"""
self.config_tester.run_common_tests()
@slow
def _lowerCAmelCase ( self : List[str] ) -> Union[str, Any]:
"""simple docstring"""
for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_UpperCAmelCase : Optional[int] = TFXGLMModel.from_pretrained(lowerCAmelCase__ )
self.assertIsNotNone(lowerCAmelCase__ )
@unittest.skip(reason="Currently, model embeddings are going to undergo a major refactor." )
def _lowerCAmelCase ( self : Union[str, Any] ) -> int:
"""simple docstring"""
super().test_resize_token_embeddings()
@require_tf
class A__ ( unittest.TestCase ):
"""simple docstring"""
@slow
def _lowerCAmelCase ( self : Optional[int] , lowerCAmelCase__ : Optional[Any]=True ) -> Tuple:
"""simple docstring"""
_UpperCAmelCase : Optional[int] = TFXGLMForCausalLM.from_pretrained("facebook/xglm-564M" )
_UpperCAmelCase : Any = tf.convert_to_tensor([[2, 2_6_8, 9_8_6_5]] , dtype=tf.intaa ) # The dog
# </s> The dog is a very friendly dog. He is very affectionate and loves to play with other
# fmt: off
_UpperCAmelCase : int = [2, 2_6_8, 9_8_6_5, 6_7, 1_1, 1_9_8_8, 5_7_2_5_2, 9_8_6_5, 5, 9_8_4, 6_7, 1_9_8_8, 2_1_3_8_3_8, 1_6_5_8, 5_3, 7_0_4_4_6, 3_3, 6_6_5_7, 2_7_8, 1_5_8_1]
# fmt: on
_UpperCAmelCase : Dict = model.generate(lowerCAmelCase__ , do_sample=lowerCAmelCase__ , num_beams=1 )
if verify_outputs:
self.assertListEqual(output_ids[0].numpy().tolist() , lowerCAmelCase__ )
@slow
def _lowerCAmelCase ( self : List[Any] ) -> str:
"""simple docstring"""
_UpperCAmelCase : List[str] = XGLMTokenizer.from_pretrained("facebook/xglm-564M" )
_UpperCAmelCase : Optional[Any] = TFXGLMForCausalLM.from_pretrained("facebook/xglm-564M" )
tf.random.set_seed(0 )
_UpperCAmelCase : Any = tokenizer("Today is a nice day and" , return_tensors="tf" )
_UpperCAmelCase : int = tokenized.input_ids
# forces the generation to happen on CPU, to avoid GPU-related quirks (and assure same output regardless of the available devices)
with tf.device(":/CPU:0" ):
_UpperCAmelCase : List[Any] = model.generate(lowerCAmelCase__ , do_sample=lowerCAmelCase__ , seed=[7, 0] )
_UpperCAmelCase : Any = tokenizer.decode(output_ids[0] , skip_special_tokens=lowerCAmelCase__ )
_UpperCAmelCase : List[Any] = (
"Today is a nice day and warm evening here over Southern Alberta!! Today when they closed schools due"
)
self.assertEqual(lowerCAmelCase__ , lowerCAmelCase__ )
@slow
def _lowerCAmelCase ( self : Optional[int] ) -> str:
"""simple docstring"""
_UpperCAmelCase : Optional[Any] = TFXGLMForCausalLM.from_pretrained("facebook/xglm-564M" )
_UpperCAmelCase : List[Any] = XGLMTokenizer.from_pretrained("facebook/xglm-564M" )
_UpperCAmelCase : Optional[int] = "left"
# use different length sentences to test batching
_UpperCAmelCase : Tuple = [
"This is an extremelly long sentence that only exists to test the ability of the model to cope with "
"left-padding, such as in batched generation. The output for the sequence below should be the same "
"regardless of whether left padding is applied or not. When",
"Hello, my dog is a little",
]
_UpperCAmelCase : Dict = tokenizer(lowerCAmelCase__ , return_tensors="tf" , padding=lowerCAmelCase__ )
_UpperCAmelCase : List[Any] = inputs["input_ids"]
_UpperCAmelCase : Dict = model.generate(input_ids=lowerCAmelCase__ , attention_mask=inputs["attention_mask"] , max_new_tokens=1_2 )
_UpperCAmelCase : int = tokenizer(sentences[0] , return_tensors="tf" ).input_ids
_UpperCAmelCase : Dict = model.generate(input_ids=lowerCAmelCase__ , max_new_tokens=1_2 )
_UpperCAmelCase : Optional[int] = tokenizer(sentences[1] , return_tensors="tf" ).input_ids
_UpperCAmelCase : List[Any] = model.generate(input_ids=lowerCAmelCase__ , max_new_tokens=1_2 )
_UpperCAmelCase : List[str] = tokenizer.batch_decode(lowerCAmelCase__ , skip_special_tokens=lowerCAmelCase__ )
_UpperCAmelCase : Tuple = tokenizer.decode(output_non_padded[0] , skip_special_tokens=lowerCAmelCase__ )
_UpperCAmelCase : List[str] = tokenizer.decode(output_padded[0] , skip_special_tokens=lowerCAmelCase__ )
_UpperCAmelCase : Union[str, Any] = [
"This is an extremelly long sentence that only exists to test the ability of the model to cope with "
"left-padding, such as in batched generation. The output for the sequence below should be the same "
"regardless of whether left padding is applied or not. When left padding is applied, the sequence will be "
"a single",
"Hello, my dog is a little bit of a shy one, but he is very friendly",
]
self.assertListEqual(lowerCAmelCase__ , lowerCAmelCase__ )
self.assertListEqual(lowerCAmelCase__ , [non_padded_sentence, padded_sentence] )
| 17
| 0
|
'''simple docstring'''
import math
def __UpperCAmelCase ( a_: int ):
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5, int(math.sqrt(UpperCAmelCase__ ) + 1 ), 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def __UpperCAmelCase ( a_: int = 10_001 ):
try:
_UpperCAmelCase : Any = int(UpperCAmelCase__ )
except (TypeError, ValueError):
raise TypeError("Parameter nth must be int or castable to int." ) from None
if nth <= 0:
raise ValueError("Parameter nth must be greater than or equal to one." )
_UpperCAmelCase : list[int] = []
_UpperCAmelCase : List[str] = 2
while len(UpperCAmelCase__ ) < nth:
if is_prime(UpperCAmelCase__ ):
primes.append(UpperCAmelCase__ )
num += 1
else:
num += 1
return primes[len(UpperCAmelCase__ ) - 1]
if __name__ == "__main__":
print(f'{solution() = }')
| 370
|
'''simple docstring'''
import os
import pytest
import yaml
from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
"files", [
["full:README.md", "dataset_infos.json"],
["empty:README.md", "dataset_infos.json"],
["dataset_infos.json"],
["full:README.md"],
], )
def __UpperCAmelCase ( a_: Tuple, a_: Any ):
_UpperCAmelCase : Union[str, Any] = tmp_path_factory.mktemp("dset_infos_dir" )
if "full:README.md" in files:
with open(dataset_infos_dir / "README.md", "w" ) as f:
f.write("---\ndataset_info:\n dataset_size: 42\n---" )
if "empty:README.md" in files:
with open(dataset_infos_dir / "README.md", "w" ) as f:
f.write("" )
# we want to support dataset_infos.json for backward compatibility
if "dataset_infos.json" in files:
with open(dataset_infos_dir / "dataset_infos.json", "w" ) as f:
f.write("{\"default\": {\"dataset_size\": 42}}" )
_UpperCAmelCase : List[str] = DatasetInfosDict.from_directory(a_ )
assert dataset_infos
assert dataset_infos["default"].dataset_size == 42
@pytest.mark.parametrize(
"dataset_info", [
DatasetInfo(),
DatasetInfo(
description="foo", features=Features({"a": Value("int32" )} ), builder_name="builder", config_name="config", version="1.0.0", splits=[{"name": "train"}], download_size=42, ),
], )
def __UpperCAmelCase ( a_: Union[str, Any], a_: DatasetInfo ):
_UpperCAmelCase : Tuple = str(a_ )
dataset_info.write_to_directory(a_ )
_UpperCAmelCase : Any = DatasetInfo.from_directory(a_ )
assert dataset_info == reloaded
assert os.path.exists(os.path.join(a_, "dataset_info.json" ) )
def __UpperCAmelCase ( ):
_UpperCAmelCase : Optional[int] = DatasetInfo(
description="foo", citation="bar", homepage="https://foo.bar", license="CC0", features=Features({"a": Value("int32" )} ), post_processed={}, supervised_keys=(), task_templates=[], builder_name="builder", config_name="config", version="1.0.0", splits=[{"name": "train", "num_examples": 42}], download_checksums={}, download_size=1_337, post_processing_size=442, dataset_size=1_234, size_in_bytes=1_337 + 442 + 1_234, )
_UpperCAmelCase : Tuple = dataset_info._to_yaml_dict()
assert sorted(a_ ) == sorted(DatasetInfo._INCLUDED_INFO_IN_YAML )
for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
assert key in dataset_info_yaml_dict
assert isinstance(dataset_info_yaml_dict[key], (list, dict, int, str) )
_UpperCAmelCase : List[Any] = yaml.safe_dump(a_ )
_UpperCAmelCase : Optional[int] = yaml.safe_load(a_ )
assert dataset_info_yaml_dict == reloaded
def __UpperCAmelCase ( ):
_UpperCAmelCase : str = DatasetInfo()
_UpperCAmelCase : List[str] = dataset_info._to_yaml_dict()
assert dataset_info_yaml_dict == {}
@pytest.mark.parametrize(
"dataset_infos_dict", [
DatasetInfosDict(),
DatasetInfosDict({"default": DatasetInfo()} ),
DatasetInfosDict({"my_config_name": DatasetInfo()} ),
DatasetInfosDict(
{
"default": DatasetInfo(
description="foo", features=Features({"a": Value("int32" )} ), builder_name="builder", config_name="config", version="1.0.0", splits=[{"name": "train"}], download_size=42, )
} ),
DatasetInfosDict(
{
"v1": DatasetInfo(dataset_size=42 ),
"v2": DatasetInfo(dataset_size=1_337 ),
} ),
], )
def __UpperCAmelCase ( a_: str, a_: DatasetInfosDict ):
_UpperCAmelCase : Union[str, Any] = str(a_ )
dataset_infos_dict.write_to_directory(a_ )
_UpperCAmelCase : Union[str, Any] = DatasetInfosDict.from_directory(a_ )
# the config_name of the dataset_infos_dict take over the attribute
for config_name, dataset_info in dataset_infos_dict.items():
_UpperCAmelCase : Optional[int] = config_name
# the yaml representation doesn't include fields like description or citation
# so we just test that we can recover what we can from the yaml
_UpperCAmelCase : List[str] = DatasetInfo._from_yaml_dict(dataset_info._to_yaml_dict() )
assert dataset_infos_dict == reloaded
if dataset_infos_dict:
assert os.path.exists(os.path.join(a_, "README.md" ) )
| 17
| 0
|
'''simple docstring'''
def __UpperCAmelCase ( a_: int ):
_UpperCAmelCase : Union[str, Any] = int(__a )
if n_element < 1:
_UpperCAmelCase : Dict = ValueError("a should be a positive number" )
raise my_error
_UpperCAmelCase : Dict = [1]
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase : Optional[int] = (0, 0, 0)
_UpperCAmelCase : List[str] = 1
while index < n_element:
while hamming_list[i] * 2 <= hamming_list[-1]:
i += 1
while hamming_list[j] * 3 <= hamming_list[-1]:
j += 1
while hamming_list[k] * 5 <= hamming_list[-1]:
k += 1
hamming_list.append(
min(hamming_list[i] * 2, hamming_list[j] * 3, hamming_list[k] * 5 ) )
index += 1
return hamming_list
if __name__ == "__main__":
__a = input('Enter the last number (nth term) of the Hamming Number Series: ')
print('Formula of Hamming Number Series => 2^i * 3^j * 5^k')
__a = hamming(int(n))
print('-----------------------------------------------------')
print(f'The list with nth numbers is: {hamming_numbers}')
print('-----------------------------------------------------')
| 371
|
'''simple docstring'''
from math import factorial
def __UpperCAmelCase ( a_: int = 100 ):
return sum(map(a_, str(factorial(a_ ) ) ) )
if __name__ == "__main__":
print(solution(int(input('Enter the Number: ').strip())))
| 17
| 0
|
'''simple docstring'''
import json
import os
from pathlib import Path
import pytest
from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadManager
from datasets.utils.file_utils import hash_url_to_filename
__a = 'http://www.mocksite.com/file1.txt'
__a = '"text": ["foo", "foo"]'
__a = '6d8ce9aa78a471c7477201efbeabd3bb01ac2e7d100a6dc024ba1608361f90a8'
class A__ :
"""simple docstring"""
UpperCamelCase_ : Tuple = 2_00
UpperCamelCase_ : Dict = {"""Content-Length""": """100"""}
UpperCamelCase_ : Dict = {}
def _lowerCAmelCase ( self : List[Any] , **lowerCAmelCase__ : Tuple ) -> Optional[int]:
"""simple docstring"""
return [bytes(lowercase_ , "utf-8" )]
def __UpperCAmelCase ( *a_: Union[str, Any], **a_: Tuple ):
return MockResponse()
@pytest.mark.parametrize("urls_type", [str, list, dict] )
def __UpperCAmelCase ( a_: List[Any], a_: Union[str, Any], a_: List[str] ):
import requests
monkeypatch.setattr(__lowerCamelCase, "request", __lowerCamelCase )
_UpperCAmelCase : Tuple = URL
if issubclass(__lowerCamelCase, __lowerCamelCase ):
_UpperCAmelCase : Union[str, Any] = url
elif issubclass(__lowerCamelCase, __lowerCamelCase ):
_UpperCAmelCase : Tuple = [url]
elif issubclass(__lowerCamelCase, __lowerCamelCase ):
_UpperCAmelCase : List[str] = {"train": url}
_UpperCAmelCase : Union[str, Any] = "dummy"
_UpperCAmelCase : Optional[Any] = "downloads"
_UpperCAmelCase : Optional[int] = tmp_path
_UpperCAmelCase : List[str] = DownloadConfig(
cache_dir=os.path.join(__lowerCamelCase, __lowerCamelCase ), use_etag=__lowerCamelCase, )
_UpperCAmelCase : List[Any] = DownloadManager(dataset_name=__lowerCamelCase, download_config=__lowerCamelCase )
_UpperCAmelCase : List[Any] = dl_manager.download(__lowerCamelCase )
_UpperCAmelCase : List[Any] = urls
for downloaded_paths in [downloaded_paths]:
if isinstance(__lowerCamelCase, __lowerCamelCase ):
_UpperCAmelCase : str = [downloaded_paths]
_UpperCAmelCase : int = [urls]
elif isinstance(__lowerCamelCase, __lowerCamelCase ):
assert "train" in downloaded_paths.keys()
_UpperCAmelCase : Tuple = downloaded_paths.values()
_UpperCAmelCase : str = urls.values()
assert downloaded_paths
for downloaded_path, input_url in zip(__lowerCamelCase, __lowerCamelCase ):
assert downloaded_path == dl_manager.downloaded_paths[input_url]
_UpperCAmelCase : List[str] = Path(__lowerCamelCase )
_UpperCAmelCase : List[str] = downloaded_path.parts
assert parts[-1] == HASH
assert parts[-2] == cache_subdir
assert downloaded_path.exists()
_UpperCAmelCase : Union[str, Any] = downloaded_path.read_text()
assert content == CONTENT
_UpperCAmelCase : str = downloaded_path.with_suffix(".json" )
assert metadata_downloaded_path.exists()
_UpperCAmelCase : Dict = json.loads(metadata_downloaded_path.read_text() )
assert metadata_content == {"url": URL, "etag": None}
@pytest.mark.parametrize("paths_type", [str, list, dict] )
def __UpperCAmelCase ( a_: Optional[int], a_: Tuple, a_: int ):
_UpperCAmelCase : Dict = str(__lowerCamelCase )
if issubclass(__lowerCamelCase, __lowerCamelCase ):
_UpperCAmelCase : List[str] = filename
elif issubclass(__lowerCamelCase, __lowerCamelCase ):
_UpperCAmelCase : Optional[int] = [filename]
elif issubclass(__lowerCamelCase, __lowerCamelCase ):
_UpperCAmelCase : Optional[Any] = {"train": filename}
_UpperCAmelCase : Optional[int] = "dummy"
_UpperCAmelCase : List[Any] = xz_file.parent
_UpperCAmelCase : List[Any] = "extracted"
_UpperCAmelCase : Tuple = DownloadConfig(
cache_dir=__lowerCamelCase, use_etag=__lowerCamelCase, )
_UpperCAmelCase : Any = DownloadManager(dataset_name=__lowerCamelCase, download_config=__lowerCamelCase )
_UpperCAmelCase : List[str] = dl_manager.extract(__lowerCamelCase )
_UpperCAmelCase : Dict = paths
for extracted_paths in [extracted_paths]:
if isinstance(__lowerCamelCase, __lowerCamelCase ):
_UpperCAmelCase : Optional[int] = [extracted_paths]
_UpperCAmelCase : str = [paths]
elif isinstance(__lowerCamelCase, __lowerCamelCase ):
assert "train" in extracted_paths.keys()
_UpperCAmelCase : str = extracted_paths.values()
_UpperCAmelCase : Tuple = paths.values()
assert extracted_paths
for extracted_path, input_path in zip(__lowerCamelCase, __lowerCamelCase ):
assert extracted_path == dl_manager.extracted_paths[input_path]
_UpperCAmelCase : Union[str, Any] = Path(__lowerCamelCase )
_UpperCAmelCase : List[str] = extracted_path.parts
assert parts[-1] == hash_url_to_filename(__lowerCamelCase, etag=__lowerCamelCase )
assert parts[-2] == extracted_subdir
assert extracted_path.exists()
_UpperCAmelCase : List[str] = extracted_path.read_text()
_UpperCAmelCase : str = text_file.read_text()
assert extracted_file_content == expected_file_content
def __UpperCAmelCase ( a_: List[str], a_: Union[str, Any] ):
assert path.endswith(".jsonl" )
for num_items, line in enumerate(__lowerCamelCase, start=1 ):
_UpperCAmelCase : Any = json.loads(line.decode("utf-8" ) )
assert item.keys() == {"col_1", "col_2", "col_3"}
assert num_items == 4
@pytest.mark.parametrize("archive_jsonl", ["tar_jsonl_path", "zip_jsonl_path"] )
def __UpperCAmelCase ( a_: int, a_: Union[str, Any] ):
_UpperCAmelCase : int = request.getfixturevalue(__lowerCamelCase )
_UpperCAmelCase : Dict = DownloadManager()
for num_jsonl, (path, file) in enumerate(dl_manager.iter_archive(__lowerCamelCase ), start=1 ):
_test_jsonl(__lowerCamelCase, __lowerCamelCase )
assert num_jsonl == 2
@pytest.mark.parametrize("archive_nested_jsonl", ["tar_nested_jsonl_path", "zip_nested_jsonl_path"] )
def __UpperCAmelCase ( a_: Any, a_: Tuple ):
_UpperCAmelCase : Optional[int] = request.getfixturevalue(__lowerCamelCase )
_UpperCAmelCase : int = DownloadManager()
for num_tar, (path, file) in enumerate(dl_manager.iter_archive(__lowerCamelCase ), start=1 ):
for num_jsonl, (subpath, subfile) in enumerate(dl_manager.iter_archive(__lowerCamelCase ), start=1 ):
_test_jsonl(__lowerCamelCase, __lowerCamelCase )
assert num_tar == 1
assert num_jsonl == 2
def __UpperCAmelCase ( a_: Optional[int] ):
_UpperCAmelCase : List[Any] = DownloadManager()
for num_file, file in enumerate(dl_manager.iter_files(__lowerCamelCase ), start=1 ):
assert os.path.basename(__lowerCamelCase ) == ("test.txt" if num_file == 1 else "train.txt")
assert num_file == 2
| 350
|
'''simple docstring'''
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
__a = (3, 9, -11, 0, 7, 5, 1, -1)
__a = (4, 6, 2, 0, 8, 10, 3, -2)
@dataclass
class A__ :
"""simple docstring"""
UpperCamelCase_ : int
UpperCamelCase_ : Node | None
class A__ :
"""simple docstring"""
def __init__( self : Dict , lowerCAmelCase__ : Iterable[int] ) -> None:
"""simple docstring"""
_UpperCAmelCase : Node | None = None
for i in sorted(lowerCAmelCase__ , reverse=lowerCAmelCase__ ):
_UpperCAmelCase : str = Node(lowerCAmelCase__ , self.head )
def __iter__( self : int ) -> Iterator[int]:
"""simple docstring"""
_UpperCAmelCase : List[Any] = self.head
while node:
yield node.data
_UpperCAmelCase : List[str] = node.next_node
def __len__( self : Any ) -> int:
"""simple docstring"""
return sum(1 for _ in self )
def __str__( self : Union[str, Any] ) -> str:
"""simple docstring"""
return " -> ".join([str(lowerCAmelCase__ ) for node in self] )
def __UpperCAmelCase ( a_: SortedLinkedList, a_: SortedLinkedList ):
return SortedLinkedList(list(a_ ) + list(a_ ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
__a = SortedLinkedList
print(merge_lists(SSL(test_data_odd), SSL(test_data_even)))
| 17
| 0
|
'''simple docstring'''
import os
import tempfile
import unittest
from transformers import DistilBertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
DistilBertForMaskedLM,
DistilBertForMultipleChoice,
DistilBertForQuestionAnswering,
DistilBertForSequenceClassification,
DistilBertForTokenClassification,
DistilBertModel,
)
class A__ ( __snake_case ):
"""simple docstring"""
def __init__( self : Dict , lowerCAmelCase__ : List[str] , lowerCAmelCase__ : Optional[Any]=1_3 , lowerCAmelCase__ : Optional[int]=7 , lowerCAmelCase__ : str=True , lowerCAmelCase__ : Any=True , lowerCAmelCase__ : str=False , lowerCAmelCase__ : str=True , lowerCAmelCase__ : Union[str, Any]=9_9 , lowerCAmelCase__ : Tuple=3_2 , lowerCAmelCase__ : Dict=5 , lowerCAmelCase__ : List[str]=4 , lowerCAmelCase__ : List[str]=3_7 , lowerCAmelCase__ : List[Any]="gelu" , lowerCAmelCase__ : str=0.1 , lowerCAmelCase__ : List[str]=0.1 , lowerCAmelCase__ : str=5_1_2 , lowerCAmelCase__ : int=1_6 , lowerCAmelCase__ : List[str]=2 , lowerCAmelCase__ : List[Any]=0.02 , lowerCAmelCase__ : List[str]=3 , lowerCAmelCase__ : List[str]=4 , lowerCAmelCase__ : str=None , ) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase : List[Any] = parent
_UpperCAmelCase : List[Any] = batch_size
_UpperCAmelCase : str = seq_length
_UpperCAmelCase : Any = is_training
_UpperCAmelCase : Any = use_input_mask
_UpperCAmelCase : str = use_token_type_ids
_UpperCAmelCase : Dict = use_labels
_UpperCAmelCase : int = vocab_size
_UpperCAmelCase : Union[str, Any] = hidden_size
_UpperCAmelCase : List[str] = num_hidden_layers
_UpperCAmelCase : str = num_attention_heads
_UpperCAmelCase : Optional[int] = intermediate_size
_UpperCAmelCase : str = hidden_act
_UpperCAmelCase : Union[str, Any] = hidden_dropout_prob
_UpperCAmelCase : Optional[Any] = attention_probs_dropout_prob
_UpperCAmelCase : str = max_position_embeddings
_UpperCAmelCase : Dict = type_vocab_size
_UpperCAmelCase : List[Any] = type_sequence_label_size
_UpperCAmelCase : Union[str, Any] = initializer_range
_UpperCAmelCase : str = num_labels
_UpperCAmelCase : Dict = num_choices
_UpperCAmelCase : Optional[int] = scope
def _lowerCAmelCase ( self : Optional[Any] ) -> Dict:
"""simple docstring"""
_UpperCAmelCase : Dict = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_UpperCAmelCase : Dict = None
if self.use_input_mask:
_UpperCAmelCase : List[Any] = random_attention_mask([self.batch_size, self.seq_length] )
_UpperCAmelCase : Tuple = None
_UpperCAmelCase : List[str] = None
_UpperCAmelCase : Dict = None
if self.use_labels:
_UpperCAmelCase : List[str] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_UpperCAmelCase : int = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
_UpperCAmelCase : Optional[Any] = ids_tensor([self.batch_size] , self.num_choices )
_UpperCAmelCase : List[Any] = self.get_config()
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def _lowerCAmelCase ( self : Any ) -> Optional[int]:
"""simple docstring"""
return DistilBertConfig(
vocab_size=self.vocab_size , dim=self.hidden_size , n_layers=self.num_hidden_layers , n_heads=self.num_attention_heads , hidden_dim=self.intermediate_size , hidden_act=self.hidden_act , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , )
def _lowerCAmelCase ( self : Tuple , lowerCAmelCase__ : Tuple , lowerCAmelCase__ : List[Any] , lowerCAmelCase__ : Optional[Any] , lowerCAmelCase__ : Union[str, Any] , lowerCAmelCase__ : str , lowerCAmelCase__ : int ) -> Union[str, Any]:
"""simple docstring"""
_UpperCAmelCase : List[str] = DistilBertModel(config=a_ )
model.to(a_ )
model.eval()
_UpperCAmelCase : int = model(a_ , a_ )
_UpperCAmelCase : List[Any] = model(a_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def _lowerCAmelCase ( self : List[Any] , lowerCAmelCase__ : Dict , lowerCAmelCase__ : int , lowerCAmelCase__ : Dict , lowerCAmelCase__ : List[str] , lowerCAmelCase__ : int , lowerCAmelCase__ : Optional[int] ) -> Optional[int]:
"""simple docstring"""
_UpperCAmelCase : Optional[Any] = DistilBertForMaskedLM(config=a_ )
model.to(a_ )
model.eval()
_UpperCAmelCase : Union[str, Any] = model(a_ , attention_mask=a_ , labels=a_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def _lowerCAmelCase ( self : Union[str, Any] , lowerCAmelCase__ : Union[str, Any] , lowerCAmelCase__ : Any , lowerCAmelCase__ : Dict , lowerCAmelCase__ : Union[str, Any] , lowerCAmelCase__ : Union[str, Any] , lowerCAmelCase__ : List[Any] ) -> Tuple:
"""simple docstring"""
_UpperCAmelCase : Tuple = DistilBertForQuestionAnswering(config=a_ )
model.to(a_ )
model.eval()
_UpperCAmelCase : Optional[Any] = model(
a_ , attention_mask=a_ , start_positions=a_ , end_positions=a_ )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def _lowerCAmelCase ( self : List[Any] , lowerCAmelCase__ : Any , lowerCAmelCase__ : Tuple , lowerCAmelCase__ : Optional[int] , lowerCAmelCase__ : Any , lowerCAmelCase__ : Tuple , lowerCAmelCase__ : List[Any] ) -> int:
"""simple docstring"""
_UpperCAmelCase : Any = self.num_labels
_UpperCAmelCase : Optional[int] = DistilBertForSequenceClassification(a_ )
model.to(a_ )
model.eval()
_UpperCAmelCase : Union[str, Any] = model(a_ , attention_mask=a_ , labels=a_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _lowerCAmelCase ( self : int , lowerCAmelCase__ : Union[str, Any] , lowerCAmelCase__ : Optional[int] , lowerCAmelCase__ : Union[str, Any] , lowerCAmelCase__ : Dict , lowerCAmelCase__ : List[str] , lowerCAmelCase__ : List[str] ) -> List[Any]:
"""simple docstring"""
_UpperCAmelCase : Union[str, Any] = self.num_labels
_UpperCAmelCase : Optional[int] = DistilBertForTokenClassification(config=a_ )
model.to(a_ )
model.eval()
_UpperCAmelCase : Dict = model(a_ , attention_mask=a_ , labels=a_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def _lowerCAmelCase ( self : Union[str, Any] , lowerCAmelCase__ : Optional[Any] , lowerCAmelCase__ : Optional[Any] , lowerCAmelCase__ : Any , lowerCAmelCase__ : Any , lowerCAmelCase__ : Dict , lowerCAmelCase__ : Union[str, Any] ) -> Optional[int]:
"""simple docstring"""
_UpperCAmelCase : List[Any] = self.num_choices
_UpperCAmelCase : Any = DistilBertForMultipleChoice(config=a_ )
model.to(a_ )
model.eval()
_UpperCAmelCase : Union[str, Any] = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
_UpperCAmelCase : List[Any] = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
_UpperCAmelCase : Optional[int] = model(
a_ , attention_mask=a_ , labels=a_ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def _lowerCAmelCase ( self : Any ) -> Tuple:
"""simple docstring"""
_UpperCAmelCase : List[Any] = self.prepare_config_and_inputs()
(_UpperCAmelCase) : str = config_and_inputs
_UpperCAmelCase : Optional[Any] = {'''input_ids''': input_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_torch
class A__ ( __snake_case , __snake_case , unittest.TestCase ):
"""simple docstring"""
UpperCamelCase_ : Optional[Any] = (
(
DistilBertModel,
DistilBertForMaskedLM,
DistilBertForMultipleChoice,
DistilBertForQuestionAnswering,
DistilBertForSequenceClassification,
DistilBertForTokenClassification,
)
if is_torch_available()
else None
)
UpperCamelCase_ : Optional[Any] = (
{
'''feature-extraction''': DistilBertModel,
'''fill-mask''': DistilBertForMaskedLM,
'''question-answering''': DistilBertForQuestionAnswering,
'''text-classification''': DistilBertForSequenceClassification,
'''token-classification''': DistilBertForTokenClassification,
'''zero-shot''': DistilBertForSequenceClassification,
}
if is_torch_available()
else {}
)
UpperCamelCase_ : Tuple = True
UpperCamelCase_ : Any = True
UpperCamelCase_ : List[Any] = True
UpperCamelCase_ : Any = True
def _lowerCAmelCase ( self : str ) -> str:
"""simple docstring"""
_UpperCAmelCase : Any = DistilBertModelTester(self )
_UpperCAmelCase : List[str] = ConfigTester(self , config_class=a_ , dim=3_7 )
def _lowerCAmelCase ( self : Optional[Any] ) -> int:
"""simple docstring"""
self.config_tester.run_common_tests()
def _lowerCAmelCase ( self : Dict ) -> Union[str, Any]:
"""simple docstring"""
_UpperCAmelCase : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_model(*a_ )
def _lowerCAmelCase ( self : Union[str, Any] ) -> Any:
"""simple docstring"""
_UpperCAmelCase : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_masked_lm(*a_ )
def _lowerCAmelCase ( self : Tuple ) -> List[str]:
"""simple docstring"""
_UpperCAmelCase : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_question_answering(*a_ )
def _lowerCAmelCase ( self : Optional[Any] ) -> Any:
"""simple docstring"""
_UpperCAmelCase : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_sequence_classification(*a_ )
def _lowerCAmelCase ( self : Any ) -> List[str]:
"""simple docstring"""
_UpperCAmelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_token_classification(*a_ )
def _lowerCAmelCase ( self : Union[str, Any] ) -> Any:
"""simple docstring"""
_UpperCAmelCase : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_multiple_choice(*a_ )
@slow
def _lowerCAmelCase ( self : Optional[int] ) -> Tuple:
"""simple docstring"""
for model_name in DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_UpperCAmelCase : Tuple = DistilBertModel.from_pretrained(a_ )
self.assertIsNotNone(a_ )
@slow
@require_torch_gpu
def _lowerCAmelCase ( self : List[Any] ) -> int:
"""simple docstring"""
_UpperCAmelCase : Dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
# BertForMultipleChoice behaves incorrectly in JIT environments.
if model_class == DistilBertForMultipleChoice:
return
_UpperCAmelCase : List[str] = True
_UpperCAmelCase : Tuple = model_class(config=a_ )
_UpperCAmelCase : Any = self._prepare_for_class(a_ , a_ )
_UpperCAmelCase : Dict = torch.jit.trace(
a_ , (inputs_dict["input_ids"].to("cpu" ), inputs_dict["attention_mask"].to("cpu" )) )
with tempfile.TemporaryDirectory() as tmp:
torch.jit.save(a_ , os.path.join(a_ , "traced_model.pt" ) )
_UpperCAmelCase : int = torch.jit.load(os.path.join(a_ , "traced_model.pt" ) , map_location=a_ )
loaded(inputs_dict["input_ids"].to(a_ ) , inputs_dict["attention_mask"].to(a_ ) )
@require_torch
class A__ ( unittest.TestCase ):
"""simple docstring"""
@slow
def _lowerCAmelCase ( self : Optional[int] ) -> List[str]:
"""simple docstring"""
_UpperCAmelCase : Optional[int] = DistilBertModel.from_pretrained("distilbert-base-uncased" )
_UpperCAmelCase : List[Any] = torch.tensor([[0, 3_4_5, 2_3_2, 3_2_8, 7_4_0, 1_4_0, 1_6_9_5, 6_9, 6_0_7_8, 1_5_8_8, 2]] )
_UpperCAmelCase : Any = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
with torch.no_grad():
_UpperCAmelCase : List[Any] = model(a_ , attention_mask=a_ )[0]
_UpperCAmelCase : Tuple = torch.Size((1, 1_1, 7_6_8) )
self.assertEqual(output.shape , a_ )
_UpperCAmelCase : Optional[int] = torch.tensor(
[[[-0.1639, 0.3299, 0.1648], [-0.1746, 0.3289, 0.1710], [-0.1884, 0.3357, 0.1810]]] )
self.assertTrue(torch.allclose(output[:, 1:4, 1:4] , a_ , atol=1e-4 ) )
| 351
|
'''simple docstring'''
def __UpperCAmelCase ( a_: str ):
if not all(char in "01" for char in bin_string ):
raise ValueError("Non-binary value was passed to the function" )
if not bin_string:
raise ValueError("Empty string was passed to the function" )
_UpperCAmelCase : Optional[Any] = ""
while len(a_ ) % 3 != 0:
_UpperCAmelCase : List[Any] = "0" + bin_string
_UpperCAmelCase : Dict = [
bin_string[index : index + 3]
for index in range(len(a_ ) )
if index % 3 == 0
]
for bin_group in bin_string_in_3_list:
_UpperCAmelCase : Optional[Any] = 0
for index, val in enumerate(a_ ):
oct_val += int(2 ** (2 - index) * int(a_ ) )
oct_string += str(a_ )
return oct_string
if __name__ == "__main__":
from doctest import testmod
testmod()
| 17
| 0
|
'''simple docstring'''
from math import sqrt
def __UpperCAmelCase ( a_: str ) -> Any:
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5, int(sqrt(_A ) + 1 ), 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def __UpperCAmelCase ( a_: Tuple = 10_001 ) -> Dict:
_UpperCAmelCase : Tuple = 0
_UpperCAmelCase : Optional[Any] = 1
while count != nth and number < 3:
number += 1
if is_prime(_A ):
count += 1
while count != nth:
number += 2
if is_prime(_A ):
count += 1
return number
if __name__ == "__main__":
print(f'{solution() = }')
| 352
|
'''simple docstring'''
from datetime import datetime
import matplotlib.pyplot as plt
import torch
def __UpperCAmelCase ( a_: str ):
for param in module.parameters():
_UpperCAmelCase : Any = False
def __UpperCAmelCase ( ):
_UpperCAmelCase : Union[str, Any] = "cuda" if torch.cuda.is_available() else "cpu"
if torch.backends.mps.is_available() and torch.backends.mps.is_built():
_UpperCAmelCase : int = "mps"
if device == "mps":
print(
"WARNING: MPS currently doesn't seem to work, and messes up backpropagation without any visible torch"
" errors. I recommend using CUDA on a colab notebook or CPU instead if you're facing inexplicable issues"
" with generations." )
return device
def __UpperCAmelCase ( a_: Optional[Any] ):
_UpperCAmelCase : int = plt.imshow(a_ )
fig.axes.get_xaxis().set_visible(a_ )
fig.axes.get_yaxis().set_visible(a_ )
plt.show()
def __UpperCAmelCase ( ):
_UpperCAmelCase : Dict = datetime.now()
_UpperCAmelCase : List[str] = current_time.strftime("%H:%M:%S" )
return timestamp
| 17
| 0
|
'''simple docstring'''
import enum
import shutil
import sys
__a , __a = shutil.get_terminal_size()
__a = {'UP': 'A', 'DOWN': 'B', 'RIGHT': 'C', 'LEFT': 'D'}
class A__ ( enum.Enum ):
"""simple docstring"""
UpperCamelCase_ : Tuple = 0
UpperCamelCase_ : List[str] = 1
def __UpperCAmelCase ( a_: Dict, a_: Any="" ):
sys.stdout.write(str(a_ ) + end )
sys.stdout.flush()
def __UpperCAmelCase ( a_: str, a_: List[Any], a_: str="" ):
forceWrite(f"""\u001b[{color}m{content}\u001b[0m""", a_ )
def __UpperCAmelCase ( ):
forceWrite("\r" )
def __UpperCAmelCase ( a_: int, a_: str ):
forceWrite(f"""\033[{num_lines}{CURSOR_TO_CHAR[direction.upper()]}""" )
def __UpperCAmelCase ( ):
forceWrite(" " * TERMINAL_WIDTH )
reset_cursor()
def __UpperCAmelCase ( ):
reset_cursor()
forceWrite("-" * TERMINAL_WIDTH )
| 353
|
'''simple docstring'''
import torch
from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class A__ ( UpperCamelCase ):
"""simple docstring"""
UpperCamelCase_ : Optional[int] = (EulerDiscreteScheduler,)
UpperCamelCase_ : Tuple = 10
def _lowerCAmelCase ( self : Dict , **lowerCAmelCase__ : Tuple ) -> Any:
"""simple docstring"""
_UpperCAmelCase : str = {
"num_train_timesteps": 1_1_0_0,
"beta_start": 0.0001,
"beta_end": 0.02,
"beta_schedule": "linear",
}
config.update(**lowerCAmelCase__ )
return config
def _lowerCAmelCase ( self : Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
self.check_over_configs(num_train_timesteps=lowerCAmelCase__ )
def _lowerCAmelCase ( self : Any ) -> List[str]:
"""simple docstring"""
for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001] , [0.0002, 0.002, 0.02] ):
self.check_over_configs(beta_start=lowerCAmelCase__ , beta_end=lowerCAmelCase__ )
def _lowerCAmelCase ( self : List[str] ) -> List[str]:
"""simple docstring"""
for schedule in ["linear", "scaled_linear"]:
self.check_over_configs(beta_schedule=lowerCAmelCase__ )
def _lowerCAmelCase ( self : Union[str, Any] ) -> Optional[Any]:
"""simple docstring"""
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=lowerCAmelCase__ )
def _lowerCAmelCase ( self : List[Any] ) -> List[Any]:
"""simple docstring"""
_UpperCAmelCase : List[str] = self.scheduler_classes[0]
_UpperCAmelCase : int = self.get_scheduler_config()
_UpperCAmelCase : Optional[int] = scheduler_class(**lowerCAmelCase__ )
scheduler.set_timesteps(self.num_inference_steps )
_UpperCAmelCase : int = torch.manual_seed(0 )
_UpperCAmelCase : Any = self.dummy_model()
_UpperCAmelCase : List[str] = self.dummy_sample_deter * scheduler.init_noise_sigma
_UpperCAmelCase : List[Any] = sample.to(lowerCAmelCase__ )
for i, t in enumerate(scheduler.timesteps ):
_UpperCAmelCase : List[str] = scheduler.scale_model_input(lowerCAmelCase__ , lowerCAmelCase__ )
_UpperCAmelCase : int = model(lowerCAmelCase__ , lowerCAmelCase__ )
_UpperCAmelCase : int = scheduler.step(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , generator=lowerCAmelCase__ )
_UpperCAmelCase : Optional[int] = output.prev_sample
_UpperCAmelCase : Optional[Any] = torch.sum(torch.abs(lowerCAmelCase__ ) )
_UpperCAmelCase : Tuple = torch.mean(torch.abs(lowerCAmelCase__ ) )
assert abs(result_sum.item() - 10.0807 ) < 1e-2
assert abs(result_mean.item() - 0.0131 ) < 1e-3
def _lowerCAmelCase ( self : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
_UpperCAmelCase : Any = self.scheduler_classes[0]
_UpperCAmelCase : List[Any] = self.get_scheduler_config(prediction_type="v_prediction" )
_UpperCAmelCase : Any = scheduler_class(**lowerCAmelCase__ )
scheduler.set_timesteps(self.num_inference_steps )
_UpperCAmelCase : str = torch.manual_seed(0 )
_UpperCAmelCase : Optional[Any] = self.dummy_model()
_UpperCAmelCase : Union[str, Any] = self.dummy_sample_deter * scheduler.init_noise_sigma
_UpperCAmelCase : Tuple = sample.to(lowerCAmelCase__ )
for i, t in enumerate(scheduler.timesteps ):
_UpperCAmelCase : Union[str, Any] = scheduler.scale_model_input(lowerCAmelCase__ , lowerCAmelCase__ )
_UpperCAmelCase : int = model(lowerCAmelCase__ , lowerCAmelCase__ )
_UpperCAmelCase : Union[str, Any] = scheduler.step(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , generator=lowerCAmelCase__ )
_UpperCAmelCase : Union[str, Any] = output.prev_sample
_UpperCAmelCase : Tuple = torch.sum(torch.abs(lowerCAmelCase__ ) )
_UpperCAmelCase : Any = torch.mean(torch.abs(lowerCAmelCase__ ) )
assert abs(result_sum.item() - 0.0002 ) < 1e-2
assert abs(result_mean.item() - 2.26_76e-06 ) < 1e-3
def _lowerCAmelCase ( self : Tuple ) -> str:
"""simple docstring"""
_UpperCAmelCase : Optional[int] = self.scheduler_classes[0]
_UpperCAmelCase : List[Any] = self.get_scheduler_config()
_UpperCAmelCase : int = scheduler_class(**lowerCAmelCase__ )
scheduler.set_timesteps(self.num_inference_steps , device=lowerCAmelCase__ )
_UpperCAmelCase : Optional[int] = torch.manual_seed(0 )
_UpperCAmelCase : str = self.dummy_model()
_UpperCAmelCase : Any = self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
_UpperCAmelCase : str = sample.to(lowerCAmelCase__ )
for t in scheduler.timesteps:
_UpperCAmelCase : List[str] = scheduler.scale_model_input(lowerCAmelCase__ , lowerCAmelCase__ )
_UpperCAmelCase : Any = model(lowerCAmelCase__ , lowerCAmelCase__ )
_UpperCAmelCase : Tuple = scheduler.step(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , generator=lowerCAmelCase__ )
_UpperCAmelCase : int = output.prev_sample
_UpperCAmelCase : List[Any] = torch.sum(torch.abs(lowerCAmelCase__ ) )
_UpperCAmelCase : str = torch.mean(torch.abs(lowerCAmelCase__ ) )
assert abs(result_sum.item() - 10.0807 ) < 1e-2
assert abs(result_mean.item() - 0.0131 ) < 1e-3
def _lowerCAmelCase ( self : List[str] ) -> int:
"""simple docstring"""
_UpperCAmelCase : List[Any] = self.scheduler_classes[0]
_UpperCAmelCase : int = self.get_scheduler_config()
_UpperCAmelCase : Union[str, Any] = scheduler_class(**lowerCAmelCase__ , use_karras_sigmas=lowerCAmelCase__ )
scheduler.set_timesteps(self.num_inference_steps , device=lowerCAmelCase__ )
_UpperCAmelCase : Optional[int] = torch.manual_seed(0 )
_UpperCAmelCase : List[str] = self.dummy_model()
_UpperCAmelCase : str = self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
_UpperCAmelCase : Optional[int] = sample.to(lowerCAmelCase__ )
for t in scheduler.timesteps:
_UpperCAmelCase : List[Any] = scheduler.scale_model_input(lowerCAmelCase__ , lowerCAmelCase__ )
_UpperCAmelCase : str = model(lowerCAmelCase__ , lowerCAmelCase__ )
_UpperCAmelCase : Optional[Any] = scheduler.step(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , generator=lowerCAmelCase__ )
_UpperCAmelCase : List[Any] = output.prev_sample
_UpperCAmelCase : List[Any] = torch.sum(torch.abs(lowerCAmelCase__ ) )
_UpperCAmelCase : Optional[Any] = torch.mean(torch.abs(lowerCAmelCase__ ) )
assert abs(result_sum.item() - 124.52_2994_9951_1719 ) < 1e-2
assert abs(result_mean.item() - 0.1_6213_9326_3339_9963 ) < 1e-3
| 17
| 0
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__a = {'''configuration_yolos''': ['''YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''YolosConfig''', '''YolosOnnxConfig''']}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = ['''YolosFeatureExtractor''']
__a = ['''YolosImageProcessor''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = [
'''YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''YolosForObjectDetection''',
'''YolosModel''',
'''YolosPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_yolos import YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP, YolosConfig, YolosOnnxConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_yolos import YolosFeatureExtractor
from .image_processing_yolos import YolosImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_yolos import (
YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST,
YolosForObjectDetection,
YolosModel,
YolosPreTrainedModel,
)
else:
import sys
__a = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 354
|
'''simple docstring'''
def __UpperCAmelCase ( a_: int, a_: int ):
if a < 0 or b < 0:
raise ValueError("the value of both inputs must be positive" )
_UpperCAmelCase : List[str] = str(bin(a_ ) )[2:] # remove the leading "0b"
_UpperCAmelCase : Any = str(bin(a_ ) )[2:] # remove the leading "0b"
_UpperCAmelCase : Dict = max(len(a_ ), len(a_ ) )
return "0b" + "".join(
str(int(char_a == "1" and char_b == "1" ) )
for char_a, char_b in zip(a_binary.zfill(a_ ), b_binary.zfill(a_ ) ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 17
| 0
|
'''simple docstring'''
def __UpperCAmelCase ( ):
_UpperCAmelCase : List[str] = 0
for i in range(1, 1_001 ):
total += i**i
return str(lowercase__ )[-10:]
if __name__ == "__main__":
print(solution())
| 355
|
'''simple docstring'''
from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def __UpperCAmelCase ( a_: int ):
# A local function to see if a dot lands in the circle.
def is_in_circle(a_: float, a_: float ) -> bool:
_UpperCAmelCase : Optional[Any] = sqrt((x**2) + (y**2) )
# Our circle has a radius of 1, so a distance
# greater than 1 would land outside the circle.
return distance_from_centre <= 1
# The proportion of guesses that landed in the circle
_UpperCAmelCase : str = mean(
int(is_in_circle(uniform(-1.0, 1.0 ), uniform(-1.0, 1.0 ) ) )
for _ in range(a_ ) )
# The ratio of the area for circle to square is pi/4.
_UpperCAmelCase : Optional[int] = proportion * 4
print(f"""The estimated value of pi is {pi_estimate}""" )
print(f"""The numpy value of pi is {pi}""" )
print(f"""The total error is {abs(pi - pi_estimate )}""" )
def __UpperCAmelCase ( a_: int, a_: Callable[[float], float], a_: float = 0.0, a_: float = 1.0, ):
return mean(
function_to_integrate(uniform(a_, a_ ) ) for _ in range(a_ ) ) * (max_value - min_value)
def __UpperCAmelCase ( a_: int, a_: float = 0.0, a_: float = 1.0 ):
def identity_function(a_: float ) -> float:
return x
_UpperCAmelCase : Union[str, Any] = area_under_curve_estimator(
a_, a_, a_, a_ )
_UpperCAmelCase : List[str] = (max_value * max_value - min_value * min_value) / 2
print("******************" )
print(f"""Estimating area under y=x where x varies from {min_value} to {max_value}""" )
print(f"""Estimated value is {estimated_value}""" )
print(f"""Expected value is {expected_value}""" )
print(f"""Total error is {abs(estimated_value - expected_value )}""" )
print("******************" )
def __UpperCAmelCase ( a_: int ):
def function_to_integrate(a_: float ) -> float:
return sqrt(4.0 - x * x )
_UpperCAmelCase : List[str] = area_under_curve_estimator(
a_, a_, 0.0, 2.0 )
print("******************" )
print("Estimating pi using area_under_curve_estimator" )
print(f"""Estimated value is {estimated_value}""" )
print(f"""Expected value is {pi}""" )
print(f"""Total error is {abs(estimated_value - pi )}""" )
print("******************" )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 17
| 0
|
'''simple docstring'''
from typing import List
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a = logging.get_logger(__name__)
__a = {
'snap-research/efficientformer-l1-300': (
'https://huggingface.co/snap-research/efficientformer-l1-300/resolve/main/config.json'
),
}
class A__ ( __lowerCamelCase ):
"""simple docstring"""
UpperCamelCase_ : Optional[Any] = 'efficientformer'
def __init__( self : str , lowerCAmelCase__ : List[int] = [3, 2, 6, 4] , lowerCAmelCase__ : List[int] = [4_8, 9_6, 2_2_4, 4_4_8] , lowerCAmelCase__ : List[bool] = [True, True, True, True] , lowerCAmelCase__ : int = 4_4_8 , lowerCAmelCase__ : int = 3_2 , lowerCAmelCase__ : int = 4 , lowerCAmelCase__ : int = 7 , lowerCAmelCase__ : int = 5 , lowerCAmelCase__ : int = 8 , lowerCAmelCase__ : int = 4 , lowerCAmelCase__ : float = 0.0 , lowerCAmelCase__ : int = 1_6 , lowerCAmelCase__ : int = 3 , lowerCAmelCase__ : int = 3 , lowerCAmelCase__ : int = 3 , lowerCAmelCase__ : int = 2 , lowerCAmelCase__ : int = 1 , lowerCAmelCase__ : float = 0.0 , lowerCAmelCase__ : int = 1 , lowerCAmelCase__ : bool = True , lowerCAmelCase__ : bool = True , lowerCAmelCase__ : float = 1e-5 , lowerCAmelCase__ : str = "gelu" , lowerCAmelCase__ : float = 0.02 , lowerCAmelCase__ : float = 1e-12 , lowerCAmelCase__ : int = 2_2_4 , lowerCAmelCase__ : float = 1e-05 , **lowerCAmelCase__ : Tuple , ) -> Union[str, Any]:
"""simple docstring"""
super().__init__(**UpperCamelCase_ )
_UpperCAmelCase : List[str] = hidden_act
_UpperCAmelCase : List[Any] = hidden_dropout_prob
_UpperCAmelCase : Dict = hidden_sizes
_UpperCAmelCase : Dict = num_hidden_layers
_UpperCAmelCase : List[str] = num_attention_heads
_UpperCAmelCase : int = initializer_range
_UpperCAmelCase : Optional[Any] = layer_norm_eps
_UpperCAmelCase : List[str] = patch_size
_UpperCAmelCase : Optional[Any] = num_channels
_UpperCAmelCase : List[str] = depths
_UpperCAmelCase : int = mlp_expansion_ratio
_UpperCAmelCase : List[str] = downsamples
_UpperCAmelCase : List[str] = dim
_UpperCAmelCase : List[str] = key_dim
_UpperCAmelCase : Optional[Any] = attention_ratio
_UpperCAmelCase : Tuple = resolution
_UpperCAmelCase : List[Any] = pool_size
_UpperCAmelCase : int = downsample_patch_size
_UpperCAmelCase : Tuple = downsample_stride
_UpperCAmelCase : Optional[int] = downsample_pad
_UpperCAmelCase : Union[str, Any] = drop_path_rate
_UpperCAmelCase : List[Any] = num_metaad_blocks
_UpperCAmelCase : Union[str, Any] = distillation
_UpperCAmelCase : int = use_layer_scale
_UpperCAmelCase : Optional[int] = layer_scale_init_value
_UpperCAmelCase : Any = image_size
_UpperCAmelCase : Tuple = batch_norm_eps
| 356
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
__a = {
'configuration_layoutlmv2': ['LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LayoutLMv2Config'],
'processing_layoutlmv2': ['LayoutLMv2Processor'],
'tokenization_layoutlmv2': ['LayoutLMv2Tokenizer'],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = ['LayoutLMv2TokenizerFast']
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = ['LayoutLMv2FeatureExtractor']
__a = ['LayoutLMv2ImageProcessor']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = [
'LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST',
'LayoutLMv2ForQuestionAnswering',
'LayoutLMv2ForSequenceClassification',
'LayoutLMv2ForTokenClassification',
'LayoutLMv2Layer',
'LayoutLMv2Model',
'LayoutLMv2PreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_layoutlmva import LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMvaConfig
from .processing_layoutlmva import LayoutLMvaProcessor
from .tokenization_layoutlmva import LayoutLMvaTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor, LayoutLMvaImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_layoutlmva import (
LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST,
LayoutLMvaForQuestionAnswering,
LayoutLMvaForSequenceClassification,
LayoutLMvaForTokenClassification,
LayoutLMvaLayer,
LayoutLMvaModel,
LayoutLMvaPreTrainedModel,
)
else:
import sys
__a = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 17
| 0
|
'''simple docstring'''
def __UpperCAmelCase ( a_: str = 10, a_: Optional[Any] = 22 ):
_UpperCAmelCase : Optional[Any] = range(1, a_ )
_UpperCAmelCase : List[str] = range(1, a_ )
return sum(
1 for power in powers for base in bases if len(str(base**power ) ) == power )
if __name__ == "__main__":
print(f'{solution(10, 22) = }')
| 357
|
'''simple docstring'''
def __UpperCAmelCase ( a_: int, a_: int ):
if not isinstance(a_, a_ ):
raise ValueError("iterations must be defined as integers" )
if not isinstance(a_, a_ ) or not number >= 1:
raise ValueError(
"starting number must be\n and integer and be more than 0" )
if not iterations >= 1:
raise ValueError("Iterations must be done more than 0 times to play FizzBuzz" )
_UpperCAmelCase : List[str] = ""
while number <= iterations:
if number % 3 == 0:
out += "Fizz"
if number % 5 == 0:
out += "Buzz"
if 0 not in (number % 3, number % 5):
out += str(a_ )
# print(out)
number += 1
out += " "
return out
if __name__ == "__main__":
import doctest
doctest.testmod()
| 17
| 0
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__a = {
"configuration_rag": ["RagConfig"],
"retrieval_rag": ["RagRetriever"],
"tokenization_rag": ["RagTokenizer"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = [
"RagModel",
"RagPreTrainedModel",
"RagSequenceForGeneration",
"RagTokenForGeneration",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = [
"TFRagModel",
"TFRagPreTrainedModel",
"TFRagSequenceForGeneration",
"TFRagTokenForGeneration",
]
if TYPE_CHECKING:
from .configuration_rag import RagConfig
from .retrieval_rag import RagRetriever
from .tokenization_rag import RagTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_rag import (
TFRagModel,
TFRagPreTrainedModel,
TFRagSequenceForGeneration,
TFRagTokenForGeneration,
)
else:
import sys
__a = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 358
|
'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from itertools import chain
from typing import Optional, Union
import datasets
import numpy as np
import torch
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,
AutoTokenizer,
HfArgumentParser,
Trainer,
TrainingArguments,
default_data_collator,
set_seed,
)
from transformers.tokenization_utils_base import PreTrainedTokenizerBase
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import PaddingStrategy, check_min_version, send_example_telemetry
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('4.31.0')
__a = logging.getLogger(__name__)
@dataclass
class A__ :
"""simple docstring"""
UpperCamelCase_ : str = field(
metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''} )
UpperCamelCase_ : Optional[str] = field(
default=UpperCamelCase , metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''} )
UpperCamelCase_ : Optional[str] = field(
default=UpperCamelCase , metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''} )
UpperCamelCase_ : Optional[str] = field(
default=UpperCamelCase , metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''} , )
UpperCamelCase_ : bool = field(
default=UpperCamelCase , metadata={'''help''': '''Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.'''} , )
UpperCamelCase_ : str = field(
default='''main''' , metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''} , )
UpperCamelCase_ : bool = field(
default=UpperCamelCase , metadata={
'''help''': (
'''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
'''with private models).'''
)
} , )
@dataclass
class A__ :
"""simple docstring"""
UpperCamelCase_ : Optional[str] = field(default=UpperCamelCase , metadata={'''help''': '''The input training data file (a text file).'''} )
UpperCamelCase_ : Optional[str] = field(
default=UpperCamelCase , metadata={'''help''': '''An optional input evaluation data file to evaluate the perplexity on (a text file).'''} , )
UpperCamelCase_ : bool = field(
default=UpperCamelCase , metadata={'''help''': '''Overwrite the cached training and evaluation sets'''} )
UpperCamelCase_ : Optional[int] = field(
default=UpperCamelCase , metadata={'''help''': '''The number of processes to use for the preprocessing.'''} , )
UpperCamelCase_ : Optional[int] = field(
default=UpperCamelCase , metadata={
'''help''': (
'''The maximum total input sequence length after tokenization. If passed, sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
)
} , )
UpperCamelCase_ : bool = field(
default=UpperCamelCase , metadata={
'''help''': (
'''Whether to pad all samples to the maximum sentence length. '''
'''If False, will pad the samples dynamically when batching to the maximum length in the batch. More '''
'''efficient on GPU but very bad for TPU.'''
)
} , )
UpperCamelCase_ : Optional[int] = field(
default=UpperCamelCase , metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of training examples to this '''
'''value if set.'''
)
} , )
UpperCamelCase_ : Optional[int] = field(
default=UpperCamelCase , metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
'''value if set.'''
)
} , )
def _lowerCAmelCase ( self : Any ) -> Any:
"""simple docstring"""
if self.train_file is not None:
_UpperCAmelCase : List[Any] = self.train_file.split("." )[-1]
assert extension in ["csv", "json"], "`train_file` should be a csv or a json file."
if self.validation_file is not None:
_UpperCAmelCase : List[str] = self.validation_file.split("." )[-1]
assert extension in ["csv", "json"], "`validation_file` should be a csv or a json file."
@dataclass
class A__ :
"""simple docstring"""
UpperCamelCase_ : PreTrainedTokenizerBase
UpperCamelCase_ : Union[bool, str, PaddingStrategy] = True
UpperCamelCase_ : Optional[int] = None
UpperCamelCase_ : Optional[int] = None
def __call__( self : List[Any] , lowerCAmelCase__ : List[str] ) -> List[str]:
"""simple docstring"""
_UpperCAmelCase : int = "label" if "label" in features[0].keys() else "labels"
_UpperCAmelCase : Dict = [feature.pop(lowerCAmelCase__ ) for feature in features]
_UpperCAmelCase : str = len(lowerCAmelCase__ )
_UpperCAmelCase : int = len(features[0]["input_ids"] )
_UpperCAmelCase : str = [
[{k: v[i] for k, v in feature.items()} for i in range(lowerCAmelCase__ )] for feature in features
]
_UpperCAmelCase : List[str] = list(chain(*lowerCAmelCase__ ) )
_UpperCAmelCase : Any = self.tokenizer.pad(
lowerCAmelCase__ , padding=self.padding , max_length=self.max_length , pad_to_multiple_of=self.pad_to_multiple_of , return_tensors="pt" , )
# Un-flatten
_UpperCAmelCase : Any = {k: v.view(lowerCAmelCase__ , lowerCAmelCase__ , -1 ) for k, v in batch.items()}
# Add back labels
_UpperCAmelCase : List[str] = torch.tensor(lowerCAmelCase__ , dtype=torch.intaa )
return batch
def __UpperCAmelCase ( ):
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
_UpperCAmelCase : Union[str, Any] = 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.
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase : str = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase : Dict = parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry("run_swag", a_, a_ )
# 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 )], )
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
_UpperCAmelCase : Optional[int] = training_args.get_process_log_level()
logger.setLevel(a_ )
datasets.utils.logging.set_verbosity(a_ )
transformers.utils.logging.set_verbosity(a_ )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# 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}""" )
logger.info(f"""Training/evaluation parameters {training_args}""" )
# Detecting last checkpoint.
_UpperCAmelCase : Any = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
_UpperCAmelCase : Any = 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 and training_args.resume_from_checkpoint is 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." )
# 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.train_file is not None or data_args.validation_file is not None:
_UpperCAmelCase : Union[str, Any] = {}
if data_args.train_file is not None:
_UpperCAmelCase : str = data_args.train_file
if data_args.validation_file is not None:
_UpperCAmelCase : Optional[Any] = data_args.validation_file
_UpperCAmelCase : Dict = data_args.train_file.split("." )[-1]
_UpperCAmelCase : Optional[int] = load_dataset(
a_, data_files=a_, cache_dir=model_args.cache_dir, use_auth_token=True if model_args.use_auth_token else None, )
else:
# Downloading and loading the swag dataset from the hub.
_UpperCAmelCase : Dict = load_dataset(
"swag", "regular", cache_dir=model_args.cache_dir, use_auth_token=True if model_args.use_auth_token else None, )
# 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.
_UpperCAmelCase : Any = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path, cache_dir=model_args.cache_dir, revision=model_args.model_revision, use_auth_token=True if model_args.use_auth_token else None, )
_UpperCAmelCase : Any = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path, 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, )
_UpperCAmelCase : str = AutoModelForMultipleChoice.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, )
# When using your own dataset or a different dataset from swag, you will probably need to change this.
_UpperCAmelCase : Optional[Any] = [f"""ending{i}""" for i in range(4 )]
_UpperCAmelCase : List[Any] = "sent1"
_UpperCAmelCase : Optional[int] = "sent2"
if data_args.max_seq_length is None:
_UpperCAmelCase : List[str] = tokenizer.model_max_length
if max_seq_length > 1_024:
logger.warning(
"The chosen tokenizer supports a `model_max_length` that is longer than the default `block_size` value"
" of 1024. If you would like to use a longer `block_size` up to `tokenizer.model_max_length` you can"
" override this default with `--block_size xxx`." )
_UpperCAmelCase : Dict = 1_024
else:
if data_args.max_seq_length > tokenizer.model_max_length:
logger.warning(
f"""The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the"""
f"""model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}.""" )
_UpperCAmelCase : Dict = min(data_args.max_seq_length, tokenizer.model_max_length )
# Preprocessing the datasets.
def preprocess_function(a_: Union[str, Any] ):
_UpperCAmelCase : Optional[int] = [[context] * 4 for context in examples[context_name]]
_UpperCAmelCase : Tuple = examples[question_header_name]
_UpperCAmelCase : Optional[Any] = [
[f"""{header} {examples[end][i]}""" for end in ending_names] for i, header in enumerate(a_ )
]
# Flatten out
_UpperCAmelCase : List[str] = list(chain(*a_ ) )
_UpperCAmelCase : Dict = list(chain(*a_ ) )
# Tokenize
_UpperCAmelCase : List[Any] = tokenizer(
a_, a_, truncation=a_, max_length=a_, padding="max_length" if data_args.pad_to_max_length else False, )
# Un-flatten
return {k: [v[i : i + 4] for i in range(0, len(a_ ), 4 )] for k, v in tokenized_examples.items()}
if training_args.do_train:
if "train" not in raw_datasets:
raise ValueError("--do_train requires a train dataset" )
_UpperCAmelCase : int = raw_datasets["train"]
if data_args.max_train_samples is not None:
_UpperCAmelCase : Optional[Any] = min(len(a_ ), data_args.max_train_samples )
_UpperCAmelCase : List[Any] = train_dataset.select(range(a_ ) )
with training_args.main_process_first(desc="train dataset map pre-processing" ):
_UpperCAmelCase : Union[str, Any] = train_dataset.map(
a_, batched=a_, num_proc=data_args.preprocessing_num_workers, load_from_cache_file=not data_args.overwrite_cache, )
if training_args.do_eval:
if "validation" not in raw_datasets:
raise ValueError("--do_eval requires a validation dataset" )
_UpperCAmelCase : Dict = raw_datasets["validation"]
if data_args.max_eval_samples is not None:
_UpperCAmelCase : int = min(len(a_ ), data_args.max_eval_samples )
_UpperCAmelCase : List[str] = eval_dataset.select(range(a_ ) )
with training_args.main_process_first(desc="validation dataset map pre-processing" ):
_UpperCAmelCase : Optional[int] = eval_dataset.map(
a_, batched=a_, num_proc=data_args.preprocessing_num_workers, load_from_cache_file=not data_args.overwrite_cache, )
# Data collator
_UpperCAmelCase : Tuple = (
default_data_collator
if data_args.pad_to_max_length
else DataCollatorForMultipleChoice(tokenizer=a_, pad_to_multiple_of=8 if training_args.fpaa else None )
)
# Metric
def compute_metrics(a_: Tuple ):
_UpperCAmelCase , _UpperCAmelCase : Tuple = eval_predictions
_UpperCAmelCase : Union[str, Any] = np.argmax(a_, axis=1 )
return {"accuracy": (preds == label_ids).astype(np.floataa ).mean().item()}
# Initialize our Trainer
_UpperCAmelCase : Any = Trainer(
model=a_, args=a_, train_dataset=train_dataset if training_args.do_train else None, eval_dataset=eval_dataset if training_args.do_eval else None, tokenizer=a_, data_collator=a_, compute_metrics=a_, )
# Training
if training_args.do_train:
_UpperCAmelCase : Optional[Any] = None
if training_args.resume_from_checkpoint is not None:
_UpperCAmelCase : List[Any] = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
_UpperCAmelCase : List[str] = last_checkpoint
_UpperCAmelCase : Any = trainer.train(resume_from_checkpoint=a_ )
trainer.save_model() # Saves the tokenizer too for easy upload
_UpperCAmelCase : str = train_result.metrics
_UpperCAmelCase : List[str] = (
data_args.max_train_samples if data_args.max_train_samples is not None else len(a_ )
)
_UpperCAmelCase : Union[str, Any] = min(a_, len(a_ ) )
trainer.log_metrics("train", a_ )
trainer.save_metrics("train", a_ )
trainer.save_state()
# Evaluation
if training_args.do_eval:
logger.info("*** Evaluate ***" )
_UpperCAmelCase : List[Any] = trainer.evaluate()
_UpperCAmelCase : int = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(a_ )
_UpperCAmelCase : Tuple = min(a_, len(a_ ) )
trainer.log_metrics("eval", a_ )
trainer.save_metrics("eval", a_ )
_UpperCAmelCase : int = {
"finetuned_from": model_args.model_name_or_path,
"tasks": "multiple-choice",
"dataset_tags": "swag",
"dataset_args": "regular",
"dataset": "SWAG",
"language": "en",
}
if training_args.push_to_hub:
trainer.push_to_hub(**a_ )
else:
trainer.create_model_card(**a_ )
def __UpperCAmelCase ( a_: int ):
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 17
| 0
|
'''simple docstring'''
import unittest
from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
if is_flax_available():
import os
# The slow tests are often failing with OOM error on GPU
# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
__a = """platform"""
import jax
import jax.numpy as jnp
import numpy as np
from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel
@require_flax
class A__ :
"""simple docstring"""
UpperCamelCase_ : Union[str, Any] = PegasusConfig
UpperCamelCase_ : Any = {}
UpperCamelCase_ : Optional[Any] = 'gelu'
def __init__( self : Optional[int] , lowerCAmelCase__ : str , lowerCAmelCase__ : List[str]=1_3 , lowerCAmelCase__ : Dict=7 , lowerCAmelCase__ : Optional[Any]=True , lowerCAmelCase__ : int=False , lowerCAmelCase__ : str=9_9 , lowerCAmelCase__ : Optional[Any]=3_2 , lowerCAmelCase__ : str=5 , lowerCAmelCase__ : int=4 , lowerCAmelCase__ : Union[str, Any]=3_7 , lowerCAmelCase__ : List[Any]=0.1 , lowerCAmelCase__ : List[Any]=0.1 , lowerCAmelCase__ : Optional[int]=2_0 , lowerCAmelCase__ : int=2 , lowerCAmelCase__ : List[str]=1 , lowerCAmelCase__ : Dict=0 , ) -> Any:
"""simple docstring"""
_UpperCAmelCase : Dict = parent
_UpperCAmelCase : Optional[int] = batch_size
_UpperCAmelCase : int = seq_length
_UpperCAmelCase : Any = is_training
_UpperCAmelCase : Dict = use_labels
_UpperCAmelCase : Tuple = vocab_size
_UpperCAmelCase : int = hidden_size
_UpperCAmelCase : Any = num_hidden_layers
_UpperCAmelCase : Dict = num_attention_heads
_UpperCAmelCase : List[str] = intermediate_size
_UpperCAmelCase : Optional[int] = hidden_dropout_prob
_UpperCAmelCase : Tuple = attention_probs_dropout_prob
_UpperCAmelCase : List[str] = max_position_embeddings
_UpperCAmelCase : Any = eos_token_id
_UpperCAmelCase : List[str] = pad_token_id
_UpperCAmelCase : int = bos_token_id
def _lowerCAmelCase ( self : Dict ) -> Tuple:
"""simple docstring"""
_UpperCAmelCase : List[str] = ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size ).clip(3 , self.vocab_size )
_UpperCAmelCase : Tuple = np.expand_dims(np.array([self.eos_token_id] * self.batch_size ) , 1 )
_UpperCAmelCase : Optional[Any] = np.concatenate([input_ids, eos_tensor] , axis=1 )
_UpperCAmelCase : Optional[Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_UpperCAmelCase : Tuple = self.config_cls(
vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , eos_token_ids=[2] , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.pad_token_id , **self.config_updates , )
_UpperCAmelCase : Tuple = prepare_pegasus_inputs_dict(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
return config, inputs_dict
def _lowerCAmelCase ( self : Union[str, Any] , lowerCAmelCase__ : List[Any] , lowerCAmelCase__ : str , lowerCAmelCase__ : Optional[int] ) -> str:
"""simple docstring"""
_UpperCAmelCase : Any = 2_0
_UpperCAmelCase : str = model_class_name(__lowerCAmelCase )
_UpperCAmelCase : Tuple = model.encode(inputs_dict["input_ids"] )
_UpperCAmelCase , _UpperCAmelCase : List[Any] = (
inputs_dict["decoder_input_ids"],
inputs_dict["decoder_attention_mask"],
)
_UpperCAmelCase : Dict = model.init_cache(decoder_input_ids.shape[0] , __lowerCAmelCase , __lowerCAmelCase )
_UpperCAmelCase : Optional[int] = jnp.ones((decoder_input_ids.shape[0], max_decoder_length) , dtype="i4" )
_UpperCAmelCase : List[str] = jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :] , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) , )
_UpperCAmelCase : List[str] = model.decode(
decoder_input_ids[:, :-1] , __lowerCAmelCase , decoder_attention_mask=__lowerCAmelCase , past_key_values=__lowerCAmelCase , decoder_position_ids=__lowerCAmelCase , )
_UpperCAmelCase : Optional[Any] = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] , dtype="i4" )
_UpperCAmelCase : List[Any] = model.decode(
decoder_input_ids[:, -1:] , __lowerCAmelCase , decoder_attention_mask=__lowerCAmelCase , past_key_values=outputs_cache.past_key_values , decoder_position_ids=__lowerCAmelCase , )
_UpperCAmelCase : int = model.decode(__lowerCAmelCase , __lowerCAmelCase )
_UpperCAmelCase : Union[str, Any] = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
self.parent.assertTrue(diff < 1e-3 , msg=F"""Max diff is {diff}""" )
def _lowerCAmelCase ( self : List[str] , lowerCAmelCase__ : Dict , lowerCAmelCase__ : List[Any] , lowerCAmelCase__ : Union[str, Any] ) -> Any:
"""simple docstring"""
_UpperCAmelCase : Tuple = 2_0
_UpperCAmelCase : List[str] = model_class_name(__lowerCAmelCase )
_UpperCAmelCase : Tuple = model.encode(inputs_dict["input_ids"] )
_UpperCAmelCase , _UpperCAmelCase : Union[str, Any] = (
inputs_dict["decoder_input_ids"],
inputs_dict["decoder_attention_mask"],
)
_UpperCAmelCase : int = jnp.concatenate(
[
decoder_attention_mask,
jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1]) ),
] , axis=-1 , )
_UpperCAmelCase : str = model.init_cache(decoder_input_ids.shape[0] , __lowerCAmelCase , __lowerCAmelCase )
_UpperCAmelCase : Union[str, Any] = jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :] , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) , )
_UpperCAmelCase : str = model.decode(
decoder_input_ids[:, :-1] , __lowerCAmelCase , decoder_attention_mask=__lowerCAmelCase , past_key_values=__lowerCAmelCase , decoder_position_ids=__lowerCAmelCase , )
_UpperCAmelCase : Union[str, Any] = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] , dtype="i4" )
_UpperCAmelCase : List[str] = model.decode(
decoder_input_ids[:, -1:] , __lowerCAmelCase , past_key_values=outputs_cache.past_key_values , decoder_attention_mask=__lowerCAmelCase , decoder_position_ids=__lowerCAmelCase , )
_UpperCAmelCase : List[Any] = model.decode(__lowerCAmelCase , __lowerCAmelCase , decoder_attention_mask=__lowerCAmelCase )
_UpperCAmelCase : int = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
self.parent.assertTrue(diff < 1e-3 , msg=F"""Max diff is {diff}""" )
def __UpperCAmelCase ( a_: List[str], a_: str, a_: Tuple, a_: Tuple=None, a_: Any=None, ):
if attention_mask is None:
_UpperCAmelCase : Optional[int] = np.not_equal(lowerCAmelCase__, config.pad_token_id ).astype(np.inta )
if decoder_attention_mask is None:
_UpperCAmelCase : Tuple = np.concatenate(
[
np.ones(decoder_input_ids[:, :1].shape, dtype=np.inta ),
np.not_equal(decoder_input_ids[:, 1:], config.pad_token_id ).astype(np.inta ),
], axis=-1, )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
}
@require_flax
class A__ ( UpperCamelCase , unittest.TestCase ):
"""simple docstring"""
UpperCamelCase_ : Optional[Any] = (
(
FlaxPegasusForConditionalGeneration,
FlaxPegasusModel,
)
if is_flax_available()
else ()
)
UpperCamelCase_ : Tuple = (FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
UpperCamelCase_ : List[str] = True
UpperCamelCase_ : int = False
UpperCamelCase_ : List[Any] = False
UpperCamelCase_ : List[str] = False
def _lowerCAmelCase ( self : List[Any] ) -> Tuple:
"""simple docstring"""
_UpperCAmelCase : Union[str, Any] = FlaxPegasusModelTester(self )
_UpperCAmelCase : Optional[Any] = ConfigTester(self , config_class=__lowerCAmelCase )
def _lowerCAmelCase ( self : List[Any] ) -> List[Any]:
"""simple docstring"""
self.config_tester.run_common_tests()
def _lowerCAmelCase ( self : Any ) -> int:
"""simple docstring"""
_UpperCAmelCase , _UpperCAmelCase : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
def _lowerCAmelCase ( self : List[Any] ) -> str:
"""simple docstring"""
_UpperCAmelCase , _UpperCAmelCase : Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward_with_attn_mask(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
def _lowerCAmelCase ( self : str ) -> List[str]:
"""simple docstring"""
_UpperCAmelCase , _UpperCAmelCase : Dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
_UpperCAmelCase : List[str] = self._prepare_for_class(__lowerCAmelCase , __lowerCAmelCase )
_UpperCAmelCase : str = model_class(__lowerCAmelCase )
@jax.jit
def encode_jitted(lowerCAmelCase__ : Optional[Any] , lowerCAmelCase__ : Optional[Any]=None , **lowerCAmelCase__ : str ):
return model.encode(input_ids=__lowerCAmelCase , attention_mask=__lowerCAmelCase )
with self.subTest("JIT Enabled" ):
_UpperCAmelCase : Optional[Any] = encode_jitted(**__lowerCAmelCase ).to_tuple()
with self.subTest("JIT Disabled" ):
with jax.disable_jit():
_UpperCAmelCase : str = encode_jitted(**__lowerCAmelCase ).to_tuple()
self.assertEqual(len(__lowerCAmelCase ) , len(__lowerCAmelCase ) )
for jitted_output, output in zip(__lowerCAmelCase , __lowerCAmelCase ):
self.assertEqual(jitted_output.shape , output.shape )
def _lowerCAmelCase ( self : Dict ) -> List[Any]:
"""simple docstring"""
_UpperCAmelCase , _UpperCAmelCase : Any = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
_UpperCAmelCase : str = model_class(__lowerCAmelCase )
_UpperCAmelCase : List[str] = model.encode(inputs_dict["input_ids"] , inputs_dict["attention_mask"] )
_UpperCAmelCase : List[Any] = {
"decoder_input_ids": inputs_dict["decoder_input_ids"],
"decoder_attention_mask": inputs_dict["decoder_attention_mask"],
"encoder_outputs": encoder_outputs,
}
@jax.jit
def decode_jitted(lowerCAmelCase__ : List[Any] , lowerCAmelCase__ : List[Any] , lowerCAmelCase__ : Union[str, Any] ):
return model.decode(
decoder_input_ids=__lowerCAmelCase , decoder_attention_mask=__lowerCAmelCase , encoder_outputs=__lowerCAmelCase , )
with self.subTest("JIT Enabled" ):
_UpperCAmelCase : int = decode_jitted(**__lowerCAmelCase ).to_tuple()
with self.subTest("JIT Disabled" ):
with jax.disable_jit():
_UpperCAmelCase : str = decode_jitted(**__lowerCAmelCase ).to_tuple()
self.assertEqual(len(__lowerCAmelCase ) , len(__lowerCAmelCase ) )
for jitted_output, output in zip(__lowerCAmelCase , __lowerCAmelCase ):
self.assertEqual(jitted_output.shape , output.shape )
@slow
def _lowerCAmelCase ( self : Optional[Any] ) -> Dict:
"""simple docstring"""
for model_class_name in self.all_model_classes:
_UpperCAmelCase : str = model_class_name.from_pretrained("google/pegasus-large" , from_pt=__lowerCAmelCase )
_UpperCAmelCase : Optional[int] = np.ones((1, 1) )
_UpperCAmelCase : List[Any] = model(__lowerCAmelCase )
self.assertIsNotNone(__lowerCAmelCase )
@slow
def _lowerCAmelCase ( self : int ) -> str:
"""simple docstring"""
_UpperCAmelCase : Optional[Any] = FlaxPegasusForConditionalGeneration.from_pretrained("google/pegasus-xsum" )
_UpperCAmelCase : Tuple = PegasusTokenizer.from_pretrained("google/pegasus-xsum" )
_UpperCAmelCase : Dict = [
" PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.",
" The London trio are up for best UK act and best album, as well as getting two nominations in the best song category.\"We got told like this morning 'Oh I think you're nominated'\", said Dappy.\"And I was like 'Oh yeah, which one?' And now we've got nominated for four awards. I mean, wow!\"Bandmate Fazer added: \"We thought it's best of us to come down and mingle with everyone and say hello to the cameras. And now we find we've got four nominations.\"The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn't be too disappointed if they didn't win this time around.\"At the end of the day we're grateful to be where we are in our careers.\"If it don't happen then it don't happen - live to fight another day and keep on making albums and hits for the fans.\"Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a re-working of The Killers' All These Things That I've Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year's Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border.\"We just done Edinburgh the other day,\" said Dappy.\"We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!\" ",
]
_UpperCAmelCase : Optional[int] = [
"California's largest electricity provider has turned off power to hundreds of thousands of customers.",
"Pop group N-Dubz have revealed they were surprised to get four nominations for this year's Mobo Awards.",
]
_UpperCAmelCase : List[Any] = tokenizer(__lowerCAmelCase , return_tensors="np" , truncation=__lowerCAmelCase , max_length=5_1_2 , padding=__lowerCAmelCase )
_UpperCAmelCase : Optional[int] = model.generate(**__lowerCAmelCase , num_beams=2 ).sequences
_UpperCAmelCase : int = tokenizer.batch_decode(__lowerCAmelCase , skip_special_tokens=__lowerCAmelCase )
assert tgt_text == decoded
| 359
|
'''simple docstring'''
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 : Any , lowerCAmelCase__ : Optional[Any] ) -> str:
"""simple docstring"""
super().__init__()
_UpperCAmelCase : List[str] = model
_UpperCAmelCase : Dict = 2
_UpperCAmelCase : Tuple = nn.Linear(self.model.config.hidden_size , self.num_labels )
def _lowerCAmelCase ( self : Tuple ) -> int:
"""simple docstring"""
pass
def __UpperCAmelCase ( a_: str, a_: str, a_: str ):
# load longformer model from model identifier
_UpperCAmelCase : int = LongformerModel.from_pretrained(a_ )
_UpperCAmelCase : Any = LightningModel(a_ )
_UpperCAmelCase : int = torch.load(a_, map_location=torch.device("cpu" ) )
lightning_model.load_state_dict(ckpt["state_dict"] )
# init longformer question answering model
_UpperCAmelCase : List[str] = 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__":
__a = 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.'
)
__a = parser.parse_args()
convert_longformer_qa_checkpoint_to_pytorch(
args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
)
| 17
| 0
|
'''simple docstring'''
from functools import reduce
__a = (
'73167176531330624919225119674426574742355349194934'
'96983520312774506326239578318016984801869478851843'
'85861560789112949495459501737958331952853208805511'
'12540698747158523863050715693290963295227443043557'
'66896648950445244523161731856403098711121722383113'
'62229893423380308135336276614282806444486645238749'
'30358907296290491560440772390713810515859307960866'
'70172427121883998797908792274921901699720888093776'
'65727333001053367881220235421809751254540594752243'
'52584907711670556013604839586446706324415722155397'
'53697817977846174064955149290862569321978468622482'
'83972241375657056057490261407972968652414535100474'
'82166370484403199890008895243450658541227588666881'
'16427171479924442928230863465674813919123162824586'
'17866458359124566529476545682848912883142607690042'
'24219022671055626321111109370544217506941658960408'
'07198403850962455444362981230987879927244284909188'
'84580156166097919133875499200524063689912560717606'
'05886116467109405077541002256983155200055935729725'
'71636269561882670428252483600823257530420752963450'
)
def __UpperCAmelCase ( a_: List[str] = N ):
return max(
# mypy cannot properly interpret reduce
int(reduce(lambda a_, a_ : str(int(a__ ) * int(a__ ) ), n[i : i + 13] ) )
for i in range(len(a__ ) - 12 ) )
if __name__ == "__main__":
print(f'{solution() = }')
| 360
|
'''simple docstring'''
from importlib import import_module
from .logging import get_logger
__a = get_logger(__name__)
class A__ :
"""simple docstring"""
def __init__( self : List[str] , lowerCAmelCase__ : List[Any] , lowerCAmelCase__ : Optional[Any]=None ) -> Optional[int]:
"""simple docstring"""
_UpperCAmelCase : Any = attrs or []
if module is not None:
for key in module.__dict__:
if key in attrs or not key.startswith("__" ):
setattr(self , lowerCAmelCase__ , getattr(lowerCAmelCase__ , lowerCAmelCase__ ) )
_UpperCAmelCase : int = module._original_module if isinstance(lowerCAmelCase__ , _PatchedModuleObj ) else module
class A__ :
"""simple docstring"""
UpperCamelCase_ : Union[str, Any] = []
def __init__( self : int , lowerCAmelCase__ : List[str] , lowerCAmelCase__ : str , lowerCAmelCase__ : List[str] , lowerCAmelCase__ : Optional[int]=None ) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase : List[Any] = obj
_UpperCAmelCase : int = target
_UpperCAmelCase : Optional[int] = new
_UpperCAmelCase : Any = target.split("." )[0]
_UpperCAmelCase : Optional[int] = {}
_UpperCAmelCase : Dict = attrs or []
def __enter__( self : List[str] ) -> int:
"""simple docstring"""
*_UpperCAmelCase , _UpperCAmelCase : List[str] = self.target.split("." )
# Patch modules:
# it's used to patch attributes of submodules like "os.path.join";
# in this case we need to patch "os" and "os.path"
for i in range(len(lowerCAmelCase__ ) ):
try:
_UpperCAmelCase : int = import_module(".".join(submodules[: i + 1] ) )
except ModuleNotFoundError:
continue
# We iterate over all the globals in self.obj in case we find "os" or "os.path"
for attr in self.obj.__dir__():
_UpperCAmelCase : List[Any] = getattr(self.obj , lowerCAmelCase__ )
# We don't check for the name of the global, but rather if its value *is* "os" or "os.path".
# This allows to patch renamed modules like "from os import path as ospath".
if obj_attr is submodule or (
(isinstance(lowerCAmelCase__ , _PatchedModuleObj ) and obj_attr._original_module is submodule)
):
_UpperCAmelCase : Tuple = obj_attr
# patch at top level
setattr(self.obj , lowerCAmelCase__ , _PatchedModuleObj(lowerCAmelCase__ , attrs=self.attrs ) )
_UpperCAmelCase : List[Any] = getattr(self.obj , lowerCAmelCase__ )
# construct lower levels patches
for key in submodules[i + 1 :]:
setattr(lowerCAmelCase__ , lowerCAmelCase__ , _PatchedModuleObj(getattr(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ) , attrs=self.attrs ) )
_UpperCAmelCase : Any = getattr(lowerCAmelCase__ , lowerCAmelCase__ )
# finally set the target attribute
setattr(lowerCAmelCase__ , lowerCAmelCase__ , self.new )
# Patch attribute itself:
# it's used for builtins like "open",
# and also to patch "os.path.join" we may also need to patch "join"
# itself if it was imported as "from os.path import join".
if submodules: # if it's an attribute of a submodule like "os.path.join"
try:
_UpperCAmelCase : Dict = getattr(import_module(".".join(lowerCAmelCase__ ) ) , lowerCAmelCase__ )
except (AttributeError, ModuleNotFoundError):
return
# We iterate over all the globals in self.obj in case we find "os.path.join"
for attr in self.obj.__dir__():
# We don't check for the name of the global, but rather if its value *is* "os.path.join".
# This allows to patch renamed attributes like "from os.path import join as pjoin".
if getattr(self.obj , lowerCAmelCase__ ) is attr_value:
_UpperCAmelCase : Optional[Any] = getattr(self.obj , lowerCAmelCase__ )
setattr(self.obj , lowerCAmelCase__ , self.new )
elif target_attr in globals()["__builtins__"]: # if it'a s builtin like "open"
_UpperCAmelCase : Dict = globals()["__builtins__"][target_attr]
setattr(self.obj , lowerCAmelCase__ , self.new )
else:
raise RuntimeError(F"""Tried to patch attribute {target_attr} instead of a submodule.""" )
def __exit__( self : Optional[int] , *lowerCAmelCase__ : List[str] ) -> Union[str, Any]:
"""simple docstring"""
for attr in list(self.original ):
setattr(self.obj , lowerCAmelCase__ , self.original.pop(lowerCAmelCase__ ) )
def _lowerCAmelCase ( self : Union[str, Any] ) -> Optional[Any]:
"""simple docstring"""
self.__enter__()
self._active_patches.append(self )
def _lowerCAmelCase ( self : Optional[int] ) -> Tuple:
"""simple docstring"""
try:
self._active_patches.remove(self )
except ValueError:
# If the patch hasn't been started this will fail
return None
return self.__exit__()
| 17
| 0
|
'''simple docstring'''
import qiskit
def __UpperCAmelCase ( a_: List[str], a_: Tuple ):
_UpperCAmelCase : Union[str, Any] = qiskit.Aer.get_backend("aer_simulator" )
# Create a Quantum Circuit acting on the q register
_UpperCAmelCase : List[str] = qiskit.QuantumCircuit(lowercase_, lowercase_ )
# Map the quantum measurement to the classical bits
circuit.measure([0], [0] )
# Execute the circuit on the simulator
_UpperCAmelCase : List[str] = qiskit.execute(lowercase_, lowercase_, shots=1_000 )
# Return the histogram data of the results of the experiment.
return job.result().get_counts(lowercase_ )
if __name__ == "__main__":
print(f'Total count for various states are: {single_qubit_measure(1, 1)}')
| 361
|
'''simple docstring'''
import itertools
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union
import pandas as pd
import pyarrow as pa
import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast
from datasets.utils.py_utils import Literal
__a = datasets.utils.logging.get_logger(__name__)
__a = ['names', 'prefix']
__a = ['warn_bad_lines', 'error_bad_lines', 'mangle_dupe_cols']
__a = ['encoding_errors', 'on_bad_lines']
__a = ['date_format']
@dataclass
class A__ ( datasets.BuilderConfig ):
"""simple docstring"""
UpperCamelCase_ : str = ","
UpperCamelCase_ : Optional[str] = None
UpperCamelCase_ : Optional[Union[int, List[int], str]] = "infer"
UpperCamelCase_ : Optional[List[str]] = None
UpperCamelCase_ : Optional[List[str]] = None
UpperCamelCase_ : Optional[Union[int, str, List[int], List[str]]] = None
UpperCamelCase_ : Optional[Union[List[int], List[str]]] = None
UpperCamelCase_ : Optional[str] = None
UpperCamelCase_ : bool = True
UpperCamelCase_ : Optional[Literal["c", "python", "pyarrow"]] = None
UpperCamelCase_ : Dict[Union[int, str], Callable[[Any], Any]] = None
UpperCamelCase_ : Optional[list] = None
UpperCamelCase_ : Optional[list] = None
UpperCamelCase_ : bool = False
UpperCamelCase_ : Optional[Union[int, List[int]]] = None
UpperCamelCase_ : Optional[int] = None
UpperCamelCase_ : Optional[Union[str, List[str]]] = None
UpperCamelCase_ : bool = True
UpperCamelCase_ : bool = True
UpperCamelCase_ : bool = False
UpperCamelCase_ : bool = True
UpperCamelCase_ : Optional[str] = None
UpperCamelCase_ : str = "."
UpperCamelCase_ : Optional[str] = None
UpperCamelCase_ : str = '"'
UpperCamelCase_ : int = 0
UpperCamelCase_ : Optional[str] = None
UpperCamelCase_ : Optional[str] = None
UpperCamelCase_ : Optional[str] = None
UpperCamelCase_ : Optional[str] = None
UpperCamelCase_ : bool = True
UpperCamelCase_ : bool = True
UpperCamelCase_ : int = 0
UpperCamelCase_ : bool = True
UpperCamelCase_ : bool = False
UpperCamelCase_ : Optional[str] = None
UpperCamelCase_ : int = 1_00_00
UpperCamelCase_ : Optional[datasets.Features] = None
UpperCamelCase_ : Optional[str] = "strict"
UpperCamelCase_ : Literal["error", "warn", "skip"] = "error"
UpperCamelCase_ : Optional[str] = None
def _lowerCAmelCase ( self : str ) -> Tuple:
"""simple docstring"""
if self.delimiter is not None:
_UpperCAmelCase : Any = self.delimiter
if self.column_names is not None:
_UpperCAmelCase : List[Any] = self.column_names
@property
def _lowerCAmelCase ( self : Optional[int] ) -> Optional[int]:
"""simple docstring"""
_UpperCAmelCase : Dict = {
"sep": self.sep,
"header": self.header,
"names": self.names,
"index_col": self.index_col,
"usecols": self.usecols,
"prefix": self.prefix,
"mangle_dupe_cols": self.mangle_dupe_cols,
"engine": self.engine,
"converters": self.converters,
"true_values": self.true_values,
"false_values": self.false_values,
"skipinitialspace": self.skipinitialspace,
"skiprows": self.skiprows,
"nrows": self.nrows,
"na_values": self.na_values,
"keep_default_na": self.keep_default_na,
"na_filter": self.na_filter,
"verbose": self.verbose,
"skip_blank_lines": self.skip_blank_lines,
"thousands": self.thousands,
"decimal": self.decimal,
"lineterminator": self.lineterminator,
"quotechar": self.quotechar,
"quoting": self.quoting,
"escapechar": self.escapechar,
"comment": self.comment,
"encoding": self.encoding,
"dialect": self.dialect,
"error_bad_lines": self.error_bad_lines,
"warn_bad_lines": self.warn_bad_lines,
"skipfooter": self.skipfooter,
"doublequote": self.doublequote,
"memory_map": self.memory_map,
"float_precision": self.float_precision,
"chunksize": self.chunksize,
"encoding_errors": self.encoding_errors,
"on_bad_lines": self.on_bad_lines,
"date_format": self.date_format,
}
# some kwargs must not be passed if they don't have a default value
# some others are deprecated and we can also not pass them if they are the default value
for pd_read_csv_parameter in _PANDAS_READ_CSV_NO_DEFAULT_PARAMETERS + _PANDAS_READ_CSV_DEPRECATED_PARAMETERS:
if pd_read_csv_kwargs[pd_read_csv_parameter] == getattr(CsvConfig() , lowerCAmelCase__ ):
del pd_read_csv_kwargs[pd_read_csv_parameter]
# Remove 2.0 new arguments
if not (datasets.config.PANDAS_VERSION.major >= 2):
for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_2_0_0_PARAMETERS:
del pd_read_csv_kwargs[pd_read_csv_parameter]
# Remove 1.3 new arguments
if not (datasets.config.PANDAS_VERSION.major >= 1 and datasets.config.PANDAS_VERSION.minor >= 3):
for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_1_3_0_PARAMETERS:
del pd_read_csv_kwargs[pd_read_csv_parameter]
return pd_read_csv_kwargs
class A__ ( datasets.ArrowBasedBuilder ):
"""simple docstring"""
UpperCamelCase_ : int = CsvConfig
def _lowerCAmelCase ( self : Optional[Any] ) -> Optional[int]:
"""simple docstring"""
return datasets.DatasetInfo(features=self.config.features )
def _lowerCAmelCase ( self : Tuple , lowerCAmelCase__ : str ) -> List[str]:
"""simple docstring"""
if not self.config.data_files:
raise ValueError(F"""At least one data file must be specified, but got data_files={self.config.data_files}""" )
_UpperCAmelCase : List[str] = dl_manager.download_and_extract(self.config.data_files )
if isinstance(lowerCAmelCase__ , (str, list, tuple) ):
_UpperCAmelCase : int = data_files
if isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
_UpperCAmelCase : Any = [files]
_UpperCAmelCase : List[Any] = [dl_manager.iter_files(lowerCAmelCase__ ) for file in files]
return [datasets.SplitGenerator(name=datasets.Split.TRAIN , gen_kwargs={"files": files} )]
_UpperCAmelCase : Optional[Any] = []
for split_name, files in data_files.items():
if isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
_UpperCAmelCase : str = [files]
_UpperCAmelCase : Any = [dl_manager.iter_files(lowerCAmelCase__ ) for file in files]
splits.append(datasets.SplitGenerator(name=lowerCAmelCase__ , gen_kwargs={"files": files} ) )
return splits
def _lowerCAmelCase ( self : List[Any] , lowerCAmelCase__ : pa.Table ) -> pa.Table:
"""simple docstring"""
if self.config.features is not None:
_UpperCAmelCase : Tuple = self.config.features.arrow_schema
if all(not require_storage_cast(lowerCAmelCase__ ) for feature in self.config.features.values() ):
# cheaper cast
_UpperCAmelCase : Any = pa.Table.from_arrays([pa_table[field.name] for field in schema] , schema=lowerCAmelCase__ )
else:
# more expensive cast; allows str <-> int/float or str to Audio for example
_UpperCAmelCase : int = table_cast(lowerCAmelCase__ , lowerCAmelCase__ )
return pa_table
def _lowerCAmelCase ( self : Dict , lowerCAmelCase__ : Dict ) -> Dict:
"""simple docstring"""
_UpperCAmelCase : int = self.config.features.arrow_schema if self.config.features else None
# dtype allows reading an int column as str
_UpperCAmelCase : Optional[Any] = (
{
name: dtype.to_pandas_dtype() if not require_storage_cast(lowerCAmelCase__ ) else object
for name, dtype, feature in zip(schema.names , schema.types , self.config.features.values() )
}
if schema is not None
else None
)
for file_idx, file in enumerate(itertools.chain.from_iterable(lowerCAmelCase__ ) ):
_UpperCAmelCase : Optional[Any] = pd.read_csv(lowerCAmelCase__ , iterator=lowerCAmelCase__ , dtype=lowerCAmelCase__ , **self.config.pd_read_csv_kwargs )
try:
for batch_idx, df in enumerate(lowerCAmelCase__ ):
_UpperCAmelCase : Optional[int] = pa.Table.from_pandas(lowerCAmelCase__ )
# Uncomment for debugging (will print the Arrow table size and elements)
# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
yield (file_idx, batch_idx), self._cast_table(lowerCAmelCase__ )
except ValueError as e:
logger.error(F"""Failed to read file '{file}' with error {type(lowerCAmelCase__ )}: {e}""" )
raise
| 17
| 0
|
'''simple docstring'''
import os
def __UpperCAmelCase ( ):
with open(os.path.dirname(UpperCamelCase__ ) + "/p022_names.txt" ) as file:
_UpperCAmelCase : Tuple = str(file.readlines()[0] )
_UpperCAmelCase : Dict = names.replace("\"", "" ).split("," )
names.sort()
_UpperCAmelCase : Optional[Any] = 0
_UpperCAmelCase : Tuple = 0
for i, name in enumerate(UpperCamelCase__ ):
for letter in name:
name_score += ord(UpperCamelCase__ ) - 64
total_score += (i + 1) * name_score
_UpperCAmelCase : Optional[int] = 0
return total_score
if __name__ == "__main__":
print(solution())
| 362
|
'''simple docstring'''
from __future__ import annotations
def __UpperCAmelCase ( a_: list[int] ):
if not nums:
return 0
_UpperCAmelCase : int = nums[0]
_UpperCAmelCase : Dict = 0
for num in nums[1:]:
_UpperCAmelCase , _UpperCAmelCase : Any = (
max_excluding + num,
max(a_, a_ ),
)
return max(a_, a_ )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 17
| 0
|
'''simple docstring'''
import argparse
import gc
import json
import os
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
__a = 16
__a = 32
def __UpperCAmelCase ( a_: str ):
return int(x / 2**20 )
class A__ :
"""simple docstring"""
def __enter__( self : str ) -> Optional[Any]:
"""simple docstring"""
gc.collect()
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated() # reset the peak gauge to zero
_UpperCAmelCase : List[Any] = torch.cuda.memory_allocated()
return self
def __exit__( self : int , *lowerCAmelCase__ : Optional[int] ) -> List[str]:
"""simple docstring"""
gc.collect()
torch.cuda.empty_cache()
_UpperCAmelCase : List[Any] = torch.cuda.memory_allocated()
_UpperCAmelCase : List[Any] = torch.cuda.max_memory_allocated()
_UpperCAmelCase : str = bamb(self.end - self.begin )
_UpperCAmelCase : Tuple = bamb(self.peak - self.begin )
# print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")
def __UpperCAmelCase ( a_: Accelerator, a_: int = 16, a_: str = "bert-base-cased", a_: int = 320, a_: int = 160, ):
_UpperCAmelCase : Dict = AutoTokenizer.from_pretrained(_a )
_UpperCAmelCase : Optional[int] = load_dataset(
"glue", "mrpc", split={"train": f"""train[:{n_train}]""", "validation": f"""validation[:{n_val}]"""} )
def tokenize_function(a_: Optional[int] ):
# max_length=None => use the model max length (it's actually the default)
_UpperCAmelCase : str = 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
_UpperCAmelCase : int = 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
_UpperCAmelCase : Dict = tokenized_datasets.rename_column("label", "labels" )
def collate_fn(a_: Union[str, Any] ):
# On TPU it's best to pad everything to the same length or training will be very slow.
if accelerator.distributed_type == DistributedType.TPU:
return tokenizer.pad(_a, padding="max_length", max_length=128, return_tensors="pt" )
return tokenizer.pad(_a, padding="longest", return_tensors="pt" )
# Instantiate dataloaders.
_UpperCAmelCase : str = DataLoader(
tokenized_datasets["train"], shuffle=_a, collate_fn=_a, batch_size=_a )
_UpperCAmelCase : int = DataLoader(
tokenized_datasets["validation"], shuffle=_a, collate_fn=_a, batch_size=_a )
return train_dataloader, eval_dataloader
def __UpperCAmelCase ( a_: int, a_: Any ):
# Initialize accelerator
_UpperCAmelCase : Union[str, Any] = Accelerator()
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
_UpperCAmelCase : Optional[Any] = config["lr"]
_UpperCAmelCase : List[str] = int(config["num_epochs"] )
_UpperCAmelCase : List[Any] = int(config["seed"] )
_UpperCAmelCase : List[str] = int(config["batch_size"] )
_UpperCAmelCase : Dict = args.model_name_or_path
set_seed(_a )
_UpperCAmelCase : List[Any] = get_dataloaders(_a, _a, _a, args.n_train, args.n_val )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
_UpperCAmelCase : int = AutoModelForSequenceClassification.from_pretrained(_a, return_dict=_a )
# Instantiate optimizer
_UpperCAmelCase : Optional[int] = (
AdamW
if accelerator.state.deepspeed_plugin is None
or "optimizer" not in accelerator.state.deepspeed_plugin.deepspeed_config
else DummyOptim
)
_UpperCAmelCase : int = optimizer_cls(params=model.parameters(), lr=_a )
if accelerator.state.deepspeed_plugin is not None:
_UpperCAmelCase : Any = accelerator.state.deepspeed_plugin.deepspeed_config[
"gradient_accumulation_steps"
]
else:
_UpperCAmelCase : List[Any] = 1
_UpperCAmelCase : Any = (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
):
_UpperCAmelCase : int = get_linear_schedule_with_warmup(
optimizer=_a, num_warmup_steps=0, num_training_steps=_a, )
else:
_UpperCAmelCase : Union[str, Any] = 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.
_UpperCAmelCase : List[Any] = accelerator.prepare(
_a, _a, _a, _a, _a )
# We need to keep track of how many total steps we have iterated over
_UpperCAmelCase : Tuple = 0
# We also need to keep track of the stating epoch so files are named properly
_UpperCAmelCase : Tuple = 0
# Now we train the model
_UpperCAmelCase : Tuple = {}
for epoch in range(_a, _a ):
with TorchTracemalloc() as tracemalloc:
model.train()
for step, batch in enumerate(_a ):
_UpperCAmelCase : int = model(**_a )
_UpperCAmelCase : str = outputs.loss
_UpperCAmelCase : str = loss / gradient_accumulation_steps
accelerator.backward(_a )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
overall_step += 1
# Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
accelerator.print("Memory before entering the train : {}".format(bamb(tracemalloc.begin ) ) )
accelerator.print("Memory consumed at the end of the train (end-begin): {}".format(tracemalloc.used ) )
accelerator.print("Peak Memory consumed during the train (max-begin): {}".format(tracemalloc.peaked ) )
accelerator.print(
"Total Peak Memory consumed during the train (max): {}".format(
tracemalloc.peaked + bamb(tracemalloc.begin ) ) )
_UpperCAmelCase : Union[str, Any] = tracemalloc.peaked + bamb(tracemalloc.begin )
if args.peak_memory_upper_bound is not None:
assert (
train_total_peak_memory[f"""epoch-{epoch}"""] <= args.peak_memory_upper_bound
), "Peak memory usage exceeded the upper bound"
accelerator.wait_for_everyone()
if accelerator.is_main_process:
with open(os.path.join(args.output_dir, "peak_memory_utilization.json" ), "w" ) as f:
json.dump(_a, _a )
def __UpperCAmelCase ( ):
_UpperCAmelCase : Optional[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(
"--peak_memory_upper_bound", type=_a, default=_a, help="The upper bound of peak memory usage in MB. If set, the training will throw an error if the peak memory usage exceeds this value.", )
parser.add_argument(
"--n_train", type=_a, default=320, help="Number of training examples to use.", )
parser.add_argument(
"--n_val", type=_a, default=160, help="Number of validation examples to use.", )
parser.add_argument(
"--num_epochs", type=_a, default=1, help="Number of train epochs.", )
_UpperCAmelCase : Any = parser.parse_args()
_UpperCAmelCase : int = {"lr": 2e-5, "num_epochs": args.num_epochs, "seed": 42, "batch_size": 16}
training_function(_a, _a )
if __name__ == "__main__":
main()
| 363
|
'''simple docstring'''
import argparse
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
__a = logging.get_logger(__name__)
def __UpperCAmelCase ( a_: List[str] ):
_UpperCAmelCase : Union[str, Any] = OrderedDict()
for key, value in state_dict.items():
if key.startswith("module.encoder" ):
_UpperCAmelCase : Optional[int] = key.replace("module.encoder", "glpn.encoder" )
if key.startswith("module.decoder" ):
_UpperCAmelCase : List[Any] = key.replace("module.decoder", "decoder.stages" )
if "patch_embed" in key:
# replace for example patch_embed1 by patch_embeddings.0
_UpperCAmelCase : int = key[key.find("patch_embed" ) + len("patch_embed" )]
_UpperCAmelCase : Union[str, Any] = key.replace(f"""patch_embed{idx}""", f"""patch_embeddings.{int(a_ )-1}""" )
if "norm" in key:
_UpperCAmelCase : Union[str, Any] = key.replace("norm", "layer_norm" )
if "glpn.encoder.layer_norm" in key:
# replace for example layer_norm1 by layer_norm.0
_UpperCAmelCase : str = key[key.find("glpn.encoder.layer_norm" ) + len("glpn.encoder.layer_norm" )]
_UpperCAmelCase : Optional[Any] = key.replace(f"""layer_norm{idx}""", f"""layer_norm.{int(a_ )-1}""" )
if "layer_norm1" in key:
_UpperCAmelCase : Union[str, Any] = key.replace("layer_norm1", "layer_norm_1" )
if "layer_norm2" in key:
_UpperCAmelCase : List[Any] = key.replace("layer_norm2", "layer_norm_2" )
if "block" in key:
# replace for example block1 by block.0
_UpperCAmelCase : Optional[Any] = key[key.find("block" ) + len("block" )]
_UpperCAmelCase : List[str] = key.replace(f"""block{idx}""", f"""block.{int(a_ )-1}""" )
if "attn.q" in key:
_UpperCAmelCase : Optional[int] = key.replace("attn.q", "attention.self.query" )
if "attn.proj" in key:
_UpperCAmelCase : List[str] = key.replace("attn.proj", "attention.output.dense" )
if "attn" in key:
_UpperCAmelCase : Dict = key.replace("attn", "attention.self" )
if "fc1" in key:
_UpperCAmelCase : List[Any] = key.replace("fc1", "dense1" )
if "fc2" in key:
_UpperCAmelCase : List[Any] = key.replace("fc2", "dense2" )
if "linear_pred" in key:
_UpperCAmelCase : Any = key.replace("linear_pred", "classifier" )
if "linear_fuse" in key:
_UpperCAmelCase : Dict = key.replace("linear_fuse.conv", "linear_fuse" )
_UpperCAmelCase : List[str] = key.replace("linear_fuse.bn", "batch_norm" )
if "linear_c" in key:
# replace for example linear_c4 by linear_c.3
_UpperCAmelCase : List[Any] = key[key.find("linear_c" ) + len("linear_c" )]
_UpperCAmelCase : Tuple = key.replace(f"""linear_c{idx}""", f"""linear_c.{int(a_ )-1}""" )
if "bot_conv" in key:
_UpperCAmelCase : Union[str, Any] = key.replace("bot_conv", "0.convolution" )
if "skip_conv1" in key:
_UpperCAmelCase : Optional[int] = key.replace("skip_conv1", "1.convolution" )
if "skip_conv2" in key:
_UpperCAmelCase : Optional[int] = key.replace("skip_conv2", "2.convolution" )
if "fusion1" in key:
_UpperCAmelCase : List[str] = key.replace("fusion1", "1.fusion" )
if "fusion2" in key:
_UpperCAmelCase : List[str] = key.replace("fusion2", "2.fusion" )
if "fusion3" in key:
_UpperCAmelCase : Optional[Any] = key.replace("fusion3", "3.fusion" )
if "fusion" in key and "conv" in key:
_UpperCAmelCase : List[Any] = key.replace("conv", "convolutional_layer" )
if key.startswith("module.last_layer_depth" ):
_UpperCAmelCase : Optional[int] = key.replace("module.last_layer_depth", "head.head" )
_UpperCAmelCase : int = value
return new_state_dict
def __UpperCAmelCase ( a_: str, a_: List[Any] ):
# for each of the encoder blocks:
for i in range(config.num_encoder_blocks ):
for j in range(config.depths[i] ):
# read in weights + bias of keys and values (which is a single matrix in the original implementation)
_UpperCAmelCase : Tuple = state_dict.pop(f"""glpn.encoder.block.{i}.{j}.attention.self.kv.weight""" )
_UpperCAmelCase : Union[str, Any] = state_dict.pop(f"""glpn.encoder.block.{i}.{j}.attention.self.kv.bias""" )
# next, add keys and values (in that order) to the state dict
_UpperCAmelCase : Optional[int] = kv_weight[
: config.hidden_sizes[i], :
]
_UpperCAmelCase : Dict = kv_bias[: config.hidden_sizes[i]]
_UpperCAmelCase : Optional[int] = kv_weight[
config.hidden_sizes[i] :, :
]
_UpperCAmelCase : Optional[Any] = kv_bias[config.hidden_sizes[i] :]
def __UpperCAmelCase ( ):
_UpperCAmelCase : Optional[int] = "http://images.cocodataset.org/val2017/000000039769.jpg"
_UpperCAmelCase : List[Any] = Image.open(requests.get(a_, stream=a_ ).raw )
return image
@torch.no_grad()
def __UpperCAmelCase ( a_: Tuple, a_: Any, a_: Optional[Any]=False, a_: List[Any]=None ):
_UpperCAmelCase : Optional[Any] = GLPNConfig(hidden_sizes=[64, 128, 320, 512], decoder_hidden_size=64, depths=[3, 8, 27, 3] )
# load image processor (only resize + rescale)
_UpperCAmelCase : Dict = GLPNImageProcessor()
# prepare image
_UpperCAmelCase : List[Any] = prepare_img()
_UpperCAmelCase : Optional[int] = image_processor(images=a_, return_tensors="pt" ).pixel_values
logger.info("Converting model..." )
# load original state dict
_UpperCAmelCase : Union[str, Any] = torch.load(a_, map_location=torch.device("cpu" ) )
# rename keys
_UpperCAmelCase : List[str] = rename_keys(a_ )
# key and value matrices need special treatment
read_in_k_v(a_, a_ )
# create HuggingFace model and load state dict
_UpperCAmelCase : List[str] = GLPNForDepthEstimation(a_ )
model.load_state_dict(a_ )
model.eval()
# forward pass
_UpperCAmelCase : Dict = model(a_ )
_UpperCAmelCase : List[str] = outputs.predicted_depth
# verify output
if model_name is not None:
if "nyu" in model_name:
_UpperCAmelCase : Optional[Any] = torch.tensor(
[[4.41_47, 4.08_73, 4.06_73], [3.78_90, 3.28_81, 3.15_25], [3.76_74, 3.54_23, 3.49_13]] )
elif "kitti" in model_name:
_UpperCAmelCase : Tuple = torch.tensor(
[[3.42_91, 2.78_65, 2.51_51], [3.28_41, 2.70_21, 2.35_02], [3.11_47, 2.46_25, 2.24_81]] )
else:
raise ValueError(f"""Unknown model name: {model_name}""" )
_UpperCAmelCase : Dict = torch.Size([1, 480, 640] )
assert predicted_depth.shape == expected_shape
assert torch.allclose(predicted_depth[0, :3, :3], a_, atol=1e-4 )
print("Looks ok!" )
# finally, push to hub if required
if push_to_hub:
logger.info("Pushing model and image processor to the 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__":
__a = argparse.ArgumentParser()
parser.add_argument(
'--checkpoint_path',
default=None,
type=str,
help='Path to the original PyTorch checkpoint (.pth file).',
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
)
parser.add_argument(
'--push_to_hub', action='store_true', help='Whether to upload the model to the HuggingFace hub.'
)
parser.add_argument(
'--model_name',
default='glpn-kitti',
type=str,
help='Name of the model in case you\'re pushing to the hub.',
)
__a = parser.parse_args()
convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)
| 17
| 0
|
'''simple docstring'''
import torch
from diffusers import DDPMScheduler
from .test_schedulers import SchedulerCommonTest
class A__ ( snake_case__ ):
"""simple docstring"""
UpperCamelCase_ : int = (DDPMScheduler,)
def _lowerCAmelCase ( self : Union[str, Any] , **lowerCAmelCase__ : Dict ) -> str:
"""simple docstring"""
_UpperCAmelCase : Optional[int] = {
"num_train_timesteps": 1_0_0_0,
"beta_start": 0.0001,
"beta_end": 0.02,
"beta_schedule": "linear",
"variance_type": "fixed_small",
"clip_sample": True,
}
config.update(**UpperCAmelCase_ )
return config
def _lowerCAmelCase ( self : Optional[Any] ) -> int:
"""simple docstring"""
for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
self.check_over_configs(num_train_timesteps=UpperCAmelCase_ )
def _lowerCAmelCase ( self : Optional[int] ) -> Any:
"""simple docstring"""
for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1] , [0.002, 0.02, 0.2, 2] ):
self.check_over_configs(beta_start=UpperCAmelCase_ , beta_end=UpperCAmelCase_ )
def _lowerCAmelCase ( self : Optional[int] ) -> int:
"""simple docstring"""
for schedule in ["linear", "squaredcos_cap_v2"]:
self.check_over_configs(beta_schedule=UpperCAmelCase_ )
def _lowerCAmelCase ( self : Dict ) -> Union[str, Any]:
"""simple docstring"""
for variance in ["fixed_small", "fixed_large", "other"]:
self.check_over_configs(variance_type=UpperCAmelCase_ )
def _lowerCAmelCase ( self : Any ) -> Tuple:
"""simple docstring"""
for clip_sample in [True, False]:
self.check_over_configs(clip_sample=UpperCAmelCase_ )
def _lowerCAmelCase ( self : str ) -> Dict:
"""simple docstring"""
self.check_over_configs(thresholding=UpperCAmelCase_ )
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "sample", "v_prediction"]:
self.check_over_configs(
thresholding=UpperCAmelCase_ , prediction_type=UpperCAmelCase_ , sample_max_value=UpperCAmelCase_ , )
def _lowerCAmelCase ( self : List[str] ) -> Optional[Any]:
"""simple docstring"""
for prediction_type in ["epsilon", "sample", "v_prediction"]:
self.check_over_configs(prediction_type=UpperCAmelCase_ )
def _lowerCAmelCase ( self : int ) -> Union[str, Any]:
"""simple docstring"""
for t in [0, 5_0_0, 9_9_9]:
self.check_over_forward(time_step=UpperCAmelCase_ )
def _lowerCAmelCase ( self : Tuple ) -> Dict:
"""simple docstring"""
_UpperCAmelCase : Optional[int] = self.scheduler_classes[0]
_UpperCAmelCase : List[str] = self.get_scheduler_config()
_UpperCAmelCase : Union[str, Any] = scheduler_class(**UpperCAmelCase_ )
assert torch.sum(torch.abs(scheduler._get_variance(0 ) - 0.0 ) ) < 1e-5
assert torch.sum(torch.abs(scheduler._get_variance(4_8_7 ) - 0.0_0979 ) ) < 1e-5
assert torch.sum(torch.abs(scheduler._get_variance(9_9_9 ) - 0.02 ) ) < 1e-5
def _lowerCAmelCase ( self : List[str] ) -> int:
"""simple docstring"""
_UpperCAmelCase : Union[str, Any] = self.scheduler_classes[0]
_UpperCAmelCase : Dict = self.get_scheduler_config()
_UpperCAmelCase : Optional[int] = scheduler_class(**UpperCAmelCase_ )
_UpperCAmelCase : List[Any] = len(UpperCAmelCase_ )
_UpperCAmelCase : str = self.dummy_model()
_UpperCAmelCase : str = self.dummy_sample_deter
_UpperCAmelCase : List[Any] = torch.manual_seed(0 )
for t in reversed(range(UpperCAmelCase_ ) ):
# 1. predict noise residual
_UpperCAmelCase : Any = model(UpperCAmelCase_ , UpperCAmelCase_ )
# 2. predict previous mean of sample x_t-1
_UpperCAmelCase : Union[str, Any] = scheduler.step(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , generator=UpperCAmelCase_ ).prev_sample
# if t > 0:
# noise = self.dummy_sample_deter
# variance = scheduler.get_variance(t) ** (0.5) * noise
#
# sample = pred_prev_sample + variance
_UpperCAmelCase : List[Any] = pred_prev_sample
_UpperCAmelCase : Dict = torch.sum(torch.abs(UpperCAmelCase_ ) )
_UpperCAmelCase : List[Any] = torch.mean(torch.abs(UpperCAmelCase_ ) )
assert abs(result_sum.item() - 258.9606 ) < 1e-2
assert abs(result_mean.item() - 0.3372 ) < 1e-3
def _lowerCAmelCase ( self : Dict ) -> Dict:
"""simple docstring"""
_UpperCAmelCase : List[Any] = self.scheduler_classes[0]
_UpperCAmelCase : Optional[int] = self.get_scheduler_config(prediction_type="v_prediction" )
_UpperCAmelCase : Optional[int] = scheduler_class(**UpperCAmelCase_ )
_UpperCAmelCase : List[Any] = len(UpperCAmelCase_ )
_UpperCAmelCase : Dict = self.dummy_model()
_UpperCAmelCase : Any = self.dummy_sample_deter
_UpperCAmelCase : str = torch.manual_seed(0 )
for t in reversed(range(UpperCAmelCase_ ) ):
# 1. predict noise residual
_UpperCAmelCase : Tuple = model(UpperCAmelCase_ , UpperCAmelCase_ )
# 2. predict previous mean of sample x_t-1
_UpperCAmelCase : Optional[int] = scheduler.step(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , generator=UpperCAmelCase_ ).prev_sample
# if t > 0:
# noise = self.dummy_sample_deter
# variance = scheduler.get_variance(t) ** (0.5) * noise
#
# sample = pred_prev_sample + variance
_UpperCAmelCase : Optional[int] = pred_prev_sample
_UpperCAmelCase : str = torch.sum(torch.abs(UpperCAmelCase_ ) )
_UpperCAmelCase : Optional[int] = torch.mean(torch.abs(UpperCAmelCase_ ) )
assert abs(result_sum.item() - 202.0296 ) < 1e-2
assert abs(result_mean.item() - 0.2631 ) < 1e-3
def _lowerCAmelCase ( self : List[Any] ) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase : Union[str, Any] = self.scheduler_classes[0]
_UpperCAmelCase : Any = self.get_scheduler_config()
_UpperCAmelCase : Optional[int] = scheduler_class(**UpperCAmelCase_ )
_UpperCAmelCase : Union[str, Any] = [1_0_0, 8_7, 5_0, 1, 0]
scheduler.set_timesteps(timesteps=UpperCAmelCase_ )
_UpperCAmelCase : List[Any] = scheduler.timesteps
for i, timestep in enumerate(UpperCAmelCase_ ):
if i == len(UpperCAmelCase_ ) - 1:
_UpperCAmelCase : str = -1
else:
_UpperCAmelCase : List[Any] = timesteps[i + 1]
_UpperCAmelCase : int = scheduler.previous_timestep(UpperCAmelCase_ )
_UpperCAmelCase : Dict = prev_t.item()
self.assertEqual(UpperCAmelCase_ , UpperCAmelCase_ )
def _lowerCAmelCase ( self : Tuple ) -> int:
"""simple docstring"""
_UpperCAmelCase : str = self.scheduler_classes[0]
_UpperCAmelCase : Optional[Any] = self.get_scheduler_config()
_UpperCAmelCase : Tuple = scheduler_class(**UpperCAmelCase_ )
_UpperCAmelCase : Dict = [1_0_0, 8_7, 5_0, 5_1, 0]
with self.assertRaises(UpperCAmelCase_ , msg="`custom_timesteps` must be in descending order." ):
scheduler.set_timesteps(timesteps=UpperCAmelCase_ )
def _lowerCAmelCase ( self : List[str] ) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase : Dict = self.scheduler_classes[0]
_UpperCAmelCase : int = self.get_scheduler_config()
_UpperCAmelCase : Optional[Any] = scheduler_class(**UpperCAmelCase_ )
_UpperCAmelCase : Optional[int] = [1_0_0, 8_7, 5_0, 1, 0]
_UpperCAmelCase : Optional[Any] = len(UpperCAmelCase_ )
with self.assertRaises(UpperCAmelCase_ , msg="Can only pass one of `num_inference_steps` or `custom_timesteps`." ):
scheduler.set_timesteps(num_inference_steps=UpperCAmelCase_ , timesteps=UpperCAmelCase_ )
def _lowerCAmelCase ( self : Tuple ) -> str:
"""simple docstring"""
_UpperCAmelCase : Union[str, Any] = self.scheduler_classes[0]
_UpperCAmelCase : Union[str, Any] = self.get_scheduler_config()
_UpperCAmelCase : Dict = scheduler_class(**UpperCAmelCase_ )
_UpperCAmelCase : Any = [scheduler.config.num_train_timesteps]
with self.assertRaises(
UpperCAmelCase_ , msg="`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}" , ):
scheduler.set_timesteps(timesteps=UpperCAmelCase_ )
| 364
|
'''simple docstring'''
import contextlib
import csv
import json
import os
import sqlitea
import tarfile
import textwrap
import zipfile
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
import datasets
import datasets.config
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( ):
_UpperCAmelCase : Optional[Any] = 10
_UpperCAmelCase : int = datasets.Features(
{
"tokens": datasets.Sequence(datasets.Value("string" ) ),
"labels": datasets.Sequence(datasets.ClassLabel(names=["negative", "positive"] ) ),
"answers": datasets.Sequence(
{
"text": datasets.Value("string" ),
"answer_start": datasets.Value("int32" ),
} ),
"id": datasets.Value("int64" ),
} )
_UpperCAmelCase : List[str] = datasets.Dataset.from_dict(
{
"tokens": [["foo"] * 5] * n,
"labels": [[1] * 5] * n,
"answers": [{"answer_start": [97], "text": ["1976"]}] * 10,
"id": list(range(a_ ) ),
}, features=a_, )
return dataset
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: Optional[int], a_: Dict ):
_UpperCAmelCase : Any = str(tmp_path_factory.mktemp("data" ) / "file.arrow" )
dataset.map(cache_file_name=a_ )
return filename
# FILE_CONTENT + files
__a = '\\n Text data.\n Second line of data.'
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: Dict ):
_UpperCAmelCase : Dict = tmp_path_factory.mktemp("data" ) / "file.txt"
_UpperCAmelCase : Tuple = FILE_CONTENT
with open(a_, "w" ) as f:
f.write(a_ )
return filename
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: Union[str, Any] ):
import bza
_UpperCAmelCase : str = tmp_path_factory.mktemp("data" ) / "file.txt.bz2"
_UpperCAmelCase : Optional[int] = bytes(a_, "utf-8" )
with bza.open(a_, "wb" ) as f:
f.write(a_ )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: Union[str, Any] ):
import gzip
_UpperCAmelCase : str = str(tmp_path_factory.mktemp("data" ) / "file.txt.gz" )
_UpperCAmelCase : Any = bytes(a_, "utf-8" )
with gzip.open(a_, "wb" ) as f:
f.write(a_ )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: str ):
if datasets.config.LZ4_AVAILABLE:
import lza.frame
_UpperCAmelCase : Optional[int] = tmp_path_factory.mktemp("data" ) / "file.txt.lz4"
_UpperCAmelCase : str = bytes(a_, "utf-8" )
with lza.frame.open(a_, "wb" ) as f:
f.write(a_ )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: int, a_: Any ):
if datasets.config.PY7ZR_AVAILABLE:
import pyazr
_UpperCAmelCase : Any = tmp_path_factory.mktemp("data" ) / "file.txt.7z"
with pyazr.SevenZipFile(a_, "w" ) as archive:
archive.write(a_, arcname=os.path.basename(a_ ) )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: Any, a_: List[str] ):
import tarfile
_UpperCAmelCase : Union[str, Any] = tmp_path_factory.mktemp("data" ) / "file.txt.tar"
with tarfile.TarFile(a_, "w" ) as f:
f.add(a_, arcname=os.path.basename(a_ ) )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: int ):
import lzma
_UpperCAmelCase : List[Any] = tmp_path_factory.mktemp("data" ) / "file.txt.xz"
_UpperCAmelCase : List[str] = bytes(a_, "utf-8" )
with lzma.open(a_, "wb" ) as f:
f.write(a_ )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: Dict, a_: Tuple ):
import zipfile
_UpperCAmelCase : Tuple = tmp_path_factory.mktemp("data" ) / "file.txt.zip"
with zipfile.ZipFile(a_, "w" ) as f:
f.write(a_, arcname=os.path.basename(a_ ) )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: Optional[int] ):
if datasets.config.ZSTANDARD_AVAILABLE:
import zstandard as zstd
_UpperCAmelCase : Optional[int] = tmp_path_factory.mktemp("data" ) / "file.txt.zst"
_UpperCAmelCase : int = bytes(a_, "utf-8" )
with zstd.open(a_, "wb" ) as f:
f.write(a_ )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: Optional[int] ):
_UpperCAmelCase : List[str] = tmp_path_factory.mktemp("data" ) / "file.xml"
_UpperCAmelCase : Tuple = textwrap.dedent(
"\\n <?xml version=\"1.0\" encoding=\"UTF-8\" ?>\n <tmx version=\"1.4\">\n <header segtype=\"sentence\" srclang=\"ca\" />\n <body>\n <tu>\n <tuv xml:lang=\"ca\"><seg>Contingut 1</seg></tuv>\n <tuv xml:lang=\"en\"><seg>Content 1</seg></tuv>\n </tu>\n <tu>\n <tuv xml:lang=\"ca\"><seg>Contingut 2</seg></tuv>\n <tuv xml:lang=\"en\"><seg>Content 2</seg></tuv>\n </tu>\n <tu>\n <tuv xml:lang=\"ca\"><seg>Contingut 3</seg></tuv>\n <tuv xml:lang=\"en\"><seg>Content 3</seg></tuv>\n </tu>\n <tu>\n <tuv xml:lang=\"ca\"><seg>Contingut 4</seg></tuv>\n <tuv xml:lang=\"en\"><seg>Content 4</seg></tuv>\n </tu>\n <tu>\n <tuv xml:lang=\"ca\"><seg>Contingut 5</seg></tuv>\n <tuv xml:lang=\"en\"><seg>Content 5</seg></tuv>\n </tu>\n </body>\n </tmx>" )
with open(a_, "w" ) as f:
f.write(a_ )
return filename
__a = [
{'col_1': '0', 'col_2': 0, 'col_3': 0.0},
{'col_1': '1', 'col_2': 1, 'col_3': 1.0},
{'col_1': '2', 'col_2': 2, 'col_3': 2.0},
{'col_1': '3', 'col_2': 3, 'col_3': 3.0},
]
__a = [
{'col_1': '4', 'col_2': 4, 'col_3': 4.0},
{'col_1': '5', 'col_2': 5, 'col_3': 5.0},
]
__a = {
'col_1': ['0', '1', '2', '3'],
'col_2': [0, 1, 2, 3],
'col_3': [0.0, 1.0, 2.0, 3.0],
}
__a = [
{'col_3': 0.0, 'col_1': '0', 'col_2': 0},
{'col_3': 1.0, 'col_1': '1', 'col_2': 1},
]
__a = [
{'col_1': 's0', 'col_2': 0, 'col_3': 0.0},
{'col_1': 's1', 'col_2': 1, 'col_3': 1.0},
{'col_1': 's2', 'col_2': 2, 'col_3': 2.0},
{'col_1': 's3', 'col_2': 3, 'col_3': 3.0},
]
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( ):
return DATA_DICT_OF_LISTS
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: Union[str, Any] ):
_UpperCAmelCase : str = datasets.Dataset.from_dict(a_ )
_UpperCAmelCase : Optional[int] = str(tmp_path_factory.mktemp("data" ) / "dataset.arrow" )
dataset.map(cache_file_name=a_ )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: str ):
_UpperCAmelCase : int = str(tmp_path_factory.mktemp("data" ) / "dataset.sqlite" )
with contextlib.closing(sqlitea.connect(a_ ) ) as con:
_UpperCAmelCase : List[Any] = con.cursor()
cur.execute("CREATE TABLE dataset(col_1 text, col_2 int, col_3 real)" )
for item in DATA:
cur.execute("INSERT INTO dataset(col_1, col_2, col_3) VALUES (?, ?, ?)", tuple(item.values() ) )
con.commit()
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: Any ):
_UpperCAmelCase : Dict = str(tmp_path_factory.mktemp("data" ) / "dataset.csv" )
with open(a_, "w", newline="" ) as f:
_UpperCAmelCase : Dict = csv.DictWriter(a_, fieldnames=["col_1", "col_2", "col_3"] )
writer.writeheader()
for item in DATA:
writer.writerow(a_ )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: Union[str, Any] ):
_UpperCAmelCase : Union[str, Any] = str(tmp_path_factory.mktemp("data" ) / "dataset2.csv" )
with open(a_, "w", newline="" ) as f:
_UpperCAmelCase : Optional[int] = csv.DictWriter(a_, fieldnames=["col_1", "col_2", "col_3"] )
writer.writeheader()
for item in DATA:
writer.writerow(a_ )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: str, a_: str ):
import bza
_UpperCAmelCase : str = tmp_path_factory.mktemp("data" ) / "dataset.csv.bz2"
with open(a_, "rb" ) as f:
_UpperCAmelCase : Any = f.read()
# data = bytes(FILE_CONTENT, "utf-8")
with bza.open(a_, "wb" ) as f:
f.write(a_ )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: Optional[int], a_: Dict, a_: Optional[int] ):
_UpperCAmelCase : List[Any] = tmp_path_factory.mktemp("data" ) / "dataset.csv.zip"
with zipfile.ZipFile(a_, "w" ) as f:
f.write(a_, arcname=os.path.basename(a_ ) )
f.write(a_, arcname=os.path.basename(a_ ) )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: List[str], a_: Union[str, Any], a_: int ):
_UpperCAmelCase : int = tmp_path_factory.mktemp("data" ) / "dataset.csv.zip"
with zipfile.ZipFile(a_, "w" ) as f:
f.write(a_, arcname=os.path.basename(csv_path.replace(".csv", ".CSV" ) ) )
f.write(a_, arcname=os.path.basename(csva_path.replace(".csv", ".CSV" ) ) )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: Any, a_: Union[str, Any], a_: Tuple ):
_UpperCAmelCase : Any = tmp_path_factory.mktemp("data" ) / "dataset_with_dir.csv.zip"
with zipfile.ZipFile(a_, "w" ) as f:
f.write(a_, arcname=os.path.join("main_dir", os.path.basename(a_ ) ) )
f.write(a_, arcname=os.path.join("main_dir", os.path.basename(a_ ) ) )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: Tuple ):
_UpperCAmelCase : Optional[Any] = str(tmp_path_factory.mktemp("data" ) / "dataset.parquet" )
_UpperCAmelCase : Dict = pa.schema(
{
"col_1": pa.string(),
"col_2": pa.intaa(),
"col_3": pa.floataa(),
} )
with open(a_, "wb" ) as f:
_UpperCAmelCase : Tuple = pq.ParquetWriter(a_, schema=a_ )
_UpperCAmelCase : Tuple = pa.Table.from_pydict({k: [DATA[i][k] for i in range(len(a_ ) )] for k in DATA[0]}, schema=a_ )
writer.write_table(a_ )
writer.close()
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: Any ):
_UpperCAmelCase : Union[str, Any] = str(tmp_path_factory.mktemp("data" ) / "dataset.json" )
_UpperCAmelCase : str = {"data": DATA}
with open(a_, "w" ) as f:
json.dump(a_, a_ )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: Union[str, Any] ):
_UpperCAmelCase : Optional[int] = str(tmp_path_factory.mktemp("data" ) / "dataset.json" )
_UpperCAmelCase : Dict = {"data": DATA_DICT_OF_LISTS}
with open(a_, "w" ) as f:
json.dump(a_, a_ )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: int ):
_UpperCAmelCase : Optional[Any] = str(tmp_path_factory.mktemp("data" ) / "dataset.jsonl" )
with open(a_, "w" ) as f:
for item in DATA:
f.write(json.dumps(a_ ) + "\n" )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: Tuple ):
_UpperCAmelCase : Any = str(tmp_path_factory.mktemp("data" ) / "dataset2.jsonl" )
with open(a_, "w" ) as f:
for item in DATA:
f.write(json.dumps(a_ ) + "\n" )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: Any ):
_UpperCAmelCase : int = str(tmp_path_factory.mktemp("data" ) / "dataset_312.jsonl" )
with open(a_, "w" ) as f:
for item in DATA_312:
f.write(json.dumps(a_ ) + "\n" )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: Optional[Any] ):
_UpperCAmelCase : Optional[int] = str(tmp_path_factory.mktemp("data" ) / "dataset-str.jsonl" )
with open(a_, "w" ) as f:
for item in DATA_STR:
f.write(json.dumps(a_ ) + "\n" )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: Union[str, Any], a_: Any ):
import gzip
_UpperCAmelCase : Optional[Any] = str(tmp_path_factory.mktemp("data" ) / "dataset.txt.gz" )
with open(a_, "rb" ) as orig_file:
with gzip.open(a_, "wb" ) as zipped_file:
zipped_file.writelines(a_ )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: Optional[Any], a_: Tuple ):
import gzip
_UpperCAmelCase : List[Any] = str(tmp_path_factory.mktemp("data" ) / "dataset.jsonl.gz" )
with open(a_, "rb" ) as orig_file:
with gzip.open(a_, "wb" ) as zipped_file:
zipped_file.writelines(a_ )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: Dict, a_: List[Any], a_: Union[str, Any] ):
_UpperCAmelCase : Tuple = tmp_path_factory.mktemp("data" ) / "dataset.jsonl.zip"
with zipfile.ZipFile(a_, "w" ) as f:
f.write(a_, arcname=os.path.basename(a_ ) )
f.write(a_, arcname=os.path.basename(a_ ) )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: Union[str, Any], a_: Optional[int], a_: Optional[Any], a_: Dict ):
_UpperCAmelCase : Dict = tmp_path_factory.mktemp("data" ) / "dataset_nested.jsonl.zip"
with zipfile.ZipFile(a_, "w" ) as f:
f.write(a_, arcname=os.path.join("nested", os.path.basename(a_ ) ) )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: List[Any], a_: Optional[int], a_: List[str] ):
_UpperCAmelCase : Dict = tmp_path_factory.mktemp("data" ) / "dataset_with_dir.jsonl.zip"
with zipfile.ZipFile(a_, "w" ) as f:
f.write(a_, arcname=os.path.join("main_dir", os.path.basename(a_ ) ) )
f.write(a_, arcname=os.path.join("main_dir", os.path.basename(a_ ) ) )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: List[Any], a_: List[Any], a_: str ):
_UpperCAmelCase : Optional[Any] = tmp_path_factory.mktemp("data" ) / "dataset.jsonl.tar"
with tarfile.TarFile(a_, "w" ) as f:
f.add(a_, arcname=os.path.basename(a_ ) )
f.add(a_, arcname=os.path.basename(a_ ) )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: List[str], a_: List[Any], a_: Tuple, a_: Dict ):
_UpperCAmelCase : List[Any] = tmp_path_factory.mktemp("data" ) / "dataset_nested.jsonl.tar"
with tarfile.TarFile(a_, "w" ) as f:
f.add(a_, arcname=os.path.join("nested", os.path.basename(a_ ) ) )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: List[str] ):
_UpperCAmelCase : List[str] = ["0", "1", "2", "3"]
_UpperCAmelCase : Tuple = str(tmp_path_factory.mktemp("data" ) / "dataset.txt" )
with open(a_, "w" ) as f:
for item in data:
f.write(item + "\n" )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: Union[str, Any] ):
_UpperCAmelCase : Dict = ["0", "1", "2", "3"]
_UpperCAmelCase : Optional[Any] = str(tmp_path_factory.mktemp("data" ) / "dataset2.txt" )
with open(a_, "w" ) as f:
for item in data:
f.write(item + "\n" )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: Any ):
_UpperCAmelCase : int = ["0", "1", "2", "3"]
_UpperCAmelCase : str = tmp_path_factory.mktemp("data" ) / "dataset.abc"
with open(a_, "w" ) as f:
for item in data:
f.write(item + "\n" )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: Optional[Any], a_: Any, a_: Union[str, Any] ):
_UpperCAmelCase : Union[str, Any] = tmp_path_factory.mktemp("data" ) / "dataset.text.zip"
with zipfile.ZipFile(a_, "w" ) as f:
f.write(a_, arcname=os.path.basename(a_ ) )
f.write(a_, arcname=os.path.basename(a_ ) )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: Optional[int], a_: List[Any], a_: List[Any] ):
_UpperCAmelCase : List[Any] = tmp_path_factory.mktemp("data" ) / "dataset_with_dir.text.zip"
with zipfile.ZipFile(a_, "w" ) as f:
f.write(a_, arcname=os.path.join("main_dir", os.path.basename(a_ ) ) )
f.write(a_, arcname=os.path.join("main_dir", os.path.basename(a_ ) ) )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: Any, a_: str, a_: Tuple ):
_UpperCAmelCase : List[Any] = tmp_path_factory.mktemp("data" ) / "dataset.ext.zip"
with zipfile.ZipFile(a_, "w" ) as f:
f.write(a_, arcname=os.path.basename("unsupported.ext" ) )
f.write(a_, arcname=os.path.basename("unsupported_2.ext" ) )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: Optional[Any] ):
_UpperCAmelCase : List[str] = "\n".join(["First", "Second\u2029with Unicode new line", "Third"] )
_UpperCAmelCase : str = str(tmp_path_factory.mktemp("data" ) / "dataset_with_unicode_new_lines.txt" )
with open(a_, "w", encoding="utf-8" ) as f:
f.write(a_ )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( ):
return os.path.join("tests", "features", "data", "test_image_rgb.jpg" )
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( ):
return os.path.join("tests", "features", "data", "test_audio_44100.wav" )
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: int, a_: Optional[Any] ):
_UpperCAmelCase : Union[str, Any] = tmp_path_factory.mktemp("data" ) / "dataset.img.zip"
with zipfile.ZipFile(a_, "w" ) as f:
f.write(a_, arcname=os.path.basename(a_ ) )
f.write(a_, arcname=os.path.basename(a_ ).replace(".jpg", "2.jpg" ) )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: Tuple ):
_UpperCAmelCase : Optional[Any] = tmp_path_factory.mktemp("data_dir" )
(data_dir / "subdir").mkdir()
with open(data_dir / "subdir" / "train.txt", "w" ) as f:
f.write("foo\n" * 10 )
with open(data_dir / "subdir" / "test.txt", "w" ) as f:
f.write("bar\n" * 10 )
# hidden file
with open(data_dir / "subdir" / ".test.txt", "w" ) as f:
f.write("bar\n" * 10 )
# hidden directory
(data_dir / ".subdir").mkdir()
with open(data_dir / ".subdir" / "train.txt", "w" ) as f:
f.write("foo\n" * 10 )
with open(data_dir / ".subdir" / "test.txt", "w" ) as f:
f.write("bar\n" * 10 )
return data_dir
| 17
| 0
|
'''simple docstring'''
from ..models.whisper import WhisperForConditionalGeneration, WhisperProcessor
from .base import PipelineTool
class A__ ( lowerCamelCase_ ):
"""simple docstring"""
UpperCamelCase_ : List[Any] = """openai/whisper-base"""
UpperCamelCase_ : int = (
"""This is a tool that transcribes an audio into text. It takes an input named `audio` and returns the """
"""transcribed text."""
)
UpperCamelCase_ : List[Any] = """transcriber"""
UpperCamelCase_ : Optional[int] = WhisperProcessor
UpperCamelCase_ : Optional[int] = WhisperForConditionalGeneration
UpperCamelCase_ : Any = ["""audio"""]
UpperCamelCase_ : Any = ["""text"""]
def _lowerCAmelCase ( self : Optional[Any] , lowerCAmelCase__ : Dict ) -> List[Any]:
"""simple docstring"""
return self.pre_processor(lowerCAmelCase__ , return_tensors="pt" ).input_features
def _lowerCAmelCase ( self : Tuple , lowerCAmelCase__ : Any ) -> Tuple:
"""simple docstring"""
return self.model.generate(inputs=lowerCAmelCase__ )
def _lowerCAmelCase ( self : Tuple , lowerCAmelCase__ : str ) -> List[str]:
"""simple docstring"""
return self.pre_processor.batch_decode(lowerCAmelCase__ , skip_special_tokens=lowerCAmelCase__ )[0]
| 365
|
'''simple docstring'''
import unittest
from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
@require_sentencepiece
@slow # see https://github.com/huggingface/transformers/issues/11457
class A__ ( UpperCamelCase , unittest.TestCase ):
"""simple docstring"""
UpperCamelCase_ : str = BarthezTokenizer
UpperCamelCase_ : List[Any] = BarthezTokenizerFast
UpperCamelCase_ : Optional[int] = True
UpperCamelCase_ : Optional[int] = True
def _lowerCAmelCase ( self : Optional[int] ) -> Dict:
"""simple docstring"""
super().setUp()
_UpperCAmelCase : Tuple = BarthezTokenizerFast.from_pretrained("moussaKam/mbarthez" )
tokenizer.save_pretrained(self.tmpdirname )
tokenizer.save_pretrained(self.tmpdirname , legacy_format=lowerCAmelCase__ )
_UpperCAmelCase : List[str] = tokenizer
def _lowerCAmelCase ( self : List[str] ) -> Optional[int]:
"""simple docstring"""
_UpperCAmelCase : Tuple = "<pad>"
_UpperCAmelCase : Dict = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCAmelCase__ ) , lowerCAmelCase__ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCAmelCase__ ) , lowerCAmelCase__ )
def _lowerCAmelCase ( self : Tuple ) -> Optional[int]:
"""simple docstring"""
_UpperCAmelCase : List[str] = 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(lowerCAmelCase__ ) , 1_0_1_1_2_2 )
def _lowerCAmelCase ( self : Union[str, Any] ) -> Dict:
"""simple docstring"""
self.assertEqual(self.get_tokenizer().vocab_size , 1_0_1_1_2_2 )
@require_torch
def _lowerCAmelCase ( self : Any ) -> int:
"""simple docstring"""
_UpperCAmelCase : int = ["A long paragraph for summarization.", "Another paragraph for summarization."]
_UpperCAmelCase : Optional[int] = [0, 5_7, 3_0_1_8, 7_0_3_0_7, 9_1, 2]
_UpperCAmelCase : int = self.tokenizer(
lowerCAmelCase__ , max_length=len(lowerCAmelCase__ ) , padding=lowerCAmelCase__ , truncation=lowerCAmelCase__ , return_tensors="pt" )
self.assertIsInstance(lowerCAmelCase__ , lowerCAmelCase__ )
self.assertEqual((2, 6) , batch.input_ids.shape )
self.assertEqual((2, 6) , batch.attention_mask.shape )
_UpperCAmelCase : str = batch.input_ids.tolist()[0]
self.assertListEqual(lowerCAmelCase__ , lowerCAmelCase__ )
def _lowerCAmelCase ( self : str ) -> Optional[Any]:
"""simple docstring"""
if not self.test_rust_tokenizer:
return
_UpperCAmelCase : Optional[int] = self.get_tokenizer()
_UpperCAmelCase : Optional[int] = self.get_rust_tokenizer()
_UpperCAmelCase : Tuple = "I was born in 92000, and this is falsé."
_UpperCAmelCase : Dict = tokenizer.tokenize(lowerCAmelCase__ )
_UpperCAmelCase : Union[str, Any] = rust_tokenizer.tokenize(lowerCAmelCase__ )
self.assertListEqual(lowerCAmelCase__ , lowerCAmelCase__ )
_UpperCAmelCase : Dict = tokenizer.encode(lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ )
_UpperCAmelCase : Union[str, Any] = rust_tokenizer.encode(lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ )
self.assertListEqual(lowerCAmelCase__ , lowerCAmelCase__ )
_UpperCAmelCase : Optional[Any] = self.get_rust_tokenizer()
_UpperCAmelCase : Optional[Any] = tokenizer.encode(lowerCAmelCase__ )
_UpperCAmelCase : Optional[int] = rust_tokenizer.encode(lowerCAmelCase__ )
self.assertListEqual(lowerCAmelCase__ , lowerCAmelCase__ )
@slow
def _lowerCAmelCase ( self : int ) -> int:
"""simple docstring"""
_UpperCAmelCase : Optional[Any] = {"input_ids": [[0, 4_9_0, 1_4_3_2_8, 4_5_0_7, 3_5_4, 4_7, 4_3_6_6_9, 9_5, 2_5, 7_8_1_1_7, 2_0_2_1_5, 1_9_7_7_9, 1_9_0, 2_2, 4_0_0, 4, 3_5_3_4_3, 8_0_3_1_0, 6_0_3, 8_6, 2_4_9_3_7, 1_0_5, 3_3_4_3_8, 9_4_7_6_2, 1_9_6, 3_9_6_4_2, 7, 1_5, 1_5_9_3_3, 1_7_3, 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], [0, 1_0_5_3_4, 8_7, 2_5, 6_6, 3_3_5_8, 1_9_6, 5_5_2_8_9, 8, 8_2_9_6_1, 8_1, 2_2_0_4, 7_5_2_0_3, 7, 1_5, 7_6_3, 1_2_9_5_6, 2_1_6, 1_7_8, 1_4_3_2_8, 9_5_9_5, 1_3_7_7, 6_9_6_9_3, 7, 4_4_8, 7_1_0_2_1, 1_9_6, 1_8_1_0_6, 1_4_3_7, 1_3_9_7_4, 1_0_8, 9_0_8_3, 4, 4_9_3_1_5, 7, 3_9, 8_6, 1_3_2_6, 2_7_9_3, 4_6_3_3_3, 4, 4_4_8, 1_9_6, 7_4_5_8_8, 7, 4_9_3_1_5, 7, 3_9, 2_1, 8_2_2, 3_8_4_7_0, 7_4, 2_1, 6_6_7_2_3, 6_2_4_8_0, 8, 2_2_0_5_0, 5, 2]], "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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]} # noqa: E501
# fmt: on
# moussaKam/mbarthez is a french model. So we also use french texts.
_UpperCAmelCase : Tuple = [
"Le transformeur est un modèle d'apprentissage profond introduit en 2017, "
"utilisé principalement dans le domaine du traitement automatique des langues (TAL).",
"À l'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus "
"pour gérer des données séquentielles, telles que le langage naturel, pour des tâches "
"telles que la traduction et la synthèse de texte.",
]
self.tokenizer_integration_test_util(
expected_encoding=lowerCAmelCase__ , model_name="moussaKam/mbarthez" , revision="c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6" , sequences=lowerCAmelCase__ , )
| 17
| 0
|
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
enable_full_determinism()
@slow
@require_torch_gpu
class A__ ( unittest.TestCase ):
"""simple docstring"""
def _lowerCAmelCase ( self : str ) -> Dict:
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _lowerCAmelCase ( self : int ) -> Dict:
"""simple docstring"""
_UpperCAmelCase : List[str] = StableDiffusionKDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4" )
_UpperCAmelCase : List[str] = sd_pipe.to(__lowerCamelCase )
sd_pipe.set_progress_bar_config(disable=__lowerCamelCase )
sd_pipe.set_scheduler("sample_euler" )
_UpperCAmelCase : List[Any] = "A painting of a squirrel eating a burger"
_UpperCAmelCase : Dict = torch.manual_seed(0 )
_UpperCAmelCase : List[str] = sd_pipe([prompt] , generator=__lowerCamelCase , guidance_scale=9.0 , num_inference_steps=2_0 , output_type="np" )
_UpperCAmelCase : Optional[int] = output.images
_UpperCAmelCase : Tuple = image[0, -3:, -3:, -1]
assert image.shape == (1, 5_1_2, 5_1_2, 3)
_UpperCAmelCase : Union[str, Any] = np.array([0.0447, 0.0492, 0.0468, 0.0408, 0.0383, 0.0408, 0.0354, 0.0380, 0.0339] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def _lowerCAmelCase ( self : Dict ) -> List[Any]:
"""simple docstring"""
_UpperCAmelCase : Tuple = StableDiffusionKDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base" )
_UpperCAmelCase : str = sd_pipe.to(__lowerCamelCase )
sd_pipe.set_progress_bar_config(disable=__lowerCamelCase )
sd_pipe.set_scheduler("sample_euler" )
_UpperCAmelCase : Dict = "A painting of a squirrel eating a burger"
_UpperCAmelCase : Optional[int] = torch.manual_seed(0 )
_UpperCAmelCase : Dict = sd_pipe([prompt] , generator=__lowerCamelCase , guidance_scale=9.0 , num_inference_steps=2_0 , output_type="np" )
_UpperCAmelCase : List[str] = output.images
_UpperCAmelCase : Optional[int] = image[0, -3:, -3:, -1]
assert image.shape == (1, 5_1_2, 5_1_2, 3)
_UpperCAmelCase : str = np.array([0.1237, 0.1320, 0.1438, 0.1359, 0.1390, 0.1132, 0.1277, 0.1175, 0.1112] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 5e-1
def _lowerCAmelCase ( self : int ) -> str:
"""simple docstring"""
_UpperCAmelCase : Tuple = StableDiffusionKDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base" )
_UpperCAmelCase : Optional[int] = sd_pipe.to(__lowerCamelCase )
sd_pipe.set_progress_bar_config(disable=__lowerCamelCase )
sd_pipe.set_scheduler("sample_dpmpp_2m" )
_UpperCAmelCase : Tuple = "A painting of a squirrel eating a burger"
_UpperCAmelCase : List[Any] = torch.manual_seed(0 )
_UpperCAmelCase : Any = sd_pipe(
[prompt] , generator=__lowerCamelCase , guidance_scale=7.5 , num_inference_steps=1_5 , output_type="np" , use_karras_sigmas=__lowerCamelCase , )
_UpperCAmelCase : str = output.images
_UpperCAmelCase : Any = image[0, -3:, -3:, -1]
assert image.shape == (1, 5_1_2, 5_1_2, 3)
_UpperCAmelCase : str = np.array(
[0.1138_1689, 0.1211_2921, 0.138_9457, 0.1254_9606, 0.124_4964, 0.1083_1517, 0.1156_2866, 0.1086_7816, 0.1049_9048] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
| 366
|
'''simple docstring'''
import unittest
import numpy as np
import requests
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
__a = False
if is_vision_available():
from PIL import Image
from transformers import PixaStructImageProcessor
class A__ ( unittest.TestCase ):
"""simple docstring"""
def __init__( self : int , lowerCAmelCase__ : Optional[int] , lowerCAmelCase__ : Optional[Any]=7 , lowerCAmelCase__ : int=3 , lowerCAmelCase__ : List[Any]=1_8 , lowerCAmelCase__ : str=3_0 , lowerCAmelCase__ : str=4_0_0 , lowerCAmelCase__ : Union[str, Any]=None , lowerCAmelCase__ : Optional[Any]=True , lowerCAmelCase__ : str=True , lowerCAmelCase__ : List[Any]=None , ) -> List[Any]:
"""simple docstring"""
_UpperCAmelCase : List[Any] = size if size is not None else {"height": 2_0, "width": 2_0}
_UpperCAmelCase : Optional[Any] = parent
_UpperCAmelCase : Tuple = batch_size
_UpperCAmelCase : str = num_channels
_UpperCAmelCase : Optional[Any] = image_size
_UpperCAmelCase : Dict = min_resolution
_UpperCAmelCase : str = max_resolution
_UpperCAmelCase : List[Any] = size
_UpperCAmelCase : Union[str, Any] = do_normalize
_UpperCAmelCase : Optional[Any] = do_convert_rgb
_UpperCAmelCase : str = [5_1_2, 1_0_2_4, 2_0_4_8, 4_0_9_6]
_UpperCAmelCase : str = patch_size if patch_size is not None else {"height": 1_6, "width": 1_6}
def _lowerCAmelCase ( self : List[str] ) -> int:
"""simple docstring"""
return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}
def _lowerCAmelCase ( self : Any ) -> str:
"""simple docstring"""
_UpperCAmelCase : Dict = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg"
_UpperCAmelCase : Optional[Any] = Image.open(requests.get(lowerCAmelCase__ , stream=lowerCAmelCase__ ).raw ).convert("RGB" )
return raw_image
@unittest.skipIf(
not is_torch_greater_or_equal_than_1_11 , reason='''`Pix2StructImageProcessor` requires `torch>=1.11.0`.''' , )
@require_torch
@require_vision
class A__ ( UpperCamelCase , unittest.TestCase ):
"""simple docstring"""
UpperCamelCase_ : Any = PixaStructImageProcessor if is_vision_available() else None
def _lowerCAmelCase ( self : Any ) -> Optional[int]:
"""simple docstring"""
_UpperCAmelCase : Tuple = PixaStructImageProcessingTester(self )
@property
def _lowerCAmelCase ( self : Tuple ) -> int:
"""simple docstring"""
return self.image_processor_tester.prepare_image_processor_dict()
def _lowerCAmelCase ( self : Any ) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase : str = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(lowerCAmelCase__ , "do_normalize" ) )
self.assertTrue(hasattr(lowerCAmelCase__ , "do_convert_rgb" ) )
def _lowerCAmelCase ( self : Optional[Any] ) -> Dict:
"""simple docstring"""
_UpperCAmelCase : Optional[Any] = self.image_processor_tester.prepare_dummy_image()
_UpperCAmelCase : Optional[int] = self.image_processing_class(**self.image_processor_dict )
_UpperCAmelCase : str = 2_0_4_8
_UpperCAmelCase : Any = image_processor(lowerCAmelCase__ , return_tensors="pt" , max_patches=lowerCAmelCase__ )
self.assertTrue(torch.allclose(inputs.flattened_patches.mean() , torch.tensor(0.0606 ) , atol=1e-3 , rtol=1e-3 ) )
def _lowerCAmelCase ( self : Dict ) -> int:
"""simple docstring"""
_UpperCAmelCase : Optional[Any] = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
_UpperCAmelCase : Tuple = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCAmelCase__ )
for image in image_inputs:
self.assertIsInstance(lowerCAmelCase__ , Image.Image )
# Test not batched input
_UpperCAmelCase : List[str] = (
(self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
* self.image_processor_tester.num_channels
) + 2
for max_patch in self.image_processor_tester.max_patches:
# Test not batched input
_UpperCAmelCase : Union[str, Any] = image_processor(
image_inputs[0] , return_tensors="pt" , max_patches=lowerCAmelCase__ ).flattened_patches
self.assertEqual(
encoded_images.shape , (1, max_patch, expected_hidden_dim) , )
# Test batched
_UpperCAmelCase : str = image_processor(
lowerCAmelCase__ , return_tensors="pt" , max_patches=lowerCAmelCase__ ).flattened_patches
self.assertEqual(
encoded_images.shape , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) , )
def _lowerCAmelCase ( self : Optional[int] ) -> List[str]:
"""simple docstring"""
_UpperCAmelCase : Any = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
_UpperCAmelCase : List[Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCAmelCase__ )
for image in image_inputs:
self.assertIsInstance(lowerCAmelCase__ , Image.Image )
# Test not batched input
_UpperCAmelCase : Union[str, Any] = (
(self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
* self.image_processor_tester.num_channels
) + 2
_UpperCAmelCase : str = True
for max_patch in self.image_processor_tester.max_patches:
# Test not batched input
with self.assertRaises(lowerCAmelCase__ ):
_UpperCAmelCase : str = image_processor(
image_inputs[0] , return_tensors="pt" , max_patches=lowerCAmelCase__ ).flattened_patches
_UpperCAmelCase : Any = "Hello"
_UpperCAmelCase : Optional[int] = image_processor(
image_inputs[0] , return_tensors="pt" , max_patches=lowerCAmelCase__ , header_text=lowerCAmelCase__ ).flattened_patches
self.assertEqual(
encoded_images.shape , (1, max_patch, expected_hidden_dim) , )
# Test batched
_UpperCAmelCase : List[Any] = image_processor(
lowerCAmelCase__ , return_tensors="pt" , max_patches=lowerCAmelCase__ , header_text=lowerCAmelCase__ ).flattened_patches
self.assertEqual(
encoded_images.shape , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) , )
def _lowerCAmelCase ( self : List[str] ) -> List[Any]:
"""simple docstring"""
_UpperCAmelCase : Union[str, Any] = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
_UpperCAmelCase : List[str] = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCAmelCase__ , numpify=lowerCAmelCase__ )
for image in image_inputs:
self.assertIsInstance(lowerCAmelCase__ , np.ndarray )
_UpperCAmelCase : Any = (
(self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
* self.image_processor_tester.num_channels
) + 2
for max_patch in self.image_processor_tester.max_patches:
# Test not batched input
_UpperCAmelCase : int = image_processor(
image_inputs[0] , return_tensors="pt" , max_patches=lowerCAmelCase__ ).flattened_patches
self.assertEqual(
encoded_images.shape , (1, max_patch, expected_hidden_dim) , )
# Test batched
_UpperCAmelCase : Union[str, Any] = image_processor(
lowerCAmelCase__ , return_tensors="pt" , max_patches=lowerCAmelCase__ ).flattened_patches
self.assertEqual(
encoded_images.shape , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) , )
def _lowerCAmelCase ( self : int ) -> str:
"""simple docstring"""
_UpperCAmelCase : List[str] = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
_UpperCAmelCase : Any = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCAmelCase__ , torchify=lowerCAmelCase__ )
for image in image_inputs:
self.assertIsInstance(lowerCAmelCase__ , torch.Tensor )
# Test not batched input
_UpperCAmelCase : List[str] = (
(self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
* self.image_processor_tester.num_channels
) + 2
for max_patch in self.image_processor_tester.max_patches:
# Test not batched input
_UpperCAmelCase : Union[str, Any] = image_processor(
image_inputs[0] , return_tensors="pt" , max_patches=lowerCAmelCase__ ).flattened_patches
self.assertEqual(
encoded_images.shape , (1, max_patch, expected_hidden_dim) , )
# Test batched
_UpperCAmelCase : str = image_processor(
lowerCAmelCase__ , return_tensors="pt" , max_patches=lowerCAmelCase__ ).flattened_patches
self.assertEqual(
encoded_images.shape , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) , )
@unittest.skipIf(
not is_torch_greater_or_equal_than_1_11 , reason='''`Pix2StructImageProcessor` requires `torch>=1.11.0`.''' , )
@require_torch
@require_vision
class A__ ( UpperCamelCase , unittest.TestCase ):
"""simple docstring"""
UpperCamelCase_ : List[Any] = PixaStructImageProcessor if is_vision_available() else None
def _lowerCAmelCase ( self : Union[str, Any] ) -> Union[str, Any]:
"""simple docstring"""
_UpperCAmelCase : Any = PixaStructImageProcessingTester(self , num_channels=4 )
_UpperCAmelCase : List[Any] = 3
@property
def _lowerCAmelCase ( self : Union[str, Any] ) -> Tuple:
"""simple docstring"""
return self.image_processor_tester.prepare_image_processor_dict()
def _lowerCAmelCase ( self : Dict ) -> Any:
"""simple docstring"""
_UpperCAmelCase : str = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(lowerCAmelCase__ , "do_normalize" ) )
self.assertTrue(hasattr(lowerCAmelCase__ , "do_convert_rgb" ) )
def _lowerCAmelCase ( self : int ) -> List[str]:
"""simple docstring"""
_UpperCAmelCase : Optional[int] = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
_UpperCAmelCase : List[str] = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCAmelCase__ )
for image in image_inputs:
self.assertIsInstance(lowerCAmelCase__ , Image.Image )
# Test not batched input
_UpperCAmelCase : str = (
(self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
* (self.image_processor_tester.num_channels - 1)
) + 2
for max_patch in self.image_processor_tester.max_patches:
# Test not batched input
_UpperCAmelCase : Any = image_processor(
image_inputs[0] , return_tensors="pt" , max_patches=lowerCAmelCase__ ).flattened_patches
self.assertEqual(
encoded_images.shape , (1, max_patch, expected_hidden_dim) , )
# Test batched
_UpperCAmelCase : Tuple = image_processor(
lowerCAmelCase__ , return_tensors="pt" , max_patches=lowerCAmelCase__ ).flattened_patches
self.assertEqual(
encoded_images.shape , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) , )
| 17
| 0
|
'''simple docstring'''
def __UpperCAmelCase ( a_: int, a_: int ):
return 1 if input_a == input_a else 0
def __UpperCAmelCase ( ):
assert xnor_gate(0, 0 ) == 1
assert xnor_gate(0, 1 ) == 0
assert xnor_gate(1, 0 ) == 0
assert xnor_gate(1, 1 ) == 1
if __name__ == "__main__":
print(xnor_gate(0, 0))
print(xnor_gate(0, 1))
print(xnor_gate(1, 0))
print(xnor_gate(1, 1))
| 367
|
'''simple docstring'''
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a = logging.get_logger(__name__)
__a = {
'huggingface/time-series-transformer-tourism-monthly': (
'https://huggingface.co/huggingface/time-series-transformer-tourism-monthly/resolve/main/config.json'
),
# See all TimeSeriesTransformer models at https://huggingface.co/models?filter=time_series_transformer
}
class A__ ( UpperCamelCase ):
"""simple docstring"""
UpperCamelCase_ : Tuple = '''time_series_transformer'''
UpperCamelCase_ : Optional[Any] = {
'''hidden_size''': '''d_model''',
'''num_attention_heads''': '''encoder_attention_heads''',
'''num_hidden_layers''': '''encoder_layers''',
}
def __init__( self : Optional[int] , lowerCAmelCase__ : Optional[int] = None , lowerCAmelCase__ : Optional[int] = None , lowerCAmelCase__ : str = "student_t" , lowerCAmelCase__ : str = "nll" , lowerCAmelCase__ : int = 1 , lowerCAmelCase__ : List[int] = [1, 2, 3, 4, 5, 6, 7] , lowerCAmelCase__ : Optional[Union[str, bool]] = "mean" , lowerCAmelCase__ : int = 0 , lowerCAmelCase__ : int = 0 , lowerCAmelCase__ : int = 0 , lowerCAmelCase__ : int = 0 , lowerCAmelCase__ : Optional[List[int]] = None , lowerCAmelCase__ : Optional[List[int]] = None , lowerCAmelCase__ : int = 3_2 , lowerCAmelCase__ : int = 3_2 , lowerCAmelCase__ : int = 2 , lowerCAmelCase__ : int = 2 , lowerCAmelCase__ : int = 2 , lowerCAmelCase__ : int = 2 , lowerCAmelCase__ : bool = True , lowerCAmelCase__ : str = "gelu" , lowerCAmelCase__ : int = 6_4 , lowerCAmelCase__ : float = 0.1 , lowerCAmelCase__ : float = 0.1 , lowerCAmelCase__ : float = 0.1 , lowerCAmelCase__ : float = 0.1 , lowerCAmelCase__ : float = 0.1 , lowerCAmelCase__ : int = 1_0_0 , lowerCAmelCase__ : float = 0.02 , lowerCAmelCase__ : Dict=True , **lowerCAmelCase__ : Tuple , ) -> Tuple:
"""simple docstring"""
_UpperCAmelCase : Optional[int] = prediction_length
_UpperCAmelCase : Optional[Any] = context_length or prediction_length
_UpperCAmelCase : Optional[Any] = distribution_output
_UpperCAmelCase : Union[str, Any] = loss
_UpperCAmelCase : Dict = input_size
_UpperCAmelCase : int = num_time_features
_UpperCAmelCase : Any = lags_sequence
_UpperCAmelCase : Dict = scaling
_UpperCAmelCase : Tuple = num_dynamic_real_features
_UpperCAmelCase : Dict = num_static_real_features
_UpperCAmelCase : Union[str, Any] = num_static_categorical_features
if cardinality and num_static_categorical_features > 0:
if len(lowerCAmelCase__ ) != num_static_categorical_features:
raise ValueError(
"The cardinality should be a list of the same length as `num_static_categorical_features`" )
_UpperCAmelCase : Optional[int] = cardinality
else:
_UpperCAmelCase : Optional[Any] = [0]
if embedding_dimension and num_static_categorical_features > 0:
if len(lowerCAmelCase__ ) != num_static_categorical_features:
raise ValueError(
"The embedding dimension should be a list of the same length as `num_static_categorical_features`" )
_UpperCAmelCase : List[Any] = embedding_dimension
else:
_UpperCAmelCase : Optional[Any] = [min(5_0 , (cat + 1) // 2 ) for cat in self.cardinality]
_UpperCAmelCase : str = num_parallel_samples
# Transformer architecture configuration
_UpperCAmelCase : Union[str, Any] = input_size * len(lowerCAmelCase__ ) + self._number_of_features
_UpperCAmelCase : str = d_model
_UpperCAmelCase : Optional[Any] = encoder_attention_heads
_UpperCAmelCase : Dict = decoder_attention_heads
_UpperCAmelCase : List[Any] = encoder_ffn_dim
_UpperCAmelCase : str = decoder_ffn_dim
_UpperCAmelCase : Dict = encoder_layers
_UpperCAmelCase : str = decoder_layers
_UpperCAmelCase : Any = dropout
_UpperCAmelCase : str = attention_dropout
_UpperCAmelCase : List[Any] = activation_dropout
_UpperCAmelCase : Dict = encoder_layerdrop
_UpperCAmelCase : Any = decoder_layerdrop
_UpperCAmelCase : Optional[Any] = activation_function
_UpperCAmelCase : Tuple = init_std
_UpperCAmelCase : List[str] = use_cache
super().__init__(is_encoder_decoder=lowerCAmelCase__ , **lowerCAmelCase__ )
@property
def _lowerCAmelCase ( self : str ) -> int:
"""simple docstring"""
return (
sum(self.embedding_dimension )
+ self.num_dynamic_real_features
+ self.num_time_features
+ self.num_static_real_features
+ self.input_size * 2 # the log1p(abs(loc)) and log(scale) features
)
| 17
| 0
|
'''simple docstring'''
import os
import string
import sys
__a = 1 << 8
__a = {
"tab": ord('\t'),
"newline": ord('\r'),
"esc": 27,
"up": 65 + ARROW_KEY_FLAG,
"down": 66 + ARROW_KEY_FLAG,
"right": 67 + ARROW_KEY_FLAG,
"left": 68 + ARROW_KEY_FLAG,
"mod_int": 91,
"undefined": sys.maxsize,
"interrupt": 3,
"insert": 50,
"delete": 51,
"pg_up": 53,
"pg_down": 54,
}
__a = KEYMAP["up"]
__a = KEYMAP["left"]
if sys.platform == "win32":
__a = []
__a = {
b"\xe0H": KEYMAP["up"] - ARROW_KEY_FLAG,
b"\x00H": KEYMAP["up"] - ARROW_KEY_FLAG,
b"\xe0P": KEYMAP["down"] - ARROW_KEY_FLAG,
b"\x00P": KEYMAP["down"] - ARROW_KEY_FLAG,
b"\xe0M": KEYMAP["right"] - ARROW_KEY_FLAG,
b"\x00M": KEYMAP["right"] - ARROW_KEY_FLAG,
b"\xe0K": KEYMAP["left"] - ARROW_KEY_FLAG,
b"\x00K": KEYMAP["left"] - ARROW_KEY_FLAG,
}
for i in range(10):
__a = ord(str(i))
def __UpperCAmelCase ( ):
if os.name == "nt":
import msvcrt
_UpperCAmelCase : str = "mbcs"
# Flush the keyboard buffer
while msvcrt.kbhit():
msvcrt.getch()
if len(_snake_case ) == 0:
# Read the keystroke
_UpperCAmelCase : Optional[Any] = msvcrt.getch()
# If it is a prefix char, get second part
if ch in (b"\x00", b"\xe0"):
_UpperCAmelCase : int = ch + msvcrt.getch()
# Translate actual Win chars to bullet char types
try:
_UpperCAmelCase : Optional[int] = chr(WIN_KEYMAP[cha] )
WIN_CH_BUFFER.append(chr(KEYMAP["mod_int"] ) )
WIN_CH_BUFFER.append(_snake_case )
if ord(_snake_case ) in (
KEYMAP["insert"] - 1 << 9,
KEYMAP["delete"] - 1 << 9,
KEYMAP["pg_up"] - 1 << 9,
KEYMAP["pg_down"] - 1 << 9,
):
WIN_CH_BUFFER.append(chr(126 ) )
_UpperCAmelCase : str = chr(KEYMAP["esc"] )
except KeyError:
_UpperCAmelCase : str = cha[1]
else:
_UpperCAmelCase : Optional[int] = ch.decode(_snake_case )
else:
_UpperCAmelCase : int = WIN_CH_BUFFER.pop(0 )
elif os.name == "posix":
import termios
import tty
_UpperCAmelCase : int = sys.stdin.fileno()
_UpperCAmelCase : str = termios.tcgetattr(_snake_case )
try:
tty.setraw(_snake_case )
_UpperCAmelCase : Optional[Any] = sys.stdin.read(1 )
finally:
termios.tcsetattr(_snake_case, termios.TCSADRAIN, _snake_case )
return ch
def __UpperCAmelCase ( ):
_UpperCAmelCase : Tuple = get_raw_chars()
if ord(_snake_case ) in [KEYMAP["interrupt"], KEYMAP["newline"]]:
return char
elif ord(_snake_case ) == KEYMAP["esc"]:
_UpperCAmelCase : str = get_raw_chars()
if ord(_snake_case ) == KEYMAP["mod_int"]:
_UpperCAmelCase : Optional[Any] = get_raw_chars()
if ord(_snake_case ) >= KEYMAP["arrow_begin"] - ARROW_KEY_FLAG and ord(_snake_case ) <= KEYMAP["arrow_end"] - ARROW_KEY_FLAG:
return chr(ord(_snake_case ) + ARROW_KEY_FLAG )
else:
return KEYMAP["undefined"]
else:
return get_raw_chars()
else:
if char in string.printable:
return char
else:
return KEYMAP["undefined"]
| 368
|
'''simple docstring'''
import baseaa
def __UpperCAmelCase ( a_: str ):
return baseaa.baaencode(string.encode("utf-8" ) )
def __UpperCAmelCase ( a_: bytes ):
return baseaa.baadecode(a_ ).decode("utf-8" )
if __name__ == "__main__":
__a = 'Hello World!'
__a = baseaa_encode(test)
print(encoded)
__a = baseaa_decode(encoded)
print(decoded)
| 17
| 0
|
'''simple docstring'''
import os
import warnings
from typing import List, Optional
from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig
__a = logging.get_logger(__name__)
class A__ :
"""simple docstring"""
def __init__( self : str , lowerCAmelCase__ : Dict , lowerCAmelCase__ : List[str] ) -> List[str]:
"""simple docstring"""
_UpperCAmelCase : Optional[int] = question_encoder
_UpperCAmelCase : Any = generator
_UpperCAmelCase : Union[str, Any] = self.question_encoder
def _lowerCAmelCase ( self : str , lowerCAmelCase__ : Union[str, Any] ) -> Any:
"""simple docstring"""
if os.path.isfile(_a ):
raise ValueError(F"""Provided path ({save_directory}) should be a directory, not a file""" )
os.makedirs(_a , exist_ok=_a )
_UpperCAmelCase : Optional[Any] = os.path.join(_a , "question_encoder_tokenizer" )
_UpperCAmelCase : Dict = os.path.join(_a , "generator_tokenizer" )
self.question_encoder.save_pretrained(_a )
self.generator.save_pretrained(_a )
@classmethod
def _lowerCAmelCase ( cls : Any , lowerCAmelCase__ : Any , **lowerCAmelCase__ : Union[str, Any] ) -> Any:
"""simple docstring"""
from ..auto.tokenization_auto import AutoTokenizer
_UpperCAmelCase : Any = kwargs.pop("config" , _a )
if config is None:
_UpperCAmelCase : str = RagConfig.from_pretrained(_a )
_UpperCAmelCase : List[Any] = AutoTokenizer.from_pretrained(
_a , config=config.question_encoder , subfolder="question_encoder_tokenizer" )
_UpperCAmelCase : Optional[int] = AutoTokenizer.from_pretrained(
_a , config=config.generator , subfolder="generator_tokenizer" )
return cls(question_encoder=_a , generator=_a )
def __call__( self : str , *lowerCAmelCase__ : Optional[int] , **lowerCAmelCase__ : Union[str, Any] ) -> int:
"""simple docstring"""
return self.current_tokenizer(*_a , **_a )
def _lowerCAmelCase ( self : List[str] , *lowerCAmelCase__ : List[str] , **lowerCAmelCase__ : List[str] ) -> str:
"""simple docstring"""
return self.generator.batch_decode(*_a , **_a )
def _lowerCAmelCase ( self : Tuple , *lowerCAmelCase__ : Dict , **lowerCAmelCase__ : Optional[int] ) -> List[Any]:
"""simple docstring"""
return self.generator.decode(*_a , **_a )
def _lowerCAmelCase ( self : str ) -> int:
"""simple docstring"""
_UpperCAmelCase : Optional[int] = self.question_encoder
def _lowerCAmelCase ( self : Tuple ) -> Optional[int]:
"""simple docstring"""
_UpperCAmelCase : Optional[int] = self.generator
def _lowerCAmelCase ( self : Union[str, Any] , lowerCAmelCase__ : List[str] , lowerCAmelCase__ : Optional[List[str]] = None , lowerCAmelCase__ : Optional[int] = None , lowerCAmelCase__ : Optional[int] = None , lowerCAmelCase__ : str = "longest" , lowerCAmelCase__ : str = None , lowerCAmelCase__ : bool = True , **lowerCAmelCase__ : List[Any] , ) -> Tuple:
"""simple docstring"""
warnings.warn(
"`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the "
"regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` "
"context manager to prepare your targets. See the documentation of your specific tokenizer for more "
"details" , _a , )
if max_length is None:
_UpperCAmelCase : int = self.current_tokenizer.model_max_length
_UpperCAmelCase : Tuple = self(
_a , add_special_tokens=_a , return_tensors=_a , max_length=_a , padding=_a , truncation=_a , **_a , )
if tgt_texts is None:
return model_inputs
# Process tgt_texts
if max_target_length is None:
_UpperCAmelCase : List[Any] = self.current_tokenizer.model_max_length
_UpperCAmelCase : Union[str, Any] = self(
text_target=_a , add_special_tokens=_a , return_tensors=_a , padding=_a , max_length=_a , truncation=_a , **_a , )
_UpperCAmelCase : Dict = labels["input_ids"]
return model_inputs
| 369
|
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow
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.models.xglm.modeling_tf_xglm import (
TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFXGLMForCausalLM,
TFXGLMModel,
)
@require_tf
class A__ :
"""simple docstring"""
UpperCamelCase_ : Any = XGLMConfig
UpperCamelCase_ : Union[str, Any] = {}
UpperCamelCase_ : Dict = '''gelu'''
def __init__( self : Optional[int] , lowerCAmelCase__ : Union[str, Any] , lowerCAmelCase__ : Optional[Any]=1_4 , lowerCAmelCase__ : Any=7 , lowerCAmelCase__ : Optional[Any]=True , lowerCAmelCase__ : List[str]=True , lowerCAmelCase__ : Any=True , lowerCAmelCase__ : List[str]=9_9 , lowerCAmelCase__ : Any=3_2 , lowerCAmelCase__ : Optional[int]=2 , lowerCAmelCase__ : List[Any]=4 , lowerCAmelCase__ : Any=3_7 , lowerCAmelCase__ : List[Any]="gelu" , lowerCAmelCase__ : List[Any]=0.1 , lowerCAmelCase__ : Dict=0.1 , lowerCAmelCase__ : Optional[int]=5_1_2 , lowerCAmelCase__ : Optional[Any]=0.02 , ) -> int:
"""simple docstring"""
_UpperCAmelCase : Optional[Any] = parent
_UpperCAmelCase : str = batch_size
_UpperCAmelCase : str = seq_length
_UpperCAmelCase : int = is_training
_UpperCAmelCase : List[Any] = use_input_mask
_UpperCAmelCase : Optional[int] = use_labels
_UpperCAmelCase : str = vocab_size
_UpperCAmelCase : int = d_model
_UpperCAmelCase : Tuple = num_hidden_layers
_UpperCAmelCase : Tuple = num_attention_heads
_UpperCAmelCase : Tuple = ffn_dim
_UpperCAmelCase : Any = activation_function
_UpperCAmelCase : Union[str, Any] = activation_dropout
_UpperCAmelCase : Union[str, Any] = attention_dropout
_UpperCAmelCase : Any = max_position_embeddings
_UpperCAmelCase : int = initializer_range
_UpperCAmelCase : Any = None
_UpperCAmelCase : int = 0
_UpperCAmelCase : Union[str, Any] = 2
_UpperCAmelCase : Tuple = 1
def _lowerCAmelCase ( self : Optional[Any] ) -> List[Any]:
"""simple docstring"""
return XGLMConfig.from_pretrained("facebook/xglm-564M" )
def _lowerCAmelCase ( self : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
_UpperCAmelCase : int = tf.clip_by_value(
ids_tensor([self.batch_size, self.seq_length] , self.vocab_size ) , clip_value_min=0 , clip_value_max=3 )
_UpperCAmelCase : Any = None
if self.use_input_mask:
_UpperCAmelCase : Union[str, Any] = random_attention_mask([self.batch_size, self.seq_length] )
_UpperCAmelCase : Optional[Any] = self.get_config()
_UpperCAmelCase : Dict = floats_tensor([self.num_hidden_layers, self.num_attention_heads] , 2 )
return (
config,
input_ids,
input_mask,
head_mask,
)
def _lowerCAmelCase ( self : int ) -> Any:
"""simple docstring"""
return XGLMConfig(
vocab_size=self.vocab_size , d_model=self.hidden_size , num_layers=self.num_hidden_layers , attention_heads=self.num_attention_heads , ffn_dim=self.ffn_dim , activation_function=self.activation_function , activation_dropout=self.activation_dropout , attention_dropout=self.attention_dropout , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , use_cache=lowerCAmelCase__ , bos_token_id=self.bos_token_id , eos_token_id=self.eos_token_id , pad_token_id=self.pad_token_id , return_dict=lowerCAmelCase__ , )
def _lowerCAmelCase ( self : Tuple ) -> Union[str, Any]:
"""simple docstring"""
_UpperCAmelCase : Union[str, Any] = self.prepare_config_and_inputs()
(
(
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) ,
) : List[Any] = config_and_inputs
_UpperCAmelCase : Optional[int] = {
"input_ids": input_ids,
"head_mask": head_mask,
}
return config, inputs_dict
@require_tf
class A__ ( UpperCamelCase , UpperCamelCase , unittest.TestCase ):
"""simple docstring"""
UpperCamelCase_ : str = (TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
UpperCamelCase_ : Any = (TFXGLMForCausalLM,) if is_tf_available() else ()
UpperCamelCase_ : Tuple = (
{'''feature-extraction''': TFXGLMModel, '''text-generation''': TFXGLMForCausalLM} if is_tf_available() else {}
)
UpperCamelCase_ : Dict = False
UpperCamelCase_ : List[Any] = False
UpperCamelCase_ : Tuple = False
def _lowerCAmelCase ( self : List[str] ) -> int:
"""simple docstring"""
_UpperCAmelCase : Dict = TFXGLMModelTester(self )
_UpperCAmelCase : Dict = ConfigTester(self , config_class=lowerCAmelCase__ , n_embd=3_7 )
def _lowerCAmelCase ( self : List[str] ) -> Dict:
"""simple docstring"""
self.config_tester.run_common_tests()
@slow
def _lowerCAmelCase ( self : List[str] ) -> Union[str, Any]:
"""simple docstring"""
for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_UpperCAmelCase : Optional[int] = TFXGLMModel.from_pretrained(lowerCAmelCase__ )
self.assertIsNotNone(lowerCAmelCase__ )
@unittest.skip(reason="Currently, model embeddings are going to undergo a major refactor." )
def _lowerCAmelCase ( self : Union[str, Any] ) -> int:
"""simple docstring"""
super().test_resize_token_embeddings()
@require_tf
class A__ ( unittest.TestCase ):
"""simple docstring"""
@slow
def _lowerCAmelCase ( self : Optional[int] , lowerCAmelCase__ : Optional[Any]=True ) -> Tuple:
"""simple docstring"""
_UpperCAmelCase : Optional[int] = TFXGLMForCausalLM.from_pretrained("facebook/xglm-564M" )
_UpperCAmelCase : Any = tf.convert_to_tensor([[2, 2_6_8, 9_8_6_5]] , dtype=tf.intaa ) # The dog
# </s> The dog is a very friendly dog. He is very affectionate and loves to play with other
# fmt: off
_UpperCAmelCase : int = [2, 2_6_8, 9_8_6_5, 6_7, 1_1, 1_9_8_8, 5_7_2_5_2, 9_8_6_5, 5, 9_8_4, 6_7, 1_9_8_8, 2_1_3_8_3_8, 1_6_5_8, 5_3, 7_0_4_4_6, 3_3, 6_6_5_7, 2_7_8, 1_5_8_1]
# fmt: on
_UpperCAmelCase : Dict = model.generate(lowerCAmelCase__ , do_sample=lowerCAmelCase__ , num_beams=1 )
if verify_outputs:
self.assertListEqual(output_ids[0].numpy().tolist() , lowerCAmelCase__ )
@slow
def _lowerCAmelCase ( self : List[Any] ) -> str:
"""simple docstring"""
_UpperCAmelCase : List[str] = XGLMTokenizer.from_pretrained("facebook/xglm-564M" )
_UpperCAmelCase : Optional[Any] = TFXGLMForCausalLM.from_pretrained("facebook/xglm-564M" )
tf.random.set_seed(0 )
_UpperCAmelCase : Any = tokenizer("Today is a nice day and" , return_tensors="tf" )
_UpperCAmelCase : int = tokenized.input_ids
# forces the generation to happen on CPU, to avoid GPU-related quirks (and assure same output regardless of the available devices)
with tf.device(":/CPU:0" ):
_UpperCAmelCase : List[Any] = model.generate(lowerCAmelCase__ , do_sample=lowerCAmelCase__ , seed=[7, 0] )
_UpperCAmelCase : Any = tokenizer.decode(output_ids[0] , skip_special_tokens=lowerCAmelCase__ )
_UpperCAmelCase : List[Any] = (
"Today is a nice day and warm evening here over Southern Alberta!! Today when they closed schools due"
)
self.assertEqual(lowerCAmelCase__ , lowerCAmelCase__ )
@slow
def _lowerCAmelCase ( self : Optional[int] ) -> str:
"""simple docstring"""
_UpperCAmelCase : Optional[Any] = TFXGLMForCausalLM.from_pretrained("facebook/xglm-564M" )
_UpperCAmelCase : List[Any] = XGLMTokenizer.from_pretrained("facebook/xglm-564M" )
_UpperCAmelCase : Optional[int] = "left"
# use different length sentences to test batching
_UpperCAmelCase : Tuple = [
"This is an extremelly long sentence that only exists to test the ability of the model to cope with "
"left-padding, such as in batched generation. The output for the sequence below should be the same "
"regardless of whether left padding is applied or not. When",
"Hello, my dog is a little",
]
_UpperCAmelCase : Dict = tokenizer(lowerCAmelCase__ , return_tensors="tf" , padding=lowerCAmelCase__ )
_UpperCAmelCase : List[Any] = inputs["input_ids"]
_UpperCAmelCase : Dict = model.generate(input_ids=lowerCAmelCase__ , attention_mask=inputs["attention_mask"] , max_new_tokens=1_2 )
_UpperCAmelCase : int = tokenizer(sentences[0] , return_tensors="tf" ).input_ids
_UpperCAmelCase : Dict = model.generate(input_ids=lowerCAmelCase__ , max_new_tokens=1_2 )
_UpperCAmelCase : Optional[int] = tokenizer(sentences[1] , return_tensors="tf" ).input_ids
_UpperCAmelCase : List[Any] = model.generate(input_ids=lowerCAmelCase__ , max_new_tokens=1_2 )
_UpperCAmelCase : List[str] = tokenizer.batch_decode(lowerCAmelCase__ , skip_special_tokens=lowerCAmelCase__ )
_UpperCAmelCase : Tuple = tokenizer.decode(output_non_padded[0] , skip_special_tokens=lowerCAmelCase__ )
_UpperCAmelCase : List[str] = tokenizer.decode(output_padded[0] , skip_special_tokens=lowerCAmelCase__ )
_UpperCAmelCase : Union[str, Any] = [
"This is an extremelly long sentence that only exists to test the ability of the model to cope with "
"left-padding, such as in batched generation. The output for the sequence below should be the same "
"regardless of whether left padding is applied or not. When left padding is applied, the sequence will be "
"a single",
"Hello, my dog is a little bit of a shy one, but he is very friendly",
]
self.assertListEqual(lowerCAmelCase__ , lowerCAmelCase__ )
self.assertListEqual(lowerCAmelCase__ , [non_padded_sentence, padded_sentence] )
| 17
| 0
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
__a = {
'configuration_layoutlmv2': ['LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LayoutLMv2Config'],
'processing_layoutlmv2': ['LayoutLMv2Processor'],
'tokenization_layoutlmv2': ['LayoutLMv2Tokenizer'],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = ['LayoutLMv2TokenizerFast']
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = ['LayoutLMv2FeatureExtractor']
__a = ['LayoutLMv2ImageProcessor']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = [
'LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST',
'LayoutLMv2ForQuestionAnswering',
'LayoutLMv2ForSequenceClassification',
'LayoutLMv2ForTokenClassification',
'LayoutLMv2Layer',
'LayoutLMv2Model',
'LayoutLMv2PreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_layoutlmva import LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMvaConfig
from .processing_layoutlmva import LayoutLMvaProcessor
from .tokenization_layoutlmva import LayoutLMvaTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor, LayoutLMvaImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_layoutlmva import (
LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST,
LayoutLMvaForQuestionAnswering,
LayoutLMvaForSequenceClassification,
LayoutLMvaForTokenClassification,
LayoutLMvaLayer,
LayoutLMvaModel,
LayoutLMvaPreTrainedModel,
)
else:
import sys
__a = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 370
|
'''simple docstring'''
import os
import pytest
import yaml
from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
"files", [
["full:README.md", "dataset_infos.json"],
["empty:README.md", "dataset_infos.json"],
["dataset_infos.json"],
["full:README.md"],
], )
def __UpperCAmelCase ( a_: Tuple, a_: Any ):
_UpperCAmelCase : Union[str, Any] = tmp_path_factory.mktemp("dset_infos_dir" )
if "full:README.md" in files:
with open(dataset_infos_dir / "README.md", "w" ) as f:
f.write("---\ndataset_info:\n dataset_size: 42\n---" )
if "empty:README.md" in files:
with open(dataset_infos_dir / "README.md", "w" ) as f:
f.write("" )
# we want to support dataset_infos.json for backward compatibility
if "dataset_infos.json" in files:
with open(dataset_infos_dir / "dataset_infos.json", "w" ) as f:
f.write("{\"default\": {\"dataset_size\": 42}}" )
_UpperCAmelCase : List[str] = DatasetInfosDict.from_directory(a_ )
assert dataset_infos
assert dataset_infos["default"].dataset_size == 42
@pytest.mark.parametrize(
"dataset_info", [
DatasetInfo(),
DatasetInfo(
description="foo", features=Features({"a": Value("int32" )} ), builder_name="builder", config_name="config", version="1.0.0", splits=[{"name": "train"}], download_size=42, ),
], )
def __UpperCAmelCase ( a_: Union[str, Any], a_: DatasetInfo ):
_UpperCAmelCase : Tuple = str(a_ )
dataset_info.write_to_directory(a_ )
_UpperCAmelCase : Any = DatasetInfo.from_directory(a_ )
assert dataset_info == reloaded
assert os.path.exists(os.path.join(a_, "dataset_info.json" ) )
def __UpperCAmelCase ( ):
_UpperCAmelCase : Optional[int] = DatasetInfo(
description="foo", citation="bar", homepage="https://foo.bar", license="CC0", features=Features({"a": Value("int32" )} ), post_processed={}, supervised_keys=(), task_templates=[], builder_name="builder", config_name="config", version="1.0.0", splits=[{"name": "train", "num_examples": 42}], download_checksums={}, download_size=1_337, post_processing_size=442, dataset_size=1_234, size_in_bytes=1_337 + 442 + 1_234, )
_UpperCAmelCase : Tuple = dataset_info._to_yaml_dict()
assert sorted(a_ ) == sorted(DatasetInfo._INCLUDED_INFO_IN_YAML )
for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
assert key in dataset_info_yaml_dict
assert isinstance(dataset_info_yaml_dict[key], (list, dict, int, str) )
_UpperCAmelCase : List[Any] = yaml.safe_dump(a_ )
_UpperCAmelCase : Optional[int] = yaml.safe_load(a_ )
assert dataset_info_yaml_dict == reloaded
def __UpperCAmelCase ( ):
_UpperCAmelCase : str = DatasetInfo()
_UpperCAmelCase : List[str] = dataset_info._to_yaml_dict()
assert dataset_info_yaml_dict == {}
@pytest.mark.parametrize(
"dataset_infos_dict", [
DatasetInfosDict(),
DatasetInfosDict({"default": DatasetInfo()} ),
DatasetInfosDict({"my_config_name": DatasetInfo()} ),
DatasetInfosDict(
{
"default": DatasetInfo(
description="foo", features=Features({"a": Value("int32" )} ), builder_name="builder", config_name="config", version="1.0.0", splits=[{"name": "train"}], download_size=42, )
} ),
DatasetInfosDict(
{
"v1": DatasetInfo(dataset_size=42 ),
"v2": DatasetInfo(dataset_size=1_337 ),
} ),
], )
def __UpperCAmelCase ( a_: str, a_: DatasetInfosDict ):
_UpperCAmelCase : Union[str, Any] = str(a_ )
dataset_infos_dict.write_to_directory(a_ )
_UpperCAmelCase : Union[str, Any] = DatasetInfosDict.from_directory(a_ )
# the config_name of the dataset_infos_dict take over the attribute
for config_name, dataset_info in dataset_infos_dict.items():
_UpperCAmelCase : Optional[int] = config_name
# the yaml representation doesn't include fields like description or citation
# so we just test that we can recover what we can from the yaml
_UpperCAmelCase : List[str] = DatasetInfo._from_yaml_dict(dataset_info._to_yaml_dict() )
assert dataset_infos_dict == reloaded
if dataset_infos_dict:
assert os.path.exists(os.path.join(a_, "README.md" ) )
| 17
| 0
|
'''simple docstring'''
from collections import Counter
from timeit import timeit
def __UpperCAmelCase ( a_: Tuple = "", ):
return sum(c % 2 for c in Counter(input_str.replace(" ", "" ).lower() ).values() ) < 2
def __UpperCAmelCase ( a_: List[str] = "" ):
if len(__lowerCAmelCase ) == 0:
return True
_UpperCAmelCase : List[str] = input_str.replace(" ", "" ).lower()
# character_freq_dict: Stores the frequency of every character in the input string
_UpperCAmelCase : dict[str, int] = {}
for character in lower_case_input_str:
_UpperCAmelCase : Optional[Any] = character_freq_dict.get(__lowerCAmelCase, 0 ) + 1
_UpperCAmelCase : Tuple = 0
for character_count in character_freq_dict.values():
if character_count % 2:
odd_char += 1
if odd_char > 1:
return False
return True
def __UpperCAmelCase ( a_: str = "" ):
print("\nFor string = ", __lowerCAmelCase, ":" )
print(
"> can_string_be_rearranged_as_palindrome_counter()", "\tans =", can_string_be_rearranged_as_palindrome_counter(__lowerCAmelCase ), "\ttime =", timeit(
"z.can_string_be_rearranged_as_palindrome_counter(z.check_str)", setup="import __main__ as z", ), "seconds", )
print(
"> can_string_be_rearranged_as_palindrome()", "\tans =", can_string_be_rearranged_as_palindrome(__lowerCAmelCase ), "\ttime =", timeit(
"z.can_string_be_rearranged_as_palindrome(z.check_str)", setup="import __main__ as z", ), "seconds", )
if __name__ == "__main__":
__a = input(
'Enter string to determine if it can be rearranged as a palindrome or not: '
).strip()
benchmark(check_str)
__a = can_string_be_rearranged_as_palindrome_counter(check_str)
print(f'{check_str} can {"" if status else "not "}be rearranged as a palindrome')
| 371
|
'''simple docstring'''
from math import factorial
def __UpperCAmelCase ( a_: int = 100 ):
return sum(map(a_, str(factorial(a_ ) ) ) )
if __name__ == "__main__":
print(solution(int(input('Enter the Number: ').strip())))
| 17
| 0
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__a = {
"""configuration_roformer""": ["""ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RoFormerConfig""", """RoFormerOnnxConfig"""],
"""tokenization_roformer""": ["""RoFormerTokenizer"""],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = ["""RoFormerTokenizerFast"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = [
"""ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""RoFormerForCausalLM""",
"""RoFormerForMaskedLM""",
"""RoFormerForMultipleChoice""",
"""RoFormerForQuestionAnswering""",
"""RoFormerForSequenceClassification""",
"""RoFormerForTokenClassification""",
"""RoFormerLayer""",
"""RoFormerModel""",
"""RoFormerPreTrainedModel""",
"""load_tf_weights_in_roformer""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = [
"""TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TFRoFormerForCausalLM""",
"""TFRoFormerForMaskedLM""",
"""TFRoFormerForMultipleChoice""",
"""TFRoFormerForQuestionAnswering""",
"""TFRoFormerForSequenceClassification""",
"""TFRoFormerForTokenClassification""",
"""TFRoFormerLayer""",
"""TFRoFormerModel""",
"""TFRoFormerPreTrainedModel""",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = [
"""FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""FlaxRoFormerForMaskedLM""",
"""FlaxRoFormerForMultipleChoice""",
"""FlaxRoFormerForQuestionAnswering""",
"""FlaxRoFormerForSequenceClassification""",
"""FlaxRoFormerForTokenClassification""",
"""FlaxRoFormerModel""",
"""FlaxRoFormerPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
from .tokenization_roformer import RoFormerTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_roformer_fast import RoFormerTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_roformer import (
ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
RoFormerForCausalLM,
RoFormerForMaskedLM,
RoFormerForMultipleChoice,
RoFormerForQuestionAnswering,
RoFormerForSequenceClassification,
RoFormerForTokenClassification,
RoFormerLayer,
RoFormerModel,
RoFormerPreTrainedModel,
load_tf_weights_in_roformer,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_roformer import (
TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
TFRoFormerForCausalLM,
TFRoFormerForMaskedLM,
TFRoFormerForMultipleChoice,
TFRoFormerForQuestionAnswering,
TFRoFormerForSequenceClassification,
TFRoFormerForTokenClassification,
TFRoFormerLayer,
TFRoFormerModel,
TFRoFormerPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_roformer import (
FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
FlaxRoFormerForMaskedLM,
FlaxRoFormerForMultipleChoice,
FlaxRoFormerForQuestionAnswering,
FlaxRoFormerForSequenceClassification,
FlaxRoFormerForTokenClassification,
FlaxRoFormerModel,
FlaxRoFormerPreTrainedModel,
)
else:
import sys
__a = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 350
|
'''simple docstring'''
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
__a = (3, 9, -11, 0, 7, 5, 1, -1)
__a = (4, 6, 2, 0, 8, 10, 3, -2)
@dataclass
class A__ :
"""simple docstring"""
UpperCamelCase_ : int
UpperCamelCase_ : Node | None
class A__ :
"""simple docstring"""
def __init__( self : Dict , lowerCAmelCase__ : Iterable[int] ) -> None:
"""simple docstring"""
_UpperCAmelCase : Node | None = None
for i in sorted(lowerCAmelCase__ , reverse=lowerCAmelCase__ ):
_UpperCAmelCase : str = Node(lowerCAmelCase__ , self.head )
def __iter__( self : int ) -> Iterator[int]:
"""simple docstring"""
_UpperCAmelCase : List[Any] = self.head
while node:
yield node.data
_UpperCAmelCase : List[str] = node.next_node
def __len__( self : Any ) -> int:
"""simple docstring"""
return sum(1 for _ in self )
def __str__( self : Union[str, Any] ) -> str:
"""simple docstring"""
return " -> ".join([str(lowerCAmelCase__ ) for node in self] )
def __UpperCAmelCase ( a_: SortedLinkedList, a_: SortedLinkedList ):
return SortedLinkedList(list(a_ ) + list(a_ ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
__a = SortedLinkedList
print(merge_lists(SSL(test_data_odd), SSL(test_data_even)))
| 17
| 0
|
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import YolosConfig, YolosForObjectDetection, YolosImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
__a = logging.get_logger(__name__)
def __UpperCAmelCase ( a_: Optional[Any] ):
_UpperCAmelCase : Union[str, Any] = YolosConfig()
# size of the architecture
if "yolos_ti" in yolos_name:
_UpperCAmelCase : Dict = 192
_UpperCAmelCase : Any = 768
_UpperCAmelCase : Dict = 12
_UpperCAmelCase : Any = 3
_UpperCAmelCase : Tuple = [800, 1_333]
_UpperCAmelCase : str = False
elif yolos_name == "yolos_s_dWr":
_UpperCAmelCase : Dict = 330
_UpperCAmelCase : Union[str, Any] = 14
_UpperCAmelCase : Dict = 6
_UpperCAmelCase : Union[str, Any] = 1_320
elif "yolos_s" in yolos_name:
_UpperCAmelCase : Tuple = 384
_UpperCAmelCase : List[str] = 1_536
_UpperCAmelCase : Optional[int] = 12
_UpperCAmelCase : List[Any] = 6
elif "yolos_b" in yolos_name:
_UpperCAmelCase : Any = [800, 1_344]
_UpperCAmelCase : Union[str, Any] = 91
_UpperCAmelCase : str = "huggingface/label-files"
_UpperCAmelCase : Tuple = "coco-detection-id2label.json"
_UpperCAmelCase : Union[str, Any] = json.load(open(hf_hub_download(a_, a_, repo_type="dataset" ), "r" ) )
_UpperCAmelCase : Any = {int(a_ ): v for k, v in idalabel.items()}
_UpperCAmelCase : str = idalabel
_UpperCAmelCase : int = {v: k for k, v in idalabel.items()}
return config
def __UpperCAmelCase ( a_: Tuple, a_: Any, a_: Tuple = False ):
for i in range(config.num_hidden_layers ):
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
_UpperCAmelCase : Dict = state_dict.pop(f"""blocks.{i}.attn.qkv.weight""" )
_UpperCAmelCase : Tuple = state_dict.pop(f"""blocks.{i}.attn.qkv.bias""" )
# next, add query, keys and values (in that order) to the state dict
_UpperCAmelCase : List[str] = in_proj_weight[: config.hidden_size, :]
_UpperCAmelCase : Dict = in_proj_bias[: config.hidden_size]
_UpperCAmelCase : Any = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
_UpperCAmelCase : Dict = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
_UpperCAmelCase : List[str] = in_proj_weight[-config.hidden_size :, :]
_UpperCAmelCase : Optional[int] = in_proj_bias[-config.hidden_size :]
def __UpperCAmelCase ( a_: Optional[int] ):
if "backbone" in name:
_UpperCAmelCase : Tuple = name.replace("backbone", "vit" )
if "cls_token" in name:
_UpperCAmelCase : int = name.replace("cls_token", "embeddings.cls_token" )
if "det_token" in name:
_UpperCAmelCase : List[Any] = name.replace("det_token", "embeddings.detection_tokens" )
if "mid_pos_embed" in name:
_UpperCAmelCase : Dict = name.replace("mid_pos_embed", "encoder.mid_position_embeddings" )
if "pos_embed" in name:
_UpperCAmelCase : Union[str, Any] = name.replace("pos_embed", "embeddings.position_embeddings" )
if "patch_embed.proj" in name:
_UpperCAmelCase : List[Any] = name.replace("patch_embed.proj", "embeddings.patch_embeddings.projection" )
if "blocks" in name:
_UpperCAmelCase : Any = name.replace("blocks", "encoder.layer" )
if "attn.proj" in name:
_UpperCAmelCase : Dict = name.replace("attn.proj", "attention.output.dense" )
if "attn" in name:
_UpperCAmelCase : Optional[Any] = name.replace("attn", "attention.self" )
if "norm1" in name:
_UpperCAmelCase : Dict = name.replace("norm1", "layernorm_before" )
if "norm2" in name:
_UpperCAmelCase : List[str] = name.replace("norm2", "layernorm_after" )
if "mlp.fc1" in name:
_UpperCAmelCase : List[str] = name.replace("mlp.fc1", "intermediate.dense" )
if "mlp.fc2" in name:
_UpperCAmelCase : Optional[int] = name.replace("mlp.fc2", "output.dense" )
if "class_embed" in name:
_UpperCAmelCase : int = name.replace("class_embed", "class_labels_classifier" )
if "bbox_embed" in name:
_UpperCAmelCase : Union[str, Any] = name.replace("bbox_embed", "bbox_predictor" )
if "vit.norm" in name:
_UpperCAmelCase : Any = name.replace("vit.norm", "vit.layernorm" )
return name
def __UpperCAmelCase ( a_: int, a_: Union[str, Any] ):
for key in orig_state_dict.copy().keys():
_UpperCAmelCase : Dict = orig_state_dict.pop(a_ )
if "qkv" in key:
_UpperCAmelCase : int = key.split("." )
_UpperCAmelCase : Dict = int(key_split[2] )
_UpperCAmelCase : int = model.vit.encoder.layer[layer_num].attention.attention.all_head_size
if "weight" in key:
_UpperCAmelCase : Optional[int] = val[:dim, :]
_UpperCAmelCase : Optional[Any] = val[
dim : dim * 2, :
]
_UpperCAmelCase : Optional[int] = val[-dim:, :]
else:
_UpperCAmelCase : Any = val[:dim]
_UpperCAmelCase : Optional[Any] = val[dim : dim * 2]
_UpperCAmelCase : Union[str, Any] = val[-dim:]
else:
_UpperCAmelCase : List[Any] = val
return orig_state_dict
def __UpperCAmelCase ( ):
_UpperCAmelCase : Any = "http://images.cocodataset.org/val2017/000000039769.jpg"
_UpperCAmelCase : Optional[int] = Image.open(requests.get(a_, stream=a_ ).raw )
return im
@torch.no_grad()
def __UpperCAmelCase ( a_: Optional[Any], a_: List[str], a_: Union[str, Any], a_: Tuple = False ):
_UpperCAmelCase : Union[str, Any] = get_yolos_config(a_ )
# load original state_dict
_UpperCAmelCase : int = torch.load(a_, map_location="cpu" )["model"]
# load 🤗 model
_UpperCAmelCase : Optional[int] = YolosForObjectDetection(a_ )
model.eval()
_UpperCAmelCase : Tuple = convert_state_dict(a_, a_ )
model.load_state_dict(a_ )
# Check outputs on an image, prepared by YolosImageProcessor
_UpperCAmelCase : Optional[Any] = 800 if yolos_name != "yolos_ti" else 512
_UpperCAmelCase : List[str] = YolosImageProcessor(format="coco_detection", size=a_ )
_UpperCAmelCase : List[str] = image_processor(images=prepare_img(), return_tensors="pt" )
_UpperCAmelCase : Tuple = model(**a_ )
_UpperCAmelCase , _UpperCAmelCase : int = outputs.logits, outputs.pred_boxes
_UpperCAmelCase , _UpperCAmelCase : Dict = None, None
if yolos_name == "yolos_ti":
_UpperCAmelCase : str = torch.tensor(
[[-39.50_22, -11.98_20, -17.68_88], [-29.95_74, -9.97_69, -17.76_91], [-42.32_81, -20.72_00, -30.62_94]] )
_UpperCAmelCase : Union[str, Any] = torch.tensor(
[[0.40_21, 0.08_36, 0.79_79], [0.01_84, 0.26_09, 0.03_64], [0.17_81, 0.20_04, 0.20_95]] )
elif yolos_name == "yolos_s_200_pre":
_UpperCAmelCase : Any = torch.tensor(
[[-24.02_48, -10.30_24, -14.82_90], [-42.03_92, -16.82_00, -27.43_34], [-27.27_43, -11.81_54, -18.71_48]] )
_UpperCAmelCase : Any = torch.tensor(
[[0.25_59, 0.54_55, 0.47_06], [0.29_89, 0.72_79, 0.18_75], [0.77_32, 0.40_17, 0.44_62]] )
elif yolos_name == "yolos_s_300_pre":
_UpperCAmelCase : Dict = torch.tensor(
[[-36.22_20, -14.43_85, -23.54_57], [-35.69_70, -14.75_83, -21.39_35], [-31.59_39, -13.60_42, -16.80_49]] )
_UpperCAmelCase : List[Any] = torch.tensor(
[[0.76_14, 0.23_16, 0.47_28], [0.71_68, 0.44_95, 0.38_55], [0.49_96, 0.14_66, 0.99_96]] )
elif yolos_name == "yolos_s_dWr":
_UpperCAmelCase : Optional[Any] = torch.tensor(
[[-42.86_68, -24.10_49, -41.16_90], [-34.74_56, -14.12_74, -24.91_94], [-33.78_98, -12.19_46, -25.64_95]] )
_UpperCAmelCase : Any = torch.tensor(
[[0.55_87, 0.27_73, 0.06_05], [0.50_04, 0.30_14, 0.99_94], [0.49_99, 0.15_48, 0.99_94]] )
elif yolos_name == "yolos_base":
_UpperCAmelCase : List[str] = torch.tensor(
[[-40.60_64, -24.30_84, -32.64_47], [-55.19_90, -30.77_19, -35.58_77], [-51.43_11, -33.35_07, -35.64_62]] )
_UpperCAmelCase : str = torch.tensor(
[[0.55_55, 0.27_94, 0.06_55], [0.90_49, 0.26_64, 0.18_94], [0.91_83, 0.19_84, 0.16_35]] )
else:
raise ValueError(f"""Unknown yolos_name: {yolos_name}""" )
assert torch.allclose(logits[0, :3, :3], a_, atol=1e-4 )
assert torch.allclose(pred_boxes[0, :3, :3], a_, atol=1e-4 )
Path(a_ ).mkdir(exist_ok=a_ )
print(f"""Saving model {yolos_name} 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:
_UpperCAmelCase : Dict = {
"yolos_ti": "yolos-tiny",
"yolos_s_200_pre": "yolos-small",
"yolos_s_300_pre": "yolos-small-300",
"yolos_s_dWr": "yolos-small-dwr",
"yolos_base": "yolos-base",
}
print("Pushing to the hub..." )
_UpperCAmelCase : List[Any] = model_mapping[yolos_name]
image_processor.push_to_hub(a_, organization="hustvl" )
model.push_to_hub(a_, organization="hustvl" )
if __name__ == "__main__":
__a = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--yolos_name',
default='yolos_s_200_pre',
type=str,
help=(
'Name of the YOLOS model you\'d like to convert. Should be one of \'yolos_ti\', \'yolos_s_200_pre\','
' \'yolos_s_300_pre\', \'yolos_s_dWr\', \'yolos_base\'.'
),
)
parser.add_argument(
'--checkpoint_path', default=None, type=str, help='Path to the original state dict (.pth file).'
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
)
parser.add_argument(
'--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
)
__a = parser.parse_args()
convert_yolos_checkpoint(args.yolos_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)
| 351
|
'''simple docstring'''
def __UpperCAmelCase ( a_: str ):
if not all(char in "01" for char in bin_string ):
raise ValueError("Non-binary value was passed to the function" )
if not bin_string:
raise ValueError("Empty string was passed to the function" )
_UpperCAmelCase : Optional[Any] = ""
while len(a_ ) % 3 != 0:
_UpperCAmelCase : List[Any] = "0" + bin_string
_UpperCAmelCase : Dict = [
bin_string[index : index + 3]
for index in range(len(a_ ) )
if index % 3 == 0
]
for bin_group in bin_string_in_3_list:
_UpperCAmelCase : Optional[Any] = 0
for index, val in enumerate(a_ ):
oct_val += int(2 ** (2 - index) * int(a_ ) )
oct_string += str(a_ )
return oct_string
if __name__ == "__main__":
from doctest import testmod
testmod()
| 17
| 0
|
'''simple docstring'''
def __UpperCAmelCase ( a_: List[str] = 1_000 ) -> Optional[Any]:
_UpperCAmelCase , _UpperCAmelCase : Union[str, Any] = 1, 1
_UpperCAmelCase : str = []
for i in range(1, n + 1 ):
_UpperCAmelCase : List[str] = prev_numerator + 2 * prev_denominator
_UpperCAmelCase : Dict = prev_numerator + prev_denominator
if len(str(lowerCamelCase__ ) ) > len(str(lowerCamelCase__ ) ):
result.append(lowerCamelCase__ )
_UpperCAmelCase : int = numerator
_UpperCAmelCase : List[str] = denominator
return len(lowerCamelCase__ )
if __name__ == "__main__":
print(f'{solution() = }')
| 352
|
'''simple docstring'''
from datetime import datetime
import matplotlib.pyplot as plt
import torch
def __UpperCAmelCase ( a_: str ):
for param in module.parameters():
_UpperCAmelCase : Any = False
def __UpperCAmelCase ( ):
_UpperCAmelCase : Union[str, Any] = "cuda" if torch.cuda.is_available() else "cpu"
if torch.backends.mps.is_available() and torch.backends.mps.is_built():
_UpperCAmelCase : int = "mps"
if device == "mps":
print(
"WARNING: MPS currently doesn't seem to work, and messes up backpropagation without any visible torch"
" errors. I recommend using CUDA on a colab notebook or CPU instead if you're facing inexplicable issues"
" with generations." )
return device
def __UpperCAmelCase ( a_: Optional[Any] ):
_UpperCAmelCase : int = plt.imshow(a_ )
fig.axes.get_xaxis().set_visible(a_ )
fig.axes.get_yaxis().set_visible(a_ )
plt.show()
def __UpperCAmelCase ( ):
_UpperCAmelCase : Dict = datetime.now()
_UpperCAmelCase : List[str] = current_time.strftime("%H:%M:%S" )
return timestamp
| 17
| 0
|
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__a = logging.get_logger(__name__)
__a = {'vocab_file': 'spm_char.model'}
__a = {
'vocab_file': {
'microsoft/speecht5_asr': 'https://huggingface.co/microsoft/speecht5_asr/resolve/main/spm_char.model',
'microsoft/speecht5_tts': 'https://huggingface.co/microsoft/speecht5_tts/resolve/main/spm_char.model',
'microsoft/speecht5_vc': 'https://huggingface.co/microsoft/speecht5_vc/resolve/main/spm_char.model',
}
}
__a = {
'microsoft/speecht5_asr': 1_024,
'microsoft/speecht5_tts': 1_024,
'microsoft/speecht5_vc': 1_024,
}
class A__ ( UpperCamelCase ):
"""simple docstring"""
UpperCamelCase_ : Any = VOCAB_FILES_NAMES
UpperCamelCase_ : Any = PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase_ : Any = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase_ : List[str] = ['''input_ids''', '''attention_mask''']
def __init__( self : List[str] , lowerCAmelCase__ : Union[str, Any] , lowerCAmelCase__ : Optional[Any]="<s>" , lowerCAmelCase__ : int="</s>" , lowerCAmelCase__ : Any="<unk>" , lowerCAmelCase__ : int="<pad>" , lowerCAmelCase__ : Optional[Dict[str, Any]] = None , **lowerCAmelCase__ : List[Any] , ) -> str:
"""simple docstring"""
_UpperCAmelCase : List[str] = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=_lowerCAmelCase , eos_token=_lowerCAmelCase , unk_token=_lowerCAmelCase , pad_token=_lowerCAmelCase , sp_model_kwargs=self.sp_model_kwargs , **_lowerCAmelCase , )
_UpperCAmelCase : Tuple = vocab_file
_UpperCAmelCase : Any = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(_lowerCAmelCase )
@property
def _lowerCAmelCase ( self : Tuple ) -> Optional[Any]:
"""simple docstring"""
return self.sp_model.get_piece_size()
def _lowerCAmelCase ( self : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase : int = {self.convert_ids_to_tokens(_lowerCAmelCase ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self : Dict ) -> Dict:
"""simple docstring"""
_UpperCAmelCase : Dict = self.__dict__.copy()
_UpperCAmelCase : Optional[int] = None
return state
def __setstate__( self : Union[str, Any] , lowerCAmelCase__ : Optional[Any] ) -> Optional[int]:
"""simple docstring"""
_UpperCAmelCase : Any = d
# for backward compatibility
if not hasattr(self , "sp_model_kwargs" ):
_UpperCAmelCase : List[str] = {}
_UpperCAmelCase : Union[str, Any] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def _lowerCAmelCase ( self : Union[str, Any] , lowerCAmelCase__ : str ) -> Union[str, Any]:
"""simple docstring"""
return self.sp_model.encode(_lowerCAmelCase , out_type=_lowerCAmelCase )
def _lowerCAmelCase ( self : Dict , lowerCAmelCase__ : Optional[Any] ) -> Optional[int]:
"""simple docstring"""
return self.sp_model.piece_to_id(_lowerCAmelCase )
def _lowerCAmelCase ( self : int , lowerCAmelCase__ : int ) -> str:
"""simple docstring"""
_UpperCAmelCase : Optional[int] = self.sp_model.IdToPiece(_lowerCAmelCase )
return token
def _lowerCAmelCase ( self : Union[str, Any] , lowerCAmelCase__ : List[str] ) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase : List[str] = []
_UpperCAmelCase : Tuple = ""
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
out_string += self.sp_model.decode(_lowerCAmelCase ) + token
_UpperCAmelCase : str = []
else:
current_sub_tokens.append(_lowerCAmelCase )
out_string += self.sp_model.decode(_lowerCAmelCase )
return out_string.strip()
def _lowerCAmelCase ( self : Tuple , lowerCAmelCase__ : Optional[Any] , lowerCAmelCase__ : Any=None ) -> Union[str, Any]:
"""simple docstring"""
if token_ids_a is None:
return token_ids_a + [self.eos_token_id]
# We don't expect to process pairs, but leave the pair logic for API consistency
return token_ids_a + token_ids_a + [self.eos_token_id]
def _lowerCAmelCase ( self : str , lowerCAmelCase__ : List[int] , lowerCAmelCase__ : Optional[List[int]] = None , lowerCAmelCase__ : bool = False ) -> Union[str, Any]:
"""simple docstring"""
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=_lowerCAmelCase , token_ids_a=_lowerCAmelCase , already_has_special_tokens=_lowerCAmelCase )
_UpperCAmelCase : str = [1]
if token_ids_a is None:
return ([0] * len(_lowerCAmelCase )) + suffix_ones
return ([0] * len(_lowerCAmelCase )) + ([0] * len(_lowerCAmelCase )) + suffix_ones
def _lowerCAmelCase ( self : Optional[Any] , lowerCAmelCase__ : str , lowerCAmelCase__ : Optional[str] = None ) -> List[Any]:
"""simple docstring"""
if not os.path.isdir(_lowerCAmelCase ):
logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
return
_UpperCAmelCase : List[Any] = os.path.join(
_lowerCAmelCase , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_lowerCAmelCase ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , _lowerCAmelCase )
elif not os.path.isfile(self.vocab_file ):
with open(_lowerCAmelCase , "wb" ) as fi:
_UpperCAmelCase : int = self.sp_model.serialized_model_proto()
fi.write(_lowerCAmelCase )
return (out_vocab_file,)
| 353
|
'''simple docstring'''
import torch
from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class A__ ( UpperCamelCase ):
"""simple docstring"""
UpperCamelCase_ : Optional[int] = (EulerDiscreteScheduler,)
UpperCamelCase_ : Tuple = 10
def _lowerCAmelCase ( self : Dict , **lowerCAmelCase__ : Tuple ) -> Any:
"""simple docstring"""
_UpperCAmelCase : str = {
"num_train_timesteps": 1_1_0_0,
"beta_start": 0.0001,
"beta_end": 0.02,
"beta_schedule": "linear",
}
config.update(**lowerCAmelCase__ )
return config
def _lowerCAmelCase ( self : Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
self.check_over_configs(num_train_timesteps=lowerCAmelCase__ )
def _lowerCAmelCase ( self : Any ) -> List[str]:
"""simple docstring"""
for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001] , [0.0002, 0.002, 0.02] ):
self.check_over_configs(beta_start=lowerCAmelCase__ , beta_end=lowerCAmelCase__ )
def _lowerCAmelCase ( self : List[str] ) -> List[str]:
"""simple docstring"""
for schedule in ["linear", "scaled_linear"]:
self.check_over_configs(beta_schedule=lowerCAmelCase__ )
def _lowerCAmelCase ( self : Union[str, Any] ) -> Optional[Any]:
"""simple docstring"""
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=lowerCAmelCase__ )
def _lowerCAmelCase ( self : List[Any] ) -> List[Any]:
"""simple docstring"""
_UpperCAmelCase : List[str] = self.scheduler_classes[0]
_UpperCAmelCase : int = self.get_scheduler_config()
_UpperCAmelCase : Optional[int] = scheduler_class(**lowerCAmelCase__ )
scheduler.set_timesteps(self.num_inference_steps )
_UpperCAmelCase : int = torch.manual_seed(0 )
_UpperCAmelCase : Any = self.dummy_model()
_UpperCAmelCase : List[str] = self.dummy_sample_deter * scheduler.init_noise_sigma
_UpperCAmelCase : List[Any] = sample.to(lowerCAmelCase__ )
for i, t in enumerate(scheduler.timesteps ):
_UpperCAmelCase : List[str] = scheduler.scale_model_input(lowerCAmelCase__ , lowerCAmelCase__ )
_UpperCAmelCase : int = model(lowerCAmelCase__ , lowerCAmelCase__ )
_UpperCAmelCase : int = scheduler.step(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , generator=lowerCAmelCase__ )
_UpperCAmelCase : Optional[int] = output.prev_sample
_UpperCAmelCase : Optional[Any] = torch.sum(torch.abs(lowerCAmelCase__ ) )
_UpperCAmelCase : Tuple = torch.mean(torch.abs(lowerCAmelCase__ ) )
assert abs(result_sum.item() - 10.0807 ) < 1e-2
assert abs(result_mean.item() - 0.0131 ) < 1e-3
def _lowerCAmelCase ( self : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
_UpperCAmelCase : Any = self.scheduler_classes[0]
_UpperCAmelCase : List[Any] = self.get_scheduler_config(prediction_type="v_prediction" )
_UpperCAmelCase : Any = scheduler_class(**lowerCAmelCase__ )
scheduler.set_timesteps(self.num_inference_steps )
_UpperCAmelCase : str = torch.manual_seed(0 )
_UpperCAmelCase : Optional[Any] = self.dummy_model()
_UpperCAmelCase : Union[str, Any] = self.dummy_sample_deter * scheduler.init_noise_sigma
_UpperCAmelCase : Tuple = sample.to(lowerCAmelCase__ )
for i, t in enumerate(scheduler.timesteps ):
_UpperCAmelCase : Union[str, Any] = scheduler.scale_model_input(lowerCAmelCase__ , lowerCAmelCase__ )
_UpperCAmelCase : int = model(lowerCAmelCase__ , lowerCAmelCase__ )
_UpperCAmelCase : Union[str, Any] = scheduler.step(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , generator=lowerCAmelCase__ )
_UpperCAmelCase : Union[str, Any] = output.prev_sample
_UpperCAmelCase : Tuple = torch.sum(torch.abs(lowerCAmelCase__ ) )
_UpperCAmelCase : Any = torch.mean(torch.abs(lowerCAmelCase__ ) )
assert abs(result_sum.item() - 0.0002 ) < 1e-2
assert abs(result_mean.item() - 2.26_76e-06 ) < 1e-3
def _lowerCAmelCase ( self : Tuple ) -> str:
"""simple docstring"""
_UpperCAmelCase : Optional[int] = self.scheduler_classes[0]
_UpperCAmelCase : List[Any] = self.get_scheduler_config()
_UpperCAmelCase : int = scheduler_class(**lowerCAmelCase__ )
scheduler.set_timesteps(self.num_inference_steps , device=lowerCAmelCase__ )
_UpperCAmelCase : Optional[int] = torch.manual_seed(0 )
_UpperCAmelCase : str = self.dummy_model()
_UpperCAmelCase : Any = self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
_UpperCAmelCase : str = sample.to(lowerCAmelCase__ )
for t in scheduler.timesteps:
_UpperCAmelCase : List[str] = scheduler.scale_model_input(lowerCAmelCase__ , lowerCAmelCase__ )
_UpperCAmelCase : Any = model(lowerCAmelCase__ , lowerCAmelCase__ )
_UpperCAmelCase : Tuple = scheduler.step(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , generator=lowerCAmelCase__ )
_UpperCAmelCase : int = output.prev_sample
_UpperCAmelCase : List[Any] = torch.sum(torch.abs(lowerCAmelCase__ ) )
_UpperCAmelCase : str = torch.mean(torch.abs(lowerCAmelCase__ ) )
assert abs(result_sum.item() - 10.0807 ) < 1e-2
assert abs(result_mean.item() - 0.0131 ) < 1e-3
def _lowerCAmelCase ( self : List[str] ) -> int:
"""simple docstring"""
_UpperCAmelCase : List[Any] = self.scheduler_classes[0]
_UpperCAmelCase : int = self.get_scheduler_config()
_UpperCAmelCase : Union[str, Any] = scheduler_class(**lowerCAmelCase__ , use_karras_sigmas=lowerCAmelCase__ )
scheduler.set_timesteps(self.num_inference_steps , device=lowerCAmelCase__ )
_UpperCAmelCase : Optional[int] = torch.manual_seed(0 )
_UpperCAmelCase : List[str] = self.dummy_model()
_UpperCAmelCase : str = self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
_UpperCAmelCase : Optional[int] = sample.to(lowerCAmelCase__ )
for t in scheduler.timesteps:
_UpperCAmelCase : List[Any] = scheduler.scale_model_input(lowerCAmelCase__ , lowerCAmelCase__ )
_UpperCAmelCase : str = model(lowerCAmelCase__ , lowerCAmelCase__ )
_UpperCAmelCase : Optional[Any] = scheduler.step(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , generator=lowerCAmelCase__ )
_UpperCAmelCase : List[Any] = output.prev_sample
_UpperCAmelCase : List[Any] = torch.sum(torch.abs(lowerCAmelCase__ ) )
_UpperCAmelCase : Optional[Any] = torch.mean(torch.abs(lowerCAmelCase__ ) )
assert abs(result_sum.item() - 124.52_2994_9951_1719 ) < 1e-2
assert abs(result_mean.item() - 0.1_6213_9326_3339_9963 ) < 1e-3
| 17
| 0
|
'''simple docstring'''
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a = logging.get_logger(__name__)
__a = {
'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json',
}
class A__ ( lowerCAmelCase__ ):
"""simple docstring"""
UpperCamelCase_ : List[Any] = "mvp"
UpperCamelCase_ : Dict = ["past_key_values"]
UpperCamelCase_ : Any = {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}
def __init__( self : List[str] , lowerCAmelCase__ : Any=5_0_2_6_7 , lowerCAmelCase__ : List[Any]=1_0_2_4 , lowerCAmelCase__ : Any=1_2 , lowerCAmelCase__ : Any=4_0_9_6 , lowerCAmelCase__ : Dict=1_6 , lowerCAmelCase__ : List[Any]=1_2 , lowerCAmelCase__ : Dict=4_0_9_6 , lowerCAmelCase__ : Dict=1_6 , lowerCAmelCase__ : Dict=0.0 , lowerCAmelCase__ : Dict=0.0 , lowerCAmelCase__ : Optional[Any]="gelu" , lowerCAmelCase__ : Optional[Any]=1_0_2_4 , lowerCAmelCase__ : Optional[int]=0.1 , lowerCAmelCase__ : Union[str, Any]=0.0 , lowerCAmelCase__ : Optional[Any]=0.0 , lowerCAmelCase__ : Any=0.02 , lowerCAmelCase__ : int=0.0 , lowerCAmelCase__ : Optional[Any]=False , lowerCAmelCase__ : Optional[Any]=True , lowerCAmelCase__ : List[str]=1 , lowerCAmelCase__ : Optional[int]=0 , lowerCAmelCase__ : Any=2 , lowerCAmelCase__ : Any=True , lowerCAmelCase__ : Union[str, Any]=2 , lowerCAmelCase__ : Tuple=2 , lowerCAmelCase__ : int=False , lowerCAmelCase__ : Any=1_0_0 , lowerCAmelCase__ : List[Any]=8_0_0 , **lowerCAmelCase__ : Optional[int] , ) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase : str = vocab_size
_UpperCAmelCase : Union[str, Any] = max_position_embeddings
_UpperCAmelCase : List[str] = d_model
_UpperCAmelCase : Optional[Any] = encoder_ffn_dim
_UpperCAmelCase : Optional[Any] = encoder_layers
_UpperCAmelCase : int = encoder_attention_heads
_UpperCAmelCase : Dict = decoder_ffn_dim
_UpperCAmelCase : List[Any] = decoder_layers
_UpperCAmelCase : Union[str, Any] = decoder_attention_heads
_UpperCAmelCase : Optional[Any] = dropout
_UpperCAmelCase : Tuple = attention_dropout
_UpperCAmelCase : Optional[Any] = activation_dropout
_UpperCAmelCase : str = activation_function
_UpperCAmelCase : str = init_std
_UpperCAmelCase : Optional[int] = encoder_layerdrop
_UpperCAmelCase : List[Any] = decoder_layerdrop
_UpperCAmelCase : Tuple = classifier_dropout
_UpperCAmelCase : int = use_cache
_UpperCAmelCase : List[Any] = encoder_layers
_UpperCAmelCase : str = scale_embedding # scale factor will be sqrt(d_model) if True
_UpperCAmelCase : str = use_prompt
_UpperCAmelCase : Tuple = prompt_length
_UpperCAmelCase : int = prompt_mid_dim
super().__init__(
pad_token_id=_SCREAMING_SNAKE_CASE , bos_token_id=_SCREAMING_SNAKE_CASE , eos_token_id=_SCREAMING_SNAKE_CASE , is_encoder_decoder=_SCREAMING_SNAKE_CASE , decoder_start_token_id=_SCREAMING_SNAKE_CASE , forced_eos_token_id=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE , )
if self.forced_bos_token_id is None and kwargs.get("force_bos_token_to_be_generated" , _SCREAMING_SNAKE_CASE ):
_UpperCAmelCase : List[str] = self.bos_token_id
warnings.warn(
F"""Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. """
"The config can simply be saved and uploaded again to be fixed." )
| 354
|
'''simple docstring'''
def __UpperCAmelCase ( a_: int, a_: int ):
if a < 0 or b < 0:
raise ValueError("the value of both inputs must be positive" )
_UpperCAmelCase : List[str] = str(bin(a_ ) )[2:] # remove the leading "0b"
_UpperCAmelCase : Any = str(bin(a_ ) )[2:] # remove the leading "0b"
_UpperCAmelCase : Dict = max(len(a_ ), len(a_ ) )
return "0b" + "".join(
str(int(char_a == "1" and char_b == "1" ) )
for char_a, char_b in zip(a_binary.zfill(a_ ), b_binary.zfill(a_ ) ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 17
| 0
|
'''simple docstring'''
import json
import os
from typing import Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__a = logging.get_logger(__name__)
__a = {
'vocab_file': 'vocab.json',
'merges_file': 'merges.txt',
}
__a = {
'vocab_file': {'ctrl': 'https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-vocab.json'},
'merges_file': {'ctrl': 'https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-merges.txt'},
}
__a = {
'ctrl': 256,
}
__a = {
'Pregnancy': 168_629,
'Christianity': 7_675,
'Explain': 106_423,
'Fitness': 63_440,
'Saving': 63_163,
'Ask': 27_171,
'Ass': 95_985,
'Joke': 163_509,
'Questions': 45_622,
'Thoughts': 49_605,
'Retail': 52_342,
'Feminism': 164_338,
'Writing': 11_992,
'Atheism': 192_263,
'Netflix': 48_616,
'Computing': 39_639,
'Opinion': 43_213,
'Alone': 44_967,
'Funny': 58_917,
'Gaming': 40_358,
'Human': 4_088,
'India': 1_331,
'Joker': 77_138,
'Diet': 36_206,
'Legal': 11_859,
'Norman': 4_939,
'Tip': 72_689,
'Weight': 52_343,
'Movies': 46_273,
'Running': 23_425,
'Science': 2_090,
'Horror': 37_793,
'Confession': 60_572,
'Finance': 12_250,
'Politics': 16_360,
'Scary': 191_985,
'Support': 12_654,
'Technologies': 32_516,
'Teenage': 66_160,
'Event': 32_769,
'Learned': 67_460,
'Notion': 182_770,
'Wikipedia': 37_583,
'Books': 6_665,
'Extract': 76_050,
'Confessions': 102_701,
'Conspiracy': 75_932,
'Links': 63_674,
'Narcissus': 150_425,
'Relationship': 54_766,
'Relationships': 134_796,
'Reviews': 41_671,
'News': 4_256,
'Translation': 26_820,
'multilingual': 128_406,
}
def __UpperCAmelCase ( a_: List[str] ):
_UpperCAmelCase : Optional[int] = set()
_UpperCAmelCase : Any = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
_UpperCAmelCase : Any = char
_UpperCAmelCase : Any = set(a_ )
return pairs
class A__ ( snake_case_ ):
"""simple docstring"""
UpperCamelCase_ : List[Any] = VOCAB_FILES_NAMES
UpperCamelCase_ : List[str] = PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase_ : Optional[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase_ : Union[str, Any] = CONTROL_CODES
def __init__( self : Optional[int] , lowerCAmelCase__ : Union[str, Any] , lowerCAmelCase__ : Tuple , lowerCAmelCase__ : Tuple="<unk>" , **lowerCAmelCase__ : List[Any] ) -> List[str]:
"""simple docstring"""
super().__init__(unk_token=lowerCAmelCase__ , **lowerCAmelCase__ )
with open(lowerCAmelCase__ , encoding="utf-8" ) as vocab_handle:
_UpperCAmelCase : Optional[Any] = json.load(lowerCAmelCase__ )
_UpperCAmelCase : Any = {v: k for k, v in self.encoder.items()}
with open(lowerCAmelCase__ , encoding="utf-8" ) as merges_handle:
_UpperCAmelCase : str = merges_handle.read().split("\n" )[1:-1]
_UpperCAmelCase : Any = [tuple(merge.split() ) for merge in merges]
_UpperCAmelCase : int = dict(zip(lowerCAmelCase__ , range(len(lowerCAmelCase__ ) ) ) )
_UpperCAmelCase : Union[str, Any] = {}
@property
def _lowerCAmelCase ( self : Optional[int] ) -> Any:
"""simple docstring"""
return len(self.encoder )
def _lowerCAmelCase ( self : Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
return dict(self.encoder , **self.added_tokens_encoder )
def _lowerCAmelCase ( self : Dict , lowerCAmelCase__ : Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
if token in self.cache:
return self.cache[token]
_UpperCAmelCase : List[Any] = tuple(lowerCAmelCase__ )
_UpperCAmelCase : Optional[int] = tuple(list(word[:-1] ) + [word[-1] + "</w>"] )
_UpperCAmelCase : Optional[Any] = get_pairs(lowerCAmelCase__ )
if not pairs:
return token
while True:
_UpperCAmelCase : Any = min(lowerCAmelCase__ , key=lambda lowerCAmelCase__ : self.bpe_ranks.get(lowerCAmelCase__ , float("inf" ) ) )
if bigram not in self.bpe_ranks:
break
_UpperCAmelCase : Optional[int] = bigram
_UpperCAmelCase : Any = []
_UpperCAmelCase : Dict = 0
while i < len(lowerCAmelCase__ ):
try:
_UpperCAmelCase : Optional[int] = word.index(lowerCAmelCase__ , lowerCAmelCase__ )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
_UpperCAmelCase : Tuple = j
if word[i] == first and i < len(lowerCAmelCase__ ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
_UpperCAmelCase : int = tuple(lowerCAmelCase__ )
_UpperCAmelCase : Optional[int] = new_word
if len(lowerCAmelCase__ ) == 1:
break
else:
_UpperCAmelCase : str = get_pairs(lowerCAmelCase__ )
_UpperCAmelCase : Tuple = '@@ '.join(lowerCAmelCase__ )
_UpperCAmelCase : int = word[:-4]
_UpperCAmelCase : List[Any] = word
return word
def _lowerCAmelCase ( self : Tuple , lowerCAmelCase__ : int ) -> Dict:
"""simple docstring"""
_UpperCAmelCase : Tuple = []
_UpperCAmelCase : str = re.findall(R"\S+\n?" , lowerCAmelCase__ )
for token in words:
split_tokens.extend(list(self.bpe(lowerCAmelCase__ ).split(" " ) ) )
return split_tokens
def _lowerCAmelCase ( self : int , lowerCAmelCase__ : Dict ) -> Any:
"""simple docstring"""
return self.encoder.get(lowerCAmelCase__ , self.encoder.get(self.unk_token ) )
def _lowerCAmelCase ( self : Union[str, Any] , lowerCAmelCase__ : int ) -> Dict:
"""simple docstring"""
return self.decoder.get(lowerCAmelCase__ , self.unk_token )
def _lowerCAmelCase ( self : List[Any] , lowerCAmelCase__ : Tuple ) -> int:
"""simple docstring"""
_UpperCAmelCase : str = ' '.join(lowerCAmelCase__ ).replace("@@ " , "" ).strip()
return out_string
def _lowerCAmelCase ( self : List[Any] , lowerCAmelCase__ : str , lowerCAmelCase__ : Optional[str] = None ) -> Tuple[str]:
"""simple docstring"""
if not os.path.isdir(lowerCAmelCase__ ):
logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
return
_UpperCAmelCase : int = os.path.join(
lowerCAmelCase__ , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
_UpperCAmelCase : Optional[Any] = os.path.join(
lowerCAmelCase__ , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"] )
with open(lowerCAmelCase__ , "w" , encoding="utf-8" ) as f:
f.write(json.dumps(self.encoder , indent=2 , sort_keys=lowerCAmelCase__ , ensure_ascii=lowerCAmelCase__ ) + "\n" )
_UpperCAmelCase : Tuple = 0
with open(lowerCAmelCase__ , "w" , encoding="utf-8" ) as writer:
writer.write("#version: 0.2\n" )
for bpe_tokens, token_index in sorted(self.bpe_ranks.items() , key=lambda lowerCAmelCase__ : kv[1] ):
if index != token_index:
logger.warning(
F"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
" Please check that the tokenizer is not corrupted!" )
_UpperCAmelCase : Dict = token_index
writer.write(" ".join(lowerCAmelCase__ ) + "\n" )
index += 1
return vocab_file, merge_file
# def decode(self, token_ids, skip_special_tokens=False, clean_up_tokenization_spaces=True):
# filtered_tokens = ' '.join(self.convert_ids_to_tokens(token_ids, skip_special_tokens=skip_special_tokens))
# tokens_generated_so_far = re.sub('(@@ )', '', string=filtered_tokens)
# tokens_generated_so_far = re.sub('(@@ ?$)', '', string=tokens_generated_so_far)
# return ''.join(tokens_generated_so_far)
| 355
|
'''simple docstring'''
from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def __UpperCAmelCase ( a_: int ):
# A local function to see if a dot lands in the circle.
def is_in_circle(a_: float, a_: float ) -> bool:
_UpperCAmelCase : Optional[Any] = sqrt((x**2) + (y**2) )
# Our circle has a radius of 1, so a distance
# greater than 1 would land outside the circle.
return distance_from_centre <= 1
# The proportion of guesses that landed in the circle
_UpperCAmelCase : str = mean(
int(is_in_circle(uniform(-1.0, 1.0 ), uniform(-1.0, 1.0 ) ) )
for _ in range(a_ ) )
# The ratio of the area for circle to square is pi/4.
_UpperCAmelCase : Optional[int] = proportion * 4
print(f"""The estimated value of pi is {pi_estimate}""" )
print(f"""The numpy value of pi is {pi}""" )
print(f"""The total error is {abs(pi - pi_estimate )}""" )
def __UpperCAmelCase ( a_: int, a_: Callable[[float], float], a_: float = 0.0, a_: float = 1.0, ):
return mean(
function_to_integrate(uniform(a_, a_ ) ) for _ in range(a_ ) ) * (max_value - min_value)
def __UpperCAmelCase ( a_: int, a_: float = 0.0, a_: float = 1.0 ):
def identity_function(a_: float ) -> float:
return x
_UpperCAmelCase : Union[str, Any] = area_under_curve_estimator(
a_, a_, a_, a_ )
_UpperCAmelCase : List[str] = (max_value * max_value - min_value * min_value) / 2
print("******************" )
print(f"""Estimating area under y=x where x varies from {min_value} to {max_value}""" )
print(f"""Estimated value is {estimated_value}""" )
print(f"""Expected value is {expected_value}""" )
print(f"""Total error is {abs(estimated_value - expected_value )}""" )
print("******************" )
def __UpperCAmelCase ( a_: int ):
def function_to_integrate(a_: float ) -> float:
return sqrt(4.0 - x * x )
_UpperCAmelCase : List[str] = area_under_curve_estimator(
a_, a_, 0.0, 2.0 )
print("******************" )
print("Estimating pi using area_under_curve_estimator" )
print(f"""Estimated value is {estimated_value}""" )
print(f"""Expected value is {pi}""" )
print(f"""Total error is {abs(estimated_value - pi )}""" )
print("******************" )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 17
| 0
|
'''simple docstring'''
import copy
import random
from transformers import CLIPTokenizer
class A__ ( __lowerCAmelCase ):
"""simple docstring"""
def __init__( self : str , *lowerCAmelCase__ : List[str] , **lowerCAmelCase__ : Optional[Any] ) -> Any:
"""simple docstring"""
super().__init__(*lowerCamelCase__ , **lowerCamelCase__ )
_UpperCAmelCase : Any = {}
def _lowerCAmelCase ( self : Any , lowerCAmelCase__ : Dict , *lowerCAmelCase__ : Union[str, Any] , **lowerCAmelCase__ : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase : List[Any] = super().add_tokens(lowerCamelCase__ , *lowerCamelCase__ , **lowerCamelCase__ )
if num_added_tokens == 0:
raise ValueError(
F"""The tokenizer already contains the token {placeholder_token}. Please pass a different"""
" `placeholder_token` that is not already in the tokenizer." )
def _lowerCAmelCase ( self : str , lowerCAmelCase__ : Tuple , *lowerCAmelCase__ : Optional[int] , lowerCAmelCase__ : Tuple=1 , **lowerCAmelCase__ : Union[str, Any] ) -> Any:
"""simple docstring"""
_UpperCAmelCase : str = []
if num_vec_per_token == 1:
self.try_adding_tokens(lowerCamelCase__ , *lowerCamelCase__ , **lowerCamelCase__ )
output.append(lowerCamelCase__ )
else:
_UpperCAmelCase : int = []
for i in range(lowerCamelCase__ ):
_UpperCAmelCase : Tuple = placeholder_token + F"""_{i}"""
self.try_adding_tokens(lowerCamelCase__ , *lowerCamelCase__ , **lowerCamelCase__ )
output.append(lowerCamelCase__ )
# handle cases where there is a new placeholder token that contains the current placeholder token but is larger
for token in self.token_map:
if token in placeholder_token:
raise ValueError(
F"""The tokenizer already has placeholder token {token} that can get confused with"""
F""" {placeholder_token}keep placeholder tokens independent""" )
_UpperCAmelCase : int = output
def _lowerCAmelCase ( self : int , lowerCAmelCase__ : Optional[int] , lowerCAmelCase__ : List[str]=False , lowerCAmelCase__ : Optional[int]=1.0 ) -> Optional[int]:
"""simple docstring"""
if isinstance(lowerCamelCase__ , lowerCamelCase__ ):
_UpperCAmelCase : int = []
for i in range(len(lowerCamelCase__ ) ):
output.append(self.replace_placeholder_tokens_in_text(text[i] , vector_shuffle=lowerCamelCase__ ) )
return output
for placeholder_token in self.token_map:
if placeholder_token in text:
_UpperCAmelCase : Any = self.token_map[placeholder_token]
_UpperCAmelCase : Union[str, Any] = tokens[: 1 + int(len(lowerCamelCase__ ) * prop_tokens_to_load )]
if vector_shuffle:
_UpperCAmelCase : List[str] = copy.copy(lowerCamelCase__ )
random.shuffle(lowerCamelCase__ )
_UpperCAmelCase : Optional[Any] = text.replace(lowerCamelCase__ , " ".join(lowerCamelCase__ ) )
return text
def __call__( self : Dict , lowerCAmelCase__ : Tuple , *lowerCAmelCase__ : List[Any] , lowerCAmelCase__ : Optional[int]=False , lowerCAmelCase__ : List[str]=1.0 , **lowerCAmelCase__ : Dict ) -> Dict:
"""simple docstring"""
return super().__call__(
self.replace_placeholder_tokens_in_text(
lowerCamelCase__ , vector_shuffle=lowerCamelCase__ , prop_tokens_to_load=lowerCamelCase__ ) , *lowerCamelCase__ , **lowerCamelCase__ , )
def _lowerCAmelCase ( self : str , lowerCAmelCase__ : Optional[Any] , *lowerCAmelCase__ : Any , lowerCAmelCase__ : Optional[int]=False , lowerCAmelCase__ : str=1.0 , **lowerCAmelCase__ : Any ) -> int:
"""simple docstring"""
return super().encode(
self.replace_placeholder_tokens_in_text(
lowerCamelCase__ , vector_shuffle=lowerCamelCase__ , prop_tokens_to_load=lowerCamelCase__ ) , *lowerCamelCase__ , **lowerCamelCase__ , )
| 356
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
__a = {
'configuration_layoutlmv2': ['LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LayoutLMv2Config'],
'processing_layoutlmv2': ['LayoutLMv2Processor'],
'tokenization_layoutlmv2': ['LayoutLMv2Tokenizer'],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = ['LayoutLMv2TokenizerFast']
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = ['LayoutLMv2FeatureExtractor']
__a = ['LayoutLMv2ImageProcessor']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = [
'LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST',
'LayoutLMv2ForQuestionAnswering',
'LayoutLMv2ForSequenceClassification',
'LayoutLMv2ForTokenClassification',
'LayoutLMv2Layer',
'LayoutLMv2Model',
'LayoutLMv2PreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_layoutlmva import LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMvaConfig
from .processing_layoutlmva import LayoutLMvaProcessor
from .tokenization_layoutlmva import LayoutLMvaTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor, LayoutLMvaImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_layoutlmva import (
LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST,
LayoutLMvaForQuestionAnswering,
LayoutLMvaForSequenceClassification,
LayoutLMvaForTokenClassification,
LayoutLMvaLayer,
LayoutLMvaModel,
LayoutLMvaPreTrainedModel,
)
else:
import sys
__a = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 17
| 0
|
'''simple docstring'''
def __UpperCAmelCase ( a_: Dict ):
_UpperCAmelCase : List[str] = int(A__ )
if decimal in (0, 1): # Exit cases for the recursion
return str(A__ )
_UpperCAmelCase , _UpperCAmelCase : List[str] = divmod(A__, 2 )
return binary_recursive(A__ ) + str(A__ )
def __UpperCAmelCase ( a_: List[Any] ):
_UpperCAmelCase : str = str(A__ ).strip()
if not number:
raise ValueError("No input value was provided" )
_UpperCAmelCase : str = "-" if number.startswith("-" ) else ""
_UpperCAmelCase : List[Any] = number.lstrip("-" )
if not number.isnumeric():
raise ValueError("Input value is not an integer" )
return f"""{negative}0b{binary_recursive(int(A__ ) )}"""
if __name__ == "__main__":
from doctest import testmod
testmod()
| 357
|
'''simple docstring'''
def __UpperCAmelCase ( a_: int, a_: int ):
if not isinstance(a_, a_ ):
raise ValueError("iterations must be defined as integers" )
if not isinstance(a_, a_ ) or not number >= 1:
raise ValueError(
"starting number must be\n and integer and be more than 0" )
if not iterations >= 1:
raise ValueError("Iterations must be done more than 0 times to play FizzBuzz" )
_UpperCAmelCase : List[str] = ""
while number <= iterations:
if number % 3 == 0:
out += "Fizz"
if number % 5 == 0:
out += "Buzz"
if 0 not in (number % 3, number % 5):
out += str(a_ )
# print(out)
number += 1
out += " "
return out
if __name__ == "__main__":
import doctest
doctest.testmod()
| 17
| 0
|
'''simple docstring'''
from typing import Optional, Union
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin
class A__ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
@register_to_config
def __init__( self : List[Any] , lowerCAmelCase__ : int = 7_6_8 , ) -> int:
"""simple docstring"""
super().__init__()
_UpperCAmelCase : int = nn.Parameter(torch.zeros(1 , lowerCAmelCase__ ) )
_UpperCAmelCase : str = nn.Parameter(torch.ones(1 , lowerCAmelCase__ ) )
def _lowerCAmelCase ( self : List[str] , lowerCAmelCase__ : Optional[Union[str, torch.device]] = None , lowerCAmelCase__ : Optional[torch.dtype] = None , ) -> Optional[int]:
"""simple docstring"""
_UpperCAmelCase : Any = nn.Parameter(self.mean.to(lowerCAmelCase__ ).to(lowerCAmelCase__ ) )
_UpperCAmelCase : Optional[Any] = nn.Parameter(self.std.to(lowerCAmelCase__ ).to(lowerCAmelCase__ ) )
return self
def _lowerCAmelCase ( self : Dict , lowerCAmelCase__ : Union[str, Any] ) -> int:
"""simple docstring"""
_UpperCAmelCase : str = (embeds - self.mean) * 1.0 / self.std
return embeds
def _lowerCAmelCase ( self : int , lowerCAmelCase__ : Tuple ) -> str:
"""simple docstring"""
_UpperCAmelCase : Any = (embeds * self.std) + self.mean
return embeds
| 358
|
'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from itertools import chain
from typing import Optional, Union
import datasets
import numpy as np
import torch
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,
AutoTokenizer,
HfArgumentParser,
Trainer,
TrainingArguments,
default_data_collator,
set_seed,
)
from transformers.tokenization_utils_base import PreTrainedTokenizerBase
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import PaddingStrategy, check_min_version, send_example_telemetry
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('4.31.0')
__a = logging.getLogger(__name__)
@dataclass
class A__ :
"""simple docstring"""
UpperCamelCase_ : str = field(
metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''} )
UpperCamelCase_ : Optional[str] = field(
default=UpperCamelCase , metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''} )
UpperCamelCase_ : Optional[str] = field(
default=UpperCamelCase , metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''} )
UpperCamelCase_ : Optional[str] = field(
default=UpperCamelCase , metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''} , )
UpperCamelCase_ : bool = field(
default=UpperCamelCase , metadata={'''help''': '''Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.'''} , )
UpperCamelCase_ : str = field(
default='''main''' , metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''} , )
UpperCamelCase_ : bool = field(
default=UpperCamelCase , metadata={
'''help''': (
'''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
'''with private models).'''
)
} , )
@dataclass
class A__ :
"""simple docstring"""
UpperCamelCase_ : Optional[str] = field(default=UpperCamelCase , metadata={'''help''': '''The input training data file (a text file).'''} )
UpperCamelCase_ : Optional[str] = field(
default=UpperCamelCase , metadata={'''help''': '''An optional input evaluation data file to evaluate the perplexity on (a text file).'''} , )
UpperCamelCase_ : bool = field(
default=UpperCamelCase , metadata={'''help''': '''Overwrite the cached training and evaluation sets'''} )
UpperCamelCase_ : Optional[int] = field(
default=UpperCamelCase , metadata={'''help''': '''The number of processes to use for the preprocessing.'''} , )
UpperCamelCase_ : Optional[int] = field(
default=UpperCamelCase , metadata={
'''help''': (
'''The maximum total input sequence length after tokenization. If passed, sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
)
} , )
UpperCamelCase_ : bool = field(
default=UpperCamelCase , metadata={
'''help''': (
'''Whether to pad all samples to the maximum sentence length. '''
'''If False, will pad the samples dynamically when batching to the maximum length in the batch. More '''
'''efficient on GPU but very bad for TPU.'''
)
} , )
UpperCamelCase_ : Optional[int] = field(
default=UpperCamelCase , metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of training examples to this '''
'''value if set.'''
)
} , )
UpperCamelCase_ : Optional[int] = field(
default=UpperCamelCase , metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
'''value if set.'''
)
} , )
def _lowerCAmelCase ( self : Any ) -> Any:
"""simple docstring"""
if self.train_file is not None:
_UpperCAmelCase : List[Any] = self.train_file.split("." )[-1]
assert extension in ["csv", "json"], "`train_file` should be a csv or a json file."
if self.validation_file is not None:
_UpperCAmelCase : List[str] = self.validation_file.split("." )[-1]
assert extension in ["csv", "json"], "`validation_file` should be a csv or a json file."
@dataclass
class A__ :
"""simple docstring"""
UpperCamelCase_ : PreTrainedTokenizerBase
UpperCamelCase_ : Union[bool, str, PaddingStrategy] = True
UpperCamelCase_ : Optional[int] = None
UpperCamelCase_ : Optional[int] = None
def __call__( self : List[Any] , lowerCAmelCase__ : List[str] ) -> List[str]:
"""simple docstring"""
_UpperCAmelCase : int = "label" if "label" in features[0].keys() else "labels"
_UpperCAmelCase : Dict = [feature.pop(lowerCAmelCase__ ) for feature in features]
_UpperCAmelCase : str = len(lowerCAmelCase__ )
_UpperCAmelCase : int = len(features[0]["input_ids"] )
_UpperCAmelCase : str = [
[{k: v[i] for k, v in feature.items()} for i in range(lowerCAmelCase__ )] for feature in features
]
_UpperCAmelCase : List[str] = list(chain(*lowerCAmelCase__ ) )
_UpperCAmelCase : Any = self.tokenizer.pad(
lowerCAmelCase__ , padding=self.padding , max_length=self.max_length , pad_to_multiple_of=self.pad_to_multiple_of , return_tensors="pt" , )
# Un-flatten
_UpperCAmelCase : Any = {k: v.view(lowerCAmelCase__ , lowerCAmelCase__ , -1 ) for k, v in batch.items()}
# Add back labels
_UpperCAmelCase : List[str] = torch.tensor(lowerCAmelCase__ , dtype=torch.intaa )
return batch
def __UpperCAmelCase ( ):
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
_UpperCAmelCase : Union[str, Any] = 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.
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase : str = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase : Dict = parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry("run_swag", a_, a_ )
# 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 )], )
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
_UpperCAmelCase : Optional[int] = training_args.get_process_log_level()
logger.setLevel(a_ )
datasets.utils.logging.set_verbosity(a_ )
transformers.utils.logging.set_verbosity(a_ )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# 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}""" )
logger.info(f"""Training/evaluation parameters {training_args}""" )
# Detecting last checkpoint.
_UpperCAmelCase : Any = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
_UpperCAmelCase : Any = 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 and training_args.resume_from_checkpoint is 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." )
# 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.train_file is not None or data_args.validation_file is not None:
_UpperCAmelCase : Union[str, Any] = {}
if data_args.train_file is not None:
_UpperCAmelCase : str = data_args.train_file
if data_args.validation_file is not None:
_UpperCAmelCase : Optional[Any] = data_args.validation_file
_UpperCAmelCase : Dict = data_args.train_file.split("." )[-1]
_UpperCAmelCase : Optional[int] = load_dataset(
a_, data_files=a_, cache_dir=model_args.cache_dir, use_auth_token=True if model_args.use_auth_token else None, )
else:
# Downloading and loading the swag dataset from the hub.
_UpperCAmelCase : Dict = load_dataset(
"swag", "regular", cache_dir=model_args.cache_dir, use_auth_token=True if model_args.use_auth_token else None, )
# 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.
_UpperCAmelCase : Any = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path, cache_dir=model_args.cache_dir, revision=model_args.model_revision, use_auth_token=True if model_args.use_auth_token else None, )
_UpperCAmelCase : Any = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path, 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, )
_UpperCAmelCase : str = AutoModelForMultipleChoice.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, )
# When using your own dataset or a different dataset from swag, you will probably need to change this.
_UpperCAmelCase : Optional[Any] = [f"""ending{i}""" for i in range(4 )]
_UpperCAmelCase : List[Any] = "sent1"
_UpperCAmelCase : Optional[int] = "sent2"
if data_args.max_seq_length is None:
_UpperCAmelCase : List[str] = tokenizer.model_max_length
if max_seq_length > 1_024:
logger.warning(
"The chosen tokenizer supports a `model_max_length` that is longer than the default `block_size` value"
" of 1024. If you would like to use a longer `block_size` up to `tokenizer.model_max_length` you can"
" override this default with `--block_size xxx`." )
_UpperCAmelCase : Dict = 1_024
else:
if data_args.max_seq_length > tokenizer.model_max_length:
logger.warning(
f"""The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the"""
f"""model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}.""" )
_UpperCAmelCase : Dict = min(data_args.max_seq_length, tokenizer.model_max_length )
# Preprocessing the datasets.
def preprocess_function(a_: Union[str, Any] ):
_UpperCAmelCase : Optional[int] = [[context] * 4 for context in examples[context_name]]
_UpperCAmelCase : Tuple = examples[question_header_name]
_UpperCAmelCase : Optional[Any] = [
[f"""{header} {examples[end][i]}""" for end in ending_names] for i, header in enumerate(a_ )
]
# Flatten out
_UpperCAmelCase : List[str] = list(chain(*a_ ) )
_UpperCAmelCase : Dict = list(chain(*a_ ) )
# Tokenize
_UpperCAmelCase : List[Any] = tokenizer(
a_, a_, truncation=a_, max_length=a_, padding="max_length" if data_args.pad_to_max_length else False, )
# Un-flatten
return {k: [v[i : i + 4] for i in range(0, len(a_ ), 4 )] for k, v in tokenized_examples.items()}
if training_args.do_train:
if "train" not in raw_datasets:
raise ValueError("--do_train requires a train dataset" )
_UpperCAmelCase : int = raw_datasets["train"]
if data_args.max_train_samples is not None:
_UpperCAmelCase : Optional[Any] = min(len(a_ ), data_args.max_train_samples )
_UpperCAmelCase : List[Any] = train_dataset.select(range(a_ ) )
with training_args.main_process_first(desc="train dataset map pre-processing" ):
_UpperCAmelCase : Union[str, Any] = train_dataset.map(
a_, batched=a_, num_proc=data_args.preprocessing_num_workers, load_from_cache_file=not data_args.overwrite_cache, )
if training_args.do_eval:
if "validation" not in raw_datasets:
raise ValueError("--do_eval requires a validation dataset" )
_UpperCAmelCase : Dict = raw_datasets["validation"]
if data_args.max_eval_samples is not None:
_UpperCAmelCase : int = min(len(a_ ), data_args.max_eval_samples )
_UpperCAmelCase : List[str] = eval_dataset.select(range(a_ ) )
with training_args.main_process_first(desc="validation dataset map pre-processing" ):
_UpperCAmelCase : Optional[int] = eval_dataset.map(
a_, batched=a_, num_proc=data_args.preprocessing_num_workers, load_from_cache_file=not data_args.overwrite_cache, )
# Data collator
_UpperCAmelCase : Tuple = (
default_data_collator
if data_args.pad_to_max_length
else DataCollatorForMultipleChoice(tokenizer=a_, pad_to_multiple_of=8 if training_args.fpaa else None )
)
# Metric
def compute_metrics(a_: Tuple ):
_UpperCAmelCase , _UpperCAmelCase : Tuple = eval_predictions
_UpperCAmelCase : Union[str, Any] = np.argmax(a_, axis=1 )
return {"accuracy": (preds == label_ids).astype(np.floataa ).mean().item()}
# Initialize our Trainer
_UpperCAmelCase : Any = Trainer(
model=a_, args=a_, train_dataset=train_dataset if training_args.do_train else None, eval_dataset=eval_dataset if training_args.do_eval else None, tokenizer=a_, data_collator=a_, compute_metrics=a_, )
# Training
if training_args.do_train:
_UpperCAmelCase : Optional[Any] = None
if training_args.resume_from_checkpoint is not None:
_UpperCAmelCase : List[Any] = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
_UpperCAmelCase : List[str] = last_checkpoint
_UpperCAmelCase : Any = trainer.train(resume_from_checkpoint=a_ )
trainer.save_model() # Saves the tokenizer too for easy upload
_UpperCAmelCase : str = train_result.metrics
_UpperCAmelCase : List[str] = (
data_args.max_train_samples if data_args.max_train_samples is not None else len(a_ )
)
_UpperCAmelCase : Union[str, Any] = min(a_, len(a_ ) )
trainer.log_metrics("train", a_ )
trainer.save_metrics("train", a_ )
trainer.save_state()
# Evaluation
if training_args.do_eval:
logger.info("*** Evaluate ***" )
_UpperCAmelCase : List[Any] = trainer.evaluate()
_UpperCAmelCase : int = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(a_ )
_UpperCAmelCase : Tuple = min(a_, len(a_ ) )
trainer.log_metrics("eval", a_ )
trainer.save_metrics("eval", a_ )
_UpperCAmelCase : int = {
"finetuned_from": model_args.model_name_or_path,
"tasks": "multiple-choice",
"dataset_tags": "swag",
"dataset_args": "regular",
"dataset": "SWAG",
"language": "en",
}
if training_args.push_to_hub:
trainer.push_to_hub(**a_ )
else:
trainer.create_model_card(**a_ )
def __UpperCAmelCase ( a_: int ):
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 17
| 0
|
'''simple docstring'''
def __UpperCAmelCase ( ):
return 1
def __UpperCAmelCase ( a_: int ):
return 0 if x < 0 else two_pence(x - 2 ) + one_pence()
def __UpperCAmelCase ( a_: int ):
return 0 if x < 0 else five_pence(x - 5 ) + two_pence(a__ )
def __UpperCAmelCase ( a_: int ):
return 0 if x < 0 else ten_pence(x - 10 ) + five_pence(a__ )
def __UpperCAmelCase ( a_: int ):
return 0 if x < 0 else twenty_pence(x - 20 ) + ten_pence(a__ )
def __UpperCAmelCase ( a_: int ):
return 0 if x < 0 else fifty_pence(x - 50 ) + twenty_pence(a__ )
def __UpperCAmelCase ( a_: int ):
return 0 if x < 0 else one_pound(x - 100 ) + fifty_pence(a__ )
def __UpperCAmelCase ( a_: int ):
return 0 if x < 0 else two_pound(x - 200 ) + one_pound(a__ )
def __UpperCAmelCase ( a_: int = 200 ):
return two_pound(a__ )
if __name__ == "__main__":
print(solution(int(input().strip())))
| 359
|
'''simple docstring'''
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 : Any , lowerCAmelCase__ : Optional[Any] ) -> str:
"""simple docstring"""
super().__init__()
_UpperCAmelCase : List[str] = model
_UpperCAmelCase : Dict = 2
_UpperCAmelCase : Tuple = nn.Linear(self.model.config.hidden_size , self.num_labels )
def _lowerCAmelCase ( self : Tuple ) -> int:
"""simple docstring"""
pass
def __UpperCAmelCase ( a_: str, a_: str, a_: str ):
# load longformer model from model identifier
_UpperCAmelCase : int = LongformerModel.from_pretrained(a_ )
_UpperCAmelCase : Any = LightningModel(a_ )
_UpperCAmelCase : int = torch.load(a_, map_location=torch.device("cpu" ) )
lightning_model.load_state_dict(ckpt["state_dict"] )
# init longformer question answering model
_UpperCAmelCase : List[str] = 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__":
__a = 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.'
)
__a = parser.parse_args()
convert_longformer_qa_checkpoint_to_pytorch(
args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
)
| 17
| 0
|
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, logging
__a = logging.get_logger(__name__)
class A__ ( __lowercase ):
"""simple docstring"""
UpperCamelCase_ : Union[str, Any] = ['''pixel_values''']
def __init__( self : int , lowerCAmelCase__ : bool = True , lowerCAmelCase__ : Optional[Dict[str, int]] = None , lowerCAmelCase__ : PILImageResampling = PILImageResampling.BILINEAR , lowerCAmelCase__ : bool = True , lowerCAmelCase__ : Dict[str, int] = None , lowerCAmelCase__ : bool = True , lowerCAmelCase__ : Union[int, float] = 1 / 2_5_5 , lowerCAmelCase__ : bool = True , lowerCAmelCase__ : Optional[Union[float, List[float]]] = None , lowerCAmelCase__ : Optional[Union[float, List[float]]] = None , **lowerCAmelCase__ : Any , ) -> None:
"""simple docstring"""
super().__init__(**UpperCAmelCase__ )
_UpperCAmelCase : Optional[Any] = size if size is not None else {"shortest_edge": 2_5_6}
_UpperCAmelCase : Union[str, Any] = get_size_dict(UpperCAmelCase__ , default_to_square=UpperCAmelCase__ )
_UpperCAmelCase : List[str] = crop_size if crop_size is not None else {"height": 2_2_4, "width": 2_2_4}
_UpperCAmelCase : Optional[Any] = get_size_dict(UpperCAmelCase__ )
_UpperCAmelCase : str = do_resize
_UpperCAmelCase : List[Any] = size
_UpperCAmelCase : Any = resample
_UpperCAmelCase : Dict = do_center_crop
_UpperCAmelCase : Any = crop_size
_UpperCAmelCase : Dict = do_rescale
_UpperCAmelCase : Optional[Any] = rescale_factor
_UpperCAmelCase : List[str] = do_normalize
_UpperCAmelCase : Optional[int] = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
_UpperCAmelCase : Dict = image_std if image_std is not None else IMAGENET_STANDARD_STD
def _lowerCAmelCase ( self : Tuple , lowerCAmelCase__ : np.ndarray , lowerCAmelCase__ : Dict[str, int] , lowerCAmelCase__ : PILImageResampling = PILImageResampling.BICUBIC , lowerCAmelCase__ : Optional[Union[str, ChannelDimension]] = None , **lowerCAmelCase__ : Tuple , ) -> np.ndarray:
"""simple docstring"""
_UpperCAmelCase : Optional[Any] = get_size_dict(UpperCAmelCase__ , default_to_square=UpperCAmelCase__ )
if "shortest_edge" not in size:
raise ValueError(F"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}""" )
_UpperCAmelCase : List[str] = get_resize_output_image_size(UpperCAmelCase__ , size=size["shortest_edge"] , default_to_square=UpperCAmelCase__ )
return resize(UpperCAmelCase__ , size=UpperCAmelCase__ , resample=UpperCAmelCase__ , data_format=UpperCAmelCase__ , **UpperCAmelCase__ )
def _lowerCAmelCase ( self : Optional[Any] , lowerCAmelCase__ : np.ndarray , lowerCAmelCase__ : Dict[str, int] , lowerCAmelCase__ : Optional[Union[str, ChannelDimension]] = None , **lowerCAmelCase__ : int , ) -> np.ndarray:
"""simple docstring"""
_UpperCAmelCase : Optional[int] = get_size_dict(UpperCAmelCase__ )
return center_crop(UpperCAmelCase__ , size=(size["height"], size["width"]) , data_format=UpperCAmelCase__ , **UpperCAmelCase__ )
def _lowerCAmelCase ( self : List[Any] , lowerCAmelCase__ : np.ndarray , lowerCAmelCase__ : float , lowerCAmelCase__ : Optional[Union[str, ChannelDimension]] = None , **lowerCAmelCase__ : Optional[int] ) -> np.ndarray:
"""simple docstring"""
return rescale(UpperCAmelCase__ , scale=UpperCAmelCase__ , data_format=UpperCAmelCase__ , **UpperCAmelCase__ )
def _lowerCAmelCase ( self : List[Any] , lowerCAmelCase__ : np.ndarray , lowerCAmelCase__ : Union[float, List[float]] , lowerCAmelCase__ : Union[float, List[float]] , lowerCAmelCase__ : Optional[Union[str, ChannelDimension]] = None , **lowerCAmelCase__ : Tuple , ) -> np.ndarray:
"""simple docstring"""
return normalize(UpperCAmelCase__ , mean=UpperCAmelCase__ , std=UpperCAmelCase__ , data_format=UpperCAmelCase__ , **UpperCAmelCase__ )
def _lowerCAmelCase ( self : Optional[int] , lowerCAmelCase__ : ImageInput , lowerCAmelCase__ : Optional[bool] = None , lowerCAmelCase__ : Dict[str, int] = None , lowerCAmelCase__ : PILImageResampling = None , lowerCAmelCase__ : bool = None , lowerCAmelCase__ : Dict[str, int] = None , lowerCAmelCase__ : Optional[bool] = None , lowerCAmelCase__ : Optional[float] = None , lowerCAmelCase__ : Optional[bool] = None , lowerCAmelCase__ : Optional[Union[float, List[float]]] = None , lowerCAmelCase__ : Optional[Union[float, List[float]]] = None , lowerCAmelCase__ : Optional[Union[str, TensorType]] = None , lowerCAmelCase__ : Union[str, ChannelDimension] = ChannelDimension.FIRST , **lowerCAmelCase__ : List[Any] , ) -> Dict:
"""simple docstring"""
_UpperCAmelCase : Union[str, Any] = do_resize if do_resize is not None else self.do_resize
_UpperCAmelCase : Tuple = size if size is not None else self.size
_UpperCAmelCase : List[Any] = get_size_dict(UpperCAmelCase__ , default_to_square=UpperCAmelCase__ )
_UpperCAmelCase : Union[str, Any] = resample if resample is not None else self.resample
_UpperCAmelCase : int = do_center_crop if do_center_crop is not None else self.do_center_crop
_UpperCAmelCase : Union[str, Any] = crop_size if crop_size is not None else self.crop_size
_UpperCAmelCase : List[str] = get_size_dict(UpperCAmelCase__ )
_UpperCAmelCase : Dict = do_rescale if do_rescale is not None else self.do_rescale
_UpperCAmelCase : Optional[Any] = rescale_factor if rescale_factor is not None else self.rescale_factor
_UpperCAmelCase : Tuple = do_normalize if do_normalize is not None else self.do_normalize
_UpperCAmelCase : Tuple = image_mean if image_mean is not None else self.image_mean
_UpperCAmelCase : Any = image_std if image_std is not None else self.image_std
_UpperCAmelCase : Optional[int] = make_list_of_images(UpperCAmelCase__ )
if not valid_images(UpperCAmelCase__ ):
raise ValueError(
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
"torch.Tensor, tf.Tensor or jax.ndarray." )
if do_resize and size is None:
raise ValueError("Size must be specified if do_resize is True." )
if do_center_crop and crop_size is None:
raise ValueError("Crop size must be specified if do_center_crop is True." )
if do_rescale and rescale_factor is None:
raise ValueError("Rescale factor must be specified if do_rescale is True." )
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.
_UpperCAmelCase : Optional[Any] = [to_numpy_array(UpperCAmelCase__ ) for image in images]
if do_resize:
_UpperCAmelCase : Optional[Any] = [self.resize(image=UpperCAmelCase__ , size=UpperCAmelCase__ , resample=UpperCAmelCase__ ) for image in images]
if do_center_crop:
_UpperCAmelCase : Union[str, Any] = [self.center_crop(image=UpperCAmelCase__ , size=UpperCAmelCase__ ) for image in images]
if do_rescale:
_UpperCAmelCase : Tuple = [self.rescale(image=UpperCAmelCase__ , scale=UpperCAmelCase__ ) for image in images]
if do_normalize:
_UpperCAmelCase : Optional[int] = [self.normalize(image=UpperCAmelCase__ , mean=UpperCAmelCase__ , std=UpperCAmelCase__ ) for image in images]
_UpperCAmelCase : Tuple = [to_channel_dimension_format(UpperCAmelCase__ , UpperCAmelCase__ ) for image in images]
_UpperCAmelCase : Optional[int] = {"pixel_values": images}
return BatchFeature(data=UpperCAmelCase__ , tensor_type=UpperCAmelCase__ )
| 360
|
'''simple docstring'''
from importlib import import_module
from .logging import get_logger
__a = get_logger(__name__)
class A__ :
"""simple docstring"""
def __init__( self : List[str] , lowerCAmelCase__ : List[Any] , lowerCAmelCase__ : Optional[Any]=None ) -> Optional[int]:
"""simple docstring"""
_UpperCAmelCase : Any = attrs or []
if module is not None:
for key in module.__dict__:
if key in attrs or not key.startswith("__" ):
setattr(self , lowerCAmelCase__ , getattr(lowerCAmelCase__ , lowerCAmelCase__ ) )
_UpperCAmelCase : int = module._original_module if isinstance(lowerCAmelCase__ , _PatchedModuleObj ) else module
class A__ :
"""simple docstring"""
UpperCamelCase_ : Union[str, Any] = []
def __init__( self : int , lowerCAmelCase__ : List[str] , lowerCAmelCase__ : str , lowerCAmelCase__ : List[str] , lowerCAmelCase__ : Optional[int]=None ) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase : List[Any] = obj
_UpperCAmelCase : int = target
_UpperCAmelCase : Optional[int] = new
_UpperCAmelCase : Any = target.split("." )[0]
_UpperCAmelCase : Optional[int] = {}
_UpperCAmelCase : Dict = attrs or []
def __enter__( self : List[str] ) -> int:
"""simple docstring"""
*_UpperCAmelCase , _UpperCAmelCase : List[str] = self.target.split("." )
# Patch modules:
# it's used to patch attributes of submodules like "os.path.join";
# in this case we need to patch "os" and "os.path"
for i in range(len(lowerCAmelCase__ ) ):
try:
_UpperCAmelCase : int = import_module(".".join(submodules[: i + 1] ) )
except ModuleNotFoundError:
continue
# We iterate over all the globals in self.obj in case we find "os" or "os.path"
for attr in self.obj.__dir__():
_UpperCAmelCase : List[Any] = getattr(self.obj , lowerCAmelCase__ )
# We don't check for the name of the global, but rather if its value *is* "os" or "os.path".
# This allows to patch renamed modules like "from os import path as ospath".
if obj_attr is submodule or (
(isinstance(lowerCAmelCase__ , _PatchedModuleObj ) and obj_attr._original_module is submodule)
):
_UpperCAmelCase : Tuple = obj_attr
# patch at top level
setattr(self.obj , lowerCAmelCase__ , _PatchedModuleObj(lowerCAmelCase__ , attrs=self.attrs ) )
_UpperCAmelCase : List[Any] = getattr(self.obj , lowerCAmelCase__ )
# construct lower levels patches
for key in submodules[i + 1 :]:
setattr(lowerCAmelCase__ , lowerCAmelCase__ , _PatchedModuleObj(getattr(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ) , attrs=self.attrs ) )
_UpperCAmelCase : Any = getattr(lowerCAmelCase__ , lowerCAmelCase__ )
# finally set the target attribute
setattr(lowerCAmelCase__ , lowerCAmelCase__ , self.new )
# Patch attribute itself:
# it's used for builtins like "open",
# and also to patch "os.path.join" we may also need to patch "join"
# itself if it was imported as "from os.path import join".
if submodules: # if it's an attribute of a submodule like "os.path.join"
try:
_UpperCAmelCase : Dict = getattr(import_module(".".join(lowerCAmelCase__ ) ) , lowerCAmelCase__ )
except (AttributeError, ModuleNotFoundError):
return
# We iterate over all the globals in self.obj in case we find "os.path.join"
for attr in self.obj.__dir__():
# We don't check for the name of the global, but rather if its value *is* "os.path.join".
# This allows to patch renamed attributes like "from os.path import join as pjoin".
if getattr(self.obj , lowerCAmelCase__ ) is attr_value:
_UpperCAmelCase : Optional[Any] = getattr(self.obj , lowerCAmelCase__ )
setattr(self.obj , lowerCAmelCase__ , self.new )
elif target_attr in globals()["__builtins__"]: # if it'a s builtin like "open"
_UpperCAmelCase : Dict = globals()["__builtins__"][target_attr]
setattr(self.obj , lowerCAmelCase__ , self.new )
else:
raise RuntimeError(F"""Tried to patch attribute {target_attr} instead of a submodule.""" )
def __exit__( self : Optional[int] , *lowerCAmelCase__ : List[str] ) -> Union[str, Any]:
"""simple docstring"""
for attr in list(self.original ):
setattr(self.obj , lowerCAmelCase__ , self.original.pop(lowerCAmelCase__ ) )
def _lowerCAmelCase ( self : Union[str, Any] ) -> Optional[Any]:
"""simple docstring"""
self.__enter__()
self._active_patches.append(self )
def _lowerCAmelCase ( self : Optional[int] ) -> Tuple:
"""simple docstring"""
try:
self._active_patches.remove(self )
except ValueError:
# If the patch hasn't been started this will fail
return None
return self.__exit__()
| 17
| 0
|
'''simple docstring'''
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import require_keras_nlp, require_tf, slow
if is_tf_available():
import tensorflow as tf
if is_keras_nlp_available():
from transformers.models.gpta import TFGPTaTokenizer
__a = ["gpt2"]
__a = "gpt2"
if is_tf_available():
class A__ ( tf.Module ):
"""simple docstring"""
def __init__( self : int , lowerCAmelCase__ : Any ) -> Optional[Any]:
"""simple docstring"""
super().__init__()
_UpperCAmelCase : Dict = tokenizer
_UpperCAmelCase : List[Any] = AutoConfig.from_pretrained(_a )
_UpperCAmelCase : str = TFGPTaLMHeadModel.from_config(_a )
@tf.function(input_signature=(tf.TensorSpec((None,) , tf.string , name="text" ),) )
def _lowerCAmelCase ( self : Tuple , lowerCAmelCase__ : List[str] ) -> List[str]:
"""simple docstring"""
_UpperCAmelCase : str = self.tokenizer(_a )
_UpperCAmelCase : List[Any] = tokenized["""input_ids"""].to_tensor()
_UpperCAmelCase : List[Any] = tf.cast(input_ids_dense > 0 , tf.intaa )
# input_mask = tf.reshape(input_mask, [-1, MAX_SEQ_LEN])
_UpperCAmelCase : int = self.model(input_ids=_a , attention_mask=_a )["""logits"""]
return outputs
@require_tf
@require_keras_nlp
class A__ ( unittest.TestCase ):
"""simple docstring"""
def _lowerCAmelCase ( self : Optional[Any] ) -> List[str]:
"""simple docstring"""
super().setUp()
_UpperCAmelCase : Optional[Any] = [GPTaTokenizer.from_pretrained(_a ) for checkpoint in (TOKENIZER_CHECKPOINTS)]
_UpperCAmelCase : Tuple = [TFGPTaTokenizer.from_pretrained(_a ) for checkpoint in TOKENIZER_CHECKPOINTS]
assert len(self.tokenizers ) == len(self.tf_tokenizers )
_UpperCAmelCase : List[Any] = [
"""This is a straightforward English test sentence.""",
"""This one has some weird characters\rto\nsee\r\nif those\u00E9break things.""",
"""Now we're going to add some Chinese: 一 二 三 一二三""",
"""And some much more rare Chinese: 齉 堃 齉堃""",
"""Je vais aussi écrire en français pour tester les accents""",
"""Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ""",
]
_UpperCAmelCase : List[str] = list(zip(self.test_sentences , self.test_sentences[::-1] ) )
def _lowerCAmelCase ( self : str ) -> List[str]:
"""simple docstring"""
for tokenizer, tf_tokenizer in zip(self.tokenizers , self.tf_tokenizers ):
for test_inputs in self.test_sentences:
_UpperCAmelCase : Union[str, Any] = tokenizer([test_inputs] , return_tensors="tf" )
_UpperCAmelCase : Optional[int] = tf_tokenizer([test_inputs] )
for key in python_outputs.keys():
# convert them to numpy to avoid messing with ragged tensors
_UpperCAmelCase : List[str] = python_outputs[key].numpy()
_UpperCAmelCase : Tuple = tf_outputs[key].numpy()
self.assertTrue(tf.reduce_all(python_outputs_values.shape == tf_outputs_values.shape ) )
self.assertTrue(tf.reduce_all(tf.cast(_a , tf.intaa ) == tf_outputs_values ) )
@slow
def _lowerCAmelCase ( self : Any ) -> int:
"""simple docstring"""
for tf_tokenizer in self.tf_tokenizers:
_UpperCAmelCase : str = tf.function(_a )
for test_inputs in self.test_sentences:
_UpperCAmelCase : Optional[int] = tf.constant(_a )
_UpperCAmelCase : Tuple = compiled_tokenizer(_a )
_UpperCAmelCase : Dict = tf_tokenizer(_a )
for key in eager_outputs.keys():
self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key] ) )
@slow
def _lowerCAmelCase ( self : str ) -> int:
"""simple docstring"""
for tf_tokenizer in self.tf_tokenizers:
_UpperCAmelCase : Tuple = ModelToSave(tokenizer=_a )
_UpperCAmelCase : str = tf.convert_to_tensor([self.test_sentences[0]] )
_UpperCAmelCase : Tuple = model.serving(_a ) # Build model with some sample inputs
with TemporaryDirectory() as tempdir:
_UpperCAmelCase : List[str] = Path(_a ) / """saved.model"""
tf.saved_model.save(_a , _a , signatures={"serving_default": model.serving} )
_UpperCAmelCase : Any = tf.saved_model.load(_a )
_UpperCAmelCase : int = loaded_model.signatures["""serving_default"""](_a )["""output_0"""]
# We may see small differences because the loaded model is compiled, so we need an epsilon for the test
self.assertTrue(tf.reduce_all(out == loaded_output ) )
@slow
def _lowerCAmelCase ( self : Union[str, Any] ) -> Dict:
"""simple docstring"""
for tf_tokenizer in self.tf_tokenizers:
_UpperCAmelCase : Dict = tf.convert_to_tensor([self.test_sentences[0]] )
_UpperCAmelCase : Union[str, Any] = tf_tokenizer(_a ) # Build model with some sample inputs
_UpperCAmelCase : str = tf_tokenizer.get_config()
_UpperCAmelCase : str = TFGPTaTokenizer.from_config(_a )
_UpperCAmelCase : List[Any] = model_from_config(_a )
for key in from_config_output.keys():
self.assertTrue(tf.reduce_all(from_config_output[key] == out[key] ) )
@slow
def _lowerCAmelCase ( self : Tuple ) -> int:
"""simple docstring"""
for tf_tokenizer in self.tf_tokenizers:
# for the test to run
_UpperCAmelCase : Optional[Any] = 1_2_3_1_2_3
for max_length in [3, 5, 1_0_2_4]:
_UpperCAmelCase : List[Any] = tf.convert_to_tensor([self.test_sentences[0]] )
_UpperCAmelCase : Dict = tf_tokenizer(_a , max_length=_a )
_UpperCAmelCase : Dict = out["""input_ids"""].numpy().shape[1]
assert out_length == max_length
| 361
|
'''simple docstring'''
import itertools
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union
import pandas as pd
import pyarrow as pa
import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast
from datasets.utils.py_utils import Literal
__a = datasets.utils.logging.get_logger(__name__)
__a = ['names', 'prefix']
__a = ['warn_bad_lines', 'error_bad_lines', 'mangle_dupe_cols']
__a = ['encoding_errors', 'on_bad_lines']
__a = ['date_format']
@dataclass
class A__ ( datasets.BuilderConfig ):
"""simple docstring"""
UpperCamelCase_ : str = ","
UpperCamelCase_ : Optional[str] = None
UpperCamelCase_ : Optional[Union[int, List[int], str]] = "infer"
UpperCamelCase_ : Optional[List[str]] = None
UpperCamelCase_ : Optional[List[str]] = None
UpperCamelCase_ : Optional[Union[int, str, List[int], List[str]]] = None
UpperCamelCase_ : Optional[Union[List[int], List[str]]] = None
UpperCamelCase_ : Optional[str] = None
UpperCamelCase_ : bool = True
UpperCamelCase_ : Optional[Literal["c", "python", "pyarrow"]] = None
UpperCamelCase_ : Dict[Union[int, str], Callable[[Any], Any]] = None
UpperCamelCase_ : Optional[list] = None
UpperCamelCase_ : Optional[list] = None
UpperCamelCase_ : bool = False
UpperCamelCase_ : Optional[Union[int, List[int]]] = None
UpperCamelCase_ : Optional[int] = None
UpperCamelCase_ : Optional[Union[str, List[str]]] = None
UpperCamelCase_ : bool = True
UpperCamelCase_ : bool = True
UpperCamelCase_ : bool = False
UpperCamelCase_ : bool = True
UpperCamelCase_ : Optional[str] = None
UpperCamelCase_ : str = "."
UpperCamelCase_ : Optional[str] = None
UpperCamelCase_ : str = '"'
UpperCamelCase_ : int = 0
UpperCamelCase_ : Optional[str] = None
UpperCamelCase_ : Optional[str] = None
UpperCamelCase_ : Optional[str] = None
UpperCamelCase_ : Optional[str] = None
UpperCamelCase_ : bool = True
UpperCamelCase_ : bool = True
UpperCamelCase_ : int = 0
UpperCamelCase_ : bool = True
UpperCamelCase_ : bool = False
UpperCamelCase_ : Optional[str] = None
UpperCamelCase_ : int = 1_00_00
UpperCamelCase_ : Optional[datasets.Features] = None
UpperCamelCase_ : Optional[str] = "strict"
UpperCamelCase_ : Literal["error", "warn", "skip"] = "error"
UpperCamelCase_ : Optional[str] = None
def _lowerCAmelCase ( self : str ) -> Tuple:
"""simple docstring"""
if self.delimiter is not None:
_UpperCAmelCase : Any = self.delimiter
if self.column_names is not None:
_UpperCAmelCase : List[Any] = self.column_names
@property
def _lowerCAmelCase ( self : Optional[int] ) -> Optional[int]:
"""simple docstring"""
_UpperCAmelCase : Dict = {
"sep": self.sep,
"header": self.header,
"names": self.names,
"index_col": self.index_col,
"usecols": self.usecols,
"prefix": self.prefix,
"mangle_dupe_cols": self.mangle_dupe_cols,
"engine": self.engine,
"converters": self.converters,
"true_values": self.true_values,
"false_values": self.false_values,
"skipinitialspace": self.skipinitialspace,
"skiprows": self.skiprows,
"nrows": self.nrows,
"na_values": self.na_values,
"keep_default_na": self.keep_default_na,
"na_filter": self.na_filter,
"verbose": self.verbose,
"skip_blank_lines": self.skip_blank_lines,
"thousands": self.thousands,
"decimal": self.decimal,
"lineterminator": self.lineterminator,
"quotechar": self.quotechar,
"quoting": self.quoting,
"escapechar": self.escapechar,
"comment": self.comment,
"encoding": self.encoding,
"dialect": self.dialect,
"error_bad_lines": self.error_bad_lines,
"warn_bad_lines": self.warn_bad_lines,
"skipfooter": self.skipfooter,
"doublequote": self.doublequote,
"memory_map": self.memory_map,
"float_precision": self.float_precision,
"chunksize": self.chunksize,
"encoding_errors": self.encoding_errors,
"on_bad_lines": self.on_bad_lines,
"date_format": self.date_format,
}
# some kwargs must not be passed if they don't have a default value
# some others are deprecated and we can also not pass them if they are the default value
for pd_read_csv_parameter in _PANDAS_READ_CSV_NO_DEFAULT_PARAMETERS + _PANDAS_READ_CSV_DEPRECATED_PARAMETERS:
if pd_read_csv_kwargs[pd_read_csv_parameter] == getattr(CsvConfig() , lowerCAmelCase__ ):
del pd_read_csv_kwargs[pd_read_csv_parameter]
# Remove 2.0 new arguments
if not (datasets.config.PANDAS_VERSION.major >= 2):
for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_2_0_0_PARAMETERS:
del pd_read_csv_kwargs[pd_read_csv_parameter]
# Remove 1.3 new arguments
if not (datasets.config.PANDAS_VERSION.major >= 1 and datasets.config.PANDAS_VERSION.minor >= 3):
for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_1_3_0_PARAMETERS:
del pd_read_csv_kwargs[pd_read_csv_parameter]
return pd_read_csv_kwargs
class A__ ( datasets.ArrowBasedBuilder ):
"""simple docstring"""
UpperCamelCase_ : int = CsvConfig
def _lowerCAmelCase ( self : Optional[Any] ) -> Optional[int]:
"""simple docstring"""
return datasets.DatasetInfo(features=self.config.features )
def _lowerCAmelCase ( self : Tuple , lowerCAmelCase__ : str ) -> List[str]:
"""simple docstring"""
if not self.config.data_files:
raise ValueError(F"""At least one data file must be specified, but got data_files={self.config.data_files}""" )
_UpperCAmelCase : List[str] = dl_manager.download_and_extract(self.config.data_files )
if isinstance(lowerCAmelCase__ , (str, list, tuple) ):
_UpperCAmelCase : int = data_files
if isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
_UpperCAmelCase : Any = [files]
_UpperCAmelCase : List[Any] = [dl_manager.iter_files(lowerCAmelCase__ ) for file in files]
return [datasets.SplitGenerator(name=datasets.Split.TRAIN , gen_kwargs={"files": files} )]
_UpperCAmelCase : Optional[Any] = []
for split_name, files in data_files.items():
if isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
_UpperCAmelCase : str = [files]
_UpperCAmelCase : Any = [dl_manager.iter_files(lowerCAmelCase__ ) for file in files]
splits.append(datasets.SplitGenerator(name=lowerCAmelCase__ , gen_kwargs={"files": files} ) )
return splits
def _lowerCAmelCase ( self : List[Any] , lowerCAmelCase__ : pa.Table ) -> pa.Table:
"""simple docstring"""
if self.config.features is not None:
_UpperCAmelCase : Tuple = self.config.features.arrow_schema
if all(not require_storage_cast(lowerCAmelCase__ ) for feature in self.config.features.values() ):
# cheaper cast
_UpperCAmelCase : Any = pa.Table.from_arrays([pa_table[field.name] for field in schema] , schema=lowerCAmelCase__ )
else:
# more expensive cast; allows str <-> int/float or str to Audio for example
_UpperCAmelCase : int = table_cast(lowerCAmelCase__ , lowerCAmelCase__ )
return pa_table
def _lowerCAmelCase ( self : Dict , lowerCAmelCase__ : Dict ) -> Dict:
"""simple docstring"""
_UpperCAmelCase : int = self.config.features.arrow_schema if self.config.features else None
# dtype allows reading an int column as str
_UpperCAmelCase : Optional[Any] = (
{
name: dtype.to_pandas_dtype() if not require_storage_cast(lowerCAmelCase__ ) else object
for name, dtype, feature in zip(schema.names , schema.types , self.config.features.values() )
}
if schema is not None
else None
)
for file_idx, file in enumerate(itertools.chain.from_iterable(lowerCAmelCase__ ) ):
_UpperCAmelCase : Optional[Any] = pd.read_csv(lowerCAmelCase__ , iterator=lowerCAmelCase__ , dtype=lowerCAmelCase__ , **self.config.pd_read_csv_kwargs )
try:
for batch_idx, df in enumerate(lowerCAmelCase__ ):
_UpperCAmelCase : Optional[int] = pa.Table.from_pandas(lowerCAmelCase__ )
# Uncomment for debugging (will print the Arrow table size and elements)
# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
yield (file_idx, batch_idx), self._cast_table(lowerCAmelCase__ )
except ValueError as e:
logger.error(F"""Failed to read file '{file}' with error {type(lowerCAmelCase__ )}: {e}""" )
raise
| 17
| 0
|
'''simple docstring'''
import os
import string
import sys
__a = 1 << 8
__a = {
'tab': ord('\t'),
'newline': ord('\r'),
'esc': 27,
'up': 65 + ARROW_KEY_FLAG,
'down': 66 + ARROW_KEY_FLAG,
'right': 67 + ARROW_KEY_FLAG,
'left': 68 + ARROW_KEY_FLAG,
'mod_int': 91,
'undefined': sys.maxsize,
'interrupt': 3,
'insert': 50,
'delete': 51,
'pg_up': 53,
'pg_down': 54,
}
__a = KEYMAP['up']
__a = KEYMAP['left']
if sys.platform == "win32":
__a = []
__a = {
b'\xe0H': KEYMAP['up'] - ARROW_KEY_FLAG,
b'\x00H': KEYMAP['up'] - ARROW_KEY_FLAG,
b'\xe0P': KEYMAP['down'] - ARROW_KEY_FLAG,
b'\x00P': KEYMAP['down'] - ARROW_KEY_FLAG,
b'\xe0M': KEYMAP['right'] - ARROW_KEY_FLAG,
b'\x00M': KEYMAP['right'] - ARROW_KEY_FLAG,
b'\xe0K': KEYMAP['left'] - ARROW_KEY_FLAG,
b'\x00K': KEYMAP['left'] - ARROW_KEY_FLAG,
}
for i in range(10):
__a = ord(str(i))
def __UpperCAmelCase ( ):
if os.name == "nt":
import msvcrt
_UpperCAmelCase : Tuple = "mbcs"
# Flush the keyboard buffer
while msvcrt.kbhit():
msvcrt.getch()
if len(lowercase__ ) == 0:
# Read the keystroke
_UpperCAmelCase : List[Any] = msvcrt.getch()
# If it is a prefix char, get second part
if ch in (b"\x00", b"\xe0"):
_UpperCAmelCase : List[Any] = ch + msvcrt.getch()
# Translate actual Win chars to bullet char types
try:
_UpperCAmelCase : Any = chr(WIN_KEYMAP[cha] )
WIN_CH_BUFFER.append(chr(KEYMAP["mod_int"] ) )
WIN_CH_BUFFER.append(lowercase__ )
if ord(lowercase__ ) in (
KEYMAP["insert"] - 1 << 9,
KEYMAP["delete"] - 1 << 9,
KEYMAP["pg_up"] - 1 << 9,
KEYMAP["pg_down"] - 1 << 9,
):
WIN_CH_BUFFER.append(chr(126 ) )
_UpperCAmelCase : List[str] = chr(KEYMAP["esc"] )
except KeyError:
_UpperCAmelCase : Tuple = cha[1]
else:
_UpperCAmelCase : Union[str, Any] = ch.decode(lowercase__ )
else:
_UpperCAmelCase : Optional[Any] = WIN_CH_BUFFER.pop(0 )
elif os.name == "posix":
import termios
import tty
_UpperCAmelCase : List[str] = sys.stdin.fileno()
_UpperCAmelCase : List[str] = termios.tcgetattr(lowercase__ )
try:
tty.setraw(lowercase__ )
_UpperCAmelCase : Tuple = sys.stdin.read(1 )
finally:
termios.tcsetattr(lowercase__, termios.TCSADRAIN, lowercase__ )
return ch
def __UpperCAmelCase ( ):
_UpperCAmelCase : Optional[int] = get_raw_chars()
if ord(lowercase__ ) in [KEYMAP["interrupt"], KEYMAP["newline"]]:
return char
elif ord(lowercase__ ) == KEYMAP["esc"]:
_UpperCAmelCase : Any = get_raw_chars()
if ord(lowercase__ ) == KEYMAP["mod_int"]:
_UpperCAmelCase : Optional[Any] = get_raw_chars()
if ord(lowercase__ ) >= KEYMAP["arrow_begin"] - ARROW_KEY_FLAG and ord(lowercase__ ) <= KEYMAP["arrow_end"] - ARROW_KEY_FLAG:
return chr(ord(lowercase__ ) + ARROW_KEY_FLAG )
else:
return KEYMAP["undefined"]
else:
return get_raw_chars()
else:
if char in string.printable:
return char
else:
return KEYMAP["undefined"]
| 362
|
'''simple docstring'''
from __future__ import annotations
def __UpperCAmelCase ( a_: list[int] ):
if not nums:
return 0
_UpperCAmelCase : int = nums[0]
_UpperCAmelCase : Dict = 0
for num in nums[1:]:
_UpperCAmelCase , _UpperCAmelCase : Any = (
max_excluding + num,
max(a_, a_ ),
)
return max(a_, a_ )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 17
| 0
|
'''simple docstring'''
import random
def __UpperCAmelCase ( a_: Optional[Any], a_: Optional[Any], a_: Optional[Any] ):
_UpperCAmelCase : Union[str, Any] = a[left_index]
_UpperCAmelCase : Union[str, Any] = left_index + 1
for j in range(left_index + 1, __snake_case ):
if a[j] < pivot:
_UpperCAmelCase : Any = a[i], a[j]
i += 1
_UpperCAmelCase : Any = a[i - 1], a[left_index]
return i - 1
def __UpperCAmelCase ( a_: int, a_: str, a_: Any ):
if left < right:
_UpperCAmelCase : List[Any] = random.randint(__snake_case, right - 1 )
_UpperCAmelCase : List[Any] = (
a[left],
a[pivot],
) # switches the pivot with the left most bound
_UpperCAmelCase : Tuple = partition(__snake_case, __snake_case, __snake_case )
quick_sort_random(
__snake_case, __snake_case, __snake_case ) # recursive quicksort to the left of the pivot point
quick_sort_random(
__snake_case, pivot_index + 1, __snake_case ) # recursive quicksort to the right of the pivot point
def __UpperCAmelCase ( ):
_UpperCAmelCase : Any = input("Enter numbers separated by a comma:\n" ).strip()
_UpperCAmelCase : List[Any] = [int(__snake_case ) for item in user_input.split("," )]
quick_sort_random(__snake_case, 0, len(__snake_case ) )
print(__snake_case )
if __name__ == "__main__":
main()
| 363
|
'''simple docstring'''
import argparse
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
__a = logging.get_logger(__name__)
def __UpperCAmelCase ( a_: List[str] ):
_UpperCAmelCase : Union[str, Any] = OrderedDict()
for key, value in state_dict.items():
if key.startswith("module.encoder" ):
_UpperCAmelCase : Optional[int] = key.replace("module.encoder", "glpn.encoder" )
if key.startswith("module.decoder" ):
_UpperCAmelCase : List[Any] = key.replace("module.decoder", "decoder.stages" )
if "patch_embed" in key:
# replace for example patch_embed1 by patch_embeddings.0
_UpperCAmelCase : int = key[key.find("patch_embed" ) + len("patch_embed" )]
_UpperCAmelCase : Union[str, Any] = key.replace(f"""patch_embed{idx}""", f"""patch_embeddings.{int(a_ )-1}""" )
if "norm" in key:
_UpperCAmelCase : Union[str, Any] = key.replace("norm", "layer_norm" )
if "glpn.encoder.layer_norm" in key:
# replace for example layer_norm1 by layer_norm.0
_UpperCAmelCase : str = key[key.find("glpn.encoder.layer_norm" ) + len("glpn.encoder.layer_norm" )]
_UpperCAmelCase : Optional[Any] = key.replace(f"""layer_norm{idx}""", f"""layer_norm.{int(a_ )-1}""" )
if "layer_norm1" in key:
_UpperCAmelCase : Union[str, Any] = key.replace("layer_norm1", "layer_norm_1" )
if "layer_norm2" in key:
_UpperCAmelCase : List[Any] = key.replace("layer_norm2", "layer_norm_2" )
if "block" in key:
# replace for example block1 by block.0
_UpperCAmelCase : Optional[Any] = key[key.find("block" ) + len("block" )]
_UpperCAmelCase : List[str] = key.replace(f"""block{idx}""", f"""block.{int(a_ )-1}""" )
if "attn.q" in key:
_UpperCAmelCase : Optional[int] = key.replace("attn.q", "attention.self.query" )
if "attn.proj" in key:
_UpperCAmelCase : List[str] = key.replace("attn.proj", "attention.output.dense" )
if "attn" in key:
_UpperCAmelCase : Dict = key.replace("attn", "attention.self" )
if "fc1" in key:
_UpperCAmelCase : List[Any] = key.replace("fc1", "dense1" )
if "fc2" in key:
_UpperCAmelCase : List[Any] = key.replace("fc2", "dense2" )
if "linear_pred" in key:
_UpperCAmelCase : Any = key.replace("linear_pred", "classifier" )
if "linear_fuse" in key:
_UpperCAmelCase : Dict = key.replace("linear_fuse.conv", "linear_fuse" )
_UpperCAmelCase : List[str] = key.replace("linear_fuse.bn", "batch_norm" )
if "linear_c" in key:
# replace for example linear_c4 by linear_c.3
_UpperCAmelCase : List[Any] = key[key.find("linear_c" ) + len("linear_c" )]
_UpperCAmelCase : Tuple = key.replace(f"""linear_c{idx}""", f"""linear_c.{int(a_ )-1}""" )
if "bot_conv" in key:
_UpperCAmelCase : Union[str, Any] = key.replace("bot_conv", "0.convolution" )
if "skip_conv1" in key:
_UpperCAmelCase : Optional[int] = key.replace("skip_conv1", "1.convolution" )
if "skip_conv2" in key:
_UpperCAmelCase : Optional[int] = key.replace("skip_conv2", "2.convolution" )
if "fusion1" in key:
_UpperCAmelCase : List[str] = key.replace("fusion1", "1.fusion" )
if "fusion2" in key:
_UpperCAmelCase : List[str] = key.replace("fusion2", "2.fusion" )
if "fusion3" in key:
_UpperCAmelCase : Optional[Any] = key.replace("fusion3", "3.fusion" )
if "fusion" in key and "conv" in key:
_UpperCAmelCase : List[Any] = key.replace("conv", "convolutional_layer" )
if key.startswith("module.last_layer_depth" ):
_UpperCAmelCase : Optional[int] = key.replace("module.last_layer_depth", "head.head" )
_UpperCAmelCase : int = value
return new_state_dict
def __UpperCAmelCase ( a_: str, a_: List[Any] ):
# for each of the encoder blocks:
for i in range(config.num_encoder_blocks ):
for j in range(config.depths[i] ):
# read in weights + bias of keys and values (which is a single matrix in the original implementation)
_UpperCAmelCase : Tuple = state_dict.pop(f"""glpn.encoder.block.{i}.{j}.attention.self.kv.weight""" )
_UpperCAmelCase : Union[str, Any] = state_dict.pop(f"""glpn.encoder.block.{i}.{j}.attention.self.kv.bias""" )
# next, add keys and values (in that order) to the state dict
_UpperCAmelCase : Optional[int] = kv_weight[
: config.hidden_sizes[i], :
]
_UpperCAmelCase : Dict = kv_bias[: config.hidden_sizes[i]]
_UpperCAmelCase : Optional[int] = kv_weight[
config.hidden_sizes[i] :, :
]
_UpperCAmelCase : Optional[Any] = kv_bias[config.hidden_sizes[i] :]
def __UpperCAmelCase ( ):
_UpperCAmelCase : Optional[int] = "http://images.cocodataset.org/val2017/000000039769.jpg"
_UpperCAmelCase : List[Any] = Image.open(requests.get(a_, stream=a_ ).raw )
return image
@torch.no_grad()
def __UpperCAmelCase ( a_: Tuple, a_: Any, a_: Optional[Any]=False, a_: List[Any]=None ):
_UpperCAmelCase : Optional[Any] = GLPNConfig(hidden_sizes=[64, 128, 320, 512], decoder_hidden_size=64, depths=[3, 8, 27, 3] )
# load image processor (only resize + rescale)
_UpperCAmelCase : Dict = GLPNImageProcessor()
# prepare image
_UpperCAmelCase : List[Any] = prepare_img()
_UpperCAmelCase : Optional[int] = image_processor(images=a_, return_tensors="pt" ).pixel_values
logger.info("Converting model..." )
# load original state dict
_UpperCAmelCase : Union[str, Any] = torch.load(a_, map_location=torch.device("cpu" ) )
# rename keys
_UpperCAmelCase : List[str] = rename_keys(a_ )
# key and value matrices need special treatment
read_in_k_v(a_, a_ )
# create HuggingFace model and load state dict
_UpperCAmelCase : List[str] = GLPNForDepthEstimation(a_ )
model.load_state_dict(a_ )
model.eval()
# forward pass
_UpperCAmelCase : Dict = model(a_ )
_UpperCAmelCase : List[str] = outputs.predicted_depth
# verify output
if model_name is not None:
if "nyu" in model_name:
_UpperCAmelCase : Optional[Any] = torch.tensor(
[[4.41_47, 4.08_73, 4.06_73], [3.78_90, 3.28_81, 3.15_25], [3.76_74, 3.54_23, 3.49_13]] )
elif "kitti" in model_name:
_UpperCAmelCase : Tuple = torch.tensor(
[[3.42_91, 2.78_65, 2.51_51], [3.28_41, 2.70_21, 2.35_02], [3.11_47, 2.46_25, 2.24_81]] )
else:
raise ValueError(f"""Unknown model name: {model_name}""" )
_UpperCAmelCase : Dict = torch.Size([1, 480, 640] )
assert predicted_depth.shape == expected_shape
assert torch.allclose(predicted_depth[0, :3, :3], a_, atol=1e-4 )
print("Looks ok!" )
# finally, push to hub if required
if push_to_hub:
logger.info("Pushing model and image processor to the 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__":
__a = argparse.ArgumentParser()
parser.add_argument(
'--checkpoint_path',
default=None,
type=str,
help='Path to the original PyTorch checkpoint (.pth file).',
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
)
parser.add_argument(
'--push_to_hub', action='store_true', help='Whether to upload the model to the HuggingFace hub.'
)
parser.add_argument(
'--model_name',
default='glpn-kitti',
type=str,
help='Name of the model in case you\'re pushing to the hub.',
)
__a = parser.parse_args()
convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)
| 17
| 0
|
'''simple docstring'''
import argparse
import os
import torch
from transformers.utils import WEIGHTS_NAME
__a = ['small', 'medium', 'large']
__a = 'lm_head.decoder.weight'
__a = 'lm_head.weight'
def __UpperCAmelCase ( a_: str, a_: str ):
_UpperCAmelCase : Union[str, Any] = torch.load(_a )
_UpperCAmelCase : Tuple = d.pop(_a )
os.makedirs(_a, exist_ok=_a )
torch.save(_a, os.path.join(_a, _a ) )
if __name__ == "__main__":
__a = argparse.ArgumentParser()
parser.add_argument('--dialogpt_path', default='.', type=str)
__a = parser.parse_args()
for MODEL in DIALOGPT_MODELS:
__a = os.path.join(args.dialogpt_path, f'{MODEL}_ft.pkl')
__a = f'./DialoGPT-{MODEL}'
convert_dialogpt_checkpoint(
checkpoint_path,
pytorch_dump_folder_path,
)
| 364
|
'''simple docstring'''
import contextlib
import csv
import json
import os
import sqlitea
import tarfile
import textwrap
import zipfile
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
import datasets
import datasets.config
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( ):
_UpperCAmelCase : Optional[Any] = 10
_UpperCAmelCase : int = datasets.Features(
{
"tokens": datasets.Sequence(datasets.Value("string" ) ),
"labels": datasets.Sequence(datasets.ClassLabel(names=["negative", "positive"] ) ),
"answers": datasets.Sequence(
{
"text": datasets.Value("string" ),
"answer_start": datasets.Value("int32" ),
} ),
"id": datasets.Value("int64" ),
} )
_UpperCAmelCase : List[str] = datasets.Dataset.from_dict(
{
"tokens": [["foo"] * 5] * n,
"labels": [[1] * 5] * n,
"answers": [{"answer_start": [97], "text": ["1976"]}] * 10,
"id": list(range(a_ ) ),
}, features=a_, )
return dataset
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: Optional[int], a_: Dict ):
_UpperCAmelCase : Any = str(tmp_path_factory.mktemp("data" ) / "file.arrow" )
dataset.map(cache_file_name=a_ )
return filename
# FILE_CONTENT + files
__a = '\\n Text data.\n Second line of data.'
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: Dict ):
_UpperCAmelCase : Dict = tmp_path_factory.mktemp("data" ) / "file.txt"
_UpperCAmelCase : Tuple = FILE_CONTENT
with open(a_, "w" ) as f:
f.write(a_ )
return filename
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: Union[str, Any] ):
import bza
_UpperCAmelCase : str = tmp_path_factory.mktemp("data" ) / "file.txt.bz2"
_UpperCAmelCase : Optional[int] = bytes(a_, "utf-8" )
with bza.open(a_, "wb" ) as f:
f.write(a_ )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: Union[str, Any] ):
import gzip
_UpperCAmelCase : str = str(tmp_path_factory.mktemp("data" ) / "file.txt.gz" )
_UpperCAmelCase : Any = bytes(a_, "utf-8" )
with gzip.open(a_, "wb" ) as f:
f.write(a_ )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: str ):
if datasets.config.LZ4_AVAILABLE:
import lza.frame
_UpperCAmelCase : Optional[int] = tmp_path_factory.mktemp("data" ) / "file.txt.lz4"
_UpperCAmelCase : str = bytes(a_, "utf-8" )
with lza.frame.open(a_, "wb" ) as f:
f.write(a_ )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: int, a_: Any ):
if datasets.config.PY7ZR_AVAILABLE:
import pyazr
_UpperCAmelCase : Any = tmp_path_factory.mktemp("data" ) / "file.txt.7z"
with pyazr.SevenZipFile(a_, "w" ) as archive:
archive.write(a_, arcname=os.path.basename(a_ ) )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: Any, a_: List[str] ):
import tarfile
_UpperCAmelCase : Union[str, Any] = tmp_path_factory.mktemp("data" ) / "file.txt.tar"
with tarfile.TarFile(a_, "w" ) as f:
f.add(a_, arcname=os.path.basename(a_ ) )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: int ):
import lzma
_UpperCAmelCase : List[Any] = tmp_path_factory.mktemp("data" ) / "file.txt.xz"
_UpperCAmelCase : List[str] = bytes(a_, "utf-8" )
with lzma.open(a_, "wb" ) as f:
f.write(a_ )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: Dict, a_: Tuple ):
import zipfile
_UpperCAmelCase : Tuple = tmp_path_factory.mktemp("data" ) / "file.txt.zip"
with zipfile.ZipFile(a_, "w" ) as f:
f.write(a_, arcname=os.path.basename(a_ ) )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: Optional[int] ):
if datasets.config.ZSTANDARD_AVAILABLE:
import zstandard as zstd
_UpperCAmelCase : Optional[int] = tmp_path_factory.mktemp("data" ) / "file.txt.zst"
_UpperCAmelCase : int = bytes(a_, "utf-8" )
with zstd.open(a_, "wb" ) as f:
f.write(a_ )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: Optional[int] ):
_UpperCAmelCase : List[str] = tmp_path_factory.mktemp("data" ) / "file.xml"
_UpperCAmelCase : Tuple = textwrap.dedent(
"\\n <?xml version=\"1.0\" encoding=\"UTF-8\" ?>\n <tmx version=\"1.4\">\n <header segtype=\"sentence\" srclang=\"ca\" />\n <body>\n <tu>\n <tuv xml:lang=\"ca\"><seg>Contingut 1</seg></tuv>\n <tuv xml:lang=\"en\"><seg>Content 1</seg></tuv>\n </tu>\n <tu>\n <tuv xml:lang=\"ca\"><seg>Contingut 2</seg></tuv>\n <tuv xml:lang=\"en\"><seg>Content 2</seg></tuv>\n </tu>\n <tu>\n <tuv xml:lang=\"ca\"><seg>Contingut 3</seg></tuv>\n <tuv xml:lang=\"en\"><seg>Content 3</seg></tuv>\n </tu>\n <tu>\n <tuv xml:lang=\"ca\"><seg>Contingut 4</seg></tuv>\n <tuv xml:lang=\"en\"><seg>Content 4</seg></tuv>\n </tu>\n <tu>\n <tuv xml:lang=\"ca\"><seg>Contingut 5</seg></tuv>\n <tuv xml:lang=\"en\"><seg>Content 5</seg></tuv>\n </tu>\n </body>\n </tmx>" )
with open(a_, "w" ) as f:
f.write(a_ )
return filename
__a = [
{'col_1': '0', 'col_2': 0, 'col_3': 0.0},
{'col_1': '1', 'col_2': 1, 'col_3': 1.0},
{'col_1': '2', 'col_2': 2, 'col_3': 2.0},
{'col_1': '3', 'col_2': 3, 'col_3': 3.0},
]
__a = [
{'col_1': '4', 'col_2': 4, 'col_3': 4.0},
{'col_1': '5', 'col_2': 5, 'col_3': 5.0},
]
__a = {
'col_1': ['0', '1', '2', '3'],
'col_2': [0, 1, 2, 3],
'col_3': [0.0, 1.0, 2.0, 3.0],
}
__a = [
{'col_3': 0.0, 'col_1': '0', 'col_2': 0},
{'col_3': 1.0, 'col_1': '1', 'col_2': 1},
]
__a = [
{'col_1': 's0', 'col_2': 0, 'col_3': 0.0},
{'col_1': 's1', 'col_2': 1, 'col_3': 1.0},
{'col_1': 's2', 'col_2': 2, 'col_3': 2.0},
{'col_1': 's3', 'col_2': 3, 'col_3': 3.0},
]
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( ):
return DATA_DICT_OF_LISTS
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: Union[str, Any] ):
_UpperCAmelCase : str = datasets.Dataset.from_dict(a_ )
_UpperCAmelCase : Optional[int] = str(tmp_path_factory.mktemp("data" ) / "dataset.arrow" )
dataset.map(cache_file_name=a_ )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: str ):
_UpperCAmelCase : int = str(tmp_path_factory.mktemp("data" ) / "dataset.sqlite" )
with contextlib.closing(sqlitea.connect(a_ ) ) as con:
_UpperCAmelCase : List[Any] = con.cursor()
cur.execute("CREATE TABLE dataset(col_1 text, col_2 int, col_3 real)" )
for item in DATA:
cur.execute("INSERT INTO dataset(col_1, col_2, col_3) VALUES (?, ?, ?)", tuple(item.values() ) )
con.commit()
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: Any ):
_UpperCAmelCase : Dict = str(tmp_path_factory.mktemp("data" ) / "dataset.csv" )
with open(a_, "w", newline="" ) as f:
_UpperCAmelCase : Dict = csv.DictWriter(a_, fieldnames=["col_1", "col_2", "col_3"] )
writer.writeheader()
for item in DATA:
writer.writerow(a_ )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: Union[str, Any] ):
_UpperCAmelCase : Union[str, Any] = str(tmp_path_factory.mktemp("data" ) / "dataset2.csv" )
with open(a_, "w", newline="" ) as f:
_UpperCAmelCase : Optional[int] = csv.DictWriter(a_, fieldnames=["col_1", "col_2", "col_3"] )
writer.writeheader()
for item in DATA:
writer.writerow(a_ )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: str, a_: str ):
import bza
_UpperCAmelCase : str = tmp_path_factory.mktemp("data" ) / "dataset.csv.bz2"
with open(a_, "rb" ) as f:
_UpperCAmelCase : Any = f.read()
# data = bytes(FILE_CONTENT, "utf-8")
with bza.open(a_, "wb" ) as f:
f.write(a_ )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: Optional[int], a_: Dict, a_: Optional[int] ):
_UpperCAmelCase : List[Any] = tmp_path_factory.mktemp("data" ) / "dataset.csv.zip"
with zipfile.ZipFile(a_, "w" ) as f:
f.write(a_, arcname=os.path.basename(a_ ) )
f.write(a_, arcname=os.path.basename(a_ ) )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: List[str], a_: Union[str, Any], a_: int ):
_UpperCAmelCase : int = tmp_path_factory.mktemp("data" ) / "dataset.csv.zip"
with zipfile.ZipFile(a_, "w" ) as f:
f.write(a_, arcname=os.path.basename(csv_path.replace(".csv", ".CSV" ) ) )
f.write(a_, arcname=os.path.basename(csva_path.replace(".csv", ".CSV" ) ) )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: Any, a_: Union[str, Any], a_: Tuple ):
_UpperCAmelCase : Any = tmp_path_factory.mktemp("data" ) / "dataset_with_dir.csv.zip"
with zipfile.ZipFile(a_, "w" ) as f:
f.write(a_, arcname=os.path.join("main_dir", os.path.basename(a_ ) ) )
f.write(a_, arcname=os.path.join("main_dir", os.path.basename(a_ ) ) )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: Tuple ):
_UpperCAmelCase : Optional[Any] = str(tmp_path_factory.mktemp("data" ) / "dataset.parquet" )
_UpperCAmelCase : Dict = pa.schema(
{
"col_1": pa.string(),
"col_2": pa.intaa(),
"col_3": pa.floataa(),
} )
with open(a_, "wb" ) as f:
_UpperCAmelCase : Tuple = pq.ParquetWriter(a_, schema=a_ )
_UpperCAmelCase : Tuple = pa.Table.from_pydict({k: [DATA[i][k] for i in range(len(a_ ) )] for k in DATA[0]}, schema=a_ )
writer.write_table(a_ )
writer.close()
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: Any ):
_UpperCAmelCase : Union[str, Any] = str(tmp_path_factory.mktemp("data" ) / "dataset.json" )
_UpperCAmelCase : str = {"data": DATA}
with open(a_, "w" ) as f:
json.dump(a_, a_ )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: Union[str, Any] ):
_UpperCAmelCase : Optional[int] = str(tmp_path_factory.mktemp("data" ) / "dataset.json" )
_UpperCAmelCase : Dict = {"data": DATA_DICT_OF_LISTS}
with open(a_, "w" ) as f:
json.dump(a_, a_ )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: int ):
_UpperCAmelCase : Optional[Any] = str(tmp_path_factory.mktemp("data" ) / "dataset.jsonl" )
with open(a_, "w" ) as f:
for item in DATA:
f.write(json.dumps(a_ ) + "\n" )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: Tuple ):
_UpperCAmelCase : Any = str(tmp_path_factory.mktemp("data" ) / "dataset2.jsonl" )
with open(a_, "w" ) as f:
for item in DATA:
f.write(json.dumps(a_ ) + "\n" )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: Any ):
_UpperCAmelCase : int = str(tmp_path_factory.mktemp("data" ) / "dataset_312.jsonl" )
with open(a_, "w" ) as f:
for item in DATA_312:
f.write(json.dumps(a_ ) + "\n" )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: Optional[Any] ):
_UpperCAmelCase : Optional[int] = str(tmp_path_factory.mktemp("data" ) / "dataset-str.jsonl" )
with open(a_, "w" ) as f:
for item in DATA_STR:
f.write(json.dumps(a_ ) + "\n" )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: Union[str, Any], a_: Any ):
import gzip
_UpperCAmelCase : Optional[Any] = str(tmp_path_factory.mktemp("data" ) / "dataset.txt.gz" )
with open(a_, "rb" ) as orig_file:
with gzip.open(a_, "wb" ) as zipped_file:
zipped_file.writelines(a_ )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: Optional[Any], a_: Tuple ):
import gzip
_UpperCAmelCase : List[Any] = str(tmp_path_factory.mktemp("data" ) / "dataset.jsonl.gz" )
with open(a_, "rb" ) as orig_file:
with gzip.open(a_, "wb" ) as zipped_file:
zipped_file.writelines(a_ )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: Dict, a_: List[Any], a_: Union[str, Any] ):
_UpperCAmelCase : Tuple = tmp_path_factory.mktemp("data" ) / "dataset.jsonl.zip"
with zipfile.ZipFile(a_, "w" ) as f:
f.write(a_, arcname=os.path.basename(a_ ) )
f.write(a_, arcname=os.path.basename(a_ ) )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: Union[str, Any], a_: Optional[int], a_: Optional[Any], a_: Dict ):
_UpperCAmelCase : Dict = tmp_path_factory.mktemp("data" ) / "dataset_nested.jsonl.zip"
with zipfile.ZipFile(a_, "w" ) as f:
f.write(a_, arcname=os.path.join("nested", os.path.basename(a_ ) ) )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: List[Any], a_: Optional[int], a_: List[str] ):
_UpperCAmelCase : Dict = tmp_path_factory.mktemp("data" ) / "dataset_with_dir.jsonl.zip"
with zipfile.ZipFile(a_, "w" ) as f:
f.write(a_, arcname=os.path.join("main_dir", os.path.basename(a_ ) ) )
f.write(a_, arcname=os.path.join("main_dir", os.path.basename(a_ ) ) )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: List[Any], a_: List[Any], a_: str ):
_UpperCAmelCase : Optional[Any] = tmp_path_factory.mktemp("data" ) / "dataset.jsonl.tar"
with tarfile.TarFile(a_, "w" ) as f:
f.add(a_, arcname=os.path.basename(a_ ) )
f.add(a_, arcname=os.path.basename(a_ ) )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: List[str], a_: List[Any], a_: Tuple, a_: Dict ):
_UpperCAmelCase : List[Any] = tmp_path_factory.mktemp("data" ) / "dataset_nested.jsonl.tar"
with tarfile.TarFile(a_, "w" ) as f:
f.add(a_, arcname=os.path.join("nested", os.path.basename(a_ ) ) )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: List[str] ):
_UpperCAmelCase : List[str] = ["0", "1", "2", "3"]
_UpperCAmelCase : Tuple = str(tmp_path_factory.mktemp("data" ) / "dataset.txt" )
with open(a_, "w" ) as f:
for item in data:
f.write(item + "\n" )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: Union[str, Any] ):
_UpperCAmelCase : Dict = ["0", "1", "2", "3"]
_UpperCAmelCase : Optional[Any] = str(tmp_path_factory.mktemp("data" ) / "dataset2.txt" )
with open(a_, "w" ) as f:
for item in data:
f.write(item + "\n" )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: Any ):
_UpperCAmelCase : int = ["0", "1", "2", "3"]
_UpperCAmelCase : str = tmp_path_factory.mktemp("data" ) / "dataset.abc"
with open(a_, "w" ) as f:
for item in data:
f.write(item + "\n" )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: Optional[Any], a_: Any, a_: Union[str, Any] ):
_UpperCAmelCase : Union[str, Any] = tmp_path_factory.mktemp("data" ) / "dataset.text.zip"
with zipfile.ZipFile(a_, "w" ) as f:
f.write(a_, arcname=os.path.basename(a_ ) )
f.write(a_, arcname=os.path.basename(a_ ) )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: Optional[int], a_: List[Any], a_: List[Any] ):
_UpperCAmelCase : List[Any] = tmp_path_factory.mktemp("data" ) / "dataset_with_dir.text.zip"
with zipfile.ZipFile(a_, "w" ) as f:
f.write(a_, arcname=os.path.join("main_dir", os.path.basename(a_ ) ) )
f.write(a_, arcname=os.path.join("main_dir", os.path.basename(a_ ) ) )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: Any, a_: str, a_: Tuple ):
_UpperCAmelCase : List[Any] = tmp_path_factory.mktemp("data" ) / "dataset.ext.zip"
with zipfile.ZipFile(a_, "w" ) as f:
f.write(a_, arcname=os.path.basename("unsupported.ext" ) )
f.write(a_, arcname=os.path.basename("unsupported_2.ext" ) )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: Optional[Any] ):
_UpperCAmelCase : List[str] = "\n".join(["First", "Second\u2029with Unicode new line", "Third"] )
_UpperCAmelCase : str = str(tmp_path_factory.mktemp("data" ) / "dataset_with_unicode_new_lines.txt" )
with open(a_, "w", encoding="utf-8" ) as f:
f.write(a_ )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( ):
return os.path.join("tests", "features", "data", "test_image_rgb.jpg" )
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( ):
return os.path.join("tests", "features", "data", "test_audio_44100.wav" )
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: int, a_: Optional[Any] ):
_UpperCAmelCase : Union[str, Any] = tmp_path_factory.mktemp("data" ) / "dataset.img.zip"
with zipfile.ZipFile(a_, "w" ) as f:
f.write(a_, arcname=os.path.basename(a_ ) )
f.write(a_, arcname=os.path.basename(a_ ).replace(".jpg", "2.jpg" ) )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: Tuple ):
_UpperCAmelCase : Optional[Any] = tmp_path_factory.mktemp("data_dir" )
(data_dir / "subdir").mkdir()
with open(data_dir / "subdir" / "train.txt", "w" ) as f:
f.write("foo\n" * 10 )
with open(data_dir / "subdir" / "test.txt", "w" ) as f:
f.write("bar\n" * 10 )
# hidden file
with open(data_dir / "subdir" / ".test.txt", "w" ) as f:
f.write("bar\n" * 10 )
# hidden directory
(data_dir / ".subdir").mkdir()
with open(data_dir / ".subdir" / "train.txt", "w" ) as f:
f.write("foo\n" * 10 )
with open(data_dir / ".subdir" / "test.txt", "w" ) as f:
f.write("bar\n" * 10 )
return data_dir
| 17
| 0
|
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_deit import DeiTImageProcessor
__a = logging.get_logger(__name__)
class A__ ( UpperCamelCase ):
"""simple docstring"""
def __init__( self : Dict , *lowerCAmelCase__ : Optional[int] , **lowerCAmelCase__ : Any ) -> None:
"""simple docstring"""
warnings.warn(
"The class DeiTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
" use DeiTImageProcessor instead." , _lowercase , )
super().__init__(*_lowercase , **_lowercase )
| 365
|
'''simple docstring'''
import unittest
from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
@require_sentencepiece
@slow # see https://github.com/huggingface/transformers/issues/11457
class A__ ( UpperCamelCase , unittest.TestCase ):
"""simple docstring"""
UpperCamelCase_ : str = BarthezTokenizer
UpperCamelCase_ : List[Any] = BarthezTokenizerFast
UpperCamelCase_ : Optional[int] = True
UpperCamelCase_ : Optional[int] = True
def _lowerCAmelCase ( self : Optional[int] ) -> Dict:
"""simple docstring"""
super().setUp()
_UpperCAmelCase : Tuple = BarthezTokenizerFast.from_pretrained("moussaKam/mbarthez" )
tokenizer.save_pretrained(self.tmpdirname )
tokenizer.save_pretrained(self.tmpdirname , legacy_format=lowerCAmelCase__ )
_UpperCAmelCase : List[str] = tokenizer
def _lowerCAmelCase ( self : List[str] ) -> Optional[int]:
"""simple docstring"""
_UpperCAmelCase : Tuple = "<pad>"
_UpperCAmelCase : Dict = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCAmelCase__ ) , lowerCAmelCase__ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCAmelCase__ ) , lowerCAmelCase__ )
def _lowerCAmelCase ( self : Tuple ) -> Optional[int]:
"""simple docstring"""
_UpperCAmelCase : List[str] = 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(lowerCAmelCase__ ) , 1_0_1_1_2_2 )
def _lowerCAmelCase ( self : Union[str, Any] ) -> Dict:
"""simple docstring"""
self.assertEqual(self.get_tokenizer().vocab_size , 1_0_1_1_2_2 )
@require_torch
def _lowerCAmelCase ( self : Any ) -> int:
"""simple docstring"""
_UpperCAmelCase : int = ["A long paragraph for summarization.", "Another paragraph for summarization."]
_UpperCAmelCase : Optional[int] = [0, 5_7, 3_0_1_8, 7_0_3_0_7, 9_1, 2]
_UpperCAmelCase : int = self.tokenizer(
lowerCAmelCase__ , max_length=len(lowerCAmelCase__ ) , padding=lowerCAmelCase__ , truncation=lowerCAmelCase__ , return_tensors="pt" )
self.assertIsInstance(lowerCAmelCase__ , lowerCAmelCase__ )
self.assertEqual((2, 6) , batch.input_ids.shape )
self.assertEqual((2, 6) , batch.attention_mask.shape )
_UpperCAmelCase : str = batch.input_ids.tolist()[0]
self.assertListEqual(lowerCAmelCase__ , lowerCAmelCase__ )
def _lowerCAmelCase ( self : str ) -> Optional[Any]:
"""simple docstring"""
if not self.test_rust_tokenizer:
return
_UpperCAmelCase : Optional[int] = self.get_tokenizer()
_UpperCAmelCase : Optional[int] = self.get_rust_tokenizer()
_UpperCAmelCase : Tuple = "I was born in 92000, and this is falsé."
_UpperCAmelCase : Dict = tokenizer.tokenize(lowerCAmelCase__ )
_UpperCAmelCase : Union[str, Any] = rust_tokenizer.tokenize(lowerCAmelCase__ )
self.assertListEqual(lowerCAmelCase__ , lowerCAmelCase__ )
_UpperCAmelCase : Dict = tokenizer.encode(lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ )
_UpperCAmelCase : Union[str, Any] = rust_tokenizer.encode(lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ )
self.assertListEqual(lowerCAmelCase__ , lowerCAmelCase__ )
_UpperCAmelCase : Optional[Any] = self.get_rust_tokenizer()
_UpperCAmelCase : Optional[Any] = tokenizer.encode(lowerCAmelCase__ )
_UpperCAmelCase : Optional[int] = rust_tokenizer.encode(lowerCAmelCase__ )
self.assertListEqual(lowerCAmelCase__ , lowerCAmelCase__ )
@slow
def _lowerCAmelCase ( self : int ) -> int:
"""simple docstring"""
_UpperCAmelCase : Optional[Any] = {"input_ids": [[0, 4_9_0, 1_4_3_2_8, 4_5_0_7, 3_5_4, 4_7, 4_3_6_6_9, 9_5, 2_5, 7_8_1_1_7, 2_0_2_1_5, 1_9_7_7_9, 1_9_0, 2_2, 4_0_0, 4, 3_5_3_4_3, 8_0_3_1_0, 6_0_3, 8_6, 2_4_9_3_7, 1_0_5, 3_3_4_3_8, 9_4_7_6_2, 1_9_6, 3_9_6_4_2, 7, 1_5, 1_5_9_3_3, 1_7_3, 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], [0, 1_0_5_3_4, 8_7, 2_5, 6_6, 3_3_5_8, 1_9_6, 5_5_2_8_9, 8, 8_2_9_6_1, 8_1, 2_2_0_4, 7_5_2_0_3, 7, 1_5, 7_6_3, 1_2_9_5_6, 2_1_6, 1_7_8, 1_4_3_2_8, 9_5_9_5, 1_3_7_7, 6_9_6_9_3, 7, 4_4_8, 7_1_0_2_1, 1_9_6, 1_8_1_0_6, 1_4_3_7, 1_3_9_7_4, 1_0_8, 9_0_8_3, 4, 4_9_3_1_5, 7, 3_9, 8_6, 1_3_2_6, 2_7_9_3, 4_6_3_3_3, 4, 4_4_8, 1_9_6, 7_4_5_8_8, 7, 4_9_3_1_5, 7, 3_9, 2_1, 8_2_2, 3_8_4_7_0, 7_4, 2_1, 6_6_7_2_3, 6_2_4_8_0, 8, 2_2_0_5_0, 5, 2]], "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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]} # noqa: E501
# fmt: on
# moussaKam/mbarthez is a french model. So we also use french texts.
_UpperCAmelCase : Tuple = [
"Le transformeur est un modèle d'apprentissage profond introduit en 2017, "
"utilisé principalement dans le domaine du traitement automatique des langues (TAL).",
"À l'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus "
"pour gérer des données séquentielles, telles que le langage naturel, pour des tâches "
"telles que la traduction et la synthèse de texte.",
]
self.tokenizer_integration_test_util(
expected_encoding=lowerCAmelCase__ , model_name="moussaKam/mbarthez" , revision="c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6" , sequences=lowerCAmelCase__ , )
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'''simple docstring'''
def __UpperCAmelCase ( a_: list[int], a_: list[int] ):
if not len(SCREAMING_SNAKE_CASE__ ) == len(SCREAMING_SNAKE_CASE__ ) == 3:
raise ValueError("Please enter a valid equation." )
if equationa[0] == equationa[1] == equationa[0] == equationa[1] == 0:
raise ValueError("Both a & b of two equations can't be zero." )
# Extract the coefficients
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase : Dict = equationa
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase : Any = equationa
# Calculate the determinants of the matrices
_UpperCAmelCase : List[str] = aa * ba - aa * ba
_UpperCAmelCase : Optional[Any] = ca * ba - ca * ba
_UpperCAmelCase : Optional[Any] = aa * ca - aa * ca
# Check if the system of linear equations has a solution (using Cramer's rule)
if determinant == 0:
if determinant_x == determinant_y == 0:
raise ValueError("Infinite solutions. (Consistent system)" )
else:
raise ValueError("No solution. (Inconsistent system)" )
else:
if determinant_x == determinant_y == 0:
# Trivial solution (Inconsistent system)
return (0.0, 0.0)
else:
_UpperCAmelCase : List[str] = determinant_x / determinant
_UpperCAmelCase : Optional[Any] = determinant_y / determinant
# Non-Trivial Solution (Consistent system)
return (x, y)
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'''simple docstring'''
import unittest
import numpy as np
import requests
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
__a = False
if is_vision_available():
from PIL import Image
from transformers import PixaStructImageProcessor
class A__ ( unittest.TestCase ):
"""simple docstring"""
def __init__( self : int , lowerCAmelCase__ : Optional[int] , lowerCAmelCase__ : Optional[Any]=7 , lowerCAmelCase__ : int=3 , lowerCAmelCase__ : List[Any]=1_8 , lowerCAmelCase__ : str=3_0 , lowerCAmelCase__ : str=4_0_0 , lowerCAmelCase__ : Union[str, Any]=None , lowerCAmelCase__ : Optional[Any]=True , lowerCAmelCase__ : str=True , lowerCAmelCase__ : List[Any]=None , ) -> List[Any]:
"""simple docstring"""
_UpperCAmelCase : List[Any] = size if size is not None else {"height": 2_0, "width": 2_0}
_UpperCAmelCase : Optional[Any] = parent
_UpperCAmelCase : Tuple = batch_size
_UpperCAmelCase : str = num_channels
_UpperCAmelCase : Optional[Any] = image_size
_UpperCAmelCase : Dict = min_resolution
_UpperCAmelCase : str = max_resolution
_UpperCAmelCase : List[Any] = size
_UpperCAmelCase : Union[str, Any] = do_normalize
_UpperCAmelCase : Optional[Any] = do_convert_rgb
_UpperCAmelCase : str = [5_1_2, 1_0_2_4, 2_0_4_8, 4_0_9_6]
_UpperCAmelCase : str = patch_size if patch_size is not None else {"height": 1_6, "width": 1_6}
def _lowerCAmelCase ( self : List[str] ) -> int:
"""simple docstring"""
return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}
def _lowerCAmelCase ( self : Any ) -> str:
"""simple docstring"""
_UpperCAmelCase : Dict = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg"
_UpperCAmelCase : Optional[Any] = Image.open(requests.get(lowerCAmelCase__ , stream=lowerCAmelCase__ ).raw ).convert("RGB" )
return raw_image
@unittest.skipIf(
not is_torch_greater_or_equal_than_1_11 , reason='''`Pix2StructImageProcessor` requires `torch>=1.11.0`.''' , )
@require_torch
@require_vision
class A__ ( UpperCamelCase , unittest.TestCase ):
"""simple docstring"""
UpperCamelCase_ : Any = PixaStructImageProcessor if is_vision_available() else None
def _lowerCAmelCase ( self : Any ) -> Optional[int]:
"""simple docstring"""
_UpperCAmelCase : Tuple = PixaStructImageProcessingTester(self )
@property
def _lowerCAmelCase ( self : Tuple ) -> int:
"""simple docstring"""
return self.image_processor_tester.prepare_image_processor_dict()
def _lowerCAmelCase ( self : Any ) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase : str = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(lowerCAmelCase__ , "do_normalize" ) )
self.assertTrue(hasattr(lowerCAmelCase__ , "do_convert_rgb" ) )
def _lowerCAmelCase ( self : Optional[Any] ) -> Dict:
"""simple docstring"""
_UpperCAmelCase : Optional[Any] = self.image_processor_tester.prepare_dummy_image()
_UpperCAmelCase : Optional[int] = self.image_processing_class(**self.image_processor_dict )
_UpperCAmelCase : str = 2_0_4_8
_UpperCAmelCase : Any = image_processor(lowerCAmelCase__ , return_tensors="pt" , max_patches=lowerCAmelCase__ )
self.assertTrue(torch.allclose(inputs.flattened_patches.mean() , torch.tensor(0.0606 ) , atol=1e-3 , rtol=1e-3 ) )
def _lowerCAmelCase ( self : Dict ) -> int:
"""simple docstring"""
_UpperCAmelCase : Optional[Any] = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
_UpperCAmelCase : Tuple = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCAmelCase__ )
for image in image_inputs:
self.assertIsInstance(lowerCAmelCase__ , Image.Image )
# Test not batched input
_UpperCAmelCase : List[str] = (
(self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
* self.image_processor_tester.num_channels
) + 2
for max_patch in self.image_processor_tester.max_patches:
# Test not batched input
_UpperCAmelCase : Union[str, Any] = image_processor(
image_inputs[0] , return_tensors="pt" , max_patches=lowerCAmelCase__ ).flattened_patches
self.assertEqual(
encoded_images.shape , (1, max_patch, expected_hidden_dim) , )
# Test batched
_UpperCAmelCase : str = image_processor(
lowerCAmelCase__ , return_tensors="pt" , max_patches=lowerCAmelCase__ ).flattened_patches
self.assertEqual(
encoded_images.shape , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) , )
def _lowerCAmelCase ( self : Optional[int] ) -> List[str]:
"""simple docstring"""
_UpperCAmelCase : Any = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
_UpperCAmelCase : List[Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCAmelCase__ )
for image in image_inputs:
self.assertIsInstance(lowerCAmelCase__ , Image.Image )
# Test not batched input
_UpperCAmelCase : Union[str, Any] = (
(self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
* self.image_processor_tester.num_channels
) + 2
_UpperCAmelCase : str = True
for max_patch in self.image_processor_tester.max_patches:
# Test not batched input
with self.assertRaises(lowerCAmelCase__ ):
_UpperCAmelCase : str = image_processor(
image_inputs[0] , return_tensors="pt" , max_patches=lowerCAmelCase__ ).flattened_patches
_UpperCAmelCase : Any = "Hello"
_UpperCAmelCase : Optional[int] = image_processor(
image_inputs[0] , return_tensors="pt" , max_patches=lowerCAmelCase__ , header_text=lowerCAmelCase__ ).flattened_patches
self.assertEqual(
encoded_images.shape , (1, max_patch, expected_hidden_dim) , )
# Test batched
_UpperCAmelCase : List[Any] = image_processor(
lowerCAmelCase__ , return_tensors="pt" , max_patches=lowerCAmelCase__ , header_text=lowerCAmelCase__ ).flattened_patches
self.assertEqual(
encoded_images.shape , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) , )
def _lowerCAmelCase ( self : List[str] ) -> List[Any]:
"""simple docstring"""
_UpperCAmelCase : Union[str, Any] = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
_UpperCAmelCase : List[str] = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCAmelCase__ , numpify=lowerCAmelCase__ )
for image in image_inputs:
self.assertIsInstance(lowerCAmelCase__ , np.ndarray )
_UpperCAmelCase : Any = (
(self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
* self.image_processor_tester.num_channels
) + 2
for max_patch in self.image_processor_tester.max_patches:
# Test not batched input
_UpperCAmelCase : int = image_processor(
image_inputs[0] , return_tensors="pt" , max_patches=lowerCAmelCase__ ).flattened_patches
self.assertEqual(
encoded_images.shape , (1, max_patch, expected_hidden_dim) , )
# Test batched
_UpperCAmelCase : Union[str, Any] = image_processor(
lowerCAmelCase__ , return_tensors="pt" , max_patches=lowerCAmelCase__ ).flattened_patches
self.assertEqual(
encoded_images.shape , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) , )
def _lowerCAmelCase ( self : int ) -> str:
"""simple docstring"""
_UpperCAmelCase : List[str] = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
_UpperCAmelCase : Any = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCAmelCase__ , torchify=lowerCAmelCase__ )
for image in image_inputs:
self.assertIsInstance(lowerCAmelCase__ , torch.Tensor )
# Test not batched input
_UpperCAmelCase : List[str] = (
(self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
* self.image_processor_tester.num_channels
) + 2
for max_patch in self.image_processor_tester.max_patches:
# Test not batched input
_UpperCAmelCase : Union[str, Any] = image_processor(
image_inputs[0] , return_tensors="pt" , max_patches=lowerCAmelCase__ ).flattened_patches
self.assertEqual(
encoded_images.shape , (1, max_patch, expected_hidden_dim) , )
# Test batched
_UpperCAmelCase : str = image_processor(
lowerCAmelCase__ , return_tensors="pt" , max_patches=lowerCAmelCase__ ).flattened_patches
self.assertEqual(
encoded_images.shape , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) , )
@unittest.skipIf(
not is_torch_greater_or_equal_than_1_11 , reason='''`Pix2StructImageProcessor` requires `torch>=1.11.0`.''' , )
@require_torch
@require_vision
class A__ ( UpperCamelCase , unittest.TestCase ):
"""simple docstring"""
UpperCamelCase_ : List[Any] = PixaStructImageProcessor if is_vision_available() else None
def _lowerCAmelCase ( self : Union[str, Any] ) -> Union[str, Any]:
"""simple docstring"""
_UpperCAmelCase : Any = PixaStructImageProcessingTester(self , num_channels=4 )
_UpperCAmelCase : List[Any] = 3
@property
def _lowerCAmelCase ( self : Union[str, Any] ) -> Tuple:
"""simple docstring"""
return self.image_processor_tester.prepare_image_processor_dict()
def _lowerCAmelCase ( self : Dict ) -> Any:
"""simple docstring"""
_UpperCAmelCase : str = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(lowerCAmelCase__ , "do_normalize" ) )
self.assertTrue(hasattr(lowerCAmelCase__ , "do_convert_rgb" ) )
def _lowerCAmelCase ( self : int ) -> List[str]:
"""simple docstring"""
_UpperCAmelCase : Optional[int] = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
_UpperCAmelCase : List[str] = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCAmelCase__ )
for image in image_inputs:
self.assertIsInstance(lowerCAmelCase__ , Image.Image )
# Test not batched input
_UpperCAmelCase : str = (
(self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
* (self.image_processor_tester.num_channels - 1)
) + 2
for max_patch in self.image_processor_tester.max_patches:
# Test not batched input
_UpperCAmelCase : Any = image_processor(
image_inputs[0] , return_tensors="pt" , max_patches=lowerCAmelCase__ ).flattened_patches
self.assertEqual(
encoded_images.shape , (1, max_patch, expected_hidden_dim) , )
# Test batched
_UpperCAmelCase : Tuple = image_processor(
lowerCAmelCase__ , return_tensors="pt" , max_patches=lowerCAmelCase__ ).flattened_patches
self.assertEqual(
encoded_images.shape , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) , )
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'''simple docstring'''
import functools
from typing import Any
def __UpperCAmelCase ( a_: str, a_: list[str] ):
if not isinstance(a_, a_ ) or len(a_ ) == 0:
raise ValueError("the string should be not empty string" )
if not isinstance(a_, a_ ) or not all(
isinstance(a_, a_ ) and len(a_ ) > 0 for item in words ):
raise ValueError("the words should be a list of non-empty strings" )
# Build trie
_UpperCAmelCase : dict[str, Any] = {}
_UpperCAmelCase : Tuple = """WORD_KEEPER"""
for word in words:
_UpperCAmelCase : List[str] = trie
for c in word:
if c not in trie_node:
_UpperCAmelCase : Tuple = {}
_UpperCAmelCase : Dict = trie_node[c]
_UpperCAmelCase : Any = True
_UpperCAmelCase : str = len(a_ )
# Dynamic programming method
@functools.cache
def is_breakable(a_: int ) -> bool:
if index == len_string:
return True
_UpperCAmelCase : Optional[int] = trie
for i in range(a_, a_ ):
_UpperCAmelCase : Tuple = trie_node.get(string[i], a_ )
if trie_node is None:
return False
if trie_node.get(a_, a_ ) and is_breakable(i + 1 ):
return True
return False
return is_breakable(0 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 367
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'''simple docstring'''
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a = logging.get_logger(__name__)
__a = {
'huggingface/time-series-transformer-tourism-monthly': (
'https://huggingface.co/huggingface/time-series-transformer-tourism-monthly/resolve/main/config.json'
),
# See all TimeSeriesTransformer models at https://huggingface.co/models?filter=time_series_transformer
}
class A__ ( UpperCamelCase ):
"""simple docstring"""
UpperCamelCase_ : Tuple = '''time_series_transformer'''
UpperCamelCase_ : Optional[Any] = {
'''hidden_size''': '''d_model''',
'''num_attention_heads''': '''encoder_attention_heads''',
'''num_hidden_layers''': '''encoder_layers''',
}
def __init__( self : Optional[int] , lowerCAmelCase__ : Optional[int] = None , lowerCAmelCase__ : Optional[int] = None , lowerCAmelCase__ : str = "student_t" , lowerCAmelCase__ : str = "nll" , lowerCAmelCase__ : int = 1 , lowerCAmelCase__ : List[int] = [1, 2, 3, 4, 5, 6, 7] , lowerCAmelCase__ : Optional[Union[str, bool]] = "mean" , lowerCAmelCase__ : int = 0 , lowerCAmelCase__ : int = 0 , lowerCAmelCase__ : int = 0 , lowerCAmelCase__ : int = 0 , lowerCAmelCase__ : Optional[List[int]] = None , lowerCAmelCase__ : Optional[List[int]] = None , lowerCAmelCase__ : int = 3_2 , lowerCAmelCase__ : int = 3_2 , lowerCAmelCase__ : int = 2 , lowerCAmelCase__ : int = 2 , lowerCAmelCase__ : int = 2 , lowerCAmelCase__ : int = 2 , lowerCAmelCase__ : bool = True , lowerCAmelCase__ : str = "gelu" , lowerCAmelCase__ : int = 6_4 , lowerCAmelCase__ : float = 0.1 , lowerCAmelCase__ : float = 0.1 , lowerCAmelCase__ : float = 0.1 , lowerCAmelCase__ : float = 0.1 , lowerCAmelCase__ : float = 0.1 , lowerCAmelCase__ : int = 1_0_0 , lowerCAmelCase__ : float = 0.02 , lowerCAmelCase__ : Dict=True , **lowerCAmelCase__ : Tuple , ) -> Tuple:
"""simple docstring"""
_UpperCAmelCase : Optional[int] = prediction_length
_UpperCAmelCase : Optional[Any] = context_length or prediction_length
_UpperCAmelCase : Optional[Any] = distribution_output
_UpperCAmelCase : Union[str, Any] = loss
_UpperCAmelCase : Dict = input_size
_UpperCAmelCase : int = num_time_features
_UpperCAmelCase : Any = lags_sequence
_UpperCAmelCase : Dict = scaling
_UpperCAmelCase : Tuple = num_dynamic_real_features
_UpperCAmelCase : Dict = num_static_real_features
_UpperCAmelCase : Union[str, Any] = num_static_categorical_features
if cardinality and num_static_categorical_features > 0:
if len(lowerCAmelCase__ ) != num_static_categorical_features:
raise ValueError(
"The cardinality should be a list of the same length as `num_static_categorical_features`" )
_UpperCAmelCase : Optional[int] = cardinality
else:
_UpperCAmelCase : Optional[Any] = [0]
if embedding_dimension and num_static_categorical_features > 0:
if len(lowerCAmelCase__ ) != num_static_categorical_features:
raise ValueError(
"The embedding dimension should be a list of the same length as `num_static_categorical_features`" )
_UpperCAmelCase : List[Any] = embedding_dimension
else:
_UpperCAmelCase : Optional[Any] = [min(5_0 , (cat + 1) // 2 ) for cat in self.cardinality]
_UpperCAmelCase : str = num_parallel_samples
# Transformer architecture configuration
_UpperCAmelCase : Union[str, Any] = input_size * len(lowerCAmelCase__ ) + self._number_of_features
_UpperCAmelCase : str = d_model
_UpperCAmelCase : Optional[Any] = encoder_attention_heads
_UpperCAmelCase : Dict = decoder_attention_heads
_UpperCAmelCase : List[Any] = encoder_ffn_dim
_UpperCAmelCase : str = decoder_ffn_dim
_UpperCAmelCase : Dict = encoder_layers
_UpperCAmelCase : str = decoder_layers
_UpperCAmelCase : Any = dropout
_UpperCAmelCase : str = attention_dropout
_UpperCAmelCase : List[Any] = activation_dropout
_UpperCAmelCase : Dict = encoder_layerdrop
_UpperCAmelCase : Any = decoder_layerdrop
_UpperCAmelCase : Optional[Any] = activation_function
_UpperCAmelCase : Tuple = init_std
_UpperCAmelCase : List[str] = use_cache
super().__init__(is_encoder_decoder=lowerCAmelCase__ , **lowerCAmelCase__ )
@property
def _lowerCAmelCase ( self : str ) -> int:
"""simple docstring"""
return (
sum(self.embedding_dimension )
+ self.num_dynamic_real_features
+ self.num_time_features
+ self.num_static_real_features
+ self.input_size * 2 # the log1p(abs(loc)) and log(scale) features
)
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'''simple docstring'''
import fire
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save
def __UpperCAmelCase ( a_: Optional[Any], a_: Dict, a_: Dict=1_024, a_: Tuple=1_024, a_: Tuple=False, **a_: Dict ):
_UpperCAmelCase : Optional[Any] = AutoTokenizer.from_pretrained(a__ )
_UpperCAmelCase : Union[str, Any] = SeqaSeqDataset(a__, a__, a__, a__, type_path="train", **a__ )
_UpperCAmelCase : Optional[Any] = tok.pad_token_id
def get_lens(a_: Tuple ):
_UpperCAmelCase : List[str] = tqdm(
DataLoader(a__, batch_size=512, num_workers=8, shuffle=a__, collate_fn=ds.collate_fn ), desc=str(ds.len_file ), )
_UpperCAmelCase : Tuple = []
for batch in dl:
_UpperCAmelCase : Optional[Any] = batch["input_ids"].ne(a__ ).sum(1 ).tolist()
_UpperCAmelCase : List[str] = batch["labels"].ne(a__ ).sum(1 ).tolist()
if consider_target:
for src, tgt in zip(a__, a__ ):
max_lens.append(max(a__, a__ ) )
else:
max_lens.extend(a__ )
return max_lens
_UpperCAmelCase : Union[str, Any] = get_lens(a__ )
_UpperCAmelCase : Union[str, Any] = SeqaSeqDataset(a__, a__, a__, a__, type_path="val", **a__ )
_UpperCAmelCase : List[Any] = get_lens(a__ )
pickle_save(a__, train_ds.len_file )
pickle_save(a__, val_ds.len_file )
if __name__ == "__main__":
fire.Fire(save_len_file)
| 368
|
'''simple docstring'''
import baseaa
def __UpperCAmelCase ( a_: str ):
return baseaa.baaencode(string.encode("utf-8" ) )
def __UpperCAmelCase ( a_: bytes ):
return baseaa.baadecode(a_ ).decode("utf-8" )
if __name__ == "__main__":
__a = 'Hello World!'
__a = baseaa_encode(test)
print(encoded)
__a = baseaa_decode(encoded)
print(decoded)
| 17
| 0
|
'''simple docstring'''
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment
class A__ ( unittest.TestCase ):
"""simple docstring"""
def _lowerCAmelCase ( self : Dict ) -> Any:
"""simple docstring"""
_UpperCAmelCase : List[str] = inspect.getfile(accelerate.test_utils )
_UpperCAmelCase : List[Any] = os.path.sep.join(mod_file.split(os.path.sep )[:-1] + ["scripts", "test_script.py"] )
_UpperCAmelCase : Any = os.path.sep.join(
mod_file.split(os.path.sep )[:-1] + ["scripts", "test_distributed_data_loop.py"] )
_UpperCAmelCase : Tuple = os.path.sep.join(mod_file.split(os.path.sep )[:-1] + ["scripts", "test_ops.py"] )
@require_multi_gpu
def _lowerCAmelCase ( self : int ) -> Optional[int]:
"""simple docstring"""
print(F"""Found {torch.cuda.device_count()} devices.""" )
_UpperCAmelCase : Union[str, Any] = ["torchrun", F"""--nproc_per_node={torch.cuda.device_count()}""", self.test_file_path]
with patch_environment(omp_num_threads=1 ):
execute_subprocess_async(lowerCAmelCase__ , env=os.environ.copy() )
@require_multi_gpu
def _lowerCAmelCase ( self : List[str] ) -> List[str]:
"""simple docstring"""
print(F"""Found {torch.cuda.device_count()} devices.""" )
_UpperCAmelCase : str = ["torchrun", F"""--nproc_per_node={torch.cuda.device_count()}""", self.operation_file_path]
print(F"""Command: {cmd}""" )
with patch_environment(omp_num_threads=1 ):
execute_subprocess_async(lowerCAmelCase__ , env=os.environ.copy() )
@require_multi_gpu
def _lowerCAmelCase ( self : Any ) -> Tuple:
"""simple docstring"""
_UpperCAmelCase : List[Any] = ["torchrun", F"""--nproc_per_node={torch.cuda.device_count()}""", inspect.getfile(self.__class__ )]
with patch_environment(omp_num_threads=1 ):
execute_subprocess_async(lowerCAmelCase__ , env=os.environ.copy() )
@require_multi_gpu
def _lowerCAmelCase ( self : Any ) -> int:
"""simple docstring"""
print(F"""Found {torch.cuda.device_count()} devices, using 2 devices only""" )
_UpperCAmelCase : Tuple = ["torchrun", F"""--nproc_per_node={torch.cuda.device_count()}""", self.data_loop_file_path]
with patch_environment(omp_num_threads=1 , cuda_visible_devices="0,1" ):
execute_subprocess_async(lowerCAmelCase__ , env=os.environ.copy() )
if __name__ == "__main__":
__a = Accelerator()
__a = (accelerator.state.process_index + 2, 10)
__a = torch.randint(0, 10, shape).to(accelerator.device)
__a = ''
__a = accelerator.pad_across_processes(tensor)
if tensora.shape[0] != accelerator.state.num_processes + 1:
error_msg += f"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
if not torch.equal(tensora[: accelerator.state.process_index + 2], tensor):
error_msg += "Tensors have different values."
if not torch.all(tensora[accelerator.state.process_index + 2 :] == 0):
error_msg += "Padding was not done with the right value (0)."
__a = accelerator.pad_across_processes(tensor, pad_first=True)
if tensora.shape[0] != accelerator.state.num_processes + 1:
error_msg += f"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
__a = accelerator.state.num_processes - accelerator.state.process_index - 1
if not torch.equal(tensora[index:], tensor):
error_msg += "Tensors have different values."
if not torch.all(tensora[:index] == 0):
error_msg += "Padding was not done with the right value (0)."
# Raise error at the end to make sure we don't stop at the first failure.
if len(error_msg) > 0:
raise ValueError(error_msg)
| 369
|
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow
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.models.xglm.modeling_tf_xglm import (
TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFXGLMForCausalLM,
TFXGLMModel,
)
@require_tf
class A__ :
"""simple docstring"""
UpperCamelCase_ : Any = XGLMConfig
UpperCamelCase_ : Union[str, Any] = {}
UpperCamelCase_ : Dict = '''gelu'''
def __init__( self : Optional[int] , lowerCAmelCase__ : Union[str, Any] , lowerCAmelCase__ : Optional[Any]=1_4 , lowerCAmelCase__ : Any=7 , lowerCAmelCase__ : Optional[Any]=True , lowerCAmelCase__ : List[str]=True , lowerCAmelCase__ : Any=True , lowerCAmelCase__ : List[str]=9_9 , lowerCAmelCase__ : Any=3_2 , lowerCAmelCase__ : Optional[int]=2 , lowerCAmelCase__ : List[Any]=4 , lowerCAmelCase__ : Any=3_7 , lowerCAmelCase__ : List[Any]="gelu" , lowerCAmelCase__ : List[Any]=0.1 , lowerCAmelCase__ : Dict=0.1 , lowerCAmelCase__ : Optional[int]=5_1_2 , lowerCAmelCase__ : Optional[Any]=0.02 , ) -> int:
"""simple docstring"""
_UpperCAmelCase : Optional[Any] = parent
_UpperCAmelCase : str = batch_size
_UpperCAmelCase : str = seq_length
_UpperCAmelCase : int = is_training
_UpperCAmelCase : List[Any] = use_input_mask
_UpperCAmelCase : Optional[int] = use_labels
_UpperCAmelCase : str = vocab_size
_UpperCAmelCase : int = d_model
_UpperCAmelCase : Tuple = num_hidden_layers
_UpperCAmelCase : Tuple = num_attention_heads
_UpperCAmelCase : Tuple = ffn_dim
_UpperCAmelCase : Any = activation_function
_UpperCAmelCase : Union[str, Any] = activation_dropout
_UpperCAmelCase : Union[str, Any] = attention_dropout
_UpperCAmelCase : Any = max_position_embeddings
_UpperCAmelCase : int = initializer_range
_UpperCAmelCase : Any = None
_UpperCAmelCase : int = 0
_UpperCAmelCase : Union[str, Any] = 2
_UpperCAmelCase : Tuple = 1
def _lowerCAmelCase ( self : Optional[Any] ) -> List[Any]:
"""simple docstring"""
return XGLMConfig.from_pretrained("facebook/xglm-564M" )
def _lowerCAmelCase ( self : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
_UpperCAmelCase : int = tf.clip_by_value(
ids_tensor([self.batch_size, self.seq_length] , self.vocab_size ) , clip_value_min=0 , clip_value_max=3 )
_UpperCAmelCase : Any = None
if self.use_input_mask:
_UpperCAmelCase : Union[str, Any] = random_attention_mask([self.batch_size, self.seq_length] )
_UpperCAmelCase : Optional[Any] = self.get_config()
_UpperCAmelCase : Dict = floats_tensor([self.num_hidden_layers, self.num_attention_heads] , 2 )
return (
config,
input_ids,
input_mask,
head_mask,
)
def _lowerCAmelCase ( self : int ) -> Any:
"""simple docstring"""
return XGLMConfig(
vocab_size=self.vocab_size , d_model=self.hidden_size , num_layers=self.num_hidden_layers , attention_heads=self.num_attention_heads , ffn_dim=self.ffn_dim , activation_function=self.activation_function , activation_dropout=self.activation_dropout , attention_dropout=self.attention_dropout , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , use_cache=lowerCAmelCase__ , bos_token_id=self.bos_token_id , eos_token_id=self.eos_token_id , pad_token_id=self.pad_token_id , return_dict=lowerCAmelCase__ , )
def _lowerCAmelCase ( self : Tuple ) -> Union[str, Any]:
"""simple docstring"""
_UpperCAmelCase : Union[str, Any] = self.prepare_config_and_inputs()
(
(
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) ,
) : List[Any] = config_and_inputs
_UpperCAmelCase : Optional[int] = {
"input_ids": input_ids,
"head_mask": head_mask,
}
return config, inputs_dict
@require_tf
class A__ ( UpperCamelCase , UpperCamelCase , unittest.TestCase ):
"""simple docstring"""
UpperCamelCase_ : str = (TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
UpperCamelCase_ : Any = (TFXGLMForCausalLM,) if is_tf_available() else ()
UpperCamelCase_ : Tuple = (
{'''feature-extraction''': TFXGLMModel, '''text-generation''': TFXGLMForCausalLM} if is_tf_available() else {}
)
UpperCamelCase_ : Dict = False
UpperCamelCase_ : List[Any] = False
UpperCamelCase_ : Tuple = False
def _lowerCAmelCase ( self : List[str] ) -> int:
"""simple docstring"""
_UpperCAmelCase : Dict = TFXGLMModelTester(self )
_UpperCAmelCase : Dict = ConfigTester(self , config_class=lowerCAmelCase__ , n_embd=3_7 )
def _lowerCAmelCase ( self : List[str] ) -> Dict:
"""simple docstring"""
self.config_tester.run_common_tests()
@slow
def _lowerCAmelCase ( self : List[str] ) -> Union[str, Any]:
"""simple docstring"""
for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_UpperCAmelCase : Optional[int] = TFXGLMModel.from_pretrained(lowerCAmelCase__ )
self.assertIsNotNone(lowerCAmelCase__ )
@unittest.skip(reason="Currently, model embeddings are going to undergo a major refactor." )
def _lowerCAmelCase ( self : Union[str, Any] ) -> int:
"""simple docstring"""
super().test_resize_token_embeddings()
@require_tf
class A__ ( unittest.TestCase ):
"""simple docstring"""
@slow
def _lowerCAmelCase ( self : Optional[int] , lowerCAmelCase__ : Optional[Any]=True ) -> Tuple:
"""simple docstring"""
_UpperCAmelCase : Optional[int] = TFXGLMForCausalLM.from_pretrained("facebook/xglm-564M" )
_UpperCAmelCase : Any = tf.convert_to_tensor([[2, 2_6_8, 9_8_6_5]] , dtype=tf.intaa ) # The dog
# </s> The dog is a very friendly dog. He is very affectionate and loves to play with other
# fmt: off
_UpperCAmelCase : int = [2, 2_6_8, 9_8_6_5, 6_7, 1_1, 1_9_8_8, 5_7_2_5_2, 9_8_6_5, 5, 9_8_4, 6_7, 1_9_8_8, 2_1_3_8_3_8, 1_6_5_8, 5_3, 7_0_4_4_6, 3_3, 6_6_5_7, 2_7_8, 1_5_8_1]
# fmt: on
_UpperCAmelCase : Dict = model.generate(lowerCAmelCase__ , do_sample=lowerCAmelCase__ , num_beams=1 )
if verify_outputs:
self.assertListEqual(output_ids[0].numpy().tolist() , lowerCAmelCase__ )
@slow
def _lowerCAmelCase ( self : List[Any] ) -> str:
"""simple docstring"""
_UpperCAmelCase : List[str] = XGLMTokenizer.from_pretrained("facebook/xglm-564M" )
_UpperCAmelCase : Optional[Any] = TFXGLMForCausalLM.from_pretrained("facebook/xglm-564M" )
tf.random.set_seed(0 )
_UpperCAmelCase : Any = tokenizer("Today is a nice day and" , return_tensors="tf" )
_UpperCAmelCase : int = tokenized.input_ids
# forces the generation to happen on CPU, to avoid GPU-related quirks (and assure same output regardless of the available devices)
with tf.device(":/CPU:0" ):
_UpperCAmelCase : List[Any] = model.generate(lowerCAmelCase__ , do_sample=lowerCAmelCase__ , seed=[7, 0] )
_UpperCAmelCase : Any = tokenizer.decode(output_ids[0] , skip_special_tokens=lowerCAmelCase__ )
_UpperCAmelCase : List[Any] = (
"Today is a nice day and warm evening here over Southern Alberta!! Today when they closed schools due"
)
self.assertEqual(lowerCAmelCase__ , lowerCAmelCase__ )
@slow
def _lowerCAmelCase ( self : Optional[int] ) -> str:
"""simple docstring"""
_UpperCAmelCase : Optional[Any] = TFXGLMForCausalLM.from_pretrained("facebook/xglm-564M" )
_UpperCAmelCase : List[Any] = XGLMTokenizer.from_pretrained("facebook/xglm-564M" )
_UpperCAmelCase : Optional[int] = "left"
# use different length sentences to test batching
_UpperCAmelCase : Tuple = [
"This is an extremelly long sentence that only exists to test the ability of the model to cope with "
"left-padding, such as in batched generation. The output for the sequence below should be the same "
"regardless of whether left padding is applied or not. When",
"Hello, my dog is a little",
]
_UpperCAmelCase : Dict = tokenizer(lowerCAmelCase__ , return_tensors="tf" , padding=lowerCAmelCase__ )
_UpperCAmelCase : List[Any] = inputs["input_ids"]
_UpperCAmelCase : Dict = model.generate(input_ids=lowerCAmelCase__ , attention_mask=inputs["attention_mask"] , max_new_tokens=1_2 )
_UpperCAmelCase : int = tokenizer(sentences[0] , return_tensors="tf" ).input_ids
_UpperCAmelCase : Dict = model.generate(input_ids=lowerCAmelCase__ , max_new_tokens=1_2 )
_UpperCAmelCase : Optional[int] = tokenizer(sentences[1] , return_tensors="tf" ).input_ids
_UpperCAmelCase : List[Any] = model.generate(input_ids=lowerCAmelCase__ , max_new_tokens=1_2 )
_UpperCAmelCase : List[str] = tokenizer.batch_decode(lowerCAmelCase__ , skip_special_tokens=lowerCAmelCase__ )
_UpperCAmelCase : Tuple = tokenizer.decode(output_non_padded[0] , skip_special_tokens=lowerCAmelCase__ )
_UpperCAmelCase : List[str] = tokenizer.decode(output_padded[0] , skip_special_tokens=lowerCAmelCase__ )
_UpperCAmelCase : Union[str, Any] = [
"This is an extremelly long sentence that only exists to test the ability of the model to cope with "
"left-padding, such as in batched generation. The output for the sequence below should be the same "
"regardless of whether left padding is applied or not. When left padding is applied, the sequence will be "
"a single",
"Hello, my dog is a little bit of a shy one, but he is very friendly",
]
self.assertListEqual(lowerCAmelCase__ , lowerCAmelCase__ )
self.assertListEqual(lowerCAmelCase__ , [non_padded_sentence, padded_sentence] )
| 17
| 0
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__a = {
'configuration_convbert': ['CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ConvBertConfig', 'ConvBertOnnxConfig'],
'tokenization_convbert': ['ConvBertTokenizer'],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = ['ConvBertTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = [
'CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
'ConvBertForMaskedLM',
'ConvBertForMultipleChoice',
'ConvBertForQuestionAnswering',
'ConvBertForSequenceClassification',
'ConvBertForTokenClassification',
'ConvBertLayer',
'ConvBertModel',
'ConvBertPreTrainedModel',
'load_tf_weights_in_convbert',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = [
'TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFConvBertForMaskedLM',
'TFConvBertForMultipleChoice',
'TFConvBertForQuestionAnswering',
'TFConvBertForSequenceClassification',
'TFConvBertForTokenClassification',
'TFConvBertLayer',
'TFConvBertModel',
'TFConvBertPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_convbert import CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvBertConfig, ConvBertOnnxConfig
from .tokenization_convbert import ConvBertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_convbert_fast import ConvBertTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_convbert import (
CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
ConvBertForMaskedLM,
ConvBertForMultipleChoice,
ConvBertForQuestionAnswering,
ConvBertForSequenceClassification,
ConvBertForTokenClassification,
ConvBertLayer,
ConvBertModel,
ConvBertPreTrainedModel,
load_tf_weights_in_convbert,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_convbert import (
TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFConvBertForMaskedLM,
TFConvBertForMultipleChoice,
TFConvBertForQuestionAnswering,
TFConvBertForSequenceClassification,
TFConvBertForTokenClassification,
TFConvBertLayer,
TFConvBertModel,
TFConvBertPreTrainedModel,
)
else:
import sys
__a = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 370
|
'''simple docstring'''
import os
import pytest
import yaml
from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
"files", [
["full:README.md", "dataset_infos.json"],
["empty:README.md", "dataset_infos.json"],
["dataset_infos.json"],
["full:README.md"],
], )
def __UpperCAmelCase ( a_: Tuple, a_: Any ):
_UpperCAmelCase : Union[str, Any] = tmp_path_factory.mktemp("dset_infos_dir" )
if "full:README.md" in files:
with open(dataset_infos_dir / "README.md", "w" ) as f:
f.write("---\ndataset_info:\n dataset_size: 42\n---" )
if "empty:README.md" in files:
with open(dataset_infos_dir / "README.md", "w" ) as f:
f.write("" )
# we want to support dataset_infos.json for backward compatibility
if "dataset_infos.json" in files:
with open(dataset_infos_dir / "dataset_infos.json", "w" ) as f:
f.write("{\"default\": {\"dataset_size\": 42}}" )
_UpperCAmelCase : List[str] = DatasetInfosDict.from_directory(a_ )
assert dataset_infos
assert dataset_infos["default"].dataset_size == 42
@pytest.mark.parametrize(
"dataset_info", [
DatasetInfo(),
DatasetInfo(
description="foo", features=Features({"a": Value("int32" )} ), builder_name="builder", config_name="config", version="1.0.0", splits=[{"name": "train"}], download_size=42, ),
], )
def __UpperCAmelCase ( a_: Union[str, Any], a_: DatasetInfo ):
_UpperCAmelCase : Tuple = str(a_ )
dataset_info.write_to_directory(a_ )
_UpperCAmelCase : Any = DatasetInfo.from_directory(a_ )
assert dataset_info == reloaded
assert os.path.exists(os.path.join(a_, "dataset_info.json" ) )
def __UpperCAmelCase ( ):
_UpperCAmelCase : Optional[int] = DatasetInfo(
description="foo", citation="bar", homepage="https://foo.bar", license="CC0", features=Features({"a": Value("int32" )} ), post_processed={}, supervised_keys=(), task_templates=[], builder_name="builder", config_name="config", version="1.0.0", splits=[{"name": "train", "num_examples": 42}], download_checksums={}, download_size=1_337, post_processing_size=442, dataset_size=1_234, size_in_bytes=1_337 + 442 + 1_234, )
_UpperCAmelCase : Tuple = dataset_info._to_yaml_dict()
assert sorted(a_ ) == sorted(DatasetInfo._INCLUDED_INFO_IN_YAML )
for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
assert key in dataset_info_yaml_dict
assert isinstance(dataset_info_yaml_dict[key], (list, dict, int, str) )
_UpperCAmelCase : List[Any] = yaml.safe_dump(a_ )
_UpperCAmelCase : Optional[int] = yaml.safe_load(a_ )
assert dataset_info_yaml_dict == reloaded
def __UpperCAmelCase ( ):
_UpperCAmelCase : str = DatasetInfo()
_UpperCAmelCase : List[str] = dataset_info._to_yaml_dict()
assert dataset_info_yaml_dict == {}
@pytest.mark.parametrize(
"dataset_infos_dict", [
DatasetInfosDict(),
DatasetInfosDict({"default": DatasetInfo()} ),
DatasetInfosDict({"my_config_name": DatasetInfo()} ),
DatasetInfosDict(
{
"default": DatasetInfo(
description="foo", features=Features({"a": Value("int32" )} ), builder_name="builder", config_name="config", version="1.0.0", splits=[{"name": "train"}], download_size=42, )
} ),
DatasetInfosDict(
{
"v1": DatasetInfo(dataset_size=42 ),
"v2": DatasetInfo(dataset_size=1_337 ),
} ),
], )
def __UpperCAmelCase ( a_: str, a_: DatasetInfosDict ):
_UpperCAmelCase : Union[str, Any] = str(a_ )
dataset_infos_dict.write_to_directory(a_ )
_UpperCAmelCase : Union[str, Any] = DatasetInfosDict.from_directory(a_ )
# the config_name of the dataset_infos_dict take over the attribute
for config_name, dataset_info in dataset_infos_dict.items():
_UpperCAmelCase : Optional[int] = config_name
# the yaml representation doesn't include fields like description or citation
# so we just test that we can recover what we can from the yaml
_UpperCAmelCase : List[str] = DatasetInfo._from_yaml_dict(dataset_info._to_yaml_dict() )
assert dataset_infos_dict == reloaded
if dataset_infos_dict:
assert os.path.exists(os.path.join(a_, "README.md" ) )
| 17
| 0
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tensorflow_text_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__a = {
"configuration_bert": ["BERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "BertConfig", "BertOnnxConfig"],
"tokenization_bert": ["BasicTokenizer", "BertTokenizer", "WordpieceTokenizer"],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = ["BertTokenizerFast"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = [
"BERT_PRETRAINED_MODEL_ARCHIVE_LIST",
"BertForMaskedLM",
"BertForMultipleChoice",
"BertForNextSentencePrediction",
"BertForPreTraining",
"BertForQuestionAnswering",
"BertForSequenceClassification",
"BertForTokenClassification",
"BertLayer",
"BertLMHeadModel",
"BertModel",
"BertPreTrainedModel",
"load_tf_weights_in_bert",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = [
"TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFBertEmbeddings",
"TFBertForMaskedLM",
"TFBertForMultipleChoice",
"TFBertForNextSentencePrediction",
"TFBertForPreTraining",
"TFBertForQuestionAnswering",
"TFBertForSequenceClassification",
"TFBertForTokenClassification",
"TFBertLMHeadModel",
"TFBertMainLayer",
"TFBertModel",
"TFBertPreTrainedModel",
]
try:
if not is_tensorflow_text_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = ["TFBertTokenizer"]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = [
"FlaxBertForCausalLM",
"FlaxBertForMaskedLM",
"FlaxBertForMultipleChoice",
"FlaxBertForNextSentencePrediction",
"FlaxBertForPreTraining",
"FlaxBertForQuestionAnswering",
"FlaxBertForSequenceClassification",
"FlaxBertForTokenClassification",
"FlaxBertModel",
"FlaxBertPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_bert import BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, BertConfig, BertOnnxConfig
from .tokenization_bert import BasicTokenizer, BertTokenizer, WordpieceTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_bert_fast import BertTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_bert import (
BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
BertForMaskedLM,
BertForMultipleChoice,
BertForNextSentencePrediction,
BertForPreTraining,
BertForQuestionAnswering,
BertForSequenceClassification,
BertForTokenClassification,
BertLayer,
BertLMHeadModel,
BertModel,
BertPreTrainedModel,
load_tf_weights_in_bert,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_bert import (
TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFBertEmbeddings,
TFBertForMaskedLM,
TFBertForMultipleChoice,
TFBertForNextSentencePrediction,
TFBertForPreTraining,
TFBertForQuestionAnswering,
TFBertForSequenceClassification,
TFBertForTokenClassification,
TFBertLMHeadModel,
TFBertMainLayer,
TFBertModel,
TFBertPreTrainedModel,
)
try:
if not is_tensorflow_text_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_bert_tf import TFBertTokenizer
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_bert import (
FlaxBertForCausalLM,
FlaxBertForMaskedLM,
FlaxBertForMultipleChoice,
FlaxBertForNextSentencePrediction,
FlaxBertForPreTraining,
FlaxBertForQuestionAnswering,
FlaxBertForSequenceClassification,
FlaxBertForTokenClassification,
FlaxBertModel,
FlaxBertPreTrainedModel,
)
else:
import sys
__a = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 371
|
'''simple docstring'''
from math import factorial
def __UpperCAmelCase ( a_: int = 100 ):
return sum(map(a_, str(factorial(a_ ) ) ) )
if __name__ == "__main__":
print(solution(int(input('Enter the Number: ').strip())))
| 17
| 0
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__a = {
'configuration_time_series_transformer': [
'TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'TimeSeriesTransformerConfig',
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = [
'TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
'TimeSeriesTransformerForPrediction',
'TimeSeriesTransformerModel',
'TimeSeriesTransformerPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_time_series_transformer import (
TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
TimeSeriesTransformerConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_time_series_transformer import (
TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
TimeSeriesTransformerForPrediction,
TimeSeriesTransformerModel,
TimeSeriesTransformerPreTrainedModel,
)
else:
import sys
__a = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 350
|
'''simple docstring'''
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
__a = (3, 9, -11, 0, 7, 5, 1, -1)
__a = (4, 6, 2, 0, 8, 10, 3, -2)
@dataclass
class A__ :
"""simple docstring"""
UpperCamelCase_ : int
UpperCamelCase_ : Node | None
class A__ :
"""simple docstring"""
def __init__( self : Dict , lowerCAmelCase__ : Iterable[int] ) -> None:
"""simple docstring"""
_UpperCAmelCase : Node | None = None
for i in sorted(lowerCAmelCase__ , reverse=lowerCAmelCase__ ):
_UpperCAmelCase : str = Node(lowerCAmelCase__ , self.head )
def __iter__( self : int ) -> Iterator[int]:
"""simple docstring"""
_UpperCAmelCase : List[Any] = self.head
while node:
yield node.data
_UpperCAmelCase : List[str] = node.next_node
def __len__( self : Any ) -> int:
"""simple docstring"""
return sum(1 for _ in self )
def __str__( self : Union[str, Any] ) -> str:
"""simple docstring"""
return " -> ".join([str(lowerCAmelCase__ ) for node in self] )
def __UpperCAmelCase ( a_: SortedLinkedList, a_: SortedLinkedList ):
return SortedLinkedList(list(a_ ) + list(a_ ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
__a = SortedLinkedList
print(merge_lists(SSL(test_data_odd), SSL(test_data_even)))
| 17
| 0
|
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaImgaImgPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
enable_full_determinism()
class A__ ( UpperCamelCase , unittest.TestCase ):
"""simple docstring"""
UpperCamelCase_ : Optional[Any] = KandinskyVaaImgaImgPipeline
UpperCamelCase_ : Any = ['''image_embeds''', '''negative_image_embeds''', '''image''']
UpperCamelCase_ : Optional[Any] = [
'''image_embeds''',
'''negative_image_embeds''',
'''image''',
]
UpperCamelCase_ : List[Any] = [
'''generator''',
'''height''',
'''width''',
'''strength''',
'''guidance_scale''',
'''num_inference_steps''',
'''return_dict''',
'''guidance_scale''',
'''num_images_per_prompt''',
'''output_type''',
'''return_dict''',
]
UpperCamelCase_ : Optional[int] = False
@property
def _lowerCAmelCase ( self : Dict ) -> Dict:
"""simple docstring"""
return 3_2
@property
def _lowerCAmelCase ( self : Union[str, Any] ) -> Tuple:
"""simple docstring"""
return 3_2
@property
def _lowerCAmelCase ( self : List[Any] ) -> Union[str, Any]:
"""simple docstring"""
return self.time_input_dim
@property
def _lowerCAmelCase ( self : List[str] ) -> Tuple:
"""simple docstring"""
return self.time_input_dim * 4
@property
def _lowerCAmelCase ( self : Any ) -> int:
"""simple docstring"""
return 1_0_0
@property
def _lowerCAmelCase ( self : Tuple ) -> Optional[int]:
"""simple docstring"""
torch.manual_seed(0 )
_UpperCAmelCase : Any = {
'''in_channels''': 4,
# Out channels is double in channels because predicts mean and variance
'''out_channels''': 8,
'''addition_embed_type''': '''image''',
'''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
'''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
'''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
'''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
'''layers_per_block''': 1,
'''encoder_hid_dim''': self.text_embedder_hidden_size,
'''encoder_hid_dim_type''': '''image_proj''',
'''cross_attention_dim''': self.cross_attention_dim,
'''attention_head_dim''': 4,
'''resnet_time_scale_shift''': '''scale_shift''',
'''class_embed_type''': None,
}
_UpperCAmelCase : str = UNetaDConditionModel(**lowerCAmelCase__ )
return model
@property
def _lowerCAmelCase ( self : Optional[int] ) -> Optional[Any]:
"""simple docstring"""
return {
"block_out_channels": [3_2, 6_4],
"down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
"in_channels": 3,
"latent_channels": 4,
"layers_per_block": 1,
"norm_num_groups": 8,
"norm_type": "spatial",
"num_vq_embeddings": 1_2,
"out_channels": 3,
"up_block_types": [
"AttnUpDecoderBlock2D",
"UpDecoderBlock2D",
],
"vq_embed_dim": 4,
}
@property
def _lowerCAmelCase ( self : List[str] ) -> Union[str, Any]:
"""simple docstring"""
torch.manual_seed(0 )
_UpperCAmelCase : int = VQModel(**self.dummy_movq_kwargs )
return model
def _lowerCAmelCase ( self : Any ) -> List[str]:
"""simple docstring"""
_UpperCAmelCase : Optional[Any] = self.dummy_unet
_UpperCAmelCase : Tuple = self.dummy_movq
_UpperCAmelCase : Optional[int] = {
'''num_train_timesteps''': 1_0_0_0,
'''beta_schedule''': '''linear''',
'''beta_start''': 0.0_0085,
'''beta_end''': 0.012,
'''clip_sample''': False,
'''set_alpha_to_one''': False,
'''steps_offset''': 0,
'''prediction_type''': '''epsilon''',
'''thresholding''': False,
}
_UpperCAmelCase : Optional[int] = DDIMScheduler(**lowerCAmelCase__ )
_UpperCAmelCase : Tuple = {
'''unet''': unet,
'''scheduler''': scheduler,
'''movq''': movq,
}
return components
def _lowerCAmelCase ( self : List[Any] , lowerCAmelCase__ : Optional[int] , lowerCAmelCase__ : str=0 ) -> int:
"""simple docstring"""
_UpperCAmelCase : Optional[int] = floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(lowerCAmelCase__ ) ).to(lowerCAmelCase__ )
_UpperCAmelCase : Tuple = floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(seed + 1 ) ).to(
lowerCAmelCase__ )
# create init_image
_UpperCAmelCase : Dict = floats_tensor((1, 3, 6_4, 6_4) , rng=random.Random(lowerCAmelCase__ ) ).to(lowerCAmelCase__ )
_UpperCAmelCase : Dict = image.cpu().permute(0 , 2 , 3 , 1 )[0]
_UpperCAmelCase : Optional[int] = Image.fromarray(np.uinta(lowerCAmelCase__ ) ).convert("RGB" ).resize((2_5_6, 2_5_6) )
if str(lowerCAmelCase__ ).startswith("mps" ):
_UpperCAmelCase : Tuple = torch.manual_seed(lowerCAmelCase__ )
else:
_UpperCAmelCase : Dict = torch.Generator(device=lowerCAmelCase__ ).manual_seed(lowerCAmelCase__ )
_UpperCAmelCase : List[Any] = {
'''image''': init_image,
'''image_embeds''': image_embeds,
'''negative_image_embeds''': negative_image_embeds,
'''generator''': generator,
'''height''': 6_4,
'''width''': 6_4,
'''num_inference_steps''': 1_0,
'''guidance_scale''': 7.0,
'''strength''': 0.2,
'''output_type''': '''np''',
}
return inputs
def _lowerCAmelCase ( self : int ) -> Tuple:
"""simple docstring"""
_UpperCAmelCase : Optional[int] = '''cpu'''
_UpperCAmelCase : List[Any] = self.get_dummy_components()
_UpperCAmelCase : Any = self.pipeline_class(**lowerCAmelCase__ )
_UpperCAmelCase : Optional[int] = pipe.to(lowerCAmelCase__ )
pipe.set_progress_bar_config(disable=lowerCAmelCase__ )
_UpperCAmelCase : List[Any] = pipe(**self.get_dummy_inputs(lowerCAmelCase__ ) )
_UpperCAmelCase : List[Any] = output.images
_UpperCAmelCase : Dict = pipe(
**self.get_dummy_inputs(lowerCAmelCase__ ) , return_dict=lowerCAmelCase__ , )[0]
_UpperCAmelCase : Optional[int] = image[0, -3:, -3:, -1]
_UpperCAmelCase : List[str] = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 6_4, 6_4, 3)
_UpperCAmelCase : Tuple = np.array(
[0.619_9778, 0.6398_4406, 0.4614_5785, 0.6294_4984, 0.562_2215, 0.4730_6132, 0.4744_1456, 0.460_7606, 0.4871_9263] )
assert (
np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
), F""" expected_slice {expected_slice}, but got {image_slice.flatten()}"""
assert (
np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
), F""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"""
@slow
@require_torch_gpu
class A__ ( unittest.TestCase ):
"""simple docstring"""
def _lowerCAmelCase ( self : Dict ) -> List[Any]:
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _lowerCAmelCase ( self : Tuple ) -> Tuple:
"""simple docstring"""
_UpperCAmelCase : Optional[int] = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/kandinskyv22/kandinskyv22_img2img_frog.npy" )
_UpperCAmelCase : Optional[int] = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png" )
_UpperCAmelCase : List[Any] = '''A red cartoon frog, 4k'''
_UpperCAmelCase : Dict = KandinskyVaaPriorPipeline.from_pretrained(
"kandinsky-community/kandinsky-2-2-prior" , torch_dtype=torch.floataa )
pipe_prior.to(lowerCAmelCase__ )
_UpperCAmelCase : Dict = KandinskyVaaImgaImgPipeline.from_pretrained(
"kandinsky-community/kandinsky-2-2-decoder" , torch_dtype=torch.floataa )
_UpperCAmelCase : str = pipeline.to(lowerCAmelCase__ )
pipeline.set_progress_bar_config(disable=lowerCAmelCase__ )
_UpperCAmelCase : Dict = torch.Generator(device="cpu" ).manual_seed(0 )
_UpperCAmelCase : Tuple = pipe_prior(
lowerCAmelCase__ , generator=lowerCAmelCase__ , num_inference_steps=5 , negative_prompt="" , ).to_tuple()
_UpperCAmelCase : List[Any] = pipeline(
image=lowerCAmelCase__ , image_embeds=lowerCAmelCase__ , negative_image_embeds=lowerCAmelCase__ , generator=lowerCAmelCase__ , num_inference_steps=1_0_0 , height=7_6_8 , width=7_6_8 , strength=0.2 , output_type="np" , )
_UpperCAmelCase : Any = output.images[0]
assert image.shape == (7_6_8, 7_6_8, 3)
assert_mean_pixel_difference(lowerCAmelCase__ , lowerCAmelCase__ )
| 351
|
'''simple docstring'''
def __UpperCAmelCase ( a_: str ):
if not all(char in "01" for char in bin_string ):
raise ValueError("Non-binary value was passed to the function" )
if not bin_string:
raise ValueError("Empty string was passed to the function" )
_UpperCAmelCase : Optional[Any] = ""
while len(a_ ) % 3 != 0:
_UpperCAmelCase : List[Any] = "0" + bin_string
_UpperCAmelCase : Dict = [
bin_string[index : index + 3]
for index in range(len(a_ ) )
if index % 3 == 0
]
for bin_group in bin_string_in_3_list:
_UpperCAmelCase : Optional[Any] = 0
for index, val in enumerate(a_ ):
oct_val += int(2 ** (2 - index) * int(a_ ) )
oct_string += str(a_ )
return oct_string
if __name__ == "__main__":
from doctest import testmod
testmod()
| 17
| 0
|
'''simple docstring'''
from typing import Any
class A__ :
"""simple docstring"""
def __init__( self : List[Any] , lowerCAmelCase__ : Any ) -> str:
"""simple docstring"""
_UpperCAmelCase : Any = data
_UpperCAmelCase : List[str] = None
class A__ :
"""simple docstring"""
def __init__( self : List[str] ) -> Tuple:
"""simple docstring"""
_UpperCAmelCase : Union[str, Any] = None
def _lowerCAmelCase ( self : Optional[Any] ) -> Optional[int]:
"""simple docstring"""
_UpperCAmelCase : Dict = self.head
while temp is not None:
print(temp.data , end=" " )
_UpperCAmelCase : List[Any] = temp.next
print()
def _lowerCAmelCase ( self : str , lowerCAmelCase__ : Any ) -> Union[str, Any]:
"""simple docstring"""
_UpperCAmelCase : Dict = Node(lowerCAmelCase__ )
_UpperCAmelCase : Tuple = self.head
_UpperCAmelCase : Any = new_node
def _lowerCAmelCase ( self : Optional[int] , lowerCAmelCase__ : Optional[int] , lowerCAmelCase__ : Union[str, Any] ) -> Union[str, Any]:
"""simple docstring"""
if node_data_a == node_data_a:
return
else:
_UpperCAmelCase : str = self.head
while node_a is not None and node_a.data != node_data_a:
_UpperCAmelCase : Optional[Any] = node_a.next
_UpperCAmelCase : Optional[int] = self.head
while node_a is not None and node_a.data != node_data_a:
_UpperCAmelCase : List[str] = node_a.next
if node_a is None or node_a is None:
return
_UpperCAmelCase , _UpperCAmelCase : Tuple = node_a.data, node_a.data
if __name__ == "__main__":
__a = LinkedList()
for i in range(5, 0, -1):
ll.push(i)
ll.print_list()
ll.swap_nodes(1, 4)
print('After swapping')
ll.print_list()
| 352
|
'''simple docstring'''
from datetime import datetime
import matplotlib.pyplot as plt
import torch
def __UpperCAmelCase ( a_: str ):
for param in module.parameters():
_UpperCAmelCase : Any = False
def __UpperCAmelCase ( ):
_UpperCAmelCase : Union[str, Any] = "cuda" if torch.cuda.is_available() else "cpu"
if torch.backends.mps.is_available() and torch.backends.mps.is_built():
_UpperCAmelCase : int = "mps"
if device == "mps":
print(
"WARNING: MPS currently doesn't seem to work, and messes up backpropagation without any visible torch"
" errors. I recommend using CUDA on a colab notebook or CPU instead if you're facing inexplicable issues"
" with generations." )
return device
def __UpperCAmelCase ( a_: Optional[Any] ):
_UpperCAmelCase : int = plt.imshow(a_ )
fig.axes.get_xaxis().set_visible(a_ )
fig.axes.get_yaxis().set_visible(a_ )
plt.show()
def __UpperCAmelCase ( ):
_UpperCAmelCase : Dict = datetime.now()
_UpperCAmelCase : List[str] = current_time.strftime("%H:%M:%S" )
return timestamp
| 17
| 0
|
'''simple docstring'''
__a = '''Alexander Joslin'''
import operator as op
from .stack import Stack
def __UpperCAmelCase ( a_: str ):
_UpperCAmelCase : Optional[Any] = {"*": op.mul, "/": op.truediv, "+": op.add, "-": op.sub}
_UpperCAmelCase : Stack[int] = Stack()
_UpperCAmelCase : Stack[str] = Stack()
for i in equation:
if i.isdigit():
# RULE 1
operand_stack.push(int(_lowerCAmelCase ) )
elif i in operators:
# RULE 2
operator_stack.push(_lowerCAmelCase )
elif i == ")":
# RULE 4
_UpperCAmelCase : List[str] = operator_stack.peek()
operator_stack.pop()
_UpperCAmelCase : Dict = operand_stack.peek()
operand_stack.pop()
_UpperCAmelCase : Optional[int] = operand_stack.peek()
operand_stack.pop()
_UpperCAmelCase : int = operators[opr](_lowerCAmelCase, _lowerCAmelCase )
operand_stack.push(_lowerCAmelCase )
# RULE 5
return operand_stack.peek()
if __name__ == "__main__":
__a = '''(5 + ((4 * 2) * (2 + 3)))'''
# answer = 45
print(f'{equation} = {dijkstras_two_stack_algorithm(equation)}')
| 353
|
'''simple docstring'''
import torch
from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class A__ ( UpperCamelCase ):
"""simple docstring"""
UpperCamelCase_ : Optional[int] = (EulerDiscreteScheduler,)
UpperCamelCase_ : Tuple = 10
def _lowerCAmelCase ( self : Dict , **lowerCAmelCase__ : Tuple ) -> Any:
"""simple docstring"""
_UpperCAmelCase : str = {
"num_train_timesteps": 1_1_0_0,
"beta_start": 0.0001,
"beta_end": 0.02,
"beta_schedule": "linear",
}
config.update(**lowerCAmelCase__ )
return config
def _lowerCAmelCase ( self : Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
self.check_over_configs(num_train_timesteps=lowerCAmelCase__ )
def _lowerCAmelCase ( self : Any ) -> List[str]:
"""simple docstring"""
for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001] , [0.0002, 0.002, 0.02] ):
self.check_over_configs(beta_start=lowerCAmelCase__ , beta_end=lowerCAmelCase__ )
def _lowerCAmelCase ( self : List[str] ) -> List[str]:
"""simple docstring"""
for schedule in ["linear", "scaled_linear"]:
self.check_over_configs(beta_schedule=lowerCAmelCase__ )
def _lowerCAmelCase ( self : Union[str, Any] ) -> Optional[Any]:
"""simple docstring"""
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=lowerCAmelCase__ )
def _lowerCAmelCase ( self : List[Any] ) -> List[Any]:
"""simple docstring"""
_UpperCAmelCase : List[str] = self.scheduler_classes[0]
_UpperCAmelCase : int = self.get_scheduler_config()
_UpperCAmelCase : Optional[int] = scheduler_class(**lowerCAmelCase__ )
scheduler.set_timesteps(self.num_inference_steps )
_UpperCAmelCase : int = torch.manual_seed(0 )
_UpperCAmelCase : Any = self.dummy_model()
_UpperCAmelCase : List[str] = self.dummy_sample_deter * scheduler.init_noise_sigma
_UpperCAmelCase : List[Any] = sample.to(lowerCAmelCase__ )
for i, t in enumerate(scheduler.timesteps ):
_UpperCAmelCase : List[str] = scheduler.scale_model_input(lowerCAmelCase__ , lowerCAmelCase__ )
_UpperCAmelCase : int = model(lowerCAmelCase__ , lowerCAmelCase__ )
_UpperCAmelCase : int = scheduler.step(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , generator=lowerCAmelCase__ )
_UpperCAmelCase : Optional[int] = output.prev_sample
_UpperCAmelCase : Optional[Any] = torch.sum(torch.abs(lowerCAmelCase__ ) )
_UpperCAmelCase : Tuple = torch.mean(torch.abs(lowerCAmelCase__ ) )
assert abs(result_sum.item() - 10.0807 ) < 1e-2
assert abs(result_mean.item() - 0.0131 ) < 1e-3
def _lowerCAmelCase ( self : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
_UpperCAmelCase : Any = self.scheduler_classes[0]
_UpperCAmelCase : List[Any] = self.get_scheduler_config(prediction_type="v_prediction" )
_UpperCAmelCase : Any = scheduler_class(**lowerCAmelCase__ )
scheduler.set_timesteps(self.num_inference_steps )
_UpperCAmelCase : str = torch.manual_seed(0 )
_UpperCAmelCase : Optional[Any] = self.dummy_model()
_UpperCAmelCase : Union[str, Any] = self.dummy_sample_deter * scheduler.init_noise_sigma
_UpperCAmelCase : Tuple = sample.to(lowerCAmelCase__ )
for i, t in enumerate(scheduler.timesteps ):
_UpperCAmelCase : Union[str, Any] = scheduler.scale_model_input(lowerCAmelCase__ , lowerCAmelCase__ )
_UpperCAmelCase : int = model(lowerCAmelCase__ , lowerCAmelCase__ )
_UpperCAmelCase : Union[str, Any] = scheduler.step(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , generator=lowerCAmelCase__ )
_UpperCAmelCase : Union[str, Any] = output.prev_sample
_UpperCAmelCase : Tuple = torch.sum(torch.abs(lowerCAmelCase__ ) )
_UpperCAmelCase : Any = torch.mean(torch.abs(lowerCAmelCase__ ) )
assert abs(result_sum.item() - 0.0002 ) < 1e-2
assert abs(result_mean.item() - 2.26_76e-06 ) < 1e-3
def _lowerCAmelCase ( self : Tuple ) -> str:
"""simple docstring"""
_UpperCAmelCase : Optional[int] = self.scheduler_classes[0]
_UpperCAmelCase : List[Any] = self.get_scheduler_config()
_UpperCAmelCase : int = scheduler_class(**lowerCAmelCase__ )
scheduler.set_timesteps(self.num_inference_steps , device=lowerCAmelCase__ )
_UpperCAmelCase : Optional[int] = torch.manual_seed(0 )
_UpperCAmelCase : str = self.dummy_model()
_UpperCAmelCase : Any = self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
_UpperCAmelCase : str = sample.to(lowerCAmelCase__ )
for t in scheduler.timesteps:
_UpperCAmelCase : List[str] = scheduler.scale_model_input(lowerCAmelCase__ , lowerCAmelCase__ )
_UpperCAmelCase : Any = model(lowerCAmelCase__ , lowerCAmelCase__ )
_UpperCAmelCase : Tuple = scheduler.step(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , generator=lowerCAmelCase__ )
_UpperCAmelCase : int = output.prev_sample
_UpperCAmelCase : List[Any] = torch.sum(torch.abs(lowerCAmelCase__ ) )
_UpperCAmelCase : str = torch.mean(torch.abs(lowerCAmelCase__ ) )
assert abs(result_sum.item() - 10.0807 ) < 1e-2
assert abs(result_mean.item() - 0.0131 ) < 1e-3
def _lowerCAmelCase ( self : List[str] ) -> int:
"""simple docstring"""
_UpperCAmelCase : List[Any] = self.scheduler_classes[0]
_UpperCAmelCase : int = self.get_scheduler_config()
_UpperCAmelCase : Union[str, Any] = scheduler_class(**lowerCAmelCase__ , use_karras_sigmas=lowerCAmelCase__ )
scheduler.set_timesteps(self.num_inference_steps , device=lowerCAmelCase__ )
_UpperCAmelCase : Optional[int] = torch.manual_seed(0 )
_UpperCAmelCase : List[str] = self.dummy_model()
_UpperCAmelCase : str = self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
_UpperCAmelCase : Optional[int] = sample.to(lowerCAmelCase__ )
for t in scheduler.timesteps:
_UpperCAmelCase : List[Any] = scheduler.scale_model_input(lowerCAmelCase__ , lowerCAmelCase__ )
_UpperCAmelCase : str = model(lowerCAmelCase__ , lowerCAmelCase__ )
_UpperCAmelCase : Optional[Any] = scheduler.step(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , generator=lowerCAmelCase__ )
_UpperCAmelCase : List[Any] = output.prev_sample
_UpperCAmelCase : List[Any] = torch.sum(torch.abs(lowerCAmelCase__ ) )
_UpperCAmelCase : Optional[Any] = torch.mean(torch.abs(lowerCAmelCase__ ) )
assert abs(result_sum.item() - 124.52_2994_9951_1719 ) < 1e-2
assert abs(result_mean.item() - 0.1_6213_9326_3339_9963 ) < 1e-3
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|
'''simple docstring'''
import argparse
import hashlib
import os
import urllib
import warnings
import torch
from torch import nn
from tqdm import tqdm
from transformers import WhisperConfig, WhisperForConditionalGeneration
__a = {
"tiny.en": "https://openaipublic.azureedge.net/main/whisper/models/d3dd57d32accea0b295c96e26691aa14d8822fac7d9d27d5dc00b4ca2826dd03/tiny.en.pt",
"tiny": "https://openaipublic.azureedge.net/main/whisper/models/65147644a518d12f04e32d6f3b26facc3f8dd46e5390956a9424a650c0ce22b9/tiny.pt",
"base.en": "https://openaipublic.azureedge.net/main/whisper/models/25a8566e1d0c1e2231d1c762132cd20e0f96a85d16145c3a00adf5d1ac670ead/base.en.pt",
"base": "https://openaipublic.azureedge.net/main/whisper/models/ed3a0b6b1c0edf879ad9b11b1af5a0e6ab5db9205f891f668f8b0e6c6326e34e/base.pt",
"small.en": "https://openaipublic.azureedge.net/main/whisper/models/f953ad0fd29cacd07d5a9eda5624af0f6bcf2258be67c92b79389873d91e0872/small.en.pt",
"small": "https://openaipublic.azureedge.net/main/whisper/models/9ecf779972d90ba49c06d968637d720dd632c55bbf19d441fb42bf17a411e794/small.pt",
"medium.en": "https://openaipublic.azureedge.net/main/whisper/models/d7440d1dc186f76616474e0ff0b3b6b879abc9d1a4926b7adfa41db2d497ab4f/medium.en.pt",
"medium": "https://openaipublic.azureedge.net/main/whisper/models/345ae4da62f9b3d59415adc60127b97c714f32e89e936602e85993674d08dcb1/medium.pt",
"large": "https://openaipublic.azureedge.net/main/whisper/models/e4b87e7e0bf463eb8e6956e646f1e277e901512310def2c24bf0e11bd3c28e9a/large.pt",
"large-v2": "https://openaipublic.azureedge.net/main/whisper/models/81f7c96c852ee8fc832187b0132e569d6c3065a3252ed18e56effd0b6a73e524/large-v2.pt",
}
def __UpperCAmelCase ( a_: List[str] ):
_UpperCAmelCase : Tuple = ["layers", "blocks"]
for k in ignore_keys:
state_dict.pop(a_, a_ )
__a = {
"blocks": "layers",
"mlp.0": "fc1",
"mlp.2": "fc2",
"mlp_ln": "final_layer_norm",
".attn.query": ".self_attn.q_proj",
".attn.key": ".self_attn.k_proj",
".attn.value": ".self_attn.v_proj",
".attn_ln": ".self_attn_layer_norm",
".attn.out": ".self_attn.out_proj",
".cross_attn.query": ".encoder_attn.q_proj",
".cross_attn.key": ".encoder_attn.k_proj",
".cross_attn.value": ".encoder_attn.v_proj",
".cross_attn_ln": ".encoder_attn_layer_norm",
".cross_attn.out": ".encoder_attn.out_proj",
"decoder.ln.": "decoder.layer_norm.",
"encoder.ln.": "encoder.layer_norm.",
"token_embedding": "embed_tokens",
"encoder.positional_embedding": "encoder.embed_positions.weight",
"decoder.positional_embedding": "decoder.embed_positions.weight",
"ln_post": "layer_norm",
}
def __UpperCAmelCase ( a_: str ):
_UpperCAmelCase : List[Any] = list(s_dict.keys() )
for key in keys:
_UpperCAmelCase : str = key
for k, v in WHISPER_MAPPING.items():
if k in key:
_UpperCAmelCase : Optional[Any] = new_key.replace(a_, a_ )
print(f"""{key} -> {new_key}""" )
_UpperCAmelCase : List[str] = s_dict.pop(a_ )
return s_dict
def __UpperCAmelCase ( a_: Union[str, Any] ):
_UpperCAmelCase , _UpperCAmelCase : str = emb.weight.shape
_UpperCAmelCase : Tuple = nn.Linear(a_, a_, bias=a_ )
_UpperCAmelCase : Optional[int] = emb.weight.data
return lin_layer
def __UpperCAmelCase ( a_: str, a_: str ):
os.makedirs(a_, exist_ok=a_ )
_UpperCAmelCase : Dict = os.path.basename(a_ )
_UpperCAmelCase : Tuple = url.split("/" )[-2]
_UpperCAmelCase : Optional[int] = os.path.join(a_, a_ )
if os.path.exists(a_ ) and not os.path.isfile(a_ ):
raise RuntimeError(f"""{download_target} exists and is not a regular file""" )
if os.path.isfile(a_ ):
_UpperCAmelCase : str = open(a_, "rb" ).read()
if hashlib.shaaaa(a_ ).hexdigest() == expected_shaaaa:
return model_bytes
else:
warnings.warn(f"""{download_target} exists, but the SHA256 checksum does not match; re-downloading the file""" )
with urllib.request.urlopen(a_ ) as source, open(a_, "wb" ) as output:
with tqdm(
total=int(source.info().get("Content-Length" ) ), ncols=80, unit="iB", unit_scale=a_, unit_divisor=1_024 ) as loop:
while True:
_UpperCAmelCase : int = source.read(8_192 )
if not buffer:
break
output.write(a_ )
loop.update(len(a_ ) )
_UpperCAmelCase : Any = open(a_, "rb" ).read()
if hashlib.shaaaa(a_ ).hexdigest() != expected_shaaaa:
raise RuntimeError(
"Model has been downloaded but the SHA256 checksum does not not match. Please retry loading the model." )
return model_bytes
def __UpperCAmelCase ( a_: List[str], a_: Union[str, Any] ):
if ".pt" not in checkpoint_path:
_UpperCAmelCase : Tuple = _download(_MODELS[checkpoint_path] )
else:
_UpperCAmelCase : Tuple = torch.load(a_, map_location="cpu" )
_UpperCAmelCase : List[Any] = original_checkpoint["dims"]
_UpperCAmelCase : Any = original_checkpoint["model_state_dict"]
_UpperCAmelCase : Dict = state_dict["decoder.token_embedding.weight"]
remove_ignore_keys_(a_ )
rename_keys(a_ )
_UpperCAmelCase : str = True
_UpperCAmelCase : Tuple = state_dict["decoder.layers.0.fc1.weight"].shape[0]
_UpperCAmelCase : str = WhisperConfig(
vocab_size=dimensions["n_vocab"], encoder_ffn_dim=a_, decoder_ffn_dim=a_, num_mel_bins=dimensions["n_mels"], d_model=dimensions["n_audio_state"], max_target_positions=dimensions["n_text_ctx"], encoder_layers=dimensions["n_audio_layer"], encoder_attention_heads=dimensions["n_audio_head"], decoder_layers=dimensions["n_text_layer"], decoder_attention_heads=dimensions["n_text_state"], max_source_positions=dimensions["n_audio_ctx"], )
_UpperCAmelCase : int = WhisperForConditionalGeneration(a_ )
_UpperCAmelCase , _UpperCAmelCase : Dict = model.model.load_state_dict(a_, strict=a_ )
if len(a_ ) > 0 and not set(a_ ) <= {
"encoder.embed_positions.weights",
"decoder.embed_positions.weights",
}:
raise ValueError(
"Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights` are allowed to be missing,"
f""" but all the following weights are missing {missing}""" )
if tie_embeds:
_UpperCAmelCase : str = make_linear_from_emb(model.model.decoder.embed_tokens )
else:
_UpperCAmelCase : Tuple = proj_out_weights
model.save_pretrained(a_ )
if __name__ == "__main__":
__a = argparse.ArgumentParser()
# # Required parameters
parser.add_argument('--checkpoint_path', type=str, help='Patht to the downloaded checkpoints')
parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
__a = parser.parse_args()
convert_openai_whisper_to_tfms(args.checkpoint_path, args.pytorch_dump_folder_path)
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|
'''simple docstring'''
def __UpperCAmelCase ( a_: int, a_: int ):
if a < 0 or b < 0:
raise ValueError("the value of both inputs must be positive" )
_UpperCAmelCase : List[str] = str(bin(a_ ) )[2:] # remove the leading "0b"
_UpperCAmelCase : Any = str(bin(a_ ) )[2:] # remove the leading "0b"
_UpperCAmelCase : Dict = max(len(a_ ), len(a_ ) )
return "0b" + "".join(
str(int(char_a == "1" and char_b == "1" ) )
for char_a, char_b in zip(a_binary.zfill(a_ ), b_binary.zfill(a_ ) ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
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|
'''simple docstring'''
import os
from pathlib import Path
import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader
from transformers import AutoTokenizer
from transformers.models.mbart.modeling_mbart import shift_tokens_right
from transformers.testing_utils import TestCasePlus, slow
from utils import FAIRSEQ_AVAILABLE, DistributedSortishSampler, LegacySeqaSeqDataset, SeqaSeqDataset
__a = 'bert-base-cased'
__a = 'google/pegasus-xsum'
__a = [' Sam ate lunch today.', 'Sams lunch ingredients.']
__a = ['A very interesting story about what I ate for lunch.', 'Avocado, celery, turkey, coffee']
__a = 'patrickvonplaten/t5-tiny-random'
__a = 'sshleifer/bart-tiny-random'
__a = 'sshleifer/tiny-mbart'
__a = 'sshleifer/tiny-marian-en-de'
def __UpperCAmelCase ( a_: Optional[int], a_: List[str] ):
_UpperCAmelCase : Optional[Any] = "\n".join(__UpperCAmelCase )
Path(__UpperCAmelCase ).open("w" ).writelines(__UpperCAmelCase )
def __UpperCAmelCase ( a_: Dict ):
for split in ["train", "val", "test"]:
_dump_articles(os.path.join(__UpperCAmelCase, f"""{split}.source""" ), __UpperCAmelCase )
_dump_articles(os.path.join(__UpperCAmelCase, f"""{split}.target""" ), __UpperCAmelCase )
return tmp_dir
class A__ ( _lowerCamelCase ):
"""simple docstring"""
@parameterized.expand(
[
MBART_TINY,
MARIAN_TINY,
T5_TINY,
BART_TINY,
PEGASUS_XSUM,
] , )
@slow
def _lowerCAmelCase ( self : int , lowerCAmelCase__ : Optional[Any] ) -> Optional[int]:
"""simple docstring"""
_UpperCAmelCase : List[Any] = AutoTokenizer.from_pretrained(lowercase_ )
_UpperCAmelCase : Dict = make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() )
_UpperCAmelCase : List[Any] = max(len(tokenizer.encode(lowercase_ ) ) for a in ARTICLES )
_UpperCAmelCase : Any = max(len(tokenizer.encode(lowercase_ ) ) for a in SUMMARIES )
_UpperCAmelCase : Dict = 4
_UpperCAmelCase : int = 8
assert max_len_target > max_src_len # Will be truncated
assert max_len_source > max_src_len # Will be truncated
_UpperCAmelCase , _UpperCAmelCase : List[str] = "ro_RO", "de_DE" # ignored for all but mbart, but never causes error.
_UpperCAmelCase : List[Any] = SeqaSeqDataset(
lowercase_ , data_dir=lowercase_ , type_path="train" , max_source_length=lowercase_ , max_target_length=lowercase_ , src_lang=lowercase_ , tgt_lang=lowercase_ , )
_UpperCAmelCase : Dict = DataLoader(lowercase_ , batch_size=2 , collate_fn=train_dataset.collate_fn )
for batch in dataloader:
assert isinstance(lowercase_ , lowercase_ )
assert batch["attention_mask"].shape == batch["input_ids"].shape
# show that articles were trimmed.
assert batch["input_ids"].shape[1] == max_src_len
# show that targets are the same len
assert batch["labels"].shape[1] == max_tgt_len
if tok_name != MBART_TINY:
continue
# check language codes in correct place
_UpperCAmelCase : Union[str, Any] = shift_tokens_right(batch["labels"] , tokenizer.pad_token_id )
assert batch["decoder_input_ids"][0, 0].item() == tokenizer.lang_code_to_id[tgt_lang]
assert batch["decoder_input_ids"][0, -1].item() == tokenizer.eos_token_id
assert batch["input_ids"][0, -2].item() == tokenizer.eos_token_id
assert batch["input_ids"][0, -1].item() == tokenizer.lang_code_to_id[src_lang]
break # No need to test every batch
@parameterized.expand([BART_TINY, BERT_BASE_CASED] )
def _lowerCAmelCase ( self : Union[str, Any] , lowerCAmelCase__ : Dict ) -> List[Any]:
"""simple docstring"""
_UpperCAmelCase : Optional[int] = AutoTokenizer.from_pretrained(lowercase_ )
_UpperCAmelCase : Dict = make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() )
_UpperCAmelCase : Optional[int] = max(len(tokenizer.encode(lowercase_ ) ) for a in ARTICLES )
_UpperCAmelCase : Any = max(len(tokenizer.encode(lowercase_ ) ) for a in SUMMARIES )
_UpperCAmelCase : Optional[int] = 4
_UpperCAmelCase : int = LegacySeqaSeqDataset(
lowercase_ , data_dir=lowercase_ , type_path="train" , max_source_length=2_0 , max_target_length=lowercase_ , )
_UpperCAmelCase : Optional[int] = DataLoader(lowercase_ , batch_size=2 , collate_fn=train_dataset.collate_fn )
for batch in dataloader:
assert batch["attention_mask"].shape == batch["input_ids"].shape
# show that articles were trimmed.
assert batch["input_ids"].shape[1] == max_len_source
assert 2_0 >= batch["input_ids"].shape[1] # trimmed significantly
# show that targets were truncated
assert batch["labels"].shape[1] == trunc_target # Truncated
assert max_len_target > trunc_target # Truncated
break # No need to test every batch
def _lowerCAmelCase ( self : Any ) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase : int = AutoTokenizer.from_pretrained("facebook/mbart-large-cc25" )
_UpperCAmelCase : Any = Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() ) )
_UpperCAmelCase : Union[str, Any] = tmp_dir.joinpath("train.source" ).open().readlines()
_UpperCAmelCase : Optional[Any] = Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() ) )
pack_data_dir(lowercase_ , lowercase_ , 1_2_8 , lowercase_ )
_UpperCAmelCase : Tuple = {x.name for x in tmp_dir.iterdir()}
_UpperCAmelCase : List[str] = {x.name for x in save_dir.iterdir()}
_UpperCAmelCase : Dict = save_dir.joinpath("train.source" ).open().readlines()
# orig: [' Sam ate lunch today.\n', 'Sams lunch ingredients.']
# desired_packed: [' Sam ate lunch today.\n Sams lunch ingredients.']
assert len(lowercase_ ) < len(lowercase_ )
assert len(lowercase_ ) == 1
assert len(packed_examples[0] ) == sum(len(lowercase_ ) for x in orig_examples )
assert orig_paths == new_paths
@pytest.mark.skipif(not FAIRSEQ_AVAILABLE , reason="This test requires fairseq" )
def _lowerCAmelCase ( self : Any ) -> Optional[int]:
"""simple docstring"""
if not FAIRSEQ_AVAILABLE:
return
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase : int = self._get_dataset(max_len=6_4 )
_UpperCAmelCase : Tuple = 6_4
_UpperCAmelCase : List[str] = ds.make_dynamic_sampler(lowercase_ , required_batch_size_multiple=lowercase_ )
_UpperCAmelCase : Tuple = [len(lowercase_ ) for x in batch_sampler]
assert len(set(lowercase_ ) ) > 1 # it's not dynamic batch size if every batch is the same length
assert sum(lowercase_ ) == len(lowercase_ ) # no dropped or added examples
_UpperCAmelCase : List[Any] = DataLoader(lowercase_ , batch_sampler=lowercase_ , collate_fn=ds.collate_fn , num_workers=2 )
_UpperCAmelCase : Optional[Any] = []
_UpperCAmelCase : Optional[int] = []
for batch in data_loader:
_UpperCAmelCase : List[Any] = batch["input_ids"].shape
_UpperCAmelCase : Tuple = src_shape[0]
assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
_UpperCAmelCase : Union[str, Any] = np.product(batch["input_ids"].shape )
num_src_per_batch.append(lowercase_ )
if num_src_tokens > (max_tokens * 1.1):
failures.append(lowercase_ )
assert num_src_per_batch[0] == max(lowercase_ )
if failures:
raise AssertionError(F"""too many tokens in {len(lowercase_ )} batches""" )
def _lowerCAmelCase ( self : List[str] ) -> List[str]:
"""simple docstring"""
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase : Union[str, Any] = self._get_dataset(max_len=5_1_2 )
_UpperCAmelCase : Union[str, Any] = 2
_UpperCAmelCase : Optional[int] = ds.make_sortish_sampler(lowercase_ , shuffle=lowercase_ )
_UpperCAmelCase : Tuple = DataLoader(lowercase_ , batch_size=lowercase_ , collate_fn=ds.collate_fn , num_workers=2 )
_UpperCAmelCase : List[Any] = DataLoader(lowercase_ , batch_size=lowercase_ , collate_fn=ds.collate_fn , num_workers=2 , sampler=lowercase_ )
_UpperCAmelCase : Any = tokenizer.pad_token_id
def count_pad_tokens(lowerCAmelCase__ : Any , lowerCAmelCase__ : int="input_ids" ):
return [batch[k].eq(lowercase_ ).sum().item() for batch in data_loader]
assert sum(count_pad_tokens(lowercase_ , k="labels" ) ) < sum(count_pad_tokens(lowercase_ , k="labels" ) )
assert sum(count_pad_tokens(lowercase_ ) ) < sum(count_pad_tokens(lowercase_ ) )
assert len(lowercase_ ) == len(lowercase_ )
def _lowerCAmelCase ( self : List[str] , lowerCAmelCase__ : Tuple=1_0_0_0 , lowerCAmelCase__ : Optional[Any]=1_2_8 ) -> List[str]:
"""simple docstring"""
if os.getenv("USE_REAL_DATA" , lowercase_ ):
_UpperCAmelCase : List[str] = "examples/seq2seq/wmt_en_ro"
_UpperCAmelCase : Any = max_len * 2 * 6_4
if not Path(lowercase_ ).joinpath("train.len" ).exists():
save_len_file(lowercase_ , lowercase_ )
else:
_UpperCAmelCase : Any = "examples/seq2seq/test_data/wmt_en_ro"
_UpperCAmelCase : Union[str, Any] = max_len * 4
save_len_file(lowercase_ , lowercase_ )
_UpperCAmelCase : Optional[int] = AutoTokenizer.from_pretrained(lowercase_ )
_UpperCAmelCase : Any = SeqaSeqDataset(
lowercase_ , data_dir=lowercase_ , type_path="train" , max_source_length=lowercase_ , max_target_length=lowercase_ , n_obs=lowercase_ , )
return ds, max_tokens, tokenizer
def _lowerCAmelCase ( self : Any ) -> int:
"""simple docstring"""
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase : Tuple = self._get_dataset()
_UpperCAmelCase : List[str] = set(DistributedSortishSampler(lowercase_ , 2_5_6 , num_replicas=2 , rank=0 , add_extra_examples=lowercase_ ) )
_UpperCAmelCase : List[str] = set(DistributedSortishSampler(lowercase_ , 2_5_6 , num_replicas=2 , rank=1 , add_extra_examples=lowercase_ ) )
assert idsa.intersection(lowercase_ ) == set()
@parameterized.expand(
[
MBART_TINY,
MARIAN_TINY,
T5_TINY,
BART_TINY,
PEGASUS_XSUM,
] , )
def _lowerCAmelCase ( self : List[str] , lowerCAmelCase__ : Optional[Any] ) -> List[str]:
"""simple docstring"""
_UpperCAmelCase : Tuple = AutoTokenizer.from_pretrained(lowercase_ , use_fast=lowercase_ )
if tok_name == MBART_TINY:
_UpperCAmelCase : List[str] = SeqaSeqDataset(
lowercase_ , data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() ) , type_path="train" , max_source_length=4 , max_target_length=8 , src_lang="EN" , tgt_lang="FR" , )
_UpperCAmelCase : List[Any] = train_dataset.dataset_kwargs
assert "src_lang" in kwargs and "tgt_lang" in kwargs
else:
_UpperCAmelCase : Optional[Any] = SeqaSeqDataset(
lowercase_ , data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() ) , type_path="train" , max_source_length=4 , max_target_length=8 , )
_UpperCAmelCase : List[str] = train_dataset.dataset_kwargs
assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
assert len(lowercase_ ) == 1 if tok_name == BART_TINY else len(lowercase_ ) == 0
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|
'''simple docstring'''
from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def __UpperCAmelCase ( a_: int ):
# A local function to see if a dot lands in the circle.
def is_in_circle(a_: float, a_: float ) -> bool:
_UpperCAmelCase : Optional[Any] = sqrt((x**2) + (y**2) )
# Our circle has a radius of 1, so a distance
# greater than 1 would land outside the circle.
return distance_from_centre <= 1
# The proportion of guesses that landed in the circle
_UpperCAmelCase : str = mean(
int(is_in_circle(uniform(-1.0, 1.0 ), uniform(-1.0, 1.0 ) ) )
for _ in range(a_ ) )
# The ratio of the area for circle to square is pi/4.
_UpperCAmelCase : Optional[int] = proportion * 4
print(f"""The estimated value of pi is {pi_estimate}""" )
print(f"""The numpy value of pi is {pi}""" )
print(f"""The total error is {abs(pi - pi_estimate )}""" )
def __UpperCAmelCase ( a_: int, a_: Callable[[float], float], a_: float = 0.0, a_: float = 1.0, ):
return mean(
function_to_integrate(uniform(a_, a_ ) ) for _ in range(a_ ) ) * (max_value - min_value)
def __UpperCAmelCase ( a_: int, a_: float = 0.0, a_: float = 1.0 ):
def identity_function(a_: float ) -> float:
return x
_UpperCAmelCase : Union[str, Any] = area_under_curve_estimator(
a_, a_, a_, a_ )
_UpperCAmelCase : List[str] = (max_value * max_value - min_value * min_value) / 2
print("******************" )
print(f"""Estimating area under y=x where x varies from {min_value} to {max_value}""" )
print(f"""Estimated value is {estimated_value}""" )
print(f"""Expected value is {expected_value}""" )
print(f"""Total error is {abs(estimated_value - expected_value )}""" )
print("******************" )
def __UpperCAmelCase ( a_: int ):
def function_to_integrate(a_: float ) -> float:
return sqrt(4.0 - x * x )
_UpperCAmelCase : List[str] = area_under_curve_estimator(
a_, a_, 0.0, 2.0 )
print("******************" )
print("Estimating pi using area_under_curve_estimator" )
print(f"""Estimated value is {estimated_value}""" )
print(f"""Expected value is {pi}""" )
print(f"""Total error is {abs(estimated_value - pi )}""" )
print("******************" )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 17
| 0
|
'''simple docstring'''
from __future__ import annotations
import math
from collections import Counter
from string import ascii_lowercase
def __UpperCAmelCase ( a_: str ):
_UpperCAmelCase , _UpperCAmelCase : Dict = analyze_text(lowercase_ )
_UpperCAmelCase : List[str] = list(" " + ascii_lowercase )
# what is our total sum of probabilities.
_UpperCAmelCase : List[Any] = sum(single_char_strings.values() )
# one length string
_UpperCAmelCase : List[str] = 0
# for each alpha we go in our dict and if it is in it we calculate entropy
for ch in my_alphas:
if ch in single_char_strings:
_UpperCAmelCase : Union[str, Any] = single_char_strings[ch]
_UpperCAmelCase : List[Any] = my_str / all_sum
my_fir_sum += prob * math.loga(lowercase_ ) # entropy formula.
# print entropy
print(f"""{round(-1 * my_fir_sum ):.1f}""" )
# two len string
_UpperCAmelCase : Optional[Any] = sum(two_char_strings.values() )
_UpperCAmelCase : Dict = 0
# for each alpha (two in size) calculate entropy.
for cha in my_alphas:
for cha in my_alphas:
_UpperCAmelCase : Optional[int] = cha + cha
if sequence in two_char_strings:
_UpperCAmelCase : List[Any] = two_char_strings[sequence]
_UpperCAmelCase : List[str] = int(lowercase_ ) / all_sum
my_sec_sum += prob * math.loga(lowercase_ )
# print second entropy
print(f"""{round(-1 * my_sec_sum ):.1f}""" )
# print the difference between them
print(f"""{round((-1 * my_sec_sum) - (-1 * my_fir_sum) ):.1f}""" )
def __UpperCAmelCase ( a_: str ):
_UpperCAmelCase : Dict = Counter() # type: ignore
_UpperCAmelCase : Optional[Any] = Counter() # type: ignore
single_char_strings[text[-1]] += 1
# first case when we have space at start.
two_char_strings[" " + text[0]] += 1
for i in range(0, len(lowercase_ ) - 1 ):
single_char_strings[text[i]] += 1
two_char_strings[text[i : i + 2]] += 1
return single_char_strings, two_char_strings
def __UpperCAmelCase ( ):
import doctest
doctest.testmod()
# text = (
# "Had repulsive dashwoods suspicion sincerity but advantage now him. Remark "
# "easily garret nor nay. Civil those mrs enjoy shy fat merry. You greatest "
# "jointure saw horrible. He private he on be imagine suppose. Fertile "
# "beloved evident through no service elderly is. Blind there if every no so "
# "at. Own neglected you preferred way sincerity delivered his attempted. To "
# "of message cottage windows do besides against uncivil. Delightful "
# "unreserved impossible few estimating men favourable see entreaties. She "
# "propriety immediate was improving. He or entrance humoured likewise "
# "moderate. Much nor game son say feel. Fat make met can must form into "
# "gate. Me we offending prevailed discovery. "
# )
# calculate_prob(text)
if __name__ == "__main__":
main()
| 356
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
__a = {
'configuration_layoutlmv2': ['LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LayoutLMv2Config'],
'processing_layoutlmv2': ['LayoutLMv2Processor'],
'tokenization_layoutlmv2': ['LayoutLMv2Tokenizer'],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = ['LayoutLMv2TokenizerFast']
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = ['LayoutLMv2FeatureExtractor']
__a = ['LayoutLMv2ImageProcessor']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = [
'LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST',
'LayoutLMv2ForQuestionAnswering',
'LayoutLMv2ForSequenceClassification',
'LayoutLMv2ForTokenClassification',
'LayoutLMv2Layer',
'LayoutLMv2Model',
'LayoutLMv2PreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_layoutlmva import LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMvaConfig
from .processing_layoutlmva import LayoutLMvaProcessor
from .tokenization_layoutlmva import LayoutLMvaTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor, LayoutLMvaImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_layoutlmva import (
LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST,
LayoutLMvaForQuestionAnswering,
LayoutLMvaForSequenceClassification,
LayoutLMvaForTokenClassification,
LayoutLMvaLayer,
LayoutLMvaModel,
LayoutLMvaPreTrainedModel,
)
else:
import sys
__a = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 17
| 0
|
'''simple docstring'''
from collections import OrderedDict
from typing import List, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__a = logging.get_logger(__name__)
__a = {
'''google/efficientnet-b7''': '''https://huggingface.co/google/efficientnet-b7/resolve/main/config.json''',
}
class A__ ( a_ ):
"""simple docstring"""
UpperCamelCase_ : Any = '''efficientnet'''
def __init__( self : Union[str, Any] , lowerCAmelCase__ : int = 3 , lowerCAmelCase__ : int = 6_0_0 , lowerCAmelCase__ : float = 2.0 , lowerCAmelCase__ : float = 3.1 , lowerCAmelCase__ : int = 8 , lowerCAmelCase__ : List[int] = [3, 3, 5, 3, 5, 5, 3] , lowerCAmelCase__ : List[int] = [3_2, 1_6, 2_4, 4_0, 8_0, 1_1_2, 1_9_2] , lowerCAmelCase__ : List[int] = [1_6, 2_4, 4_0, 8_0, 1_1_2, 1_9_2, 3_2_0] , lowerCAmelCase__ : List[int] = [] , lowerCAmelCase__ : List[int] = [1, 2, 2, 2, 1, 2, 1] , lowerCAmelCase__ : List[int] = [1, 2, 2, 3, 3, 4, 1] , lowerCAmelCase__ : List[int] = [1, 6, 6, 6, 6, 6, 6] , lowerCAmelCase__ : float = 0.25 , lowerCAmelCase__ : str = "swish" , lowerCAmelCase__ : int = 2_5_6_0 , lowerCAmelCase__ : str = "mean" , lowerCAmelCase__ : float = 0.02 , lowerCAmelCase__ : float = 0.001 , lowerCAmelCase__ : float = 0.99 , lowerCAmelCase__ : float = 0.5 , lowerCAmelCase__ : float = 0.2 , **lowerCAmelCase__ : str , ) -> List[Any]:
"""simple docstring"""
super().__init__(**lowercase_ )
_UpperCAmelCase : Tuple = num_channels
_UpperCAmelCase : str = image_size
_UpperCAmelCase : Union[str, Any] = width_coefficient
_UpperCAmelCase : Any = depth_coefficient
_UpperCAmelCase : Optional[int] = depth_divisor
_UpperCAmelCase : Union[str, Any] = kernel_sizes
_UpperCAmelCase : int = in_channels
_UpperCAmelCase : int = out_channels
_UpperCAmelCase : Tuple = depthwise_padding
_UpperCAmelCase : Dict = strides
_UpperCAmelCase : int = num_block_repeats
_UpperCAmelCase : str = expand_ratios
_UpperCAmelCase : Union[str, Any] = squeeze_expansion_ratio
_UpperCAmelCase : List[Any] = hidden_act
_UpperCAmelCase : Any = hidden_dim
_UpperCAmelCase : List[Any] = pooling_type
_UpperCAmelCase : Any = initializer_range
_UpperCAmelCase : Tuple = batch_norm_eps
_UpperCAmelCase : Tuple = batch_norm_momentum
_UpperCAmelCase : str = dropout_rate
_UpperCAmelCase : str = drop_connect_rate
_UpperCAmelCase : Optional[Any] = sum(lowercase_ ) * 4
class A__ ( a_ ):
"""simple docstring"""
UpperCamelCase_ : Any = version.parse('''1.11''' )
@property
def _lowerCAmelCase ( self : Dict ) -> Union[str, Any]:
"""simple docstring"""
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
] )
@property
def _lowerCAmelCase ( self : Optional[int] ) -> int:
"""simple docstring"""
return 1e-5
| 357
|
'''simple docstring'''
def __UpperCAmelCase ( a_: int, a_: int ):
if not isinstance(a_, a_ ):
raise ValueError("iterations must be defined as integers" )
if not isinstance(a_, a_ ) or not number >= 1:
raise ValueError(
"starting number must be\n and integer and be more than 0" )
if not iterations >= 1:
raise ValueError("Iterations must be done more than 0 times to play FizzBuzz" )
_UpperCAmelCase : List[str] = ""
while number <= iterations:
if number % 3 == 0:
out += "Fizz"
if number % 5 == 0:
out += "Buzz"
if 0 not in (number % 3, number % 5):
out += str(a_ )
# print(out)
number += 1
out += " "
return out
if __name__ == "__main__":
import doctest
doctest.testmod()
| 17
| 0
|
'''simple docstring'''
from typing import Any
class A__ :
"""simple docstring"""
def __init__( self : Tuple , lowerCAmelCase__ : Any ) -> List[Any]:
"""simple docstring"""
_UpperCAmelCase : Tuple = data
_UpperCAmelCase : Tuple = None
def __repr__( self : List[Any] ) -> str:
"""simple docstring"""
return F"""Node({self.data})"""
class A__ :
"""simple docstring"""
def __init__( self : Tuple ) -> List[Any]:
"""simple docstring"""
_UpperCAmelCase : str = None
def __iter__( self : Optional[int] ) -> Any:
"""simple docstring"""
_UpperCAmelCase : Optional[int] = self.head
while node:
yield node.data
_UpperCAmelCase : int = node.next
def __len__( self : Optional[int] ) -> int:
"""simple docstring"""
return sum(1 for _ in self )
def __repr__( self : Union[str, Any] ) -> str:
"""simple docstring"""
return "->".join([str(__A ) for item in self] )
def __getitem__( self : List[Any] , lowerCAmelCase__ : int ) -> Any:
"""simple docstring"""
if not 0 <= index < len(self ):
raise ValueError("list index out of range." )
for i, node in enumerate(self ):
if i == index:
return node
return None
def __setitem__( self : Dict , lowerCAmelCase__ : int , lowerCAmelCase__ : Any ) -> None:
"""simple docstring"""
if not 0 <= index < len(self ):
raise ValueError("list index out of range." )
_UpperCAmelCase : Optional[int] = self.head
for _ in range(__A ):
_UpperCAmelCase : List[str] = current.next
_UpperCAmelCase : Optional[int] = data
def _lowerCAmelCase ( self : int , lowerCAmelCase__ : Any ) -> None:
"""simple docstring"""
self.insert_nth(len(self ) , __A )
def _lowerCAmelCase ( self : Dict , lowerCAmelCase__ : Any ) -> None:
"""simple docstring"""
self.insert_nth(0 , __A )
def _lowerCAmelCase ( self : Tuple , lowerCAmelCase__ : int , lowerCAmelCase__ : Any ) -> None:
"""simple docstring"""
if not 0 <= index <= len(self ):
raise IndexError("list index out of range" )
_UpperCAmelCase : Optional[Any] = Node(__A )
if self.head is None:
_UpperCAmelCase : str = new_node
elif index == 0:
_UpperCAmelCase : List[Any] = self.head # link new_node to head
_UpperCAmelCase : Dict = new_node
else:
_UpperCAmelCase : Union[str, Any] = self.head
for _ in range(index - 1 ):
_UpperCAmelCase : Tuple = temp.next
_UpperCAmelCase : List[str] = temp.next
_UpperCAmelCase : List[str] = new_node
def _lowerCAmelCase ( self : Union[str, Any] ) -> None: # print every node data
"""simple docstring"""
print(self )
def _lowerCAmelCase ( self : Dict ) -> Any:
"""simple docstring"""
return self.delete_nth(0 )
def _lowerCAmelCase ( self : Dict ) -> Any: # delete from tail
"""simple docstring"""
return self.delete_nth(len(self ) - 1 )
def _lowerCAmelCase ( self : Optional[int] , lowerCAmelCase__ : int = 0 ) -> Any:
"""simple docstring"""
if not 0 <= index <= len(self ) - 1: # test if index is valid
raise IndexError("List index out of range." )
_UpperCAmelCase : List[Any] = self.head # default first node
if index == 0:
_UpperCAmelCase : List[str] = self.head.next
else:
_UpperCAmelCase : int = self.head
for _ in range(index - 1 ):
_UpperCAmelCase : Tuple = temp.next
_UpperCAmelCase : Optional[int] = temp.next
_UpperCAmelCase : Optional[Any] = temp.next.next
return delete_node.data
def _lowerCAmelCase ( self : Tuple ) -> bool:
"""simple docstring"""
return self.head is None
def _lowerCAmelCase ( self : Union[str, Any] ) -> None:
"""simple docstring"""
_UpperCAmelCase : Union[str, Any] = None
_UpperCAmelCase : str = self.head
while current:
# Store the current node's next node.
_UpperCAmelCase : List[Any] = current.next
# Make the current node's next point backwards
_UpperCAmelCase : Optional[int] = prev
# Make the previous node be the current node
_UpperCAmelCase : Any = current
# Make the current node the next node (to progress iteration)
_UpperCAmelCase : Dict = next_node
# Return prev in order to put the head at the end
_UpperCAmelCase : Tuple = prev
def __UpperCAmelCase ( ):
_UpperCAmelCase : Optional[Any] = LinkedList()
assert linked_list.is_empty() is True
assert str(_lowercase ) == ""
try:
linked_list.delete_head()
raise AssertionError # This should not happen.
except IndexError:
assert True # This should happen.
try:
linked_list.delete_tail()
raise AssertionError # This should not happen.
except IndexError:
assert True # This should happen.
for i in range(10 ):
assert len(_lowercase ) == i
linked_list.insert_nth(_lowercase, i + 1 )
assert str(_lowercase ) == "->".join(str(_lowercase ) for i in range(1, 11 ) )
linked_list.insert_head(0 )
linked_list.insert_tail(11 )
assert str(_lowercase ) == "->".join(str(_lowercase ) for i in range(0, 12 ) )
assert linked_list.delete_head() == 0
assert linked_list.delete_nth(9 ) == 10
assert linked_list.delete_tail() == 11
assert len(_lowercase ) == 9
assert str(_lowercase ) == "->".join(str(_lowercase ) for i in range(1, 10 ) )
assert all(linked_list[i] == i + 1 for i in range(0, 9 ) ) is True
for i in range(0, 9 ):
_UpperCAmelCase : int = -i
assert all(linked_list[i] == -i for i in range(0, 9 ) ) is True
linked_list.reverse()
assert str(_lowercase ) == "->".join(str(_lowercase ) for i in range(-8, 1 ) )
def __UpperCAmelCase ( ):
_UpperCAmelCase : List[Any] = [
-9,
100,
Node(77_345_112 ),
"dlrow olleH",
7,
5_555,
0,
-192.55_555,
"Hello, world!",
77.9,
Node(10 ),
None,
None,
12.20,
]
_UpperCAmelCase : Dict = LinkedList()
for i in test_input:
linked_list.insert_tail(_lowercase )
# Check if it's empty or not
assert linked_list.is_empty() is False
assert (
str(_lowercase ) == "-9->100->Node(77345112)->dlrow olleH->7->5555->0->"
"-192.55555->Hello, world!->77.9->Node(10)->None->None->12.2"
)
# Delete the head
_UpperCAmelCase : Union[str, Any] = linked_list.delete_head()
assert result == -9
assert (
str(_lowercase ) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
"Hello, world!->77.9->Node(10)->None->None->12.2"
)
# Delete the tail
_UpperCAmelCase : List[str] = linked_list.delete_tail()
assert result == 12.2
assert (
str(_lowercase ) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
"Hello, world!->77.9->Node(10)->None->None"
)
# Delete a node in specific location in linked list
_UpperCAmelCase : Optional[int] = linked_list.delete_nth(10 )
assert result is None
assert (
str(_lowercase ) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
"Hello, world!->77.9->Node(10)->None"
)
# Add a Node instance to its head
linked_list.insert_head(Node("Hello again, world!" ) )
assert (
str(_lowercase )
== "Node(Hello again, world!)->100->Node(77345112)->dlrow olleH->"
"7->5555->0->-192.55555->Hello, world!->77.9->Node(10)->None"
)
# Add None to its tail
linked_list.insert_tail(_lowercase )
assert (
str(_lowercase )
== "Node(Hello again, world!)->100->Node(77345112)->dlrow olleH->"
"7->5555->0->-192.55555->Hello, world!->77.9->Node(10)->None->None"
)
# Reverse the linked list
linked_list.reverse()
assert (
str(_lowercase )
== "None->None->Node(10)->77.9->Hello, world!->-192.55555->0->5555->"
"7->dlrow olleH->Node(77345112)->100->Node(Hello again, world!)"
)
def __UpperCAmelCase ( ):
from doctest import testmod
testmod()
_UpperCAmelCase : Optional[int] = LinkedList()
linked_list.insert_head(input("Inserting 1st at head " ).strip() )
linked_list.insert_head(input("Inserting 2nd at head " ).strip() )
print("\nPrint list:" )
linked_list.print_list()
linked_list.insert_tail(input("\nInserting 1st at tail " ).strip() )
linked_list.insert_tail(input("Inserting 2nd at tail " ).strip() )
print("\nPrint list:" )
linked_list.print_list()
print("\nDelete head" )
linked_list.delete_head()
print("Delete tail" )
linked_list.delete_tail()
print("\nPrint list:" )
linked_list.print_list()
print("\nReverse linked list" )
linked_list.reverse()
print("\nPrint list:" )
linked_list.print_list()
print("\nString representation of linked list:" )
print(_lowercase )
print("\nReading/changing Node data using indexing:" )
print(f"""Element at Position 1: {linked_list[1]}""" )
_UpperCAmelCase : int = input("Enter New Value: " ).strip()
print("New list:" )
print(_lowercase )
print(f"""length of linked_list is : {len(_lowercase )}""" )
if __name__ == "__main__":
main()
| 358
|
'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from itertools import chain
from typing import Optional, Union
import datasets
import numpy as np
import torch
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,
AutoTokenizer,
HfArgumentParser,
Trainer,
TrainingArguments,
default_data_collator,
set_seed,
)
from transformers.tokenization_utils_base import PreTrainedTokenizerBase
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import PaddingStrategy, check_min_version, send_example_telemetry
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('4.31.0')
__a = logging.getLogger(__name__)
@dataclass
class A__ :
"""simple docstring"""
UpperCamelCase_ : str = field(
metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''} )
UpperCamelCase_ : Optional[str] = field(
default=UpperCamelCase , metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''} )
UpperCamelCase_ : Optional[str] = field(
default=UpperCamelCase , metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''} )
UpperCamelCase_ : Optional[str] = field(
default=UpperCamelCase , metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''} , )
UpperCamelCase_ : bool = field(
default=UpperCamelCase , metadata={'''help''': '''Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.'''} , )
UpperCamelCase_ : str = field(
default='''main''' , metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''} , )
UpperCamelCase_ : bool = field(
default=UpperCamelCase , metadata={
'''help''': (
'''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
'''with private models).'''
)
} , )
@dataclass
class A__ :
"""simple docstring"""
UpperCamelCase_ : Optional[str] = field(default=UpperCamelCase , metadata={'''help''': '''The input training data file (a text file).'''} )
UpperCamelCase_ : Optional[str] = field(
default=UpperCamelCase , metadata={'''help''': '''An optional input evaluation data file to evaluate the perplexity on (a text file).'''} , )
UpperCamelCase_ : bool = field(
default=UpperCamelCase , metadata={'''help''': '''Overwrite the cached training and evaluation sets'''} )
UpperCamelCase_ : Optional[int] = field(
default=UpperCamelCase , metadata={'''help''': '''The number of processes to use for the preprocessing.'''} , )
UpperCamelCase_ : Optional[int] = field(
default=UpperCamelCase , metadata={
'''help''': (
'''The maximum total input sequence length after tokenization. If passed, sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
)
} , )
UpperCamelCase_ : bool = field(
default=UpperCamelCase , metadata={
'''help''': (
'''Whether to pad all samples to the maximum sentence length. '''
'''If False, will pad the samples dynamically when batching to the maximum length in the batch. More '''
'''efficient on GPU but very bad for TPU.'''
)
} , )
UpperCamelCase_ : Optional[int] = field(
default=UpperCamelCase , metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of training examples to this '''
'''value if set.'''
)
} , )
UpperCamelCase_ : Optional[int] = field(
default=UpperCamelCase , metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
'''value if set.'''
)
} , )
def _lowerCAmelCase ( self : Any ) -> Any:
"""simple docstring"""
if self.train_file is not None:
_UpperCAmelCase : List[Any] = self.train_file.split("." )[-1]
assert extension in ["csv", "json"], "`train_file` should be a csv or a json file."
if self.validation_file is not None:
_UpperCAmelCase : List[str] = self.validation_file.split("." )[-1]
assert extension in ["csv", "json"], "`validation_file` should be a csv or a json file."
@dataclass
class A__ :
"""simple docstring"""
UpperCamelCase_ : PreTrainedTokenizerBase
UpperCamelCase_ : Union[bool, str, PaddingStrategy] = True
UpperCamelCase_ : Optional[int] = None
UpperCamelCase_ : Optional[int] = None
def __call__( self : List[Any] , lowerCAmelCase__ : List[str] ) -> List[str]:
"""simple docstring"""
_UpperCAmelCase : int = "label" if "label" in features[0].keys() else "labels"
_UpperCAmelCase : Dict = [feature.pop(lowerCAmelCase__ ) for feature in features]
_UpperCAmelCase : str = len(lowerCAmelCase__ )
_UpperCAmelCase : int = len(features[0]["input_ids"] )
_UpperCAmelCase : str = [
[{k: v[i] for k, v in feature.items()} for i in range(lowerCAmelCase__ )] for feature in features
]
_UpperCAmelCase : List[str] = list(chain(*lowerCAmelCase__ ) )
_UpperCAmelCase : Any = self.tokenizer.pad(
lowerCAmelCase__ , padding=self.padding , max_length=self.max_length , pad_to_multiple_of=self.pad_to_multiple_of , return_tensors="pt" , )
# Un-flatten
_UpperCAmelCase : Any = {k: v.view(lowerCAmelCase__ , lowerCAmelCase__ , -1 ) for k, v in batch.items()}
# Add back labels
_UpperCAmelCase : List[str] = torch.tensor(lowerCAmelCase__ , dtype=torch.intaa )
return batch
def __UpperCAmelCase ( ):
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
_UpperCAmelCase : Union[str, Any] = 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.
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase : str = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase : Dict = parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry("run_swag", a_, a_ )
# 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 )], )
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
_UpperCAmelCase : Optional[int] = training_args.get_process_log_level()
logger.setLevel(a_ )
datasets.utils.logging.set_verbosity(a_ )
transformers.utils.logging.set_verbosity(a_ )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# 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}""" )
logger.info(f"""Training/evaluation parameters {training_args}""" )
# Detecting last checkpoint.
_UpperCAmelCase : Any = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
_UpperCAmelCase : Any = 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 and training_args.resume_from_checkpoint is 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." )
# 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.train_file is not None or data_args.validation_file is not None:
_UpperCAmelCase : Union[str, Any] = {}
if data_args.train_file is not None:
_UpperCAmelCase : str = data_args.train_file
if data_args.validation_file is not None:
_UpperCAmelCase : Optional[Any] = data_args.validation_file
_UpperCAmelCase : Dict = data_args.train_file.split("." )[-1]
_UpperCAmelCase : Optional[int] = load_dataset(
a_, data_files=a_, cache_dir=model_args.cache_dir, use_auth_token=True if model_args.use_auth_token else None, )
else:
# Downloading and loading the swag dataset from the hub.
_UpperCAmelCase : Dict = load_dataset(
"swag", "regular", cache_dir=model_args.cache_dir, use_auth_token=True if model_args.use_auth_token else None, )
# 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.
_UpperCAmelCase : Any = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path, cache_dir=model_args.cache_dir, revision=model_args.model_revision, use_auth_token=True if model_args.use_auth_token else None, )
_UpperCAmelCase : Any = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path, 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, )
_UpperCAmelCase : str = AutoModelForMultipleChoice.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, )
# When using your own dataset or a different dataset from swag, you will probably need to change this.
_UpperCAmelCase : Optional[Any] = [f"""ending{i}""" for i in range(4 )]
_UpperCAmelCase : List[Any] = "sent1"
_UpperCAmelCase : Optional[int] = "sent2"
if data_args.max_seq_length is None:
_UpperCAmelCase : List[str] = tokenizer.model_max_length
if max_seq_length > 1_024:
logger.warning(
"The chosen tokenizer supports a `model_max_length` that is longer than the default `block_size` value"
" of 1024. If you would like to use a longer `block_size` up to `tokenizer.model_max_length` you can"
" override this default with `--block_size xxx`." )
_UpperCAmelCase : Dict = 1_024
else:
if data_args.max_seq_length > tokenizer.model_max_length:
logger.warning(
f"""The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the"""
f"""model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}.""" )
_UpperCAmelCase : Dict = min(data_args.max_seq_length, tokenizer.model_max_length )
# Preprocessing the datasets.
def preprocess_function(a_: Union[str, Any] ):
_UpperCAmelCase : Optional[int] = [[context] * 4 for context in examples[context_name]]
_UpperCAmelCase : Tuple = examples[question_header_name]
_UpperCAmelCase : Optional[Any] = [
[f"""{header} {examples[end][i]}""" for end in ending_names] for i, header in enumerate(a_ )
]
# Flatten out
_UpperCAmelCase : List[str] = list(chain(*a_ ) )
_UpperCAmelCase : Dict = list(chain(*a_ ) )
# Tokenize
_UpperCAmelCase : List[Any] = tokenizer(
a_, a_, truncation=a_, max_length=a_, padding="max_length" if data_args.pad_to_max_length else False, )
# Un-flatten
return {k: [v[i : i + 4] for i in range(0, len(a_ ), 4 )] for k, v in tokenized_examples.items()}
if training_args.do_train:
if "train" not in raw_datasets:
raise ValueError("--do_train requires a train dataset" )
_UpperCAmelCase : int = raw_datasets["train"]
if data_args.max_train_samples is not None:
_UpperCAmelCase : Optional[Any] = min(len(a_ ), data_args.max_train_samples )
_UpperCAmelCase : List[Any] = train_dataset.select(range(a_ ) )
with training_args.main_process_first(desc="train dataset map pre-processing" ):
_UpperCAmelCase : Union[str, Any] = train_dataset.map(
a_, batched=a_, num_proc=data_args.preprocessing_num_workers, load_from_cache_file=not data_args.overwrite_cache, )
if training_args.do_eval:
if "validation" not in raw_datasets:
raise ValueError("--do_eval requires a validation dataset" )
_UpperCAmelCase : Dict = raw_datasets["validation"]
if data_args.max_eval_samples is not None:
_UpperCAmelCase : int = min(len(a_ ), data_args.max_eval_samples )
_UpperCAmelCase : List[str] = eval_dataset.select(range(a_ ) )
with training_args.main_process_first(desc="validation dataset map pre-processing" ):
_UpperCAmelCase : Optional[int] = eval_dataset.map(
a_, batched=a_, num_proc=data_args.preprocessing_num_workers, load_from_cache_file=not data_args.overwrite_cache, )
# Data collator
_UpperCAmelCase : Tuple = (
default_data_collator
if data_args.pad_to_max_length
else DataCollatorForMultipleChoice(tokenizer=a_, pad_to_multiple_of=8 if training_args.fpaa else None )
)
# Metric
def compute_metrics(a_: Tuple ):
_UpperCAmelCase , _UpperCAmelCase : Tuple = eval_predictions
_UpperCAmelCase : Union[str, Any] = np.argmax(a_, axis=1 )
return {"accuracy": (preds == label_ids).astype(np.floataa ).mean().item()}
# Initialize our Trainer
_UpperCAmelCase : Any = Trainer(
model=a_, args=a_, train_dataset=train_dataset if training_args.do_train else None, eval_dataset=eval_dataset if training_args.do_eval else None, tokenizer=a_, data_collator=a_, compute_metrics=a_, )
# Training
if training_args.do_train:
_UpperCAmelCase : Optional[Any] = None
if training_args.resume_from_checkpoint is not None:
_UpperCAmelCase : List[Any] = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
_UpperCAmelCase : List[str] = last_checkpoint
_UpperCAmelCase : Any = trainer.train(resume_from_checkpoint=a_ )
trainer.save_model() # Saves the tokenizer too for easy upload
_UpperCAmelCase : str = train_result.metrics
_UpperCAmelCase : List[str] = (
data_args.max_train_samples if data_args.max_train_samples is not None else len(a_ )
)
_UpperCAmelCase : Union[str, Any] = min(a_, len(a_ ) )
trainer.log_metrics("train", a_ )
trainer.save_metrics("train", a_ )
trainer.save_state()
# Evaluation
if training_args.do_eval:
logger.info("*** Evaluate ***" )
_UpperCAmelCase : List[Any] = trainer.evaluate()
_UpperCAmelCase : int = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(a_ )
_UpperCAmelCase : Tuple = min(a_, len(a_ ) )
trainer.log_metrics("eval", a_ )
trainer.save_metrics("eval", a_ )
_UpperCAmelCase : int = {
"finetuned_from": model_args.model_name_or_path,
"tasks": "multiple-choice",
"dataset_tags": "swag",
"dataset_args": "regular",
"dataset": "SWAG",
"language": "en",
}
if training_args.push_to_hub:
trainer.push_to_hub(**a_ )
else:
trainer.create_model_card(**a_ )
def __UpperCAmelCase ( a_: int ):
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 17
| 0
|
'''simple docstring'''
import unittest
from transformers import XLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
XLMForMultipleChoice,
XLMForQuestionAnswering,
XLMForQuestionAnsweringSimple,
XLMForSequenceClassification,
XLMForTokenClassification,
XLMModel,
XLMWithLMHeadModel,
)
from transformers.models.xlm.modeling_xlm import XLM_PRETRAINED_MODEL_ARCHIVE_LIST
class A__ :
"""simple docstring"""
def __init__( self : Dict , lowerCAmelCase__ : Optional[Any] , lowerCAmelCase__ : List[str]=1_3 , lowerCAmelCase__ : int=7 , lowerCAmelCase__ : Optional[int]=True , lowerCAmelCase__ : str=True , lowerCAmelCase__ : List[str]=True , lowerCAmelCase__ : Tuple=True , lowerCAmelCase__ : int=True , lowerCAmelCase__ : Optional[Any]=False , lowerCAmelCase__ : Dict=False , lowerCAmelCase__ : List[Any]=False , lowerCAmelCase__ : str=2 , lowerCAmelCase__ : int=9_9 , lowerCAmelCase__ : List[str]=0 , lowerCAmelCase__ : int=3_2 , lowerCAmelCase__ : Dict=5 , lowerCAmelCase__ : Optional[Any]=4 , lowerCAmelCase__ : Optional[Any]=0.1 , lowerCAmelCase__ : int=0.1 , lowerCAmelCase__ : Any=5_1_2 , lowerCAmelCase__ : Optional[Any]=2 , lowerCAmelCase__ : str=0.02 , lowerCAmelCase__ : str=2 , lowerCAmelCase__ : Optional[int]=4 , lowerCAmelCase__ : Optional[Any]="last" , lowerCAmelCase__ : Optional[int]=True , lowerCAmelCase__ : List[Any]=None , lowerCAmelCase__ : Optional[Any]=0 , ) -> Optional[int]:
"""simple docstring"""
_UpperCAmelCase : Optional[int] = parent
_UpperCAmelCase : List[str] = batch_size
_UpperCAmelCase : str = seq_length
_UpperCAmelCase : List[Any] = is_training
_UpperCAmelCase : Optional[int] = use_input_lengths
_UpperCAmelCase : List[Any] = use_token_type_ids
_UpperCAmelCase : List[Any] = use_labels
_UpperCAmelCase : List[Any] = gelu_activation
_UpperCAmelCase : Union[str, Any] = sinusoidal_embeddings
_UpperCAmelCase : List[Any] = causal
_UpperCAmelCase : List[Any] = asm
_UpperCAmelCase : Union[str, Any] = n_langs
_UpperCAmelCase : Optional[int] = vocab_size
_UpperCAmelCase : Dict = n_special
_UpperCAmelCase : List[str] = hidden_size
_UpperCAmelCase : str = num_hidden_layers
_UpperCAmelCase : Tuple = num_attention_heads
_UpperCAmelCase : Union[str, Any] = hidden_dropout_prob
_UpperCAmelCase : Tuple = attention_probs_dropout_prob
_UpperCAmelCase : Any = max_position_embeddings
_UpperCAmelCase : Optional[Any] = type_sequence_label_size
_UpperCAmelCase : Optional[int] = initializer_range
_UpperCAmelCase : List[Any] = num_labels
_UpperCAmelCase : Any = num_choices
_UpperCAmelCase : Any = summary_type
_UpperCAmelCase : Union[str, Any] = use_proj
_UpperCAmelCase : int = scope
_UpperCAmelCase : Optional[Any] = bos_token_id
def _lowerCAmelCase ( self : Optional[int] ) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase : str = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_UpperCAmelCase : List[Any] = random_attention_mask([self.batch_size, self.seq_length] )
_UpperCAmelCase : str = None
if self.use_input_lengths:
_UpperCAmelCase : str = (
ids_tensor([self.batch_size] , vocab_size=2 ) + self.seq_length - 2
) # small variation of seq_length
_UpperCAmelCase : int = None
if self.use_token_type_ids:
_UpperCAmelCase : Optional[Any] = ids_tensor([self.batch_size, self.seq_length] , self.n_langs )
_UpperCAmelCase : Any = None
_UpperCAmelCase : Optional[Any] = None
_UpperCAmelCase : List[str] = None
if self.use_labels:
_UpperCAmelCase : List[Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_UpperCAmelCase : Tuple = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
_UpperCAmelCase : Any = ids_tensor([self.batch_size] , 2 ).float()
_UpperCAmelCase : Any = ids_tensor([self.batch_size] , self.num_choices )
_UpperCAmelCase : int = self.get_config()
return (
config,
input_ids,
token_type_ids,
input_lengths,
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
)
def _lowerCAmelCase ( self : List[str] ) -> Optional[Any]:
"""simple docstring"""
return XLMConfig(
vocab_size=self.vocab_size , n_special=self.n_special , emb_dim=self.hidden_size , n_layers=self.num_hidden_layers , n_heads=self.num_attention_heads , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , gelu_activation=self.gelu_activation , sinusoidal_embeddings=self.sinusoidal_embeddings , asm=self.asm , causal=self.causal , n_langs=self.n_langs , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , summary_type=self.summary_type , use_proj=self.use_proj , num_labels=self.num_labels , bos_token_id=self.bos_token_id , )
def _lowerCAmelCase ( self : Optional[int] , lowerCAmelCase__ : Tuple , lowerCAmelCase__ : int , lowerCAmelCase__ : Tuple , lowerCAmelCase__ : Optional[Any] , lowerCAmelCase__ : List[Any] , lowerCAmelCase__ : Optional[int] , lowerCAmelCase__ : int , lowerCAmelCase__ : Tuple , lowerCAmelCase__ : List[str] , ) -> Any:
"""simple docstring"""
_UpperCAmelCase : Union[str, Any] = XLMModel(config=_SCREAMING_SNAKE_CASE )
model.to(_SCREAMING_SNAKE_CASE )
model.eval()
_UpperCAmelCase : int = model(_SCREAMING_SNAKE_CASE , lengths=_SCREAMING_SNAKE_CASE , langs=_SCREAMING_SNAKE_CASE )
_UpperCAmelCase : Optional[Any] = model(_SCREAMING_SNAKE_CASE , langs=_SCREAMING_SNAKE_CASE )
_UpperCAmelCase : int = model(_SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def _lowerCAmelCase ( self : str , lowerCAmelCase__ : Optional[Any] , lowerCAmelCase__ : List[Any] , lowerCAmelCase__ : int , lowerCAmelCase__ : List[Any] , lowerCAmelCase__ : Dict , lowerCAmelCase__ : str , lowerCAmelCase__ : Tuple , lowerCAmelCase__ : Dict , lowerCAmelCase__ : List[Any] , ) -> Dict:
"""simple docstring"""
_UpperCAmelCase : Dict = XLMWithLMHeadModel(_SCREAMING_SNAKE_CASE )
model.to(_SCREAMING_SNAKE_CASE )
model.eval()
_UpperCAmelCase : str = model(_SCREAMING_SNAKE_CASE , token_type_ids=_SCREAMING_SNAKE_CASE , labels=_SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def _lowerCAmelCase ( self : List[str] , lowerCAmelCase__ : Union[str, Any] , lowerCAmelCase__ : List[Any] , lowerCAmelCase__ : Tuple , lowerCAmelCase__ : Dict , lowerCAmelCase__ : Union[str, Any] , lowerCAmelCase__ : Union[str, Any] , lowerCAmelCase__ : int , lowerCAmelCase__ : List[str] , lowerCAmelCase__ : Tuple , ) -> str:
"""simple docstring"""
_UpperCAmelCase : List[Any] = XLMForQuestionAnsweringSimple(_SCREAMING_SNAKE_CASE )
model.to(_SCREAMING_SNAKE_CASE )
model.eval()
_UpperCAmelCase : Optional[int] = model(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase : Optional[int] = model(_SCREAMING_SNAKE_CASE , start_positions=_SCREAMING_SNAKE_CASE , end_positions=_SCREAMING_SNAKE_CASE )
_UpperCAmelCase : Tuple = outputs
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def _lowerCAmelCase ( self : Tuple , lowerCAmelCase__ : List[str] , lowerCAmelCase__ : Dict , lowerCAmelCase__ : Any , lowerCAmelCase__ : Union[str, Any] , lowerCAmelCase__ : Any , lowerCAmelCase__ : Dict , lowerCAmelCase__ : List[Any] , lowerCAmelCase__ : int , lowerCAmelCase__ : Any , ) -> Optional[int]:
"""simple docstring"""
_UpperCAmelCase : Tuple = XLMForQuestionAnswering(_SCREAMING_SNAKE_CASE )
model.to(_SCREAMING_SNAKE_CASE )
model.eval()
_UpperCAmelCase : int = model(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase : Any = model(
_SCREAMING_SNAKE_CASE , start_positions=_SCREAMING_SNAKE_CASE , end_positions=_SCREAMING_SNAKE_CASE , cls_index=_SCREAMING_SNAKE_CASE , is_impossible=_SCREAMING_SNAKE_CASE , p_mask=_SCREAMING_SNAKE_CASE , )
_UpperCAmelCase : List[str] = model(
_SCREAMING_SNAKE_CASE , start_positions=_SCREAMING_SNAKE_CASE , end_positions=_SCREAMING_SNAKE_CASE , cls_index=_SCREAMING_SNAKE_CASE , is_impossible=_SCREAMING_SNAKE_CASE , )
(_UpperCAmelCase ) : Optional[Any] = result_with_labels.to_tuple()
_UpperCAmelCase : Dict = model(_SCREAMING_SNAKE_CASE , start_positions=_SCREAMING_SNAKE_CASE , end_positions=_SCREAMING_SNAKE_CASE )
(_UpperCAmelCase ) : Dict = result_with_labels.to_tuple()
self.parent.assertEqual(result_with_labels.loss.shape , () )
self.parent.assertEqual(result.start_top_log_probs.shape , (self.batch_size, model.config.start_n_top) )
self.parent.assertEqual(result.start_top_index.shape , (self.batch_size, model.config.start_n_top) )
self.parent.assertEqual(
result.end_top_log_probs.shape , (self.batch_size, model.config.start_n_top * model.config.end_n_top) )
self.parent.assertEqual(
result.end_top_index.shape , (self.batch_size, model.config.start_n_top * model.config.end_n_top) )
self.parent.assertEqual(result.cls_logits.shape , (self.batch_size,) )
def _lowerCAmelCase ( self : Optional[Any] , lowerCAmelCase__ : Tuple , lowerCAmelCase__ : Optional[int] , lowerCAmelCase__ : List[Any] , lowerCAmelCase__ : List[Any] , lowerCAmelCase__ : Optional[int] , lowerCAmelCase__ : Tuple , lowerCAmelCase__ : Optional[Any] , lowerCAmelCase__ : str , lowerCAmelCase__ : int , ) -> Optional[int]:
"""simple docstring"""
_UpperCAmelCase : str = XLMForSequenceClassification(_SCREAMING_SNAKE_CASE )
model.to(_SCREAMING_SNAKE_CASE )
model.eval()
_UpperCAmelCase : List[str] = model(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase : int = model(_SCREAMING_SNAKE_CASE , labels=_SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def _lowerCAmelCase ( self : Union[str, Any] , lowerCAmelCase__ : Tuple , lowerCAmelCase__ : Union[str, Any] , lowerCAmelCase__ : Optional[int] , lowerCAmelCase__ : Tuple , lowerCAmelCase__ : List[str] , lowerCAmelCase__ : Tuple , lowerCAmelCase__ : Optional[Any] , lowerCAmelCase__ : Dict , lowerCAmelCase__ : int , ) -> List[str]:
"""simple docstring"""
_UpperCAmelCase : Dict = self.num_labels
_UpperCAmelCase : Any = XLMForTokenClassification(_SCREAMING_SNAKE_CASE )
model.to(_SCREAMING_SNAKE_CASE )
model.eval()
_UpperCAmelCase : Union[str, Any] = model(_SCREAMING_SNAKE_CASE , attention_mask=_SCREAMING_SNAKE_CASE , labels=_SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def _lowerCAmelCase ( self : Dict , lowerCAmelCase__ : List[Any] , lowerCAmelCase__ : Optional[Any] , lowerCAmelCase__ : int , lowerCAmelCase__ : str , lowerCAmelCase__ : Any , lowerCAmelCase__ : List[str] , lowerCAmelCase__ : List[str] , lowerCAmelCase__ : Optional[int] , lowerCAmelCase__ : Any , ) -> Union[str, Any]:
"""simple docstring"""
_UpperCAmelCase : Optional[Any] = self.num_choices
_UpperCAmelCase : Tuple = XLMForMultipleChoice(config=_SCREAMING_SNAKE_CASE )
model.to(_SCREAMING_SNAKE_CASE )
model.eval()
_UpperCAmelCase : Tuple = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
_UpperCAmelCase : Tuple = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
_UpperCAmelCase : Tuple = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
_UpperCAmelCase : Any = model(
_SCREAMING_SNAKE_CASE , attention_mask=_SCREAMING_SNAKE_CASE , token_type_ids=_SCREAMING_SNAKE_CASE , labels=_SCREAMING_SNAKE_CASE , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def _lowerCAmelCase ( self : Union[str, Any] ) -> List[str]:
"""simple docstring"""
_UpperCAmelCase : Optional[int] = self.prepare_config_and_inputs()
(
_UpperCAmelCase
) : Any = config_and_inputs
_UpperCAmelCase : Union[str, Any] = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """lengths""": input_lengths}
return config, inputs_dict
@require_torch
class A__ ( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , unittest.TestCase ):
"""simple docstring"""
UpperCamelCase_ : Union[str, Any] = (
(
XLMModel,
XLMWithLMHeadModel,
XLMForQuestionAnswering,
XLMForSequenceClassification,
XLMForQuestionAnsweringSimple,
XLMForTokenClassification,
XLMForMultipleChoice,
)
if is_torch_available()
else ()
)
UpperCamelCase_ : Optional[int] = (
(XLMWithLMHeadModel,) if is_torch_available() else ()
) # TODO (PVP): Check other models whether language generation is also applicable
UpperCamelCase_ : Dict = (
{
'feature-extraction': XLMModel,
'fill-mask': XLMWithLMHeadModel,
'question-answering': XLMForQuestionAnsweringSimple,
'text-classification': XLMForSequenceClassification,
'text-generation': XLMWithLMHeadModel,
'token-classification': XLMForTokenClassification,
'zero-shot': XLMForSequenceClassification,
}
if is_torch_available()
else {}
)
def _lowerCAmelCase ( self : Optional[Any] , lowerCAmelCase__ : Optional[int] , lowerCAmelCase__ : int , lowerCAmelCase__ : int , lowerCAmelCase__ : List[str] , lowerCAmelCase__ : Optional[Any] ) -> int:
"""simple docstring"""
if (
pipeline_test_casse_name == "QAPipelineTests"
and tokenizer_name is not None
and not tokenizer_name.endswith("Fast" )
):
# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
return True
return False
def _lowerCAmelCase ( self : List[Any] , lowerCAmelCase__ : int , lowerCAmelCase__ : Optional[int] , lowerCAmelCase__ : Tuple=False ) -> Dict:
"""simple docstring"""
_UpperCAmelCase : Optional[int] = super()._prepare_for_class(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , return_labels=_SCREAMING_SNAKE_CASE )
if return_labels:
if model_class.__name__ == "XLMForQuestionAnswering":
_UpperCAmelCase : Union[str, Any] = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=_SCREAMING_SNAKE_CASE )
_UpperCAmelCase : Optional[Any] = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=_SCREAMING_SNAKE_CASE )
return inputs_dict
def _lowerCAmelCase ( self : Union[str, Any] ) -> List[str]:
"""simple docstring"""
_UpperCAmelCase : Union[str, Any] = XLMModelTester(self )
_UpperCAmelCase : Dict = ConfigTester(self , config_class=_SCREAMING_SNAKE_CASE , emb_dim=3_7 )
def _lowerCAmelCase ( self : Tuple ) -> int:
"""simple docstring"""
self.config_tester.run_common_tests()
def _lowerCAmelCase ( self : Optional[Any] ) -> List[str]:
"""simple docstring"""
_UpperCAmelCase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_model(*_SCREAMING_SNAKE_CASE )
def _lowerCAmelCase ( self : str ) -> Optional[int]:
"""simple docstring"""
_UpperCAmelCase : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_lm_head(*_SCREAMING_SNAKE_CASE )
def _lowerCAmelCase ( self : List[Any] ) -> int:
"""simple docstring"""
_UpperCAmelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_simple_qa(*_SCREAMING_SNAKE_CASE )
def _lowerCAmelCase ( self : Optional[Any] ) -> List[Any]:
"""simple docstring"""
_UpperCAmelCase : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_qa(*_SCREAMING_SNAKE_CASE )
def _lowerCAmelCase ( self : Tuple ) -> List[Any]:
"""simple docstring"""
_UpperCAmelCase : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_sequence_classif(*_SCREAMING_SNAKE_CASE )
def _lowerCAmelCase ( self : Tuple ) -> Optional[int]:
"""simple docstring"""
_UpperCAmelCase : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_token_classif(*_SCREAMING_SNAKE_CASE )
def _lowerCAmelCase ( self : Optional[Any] ) -> int:
"""simple docstring"""
_UpperCAmelCase : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_for_multiple_choice(*_SCREAMING_SNAKE_CASE )
def _lowerCAmelCase ( self : List[str] , lowerCAmelCase__ : Union[str, Any] , lowerCAmelCase__ : Union[str, Any] , lowerCAmelCase__ : Dict , lowerCAmelCase__ : Optional[int] , lowerCAmelCase__ : Any , lowerCAmelCase__ : str=False , lowerCAmelCase__ : List[str]=1 ) -> int:
"""simple docstring"""
self.assertIsInstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
self.assertListEqual(
[isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) for iter_attentions in attentions] , [True] * len(_SCREAMING_SNAKE_CASE ) )
self.assertEqual(len(_SCREAMING_SNAKE_CASE ) , (max_length - min_length) * num_beam_groups )
for idx, iter_attentions in enumerate(_SCREAMING_SNAKE_CASE ):
# adds PAD dummy token
_UpperCAmelCase : Optional[int] = min_length + idx + 1
_UpperCAmelCase : Union[str, Any] = min_length + idx + 1
_UpperCAmelCase : str = (
batch_size * num_beam_groups,
config.num_attention_heads,
tgt_len,
src_len,
)
# check attn size
self.assertListEqual(
[layer_attention.shape for layer_attention in iter_attentions] , [expected_shape] * len(_SCREAMING_SNAKE_CASE ) )
def _lowerCAmelCase ( self : List[str] , lowerCAmelCase__ : List[str] , lowerCAmelCase__ : Optional[Any] , lowerCAmelCase__ : List[str] , lowerCAmelCase__ : List[str] , lowerCAmelCase__ : List[str] , lowerCAmelCase__ : Dict=False , lowerCAmelCase__ : Optional[int]=1 ) -> Union[str, Any]:
"""simple docstring"""
self.assertIsInstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
self.assertListEqual(
[isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) for iter_hidden_states in hidden_states] , [True] * len(_SCREAMING_SNAKE_CASE ) , )
self.assertEqual(len(_SCREAMING_SNAKE_CASE ) , (max_length - min_length) * num_beam_groups )
for idx, iter_hidden_states in enumerate(_SCREAMING_SNAKE_CASE ):
# adds PAD dummy token
_UpperCAmelCase : int = min_length + idx + 1
_UpperCAmelCase : Optional[int] = (batch_size * num_beam_groups, seq_len, config.hidden_size)
# check hidden size
self.assertListEqual(
[layer_hidden_states.shape for layer_hidden_states in iter_hidden_states] , [expected_shape] * len(_SCREAMING_SNAKE_CASE ) , )
pass
@slow
def _lowerCAmelCase ( self : Optional[Any] ) -> Tuple:
"""simple docstring"""
for model_name in XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_UpperCAmelCase : List[Any] = XLMModel.from_pretrained(_SCREAMING_SNAKE_CASE )
self.assertIsNotNone(_SCREAMING_SNAKE_CASE )
@require_torch
class A__ ( unittest.TestCase ):
"""simple docstring"""
@slow
def _lowerCAmelCase ( self : Optional[int] ) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase : Any = XLMWithLMHeadModel.from_pretrained("xlm-mlm-en-2048" )
model.to(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase : List[str] = torch.tensor([[1_4, 4_4_7]] , dtype=torch.long , device=_SCREAMING_SNAKE_CASE ) # the president
_UpperCAmelCase : Any = [
1_4,
4_4_7,
1_4,
4_4_7,
1_4,
4_4_7,
1_4,
4_4_7,
1_4,
4_4_7,
1_4,
4_4_7,
1_4,
4_4_7,
1_4,
4_4_7,
1_4,
4_4_7,
1_4,
4_4_7,
] # the president the president the president the president the president the president the president the president the president the president
# TODO(PVP): this and other input_ids I tried for generation give pretty bad results. Not sure why. Model might just not be made for auto-regressive inference
_UpperCAmelCase : Optional[Any] = model.generate(_SCREAMING_SNAKE_CASE , do_sample=_SCREAMING_SNAKE_CASE )
self.assertListEqual(output_ids[0].cpu().numpy().tolist() , _SCREAMING_SNAKE_CASE )
| 359
|
'''simple docstring'''
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 : Any , lowerCAmelCase__ : Optional[Any] ) -> str:
"""simple docstring"""
super().__init__()
_UpperCAmelCase : List[str] = model
_UpperCAmelCase : Dict = 2
_UpperCAmelCase : Tuple = nn.Linear(self.model.config.hidden_size , self.num_labels )
def _lowerCAmelCase ( self : Tuple ) -> int:
"""simple docstring"""
pass
def __UpperCAmelCase ( a_: str, a_: str, a_: str ):
# load longformer model from model identifier
_UpperCAmelCase : int = LongformerModel.from_pretrained(a_ )
_UpperCAmelCase : Any = LightningModel(a_ )
_UpperCAmelCase : int = torch.load(a_, map_location=torch.device("cpu" ) )
lightning_model.load_state_dict(ckpt["state_dict"] )
# init longformer question answering model
_UpperCAmelCase : List[str] = 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__":
__a = 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.'
)
__a = parser.parse_args()
convert_longformer_qa_checkpoint_to_pytorch(
args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
)
| 17
| 0
|
'''simple docstring'''
import os
from collections.abc import Iterator
def __UpperCAmelCase ( a_: str = "." ):
for dir_path, dir_names, filenames in os.walk(_a ):
_UpperCAmelCase : Any = [d for d in dir_names if d != "scripts" and d[0] not in "._"]
for filename in filenames:
if filename == "__init__.py":
continue
if os.path.splitext(_a )[1] in (".py", ".ipynb"):
yield os.path.join(_a, _a ).lstrip("./" )
def __UpperCAmelCase ( a_: Dict ):
return f"""{i * ' '}*""" if i else "\n##"
def __UpperCAmelCase ( a_: str, a_: str ):
_UpperCAmelCase : str = old_path.split(os.sep )
for i, new_part in enumerate(new_path.split(os.sep ) ):
if (i + 1 > len(_a ) or old_parts[i] != new_part) and new_part:
print(f"""{md_prefix(_a )} {new_part.replace('_', ' ' ).title()}""" )
return new_path
def __UpperCAmelCase ( a_: str = "." ):
_UpperCAmelCase : List[str] = ""
for filepath in sorted(good_file_paths(_a ) ):
_UpperCAmelCase , _UpperCAmelCase : Dict = os.path.split(_a )
if filepath != old_path:
_UpperCAmelCase : List[Any] = print_path(_a, _a )
_UpperCAmelCase : int = (filepath.count(os.sep ) + 1) if filepath else 0
_UpperCAmelCase : Optional[int] = f"""{filepath}/{filename}""".replace(" ", "%20" )
_UpperCAmelCase : Optional[Any] = os.path.splitext(filename.replace("_", " " ).title() )[0]
print(f"""{md_prefix(_a )} [{filename}]({url})""" )
if __name__ == "__main__":
print_directory_md('.')
| 360
|
'''simple docstring'''
from importlib import import_module
from .logging import get_logger
__a = get_logger(__name__)
class A__ :
"""simple docstring"""
def __init__( self : List[str] , lowerCAmelCase__ : List[Any] , lowerCAmelCase__ : Optional[Any]=None ) -> Optional[int]:
"""simple docstring"""
_UpperCAmelCase : Any = attrs or []
if module is not None:
for key in module.__dict__:
if key in attrs or not key.startswith("__" ):
setattr(self , lowerCAmelCase__ , getattr(lowerCAmelCase__ , lowerCAmelCase__ ) )
_UpperCAmelCase : int = module._original_module if isinstance(lowerCAmelCase__ , _PatchedModuleObj ) else module
class A__ :
"""simple docstring"""
UpperCamelCase_ : Union[str, Any] = []
def __init__( self : int , lowerCAmelCase__ : List[str] , lowerCAmelCase__ : str , lowerCAmelCase__ : List[str] , lowerCAmelCase__ : Optional[int]=None ) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase : List[Any] = obj
_UpperCAmelCase : int = target
_UpperCAmelCase : Optional[int] = new
_UpperCAmelCase : Any = target.split("." )[0]
_UpperCAmelCase : Optional[int] = {}
_UpperCAmelCase : Dict = attrs or []
def __enter__( self : List[str] ) -> int:
"""simple docstring"""
*_UpperCAmelCase , _UpperCAmelCase : List[str] = self.target.split("." )
# Patch modules:
# it's used to patch attributes of submodules like "os.path.join";
# in this case we need to patch "os" and "os.path"
for i in range(len(lowerCAmelCase__ ) ):
try:
_UpperCAmelCase : int = import_module(".".join(submodules[: i + 1] ) )
except ModuleNotFoundError:
continue
# We iterate over all the globals in self.obj in case we find "os" or "os.path"
for attr in self.obj.__dir__():
_UpperCAmelCase : List[Any] = getattr(self.obj , lowerCAmelCase__ )
# We don't check for the name of the global, but rather if its value *is* "os" or "os.path".
# This allows to patch renamed modules like "from os import path as ospath".
if obj_attr is submodule or (
(isinstance(lowerCAmelCase__ , _PatchedModuleObj ) and obj_attr._original_module is submodule)
):
_UpperCAmelCase : Tuple = obj_attr
# patch at top level
setattr(self.obj , lowerCAmelCase__ , _PatchedModuleObj(lowerCAmelCase__ , attrs=self.attrs ) )
_UpperCAmelCase : List[Any] = getattr(self.obj , lowerCAmelCase__ )
# construct lower levels patches
for key in submodules[i + 1 :]:
setattr(lowerCAmelCase__ , lowerCAmelCase__ , _PatchedModuleObj(getattr(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ) , attrs=self.attrs ) )
_UpperCAmelCase : Any = getattr(lowerCAmelCase__ , lowerCAmelCase__ )
# finally set the target attribute
setattr(lowerCAmelCase__ , lowerCAmelCase__ , self.new )
# Patch attribute itself:
# it's used for builtins like "open",
# and also to patch "os.path.join" we may also need to patch "join"
# itself if it was imported as "from os.path import join".
if submodules: # if it's an attribute of a submodule like "os.path.join"
try:
_UpperCAmelCase : Dict = getattr(import_module(".".join(lowerCAmelCase__ ) ) , lowerCAmelCase__ )
except (AttributeError, ModuleNotFoundError):
return
# We iterate over all the globals in self.obj in case we find "os.path.join"
for attr in self.obj.__dir__():
# We don't check for the name of the global, but rather if its value *is* "os.path.join".
# This allows to patch renamed attributes like "from os.path import join as pjoin".
if getattr(self.obj , lowerCAmelCase__ ) is attr_value:
_UpperCAmelCase : Optional[Any] = getattr(self.obj , lowerCAmelCase__ )
setattr(self.obj , lowerCAmelCase__ , self.new )
elif target_attr in globals()["__builtins__"]: # if it'a s builtin like "open"
_UpperCAmelCase : Dict = globals()["__builtins__"][target_attr]
setattr(self.obj , lowerCAmelCase__ , self.new )
else:
raise RuntimeError(F"""Tried to patch attribute {target_attr} instead of a submodule.""" )
def __exit__( self : Optional[int] , *lowerCAmelCase__ : List[str] ) -> Union[str, Any]:
"""simple docstring"""
for attr in list(self.original ):
setattr(self.obj , lowerCAmelCase__ , self.original.pop(lowerCAmelCase__ ) )
def _lowerCAmelCase ( self : Union[str, Any] ) -> Optional[Any]:
"""simple docstring"""
self.__enter__()
self._active_patches.append(self )
def _lowerCAmelCase ( self : Optional[int] ) -> Tuple:
"""simple docstring"""
try:
self._active_patches.remove(self )
except ValueError:
# If the patch hasn't been started this will fail
return None
return self.__exit__()
| 17
| 0
|
'''simple docstring'''
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartaaTokenizer, MBartaaTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
)
from ...test_tokenization_common import TokenizerTesterMixin
__a = get_tests_dir('fixtures/test_sentencepiece.model')
if is_torch_available():
from transformers.models.mbart.modeling_mbart import shift_tokens_right
__a = 250_004
__a = 250_020
@require_sentencepiece
@require_tokenizers
class A__ ( lowerCamelCase__ , unittest.TestCase ):
"""simple docstring"""
UpperCamelCase_ : Union[str, Any] = MBartaaTokenizer
UpperCamelCase_ : List[Any] = MBartaaTokenizerFast
UpperCamelCase_ : Union[str, Any] = True
UpperCamelCase_ : Optional[Any] = True
def _lowerCAmelCase ( self : List[Any] ) -> List[str]:
"""simple docstring"""
super().setUp()
# We have a SentencePiece fixture for testing
_UpperCAmelCase : Union[str, Any] = MBartaaTokenizer(snake_case__ , src_lang="en_XX" , tgt_lang="ro_RO" , keep_accents=snake_case__ )
tokenizer.save_pretrained(self.tmpdirname )
def _lowerCAmelCase ( self : str ) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase : Optional[int] = '''<s>'''
_UpperCAmelCase : Union[str, Any] = 0
self.assertEqual(self.get_tokenizer()._convert_token_to_id(snake_case__ ) , snake_case__ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(snake_case__ ) , snake_case__ )
def _lowerCAmelCase ( self : Optional[int] ) -> List[str]:
"""simple docstring"""
_UpperCAmelCase : List[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(snake_case__ ) , 1_0_5_4 )
def _lowerCAmelCase ( self : int ) -> int:
"""simple docstring"""
self.assertEqual(self.get_tokenizer().vocab_size , 1_0_5_4 )
def _lowerCAmelCase ( self : List[Any] ) -> Optional[int]:
"""simple docstring"""
_UpperCAmelCase : Tuple = MBartaaTokenizer(snake_case__ , src_lang="en_XX" , tgt_lang="ro_RO" , keep_accents=snake_case__ )
_UpperCAmelCase : int = tokenizer.tokenize("This is a test" )
self.assertListEqual(snake_case__ , ["▁This", "▁is", "▁a", "▁t", "est"] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(snake_case__ ) , [value + tokenizer.fairseq_offset for value in [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]] , )
_UpperCAmelCase : Tuple = tokenizer.tokenize("I was born in 92000, and this is falsé." )
self.assertListEqual(
snake_case__ , [SPIECE_UNDERLINE + "I", SPIECE_UNDERLINE + "was", SPIECE_UNDERLINE + "b", "or", "n", SPIECE_UNDERLINE + "in", SPIECE_UNDERLINE + "", "9", "2", "0", "0", "0", ",", SPIECE_UNDERLINE + "and", SPIECE_UNDERLINE + "this", SPIECE_UNDERLINE + "is", SPIECE_UNDERLINE + "f", "al", "s", "é", "."] , )
_UpperCAmelCase : Union[str, Any] = tokenizer.convert_tokens_to_ids(snake_case__ )
self.assertListEqual(
snake_case__ , [
value + tokenizer.fairseq_offset
for value in [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 2, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 2, 4]
] , )
_UpperCAmelCase : Union[str, Any] = tokenizer.convert_ids_to_tokens(snake_case__ )
self.assertListEqual(
snake_case__ , [SPIECE_UNDERLINE + "I", SPIECE_UNDERLINE + "was", SPIECE_UNDERLINE + "b", "or", "n", SPIECE_UNDERLINE + "in", SPIECE_UNDERLINE + "", "<unk>", "2", "0", "0", "0", ",", SPIECE_UNDERLINE + "and", SPIECE_UNDERLINE + "this", SPIECE_UNDERLINE + "is", SPIECE_UNDERLINE + "f", "al", "s", "<unk>", "."] , )
@slow
def _lowerCAmelCase ( self : List[str] ) -> Tuple:
"""simple docstring"""
_UpperCAmelCase : List[str] = {'''input_ids''': [[2_5_0_0_0_4, 1_1_0_6_2, 8_2_7_7_2, 7, 1_5, 8_2_7_7_2, 5_3_8, 5_1_5_2_9, 2_3_7, 1_7_1_9_8, 1_2_9_0, 2_0_6, 9, 2_1_5_1_7_5, 1_3_1_4, 1_3_6, 1_7_1_9_8, 1_2_9_0, 2_0_6, 9, 5_6_3_5_9, 4_2, 1_2_2_0_0_9, 9, 1_6_4_6_6, 1_6, 8_7_3_4_4, 4_5_3_7, 9, 4_7_1_7, 7_8_3_8_1, 6, 1_5_9_9_5_8, 7, 1_5, 2_4_4_8_0, 6_1_8, 4, 5_2_7, 2_2_6_9_3, 5_4_2_8, 4, 2_7_7_7, 2_4_4_8_0, 9_8_7_4, 4, 4_3_5_2_3, 5_9_4, 4, 8_0_3, 1_8_3_9_2, 3_3_1_8_9, 1_8, 4, 4_3_5_2_3, 2_4_4_4_7, 1_2_3_9_9, 1_0_0, 2_4_9_5_5, 8_3_6_5_8, 9_6_2_6, 1_4_4_0_5_7, 1_5, 8_3_9, 2_2_3_3_5, 1_6, 1_3_6, 2_4_9_5_5, 8_3_6_5_8, 8_3_4_7_9, 1_5, 3_9_1_0_2, 7_2_4, 1_6, 6_7_8, 6_4_5, 2_7_8_9, 1_3_2_8, 4_5_8_9, 4_2, 1_2_2_0_0_9, 1_1_5_7_7_4, 2_3, 8_0_5, 1_3_2_8, 4_6_8_7_6, 7, 1_3_6, 5_3_8_9_4, 1_9_4_0, 4_2_2_2_7, 4_1_1_5_9, 1_7_7_2_1, 8_2_3, 4_2_5, 4, 2_7_5_1_2, 9_8_7_2_2, 2_0_6, 1_3_6, 5_5_3_1, 4_9_7_0, 9_1_9, 1_7_3_3_6, 5, 2], [2_5_0_0_0_4, 2_0_0_8_0, 6_1_8, 8_3, 8_2_7_7_5, 4_7, 4_7_9, 9, 1_5_1_7, 7_3, 5_3_8_9_4, 3_3_3, 8_0_5_8_1, 1_1_0_1_1_7, 1_8_8_1_1, 5_2_5_6, 1_2_9_5, 5_1, 1_5_2_5_2_6, 2_9_7, 7_9_8_6, 3_9_0, 1_2_4_4_1_6, 5_3_8, 3_5_4_3_1, 2_1_4, 9_8, 1_5_0_4_4, 2_5_7_3_7, 1_3_6, 7_1_0_8, 4_3_7_0_1, 2_3, 7_5_6, 1_3_5_3_5_5, 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], [2_5_0_0_0_4, 5_8_1, 6_3_7_7_3, 1_1_9_4_5_5, 6, 1_4_7_7_9_7, 8_8_2_0_3, 7, 6_4_5, 7_0, 2_1, 3_2_8_5, 1_0_2_6_9, 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=snake_case__ , model_name="facebook/mbart-large-50" , revision="d3913889c59cd5c9e456b269c376325eabad57e2" , )
def _lowerCAmelCase ( self : Any ) -> Any:
"""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
_UpperCAmelCase : Optional[int] = (self.rust_tokenizer_class, '''hf-internal-testing/tiny-random-mbart50''', {})
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
_UpperCAmelCase : Union[str, Any] = self.rust_tokenizer_class.from_pretrained(snake_case__ , **snake_case__ )
_UpperCAmelCase : str = self.tokenizer_class.from_pretrained(snake_case__ , **snake_case__ )
_UpperCAmelCase : Optional[int] = tempfile.mkdtemp()
_UpperCAmelCase : Tuple = tokenizer_r.save_pretrained(snake_case__ )
_UpperCAmelCase : Tuple = tokenizer_p.save_pretrained(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 ) )
_UpperCAmelCase : Any = tuple(f for f in tokenizer_r_files if "tokenizer.json" not in f )
self.assertSequenceEqual(snake_case__ , snake_case__ )
# Checks everything loads correctly in the same way
_UpperCAmelCase : List[str] = tokenizer_r.from_pretrained(snake_case__ )
_UpperCAmelCase : str = tokenizer_p.from_pretrained(snake_case__ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(snake_case__ , 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(snake_case__ )
# Save tokenizer rust, legacy_format=True
_UpperCAmelCase : Dict = tempfile.mkdtemp()
_UpperCAmelCase : Any = tokenizer_r.save_pretrained(snake_case__ , legacy_format=snake_case__ )
_UpperCAmelCase : int = tokenizer_p.save_pretrained(snake_case__ )
# Checks it save with the same files
self.assertSequenceEqual(snake_case__ , snake_case__ )
# Checks everything loads correctly in the same way
_UpperCAmelCase : str = tokenizer_r.from_pretrained(snake_case__ )
_UpperCAmelCase : Optional[int] = tokenizer_p.from_pretrained(snake_case__ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(snake_case__ , snake_case__ ) )
shutil.rmtree(snake_case__ )
# Save tokenizer rust, legacy_format=False
_UpperCAmelCase : List[str] = tempfile.mkdtemp()
_UpperCAmelCase : Any = tokenizer_r.save_pretrained(snake_case__ , legacy_format=snake_case__ )
_UpperCAmelCase : List[str] = tokenizer_p.save_pretrained(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
_UpperCAmelCase : Union[str, Any] = tokenizer_r.from_pretrained(snake_case__ )
_UpperCAmelCase : Dict = tokenizer_p.from_pretrained(snake_case__ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(snake_case__ , snake_case__ ) )
shutil.rmtree(snake_case__ )
@require_torch
@require_sentencepiece
@require_tokenizers
class A__ ( unittest.TestCase ):
"""simple docstring"""
UpperCamelCase_ : List[Any] = """facebook/mbart-large-50-one-to-many-mmt"""
UpperCamelCase_ : str = [
""" UN Chief Says There Is No Military Solution in Syria""",
""" Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for Syria is that \"there is no military solution\" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.""",
]
UpperCamelCase_ : int = [
"""Şeful ONU declară că nu există o soluţie militară în Siria""",
"""Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei"""
""" pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi că noi arme nu vor"""
""" face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.""",
]
UpperCamelCase_ : str = [EN_CODE, 82_74, 12_78_73, 2_59_16, 7, 86_22, 20_71, 4_38, 6_74_85, 53, 18_78_95, 23, 5_17_12, 2]
@classmethod
def _lowerCAmelCase ( cls : List[str] ) -> Optional[int]:
"""simple docstring"""
_UpperCAmelCase : MBartaaTokenizer = MBartaaTokenizer.from_pretrained(
cls.checkpoint_name , src_lang="en_XX" , tgt_lang="ro_RO" )
_UpperCAmelCase : List[Any] = 1
return cls
def _lowerCAmelCase ( self : int ) -> List[Any]:
"""simple docstring"""
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ar_AR"] , 2_5_0_0_0_1 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["en_EN"] , 2_5_0_0_0_4 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ro_RO"] , 2_5_0_0_2_0 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["mr_IN"] , 2_5_0_0_3_8 )
def _lowerCAmelCase ( self : str ) -> Optional[int]:
"""simple docstring"""
_UpperCAmelCase : List[Any] = self.tokenizer.batch_encode_plus(self.src_text ).input_ids[0]
self.assertListEqual(self.expected_src_tokens , snake_case__ )
def _lowerCAmelCase ( self : str ) -> Dict:
"""simple docstring"""
self.assertIn(snake_case__ , self.tokenizer.all_special_ids )
_UpperCAmelCase : Optional[int] = [RO_CODE, 8_8_4, 9_0_1_9, 9_6, 9, 9_1_6, 8_6_7_9_2, 3_6, 1_8_7_4_3, 1_5_5_9_6, 5, 2]
_UpperCAmelCase : str = self.tokenizer.decode(snake_case__ , skip_special_tokens=snake_case__ )
_UpperCAmelCase : Any = self.tokenizer.decode(generated_ids[1:] , skip_special_tokens=snake_case__ )
self.assertEqual(snake_case__ , snake_case__ )
self.assertNotIn(self.tokenizer.eos_token , snake_case__ )
def _lowerCAmelCase ( self : Dict ) -> int:
"""simple docstring"""
_UpperCAmelCase : int = ['''this is gunna be a long sentence ''' * 2_0]
assert isinstance(src_text[0] , snake_case__ )
_UpperCAmelCase : Optional[int] = 1_0
_UpperCAmelCase : Optional[int] = self.tokenizer(snake_case__ , max_length=snake_case__ , truncation=snake_case__ ).input_ids[0]
self.assertEqual(ids[0] , snake_case__ )
self.assertEqual(ids[-1] , 2 )
self.assertEqual(len(snake_case__ ) , snake_case__ )
def _lowerCAmelCase ( self : Optional[int] ) -> Any:
"""simple docstring"""
self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["<mask>", "ar_AR"] ) , [2_5_0_0_5_3, 2_5_0_0_0_1] )
def _lowerCAmelCase ( self : Any ) -> List[Any]:
"""simple docstring"""
_UpperCAmelCase : Any = tempfile.mkdtemp()
_UpperCAmelCase : int = self.tokenizer.fairseq_tokens_to_ids
self.tokenizer.save_pretrained(snake_case__ )
_UpperCAmelCase : Optional[Any] = MBartaaTokenizer.from_pretrained(snake_case__ )
self.assertDictEqual(new_tok.fairseq_tokens_to_ids , snake_case__ )
@require_torch
def _lowerCAmelCase ( self : Tuple ) -> str:
"""simple docstring"""
_UpperCAmelCase : str = self.tokenizer(self.src_text , text_target=self.tgt_text , padding=snake_case__ , return_tensors="pt" )
_UpperCAmelCase : Dict = shift_tokens_right(batch["labels"] , self.tokenizer.pad_token_id )
# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
assert batch.input_ids[1][0] == EN_CODE
assert batch.input_ids[1][-1] == 2
assert batch.labels[1][0] == RO_CODE
assert batch.labels[1][-1] == 2
assert batch.decoder_input_ids[1][:2].tolist() == [2, RO_CODE]
@require_torch
def _lowerCAmelCase ( self : int ) -> Tuple:
"""simple docstring"""
_UpperCAmelCase : int = self.tokenizer(
self.src_text , text_target=self.tgt_text , padding=snake_case__ , truncation=snake_case__ , max_length=len(self.expected_src_tokens ) , return_tensors="pt" , )
_UpperCAmelCase : Union[str, Any] = shift_tokens_right(batch["labels"] , self.tokenizer.pad_token_id )
self.assertIsInstance(snake_case__ , snake_case__ )
self.assertEqual((2, 1_4) , batch.input_ids.shape )
self.assertEqual((2, 1_4) , batch.attention_mask.shape )
_UpperCAmelCase : Any = batch.input_ids.tolist()[0]
self.assertListEqual(self.expected_src_tokens , snake_case__ )
self.assertEqual(2 , batch.decoder_input_ids[0, 0] ) # decoder_start_token_id
# Test that special tokens are reset
self.assertEqual(self.tokenizer.prefix_tokens , [EN_CODE] )
self.assertEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id] )
def _lowerCAmelCase ( self : Tuple ) -> List[Any]:
"""simple docstring"""
_UpperCAmelCase : str = self.tokenizer(self.src_text , padding=snake_case__ , truncation=snake_case__ , max_length=3 , return_tensors="pt" )
_UpperCAmelCase : List[str] = self.tokenizer(
text_target=self.tgt_text , padding=snake_case__ , truncation=snake_case__ , max_length=1_0 , return_tensors="pt" )
_UpperCAmelCase : Any = targets['''input_ids''']
_UpperCAmelCase : str = shift_tokens_right(snake_case__ , self.tokenizer.pad_token_id )
self.assertEqual(batch.input_ids.shape[1] , 3 )
self.assertEqual(batch.decoder_input_ids.shape[1] , 1_0 )
@require_torch
def _lowerCAmelCase ( self : List[str] ) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase : List[str] = self.tokenizer._build_translation_inputs(
"A test" , return_tensors="pt" , src_lang="en_XX" , tgt_lang="ar_AR" )
self.assertEqual(
nested_simplify(snake_case__ ) , {
# en_XX, A, test, EOS
"input_ids": [[2_5_0_0_0_4, 6_2, 3_0_3_4, 2]],
"attention_mask": [[1, 1, 1, 1]],
# ar_AR
"forced_bos_token_id": 2_5_0_0_0_1,
} , )
| 361
|
'''simple docstring'''
import itertools
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union
import pandas as pd
import pyarrow as pa
import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast
from datasets.utils.py_utils import Literal
__a = datasets.utils.logging.get_logger(__name__)
__a = ['names', 'prefix']
__a = ['warn_bad_lines', 'error_bad_lines', 'mangle_dupe_cols']
__a = ['encoding_errors', 'on_bad_lines']
__a = ['date_format']
@dataclass
class A__ ( datasets.BuilderConfig ):
"""simple docstring"""
UpperCamelCase_ : str = ","
UpperCamelCase_ : Optional[str] = None
UpperCamelCase_ : Optional[Union[int, List[int], str]] = "infer"
UpperCamelCase_ : Optional[List[str]] = None
UpperCamelCase_ : Optional[List[str]] = None
UpperCamelCase_ : Optional[Union[int, str, List[int], List[str]]] = None
UpperCamelCase_ : Optional[Union[List[int], List[str]]] = None
UpperCamelCase_ : Optional[str] = None
UpperCamelCase_ : bool = True
UpperCamelCase_ : Optional[Literal["c", "python", "pyarrow"]] = None
UpperCamelCase_ : Dict[Union[int, str], Callable[[Any], Any]] = None
UpperCamelCase_ : Optional[list] = None
UpperCamelCase_ : Optional[list] = None
UpperCamelCase_ : bool = False
UpperCamelCase_ : Optional[Union[int, List[int]]] = None
UpperCamelCase_ : Optional[int] = None
UpperCamelCase_ : Optional[Union[str, List[str]]] = None
UpperCamelCase_ : bool = True
UpperCamelCase_ : bool = True
UpperCamelCase_ : bool = False
UpperCamelCase_ : bool = True
UpperCamelCase_ : Optional[str] = None
UpperCamelCase_ : str = "."
UpperCamelCase_ : Optional[str] = None
UpperCamelCase_ : str = '"'
UpperCamelCase_ : int = 0
UpperCamelCase_ : Optional[str] = None
UpperCamelCase_ : Optional[str] = None
UpperCamelCase_ : Optional[str] = None
UpperCamelCase_ : Optional[str] = None
UpperCamelCase_ : bool = True
UpperCamelCase_ : bool = True
UpperCamelCase_ : int = 0
UpperCamelCase_ : bool = True
UpperCamelCase_ : bool = False
UpperCamelCase_ : Optional[str] = None
UpperCamelCase_ : int = 1_00_00
UpperCamelCase_ : Optional[datasets.Features] = None
UpperCamelCase_ : Optional[str] = "strict"
UpperCamelCase_ : Literal["error", "warn", "skip"] = "error"
UpperCamelCase_ : Optional[str] = None
def _lowerCAmelCase ( self : str ) -> Tuple:
"""simple docstring"""
if self.delimiter is not None:
_UpperCAmelCase : Any = self.delimiter
if self.column_names is not None:
_UpperCAmelCase : List[Any] = self.column_names
@property
def _lowerCAmelCase ( self : Optional[int] ) -> Optional[int]:
"""simple docstring"""
_UpperCAmelCase : Dict = {
"sep": self.sep,
"header": self.header,
"names": self.names,
"index_col": self.index_col,
"usecols": self.usecols,
"prefix": self.prefix,
"mangle_dupe_cols": self.mangle_dupe_cols,
"engine": self.engine,
"converters": self.converters,
"true_values": self.true_values,
"false_values": self.false_values,
"skipinitialspace": self.skipinitialspace,
"skiprows": self.skiprows,
"nrows": self.nrows,
"na_values": self.na_values,
"keep_default_na": self.keep_default_na,
"na_filter": self.na_filter,
"verbose": self.verbose,
"skip_blank_lines": self.skip_blank_lines,
"thousands": self.thousands,
"decimal": self.decimal,
"lineterminator": self.lineterminator,
"quotechar": self.quotechar,
"quoting": self.quoting,
"escapechar": self.escapechar,
"comment": self.comment,
"encoding": self.encoding,
"dialect": self.dialect,
"error_bad_lines": self.error_bad_lines,
"warn_bad_lines": self.warn_bad_lines,
"skipfooter": self.skipfooter,
"doublequote": self.doublequote,
"memory_map": self.memory_map,
"float_precision": self.float_precision,
"chunksize": self.chunksize,
"encoding_errors": self.encoding_errors,
"on_bad_lines": self.on_bad_lines,
"date_format": self.date_format,
}
# some kwargs must not be passed if they don't have a default value
# some others are deprecated and we can also not pass them if they are the default value
for pd_read_csv_parameter in _PANDAS_READ_CSV_NO_DEFAULT_PARAMETERS + _PANDAS_READ_CSV_DEPRECATED_PARAMETERS:
if pd_read_csv_kwargs[pd_read_csv_parameter] == getattr(CsvConfig() , lowerCAmelCase__ ):
del pd_read_csv_kwargs[pd_read_csv_parameter]
# Remove 2.0 new arguments
if not (datasets.config.PANDAS_VERSION.major >= 2):
for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_2_0_0_PARAMETERS:
del pd_read_csv_kwargs[pd_read_csv_parameter]
# Remove 1.3 new arguments
if not (datasets.config.PANDAS_VERSION.major >= 1 and datasets.config.PANDAS_VERSION.minor >= 3):
for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_1_3_0_PARAMETERS:
del pd_read_csv_kwargs[pd_read_csv_parameter]
return pd_read_csv_kwargs
class A__ ( datasets.ArrowBasedBuilder ):
"""simple docstring"""
UpperCamelCase_ : int = CsvConfig
def _lowerCAmelCase ( self : Optional[Any] ) -> Optional[int]:
"""simple docstring"""
return datasets.DatasetInfo(features=self.config.features )
def _lowerCAmelCase ( self : Tuple , lowerCAmelCase__ : str ) -> List[str]:
"""simple docstring"""
if not self.config.data_files:
raise ValueError(F"""At least one data file must be specified, but got data_files={self.config.data_files}""" )
_UpperCAmelCase : List[str] = dl_manager.download_and_extract(self.config.data_files )
if isinstance(lowerCAmelCase__ , (str, list, tuple) ):
_UpperCAmelCase : int = data_files
if isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
_UpperCAmelCase : Any = [files]
_UpperCAmelCase : List[Any] = [dl_manager.iter_files(lowerCAmelCase__ ) for file in files]
return [datasets.SplitGenerator(name=datasets.Split.TRAIN , gen_kwargs={"files": files} )]
_UpperCAmelCase : Optional[Any] = []
for split_name, files in data_files.items():
if isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
_UpperCAmelCase : str = [files]
_UpperCAmelCase : Any = [dl_manager.iter_files(lowerCAmelCase__ ) for file in files]
splits.append(datasets.SplitGenerator(name=lowerCAmelCase__ , gen_kwargs={"files": files} ) )
return splits
def _lowerCAmelCase ( self : List[Any] , lowerCAmelCase__ : pa.Table ) -> pa.Table:
"""simple docstring"""
if self.config.features is not None:
_UpperCAmelCase : Tuple = self.config.features.arrow_schema
if all(not require_storage_cast(lowerCAmelCase__ ) for feature in self.config.features.values() ):
# cheaper cast
_UpperCAmelCase : Any = pa.Table.from_arrays([pa_table[field.name] for field in schema] , schema=lowerCAmelCase__ )
else:
# more expensive cast; allows str <-> int/float or str to Audio for example
_UpperCAmelCase : int = table_cast(lowerCAmelCase__ , lowerCAmelCase__ )
return pa_table
def _lowerCAmelCase ( self : Dict , lowerCAmelCase__ : Dict ) -> Dict:
"""simple docstring"""
_UpperCAmelCase : int = self.config.features.arrow_schema if self.config.features else None
# dtype allows reading an int column as str
_UpperCAmelCase : Optional[Any] = (
{
name: dtype.to_pandas_dtype() if not require_storage_cast(lowerCAmelCase__ ) else object
for name, dtype, feature in zip(schema.names , schema.types , self.config.features.values() )
}
if schema is not None
else None
)
for file_idx, file in enumerate(itertools.chain.from_iterable(lowerCAmelCase__ ) ):
_UpperCAmelCase : Optional[Any] = pd.read_csv(lowerCAmelCase__ , iterator=lowerCAmelCase__ , dtype=lowerCAmelCase__ , **self.config.pd_read_csv_kwargs )
try:
for batch_idx, df in enumerate(lowerCAmelCase__ ):
_UpperCAmelCase : Optional[int] = pa.Table.from_pandas(lowerCAmelCase__ )
# Uncomment for debugging (will print the Arrow table size and elements)
# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
yield (file_idx, batch_idx), self._cast_table(lowerCAmelCase__ )
except ValueError as e:
logger.error(F"""Failed to read file '{file}' with error {type(lowerCAmelCase__ )}: {e}""" )
raise
| 17
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'''simple docstring'''
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class A__ ( a__ ):
"""simple docstring"""
UpperCamelCase_ : Optional[int] = ["""image_processor""", """tokenizer"""]
UpperCamelCase_ : Tuple = """BlipImageProcessor"""
UpperCamelCase_ : Tuple = """AutoTokenizer"""
def __init__( self : Union[str, Any] , lowerCAmelCase__ : int , lowerCAmelCase__ : int ) -> str:
"""simple docstring"""
_UpperCAmelCase : int = False
super().__init__(lowerCAmelCase__ , lowerCAmelCase__ )
_UpperCAmelCase : List[str] = self.image_processor
def __call__( self : int , lowerCAmelCase__ : Tuple = None , lowerCAmelCase__ : Optional[int] = None , lowerCAmelCase__ : List[str] = True , lowerCAmelCase__ : Any = False , lowerCAmelCase__ : Any = None , lowerCAmelCase__ : Tuple = None , lowerCAmelCase__ : Optional[Any] = 0 , lowerCAmelCase__ : List[Any] = None , lowerCAmelCase__ : Any = None , lowerCAmelCase__ : Optional[Any] = False , lowerCAmelCase__ : Union[str, Any] = False , lowerCAmelCase__ : Optional[Any] = False , lowerCAmelCase__ : Union[str, Any] = False , lowerCAmelCase__ : int = False , lowerCAmelCase__ : List[Any] = True , lowerCAmelCase__ : int = None , **lowerCAmelCase__ : Optional[int] , ) -> BatchEncoding:
"""simple docstring"""
if images is None and text is None:
raise ValueError("You have to specify either images or text." )
# Get only text
if images is None:
_UpperCAmelCase : Optional[int] = self.tokenizer
_UpperCAmelCase : Optional[Any] = self.tokenizer(
text=lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ , padding=lowerCAmelCase__ , truncation=lowerCAmelCase__ , max_length=lowerCAmelCase__ , stride=lowerCAmelCase__ , pad_to_multiple_of=lowerCAmelCase__ , return_attention_mask=lowerCAmelCase__ , return_overflowing_tokens=lowerCAmelCase__ , return_special_tokens_mask=lowerCAmelCase__ , return_offsets_mapping=lowerCAmelCase__ , return_token_type_ids=lowerCAmelCase__ , return_length=lowerCAmelCase__ , verbose=lowerCAmelCase__ , return_tensors=lowerCAmelCase__ , **lowerCAmelCase__ , )
return text_encoding
# add pixel_values
_UpperCAmelCase : int = self.image_processor(lowerCAmelCase__ , return_tensors=lowerCAmelCase__ )
if text is not None:
_UpperCAmelCase : str = self.tokenizer(
text=lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ , padding=lowerCAmelCase__ , truncation=lowerCAmelCase__ , max_length=lowerCAmelCase__ , stride=lowerCAmelCase__ , pad_to_multiple_of=lowerCAmelCase__ , return_attention_mask=lowerCAmelCase__ , return_overflowing_tokens=lowerCAmelCase__ , return_special_tokens_mask=lowerCAmelCase__ , return_offsets_mapping=lowerCAmelCase__ , return_token_type_ids=lowerCAmelCase__ , return_length=lowerCAmelCase__ , verbose=lowerCAmelCase__ , return_tensors=lowerCAmelCase__ , **lowerCAmelCase__ , )
else:
_UpperCAmelCase : Optional[Any] = None
if text_encoding is not None:
encoding_image_processor.update(lowerCAmelCase__ )
return encoding_image_processor
def _lowerCAmelCase ( self : int , *lowerCAmelCase__ : Tuple , **lowerCAmelCase__ : str ) -> Optional[Any]:
"""simple docstring"""
return self.tokenizer.batch_decode(*lowerCAmelCase__ , **lowerCAmelCase__ )
def _lowerCAmelCase ( self : Tuple , *lowerCAmelCase__ : Union[str, Any] , **lowerCAmelCase__ : List[str] ) -> List[Any]:
"""simple docstring"""
return self.tokenizer.decode(*lowerCAmelCase__ , **lowerCAmelCase__ )
@property
# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
def _lowerCAmelCase ( self : Any ) -> List[str]:
"""simple docstring"""
_UpperCAmelCase : Optional[Any] = self.tokenizer.model_input_names
_UpperCAmelCase : Optional[Any] = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
| 362
|
'''simple docstring'''
from __future__ import annotations
def __UpperCAmelCase ( a_: list[int] ):
if not nums:
return 0
_UpperCAmelCase : int = nums[0]
_UpperCAmelCase : Dict = 0
for num in nums[1:]:
_UpperCAmelCase , _UpperCAmelCase : Any = (
max_excluding + num,
max(a_, a_ ),
)
return max(a_, a_ )
if __name__ == "__main__":
import doctest
doctest.testmod()
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'''simple docstring'''
from pathlib import Path
import cva
import numpy as np
from matplotlib import pyplot as plt
def __UpperCAmelCase ( a_: np.ndarray, a_: np.ndarray, a_: np.ndarray, a_: int, a_: int ):
_UpperCAmelCase : Optional[Any] = cva.getAffineTransform(a_, a_ )
return cva.warpAffine(a_, a_, (rows, cols) )
if __name__ == "__main__":
# read original image
__a = cva.imread(
str(Path(__file__).resolve().parent.parent / 'image_data' / 'lena.jpg')
)
# turn image in gray scale value
__a = cva.cvtColor(image, cva.COLOR_BGR2GRAY)
# get image shape
__a = gray_img.shape
# set different points to rotate image
__a = np.array([[50, 50], [200, 50], [50, 200]], np.floataa)
__a = np.array([[10, 100], [200, 50], [100, 250]], np.floataa)
__a = np.array([[50, 50], [150, 50], [120, 200]], np.floataa)
__a = np.array([[10, 100], [80, 50], [180, 250]], np.floataa)
# add all rotated images in a list
__a = [
gray_img,
get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
]
# plot different image rotations
__a = plt.figure(1)
__a = ["""Original""", """Rotation 1""", """Rotation 2""", """Rotation 3"""]
for i, image in enumerate(images):
plt.subplot(2, 2, i + 1), plt.imshow(image, 'gray')
plt.title(titles[i])
plt.axis('off')
plt.subplots_adjust(left=0.0, bottom=0.0_5, right=1.0, top=0.9_5)
plt.show()
| 363
|
'''simple docstring'''
import argparse
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
__a = logging.get_logger(__name__)
def __UpperCAmelCase ( a_: List[str] ):
_UpperCAmelCase : Union[str, Any] = OrderedDict()
for key, value in state_dict.items():
if key.startswith("module.encoder" ):
_UpperCAmelCase : Optional[int] = key.replace("module.encoder", "glpn.encoder" )
if key.startswith("module.decoder" ):
_UpperCAmelCase : List[Any] = key.replace("module.decoder", "decoder.stages" )
if "patch_embed" in key:
# replace for example patch_embed1 by patch_embeddings.0
_UpperCAmelCase : int = key[key.find("patch_embed" ) + len("patch_embed" )]
_UpperCAmelCase : Union[str, Any] = key.replace(f"""patch_embed{idx}""", f"""patch_embeddings.{int(a_ )-1}""" )
if "norm" in key:
_UpperCAmelCase : Union[str, Any] = key.replace("norm", "layer_norm" )
if "glpn.encoder.layer_norm" in key:
# replace for example layer_norm1 by layer_norm.0
_UpperCAmelCase : str = key[key.find("glpn.encoder.layer_norm" ) + len("glpn.encoder.layer_norm" )]
_UpperCAmelCase : Optional[Any] = key.replace(f"""layer_norm{idx}""", f"""layer_norm.{int(a_ )-1}""" )
if "layer_norm1" in key:
_UpperCAmelCase : Union[str, Any] = key.replace("layer_norm1", "layer_norm_1" )
if "layer_norm2" in key:
_UpperCAmelCase : List[Any] = key.replace("layer_norm2", "layer_norm_2" )
if "block" in key:
# replace for example block1 by block.0
_UpperCAmelCase : Optional[Any] = key[key.find("block" ) + len("block" )]
_UpperCAmelCase : List[str] = key.replace(f"""block{idx}""", f"""block.{int(a_ )-1}""" )
if "attn.q" in key:
_UpperCAmelCase : Optional[int] = key.replace("attn.q", "attention.self.query" )
if "attn.proj" in key:
_UpperCAmelCase : List[str] = key.replace("attn.proj", "attention.output.dense" )
if "attn" in key:
_UpperCAmelCase : Dict = key.replace("attn", "attention.self" )
if "fc1" in key:
_UpperCAmelCase : List[Any] = key.replace("fc1", "dense1" )
if "fc2" in key:
_UpperCAmelCase : List[Any] = key.replace("fc2", "dense2" )
if "linear_pred" in key:
_UpperCAmelCase : Any = key.replace("linear_pred", "classifier" )
if "linear_fuse" in key:
_UpperCAmelCase : Dict = key.replace("linear_fuse.conv", "linear_fuse" )
_UpperCAmelCase : List[str] = key.replace("linear_fuse.bn", "batch_norm" )
if "linear_c" in key:
# replace for example linear_c4 by linear_c.3
_UpperCAmelCase : List[Any] = key[key.find("linear_c" ) + len("linear_c" )]
_UpperCAmelCase : Tuple = key.replace(f"""linear_c{idx}""", f"""linear_c.{int(a_ )-1}""" )
if "bot_conv" in key:
_UpperCAmelCase : Union[str, Any] = key.replace("bot_conv", "0.convolution" )
if "skip_conv1" in key:
_UpperCAmelCase : Optional[int] = key.replace("skip_conv1", "1.convolution" )
if "skip_conv2" in key:
_UpperCAmelCase : Optional[int] = key.replace("skip_conv2", "2.convolution" )
if "fusion1" in key:
_UpperCAmelCase : List[str] = key.replace("fusion1", "1.fusion" )
if "fusion2" in key:
_UpperCAmelCase : List[str] = key.replace("fusion2", "2.fusion" )
if "fusion3" in key:
_UpperCAmelCase : Optional[Any] = key.replace("fusion3", "3.fusion" )
if "fusion" in key and "conv" in key:
_UpperCAmelCase : List[Any] = key.replace("conv", "convolutional_layer" )
if key.startswith("module.last_layer_depth" ):
_UpperCAmelCase : Optional[int] = key.replace("module.last_layer_depth", "head.head" )
_UpperCAmelCase : int = value
return new_state_dict
def __UpperCAmelCase ( a_: str, a_: List[Any] ):
# for each of the encoder blocks:
for i in range(config.num_encoder_blocks ):
for j in range(config.depths[i] ):
# read in weights + bias of keys and values (which is a single matrix in the original implementation)
_UpperCAmelCase : Tuple = state_dict.pop(f"""glpn.encoder.block.{i}.{j}.attention.self.kv.weight""" )
_UpperCAmelCase : Union[str, Any] = state_dict.pop(f"""glpn.encoder.block.{i}.{j}.attention.self.kv.bias""" )
# next, add keys and values (in that order) to the state dict
_UpperCAmelCase : Optional[int] = kv_weight[
: config.hidden_sizes[i], :
]
_UpperCAmelCase : Dict = kv_bias[: config.hidden_sizes[i]]
_UpperCAmelCase : Optional[int] = kv_weight[
config.hidden_sizes[i] :, :
]
_UpperCAmelCase : Optional[Any] = kv_bias[config.hidden_sizes[i] :]
def __UpperCAmelCase ( ):
_UpperCAmelCase : Optional[int] = "http://images.cocodataset.org/val2017/000000039769.jpg"
_UpperCAmelCase : List[Any] = Image.open(requests.get(a_, stream=a_ ).raw )
return image
@torch.no_grad()
def __UpperCAmelCase ( a_: Tuple, a_: Any, a_: Optional[Any]=False, a_: List[Any]=None ):
_UpperCAmelCase : Optional[Any] = GLPNConfig(hidden_sizes=[64, 128, 320, 512], decoder_hidden_size=64, depths=[3, 8, 27, 3] )
# load image processor (only resize + rescale)
_UpperCAmelCase : Dict = GLPNImageProcessor()
# prepare image
_UpperCAmelCase : List[Any] = prepare_img()
_UpperCAmelCase : Optional[int] = image_processor(images=a_, return_tensors="pt" ).pixel_values
logger.info("Converting model..." )
# load original state dict
_UpperCAmelCase : Union[str, Any] = torch.load(a_, map_location=torch.device("cpu" ) )
# rename keys
_UpperCAmelCase : List[str] = rename_keys(a_ )
# key and value matrices need special treatment
read_in_k_v(a_, a_ )
# create HuggingFace model and load state dict
_UpperCAmelCase : List[str] = GLPNForDepthEstimation(a_ )
model.load_state_dict(a_ )
model.eval()
# forward pass
_UpperCAmelCase : Dict = model(a_ )
_UpperCAmelCase : List[str] = outputs.predicted_depth
# verify output
if model_name is not None:
if "nyu" in model_name:
_UpperCAmelCase : Optional[Any] = torch.tensor(
[[4.41_47, 4.08_73, 4.06_73], [3.78_90, 3.28_81, 3.15_25], [3.76_74, 3.54_23, 3.49_13]] )
elif "kitti" in model_name:
_UpperCAmelCase : Tuple = torch.tensor(
[[3.42_91, 2.78_65, 2.51_51], [3.28_41, 2.70_21, 2.35_02], [3.11_47, 2.46_25, 2.24_81]] )
else:
raise ValueError(f"""Unknown model name: {model_name}""" )
_UpperCAmelCase : Dict = torch.Size([1, 480, 640] )
assert predicted_depth.shape == expected_shape
assert torch.allclose(predicted_depth[0, :3, :3], a_, atol=1e-4 )
print("Looks ok!" )
# finally, push to hub if required
if push_to_hub:
logger.info("Pushing model and image processor to the 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__":
__a = argparse.ArgumentParser()
parser.add_argument(
'--checkpoint_path',
default=None,
type=str,
help='Path to the original PyTorch checkpoint (.pth file).',
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
)
parser.add_argument(
'--push_to_hub', action='store_true', help='Whether to upload the model to the HuggingFace hub.'
)
parser.add_argument(
'--model_name',
default='glpn-kitti',
type=str,
help='Name of the model in case you\'re pushing to the hub.',
)
__a = parser.parse_args()
convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)
| 17
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|
'''simple docstring'''
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMInverseScheduler,
DDIMScheduler,
DPMSolverMultistepInverseScheduler,
DPMSolverMultistepScheduler,
StableDiffusionDiffEditPipeline,
UNetaDConditionModel,
)
from diffusers.utils import load_image, slow
from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, require_torch_gpu, torch_device
from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class A__ ( __lowerCAmelCase , __lowerCAmelCase , unittest.TestCase ):
"""simple docstring"""
UpperCamelCase_ : List[Any] = StableDiffusionDiffEditPipeline
UpperCamelCase_ : Tuple = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''height''', '''width''', '''image'''} | {'''image_latents'''}
UpperCamelCase_ : int = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {'''image'''} | {'''image_latents'''}
UpperCamelCase_ : int = frozenset(
[] ) # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
UpperCamelCase_ : Optional[Any] = frozenset([] )
def _lowerCAmelCase ( self : Tuple ) -> Any:
"""simple docstring"""
torch.manual_seed(0 )
_UpperCAmelCase : Tuple = UNetaDConditionModel(
block_out_channels=(3_2, 6_4) , layers_per_block=2 , sample_size=3_2 , in_channels=4 , out_channels=4 , down_block_types=("DownBlock2D", "CrossAttnDownBlock2D") , up_block_types=("CrossAttnUpBlock2D", "UpBlock2D") , cross_attention_dim=3_2 , attention_head_dim=(2, 4) , use_linear_projection=lowerCAmelCase_ , )
_UpperCAmelCase : List[Any] = DDIMScheduler(
beta_start=0.0_0085 , beta_end=0.012 , beta_schedule="scaled_linear" , clip_sample=lowerCAmelCase_ , set_alpha_to_one=lowerCAmelCase_ , )
_UpperCAmelCase : Optional[Any] = DDIMInverseScheduler(
beta_start=0.0_0085 , beta_end=0.012 , beta_schedule="scaled_linear" , clip_sample=lowerCAmelCase_ , set_alpha_to_zero=lowerCAmelCase_ , )
torch.manual_seed(0 )
_UpperCAmelCase : List[Any] = AutoencoderKL(
block_out_channels=[3_2, 6_4] , in_channels=3 , out_channels=3 , down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"] , up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"] , latent_channels=4 , sample_size=1_2_8 , )
torch.manual_seed(0 )
_UpperCAmelCase : Union[str, Any] = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=3_2 , intermediate_size=3_7 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1_0_0_0 , hidden_act="gelu" , projection_dim=5_1_2 , )
_UpperCAmelCase : int = CLIPTextModel(lowerCAmelCase_ )
_UpperCAmelCase : Dict = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )
_UpperCAmelCase : Tuple = {
"unet": unet,
"scheduler": scheduler,
"inverse_scheduler": inverse_scheduler,
"vae": vae,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"safety_checker": None,
"feature_extractor": None,
}
return components
def _lowerCAmelCase ( self : Union[str, Any] , lowerCAmelCase__ : List[Any] , lowerCAmelCase__ : Any=0 ) -> List[Any]:
"""simple docstring"""
_UpperCAmelCase : Optional[int] = floats_tensor((1, 1_6, 1_6) , rng=random.Random(lowerCAmelCase_ ) ).to(lowerCAmelCase_ )
_UpperCAmelCase : List[str] = floats_tensor((1, 2, 4, 1_6, 1_6) , rng=random.Random(lowerCAmelCase_ ) ).to(lowerCAmelCase_ )
if str(lowerCAmelCase_ ).startswith("mps" ):
_UpperCAmelCase : Union[str, Any] = torch.manual_seed(lowerCAmelCase_ )
else:
_UpperCAmelCase : Dict = torch.Generator(device=lowerCAmelCase_ ).manual_seed(lowerCAmelCase_ )
_UpperCAmelCase : List[Any] = {
"prompt": "a dog and a newt",
"mask_image": mask,
"image_latents": latents,
"generator": generator,
"num_inference_steps": 2,
"inpaint_strength": 1.0,
"guidance_scale": 6.0,
"output_type": "numpy",
}
return inputs
def _lowerCAmelCase ( self : Tuple , lowerCAmelCase__ : Dict , lowerCAmelCase__ : Any=0 ) -> List[str]:
"""simple docstring"""
_UpperCAmelCase : Union[str, Any] = floats_tensor((1, 3, 3_2, 3_2) , rng=random.Random(lowerCAmelCase_ ) ).to(lowerCAmelCase_ )
_UpperCAmelCase : Union[str, Any] = image.cpu().permute(0 , 2 , 3 , 1 )[0]
_UpperCAmelCase : int = Image.fromarray(np.uinta(lowerCAmelCase_ ) ).convert("RGB" )
if str(lowerCAmelCase_ ).startswith("mps" ):
_UpperCAmelCase : Optional[Any] = torch.manual_seed(lowerCAmelCase_ )
else:
_UpperCAmelCase : Optional[int] = torch.Generator(device=lowerCAmelCase_ ).manual_seed(lowerCAmelCase_ )
_UpperCAmelCase : Union[str, Any] = {
"image": image,
"source_prompt": "a cat and a frog",
"target_prompt": "a dog and a newt",
"generator": generator,
"num_inference_steps": 2,
"num_maps_per_mask": 2,
"mask_encode_strength": 1.0,
"guidance_scale": 6.0,
"output_type": "numpy",
}
return inputs
def _lowerCAmelCase ( self : List[str] , lowerCAmelCase__ : Dict , lowerCAmelCase__ : str=0 ) -> Union[str, Any]:
"""simple docstring"""
_UpperCAmelCase : Union[str, Any] = floats_tensor((1, 3, 3_2, 3_2) , rng=random.Random(lowerCAmelCase_ ) ).to(lowerCAmelCase_ )
_UpperCAmelCase : List[Any] = image.cpu().permute(0 , 2 , 3 , 1 )[0]
_UpperCAmelCase : List[str] = Image.fromarray(np.uinta(lowerCAmelCase_ ) ).convert("RGB" )
if str(lowerCAmelCase_ ).startswith("mps" ):
_UpperCAmelCase : Tuple = torch.manual_seed(lowerCAmelCase_ )
else:
_UpperCAmelCase : Union[str, Any] = torch.Generator(device=lowerCAmelCase_ ).manual_seed(lowerCAmelCase_ )
_UpperCAmelCase : Tuple = {
"image": image,
"prompt": "a cat and a frog",
"generator": generator,
"num_inference_steps": 2,
"inpaint_strength": 1.0,
"guidance_scale": 6.0,
"decode_latents": True,
"output_type": "numpy",
}
return inputs
def _lowerCAmelCase ( self : Union[str, Any] ) -> Optional[int]:
"""simple docstring"""
if not hasattr(self.pipeline_class , "_optional_components" ):
return
_UpperCAmelCase : int = self.get_dummy_components()
_UpperCAmelCase : Dict = self.pipeline_class(**lowerCAmelCase_ )
pipe.to(lowerCAmelCase_ )
pipe.set_progress_bar_config(disable=lowerCAmelCase_ )
# set all optional components to None and update pipeline config accordingly
for optional_component in pipe._optional_components:
setattr(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )
pipe.register_modules(**{optional_component: None for optional_component in pipe._optional_components} )
_UpperCAmelCase : List[str] = self.get_dummy_inputs(lowerCAmelCase_ )
_UpperCAmelCase : Optional[Any] = pipe(**lowerCAmelCase_ )[0]
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(lowerCAmelCase_ )
_UpperCAmelCase : Optional[Any] = self.pipeline_class.from_pretrained(lowerCAmelCase_ )
pipe_loaded.to(lowerCAmelCase_ )
pipe_loaded.set_progress_bar_config(disable=lowerCAmelCase_ )
for optional_component in pipe._optional_components:
self.assertTrue(
getattr(lowerCAmelCase_ , lowerCAmelCase_ ) is None , F"""`{optional_component}` did not stay set to None after loading.""" , )
_UpperCAmelCase : str = self.get_dummy_inputs(lowerCAmelCase_ )
_UpperCAmelCase : Dict = pipe_loaded(**lowerCAmelCase_ )[0]
_UpperCAmelCase : Optional[Any] = np.abs(output - output_loaded ).max()
self.assertLess(lowerCAmelCase_ , 1e-4 )
def _lowerCAmelCase ( self : Tuple ) -> str:
"""simple docstring"""
_UpperCAmelCase : List[Any] = "cpu"
_UpperCAmelCase : List[str] = self.get_dummy_components()
_UpperCAmelCase : Dict = self.pipeline_class(**lowerCAmelCase_ )
pipe.to(lowerCAmelCase_ )
pipe.set_progress_bar_config(disable=lowerCAmelCase_ )
_UpperCAmelCase : Dict = self.get_dummy_mask_inputs(lowerCAmelCase_ )
_UpperCAmelCase : Dict = pipe.generate_mask(**lowerCAmelCase_ )
_UpperCAmelCase : Optional[int] = mask[0, -3:, -3:]
self.assertEqual(mask.shape , (1, 1_6, 1_6) )
_UpperCAmelCase : Union[str, Any] = np.array([0] * 9 )
_UpperCAmelCase : Any = np.abs(mask_slice.flatten() - expected_slice ).max()
self.assertLessEqual(lowerCAmelCase_ , 1e-3 )
self.assertEqual(mask[0, -3, -4] , 0 )
def _lowerCAmelCase ( self : List[str] ) -> Union[str, Any]:
"""simple docstring"""
_UpperCAmelCase : Optional[Any] = "cpu"
_UpperCAmelCase : Tuple = self.get_dummy_components()
_UpperCAmelCase : Union[str, Any] = self.pipeline_class(**lowerCAmelCase_ )
pipe.to(lowerCAmelCase_ )
pipe.set_progress_bar_config(disable=lowerCAmelCase_ )
_UpperCAmelCase : List[str] = self.get_dummy_inversion_inputs(lowerCAmelCase_ )
_UpperCAmelCase : str = pipe.invert(**lowerCAmelCase_ ).images
_UpperCAmelCase : List[str] = image[0, -1, -3:, -3:]
self.assertEqual(image.shape , (2, 3_2, 3_2, 3) )
_UpperCAmelCase : Union[str, Any] = np.array(
[0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.5_1050, 0.5015, 0.4407, 0.4799] , )
_UpperCAmelCase : Any = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(lowerCAmelCase_ , 1e-3 )
def _lowerCAmelCase ( self : Optional[Any] ) -> Tuple:
"""simple docstring"""
super().test_inference_batch_single_identical(expected_max_diff=5e-3 )
def _lowerCAmelCase ( self : Union[str, Any] ) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase : str = "cpu"
_UpperCAmelCase : str = self.get_dummy_components()
_UpperCAmelCase : Optional[Any] = {"beta_start": 0.0_0085, "beta_end": 0.012, "beta_schedule": "scaled_linear"}
_UpperCAmelCase : int = DPMSolverMultistepScheduler(**lowerCAmelCase_ )
_UpperCAmelCase : List[Any] = DPMSolverMultistepInverseScheduler(**lowerCAmelCase_ )
_UpperCAmelCase : Any = self.pipeline_class(**lowerCAmelCase_ )
pipe.to(lowerCAmelCase_ )
pipe.set_progress_bar_config(disable=lowerCAmelCase_ )
_UpperCAmelCase : Dict = self.get_dummy_inversion_inputs(lowerCAmelCase_ )
_UpperCAmelCase : int = pipe.invert(**lowerCAmelCase_ ).images
_UpperCAmelCase : Optional[int] = image[0, -1, -3:, -3:]
self.assertEqual(image.shape , (2, 3_2, 3_2, 3) )
_UpperCAmelCase : Optional[int] = np.array(
[0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.5_1050, 0.5015, 0.4407, 0.4799] , )
_UpperCAmelCase : str = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(lowerCAmelCase_ , 1e-3 )
@require_torch_gpu
@slow
class A__ ( unittest.TestCase ):
"""simple docstring"""
def _lowerCAmelCase ( self : Dict ) -> Optional[Any]:
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@classmethod
def _lowerCAmelCase ( cls : List[str] ) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase : Optional[int] = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png" )
_UpperCAmelCase : Optional[int] = raw_image.convert("RGB" ).resize((7_6_8, 7_6_8) )
_UpperCAmelCase : Union[str, Any] = raw_image
def _lowerCAmelCase ( self : Tuple ) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase : List[Any] = torch.manual_seed(0 )
_UpperCAmelCase : Union[str, Any] = StableDiffusionDiffEditPipeline.from_pretrained(
"stabilityai/stable-diffusion-2-1" , safety_checker=lowerCAmelCase_ , torch_dtype=torch.floataa )
_UpperCAmelCase : Optional[int] = DDIMScheduler.from_config(pipe.scheduler.config )
_UpperCAmelCase : Dict = DDIMInverseScheduler.from_config(pipe.scheduler.config )
pipe.enable_model_cpu_offload()
pipe.set_progress_bar_config(disable=lowerCAmelCase_ )
_UpperCAmelCase : Dict = "a bowl of fruit"
_UpperCAmelCase : Union[str, Any] = "a bowl of pears"
_UpperCAmelCase : Dict = pipe.generate_mask(
image=self.raw_image , source_prompt=lowerCAmelCase_ , target_prompt=lowerCAmelCase_ , generator=lowerCAmelCase_ , )
_UpperCAmelCase : List[Any] = pipe.invert(
prompt=lowerCAmelCase_ , image=self.raw_image , inpaint_strength=0.7 , generator=lowerCAmelCase_ ).latents
_UpperCAmelCase : Tuple = pipe(
prompt=lowerCAmelCase_ , mask_image=lowerCAmelCase_ , image_latents=lowerCAmelCase_ , generator=lowerCAmelCase_ , negative_prompt=lowerCAmelCase_ , inpaint_strength=0.7 , output_type="numpy" , ).images[0]
_UpperCAmelCase : Any = (
np.array(
load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/diffedit/pears.png" ).resize((7_6_8, 7_6_8) ) )
/ 2_5_5
)
assert np.abs((expected_image - image).max() ) < 5e-1
def _lowerCAmelCase ( self : Optional[int] ) -> Union[str, Any]:
"""simple docstring"""
_UpperCAmelCase : Optional[Any] = torch.manual_seed(0 )
_UpperCAmelCase : str = StableDiffusionDiffEditPipeline.from_pretrained(
"stabilityai/stable-diffusion-2-1" , safety_checker=lowerCAmelCase_ , torch_dtype=torch.floataa )
_UpperCAmelCase : int = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
_UpperCAmelCase : str = DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config )
pipe.enable_model_cpu_offload()
pipe.set_progress_bar_config(disable=lowerCAmelCase_ )
_UpperCAmelCase : Union[str, Any] = "a bowl of fruit"
_UpperCAmelCase : Optional[Any] = "a bowl of pears"
_UpperCAmelCase : List[str] = pipe.generate_mask(
image=self.raw_image , source_prompt=lowerCAmelCase_ , target_prompt=lowerCAmelCase_ , generator=lowerCAmelCase_ , )
_UpperCAmelCase : Dict = pipe.invert(
prompt=lowerCAmelCase_ , image=self.raw_image , inpaint_strength=0.7 , generator=lowerCAmelCase_ , num_inference_steps=2_5 , ).latents
_UpperCAmelCase : Optional[Any] = pipe(
prompt=lowerCAmelCase_ , mask_image=lowerCAmelCase_ , image_latents=lowerCAmelCase_ , generator=lowerCAmelCase_ , negative_prompt=lowerCAmelCase_ , inpaint_strength=0.7 , num_inference_steps=2_5 , output_type="numpy" , ).images[0]
_UpperCAmelCase : Optional[int] = (
np.array(
load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/diffedit/pears.png" ).resize((7_6_8, 7_6_8) ) )
/ 2_5_5
)
assert np.abs((expected_image - image).max() ) < 5e-1
| 364
|
'''simple docstring'''
import contextlib
import csv
import json
import os
import sqlitea
import tarfile
import textwrap
import zipfile
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
import datasets
import datasets.config
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( ):
_UpperCAmelCase : Optional[Any] = 10
_UpperCAmelCase : int = datasets.Features(
{
"tokens": datasets.Sequence(datasets.Value("string" ) ),
"labels": datasets.Sequence(datasets.ClassLabel(names=["negative", "positive"] ) ),
"answers": datasets.Sequence(
{
"text": datasets.Value("string" ),
"answer_start": datasets.Value("int32" ),
} ),
"id": datasets.Value("int64" ),
} )
_UpperCAmelCase : List[str] = datasets.Dataset.from_dict(
{
"tokens": [["foo"] * 5] * n,
"labels": [[1] * 5] * n,
"answers": [{"answer_start": [97], "text": ["1976"]}] * 10,
"id": list(range(a_ ) ),
}, features=a_, )
return dataset
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: Optional[int], a_: Dict ):
_UpperCAmelCase : Any = str(tmp_path_factory.mktemp("data" ) / "file.arrow" )
dataset.map(cache_file_name=a_ )
return filename
# FILE_CONTENT + files
__a = '\\n Text data.\n Second line of data.'
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: Dict ):
_UpperCAmelCase : Dict = tmp_path_factory.mktemp("data" ) / "file.txt"
_UpperCAmelCase : Tuple = FILE_CONTENT
with open(a_, "w" ) as f:
f.write(a_ )
return filename
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: Union[str, Any] ):
import bza
_UpperCAmelCase : str = tmp_path_factory.mktemp("data" ) / "file.txt.bz2"
_UpperCAmelCase : Optional[int] = bytes(a_, "utf-8" )
with bza.open(a_, "wb" ) as f:
f.write(a_ )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: Union[str, Any] ):
import gzip
_UpperCAmelCase : str = str(tmp_path_factory.mktemp("data" ) / "file.txt.gz" )
_UpperCAmelCase : Any = bytes(a_, "utf-8" )
with gzip.open(a_, "wb" ) as f:
f.write(a_ )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: str ):
if datasets.config.LZ4_AVAILABLE:
import lza.frame
_UpperCAmelCase : Optional[int] = tmp_path_factory.mktemp("data" ) / "file.txt.lz4"
_UpperCAmelCase : str = bytes(a_, "utf-8" )
with lza.frame.open(a_, "wb" ) as f:
f.write(a_ )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: int, a_: Any ):
if datasets.config.PY7ZR_AVAILABLE:
import pyazr
_UpperCAmelCase : Any = tmp_path_factory.mktemp("data" ) / "file.txt.7z"
with pyazr.SevenZipFile(a_, "w" ) as archive:
archive.write(a_, arcname=os.path.basename(a_ ) )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: Any, a_: List[str] ):
import tarfile
_UpperCAmelCase : Union[str, Any] = tmp_path_factory.mktemp("data" ) / "file.txt.tar"
with tarfile.TarFile(a_, "w" ) as f:
f.add(a_, arcname=os.path.basename(a_ ) )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: int ):
import lzma
_UpperCAmelCase : List[Any] = tmp_path_factory.mktemp("data" ) / "file.txt.xz"
_UpperCAmelCase : List[str] = bytes(a_, "utf-8" )
with lzma.open(a_, "wb" ) as f:
f.write(a_ )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: Dict, a_: Tuple ):
import zipfile
_UpperCAmelCase : Tuple = tmp_path_factory.mktemp("data" ) / "file.txt.zip"
with zipfile.ZipFile(a_, "w" ) as f:
f.write(a_, arcname=os.path.basename(a_ ) )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: Optional[int] ):
if datasets.config.ZSTANDARD_AVAILABLE:
import zstandard as zstd
_UpperCAmelCase : Optional[int] = tmp_path_factory.mktemp("data" ) / "file.txt.zst"
_UpperCAmelCase : int = bytes(a_, "utf-8" )
with zstd.open(a_, "wb" ) as f:
f.write(a_ )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: Optional[int] ):
_UpperCAmelCase : List[str] = tmp_path_factory.mktemp("data" ) / "file.xml"
_UpperCAmelCase : Tuple = textwrap.dedent(
"\\n <?xml version=\"1.0\" encoding=\"UTF-8\" ?>\n <tmx version=\"1.4\">\n <header segtype=\"sentence\" srclang=\"ca\" />\n <body>\n <tu>\n <tuv xml:lang=\"ca\"><seg>Contingut 1</seg></tuv>\n <tuv xml:lang=\"en\"><seg>Content 1</seg></tuv>\n </tu>\n <tu>\n <tuv xml:lang=\"ca\"><seg>Contingut 2</seg></tuv>\n <tuv xml:lang=\"en\"><seg>Content 2</seg></tuv>\n </tu>\n <tu>\n <tuv xml:lang=\"ca\"><seg>Contingut 3</seg></tuv>\n <tuv xml:lang=\"en\"><seg>Content 3</seg></tuv>\n </tu>\n <tu>\n <tuv xml:lang=\"ca\"><seg>Contingut 4</seg></tuv>\n <tuv xml:lang=\"en\"><seg>Content 4</seg></tuv>\n </tu>\n <tu>\n <tuv xml:lang=\"ca\"><seg>Contingut 5</seg></tuv>\n <tuv xml:lang=\"en\"><seg>Content 5</seg></tuv>\n </tu>\n </body>\n </tmx>" )
with open(a_, "w" ) as f:
f.write(a_ )
return filename
__a = [
{'col_1': '0', 'col_2': 0, 'col_3': 0.0},
{'col_1': '1', 'col_2': 1, 'col_3': 1.0},
{'col_1': '2', 'col_2': 2, 'col_3': 2.0},
{'col_1': '3', 'col_2': 3, 'col_3': 3.0},
]
__a = [
{'col_1': '4', 'col_2': 4, 'col_3': 4.0},
{'col_1': '5', 'col_2': 5, 'col_3': 5.0},
]
__a = {
'col_1': ['0', '1', '2', '3'],
'col_2': [0, 1, 2, 3],
'col_3': [0.0, 1.0, 2.0, 3.0],
}
__a = [
{'col_3': 0.0, 'col_1': '0', 'col_2': 0},
{'col_3': 1.0, 'col_1': '1', 'col_2': 1},
]
__a = [
{'col_1': 's0', 'col_2': 0, 'col_3': 0.0},
{'col_1': 's1', 'col_2': 1, 'col_3': 1.0},
{'col_1': 's2', 'col_2': 2, 'col_3': 2.0},
{'col_1': 's3', 'col_2': 3, 'col_3': 3.0},
]
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( ):
return DATA_DICT_OF_LISTS
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: Union[str, Any] ):
_UpperCAmelCase : str = datasets.Dataset.from_dict(a_ )
_UpperCAmelCase : Optional[int] = str(tmp_path_factory.mktemp("data" ) / "dataset.arrow" )
dataset.map(cache_file_name=a_ )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: str ):
_UpperCAmelCase : int = str(tmp_path_factory.mktemp("data" ) / "dataset.sqlite" )
with contextlib.closing(sqlitea.connect(a_ ) ) as con:
_UpperCAmelCase : List[Any] = con.cursor()
cur.execute("CREATE TABLE dataset(col_1 text, col_2 int, col_3 real)" )
for item in DATA:
cur.execute("INSERT INTO dataset(col_1, col_2, col_3) VALUES (?, ?, ?)", tuple(item.values() ) )
con.commit()
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: Any ):
_UpperCAmelCase : Dict = str(tmp_path_factory.mktemp("data" ) / "dataset.csv" )
with open(a_, "w", newline="" ) as f:
_UpperCAmelCase : Dict = csv.DictWriter(a_, fieldnames=["col_1", "col_2", "col_3"] )
writer.writeheader()
for item in DATA:
writer.writerow(a_ )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: Union[str, Any] ):
_UpperCAmelCase : Union[str, Any] = str(tmp_path_factory.mktemp("data" ) / "dataset2.csv" )
with open(a_, "w", newline="" ) as f:
_UpperCAmelCase : Optional[int] = csv.DictWriter(a_, fieldnames=["col_1", "col_2", "col_3"] )
writer.writeheader()
for item in DATA:
writer.writerow(a_ )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: str, a_: str ):
import bza
_UpperCAmelCase : str = tmp_path_factory.mktemp("data" ) / "dataset.csv.bz2"
with open(a_, "rb" ) as f:
_UpperCAmelCase : Any = f.read()
# data = bytes(FILE_CONTENT, "utf-8")
with bza.open(a_, "wb" ) as f:
f.write(a_ )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: Optional[int], a_: Dict, a_: Optional[int] ):
_UpperCAmelCase : List[Any] = tmp_path_factory.mktemp("data" ) / "dataset.csv.zip"
with zipfile.ZipFile(a_, "w" ) as f:
f.write(a_, arcname=os.path.basename(a_ ) )
f.write(a_, arcname=os.path.basename(a_ ) )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: List[str], a_: Union[str, Any], a_: int ):
_UpperCAmelCase : int = tmp_path_factory.mktemp("data" ) / "dataset.csv.zip"
with zipfile.ZipFile(a_, "w" ) as f:
f.write(a_, arcname=os.path.basename(csv_path.replace(".csv", ".CSV" ) ) )
f.write(a_, arcname=os.path.basename(csva_path.replace(".csv", ".CSV" ) ) )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: Any, a_: Union[str, Any], a_: Tuple ):
_UpperCAmelCase : Any = tmp_path_factory.mktemp("data" ) / "dataset_with_dir.csv.zip"
with zipfile.ZipFile(a_, "w" ) as f:
f.write(a_, arcname=os.path.join("main_dir", os.path.basename(a_ ) ) )
f.write(a_, arcname=os.path.join("main_dir", os.path.basename(a_ ) ) )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: Tuple ):
_UpperCAmelCase : Optional[Any] = str(tmp_path_factory.mktemp("data" ) / "dataset.parquet" )
_UpperCAmelCase : Dict = pa.schema(
{
"col_1": pa.string(),
"col_2": pa.intaa(),
"col_3": pa.floataa(),
} )
with open(a_, "wb" ) as f:
_UpperCAmelCase : Tuple = pq.ParquetWriter(a_, schema=a_ )
_UpperCAmelCase : Tuple = pa.Table.from_pydict({k: [DATA[i][k] for i in range(len(a_ ) )] for k in DATA[0]}, schema=a_ )
writer.write_table(a_ )
writer.close()
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: Any ):
_UpperCAmelCase : Union[str, Any] = str(tmp_path_factory.mktemp("data" ) / "dataset.json" )
_UpperCAmelCase : str = {"data": DATA}
with open(a_, "w" ) as f:
json.dump(a_, a_ )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: Union[str, Any] ):
_UpperCAmelCase : Optional[int] = str(tmp_path_factory.mktemp("data" ) / "dataset.json" )
_UpperCAmelCase : Dict = {"data": DATA_DICT_OF_LISTS}
with open(a_, "w" ) as f:
json.dump(a_, a_ )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: int ):
_UpperCAmelCase : Optional[Any] = str(tmp_path_factory.mktemp("data" ) / "dataset.jsonl" )
with open(a_, "w" ) as f:
for item in DATA:
f.write(json.dumps(a_ ) + "\n" )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: Tuple ):
_UpperCAmelCase : Any = str(tmp_path_factory.mktemp("data" ) / "dataset2.jsonl" )
with open(a_, "w" ) as f:
for item in DATA:
f.write(json.dumps(a_ ) + "\n" )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: Any ):
_UpperCAmelCase : int = str(tmp_path_factory.mktemp("data" ) / "dataset_312.jsonl" )
with open(a_, "w" ) as f:
for item in DATA_312:
f.write(json.dumps(a_ ) + "\n" )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: Optional[Any] ):
_UpperCAmelCase : Optional[int] = str(tmp_path_factory.mktemp("data" ) / "dataset-str.jsonl" )
with open(a_, "w" ) as f:
for item in DATA_STR:
f.write(json.dumps(a_ ) + "\n" )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: Union[str, Any], a_: Any ):
import gzip
_UpperCAmelCase : Optional[Any] = str(tmp_path_factory.mktemp("data" ) / "dataset.txt.gz" )
with open(a_, "rb" ) as orig_file:
with gzip.open(a_, "wb" ) as zipped_file:
zipped_file.writelines(a_ )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: Optional[Any], a_: Tuple ):
import gzip
_UpperCAmelCase : List[Any] = str(tmp_path_factory.mktemp("data" ) / "dataset.jsonl.gz" )
with open(a_, "rb" ) as orig_file:
with gzip.open(a_, "wb" ) as zipped_file:
zipped_file.writelines(a_ )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: Dict, a_: List[Any], a_: Union[str, Any] ):
_UpperCAmelCase : Tuple = tmp_path_factory.mktemp("data" ) / "dataset.jsonl.zip"
with zipfile.ZipFile(a_, "w" ) as f:
f.write(a_, arcname=os.path.basename(a_ ) )
f.write(a_, arcname=os.path.basename(a_ ) )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: Union[str, Any], a_: Optional[int], a_: Optional[Any], a_: Dict ):
_UpperCAmelCase : Dict = tmp_path_factory.mktemp("data" ) / "dataset_nested.jsonl.zip"
with zipfile.ZipFile(a_, "w" ) as f:
f.write(a_, arcname=os.path.join("nested", os.path.basename(a_ ) ) )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: List[Any], a_: Optional[int], a_: List[str] ):
_UpperCAmelCase : Dict = tmp_path_factory.mktemp("data" ) / "dataset_with_dir.jsonl.zip"
with zipfile.ZipFile(a_, "w" ) as f:
f.write(a_, arcname=os.path.join("main_dir", os.path.basename(a_ ) ) )
f.write(a_, arcname=os.path.join("main_dir", os.path.basename(a_ ) ) )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: List[Any], a_: List[Any], a_: str ):
_UpperCAmelCase : Optional[Any] = tmp_path_factory.mktemp("data" ) / "dataset.jsonl.tar"
with tarfile.TarFile(a_, "w" ) as f:
f.add(a_, arcname=os.path.basename(a_ ) )
f.add(a_, arcname=os.path.basename(a_ ) )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: List[str], a_: List[Any], a_: Tuple, a_: Dict ):
_UpperCAmelCase : List[Any] = tmp_path_factory.mktemp("data" ) / "dataset_nested.jsonl.tar"
with tarfile.TarFile(a_, "w" ) as f:
f.add(a_, arcname=os.path.join("nested", os.path.basename(a_ ) ) )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: List[str] ):
_UpperCAmelCase : List[str] = ["0", "1", "2", "3"]
_UpperCAmelCase : Tuple = str(tmp_path_factory.mktemp("data" ) / "dataset.txt" )
with open(a_, "w" ) as f:
for item in data:
f.write(item + "\n" )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: Union[str, Any] ):
_UpperCAmelCase : Dict = ["0", "1", "2", "3"]
_UpperCAmelCase : Optional[Any] = str(tmp_path_factory.mktemp("data" ) / "dataset2.txt" )
with open(a_, "w" ) as f:
for item in data:
f.write(item + "\n" )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: Any ):
_UpperCAmelCase : int = ["0", "1", "2", "3"]
_UpperCAmelCase : str = tmp_path_factory.mktemp("data" ) / "dataset.abc"
with open(a_, "w" ) as f:
for item in data:
f.write(item + "\n" )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: Optional[Any], a_: Any, a_: Union[str, Any] ):
_UpperCAmelCase : Union[str, Any] = tmp_path_factory.mktemp("data" ) / "dataset.text.zip"
with zipfile.ZipFile(a_, "w" ) as f:
f.write(a_, arcname=os.path.basename(a_ ) )
f.write(a_, arcname=os.path.basename(a_ ) )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: Optional[int], a_: List[Any], a_: List[Any] ):
_UpperCAmelCase : List[Any] = tmp_path_factory.mktemp("data" ) / "dataset_with_dir.text.zip"
with zipfile.ZipFile(a_, "w" ) as f:
f.write(a_, arcname=os.path.join("main_dir", os.path.basename(a_ ) ) )
f.write(a_, arcname=os.path.join("main_dir", os.path.basename(a_ ) ) )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: Any, a_: str, a_: Tuple ):
_UpperCAmelCase : List[Any] = tmp_path_factory.mktemp("data" ) / "dataset.ext.zip"
with zipfile.ZipFile(a_, "w" ) as f:
f.write(a_, arcname=os.path.basename("unsupported.ext" ) )
f.write(a_, arcname=os.path.basename("unsupported_2.ext" ) )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: Optional[Any] ):
_UpperCAmelCase : List[str] = "\n".join(["First", "Second\u2029with Unicode new line", "Third"] )
_UpperCAmelCase : str = str(tmp_path_factory.mktemp("data" ) / "dataset_with_unicode_new_lines.txt" )
with open(a_, "w", encoding="utf-8" ) as f:
f.write(a_ )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( ):
return os.path.join("tests", "features", "data", "test_image_rgb.jpg" )
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( ):
return os.path.join("tests", "features", "data", "test_audio_44100.wav" )
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: int, a_: Optional[Any] ):
_UpperCAmelCase : Union[str, Any] = tmp_path_factory.mktemp("data" ) / "dataset.img.zip"
with zipfile.ZipFile(a_, "w" ) as f:
f.write(a_, arcname=os.path.basename(a_ ) )
f.write(a_, arcname=os.path.basename(a_ ).replace(".jpg", "2.jpg" ) )
return path
@pytest.fixture(scope="session" )
def __UpperCAmelCase ( a_: Tuple ):
_UpperCAmelCase : Optional[Any] = tmp_path_factory.mktemp("data_dir" )
(data_dir / "subdir").mkdir()
with open(data_dir / "subdir" / "train.txt", "w" ) as f:
f.write("foo\n" * 10 )
with open(data_dir / "subdir" / "test.txt", "w" ) as f:
f.write("bar\n" * 10 )
# hidden file
with open(data_dir / "subdir" / ".test.txt", "w" ) as f:
f.write("bar\n" * 10 )
# hidden directory
(data_dir / ".subdir").mkdir()
with open(data_dir / ".subdir" / "train.txt", "w" ) as f:
f.write("foo\n" * 10 )
with open(data_dir / ".subdir" / "test.txt", "w" ) as f:
f.write("bar\n" * 10 )
return data_dir
| 17
| 0
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__a = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = ['NllbTokenizer']
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = ['NllbTokenizerFast']
if TYPE_CHECKING:
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_nllb import NllbTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_nllb_fast import NllbTokenizerFast
else:
import sys
__a = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 365
|
'''simple docstring'''
import unittest
from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
@require_sentencepiece
@slow # see https://github.com/huggingface/transformers/issues/11457
class A__ ( UpperCamelCase , unittest.TestCase ):
"""simple docstring"""
UpperCamelCase_ : str = BarthezTokenizer
UpperCamelCase_ : List[Any] = BarthezTokenizerFast
UpperCamelCase_ : Optional[int] = True
UpperCamelCase_ : Optional[int] = True
def _lowerCAmelCase ( self : Optional[int] ) -> Dict:
"""simple docstring"""
super().setUp()
_UpperCAmelCase : Tuple = BarthezTokenizerFast.from_pretrained("moussaKam/mbarthez" )
tokenizer.save_pretrained(self.tmpdirname )
tokenizer.save_pretrained(self.tmpdirname , legacy_format=lowerCAmelCase__ )
_UpperCAmelCase : List[str] = tokenizer
def _lowerCAmelCase ( self : List[str] ) -> Optional[int]:
"""simple docstring"""
_UpperCAmelCase : Tuple = "<pad>"
_UpperCAmelCase : Dict = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCAmelCase__ ) , lowerCAmelCase__ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCAmelCase__ ) , lowerCAmelCase__ )
def _lowerCAmelCase ( self : Tuple ) -> Optional[int]:
"""simple docstring"""
_UpperCAmelCase : List[str] = 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(lowerCAmelCase__ ) , 1_0_1_1_2_2 )
def _lowerCAmelCase ( self : Union[str, Any] ) -> Dict:
"""simple docstring"""
self.assertEqual(self.get_tokenizer().vocab_size , 1_0_1_1_2_2 )
@require_torch
def _lowerCAmelCase ( self : Any ) -> int:
"""simple docstring"""
_UpperCAmelCase : int = ["A long paragraph for summarization.", "Another paragraph for summarization."]
_UpperCAmelCase : Optional[int] = [0, 5_7, 3_0_1_8, 7_0_3_0_7, 9_1, 2]
_UpperCAmelCase : int = self.tokenizer(
lowerCAmelCase__ , max_length=len(lowerCAmelCase__ ) , padding=lowerCAmelCase__ , truncation=lowerCAmelCase__ , return_tensors="pt" )
self.assertIsInstance(lowerCAmelCase__ , lowerCAmelCase__ )
self.assertEqual((2, 6) , batch.input_ids.shape )
self.assertEqual((2, 6) , batch.attention_mask.shape )
_UpperCAmelCase : str = batch.input_ids.tolist()[0]
self.assertListEqual(lowerCAmelCase__ , lowerCAmelCase__ )
def _lowerCAmelCase ( self : str ) -> Optional[Any]:
"""simple docstring"""
if not self.test_rust_tokenizer:
return
_UpperCAmelCase : Optional[int] = self.get_tokenizer()
_UpperCAmelCase : Optional[int] = self.get_rust_tokenizer()
_UpperCAmelCase : Tuple = "I was born in 92000, and this is falsé."
_UpperCAmelCase : Dict = tokenizer.tokenize(lowerCAmelCase__ )
_UpperCAmelCase : Union[str, Any] = rust_tokenizer.tokenize(lowerCAmelCase__ )
self.assertListEqual(lowerCAmelCase__ , lowerCAmelCase__ )
_UpperCAmelCase : Dict = tokenizer.encode(lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ )
_UpperCAmelCase : Union[str, Any] = rust_tokenizer.encode(lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ )
self.assertListEqual(lowerCAmelCase__ , lowerCAmelCase__ )
_UpperCAmelCase : Optional[Any] = self.get_rust_tokenizer()
_UpperCAmelCase : Optional[Any] = tokenizer.encode(lowerCAmelCase__ )
_UpperCAmelCase : Optional[int] = rust_tokenizer.encode(lowerCAmelCase__ )
self.assertListEqual(lowerCAmelCase__ , lowerCAmelCase__ )
@slow
def _lowerCAmelCase ( self : int ) -> int:
"""simple docstring"""
_UpperCAmelCase : Optional[Any] = {"input_ids": [[0, 4_9_0, 1_4_3_2_8, 4_5_0_7, 3_5_4, 4_7, 4_3_6_6_9, 9_5, 2_5, 7_8_1_1_7, 2_0_2_1_5, 1_9_7_7_9, 1_9_0, 2_2, 4_0_0, 4, 3_5_3_4_3, 8_0_3_1_0, 6_0_3, 8_6, 2_4_9_3_7, 1_0_5, 3_3_4_3_8, 9_4_7_6_2, 1_9_6, 3_9_6_4_2, 7, 1_5, 1_5_9_3_3, 1_7_3, 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], [0, 1_0_5_3_4, 8_7, 2_5, 6_6, 3_3_5_8, 1_9_6, 5_5_2_8_9, 8, 8_2_9_6_1, 8_1, 2_2_0_4, 7_5_2_0_3, 7, 1_5, 7_6_3, 1_2_9_5_6, 2_1_6, 1_7_8, 1_4_3_2_8, 9_5_9_5, 1_3_7_7, 6_9_6_9_3, 7, 4_4_8, 7_1_0_2_1, 1_9_6, 1_8_1_0_6, 1_4_3_7, 1_3_9_7_4, 1_0_8, 9_0_8_3, 4, 4_9_3_1_5, 7, 3_9, 8_6, 1_3_2_6, 2_7_9_3, 4_6_3_3_3, 4, 4_4_8, 1_9_6, 7_4_5_8_8, 7, 4_9_3_1_5, 7, 3_9, 2_1, 8_2_2, 3_8_4_7_0, 7_4, 2_1, 6_6_7_2_3, 6_2_4_8_0, 8, 2_2_0_5_0, 5, 2]], "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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]} # noqa: E501
# fmt: on
# moussaKam/mbarthez is a french model. So we also use french texts.
_UpperCAmelCase : Tuple = [
"Le transformeur est un modèle d'apprentissage profond introduit en 2017, "
"utilisé principalement dans le domaine du traitement automatique des langues (TAL).",
"À l'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus "
"pour gérer des données séquentielles, telles que le langage naturel, pour des tâches "
"telles que la traduction et la synthèse de texte.",
]
self.tokenizer_integration_test_util(
expected_encoding=lowerCAmelCase__ , model_name="moussaKam/mbarthez" , revision="c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6" , sequences=lowerCAmelCase__ , )
| 17
| 0
|
'''simple docstring'''
from math import sqrt
def __UpperCAmelCase ( a_: Optional[int] ):
_UpperCAmelCase : Tuple = 0
for i in range(1, int(sqrt(SCREAMING_SNAKE_CASE__ ) + 1 ) ):
if n % i == 0 and i != sqrt(SCREAMING_SNAKE_CASE__ ):
total += i + n // i
elif i == sqrt(SCREAMING_SNAKE_CASE__ ):
total += i
return total - n
def __UpperCAmelCase ( a_: str = 10_000 ):
_UpperCAmelCase : Optional[Any] = sum(
i
for i in range(1, SCREAMING_SNAKE_CASE__ )
if sum_of_divisors(sum_of_divisors(SCREAMING_SNAKE_CASE__ ) ) == i and sum_of_divisors(SCREAMING_SNAKE_CASE__ ) != i )
return total
if __name__ == "__main__":
print(solution(int(str(input()).strip())))
| 366
|
'''simple docstring'''
import unittest
import numpy as np
import requests
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
__a = False
if is_vision_available():
from PIL import Image
from transformers import PixaStructImageProcessor
class A__ ( unittest.TestCase ):
"""simple docstring"""
def __init__( self : int , lowerCAmelCase__ : Optional[int] , lowerCAmelCase__ : Optional[Any]=7 , lowerCAmelCase__ : int=3 , lowerCAmelCase__ : List[Any]=1_8 , lowerCAmelCase__ : str=3_0 , lowerCAmelCase__ : str=4_0_0 , lowerCAmelCase__ : Union[str, Any]=None , lowerCAmelCase__ : Optional[Any]=True , lowerCAmelCase__ : str=True , lowerCAmelCase__ : List[Any]=None , ) -> List[Any]:
"""simple docstring"""
_UpperCAmelCase : List[Any] = size if size is not None else {"height": 2_0, "width": 2_0}
_UpperCAmelCase : Optional[Any] = parent
_UpperCAmelCase : Tuple = batch_size
_UpperCAmelCase : str = num_channels
_UpperCAmelCase : Optional[Any] = image_size
_UpperCAmelCase : Dict = min_resolution
_UpperCAmelCase : str = max_resolution
_UpperCAmelCase : List[Any] = size
_UpperCAmelCase : Union[str, Any] = do_normalize
_UpperCAmelCase : Optional[Any] = do_convert_rgb
_UpperCAmelCase : str = [5_1_2, 1_0_2_4, 2_0_4_8, 4_0_9_6]
_UpperCAmelCase : str = patch_size if patch_size is not None else {"height": 1_6, "width": 1_6}
def _lowerCAmelCase ( self : List[str] ) -> int:
"""simple docstring"""
return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}
def _lowerCAmelCase ( self : Any ) -> str:
"""simple docstring"""
_UpperCAmelCase : Dict = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg"
_UpperCAmelCase : Optional[Any] = Image.open(requests.get(lowerCAmelCase__ , stream=lowerCAmelCase__ ).raw ).convert("RGB" )
return raw_image
@unittest.skipIf(
not is_torch_greater_or_equal_than_1_11 , reason='''`Pix2StructImageProcessor` requires `torch>=1.11.0`.''' , )
@require_torch
@require_vision
class A__ ( UpperCamelCase , unittest.TestCase ):
"""simple docstring"""
UpperCamelCase_ : Any = PixaStructImageProcessor if is_vision_available() else None
def _lowerCAmelCase ( self : Any ) -> Optional[int]:
"""simple docstring"""
_UpperCAmelCase : Tuple = PixaStructImageProcessingTester(self )
@property
def _lowerCAmelCase ( self : Tuple ) -> int:
"""simple docstring"""
return self.image_processor_tester.prepare_image_processor_dict()
def _lowerCAmelCase ( self : Any ) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase : str = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(lowerCAmelCase__ , "do_normalize" ) )
self.assertTrue(hasattr(lowerCAmelCase__ , "do_convert_rgb" ) )
def _lowerCAmelCase ( self : Optional[Any] ) -> Dict:
"""simple docstring"""
_UpperCAmelCase : Optional[Any] = self.image_processor_tester.prepare_dummy_image()
_UpperCAmelCase : Optional[int] = self.image_processing_class(**self.image_processor_dict )
_UpperCAmelCase : str = 2_0_4_8
_UpperCAmelCase : Any = image_processor(lowerCAmelCase__ , return_tensors="pt" , max_patches=lowerCAmelCase__ )
self.assertTrue(torch.allclose(inputs.flattened_patches.mean() , torch.tensor(0.0606 ) , atol=1e-3 , rtol=1e-3 ) )
def _lowerCAmelCase ( self : Dict ) -> int:
"""simple docstring"""
_UpperCAmelCase : Optional[Any] = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
_UpperCAmelCase : Tuple = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCAmelCase__ )
for image in image_inputs:
self.assertIsInstance(lowerCAmelCase__ , Image.Image )
# Test not batched input
_UpperCAmelCase : List[str] = (
(self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
* self.image_processor_tester.num_channels
) + 2
for max_patch in self.image_processor_tester.max_patches:
# Test not batched input
_UpperCAmelCase : Union[str, Any] = image_processor(
image_inputs[0] , return_tensors="pt" , max_patches=lowerCAmelCase__ ).flattened_patches
self.assertEqual(
encoded_images.shape , (1, max_patch, expected_hidden_dim) , )
# Test batched
_UpperCAmelCase : str = image_processor(
lowerCAmelCase__ , return_tensors="pt" , max_patches=lowerCAmelCase__ ).flattened_patches
self.assertEqual(
encoded_images.shape , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) , )
def _lowerCAmelCase ( self : Optional[int] ) -> List[str]:
"""simple docstring"""
_UpperCAmelCase : Any = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
_UpperCAmelCase : List[Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCAmelCase__ )
for image in image_inputs:
self.assertIsInstance(lowerCAmelCase__ , Image.Image )
# Test not batched input
_UpperCAmelCase : Union[str, Any] = (
(self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
* self.image_processor_tester.num_channels
) + 2
_UpperCAmelCase : str = True
for max_patch in self.image_processor_tester.max_patches:
# Test not batched input
with self.assertRaises(lowerCAmelCase__ ):
_UpperCAmelCase : str = image_processor(
image_inputs[0] , return_tensors="pt" , max_patches=lowerCAmelCase__ ).flattened_patches
_UpperCAmelCase : Any = "Hello"
_UpperCAmelCase : Optional[int] = image_processor(
image_inputs[0] , return_tensors="pt" , max_patches=lowerCAmelCase__ , header_text=lowerCAmelCase__ ).flattened_patches
self.assertEqual(
encoded_images.shape , (1, max_patch, expected_hidden_dim) , )
# Test batched
_UpperCAmelCase : List[Any] = image_processor(
lowerCAmelCase__ , return_tensors="pt" , max_patches=lowerCAmelCase__ , header_text=lowerCAmelCase__ ).flattened_patches
self.assertEqual(
encoded_images.shape , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) , )
def _lowerCAmelCase ( self : List[str] ) -> List[Any]:
"""simple docstring"""
_UpperCAmelCase : Union[str, Any] = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
_UpperCAmelCase : List[str] = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCAmelCase__ , numpify=lowerCAmelCase__ )
for image in image_inputs:
self.assertIsInstance(lowerCAmelCase__ , np.ndarray )
_UpperCAmelCase : Any = (
(self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
* self.image_processor_tester.num_channels
) + 2
for max_patch in self.image_processor_tester.max_patches:
# Test not batched input
_UpperCAmelCase : int = image_processor(
image_inputs[0] , return_tensors="pt" , max_patches=lowerCAmelCase__ ).flattened_patches
self.assertEqual(
encoded_images.shape , (1, max_patch, expected_hidden_dim) , )
# Test batched
_UpperCAmelCase : Union[str, Any] = image_processor(
lowerCAmelCase__ , return_tensors="pt" , max_patches=lowerCAmelCase__ ).flattened_patches
self.assertEqual(
encoded_images.shape , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) , )
def _lowerCAmelCase ( self : int ) -> str:
"""simple docstring"""
_UpperCAmelCase : List[str] = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
_UpperCAmelCase : Any = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCAmelCase__ , torchify=lowerCAmelCase__ )
for image in image_inputs:
self.assertIsInstance(lowerCAmelCase__ , torch.Tensor )
# Test not batched input
_UpperCAmelCase : List[str] = (
(self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
* self.image_processor_tester.num_channels
) + 2
for max_patch in self.image_processor_tester.max_patches:
# Test not batched input
_UpperCAmelCase : Union[str, Any] = image_processor(
image_inputs[0] , return_tensors="pt" , max_patches=lowerCAmelCase__ ).flattened_patches
self.assertEqual(
encoded_images.shape , (1, max_patch, expected_hidden_dim) , )
# Test batched
_UpperCAmelCase : str = image_processor(
lowerCAmelCase__ , return_tensors="pt" , max_patches=lowerCAmelCase__ ).flattened_patches
self.assertEqual(
encoded_images.shape , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) , )
@unittest.skipIf(
not is_torch_greater_or_equal_than_1_11 , reason='''`Pix2StructImageProcessor` requires `torch>=1.11.0`.''' , )
@require_torch
@require_vision
class A__ ( UpperCamelCase , unittest.TestCase ):
"""simple docstring"""
UpperCamelCase_ : List[Any] = PixaStructImageProcessor if is_vision_available() else None
def _lowerCAmelCase ( self : Union[str, Any] ) -> Union[str, Any]:
"""simple docstring"""
_UpperCAmelCase : Any = PixaStructImageProcessingTester(self , num_channels=4 )
_UpperCAmelCase : List[Any] = 3
@property
def _lowerCAmelCase ( self : Union[str, Any] ) -> Tuple:
"""simple docstring"""
return self.image_processor_tester.prepare_image_processor_dict()
def _lowerCAmelCase ( self : Dict ) -> Any:
"""simple docstring"""
_UpperCAmelCase : str = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(lowerCAmelCase__ , "do_normalize" ) )
self.assertTrue(hasattr(lowerCAmelCase__ , "do_convert_rgb" ) )
def _lowerCAmelCase ( self : int ) -> List[str]:
"""simple docstring"""
_UpperCAmelCase : Optional[int] = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
_UpperCAmelCase : List[str] = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCAmelCase__ )
for image in image_inputs:
self.assertIsInstance(lowerCAmelCase__ , Image.Image )
# Test not batched input
_UpperCAmelCase : str = (
(self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
* (self.image_processor_tester.num_channels - 1)
) + 2
for max_patch in self.image_processor_tester.max_patches:
# Test not batched input
_UpperCAmelCase : Any = image_processor(
image_inputs[0] , return_tensors="pt" , max_patches=lowerCAmelCase__ ).flattened_patches
self.assertEqual(
encoded_images.shape , (1, max_patch, expected_hidden_dim) , )
# Test batched
_UpperCAmelCase : Tuple = image_processor(
lowerCAmelCase__ , return_tensors="pt" , max_patches=lowerCAmelCase__ ).flattened_patches
self.assertEqual(
encoded_images.shape , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) , )
| 17
| 0
|
'''simple docstring'''
class A__ :
"""simple docstring"""
def __init__( self : Dict , lowerCAmelCase__ : Tuple ) -> str:
"""simple docstring"""
_UpperCAmelCase : str = arr.split("," )
def _lowerCAmelCase ( self : str ) -> Optional[int]:
"""simple docstring"""
_UpperCAmelCase : List[str] = [int(self.array[0] )] * len(self.array )
_UpperCAmelCase : str = [int(self.array[0] )] * len(self.array )
for i in range(1 , len(self.array ) ):
_UpperCAmelCase : Optional[Any] = max(
int(self.array[i] ) + sum_value[i - 1] , int(self.array[i] ) )
_UpperCAmelCase : Tuple = max(sum_value[i] , rear[i - 1] )
return rear[len(self.array ) - 1]
if __name__ == "__main__":
__a = input('please input some numbers:')
__a = SubArray(whole_array)
__a = array.solve_sub_array()
print(('the results is:', re))
| 367
|
'''simple docstring'''
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a = logging.get_logger(__name__)
__a = {
'huggingface/time-series-transformer-tourism-monthly': (
'https://huggingface.co/huggingface/time-series-transformer-tourism-monthly/resolve/main/config.json'
),
# See all TimeSeriesTransformer models at https://huggingface.co/models?filter=time_series_transformer
}
class A__ ( UpperCamelCase ):
"""simple docstring"""
UpperCamelCase_ : Tuple = '''time_series_transformer'''
UpperCamelCase_ : Optional[Any] = {
'''hidden_size''': '''d_model''',
'''num_attention_heads''': '''encoder_attention_heads''',
'''num_hidden_layers''': '''encoder_layers''',
}
def __init__( self : Optional[int] , lowerCAmelCase__ : Optional[int] = None , lowerCAmelCase__ : Optional[int] = None , lowerCAmelCase__ : str = "student_t" , lowerCAmelCase__ : str = "nll" , lowerCAmelCase__ : int = 1 , lowerCAmelCase__ : List[int] = [1, 2, 3, 4, 5, 6, 7] , lowerCAmelCase__ : Optional[Union[str, bool]] = "mean" , lowerCAmelCase__ : int = 0 , lowerCAmelCase__ : int = 0 , lowerCAmelCase__ : int = 0 , lowerCAmelCase__ : int = 0 , lowerCAmelCase__ : Optional[List[int]] = None , lowerCAmelCase__ : Optional[List[int]] = None , lowerCAmelCase__ : int = 3_2 , lowerCAmelCase__ : int = 3_2 , lowerCAmelCase__ : int = 2 , lowerCAmelCase__ : int = 2 , lowerCAmelCase__ : int = 2 , lowerCAmelCase__ : int = 2 , lowerCAmelCase__ : bool = True , lowerCAmelCase__ : str = "gelu" , lowerCAmelCase__ : int = 6_4 , lowerCAmelCase__ : float = 0.1 , lowerCAmelCase__ : float = 0.1 , lowerCAmelCase__ : float = 0.1 , lowerCAmelCase__ : float = 0.1 , lowerCAmelCase__ : float = 0.1 , lowerCAmelCase__ : int = 1_0_0 , lowerCAmelCase__ : float = 0.02 , lowerCAmelCase__ : Dict=True , **lowerCAmelCase__ : Tuple , ) -> Tuple:
"""simple docstring"""
_UpperCAmelCase : Optional[int] = prediction_length
_UpperCAmelCase : Optional[Any] = context_length or prediction_length
_UpperCAmelCase : Optional[Any] = distribution_output
_UpperCAmelCase : Union[str, Any] = loss
_UpperCAmelCase : Dict = input_size
_UpperCAmelCase : int = num_time_features
_UpperCAmelCase : Any = lags_sequence
_UpperCAmelCase : Dict = scaling
_UpperCAmelCase : Tuple = num_dynamic_real_features
_UpperCAmelCase : Dict = num_static_real_features
_UpperCAmelCase : Union[str, Any] = num_static_categorical_features
if cardinality and num_static_categorical_features > 0:
if len(lowerCAmelCase__ ) != num_static_categorical_features:
raise ValueError(
"The cardinality should be a list of the same length as `num_static_categorical_features`" )
_UpperCAmelCase : Optional[int] = cardinality
else:
_UpperCAmelCase : Optional[Any] = [0]
if embedding_dimension and num_static_categorical_features > 0:
if len(lowerCAmelCase__ ) != num_static_categorical_features:
raise ValueError(
"The embedding dimension should be a list of the same length as `num_static_categorical_features`" )
_UpperCAmelCase : List[Any] = embedding_dimension
else:
_UpperCAmelCase : Optional[Any] = [min(5_0 , (cat + 1) // 2 ) for cat in self.cardinality]
_UpperCAmelCase : str = num_parallel_samples
# Transformer architecture configuration
_UpperCAmelCase : Union[str, Any] = input_size * len(lowerCAmelCase__ ) + self._number_of_features
_UpperCAmelCase : str = d_model
_UpperCAmelCase : Optional[Any] = encoder_attention_heads
_UpperCAmelCase : Dict = decoder_attention_heads
_UpperCAmelCase : List[Any] = encoder_ffn_dim
_UpperCAmelCase : str = decoder_ffn_dim
_UpperCAmelCase : Dict = encoder_layers
_UpperCAmelCase : str = decoder_layers
_UpperCAmelCase : Any = dropout
_UpperCAmelCase : str = attention_dropout
_UpperCAmelCase : List[Any] = activation_dropout
_UpperCAmelCase : Dict = encoder_layerdrop
_UpperCAmelCase : Any = decoder_layerdrop
_UpperCAmelCase : Optional[Any] = activation_function
_UpperCAmelCase : Tuple = init_std
_UpperCAmelCase : List[str] = use_cache
super().__init__(is_encoder_decoder=lowerCAmelCase__ , **lowerCAmelCase__ )
@property
def _lowerCAmelCase ( self : str ) -> int:
"""simple docstring"""
return (
sum(self.embedding_dimension )
+ self.num_dynamic_real_features
+ self.num_time_features
+ self.num_static_real_features
+ self.input_size * 2 # the log1p(abs(loc)) and log(scale) features
)
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|
'''simple docstring'''
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import Callable, Dict, List, Tuple
import timm
import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams, RegNetYaagf, RegNetYaagf, RegNetYaaagf
from huggingface_hub import cached_download, hf_hub_url
from torch import Tensor
from vissl.models.model_helpers import get_trunk_forward_outputs
from transformers import AutoImageProcessor, RegNetConfig, RegNetForImageClassification, RegNetModel
from transformers.utils import logging
logging.set_verbosity_info()
__a = logging.get_logger()
@dataclass
class A__ :
"""simple docstring"""
UpperCamelCase_ : Optional[Any] = 42
UpperCamelCase_ : Tuple = field(default_factory=__a )
UpperCamelCase_ : int = field(default_factory=__a )
def _lowerCAmelCase ( self : int , lowerCAmelCase__ : List[Any] , lowerCAmelCase__ : Tensor , lowerCAmelCase__ : Tensor ) -> Dict:
"""simple docstring"""
_UpperCAmelCase : Optional[int] = len(list(m.modules() ) ) == 1 or isinstance(UpperCamelCase__ , nn.Convad ) or isinstance(UpperCamelCase__ , nn.BatchNormad )
if has_not_submodules:
self.traced.append(UpperCamelCase__ )
def __call__( self : Optional[Any] , lowerCAmelCase__ : Tensor ) -> Any:
"""simple docstring"""
for m in self.module.modules():
self.handles.append(m.register_forward_hook(self._forward_hook ) )
self.module(UpperCamelCase__ )
[x.remove() for x in self.handles]
return self
@property
def _lowerCAmelCase ( self : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
return list(filter(lambda lowerCAmelCase__ : len(list(x.state_dict().keys() ) ) > 0 , self.traced ) )
@dataclass
class A__ :
"""simple docstring"""
UpperCamelCase_ : str = 42
UpperCamelCase_ : Union[str, Any] = 42
UpperCamelCase_ : str = 1
UpperCamelCase_ : Tuple = field(default_factory=__a )
UpperCamelCase_ : List[Any] = field(default_factory=__a )
UpperCamelCase_ : int = True
def __call__( self : int , lowerCAmelCase__ : Tensor ) -> str:
"""simple docstring"""
_UpperCAmelCase : Optional[Any] = Tracker(self.dest )(UpperCamelCase__ ).parametrized
_UpperCAmelCase : Dict = Tracker(self.src )(UpperCamelCase__ ).parametrized
_UpperCAmelCase : List[Any] = list(filter(lambda lowerCAmelCase__ : type(UpperCamelCase__ ) not in self.src_skip , UpperCamelCase__ ) )
_UpperCAmelCase : Optional[Any] = list(filter(lambda lowerCAmelCase__ : type(UpperCamelCase__ ) not in self.dest_skip , UpperCamelCase__ ) )
if len(UpperCamelCase__ ) != len(UpperCamelCase__ ) and self.raise_if_mismatch:
raise Exception(
F"""Numbers of operations are different. Source module has {len(UpperCamelCase__ )} operations while"""
F""" destination module has {len(UpperCamelCase__ )}.""" )
for dest_m, src_m in zip(UpperCamelCase__ , UpperCamelCase__ ):
dest_m.load_state_dict(src_m.state_dict() )
if self.verbose == 1:
print(F"""Transfered from={src_m} to={dest_m}""" )
class A__ ( nn.Module ):
"""simple docstring"""
def __init__( self : Optional[int] , lowerCAmelCase__ : nn.Module ) -> Dict:
"""simple docstring"""
super().__init__()
_UpperCAmelCase : List[Any] = []
# - get the stem
feature_blocks.append(("conv1", model.stem) )
# - get all the feature blocks
for k, v in model.trunk_output.named_children():
assert k.startswith("block" ), F"""Unexpected layer name {k}"""
_UpperCAmelCase : Optional[Any] = len(UpperCamelCase__ ) + 1
feature_blocks.append((F"""res{block_index}""", v) )
_UpperCAmelCase : List[str] = nn.ModuleDict(UpperCamelCase__ )
def _lowerCAmelCase ( self : Union[str, Any] , lowerCAmelCase__ : Tensor ) -> Tuple:
"""simple docstring"""
return get_trunk_forward_outputs(
UpperCamelCase__ , out_feat_keys=UpperCamelCase__ , feature_blocks=self._feature_blocks , )
class A__ ( __a ):
"""simple docstring"""
def _lowerCAmelCase ( self : Tuple , lowerCAmelCase__ : str ) -> str:
"""simple docstring"""
_UpperCAmelCase : Tuple = x.split("-" )
return x_split[0] + x_split[1] + "_" + "".join(x_split[2:] )
def __getitem__( self : Union[str, Any] , lowerCAmelCase__ : str ) -> Callable[[], Tuple[nn.Module, Dict]]:
"""simple docstring"""
if x not in self:
_UpperCAmelCase : List[str] = self.convert_name_to_timm(UpperCamelCase__ )
_UpperCAmelCase : Union[str, Any] = partial(lambda: (timm.create_model(UpperCamelCase__ , pretrained=UpperCamelCase__ ).eval(), None) )
else:
_UpperCAmelCase : List[str] = super().__getitem__(UpperCamelCase__ )
return val
class A__ ( __a ):
"""simple docstring"""
def __getitem__( self : Optional[Any] , lowerCAmelCase__ : str ) -> Callable[[], nn.Module]:
"""simple docstring"""
if "seer" in x and "in1k" not in x:
_UpperCAmelCase : int = RegNetModel
else:
_UpperCAmelCase : List[str] = RegNetForImageClassification
return val
def __UpperCAmelCase ( a_: Union[str, Any], a_: List[str], a_: List[Tuple[str, str]] ):
for from_key, to_key in keys:
_UpperCAmelCase : Dict = from_state_dict[from_key].clone()
print(f"""Copied key={from_key} to={to_key}""" )
return to_state_dict
def __UpperCAmelCase ( a_: str, a_: Callable[[], nn.Module], a_: Callable[[], nn.Module], a_: RegNetConfig, a_: Path, a_: bool = True, ):
print(f"""Converting {name}...""" )
with torch.no_grad():
_UpperCAmelCase , _UpperCAmelCase : Optional[int] = from_model_func()
_UpperCAmelCase : List[str] = our_model_func(__UpperCamelCase ).eval()
_UpperCAmelCase : List[Any] = ModuleTransfer(src=__UpperCamelCase, dest=__UpperCamelCase, raise_if_mismatch=__UpperCamelCase )
_UpperCAmelCase : int = torch.randn((1, 3, 224, 224) )
module_transfer(__UpperCamelCase )
if from_state_dict is not None:
_UpperCAmelCase : List[Any] = []
# for seer - in1k finetuned we have to manually copy the head
if "seer" in name and "in1k" in name:
_UpperCAmelCase : Optional[Any] = [("0.clf.0.weight", "classifier.1.weight"), ("0.clf.0.bias", "classifier.1.bias")]
_UpperCAmelCase : Optional[int] = manually_copy_vissl_head(__UpperCamelCase, our_model.state_dict(), __UpperCamelCase )
our_model.load_state_dict(__UpperCamelCase )
_UpperCAmelCase : Optional[Any] = our_model(__UpperCamelCase, output_hidden_states=__UpperCamelCase )
_UpperCAmelCase : int = (
our_outputs.logits if isinstance(__UpperCamelCase, __UpperCamelCase ) else our_outputs.last_hidden_state
)
_UpperCAmelCase : Any = from_model(__UpperCamelCase )
_UpperCAmelCase : List[str] = from_output[-1] if type(__UpperCamelCase ) is list else from_output
# now since I don't want to use any config files, vissl seer model doesn't actually have an head, so let's just check the last hidden state
if "seer" in name and "in1k" in name:
_UpperCAmelCase : Union[str, Any] = our_outputs.hidden_states[-1]
assert torch.allclose(__UpperCamelCase, __UpperCamelCase ), "The model logits don't match the original one."
if push_to_hub:
our_model.push_to_hub(
repo_path_or_name=save_directory / name, commit_message="Add model", use_temp_dir=__UpperCamelCase, )
_UpperCAmelCase : Union[str, Any] = 224 if "seer" not in name else 384
# we can use the convnext one
_UpperCAmelCase : List[Any] = AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k", size=__UpperCamelCase )
image_processor.push_to_hub(
repo_path_or_name=save_directory / name, commit_message="Add image processor", use_temp_dir=__UpperCamelCase, )
print(f"""Pushed {name}""" )
def __UpperCAmelCase ( a_: Path, a_: str = None, a_: bool = True ):
_UpperCAmelCase : Any = "imagenet-1k-id2label.json"
_UpperCAmelCase : List[str] = 1_000
_UpperCAmelCase : Tuple = (1, num_labels)
_UpperCAmelCase : Any = "huggingface/label-files"
_UpperCAmelCase : Optional[Any] = num_labels
_UpperCAmelCase : Tuple = json.load(open(cached_download(hf_hub_url(__UpperCamelCase, __UpperCamelCase, repo_type="dataset" ) ), "r" ) )
_UpperCAmelCase : List[Any] = {int(__UpperCamelCase ): v for k, v in idalabel.items()}
_UpperCAmelCase : Optional[int] = idalabel
_UpperCAmelCase : Tuple = {v: k for k, v in idalabel.items()}
_UpperCAmelCase : Tuple = partial(__UpperCamelCase, num_labels=__UpperCamelCase, idalabel=__UpperCamelCase, labelaid=__UpperCamelCase )
_UpperCAmelCase : Union[str, Any] = {
"regnet-x-002": ImageNetPreTrainedConfig(
depths=[1, 1, 4, 7], hidden_sizes=[24, 56, 152, 368], groups_width=8, layer_type="x" ),
"regnet-x-004": ImageNetPreTrainedConfig(
depths=[1, 2, 7, 12], hidden_sizes=[32, 64, 160, 384], groups_width=16, layer_type="x" ),
"regnet-x-006": ImageNetPreTrainedConfig(
depths=[1, 3, 5, 7], hidden_sizes=[48, 96, 240, 528], groups_width=24, layer_type="x" ),
"regnet-x-008": ImageNetPreTrainedConfig(
depths=[1, 3, 7, 5], hidden_sizes=[64, 128, 288, 672], groups_width=16, layer_type="x" ),
"regnet-x-016": ImageNetPreTrainedConfig(
depths=[2, 4, 10, 2], hidden_sizes=[72, 168, 408, 912], groups_width=24, layer_type="x" ),
"regnet-x-032": ImageNetPreTrainedConfig(
depths=[2, 6, 15, 2], hidden_sizes=[96, 192, 432, 1_008], groups_width=48, layer_type="x" ),
"regnet-x-040": ImageNetPreTrainedConfig(
depths=[2, 5, 14, 2], hidden_sizes=[80, 240, 560, 1_360], groups_width=40, layer_type="x" ),
"regnet-x-064": ImageNetPreTrainedConfig(
depths=[2, 4, 10, 1], hidden_sizes=[168, 392, 784, 1_624], groups_width=56, layer_type="x" ),
"regnet-x-080": ImageNetPreTrainedConfig(
depths=[2, 5, 15, 1], hidden_sizes=[80, 240, 720, 1_920], groups_width=120, layer_type="x" ),
"regnet-x-120": ImageNetPreTrainedConfig(
depths=[2, 5, 11, 1], hidden_sizes=[224, 448, 896, 2_240], groups_width=112, layer_type="x" ),
"regnet-x-160": ImageNetPreTrainedConfig(
depths=[2, 6, 13, 1], hidden_sizes=[256, 512, 896, 2_048], groups_width=128, layer_type="x" ),
"regnet-x-320": ImageNetPreTrainedConfig(
depths=[2, 7, 13, 1], hidden_sizes=[336, 672, 1_344, 2_520], groups_width=168, layer_type="x" ),
# y variant
"regnet-y-002": ImageNetPreTrainedConfig(depths=[1, 1, 4, 7], hidden_sizes=[24, 56, 152, 368], groups_width=8 ),
"regnet-y-004": ImageNetPreTrainedConfig(
depths=[1, 3, 6, 6], hidden_sizes=[48, 104, 208, 440], groups_width=8 ),
"regnet-y-006": ImageNetPreTrainedConfig(
depths=[1, 3, 7, 4], hidden_sizes=[48, 112, 256, 608], groups_width=16 ),
"regnet-y-008": ImageNetPreTrainedConfig(
depths=[1, 3, 8, 2], hidden_sizes=[64, 128, 320, 768], groups_width=16 ),
"regnet-y-016": ImageNetPreTrainedConfig(
depths=[2, 6, 17, 2], hidden_sizes=[48, 120, 336, 888], groups_width=24 ),
"regnet-y-032": ImageNetPreTrainedConfig(
depths=[2, 5, 13, 1], hidden_sizes=[72, 216, 576, 1_512], groups_width=24 ),
"regnet-y-040": ImageNetPreTrainedConfig(
depths=[2, 6, 12, 2], hidden_sizes=[128, 192, 512, 1_088], groups_width=64 ),
"regnet-y-064": ImageNetPreTrainedConfig(
depths=[2, 7, 14, 2], hidden_sizes=[144, 288, 576, 1_296], groups_width=72 ),
"regnet-y-080": ImageNetPreTrainedConfig(
depths=[2, 4, 10, 1], hidden_sizes=[168, 448, 896, 2_016], groups_width=56 ),
"regnet-y-120": ImageNetPreTrainedConfig(
depths=[2, 5, 11, 1], hidden_sizes=[224, 448, 896, 2_240], groups_width=112 ),
"regnet-y-160": ImageNetPreTrainedConfig(
depths=[2, 4, 11, 1], hidden_sizes=[224, 448, 1_232, 3_024], groups_width=112 ),
"regnet-y-320": ImageNetPreTrainedConfig(
depths=[2, 5, 12, 1], hidden_sizes=[232, 696, 1_392, 3_712], groups_width=232 ),
# models created by SEER -> https://arxiv.org/abs/2202.08360
"regnet-y-320-seer": RegNetConfig(depths=[2, 5, 12, 1], hidden_sizes=[232, 696, 1_392, 3_712], groups_width=232 ),
"regnet-y-640-seer": RegNetConfig(depths=[2, 5, 12, 1], hidden_sizes=[328, 984, 1_968, 4_920], groups_width=328 ),
"regnet-y-1280-seer": RegNetConfig(
depths=[2, 7, 17, 1], hidden_sizes=[528, 1_056, 2_904, 7_392], groups_width=264 ),
"regnet-y-2560-seer": RegNetConfig(
depths=[3, 7, 16, 1], hidden_sizes=[640, 1_696, 2_544, 5_088], groups_width=640 ),
"regnet-y-10b-seer": ImageNetPreTrainedConfig(
depths=[2, 7, 17, 1], hidden_sizes=[2_020, 4_040, 11_110, 28_280], groups_width=1_010 ),
# finetuned on imagenet
"regnet-y-320-seer-in1k": ImageNetPreTrainedConfig(
depths=[2, 5, 12, 1], hidden_sizes=[232, 696, 1_392, 3_712], groups_width=232 ),
"regnet-y-640-seer-in1k": ImageNetPreTrainedConfig(
depths=[2, 5, 12, 1], hidden_sizes=[328, 984, 1_968, 4_920], groups_width=328 ),
"regnet-y-1280-seer-in1k": ImageNetPreTrainedConfig(
depths=[2, 7, 17, 1], hidden_sizes=[528, 1_056, 2_904, 7_392], groups_width=264 ),
"regnet-y-2560-seer-in1k": ImageNetPreTrainedConfig(
depths=[3, 7, 16, 1], hidden_sizes=[640, 1_696, 2_544, 5_088], groups_width=640 ),
"regnet-y-10b-seer-in1k": ImageNetPreTrainedConfig(
depths=[2, 7, 17, 1], hidden_sizes=[2_020, 4_040, 11_110, 28_280], groups_width=1_010 ),
}
_UpperCAmelCase : List[Any] = NameToOurModelFuncMap()
_UpperCAmelCase : List[Any] = NameToFromModelFuncMap()
# add seer weights logic
def load_using_classy_vision(a_: str, a_: Callable[[], nn.Module] ) -> Tuple[nn.Module, Dict]:
_UpperCAmelCase : str = torch.hub.load_state_dict_from_url(__UpperCamelCase, model_dir=str(__UpperCamelCase ), map_location="cpu" )
_UpperCAmelCase : Dict = model_func()
# check if we have a head, if yes add it
_UpperCAmelCase : Any = files["classy_state_dict"]["base_model"]["model"]
_UpperCAmelCase : str = model_state_dict["trunk"]
model.load_state_dict(__UpperCamelCase )
return model.eval(), model_state_dict["heads"]
# pretrained
_UpperCAmelCase : Tuple = partial(
__UpperCamelCase, "https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet32d/seer_regnet32gf_model_iteration244000.torch", lambda: FakeRegNetVisslWrapper(RegNetYaagf() ), )
_UpperCAmelCase : Optional[Any] = partial(
__UpperCamelCase, "https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet64/seer_regnet64gf_model_final_checkpoint_phase0.torch", lambda: FakeRegNetVisslWrapper(RegNetYaagf() ), )
_UpperCAmelCase : str = partial(
__UpperCamelCase, "https://dl.fbaipublicfiles.com/vissl/model_zoo/swav_ig1b_regnet128Gf_cnstant_bs32_node16_sinkhorn10_proto16k_syncBN64_warmup8k/model_final_checkpoint_phase0.torch", lambda: FakeRegNetVisslWrapper(RegNetYaaagf() ), )
_UpperCAmelCase : Tuple = partial(
__UpperCamelCase, "https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch", lambda: FakeRegNetVisslWrapper(
RegNet(RegNetParams(depth=27, group_width=1_010, w_a=1_744, w_a=620.83, w_m=2.52 ) ) ), )
# IN1K finetuned
_UpperCAmelCase : Tuple = partial(
__UpperCamelCase, "https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch", lambda: FakeRegNetVisslWrapper(RegNetYaagf() ), )
_UpperCAmelCase : int = partial(
__UpperCamelCase, "https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch", lambda: FakeRegNetVisslWrapper(RegNetYaagf() ), )
_UpperCAmelCase : Tuple = partial(
__UpperCamelCase, "https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet128_finetuned_in1k_model_final_checkpoint_phase78.torch", lambda: FakeRegNetVisslWrapper(RegNetYaaagf() ), )
_UpperCAmelCase : Optional[Any] = partial(
__UpperCamelCase, "https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch", lambda: FakeRegNetVisslWrapper(
RegNet(RegNetParams(depth=27, group_width=1_010, w_a=1_744, w_a=620.83, w_m=2.52 ) ) ), )
if model_name:
convert_weight_and_push(
__UpperCamelCase, names_to_from_model_map[model_name], names_to_ours_model_map[model_name], names_to_config[model_name], __UpperCamelCase, __UpperCamelCase, )
else:
for model_name, config in names_to_config.items():
convert_weight_and_push(
__UpperCamelCase, names_to_from_model_map[model_name], names_to_ours_model_map[model_name], __UpperCamelCase, __UpperCamelCase, __UpperCamelCase, )
return config, expected_shape
if __name__ == "__main__":
__a = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--model_name',
default=None,
type=str,
help=(
'The name of the model you wish to convert, it must be one of the supported regnet* architecture,'
' currently: regnetx-*, regnety-*. If `None`, all of them will the converted.'
),
)
parser.add_argument(
'--pytorch_dump_folder_path',
default=None,
type=Path,
required=True,
help='Path to the output PyTorch model directory.',
)
parser.add_argument(
'--push_to_hub',
default=True,
type=bool,
required=False,
help='If True, push model and image processor to the hub.',
)
__a = parser.parse_args()
__a = args.pytorch_dump_folder_path
pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)
| 368
|
'''simple docstring'''
import baseaa
def __UpperCAmelCase ( a_: str ):
return baseaa.baaencode(string.encode("utf-8" ) )
def __UpperCAmelCase ( a_: bytes ):
return baseaa.baadecode(a_ ).decode("utf-8" )
if __name__ == "__main__":
__a = 'Hello World!'
__a = baseaa_encode(test)
print(encoded)
__a = baseaa_decode(encoded)
print(decoded)
| 17
| 0
|
'''simple docstring'''
import unittest
from transformers import AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
if is_flax_available():
import jax.numpy as jnp
from transformers import FlaxXLMRobertaModel
@require_sentencepiece
@require_tokenizers
@require_flax
class A__ ( unittest.TestCase ):
"""simple docstring"""
@slow
def _lowerCAmelCase ( self : List[str] ) -> Union[str, Any]:
"""simple docstring"""
_UpperCAmelCase : str = FlaxXLMRobertaModel.from_pretrained("xlm-roberta-base" )
_UpperCAmelCase : List[Any] = AutoTokenizer.from_pretrained("xlm-roberta-base" )
_UpperCAmelCase : Any = "The dog is cute and lives in the garden house"
_UpperCAmelCase : Tuple = jnp.array([tokenizer.encode(_lowercase )] )
_UpperCAmelCase : Optional[Any] = (1, 1_2, 7_6_8) # batch_size, sequence_length, embedding_vector_dim
_UpperCAmelCase : List[str] = jnp.array(
[[-0.0101, 0.1218, -0.0803, 0.0801, 0.1327, 0.0776, -0.1215, 0.2383, 0.3338, 0.3106, 0.0300, 0.0252]] )
_UpperCAmelCase : Tuple = model(_lowercase )["last_hidden_state"]
self.assertEqual(output.shape , _lowercase )
# compare the actual values for a slice of last dim
self.assertTrue(jnp.allclose(output[:, :, -1] , _lowercase , atol=1e-3 ) )
| 369
|
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow
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.models.xglm.modeling_tf_xglm import (
TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFXGLMForCausalLM,
TFXGLMModel,
)
@require_tf
class A__ :
"""simple docstring"""
UpperCamelCase_ : Any = XGLMConfig
UpperCamelCase_ : Union[str, Any] = {}
UpperCamelCase_ : Dict = '''gelu'''
def __init__( self : Optional[int] , lowerCAmelCase__ : Union[str, Any] , lowerCAmelCase__ : Optional[Any]=1_4 , lowerCAmelCase__ : Any=7 , lowerCAmelCase__ : Optional[Any]=True , lowerCAmelCase__ : List[str]=True , lowerCAmelCase__ : Any=True , lowerCAmelCase__ : List[str]=9_9 , lowerCAmelCase__ : Any=3_2 , lowerCAmelCase__ : Optional[int]=2 , lowerCAmelCase__ : List[Any]=4 , lowerCAmelCase__ : Any=3_7 , lowerCAmelCase__ : List[Any]="gelu" , lowerCAmelCase__ : List[Any]=0.1 , lowerCAmelCase__ : Dict=0.1 , lowerCAmelCase__ : Optional[int]=5_1_2 , lowerCAmelCase__ : Optional[Any]=0.02 , ) -> int:
"""simple docstring"""
_UpperCAmelCase : Optional[Any] = parent
_UpperCAmelCase : str = batch_size
_UpperCAmelCase : str = seq_length
_UpperCAmelCase : int = is_training
_UpperCAmelCase : List[Any] = use_input_mask
_UpperCAmelCase : Optional[int] = use_labels
_UpperCAmelCase : str = vocab_size
_UpperCAmelCase : int = d_model
_UpperCAmelCase : Tuple = num_hidden_layers
_UpperCAmelCase : Tuple = num_attention_heads
_UpperCAmelCase : Tuple = ffn_dim
_UpperCAmelCase : Any = activation_function
_UpperCAmelCase : Union[str, Any] = activation_dropout
_UpperCAmelCase : Union[str, Any] = attention_dropout
_UpperCAmelCase : Any = max_position_embeddings
_UpperCAmelCase : int = initializer_range
_UpperCAmelCase : Any = None
_UpperCAmelCase : int = 0
_UpperCAmelCase : Union[str, Any] = 2
_UpperCAmelCase : Tuple = 1
def _lowerCAmelCase ( self : Optional[Any] ) -> List[Any]:
"""simple docstring"""
return XGLMConfig.from_pretrained("facebook/xglm-564M" )
def _lowerCAmelCase ( self : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
_UpperCAmelCase : int = tf.clip_by_value(
ids_tensor([self.batch_size, self.seq_length] , self.vocab_size ) , clip_value_min=0 , clip_value_max=3 )
_UpperCAmelCase : Any = None
if self.use_input_mask:
_UpperCAmelCase : Union[str, Any] = random_attention_mask([self.batch_size, self.seq_length] )
_UpperCAmelCase : Optional[Any] = self.get_config()
_UpperCAmelCase : Dict = floats_tensor([self.num_hidden_layers, self.num_attention_heads] , 2 )
return (
config,
input_ids,
input_mask,
head_mask,
)
def _lowerCAmelCase ( self : int ) -> Any:
"""simple docstring"""
return XGLMConfig(
vocab_size=self.vocab_size , d_model=self.hidden_size , num_layers=self.num_hidden_layers , attention_heads=self.num_attention_heads , ffn_dim=self.ffn_dim , activation_function=self.activation_function , activation_dropout=self.activation_dropout , attention_dropout=self.attention_dropout , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , use_cache=lowerCAmelCase__ , bos_token_id=self.bos_token_id , eos_token_id=self.eos_token_id , pad_token_id=self.pad_token_id , return_dict=lowerCAmelCase__ , )
def _lowerCAmelCase ( self : Tuple ) -> Union[str, Any]:
"""simple docstring"""
_UpperCAmelCase : Union[str, Any] = self.prepare_config_and_inputs()
(
(
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) ,
) : List[Any] = config_and_inputs
_UpperCAmelCase : Optional[int] = {
"input_ids": input_ids,
"head_mask": head_mask,
}
return config, inputs_dict
@require_tf
class A__ ( UpperCamelCase , UpperCamelCase , unittest.TestCase ):
"""simple docstring"""
UpperCamelCase_ : str = (TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
UpperCamelCase_ : Any = (TFXGLMForCausalLM,) if is_tf_available() else ()
UpperCamelCase_ : Tuple = (
{'''feature-extraction''': TFXGLMModel, '''text-generation''': TFXGLMForCausalLM} if is_tf_available() else {}
)
UpperCamelCase_ : Dict = False
UpperCamelCase_ : List[Any] = False
UpperCamelCase_ : Tuple = False
def _lowerCAmelCase ( self : List[str] ) -> int:
"""simple docstring"""
_UpperCAmelCase : Dict = TFXGLMModelTester(self )
_UpperCAmelCase : Dict = ConfigTester(self , config_class=lowerCAmelCase__ , n_embd=3_7 )
def _lowerCAmelCase ( self : List[str] ) -> Dict:
"""simple docstring"""
self.config_tester.run_common_tests()
@slow
def _lowerCAmelCase ( self : List[str] ) -> Union[str, Any]:
"""simple docstring"""
for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_UpperCAmelCase : Optional[int] = TFXGLMModel.from_pretrained(lowerCAmelCase__ )
self.assertIsNotNone(lowerCAmelCase__ )
@unittest.skip(reason="Currently, model embeddings are going to undergo a major refactor." )
def _lowerCAmelCase ( self : Union[str, Any] ) -> int:
"""simple docstring"""
super().test_resize_token_embeddings()
@require_tf
class A__ ( unittest.TestCase ):
"""simple docstring"""
@slow
def _lowerCAmelCase ( self : Optional[int] , lowerCAmelCase__ : Optional[Any]=True ) -> Tuple:
"""simple docstring"""
_UpperCAmelCase : Optional[int] = TFXGLMForCausalLM.from_pretrained("facebook/xglm-564M" )
_UpperCAmelCase : Any = tf.convert_to_tensor([[2, 2_6_8, 9_8_6_5]] , dtype=tf.intaa ) # The dog
# </s> The dog is a very friendly dog. He is very affectionate and loves to play with other
# fmt: off
_UpperCAmelCase : int = [2, 2_6_8, 9_8_6_5, 6_7, 1_1, 1_9_8_8, 5_7_2_5_2, 9_8_6_5, 5, 9_8_4, 6_7, 1_9_8_8, 2_1_3_8_3_8, 1_6_5_8, 5_3, 7_0_4_4_6, 3_3, 6_6_5_7, 2_7_8, 1_5_8_1]
# fmt: on
_UpperCAmelCase : Dict = model.generate(lowerCAmelCase__ , do_sample=lowerCAmelCase__ , num_beams=1 )
if verify_outputs:
self.assertListEqual(output_ids[0].numpy().tolist() , lowerCAmelCase__ )
@slow
def _lowerCAmelCase ( self : List[Any] ) -> str:
"""simple docstring"""
_UpperCAmelCase : List[str] = XGLMTokenizer.from_pretrained("facebook/xglm-564M" )
_UpperCAmelCase : Optional[Any] = TFXGLMForCausalLM.from_pretrained("facebook/xglm-564M" )
tf.random.set_seed(0 )
_UpperCAmelCase : Any = tokenizer("Today is a nice day and" , return_tensors="tf" )
_UpperCAmelCase : int = tokenized.input_ids
# forces the generation to happen on CPU, to avoid GPU-related quirks (and assure same output regardless of the available devices)
with tf.device(":/CPU:0" ):
_UpperCAmelCase : List[Any] = model.generate(lowerCAmelCase__ , do_sample=lowerCAmelCase__ , seed=[7, 0] )
_UpperCAmelCase : Any = tokenizer.decode(output_ids[0] , skip_special_tokens=lowerCAmelCase__ )
_UpperCAmelCase : List[Any] = (
"Today is a nice day and warm evening here over Southern Alberta!! Today when they closed schools due"
)
self.assertEqual(lowerCAmelCase__ , lowerCAmelCase__ )
@slow
def _lowerCAmelCase ( self : Optional[int] ) -> str:
"""simple docstring"""
_UpperCAmelCase : Optional[Any] = TFXGLMForCausalLM.from_pretrained("facebook/xglm-564M" )
_UpperCAmelCase : List[Any] = XGLMTokenizer.from_pretrained("facebook/xglm-564M" )
_UpperCAmelCase : Optional[int] = "left"
# use different length sentences to test batching
_UpperCAmelCase : Tuple = [
"This is an extremelly long sentence that only exists to test the ability of the model to cope with "
"left-padding, such as in batched generation. The output for the sequence below should be the same "
"regardless of whether left padding is applied or not. When",
"Hello, my dog is a little",
]
_UpperCAmelCase : Dict = tokenizer(lowerCAmelCase__ , return_tensors="tf" , padding=lowerCAmelCase__ )
_UpperCAmelCase : List[Any] = inputs["input_ids"]
_UpperCAmelCase : Dict = model.generate(input_ids=lowerCAmelCase__ , attention_mask=inputs["attention_mask"] , max_new_tokens=1_2 )
_UpperCAmelCase : int = tokenizer(sentences[0] , return_tensors="tf" ).input_ids
_UpperCAmelCase : Dict = model.generate(input_ids=lowerCAmelCase__ , max_new_tokens=1_2 )
_UpperCAmelCase : Optional[int] = tokenizer(sentences[1] , return_tensors="tf" ).input_ids
_UpperCAmelCase : List[Any] = model.generate(input_ids=lowerCAmelCase__ , max_new_tokens=1_2 )
_UpperCAmelCase : List[str] = tokenizer.batch_decode(lowerCAmelCase__ , skip_special_tokens=lowerCAmelCase__ )
_UpperCAmelCase : Tuple = tokenizer.decode(output_non_padded[0] , skip_special_tokens=lowerCAmelCase__ )
_UpperCAmelCase : List[str] = tokenizer.decode(output_padded[0] , skip_special_tokens=lowerCAmelCase__ )
_UpperCAmelCase : Union[str, Any] = [
"This is an extremelly long sentence that only exists to test the ability of the model to cope with "
"left-padding, such as in batched generation. The output for the sequence below should be the same "
"regardless of whether left padding is applied or not. When left padding is applied, the sequence will be "
"a single",
"Hello, my dog is a little bit of a shy one, but he is very friendly",
]
self.assertListEqual(lowerCAmelCase__ , lowerCAmelCase__ )
self.assertListEqual(lowerCAmelCase__ , [non_padded_sentence, padded_sentence] )
| 17
| 0
|
'''simple docstring'''
__a = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ'
def __UpperCAmelCase ( ):
_UpperCAmelCase : str = input("Enter message: " )
_UpperCAmelCase : Tuple = input("Enter key [alphanumeric]: " )
_UpperCAmelCase : List[Any] = input("Encrypt/Decrypt [e/d]: " )
if mode.lower().startswith("e" ):
_UpperCAmelCase : Union[str, Any] = "encrypt"
_UpperCAmelCase : int = encrypt_message(a__, a__ )
elif mode.lower().startswith("d" ):
_UpperCAmelCase : Optional[Any] = "decrypt"
_UpperCAmelCase : Tuple = decrypt_message(a__, a__ )
print(f"""\n{mode.title()}ed message:""" )
print(a__ )
def __UpperCAmelCase ( a_: str, a_: str ):
return translate_message(a__, a__, "encrypt" )
def __UpperCAmelCase ( a_: str, a_: str ):
return translate_message(a__, a__, "decrypt" )
def __UpperCAmelCase ( a_: str, a_: str, a_: str ):
_UpperCAmelCase : str = []
_UpperCAmelCase : Dict = 0
_UpperCAmelCase : List[Any] = key.upper()
for symbol in message:
_UpperCAmelCase : Optional[int] = LETTERS.find(symbol.upper() )
if num != -1:
if mode == "encrypt":
num += LETTERS.find(key[key_index] )
elif mode == "decrypt":
num -= LETTERS.find(key[key_index] )
num %= len(a__ )
if symbol.isupper():
translated.append(LETTERS[num] )
elif symbol.islower():
translated.append(LETTERS[num].lower() )
key_index += 1
if key_index == len(a__ ):
_UpperCAmelCase : Any = 0
else:
translated.append(a__ )
return "".join(a__ )
if __name__ == "__main__":
main()
| 370
|
'''simple docstring'''
import os
import pytest
import yaml
from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
"files", [
["full:README.md", "dataset_infos.json"],
["empty:README.md", "dataset_infos.json"],
["dataset_infos.json"],
["full:README.md"],
], )
def __UpperCAmelCase ( a_: Tuple, a_: Any ):
_UpperCAmelCase : Union[str, Any] = tmp_path_factory.mktemp("dset_infos_dir" )
if "full:README.md" in files:
with open(dataset_infos_dir / "README.md", "w" ) as f:
f.write("---\ndataset_info:\n dataset_size: 42\n---" )
if "empty:README.md" in files:
with open(dataset_infos_dir / "README.md", "w" ) as f:
f.write("" )
# we want to support dataset_infos.json for backward compatibility
if "dataset_infos.json" in files:
with open(dataset_infos_dir / "dataset_infos.json", "w" ) as f:
f.write("{\"default\": {\"dataset_size\": 42}}" )
_UpperCAmelCase : List[str] = DatasetInfosDict.from_directory(a_ )
assert dataset_infos
assert dataset_infos["default"].dataset_size == 42
@pytest.mark.parametrize(
"dataset_info", [
DatasetInfo(),
DatasetInfo(
description="foo", features=Features({"a": Value("int32" )} ), builder_name="builder", config_name="config", version="1.0.0", splits=[{"name": "train"}], download_size=42, ),
], )
def __UpperCAmelCase ( a_: Union[str, Any], a_: DatasetInfo ):
_UpperCAmelCase : Tuple = str(a_ )
dataset_info.write_to_directory(a_ )
_UpperCAmelCase : Any = DatasetInfo.from_directory(a_ )
assert dataset_info == reloaded
assert os.path.exists(os.path.join(a_, "dataset_info.json" ) )
def __UpperCAmelCase ( ):
_UpperCAmelCase : Optional[int] = DatasetInfo(
description="foo", citation="bar", homepage="https://foo.bar", license="CC0", features=Features({"a": Value("int32" )} ), post_processed={}, supervised_keys=(), task_templates=[], builder_name="builder", config_name="config", version="1.0.0", splits=[{"name": "train", "num_examples": 42}], download_checksums={}, download_size=1_337, post_processing_size=442, dataset_size=1_234, size_in_bytes=1_337 + 442 + 1_234, )
_UpperCAmelCase : Tuple = dataset_info._to_yaml_dict()
assert sorted(a_ ) == sorted(DatasetInfo._INCLUDED_INFO_IN_YAML )
for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
assert key in dataset_info_yaml_dict
assert isinstance(dataset_info_yaml_dict[key], (list, dict, int, str) )
_UpperCAmelCase : List[Any] = yaml.safe_dump(a_ )
_UpperCAmelCase : Optional[int] = yaml.safe_load(a_ )
assert dataset_info_yaml_dict == reloaded
def __UpperCAmelCase ( ):
_UpperCAmelCase : str = DatasetInfo()
_UpperCAmelCase : List[str] = dataset_info._to_yaml_dict()
assert dataset_info_yaml_dict == {}
@pytest.mark.parametrize(
"dataset_infos_dict", [
DatasetInfosDict(),
DatasetInfosDict({"default": DatasetInfo()} ),
DatasetInfosDict({"my_config_name": DatasetInfo()} ),
DatasetInfosDict(
{
"default": DatasetInfo(
description="foo", features=Features({"a": Value("int32" )} ), builder_name="builder", config_name="config", version="1.0.0", splits=[{"name": "train"}], download_size=42, )
} ),
DatasetInfosDict(
{
"v1": DatasetInfo(dataset_size=42 ),
"v2": DatasetInfo(dataset_size=1_337 ),
} ),
], )
def __UpperCAmelCase ( a_: str, a_: DatasetInfosDict ):
_UpperCAmelCase : Union[str, Any] = str(a_ )
dataset_infos_dict.write_to_directory(a_ )
_UpperCAmelCase : Union[str, Any] = DatasetInfosDict.from_directory(a_ )
# the config_name of the dataset_infos_dict take over the attribute
for config_name, dataset_info in dataset_infos_dict.items():
_UpperCAmelCase : Optional[int] = config_name
# the yaml representation doesn't include fields like description or citation
# so we just test that we can recover what we can from the yaml
_UpperCAmelCase : List[str] = DatasetInfo._from_yaml_dict(dataset_info._to_yaml_dict() )
assert dataset_infos_dict == reloaded
if dataset_infos_dict:
assert os.path.exists(os.path.join(a_, "README.md" ) )
| 17
| 0
|
'''simple docstring'''
def __UpperCAmelCase ( a_: int, a_: Optional[Any] ):
return "\n".join(
f"""{number} * {i} = {number * i}""" for i in range(1, number_of_terms + 1 ) )
if __name__ == "__main__":
print(multiplication_table(number=5, number_of_terms=10))
| 371
|
'''simple docstring'''
from math import factorial
def __UpperCAmelCase ( a_: int = 100 ):
return sum(map(a_, str(factorial(a_ ) ) ) )
if __name__ == "__main__":
print(solution(int(input('Enter the Number: ').strip())))
| 17
| 0
|
'''simple docstring'''
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 : Any , lowerCAmelCase__ : str , lowerCAmelCase__ : Dict=7 , lowerCAmelCase__ : List[Any]=3 , lowerCAmelCase__ : Tuple=1_8 , lowerCAmelCase__ : Optional[Any]=3_0 , lowerCAmelCase__ : Any=4_0_0 , lowerCAmelCase__ : Optional[int]=True , lowerCAmelCase__ : Optional[int]=None , lowerCAmelCase__ : List[str]=True , ) -> List[str]:
"""simple docstring"""
_UpperCAmelCase : Tuple = size if size is not None else {"""height""": 1_8, """width""": 1_8}
_UpperCAmelCase : Any = parent
_UpperCAmelCase : List[str] = batch_size
_UpperCAmelCase : List[str] = num_channels
_UpperCAmelCase : Dict = image_size
_UpperCAmelCase : Dict = min_resolution
_UpperCAmelCase : int = max_resolution
_UpperCAmelCase : List[Any] = do_resize
_UpperCAmelCase : List[str] = size
_UpperCAmelCase : Tuple = apply_ocr
def _lowerCAmelCase ( self : List[str] ) -> Union[str, Any]:
"""simple docstring"""
return {"do_resize": self.do_resize, "size": self.size, "apply_ocr": self.apply_ocr}
@require_torch
@require_pytesseract
class A__ ( _SCREAMING_SNAKE_CASE , unittest.TestCase ):
"""simple docstring"""
UpperCamelCase_ : Optional[int] = LayoutLMvaImageProcessor if is_pytesseract_available() else None
def _lowerCAmelCase ( self : Any ) -> Union[str, Any]:
"""simple docstring"""
_UpperCAmelCase : Any = LayoutLMvaImageProcessingTester(self )
@property
def _lowerCAmelCase ( self : Dict ) -> Union[str, Any]:
"""simple docstring"""
return self.image_processor_tester.prepare_image_processor_dict()
def _lowerCAmelCase ( self : int ) -> Tuple:
"""simple docstring"""
_UpperCAmelCase : List[Any] = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(lowerCAmelCase__ , "do_resize" ) )
self.assertTrue(hasattr(lowerCAmelCase__ , "size" ) )
self.assertTrue(hasattr(lowerCAmelCase__ , "apply_ocr" ) )
def _lowerCAmelCase ( self : Tuple ) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase : List[Any] = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {"height": 1_8, "width": 1_8} )
_UpperCAmelCase : Optional[Any] = self.image_processing_class.from_dict(self.image_processor_dict , size=4_2 )
self.assertEqual(image_processor.size , {"height": 4_2, "width": 4_2} )
def _lowerCAmelCase ( self : str ) -> List[Any]:
"""simple docstring"""
pass
def _lowerCAmelCase ( self : Union[str, Any] ) -> str:
"""simple docstring"""
_UpperCAmelCase : Optional[Any] = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
_UpperCAmelCase : Any = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCAmelCase__ )
for image in image_inputs:
self.assertIsInstance(lowerCAmelCase__ , Image.Image )
# Test not batched input
_UpperCAmelCase : List[str] = 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 , lowerCAmelCase__ )
self.assertIsInstance(encoding.boxes , lowerCAmelCase__ )
# Test batched
_UpperCAmelCase : Any = image_processing(lowerCAmelCase__ , 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 _lowerCAmelCase ( self : Any ) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase : str = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
_UpperCAmelCase : Any = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCAmelCase__ , numpify=lowerCAmelCase__ )
for image in image_inputs:
self.assertIsInstance(lowerCAmelCase__ , np.ndarray )
# Test not batched input
_UpperCAmelCase : List[Any] = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["height"],
self.image_processor_tester.size["width"],
) , )
# Test batched
_UpperCAmelCase : Any = image_processing(lowerCAmelCase__ , 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 _lowerCAmelCase ( self : str ) -> Union[str, Any]:
"""simple docstring"""
_UpperCAmelCase : str = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
_UpperCAmelCase : Optional[Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCAmelCase__ , torchify=lowerCAmelCase__ )
for image in image_inputs:
self.assertIsInstance(lowerCAmelCase__ , torch.Tensor )
# Test not batched input
_UpperCAmelCase : List[Any] = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["height"],
self.image_processor_tester.size["width"],
) , )
# Test batched
_UpperCAmelCase : str = image_processing(lowerCAmelCase__ , 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 _lowerCAmelCase ( self : Optional[int] ) -> Any:
"""simple docstring"""
_UpperCAmelCase : Tuple = LayoutLMvaImageProcessor()
from datasets import load_dataset
_UpperCAmelCase : List[Any] = load_dataset("hf-internal-testing/fixtures_docvqa" , split="test" )
_UpperCAmelCase : Optional[int] = Image.open(ds[0]["file"] ).convert("RGB" )
_UpperCAmelCase : Tuple = image_processing(lowerCAmelCase__ , return_tensors="pt" )
self.assertEqual(encoding.pixel_values.shape , (1, 3, 2_2_4, 2_2_4) )
self.assertEqual(len(encoding.words ) , len(encoding.boxes ) )
# fmt: off
# the words and boxes were obtained with Tesseract 4.1.1
_UpperCAmelCase : int = [["""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
_UpperCAmelCase : List[str] = [[[1_4_1, 5_7, 2_1_4, 6_9], [2_2_8, 5_8, 2_5_2, 6_9], [1_4_1, 7_5, 2_1_6, 8_8], [2_3_0, 7_9, 2_8_0, 8_8], [1_4_2, 2_6_0, 2_1_8, 2_7_3], [2_3_0, 2_6_1, 2_5_5, 2_7_3], [1_4_3, 2_7_9, 2_1_8, 2_9_0], [2_3_1, 2_8_2, 2_9_0, 2_9_1], [1_4_3, 3_4_2, 2_1_8, 3_5_4], [2_3_1, 3_4_5, 2_8_9, 3_5_5], [2_0_2, 3_6_2, 2_2_7, 3_7_3], [1_4_3, 3_7_9, 2_2_0, 3_9_2], [2_3_1, 3_8_2, 2_9_1, 3_9_4], [1_4_4, 7_1_4, 2_2_0, 7_2_6], [2_3_1, 7_1_5, 2_5_6, 7_2_6], [1_4_4, 7_3_2, 2_2_0, 7_4_5], [2_3_2, 7_3_6, 2_9_1, 7_4_7], [1_4_4, 7_6_9, 2_1_8, 7_8_2], [2_3_1, 7_7_0, 2_5_6, 7_8_2], [1_4_1, 7_8_8, 2_0_2, 8_0_1], [2_1_5, 7_9_1, 2_7_4, 8_0_4], [1_4_3, 8_2_6, 2_0_4, 8_3_8], [2_1_5, 8_2_6, 2_4_0, 8_3_8], [1_4_2, 8_4_4, 2_0_2, 8_5_7], [2_1_5, 8_4_7, 2_7_4, 8_5_9], [3_3_4, 5_7, 4_2_7, 6_9], [4_4_0, 5_7, 5_2_2, 6_9], [3_6_9, 7_5, 4_6_1, 8_8], [4_6_9, 7_5, 5_1_6, 8_8], [5_2_8, 7_6, 5_6_2, 8_8], [5_7_0, 7_6, 6_6_7, 8_8], [6_7_5, 7_5, 7_1_1, 8_7], [7_2_1, 7_9, 7_7_8, 8_8], [7_8_9, 7_5, 8_4_0, 8_8], [3_6_9, 9_7, 4_7_0, 1_0_7], [4_8_4, 9_4, 5_0_7, 1_0_6], [5_1_8, 9_4, 5_6_2, 1_0_7], [5_7_6, 9_4, 6_5_5, 1_1_0], [6_6_8, 9_4, 7_9_2, 1_0_9], [8_0_4, 9_5, 8_2_9, 1_0_7], [3_6_9, 1_1_3, 4_6_5, 1_2_5], [4_7_7, 1_1_6, 5_4_7, 1_2_5], [5_6_2, 1_1_3, 6_5_8, 1_2_5], [6_7_1, 1_1_6, 7_4_8, 1_2_5], [7_6_1, 1_1_3, 8_1_1, 1_2_5], [3_6_9, 1_3_1, 4_6_5, 1_4_3], [4_7_7, 1_3_3, 5_4_8, 1_4_3], [5_6_3, 1_3_0, 6_9_8, 1_4_5], [7_1_0, 1_3_0, 8_0_2, 1_4_6], [3_3_6, 1_7_1, 4_1_2, 1_8_3], [4_2_3, 1_7_1, 5_7_2, 1_8_3], [5_8_2, 1_7_0, 7_1_6, 1_8_4], [7_2_8, 1_7_1, 8_1_7, 1_8_7], [8_2_9, 1_7_1, 8_4_4, 1_8_6], [3_3_8, 1_9_7, 4_8_2, 2_1_2], [5_0_7, 1_9_6, 5_5_7, 2_0_9], [5_6_9, 1_9_6, 5_9_5, 2_0_8], [6_1_0, 1_9_6, 7_0_2, 2_0_9], [5_0_5, 2_1_4, 5_8_3, 2_2_6], [5_9_5, 2_1_4, 6_5_6, 2_2_7], [6_7_0, 2_1_5, 8_0_7, 2_2_7], [3_3_5, 2_5_9, 5_4_3, 2_7_4], [5_5_6, 2_5_9, 7_0_8, 2_7_2], [3_7_2, 2_7_9, 4_2_2, 2_9_1], [4_3_5, 2_7_9, 4_6_0, 2_9_1], [4_7_4, 2_7_9, 5_7_4, 2_9_2], [5_8_7, 2_7_8, 6_6_4, 2_9_1], [6_7_6, 2_7_8, 7_3_8, 2_9_1], [7_5_1, 2_7_9, 8_3_4, 2_9_1], [3_7_2, 2_9_8, 4_3_4, 3_1_0], [3_3_5, 3_4_1, 4_8_3, 3_5_4], [4_9_7, 3_4_1, 6_5_5, 3_5_4], [6_6_7, 3_4_1, 7_2_8, 3_5_4], [7_4_0, 3_4_1, 8_2_5, 3_5_4], [3_3_5, 3_6_0, 4_3_0, 3_7_2], [4_4_2, 3_6_0, 5_3_4, 3_7_2], [5_4_5, 3_5_9, 6_8_7, 3_7_2], [6_9_7, 3_6_0, 7_5_4, 3_7_2], [7_6_5, 3_6_0, 8_2_3, 3_7_3], [3_3_4, 3_7_8, 4_2_8, 3_9_1], [4_4_0, 3_7_8, 5_7_7, 3_9_4], [5_9_0, 3_7_8, 7_0_5, 3_9_1], [7_2_0, 3_7_8, 8_0_1, 3_9_1], [3_3_4, 3_9_7, 4_0_0, 4_0_9], [3_7_0, 4_1_6, 5_2_9, 4_2_9], [5_4_4, 4_1_6, 5_7_6, 4_3_2], [5_8_7, 4_1_6, 6_6_5, 4_2_8], [6_7_7, 4_1_6, 8_1_4, 4_2_9], [3_7_2, 4_3_5, 4_5_2, 4_5_0], [4_6_5, 4_3_4, 4_9_5, 4_4_7], [5_1_1, 4_3_4, 6_0_0, 4_4_7], [6_1_1, 4_3_6, 6_3_7, 4_4_7], [6_4_9, 4_3_6, 6_9_4, 4_5_1], [7_0_5, 4_3_8, 8_2_4, 4_4_7], [3_6_9, 4_5_3, 4_5_2, 4_6_6], [4_6_4, 4_5_4, 5_0_9, 4_6_6], [5_2_2, 4_5_3, 6_1_1, 4_6_9], [6_2_5, 4_5_3, 7_9_2, 4_6_9], [3_7_0, 4_7_2, 5_5_6, 4_8_8], [5_7_0, 4_7_2, 6_8_4, 4_8_7], [6_9_7, 4_7_2, 7_1_8, 4_8_5], [7_3_2, 4_7_2, 8_3_5, 4_8_8], [3_6_9, 4_9_0, 4_1_1, 5_0_3], [4_2_5, 4_9_0, 4_8_4, 5_0_3], [4_9_6, 4_9_0, 6_3_5, 5_0_6], [6_4_5, 4_9_0, 7_0_7, 5_0_3], [7_1_8, 4_9_1, 7_6_1, 5_0_3], [7_7_1, 4_9_0, 8_4_0, 5_0_3], [3_3_6, 5_1_0, 3_7_4, 5_2_1], [3_8_8, 5_1_0, 4_4_7, 5_2_2], [4_6_0, 5_1_0, 4_8_9, 5_2_1], [5_0_3, 5_1_0, 5_8_0, 5_2_2], [5_9_2, 5_0_9, 7_3_6, 5_2_5], [7_4_5, 5_0_9, 7_7_0, 5_2_2], [7_8_1, 5_0_9, 8_4_0, 5_2_2], [3_3_8, 5_2_8, 4_3_4, 5_4_1], [4_4_8, 5_2_8, 5_9_6, 5_4_1], [6_0_9, 5_2_7, 6_8_7, 5_4_0], [7_0_0, 5_2_8, 7_9_2, 5_4_1], [3_3_6, 5_4_6, 3_9_7, 5_5_9], [4_0_7, 5_4_6, 4_3_1, 5_5_9], [4_4_3, 5_4_6, 5_2_5, 5_6_0], [5_3_7, 5_4_6, 6_8_0, 5_6_2], [6_8_8, 5_4_6, 7_1_4, 5_5_9], [7_2_2, 5_4_6, 8_3_7, 5_6_2], [3_3_6, 5_6_5, 4_4_9, 5_8_1], [4_6_1, 5_6_5, 4_8_5, 5_7_7], [4_9_7, 5_6_5, 6_6_5, 5_8_1], [6_8_1, 5_6_5, 7_1_8, 5_7_7], [7_3_2, 5_6_5, 8_3_7, 5_8_0], [3_3_7, 5_8_4, 4_3_8, 5_9_7], [4_5_2, 5_8_3, 5_2_1, 5_9_6], [5_3_5, 5_8_4, 6_7_7, 5_9_9], [6_9_0, 5_8_3, 7_8_7, 5_9_6], [8_0_1, 5_8_3, 8_2_5, 5_9_6], [3_3_8, 6_0_2, 4_7_8, 6_1_5], [4_9_2, 6_0_2, 5_3_0, 6_1_4], [5_4_3, 6_0_2, 6_3_8, 6_1_5], [6_5_0, 6_0_2, 6_7_6, 6_1_4], [6_8_8, 6_0_2, 7_8_8, 6_1_5], [8_0_2, 6_0_2, 8_4_3, 6_1_4], [3_3_7, 6_2_1, 5_0_2, 6_3_3], [5_1_6, 6_2_1, 6_1_5, 6_3_7], [6_2_9, 6_2_1, 7_7_4, 6_3_6], [7_8_9, 6_2_1, 8_2_7, 6_3_3], [3_3_7, 6_3_9, 4_1_8, 6_5_2], [4_3_2, 6_4_0, 5_7_1, 6_5_3], [5_8_7, 6_3_9, 7_3_1, 6_5_5], [7_4_3, 6_3_9, 7_6_9, 6_5_2], [7_8_0, 6_3_9, 8_4_1, 6_5_2], [3_3_8, 6_5_8, 4_4_0, 6_7_3], [4_5_5, 6_5_8, 4_9_1, 6_7_0], [5_0_8, 6_5_8, 6_0_2, 6_7_1], [6_1_6, 6_5_8, 6_3_8, 6_7_0], [6_5_4, 6_5_8, 8_3_5, 6_7_4], [3_3_7, 6_7_7, 4_2_9, 6_8_9], [3_3_7, 7_1_4, 4_8_2, 7_2_6], [4_9_5, 7_1_4, 5_4_8, 7_2_6], [5_6_1, 7_1_4, 6_8_3, 7_2_6], [3_3_8, 7_7_0, 4_6_1, 7_8_2], [4_7_4, 7_6_9, 5_5_4, 7_8_5], [4_8_9, 7_8_8, 5_6_2, 8_0_3], [5_7_6, 7_8_8, 6_4_3, 8_0_1], [6_5_6, 7_8_7, 7_5_1, 8_0_4], [7_6_4, 7_8_8, 8_4_4, 8_0_1], [3_3_4, 8_2_5, 4_2_1, 8_3_8], [4_3_0, 8_2_4, 5_7_4, 8_3_8], [5_8_4, 8_2_4, 7_2_3, 8_4_1], [3_3_5, 8_4_4, 4_5_0, 8_5_7], [4_6_4, 8_4_3, 5_8_3, 8_6_0], [6_2_8, 8_6_2, 7_5_5, 8_7_5], [7_6_9, 8_6_1, 8_4_8, 8_7_8]]] # noqa: E231
# fmt: on
self.assertListEqual(encoding.words , lowerCAmelCase__ )
self.assertListEqual(encoding.boxes , lowerCAmelCase__ )
# with apply_OCR = False
_UpperCAmelCase : int = LayoutLMvaImageProcessor(apply_ocr=lowerCAmelCase__ )
_UpperCAmelCase : Optional[int] = image_processing(lowerCAmelCase__ , return_tensors="pt" )
self.assertEqual(encoding.pixel_values.shape , (1, 3, 2_2_4, 2_2_4) )
| 350
|
'''simple docstring'''
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
__a = (3, 9, -11, 0, 7, 5, 1, -1)
__a = (4, 6, 2, 0, 8, 10, 3, -2)
@dataclass
class A__ :
"""simple docstring"""
UpperCamelCase_ : int
UpperCamelCase_ : Node | None
class A__ :
"""simple docstring"""
def __init__( self : Dict , lowerCAmelCase__ : Iterable[int] ) -> None:
"""simple docstring"""
_UpperCAmelCase : Node | None = None
for i in sorted(lowerCAmelCase__ , reverse=lowerCAmelCase__ ):
_UpperCAmelCase : str = Node(lowerCAmelCase__ , self.head )
def __iter__( self : int ) -> Iterator[int]:
"""simple docstring"""
_UpperCAmelCase : List[Any] = self.head
while node:
yield node.data
_UpperCAmelCase : List[str] = node.next_node
def __len__( self : Any ) -> int:
"""simple docstring"""
return sum(1 for _ in self )
def __str__( self : Union[str, Any] ) -> str:
"""simple docstring"""
return " -> ".join([str(lowerCAmelCase__ ) for node in self] )
def __UpperCAmelCase ( a_: SortedLinkedList, a_: SortedLinkedList ):
return SortedLinkedList(list(a_ ) + list(a_ ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
__a = SortedLinkedList
print(merge_lists(SSL(test_data_odd), SSL(test_data_even)))
| 17
| 0
|
'''simple docstring'''
from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def __UpperCAmelCase ( a_: int ):
# A local function to see if a dot lands in the circle.
def is_in_circle(a_: float, a_: float ) -> bool:
_UpperCAmelCase : int = sqrt((x**2) + (y**2) )
# Our circle has a radius of 1, so a distance
# greater than 1 would land outside the circle.
return distance_from_centre <= 1
# The proportion of guesses that landed in the circle
_UpperCAmelCase : List[str] = mean(
int(is_in_circle(uniform(-1.0, 1.0 ), uniform(-1.0, 1.0 ) ) )
for _ in range(lowerCAmelCase__ ) )
# The ratio of the area for circle to square is pi/4.
_UpperCAmelCase : int = proportion * 4
print(f"""The estimated value of pi is {pi_estimate}""" )
print(f"""The numpy value of pi is {pi}""" )
print(f"""The total error is {abs(pi - pi_estimate )}""" )
def __UpperCAmelCase ( a_: int, a_: Callable[[float], float], a_: float = 0.0, a_: float = 1.0, ):
return mean(
function_to_integrate(uniform(lowerCAmelCase__, lowerCAmelCase__ ) ) for _ in range(lowerCAmelCase__ ) ) * (max_value - min_value)
def __UpperCAmelCase ( a_: int, a_: float = 0.0, a_: float = 1.0 ):
def identity_function(a_: float ) -> float:
return x
_UpperCAmelCase : List[str] = area_under_curve_estimator(
lowerCAmelCase__, lowerCAmelCase__, lowerCAmelCase__, lowerCAmelCase__ )
_UpperCAmelCase : Any = (max_value * max_value - min_value * min_value) / 2
print("******************" )
print(f"""Estimating area under y=x where x varies from {min_value} to {max_value}""" )
print(f"""Estimated value is {estimated_value}""" )
print(f"""Expected value is {expected_value}""" )
print(f"""Total error is {abs(estimated_value - expected_value )}""" )
print("******************" )
def __UpperCAmelCase ( a_: int ):
def function_to_integrate(a_: float ) -> float:
return sqrt(4.0 - x * x )
_UpperCAmelCase : int = area_under_curve_estimator(
lowerCAmelCase__, lowerCAmelCase__, 0.0, 2.0 )
print("******************" )
print("Estimating pi using area_under_curve_estimator" )
print(f"""Estimated value is {estimated_value}""" )
print(f"""Expected value is {pi}""" )
print(f"""Total error is {abs(estimated_value - pi )}""" )
print("******************" )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 351
|
'''simple docstring'''
def __UpperCAmelCase ( a_: str ):
if not all(char in "01" for char in bin_string ):
raise ValueError("Non-binary value was passed to the function" )
if not bin_string:
raise ValueError("Empty string was passed to the function" )
_UpperCAmelCase : Optional[Any] = ""
while len(a_ ) % 3 != 0:
_UpperCAmelCase : List[Any] = "0" + bin_string
_UpperCAmelCase : Dict = [
bin_string[index : index + 3]
for index in range(len(a_ ) )
if index % 3 == 0
]
for bin_group in bin_string_in_3_list:
_UpperCAmelCase : Optional[Any] = 0
for index, val in enumerate(a_ ):
oct_val += int(2 ** (2 - index) * int(a_ ) )
oct_string += str(a_ )
return oct_string
if __name__ == "__main__":
from doctest import testmod
testmod()
| 17
| 0
|
'''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class A__ ( UpperCamelCase ):
"""simple docstring"""
UpperCamelCase_ : Tuple = ["image_processor", "tokenizer"]
UpperCamelCase_ : Any = "CLIPImageProcessor"
UpperCamelCase_ : Optional[Any] = ("CLIPTokenizer", "CLIPTokenizerFast")
def __init__( self : Union[str, Any] , lowerCAmelCase__ : Tuple=None , lowerCAmelCase__ : Any=None , **lowerCAmelCase__ : Dict ) -> Tuple:
"""simple docstring"""
_UpperCAmelCase : List[str] = None
if "feature_extractor" in kwargs:
warnings.warn(
"The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
" instead." , lowerCAmelCase__ , )
_UpperCAmelCase : str = kwargs.pop("feature_extractor" )
_UpperCAmelCase : Optional[Any] = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError("You need to specify an `image_processor`." )
if tokenizer is None:
raise ValueError("You need to specify a `tokenizer`." )
super().__init__(lowerCAmelCase__ , lowerCAmelCase__ )
def __call__( self : Tuple , lowerCAmelCase__ : List[Any]=None , lowerCAmelCase__ : List[str]=None , lowerCAmelCase__ : str=None , **lowerCAmelCase__ : List[Any] ) -> int:
"""simple docstring"""
if text is None and images is None:
raise ValueError("You have to specify either text or images. Both cannot be none." )
if text is not None:
_UpperCAmelCase : List[str] = self.tokenizer(lowerCAmelCase__ , return_tensors=lowerCAmelCase__ , **lowerCAmelCase__ )
if images is not None:
_UpperCAmelCase : str = self.image_processor(lowerCAmelCase__ , return_tensors=lowerCAmelCase__ , **lowerCAmelCase__ )
if text is not None and images is not None:
_UpperCAmelCase : Any = image_features.pixel_values
return encoding
elif text is not None:
return encoding
else:
return BatchEncoding(data=dict(**lowerCAmelCase__ ) , tensor_type=lowerCAmelCase__ )
def _lowerCAmelCase ( self : Union[str, Any] , *lowerCAmelCase__ : Union[str, Any] , **lowerCAmelCase__ : Optional[Any] ) -> Dict:
"""simple docstring"""
return self.tokenizer.batch_decode(*lowerCAmelCase__ , **lowerCAmelCase__ )
def _lowerCAmelCase ( self : List[Any] , *lowerCAmelCase__ : List[str] , **lowerCAmelCase__ : Optional[int] ) -> int:
"""simple docstring"""
return self.tokenizer.decode(*lowerCAmelCase__ , **lowerCAmelCase__ )
@property
def _lowerCAmelCase ( self : Any ) -> Any:
"""simple docstring"""
_UpperCAmelCase : List[Any] = self.tokenizer.model_input_names
_UpperCAmelCase : Optional[Any] = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
@property
def _lowerCAmelCase ( self : Union[str, Any] ) -> str:
"""simple docstring"""
warnings.warn(
"`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead." , lowerCAmelCase__ , )
return self.image_processor_class
@property
def _lowerCAmelCase ( self : Optional[int] ) -> List[Any]:
"""simple docstring"""
warnings.warn(
"`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead." , lowerCAmelCase__ , )
return self.image_processor
| 352
|
'''simple docstring'''
from datetime import datetime
import matplotlib.pyplot as plt
import torch
def __UpperCAmelCase ( a_: str ):
for param in module.parameters():
_UpperCAmelCase : Any = False
def __UpperCAmelCase ( ):
_UpperCAmelCase : Union[str, Any] = "cuda" if torch.cuda.is_available() else "cpu"
if torch.backends.mps.is_available() and torch.backends.mps.is_built():
_UpperCAmelCase : int = "mps"
if device == "mps":
print(
"WARNING: MPS currently doesn't seem to work, and messes up backpropagation without any visible torch"
" errors. I recommend using CUDA on a colab notebook or CPU instead if you're facing inexplicable issues"
" with generations." )
return device
def __UpperCAmelCase ( a_: Optional[Any] ):
_UpperCAmelCase : int = plt.imshow(a_ )
fig.axes.get_xaxis().set_visible(a_ )
fig.axes.get_yaxis().set_visible(a_ )
plt.show()
def __UpperCAmelCase ( ):
_UpperCAmelCase : Dict = datetime.now()
_UpperCAmelCase : List[str] = current_time.strftime("%H:%M:%S" )
return timestamp
| 17
| 0
|
'''simple docstring'''
def __UpperCAmelCase ( a_: int, a_: int ):
return int((input_a, input_a).count(1 ) != 0 )
def __UpperCAmelCase ( ):
assert or_gate(0, 0 ) == 0
assert or_gate(0, 1 ) == 1
assert or_gate(1, 0 ) == 1
assert or_gate(1, 1 ) == 1
if __name__ == "__main__":
print(or_gate(0, 1))
print(or_gate(1, 0))
print(or_gate(0, 0))
print(or_gate(1, 1))
| 353
|
'''simple docstring'''
import torch
from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class A__ ( UpperCamelCase ):
"""simple docstring"""
UpperCamelCase_ : Optional[int] = (EulerDiscreteScheduler,)
UpperCamelCase_ : Tuple = 10
def _lowerCAmelCase ( self : Dict , **lowerCAmelCase__ : Tuple ) -> Any:
"""simple docstring"""
_UpperCAmelCase : str = {
"num_train_timesteps": 1_1_0_0,
"beta_start": 0.0001,
"beta_end": 0.02,
"beta_schedule": "linear",
}
config.update(**lowerCAmelCase__ )
return config
def _lowerCAmelCase ( self : Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
self.check_over_configs(num_train_timesteps=lowerCAmelCase__ )
def _lowerCAmelCase ( self : Any ) -> List[str]:
"""simple docstring"""
for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001] , [0.0002, 0.002, 0.02] ):
self.check_over_configs(beta_start=lowerCAmelCase__ , beta_end=lowerCAmelCase__ )
def _lowerCAmelCase ( self : List[str] ) -> List[str]:
"""simple docstring"""
for schedule in ["linear", "scaled_linear"]:
self.check_over_configs(beta_schedule=lowerCAmelCase__ )
def _lowerCAmelCase ( self : Union[str, Any] ) -> Optional[Any]:
"""simple docstring"""
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=lowerCAmelCase__ )
def _lowerCAmelCase ( self : List[Any] ) -> List[Any]:
"""simple docstring"""
_UpperCAmelCase : List[str] = self.scheduler_classes[0]
_UpperCAmelCase : int = self.get_scheduler_config()
_UpperCAmelCase : Optional[int] = scheduler_class(**lowerCAmelCase__ )
scheduler.set_timesteps(self.num_inference_steps )
_UpperCAmelCase : int = torch.manual_seed(0 )
_UpperCAmelCase : Any = self.dummy_model()
_UpperCAmelCase : List[str] = self.dummy_sample_deter * scheduler.init_noise_sigma
_UpperCAmelCase : List[Any] = sample.to(lowerCAmelCase__ )
for i, t in enumerate(scheduler.timesteps ):
_UpperCAmelCase : List[str] = scheduler.scale_model_input(lowerCAmelCase__ , lowerCAmelCase__ )
_UpperCAmelCase : int = model(lowerCAmelCase__ , lowerCAmelCase__ )
_UpperCAmelCase : int = scheduler.step(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , generator=lowerCAmelCase__ )
_UpperCAmelCase : Optional[int] = output.prev_sample
_UpperCAmelCase : Optional[Any] = torch.sum(torch.abs(lowerCAmelCase__ ) )
_UpperCAmelCase : Tuple = torch.mean(torch.abs(lowerCAmelCase__ ) )
assert abs(result_sum.item() - 10.0807 ) < 1e-2
assert abs(result_mean.item() - 0.0131 ) < 1e-3
def _lowerCAmelCase ( self : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
_UpperCAmelCase : Any = self.scheduler_classes[0]
_UpperCAmelCase : List[Any] = self.get_scheduler_config(prediction_type="v_prediction" )
_UpperCAmelCase : Any = scheduler_class(**lowerCAmelCase__ )
scheduler.set_timesteps(self.num_inference_steps )
_UpperCAmelCase : str = torch.manual_seed(0 )
_UpperCAmelCase : Optional[Any] = self.dummy_model()
_UpperCAmelCase : Union[str, Any] = self.dummy_sample_deter * scheduler.init_noise_sigma
_UpperCAmelCase : Tuple = sample.to(lowerCAmelCase__ )
for i, t in enumerate(scheduler.timesteps ):
_UpperCAmelCase : Union[str, Any] = scheduler.scale_model_input(lowerCAmelCase__ , lowerCAmelCase__ )
_UpperCAmelCase : int = model(lowerCAmelCase__ , lowerCAmelCase__ )
_UpperCAmelCase : Union[str, Any] = scheduler.step(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , generator=lowerCAmelCase__ )
_UpperCAmelCase : Union[str, Any] = output.prev_sample
_UpperCAmelCase : Tuple = torch.sum(torch.abs(lowerCAmelCase__ ) )
_UpperCAmelCase : Any = torch.mean(torch.abs(lowerCAmelCase__ ) )
assert abs(result_sum.item() - 0.0002 ) < 1e-2
assert abs(result_mean.item() - 2.26_76e-06 ) < 1e-3
def _lowerCAmelCase ( self : Tuple ) -> str:
"""simple docstring"""
_UpperCAmelCase : Optional[int] = self.scheduler_classes[0]
_UpperCAmelCase : List[Any] = self.get_scheduler_config()
_UpperCAmelCase : int = scheduler_class(**lowerCAmelCase__ )
scheduler.set_timesteps(self.num_inference_steps , device=lowerCAmelCase__ )
_UpperCAmelCase : Optional[int] = torch.manual_seed(0 )
_UpperCAmelCase : str = self.dummy_model()
_UpperCAmelCase : Any = self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
_UpperCAmelCase : str = sample.to(lowerCAmelCase__ )
for t in scheduler.timesteps:
_UpperCAmelCase : List[str] = scheduler.scale_model_input(lowerCAmelCase__ , lowerCAmelCase__ )
_UpperCAmelCase : Any = model(lowerCAmelCase__ , lowerCAmelCase__ )
_UpperCAmelCase : Tuple = scheduler.step(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , generator=lowerCAmelCase__ )
_UpperCAmelCase : int = output.prev_sample
_UpperCAmelCase : List[Any] = torch.sum(torch.abs(lowerCAmelCase__ ) )
_UpperCAmelCase : str = torch.mean(torch.abs(lowerCAmelCase__ ) )
assert abs(result_sum.item() - 10.0807 ) < 1e-2
assert abs(result_mean.item() - 0.0131 ) < 1e-3
def _lowerCAmelCase ( self : List[str] ) -> int:
"""simple docstring"""
_UpperCAmelCase : List[Any] = self.scheduler_classes[0]
_UpperCAmelCase : int = self.get_scheduler_config()
_UpperCAmelCase : Union[str, Any] = scheduler_class(**lowerCAmelCase__ , use_karras_sigmas=lowerCAmelCase__ )
scheduler.set_timesteps(self.num_inference_steps , device=lowerCAmelCase__ )
_UpperCAmelCase : Optional[int] = torch.manual_seed(0 )
_UpperCAmelCase : List[str] = self.dummy_model()
_UpperCAmelCase : str = self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
_UpperCAmelCase : Optional[int] = sample.to(lowerCAmelCase__ )
for t in scheduler.timesteps:
_UpperCAmelCase : List[Any] = scheduler.scale_model_input(lowerCAmelCase__ , lowerCAmelCase__ )
_UpperCAmelCase : str = model(lowerCAmelCase__ , lowerCAmelCase__ )
_UpperCAmelCase : Optional[Any] = scheduler.step(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , generator=lowerCAmelCase__ )
_UpperCAmelCase : List[Any] = output.prev_sample
_UpperCAmelCase : List[Any] = torch.sum(torch.abs(lowerCAmelCase__ ) )
_UpperCAmelCase : Optional[Any] = torch.mean(torch.abs(lowerCAmelCase__ ) )
assert abs(result_sum.item() - 124.52_2994_9951_1719 ) < 1e-2
assert abs(result_mean.item() - 0.1_6213_9326_3339_9963 ) < 1e-3
| 17
| 0
|
'''simple docstring'''
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_gpta import GPTaTokenizer
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
__a = logging.get_logger(__name__)
__a = {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}
__a = {
'vocab_file': {
'gpt2': 'https://huggingface.co/gpt2/resolve/main/vocab.json',
'gpt2-medium': 'https://huggingface.co/gpt2-medium/resolve/main/vocab.json',
'gpt2-large': 'https://huggingface.co/gpt2-large/resolve/main/vocab.json',
'gpt2-xl': 'https://huggingface.co/gpt2-xl/resolve/main/vocab.json',
'distilgpt2': 'https://huggingface.co/distilgpt2/resolve/main/vocab.json',
},
'merges_file': {
'gpt2': 'https://huggingface.co/gpt2/resolve/main/merges.txt',
'gpt2-medium': 'https://huggingface.co/gpt2-medium/resolve/main/merges.txt',
'gpt2-large': 'https://huggingface.co/gpt2-large/resolve/main/merges.txt',
'gpt2-xl': 'https://huggingface.co/gpt2-xl/resolve/main/merges.txt',
'distilgpt2': 'https://huggingface.co/distilgpt2/resolve/main/merges.txt',
},
'tokenizer_file': {
'gpt2': 'https://huggingface.co/gpt2/resolve/main/tokenizer.json',
'gpt2-medium': 'https://huggingface.co/gpt2-medium/resolve/main/tokenizer.json',
'gpt2-large': 'https://huggingface.co/gpt2-large/resolve/main/tokenizer.json',
'gpt2-xl': 'https://huggingface.co/gpt2-xl/resolve/main/tokenizer.json',
'distilgpt2': 'https://huggingface.co/distilgpt2/resolve/main/tokenizer.json',
},
}
__a = {
'gpt2': 1_024,
'gpt2-medium': 1_024,
'gpt2-large': 1_024,
'gpt2-xl': 1_024,
'distilgpt2': 1_024,
}
class A__ ( UpperCamelCase__ ):
"""simple docstring"""
UpperCamelCase_ : List[Any] = VOCAB_FILES_NAMES
UpperCamelCase_ : Optional[int] = PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase_ : Any = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase_ : List[str] = ['''input_ids''', '''attention_mask''']
UpperCamelCase_ : Optional[int] = GPTaTokenizer
def __init__( self : Any , lowerCAmelCase__ : Tuple=None , lowerCAmelCase__ : Optional[int]=None , lowerCAmelCase__ : Optional[int]=None , lowerCAmelCase__ : Dict="<|endoftext|>" , lowerCAmelCase__ : Tuple="<|endoftext|>" , lowerCAmelCase__ : List[str]="<|endoftext|>" , lowerCAmelCase__ : List[Any]=False , **lowerCAmelCase__ : str , ) -> Optional[Any]:
"""simple docstring"""
super().__init__(
UpperCamelCase_ , UpperCamelCase_ , tokenizer_file=UpperCamelCase_ , unk_token=UpperCamelCase_ , bos_token=UpperCamelCase_ , eos_token=UpperCamelCase_ , add_prefix_space=UpperCamelCase_ , **UpperCamelCase_ , )
_UpperCAmelCase : int = kwargs.pop("add_bos_token" , UpperCamelCase_ )
_UpperCAmelCase : Tuple = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get("add_prefix_space" , UpperCamelCase_ ) != add_prefix_space:
_UpperCAmelCase : int = getattr(UpperCamelCase_ , pre_tok_state.pop("type" ) )
_UpperCAmelCase : str = add_prefix_space
_UpperCAmelCase : List[str] = pre_tok_class(**UpperCamelCase_ )
_UpperCAmelCase : Union[str, Any] = add_prefix_space
def _lowerCAmelCase ( self : Dict , *lowerCAmelCase__ : int , **lowerCAmelCase__ : Optional[Any] ) -> BatchEncoding:
"""simple docstring"""
_UpperCAmelCase : Dict = kwargs.get("is_split_into_words" , UpperCamelCase_ )
assert self.add_prefix_space or not is_split_into_words, (
F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
"to use it with pretokenized inputs."
)
return super()._batch_encode_plus(*UpperCamelCase_ , **UpperCamelCase_ )
def _lowerCAmelCase ( self : Tuple , *lowerCAmelCase__ : int , **lowerCAmelCase__ : int ) -> BatchEncoding:
"""simple docstring"""
_UpperCAmelCase : Optional[int] = kwargs.get("is_split_into_words" , UpperCamelCase_ )
assert self.add_prefix_space or not is_split_into_words, (
F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
"to use it with pretokenized inputs."
)
return super()._encode_plus(*UpperCamelCase_ , **UpperCamelCase_ )
def _lowerCAmelCase ( self : Optional[int] , lowerCAmelCase__ : str , lowerCAmelCase__ : Optional[str] = None ) -> Tuple[str]:
"""simple docstring"""
_UpperCAmelCase : Any = self._tokenizer.model.save(UpperCamelCase_ , name=UpperCamelCase_ )
return tuple(UpperCamelCase_ )
def _lowerCAmelCase ( self : int , lowerCAmelCase__ : "Conversation" ) -> List[int]:
"""simple docstring"""
_UpperCAmelCase : List[Any] = []
for is_user, text in conversation.iter_texts():
input_ids.extend(self.encode(UpperCamelCase_ , add_special_tokens=UpperCamelCase_ ) + [self.eos_token_id] )
if len(UpperCamelCase_ ) > self.model_max_length:
_UpperCAmelCase : Union[str, Any] = input_ids[-self.model_max_length :]
return input_ids
| 354
|
'''simple docstring'''
def __UpperCAmelCase ( a_: int, a_: int ):
if a < 0 or b < 0:
raise ValueError("the value of both inputs must be positive" )
_UpperCAmelCase : List[str] = str(bin(a_ ) )[2:] # remove the leading "0b"
_UpperCAmelCase : Any = str(bin(a_ ) )[2:] # remove the leading "0b"
_UpperCAmelCase : Dict = max(len(a_ ), len(a_ ) )
return "0b" + "".join(
str(int(char_a == "1" and char_b == "1" ) )
for char_a, char_b in zip(a_binary.zfill(a_ ), b_binary.zfill(a_ ) ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 17
| 0
|
'''simple docstring'''
import heapq
def __UpperCAmelCase ( a_: dict ):
_UpperCAmelCase : list[list] = []
# for each node and his adjacency list add them and the rank of the node to queue
# using heapq module the queue will be filled like a Priority Queue
# heapq works with a min priority queue, so I used -1*len(v) to build it
for key, value in graph.items():
# O(log(n))
heapq.heappush(lowerCamelCase_, [-1 * len(lowerCamelCase_ ), (key, value)] )
# chosen_vertices = set of chosen vertices
_UpperCAmelCase : int = set()
# while queue isn't empty and there are still edges
# (queue[0][0] is the rank of the node with max rank)
while queue and queue[0][0] != 0:
# extract vertex with max rank from queue and add it to chosen_vertices
_UpperCAmelCase : str = heapq.heappop(lowerCamelCase_ )[1][0]
chosen_vertices.add(lowerCamelCase_ )
# Remove all arcs adjacent to argmax
for elem in queue:
# if v haven't adjacent node, skip
if elem[0] == 0:
continue
# if argmax is reachable from elem
# remove argmax from elem's adjacent list and update his rank
if argmax in elem[1][1]:
_UpperCAmelCase : Optional[int] = elem[1][1].index(lowerCamelCase_ )
del elem[1][1][index]
elem[0] += 1
# re-order the queue
heapq.heapify(lowerCamelCase_ )
return chosen_vertices
if __name__ == "__main__":
import doctest
doctest.testmod()
__a = {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
print(f'Minimum vertex cover:\n{greedy_min_vertex_cover(graph)}')
| 355
|
'''simple docstring'''
from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def __UpperCAmelCase ( a_: int ):
# A local function to see if a dot lands in the circle.
def is_in_circle(a_: float, a_: float ) -> bool:
_UpperCAmelCase : Optional[Any] = sqrt((x**2) + (y**2) )
# Our circle has a radius of 1, so a distance
# greater than 1 would land outside the circle.
return distance_from_centre <= 1
# The proportion of guesses that landed in the circle
_UpperCAmelCase : str = mean(
int(is_in_circle(uniform(-1.0, 1.0 ), uniform(-1.0, 1.0 ) ) )
for _ in range(a_ ) )
# The ratio of the area for circle to square is pi/4.
_UpperCAmelCase : Optional[int] = proportion * 4
print(f"""The estimated value of pi is {pi_estimate}""" )
print(f"""The numpy value of pi is {pi}""" )
print(f"""The total error is {abs(pi - pi_estimate )}""" )
def __UpperCAmelCase ( a_: int, a_: Callable[[float], float], a_: float = 0.0, a_: float = 1.0, ):
return mean(
function_to_integrate(uniform(a_, a_ ) ) for _ in range(a_ ) ) * (max_value - min_value)
def __UpperCAmelCase ( a_: int, a_: float = 0.0, a_: float = 1.0 ):
def identity_function(a_: float ) -> float:
return x
_UpperCAmelCase : Union[str, Any] = area_under_curve_estimator(
a_, a_, a_, a_ )
_UpperCAmelCase : List[str] = (max_value * max_value - min_value * min_value) / 2
print("******************" )
print(f"""Estimating area under y=x where x varies from {min_value} to {max_value}""" )
print(f"""Estimated value is {estimated_value}""" )
print(f"""Expected value is {expected_value}""" )
print(f"""Total error is {abs(estimated_value - expected_value )}""" )
print("******************" )
def __UpperCAmelCase ( a_: int ):
def function_to_integrate(a_: float ) -> float:
return sqrt(4.0 - x * x )
_UpperCAmelCase : List[str] = area_under_curve_estimator(
a_, a_, 0.0, 2.0 )
print("******************" )
print("Estimating pi using area_under_curve_estimator" )
print(f"""Estimated value is {estimated_value}""" )
print(f"""Expected value is {pi}""" )
print(f"""Total error is {abs(estimated_value - pi )}""" )
print("******************" )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 17
| 0
|
'''simple docstring'''
from argparse import ArgumentParser
from . import BaseTransformersCLICommand
def __UpperCAmelCase ( a_: int ):
return DownloadCommand(args.model, args.cache_dir, args.force, args.trust_remote_code )
class A__ ( UpperCamelCase ):
"""simple docstring"""
@staticmethod
def _lowerCAmelCase ( lowerCAmelCase__ : ArgumentParser ) -> Optional[int]:
"""simple docstring"""
_UpperCAmelCase : Union[str, Any] = parser.add_parser("download" )
download_parser.add_argument(
"--cache-dir" , type=_A , default=_A , help="Path to location to store the models" )
download_parser.add_argument(
"--force" , action="store_true" , help="Force the model to be download even if already in cache-dir" )
download_parser.add_argument(
"--trust-remote-code" , action="store_true" , help="Whether or not to allow for custom models defined on the Hub in their own modeling files. Use only if you\'ve reviewed the code as it will execute on your local machine" , )
download_parser.add_argument("model" , type=_A , help="Name of the model to download" )
download_parser.set_defaults(func=_A )
def __init__( self : Tuple , lowerCAmelCase__ : str , lowerCAmelCase__ : str , lowerCAmelCase__ : bool , lowerCAmelCase__ : bool ) -> Any:
"""simple docstring"""
_UpperCAmelCase : int = model
_UpperCAmelCase : Any = cache
_UpperCAmelCase : Any = force
_UpperCAmelCase : Optional[Any] = trust_remote_code
def _lowerCAmelCase ( self : Tuple ) -> List[str]:
"""simple docstring"""
from ..models.auto import AutoModel, AutoTokenizer
AutoModel.from_pretrained(
self._model , cache_dir=self._cache , force_download=self._force , trust_remote_code=self._trust_remote_code )
AutoTokenizer.from_pretrained(
self._model , cache_dir=self._cache , force_download=self._force , trust_remote_code=self._trust_remote_code )
| 356
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
__a = {
'configuration_layoutlmv2': ['LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LayoutLMv2Config'],
'processing_layoutlmv2': ['LayoutLMv2Processor'],
'tokenization_layoutlmv2': ['LayoutLMv2Tokenizer'],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = ['LayoutLMv2TokenizerFast']
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = ['LayoutLMv2FeatureExtractor']
__a = ['LayoutLMv2ImageProcessor']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = [
'LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST',
'LayoutLMv2ForQuestionAnswering',
'LayoutLMv2ForSequenceClassification',
'LayoutLMv2ForTokenClassification',
'LayoutLMv2Layer',
'LayoutLMv2Model',
'LayoutLMv2PreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_layoutlmva import LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMvaConfig
from .processing_layoutlmva import LayoutLMvaProcessor
from .tokenization_layoutlmva import LayoutLMvaTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor, LayoutLMvaImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_layoutlmva import (
LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST,
LayoutLMvaForQuestionAnswering,
LayoutLMvaForSequenceClassification,
LayoutLMvaForTokenClassification,
LayoutLMvaLayer,
LayoutLMvaModel,
LayoutLMvaPreTrainedModel,
)
else:
import sys
__a = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 17
| 0
|
'''simple docstring'''
from typing import Callable, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a = logging.get_logger(__name__)
__a = {
'microsoft/xprophetnet-large-wiki100-cased': (
'https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/config.json'
),
}
class A__ ( lowerCamelCase__ ):
"""simple docstring"""
UpperCamelCase_ : Dict = '''xlm-prophetnet'''
UpperCamelCase_ : int = ['''past_key_values''']
UpperCamelCase_ : Any = {
'''num_attention_heads''': '''num_encoder_attention_heads''',
}
def __init__( self : Union[str, Any] , lowerCAmelCase__ : Optional[float] = 0.1 , lowerCAmelCase__ : Optional[Union[str, Callable]] = "gelu" , lowerCAmelCase__ : Optional[int] = 3_0_5_2_2 , lowerCAmelCase__ : Optional[int] = 1_0_2_4 , lowerCAmelCase__ : Optional[int] = 4_0_9_6 , lowerCAmelCase__ : Optional[int] = 1_2 , lowerCAmelCase__ : Optional[int] = 1_6 , lowerCAmelCase__ : Optional[int] = 4_0_9_6 , lowerCAmelCase__ : Optional[int] = 1_2 , lowerCAmelCase__ : Optional[int] = 1_6 , lowerCAmelCase__ : Optional[float] = 0.1 , lowerCAmelCase__ : Optional[float] = 0.1 , lowerCAmelCase__ : Optional[int] = 5_1_2 , lowerCAmelCase__ : Optional[float] = 0.02 , lowerCAmelCase__ : Optional[bool] = True , lowerCAmelCase__ : Optional[bool] = True , lowerCAmelCase__ : Optional[int] = 0 , lowerCAmelCase__ : Optional[int] = 2 , lowerCAmelCase__ : Optional[int] = 3_2 , lowerCAmelCase__ : Optional[int] = 1_2_8 , lowerCAmelCase__ : Optional[bool] = False , lowerCAmelCase__ : Optional[float] = 0.0 , lowerCAmelCase__ : Optional[bool] = True , lowerCAmelCase__ : Optional[int] = 0 , lowerCAmelCase__ : Optional[int] = 1 , lowerCAmelCase__ : Optional[int] = 2 , **lowerCAmelCase__ : int , ) -> Optional[int]:
"""simple docstring"""
_UpperCAmelCase : Dict = vocab_size
_UpperCAmelCase : List[str] = hidden_size
_UpperCAmelCase : str = encoder_ffn_dim
_UpperCAmelCase : Dict = num_encoder_layers
_UpperCAmelCase : Dict = num_encoder_attention_heads
_UpperCAmelCase : int = decoder_ffn_dim
_UpperCAmelCase : List[str] = num_decoder_layers
_UpperCAmelCase : Optional[int] = num_decoder_attention_heads
_UpperCAmelCase : Optional[Any] = max_position_embeddings
_UpperCAmelCase : List[Any] = init_std # Normal(0, this parameter)
_UpperCAmelCase : Optional[Any] = activation_function
# parameters for xlmprophetnet
_UpperCAmelCase : str = ngram
_UpperCAmelCase : Dict = num_buckets
_UpperCAmelCase : str = relative_max_distance
_UpperCAmelCase : Optional[Any] = disable_ngram_loss
_UpperCAmelCase : str = eps
# 3 Types of Dropout
_UpperCAmelCase : int = attention_dropout
_UpperCAmelCase : Optional[Any] = activation_dropout
_UpperCAmelCase : List[str] = dropout
_UpperCAmelCase : Tuple = use_cache
super().__init__(
pad_token_id=__snake_case , bos_token_id=__snake_case , eos_token_id=__snake_case , is_encoder_decoder=__snake_case , add_cross_attention=__snake_case , decoder_start_token_id=__snake_case , **__snake_case , )
@property
def _lowerCAmelCase ( self : Optional[Any] ) -> List[Any]:
"""simple docstring"""
return self.num_encoder_layers + self.num_decoder_layers
@num_hidden_layers.setter
def _lowerCAmelCase ( self : Tuple , lowerCAmelCase__ : Dict ) -> Tuple:
"""simple docstring"""
raise NotImplementedError(
"This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and"
" `num_decoder_layers`." )
| 357
|
'''simple docstring'''
def __UpperCAmelCase ( a_: int, a_: int ):
if not isinstance(a_, a_ ):
raise ValueError("iterations must be defined as integers" )
if not isinstance(a_, a_ ) or not number >= 1:
raise ValueError(
"starting number must be\n and integer and be more than 0" )
if not iterations >= 1:
raise ValueError("Iterations must be done more than 0 times to play FizzBuzz" )
_UpperCAmelCase : List[str] = ""
while number <= iterations:
if number % 3 == 0:
out += "Fizz"
if number % 5 == 0:
out += "Buzz"
if 0 not in (number % 3, number % 5):
out += str(a_ )
# print(out)
number += 1
out += " "
return out
if __name__ == "__main__":
import doctest
doctest.testmod()
| 17
| 0
|
'''simple docstring'''
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
| 358
|
'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from itertools import chain
from typing import Optional, Union
import datasets
import numpy as np
import torch
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,
AutoTokenizer,
HfArgumentParser,
Trainer,
TrainingArguments,
default_data_collator,
set_seed,
)
from transformers.tokenization_utils_base import PreTrainedTokenizerBase
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import PaddingStrategy, check_min_version, send_example_telemetry
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('4.31.0')
__a = logging.getLogger(__name__)
@dataclass
class A__ :
"""simple docstring"""
UpperCamelCase_ : str = field(
metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''} )
UpperCamelCase_ : Optional[str] = field(
default=UpperCamelCase , metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''} )
UpperCamelCase_ : Optional[str] = field(
default=UpperCamelCase , metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''} )
UpperCamelCase_ : Optional[str] = field(
default=UpperCamelCase , metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''} , )
UpperCamelCase_ : bool = field(
default=UpperCamelCase , metadata={'''help''': '''Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.'''} , )
UpperCamelCase_ : str = field(
default='''main''' , metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''} , )
UpperCamelCase_ : bool = field(
default=UpperCamelCase , metadata={
'''help''': (
'''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
'''with private models).'''
)
} , )
@dataclass
class A__ :
"""simple docstring"""
UpperCamelCase_ : Optional[str] = field(default=UpperCamelCase , metadata={'''help''': '''The input training data file (a text file).'''} )
UpperCamelCase_ : Optional[str] = field(
default=UpperCamelCase , metadata={'''help''': '''An optional input evaluation data file to evaluate the perplexity on (a text file).'''} , )
UpperCamelCase_ : bool = field(
default=UpperCamelCase , metadata={'''help''': '''Overwrite the cached training and evaluation sets'''} )
UpperCamelCase_ : Optional[int] = field(
default=UpperCamelCase , metadata={'''help''': '''The number of processes to use for the preprocessing.'''} , )
UpperCamelCase_ : Optional[int] = field(
default=UpperCamelCase , metadata={
'''help''': (
'''The maximum total input sequence length after tokenization. If passed, sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
)
} , )
UpperCamelCase_ : bool = field(
default=UpperCamelCase , metadata={
'''help''': (
'''Whether to pad all samples to the maximum sentence length. '''
'''If False, will pad the samples dynamically when batching to the maximum length in the batch. More '''
'''efficient on GPU but very bad for TPU.'''
)
} , )
UpperCamelCase_ : Optional[int] = field(
default=UpperCamelCase , metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of training examples to this '''
'''value if set.'''
)
} , )
UpperCamelCase_ : Optional[int] = field(
default=UpperCamelCase , metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
'''value if set.'''
)
} , )
def _lowerCAmelCase ( self : Any ) -> Any:
"""simple docstring"""
if self.train_file is not None:
_UpperCAmelCase : List[Any] = self.train_file.split("." )[-1]
assert extension in ["csv", "json"], "`train_file` should be a csv or a json file."
if self.validation_file is not None:
_UpperCAmelCase : List[str] = self.validation_file.split("." )[-1]
assert extension in ["csv", "json"], "`validation_file` should be a csv or a json file."
@dataclass
class A__ :
"""simple docstring"""
UpperCamelCase_ : PreTrainedTokenizerBase
UpperCamelCase_ : Union[bool, str, PaddingStrategy] = True
UpperCamelCase_ : Optional[int] = None
UpperCamelCase_ : Optional[int] = None
def __call__( self : List[Any] , lowerCAmelCase__ : List[str] ) -> List[str]:
"""simple docstring"""
_UpperCAmelCase : int = "label" if "label" in features[0].keys() else "labels"
_UpperCAmelCase : Dict = [feature.pop(lowerCAmelCase__ ) for feature in features]
_UpperCAmelCase : str = len(lowerCAmelCase__ )
_UpperCAmelCase : int = len(features[0]["input_ids"] )
_UpperCAmelCase : str = [
[{k: v[i] for k, v in feature.items()} for i in range(lowerCAmelCase__ )] for feature in features
]
_UpperCAmelCase : List[str] = list(chain(*lowerCAmelCase__ ) )
_UpperCAmelCase : Any = self.tokenizer.pad(
lowerCAmelCase__ , padding=self.padding , max_length=self.max_length , pad_to_multiple_of=self.pad_to_multiple_of , return_tensors="pt" , )
# Un-flatten
_UpperCAmelCase : Any = {k: v.view(lowerCAmelCase__ , lowerCAmelCase__ , -1 ) for k, v in batch.items()}
# Add back labels
_UpperCAmelCase : List[str] = torch.tensor(lowerCAmelCase__ , dtype=torch.intaa )
return batch
def __UpperCAmelCase ( ):
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
_UpperCAmelCase : Union[str, Any] = 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.
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase : str = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase : Dict = parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry("run_swag", a_, a_ )
# 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 )], )
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
_UpperCAmelCase : Optional[int] = training_args.get_process_log_level()
logger.setLevel(a_ )
datasets.utils.logging.set_verbosity(a_ )
transformers.utils.logging.set_verbosity(a_ )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# 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}""" )
logger.info(f"""Training/evaluation parameters {training_args}""" )
# Detecting last checkpoint.
_UpperCAmelCase : Any = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
_UpperCAmelCase : Any = 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 and training_args.resume_from_checkpoint is 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." )
# 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.train_file is not None or data_args.validation_file is not None:
_UpperCAmelCase : Union[str, Any] = {}
if data_args.train_file is not None:
_UpperCAmelCase : str = data_args.train_file
if data_args.validation_file is not None:
_UpperCAmelCase : Optional[Any] = data_args.validation_file
_UpperCAmelCase : Dict = data_args.train_file.split("." )[-1]
_UpperCAmelCase : Optional[int] = load_dataset(
a_, data_files=a_, cache_dir=model_args.cache_dir, use_auth_token=True if model_args.use_auth_token else None, )
else:
# Downloading and loading the swag dataset from the hub.
_UpperCAmelCase : Dict = load_dataset(
"swag", "regular", cache_dir=model_args.cache_dir, use_auth_token=True if model_args.use_auth_token else None, )
# 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.
_UpperCAmelCase : Any = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path, cache_dir=model_args.cache_dir, revision=model_args.model_revision, use_auth_token=True if model_args.use_auth_token else None, )
_UpperCAmelCase : Any = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path, 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, )
_UpperCAmelCase : str = AutoModelForMultipleChoice.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, )
# When using your own dataset or a different dataset from swag, you will probably need to change this.
_UpperCAmelCase : Optional[Any] = [f"""ending{i}""" for i in range(4 )]
_UpperCAmelCase : List[Any] = "sent1"
_UpperCAmelCase : Optional[int] = "sent2"
if data_args.max_seq_length is None:
_UpperCAmelCase : List[str] = tokenizer.model_max_length
if max_seq_length > 1_024:
logger.warning(
"The chosen tokenizer supports a `model_max_length` that is longer than the default `block_size` value"
" of 1024. If you would like to use a longer `block_size` up to `tokenizer.model_max_length` you can"
" override this default with `--block_size xxx`." )
_UpperCAmelCase : Dict = 1_024
else:
if data_args.max_seq_length > tokenizer.model_max_length:
logger.warning(
f"""The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the"""
f"""model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}.""" )
_UpperCAmelCase : Dict = min(data_args.max_seq_length, tokenizer.model_max_length )
# Preprocessing the datasets.
def preprocess_function(a_: Union[str, Any] ):
_UpperCAmelCase : Optional[int] = [[context] * 4 for context in examples[context_name]]
_UpperCAmelCase : Tuple = examples[question_header_name]
_UpperCAmelCase : Optional[Any] = [
[f"""{header} {examples[end][i]}""" for end in ending_names] for i, header in enumerate(a_ )
]
# Flatten out
_UpperCAmelCase : List[str] = list(chain(*a_ ) )
_UpperCAmelCase : Dict = list(chain(*a_ ) )
# Tokenize
_UpperCAmelCase : List[Any] = tokenizer(
a_, a_, truncation=a_, max_length=a_, padding="max_length" if data_args.pad_to_max_length else False, )
# Un-flatten
return {k: [v[i : i + 4] for i in range(0, len(a_ ), 4 )] for k, v in tokenized_examples.items()}
if training_args.do_train:
if "train" not in raw_datasets:
raise ValueError("--do_train requires a train dataset" )
_UpperCAmelCase : int = raw_datasets["train"]
if data_args.max_train_samples is not None:
_UpperCAmelCase : Optional[Any] = min(len(a_ ), data_args.max_train_samples )
_UpperCAmelCase : List[Any] = train_dataset.select(range(a_ ) )
with training_args.main_process_first(desc="train dataset map pre-processing" ):
_UpperCAmelCase : Union[str, Any] = train_dataset.map(
a_, batched=a_, num_proc=data_args.preprocessing_num_workers, load_from_cache_file=not data_args.overwrite_cache, )
if training_args.do_eval:
if "validation" not in raw_datasets:
raise ValueError("--do_eval requires a validation dataset" )
_UpperCAmelCase : Dict = raw_datasets["validation"]
if data_args.max_eval_samples is not None:
_UpperCAmelCase : int = min(len(a_ ), data_args.max_eval_samples )
_UpperCAmelCase : List[str] = eval_dataset.select(range(a_ ) )
with training_args.main_process_first(desc="validation dataset map pre-processing" ):
_UpperCAmelCase : Optional[int] = eval_dataset.map(
a_, batched=a_, num_proc=data_args.preprocessing_num_workers, load_from_cache_file=not data_args.overwrite_cache, )
# Data collator
_UpperCAmelCase : Tuple = (
default_data_collator
if data_args.pad_to_max_length
else DataCollatorForMultipleChoice(tokenizer=a_, pad_to_multiple_of=8 if training_args.fpaa else None )
)
# Metric
def compute_metrics(a_: Tuple ):
_UpperCAmelCase , _UpperCAmelCase : Tuple = eval_predictions
_UpperCAmelCase : Union[str, Any] = np.argmax(a_, axis=1 )
return {"accuracy": (preds == label_ids).astype(np.floataa ).mean().item()}
# Initialize our Trainer
_UpperCAmelCase : Any = Trainer(
model=a_, args=a_, train_dataset=train_dataset if training_args.do_train else None, eval_dataset=eval_dataset if training_args.do_eval else None, tokenizer=a_, data_collator=a_, compute_metrics=a_, )
# Training
if training_args.do_train:
_UpperCAmelCase : Optional[Any] = None
if training_args.resume_from_checkpoint is not None:
_UpperCAmelCase : List[Any] = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
_UpperCAmelCase : List[str] = last_checkpoint
_UpperCAmelCase : Any = trainer.train(resume_from_checkpoint=a_ )
trainer.save_model() # Saves the tokenizer too for easy upload
_UpperCAmelCase : str = train_result.metrics
_UpperCAmelCase : List[str] = (
data_args.max_train_samples if data_args.max_train_samples is not None else len(a_ )
)
_UpperCAmelCase : Union[str, Any] = min(a_, len(a_ ) )
trainer.log_metrics("train", a_ )
trainer.save_metrics("train", a_ )
trainer.save_state()
# Evaluation
if training_args.do_eval:
logger.info("*** Evaluate ***" )
_UpperCAmelCase : List[Any] = trainer.evaluate()
_UpperCAmelCase : int = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(a_ )
_UpperCAmelCase : Tuple = min(a_, len(a_ ) )
trainer.log_metrics("eval", a_ )
trainer.save_metrics("eval", a_ )
_UpperCAmelCase : int = {
"finetuned_from": model_args.model_name_or_path,
"tasks": "multiple-choice",
"dataset_tags": "swag",
"dataset_args": "regular",
"dataset": "SWAG",
"language": "en",
}
if training_args.push_to_hub:
trainer.push_to_hub(**a_ )
else:
trainer.create_model_card(**a_ )
def __UpperCAmelCase ( a_: int ):
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 17
| 0
|
'''simple docstring'''
import sys
from typing import Tuple
import numpy as np
import torch
from PIL import Image
from torch import nn
from transformers.image_utils import PILImageResampling
from utils import img_tensorize
class A__ :
"""simple docstring"""
def __init__( self : Optional[Any] , lowerCAmelCase__ : Any , lowerCAmelCase__ : Tuple=sys.maxsize ) -> List[str]:
"""simple docstring"""
_UpperCAmelCase : Union[str, Any] = "bilinear"
_UpperCAmelCase : Optional[Any] = max_size
_UpperCAmelCase : Any = short_edge_length
def __call__( self : Tuple , lowerCAmelCase__ : List[str] ) -> int:
"""simple docstring"""
_UpperCAmelCase : int = []
for img in imgs:
_UpperCAmelCase , _UpperCAmelCase : Optional[int] = img.shape[:2]
# later: provide list and randomly choose index for resize
_UpperCAmelCase : Tuple = np.random.randint(self.short_edge_length[0] , self.short_edge_length[1] + 1 )
if size == 0:
return img
_UpperCAmelCase : str = size * 1.0 / min(a__ , a__ )
if h < w:
_UpperCAmelCase , _UpperCAmelCase : int = size, scale * w
else:
_UpperCAmelCase , _UpperCAmelCase : int = scale * h, size
if max(a__ , a__ ) > self.max_size:
_UpperCAmelCase : int = self.max_size * 1.0 / max(a__ , a__ )
_UpperCAmelCase : List[str] = newh * scale
_UpperCAmelCase : Tuple = neww * scale
_UpperCAmelCase : str = int(neww + 0.5 )
_UpperCAmelCase : int = int(newh + 0.5 )
if img.dtype == np.uinta:
_UpperCAmelCase : int = Image.fromarray(a__ )
_UpperCAmelCase : int = pil_image.resize((neww, newh) , PILImageResampling.BILINEAR )
_UpperCAmelCase : Union[str, Any] = np.asarray(a__ )
else:
_UpperCAmelCase : Optional[int] = img.permute(2 , 0 , 1 ).unsqueeze(0 ) # 3, 0, 1) # hw(c) -> nchw
_UpperCAmelCase : Any = nn.functional.interpolate(
a__ , (newh, neww) , mode=self.interp_method , align_corners=a__ ).squeeze(0 )
img_augs.append(a__ )
return img_augs
class A__ :
"""simple docstring"""
def __init__( self : List[Any] , lowerCAmelCase__ : Tuple ) -> Any:
"""simple docstring"""
_UpperCAmelCase : Tuple = ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST] , cfg.INPUT.MAX_SIZE_TEST )
_UpperCAmelCase : Dict = cfg.INPUT.FORMAT
_UpperCAmelCase : int = cfg.SIZE_DIVISIBILITY
_UpperCAmelCase : int = cfg.PAD_VALUE
_UpperCAmelCase : int = cfg.INPUT.MAX_SIZE_TEST
_UpperCAmelCase : Tuple = cfg.MODEL.DEVICE
_UpperCAmelCase : List[str] = torch.tensor(cfg.MODEL.PIXEL_STD ).to(self.device ).view(len(cfg.MODEL.PIXEL_STD ) , 1 , 1 )
_UpperCAmelCase : Union[str, Any] = torch.tensor(cfg.MODEL.PIXEL_MEAN ).to(self.device ).view(len(cfg.MODEL.PIXEL_STD ) , 1 , 1 )
_UpperCAmelCase : Dict = lambda lowerCAmelCase__ : (x - self.pixel_mean) / self.pixel_std
def _lowerCAmelCase ( self : Union[str, Any] , lowerCAmelCase__ : Dict ) -> Tuple:
"""simple docstring"""
_UpperCAmelCase : Dict = tuple(max(a__ ) for s in zip(*[img.shape for img in images] ) )
_UpperCAmelCase : Tuple = [im.shape[-2:] for im in images]
_UpperCAmelCase : str = [
nn.functional.pad(
a__ , [0, max_size[-1] - size[1], 0, max_size[-2] - size[0]] , value=self.pad_value , )
for size, im in zip(a__ , a__ )
]
return torch.stack(a__ ), torch.tensor(a__ )
def __call__( self : Dict , lowerCAmelCase__ : Dict , lowerCAmelCase__ : List[str]=False ) -> Union[str, Any]:
"""simple docstring"""
with torch.no_grad():
if not isinstance(a__ , a__ ):
_UpperCAmelCase : str = [images]
if single_image:
assert len(a__ ) == 1
for i in range(len(a__ ) ):
if isinstance(images[i] , torch.Tensor ):
images.insert(a__ , images.pop(a__ ).to(self.device ).float() )
elif not isinstance(images[i] , torch.Tensor ):
images.insert(
a__ , torch.as_tensor(img_tensorize(images.pop(a__ ) , input_format=self.input_format ) )
.to(self.device )
.float() , )
# resize smallest edge
_UpperCAmelCase : Tuple = torch.tensor([im.shape[:2] for im in images] )
_UpperCAmelCase : Optional[int] = self.aug(a__ )
# transpose images and convert to torch tensors
# images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
# now normalize before pad to avoid useless arithmetic
_UpperCAmelCase : List[str] = [self.normalizer(a__ ) for x in images]
# now pad them to do the following operations
_UpperCAmelCase , _UpperCAmelCase : Tuple = self.pad(a__ )
# Normalize
if self.size_divisibility > 0:
raise NotImplementedError()
# pad
_UpperCAmelCase : Union[str, Any] = torch.true_divide(a__ , a__ )
if single_image:
return images[0], sizes[0], scales_yx[0]
else:
return images, sizes, scales_yx
def __UpperCAmelCase ( a_: Optional[int], a_: Any ):
boxes[:, 0::2] *= scale_yx[:, 1]
boxes[:, 1::2] *= scale_yx[:, 0]
return boxes
def __UpperCAmelCase ( a_: Union[str, Any], a_: Tuple ):
assert torch.isfinite(a_ ).all(), "Box tensor contains infinite or NaN!"
_UpperCAmelCase , _UpperCAmelCase : Dict = box_size
tensor[:, 0].clamp_(min=0, max=a_ )
tensor[:, 1].clamp_(min=0, max=a_ )
tensor[:, 2].clamp_(min=0, max=a_ )
tensor[:, 3].clamp_(min=0, max=a_ )
| 359
|
'''simple docstring'''
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 : Any , lowerCAmelCase__ : Optional[Any] ) -> str:
"""simple docstring"""
super().__init__()
_UpperCAmelCase : List[str] = model
_UpperCAmelCase : Dict = 2
_UpperCAmelCase : Tuple = nn.Linear(self.model.config.hidden_size , self.num_labels )
def _lowerCAmelCase ( self : Tuple ) -> int:
"""simple docstring"""
pass
def __UpperCAmelCase ( a_: str, a_: str, a_: str ):
# load longformer model from model identifier
_UpperCAmelCase : int = LongformerModel.from_pretrained(a_ )
_UpperCAmelCase : Any = LightningModel(a_ )
_UpperCAmelCase : int = torch.load(a_, map_location=torch.device("cpu" ) )
lightning_model.load_state_dict(ckpt["state_dict"] )
# init longformer question answering model
_UpperCAmelCase : List[str] = 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__":
__a = 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.'
)
__a = parser.parse_args()
convert_longformer_qa_checkpoint_to_pytorch(
args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
)
| 17
| 0
|
'''simple docstring'''
from unittest import TestCase
from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset
class A__ ( lowerCamelCase__ ):
"""simple docstring"""
def _lowerCAmelCase ( self : Any ) -> str:
"""simple docstring"""
return [
{"col_1": 3, "col_2": "a"},
{"col_1": 2, "col_2": "b"},
{"col_1": 1, "col_2": "c"},
{"col_1": 0, "col_2": "d"},
]
def _lowerCAmelCase ( self : int ) -> Dict:
"""simple docstring"""
_UpperCAmelCase : Union[str, Any] = {'''col_1''': [3, 2, 1, 0], '''col_2''': ['''a''', '''b''', '''c''', '''d''']}
return Dataset.from_dict(__A )
def _lowerCAmelCase ( self : Optional[int] ) -> int:
"""simple docstring"""
_UpperCAmelCase : Union[str, Any] = self._create_example_records()
_UpperCAmelCase : Dict = Dataset.from_list(__A )
self.assertListEqual(dset.column_names , ["col_1", "col_2"] )
for i, r in enumerate(__A ):
self.assertDictEqual(__A , example_records[i] )
def _lowerCAmelCase ( self : Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
_UpperCAmelCase : int = self._create_example_records()
_UpperCAmelCase : List[Any] = Dataset.from_list(__A )
_UpperCAmelCase : Any = Dataset.from_dict({k: [r[k] for r in example_records] for k in example_records[0]} )
self.assertEqual(dset.info , dset_from_dict.info )
def _lowerCAmelCase ( self : Dict ) -> Optional[int]: # checks what happens with missing columns
"""simple docstring"""
_UpperCAmelCase : Dict = [{'''col_1''': 1}, {'''col_2''': '''x'''}]
_UpperCAmelCase : Optional[Any] = Dataset.from_list(__A )
self.assertDictEqual(dset[0] , {"col_1": 1} )
self.assertDictEqual(dset[1] , {"col_1": None} ) # NB: first record is used for columns
def _lowerCAmelCase ( self : Optional[int] ) -> Optional[int]: # checks if the type can be inferred from the second record
"""simple docstring"""
_UpperCAmelCase : Union[str, Any] = [{'''col_1''': []}, {'''col_1''': [1, 2]}]
_UpperCAmelCase : Optional[int] = Dataset.from_list(__A )
self.assertEqual(dset.info.features["col_1"] , Sequence(Value("int64" ) ) )
def _lowerCAmelCase ( self : Union[str, Any] ) -> int:
"""simple docstring"""
_UpperCAmelCase : str = Dataset.from_list([] )
self.assertEqual(len(__A ) , 0 )
self.assertListEqual(dset.column_names , [] )
| 360
|
'''simple docstring'''
from importlib import import_module
from .logging import get_logger
__a = get_logger(__name__)
class A__ :
"""simple docstring"""
def __init__( self : List[str] , lowerCAmelCase__ : List[Any] , lowerCAmelCase__ : Optional[Any]=None ) -> Optional[int]:
"""simple docstring"""
_UpperCAmelCase : Any = attrs or []
if module is not None:
for key in module.__dict__:
if key in attrs or not key.startswith("__" ):
setattr(self , lowerCAmelCase__ , getattr(lowerCAmelCase__ , lowerCAmelCase__ ) )
_UpperCAmelCase : int = module._original_module if isinstance(lowerCAmelCase__ , _PatchedModuleObj ) else module
class A__ :
"""simple docstring"""
UpperCamelCase_ : Union[str, Any] = []
def __init__( self : int , lowerCAmelCase__ : List[str] , lowerCAmelCase__ : str , lowerCAmelCase__ : List[str] , lowerCAmelCase__ : Optional[int]=None ) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase : List[Any] = obj
_UpperCAmelCase : int = target
_UpperCAmelCase : Optional[int] = new
_UpperCAmelCase : Any = target.split("." )[0]
_UpperCAmelCase : Optional[int] = {}
_UpperCAmelCase : Dict = attrs or []
def __enter__( self : List[str] ) -> int:
"""simple docstring"""
*_UpperCAmelCase , _UpperCAmelCase : List[str] = self.target.split("." )
# Patch modules:
# it's used to patch attributes of submodules like "os.path.join";
# in this case we need to patch "os" and "os.path"
for i in range(len(lowerCAmelCase__ ) ):
try:
_UpperCAmelCase : int = import_module(".".join(submodules[: i + 1] ) )
except ModuleNotFoundError:
continue
# We iterate over all the globals in self.obj in case we find "os" or "os.path"
for attr in self.obj.__dir__():
_UpperCAmelCase : List[Any] = getattr(self.obj , lowerCAmelCase__ )
# We don't check for the name of the global, but rather if its value *is* "os" or "os.path".
# This allows to patch renamed modules like "from os import path as ospath".
if obj_attr is submodule or (
(isinstance(lowerCAmelCase__ , _PatchedModuleObj ) and obj_attr._original_module is submodule)
):
_UpperCAmelCase : Tuple = obj_attr
# patch at top level
setattr(self.obj , lowerCAmelCase__ , _PatchedModuleObj(lowerCAmelCase__ , attrs=self.attrs ) )
_UpperCAmelCase : List[Any] = getattr(self.obj , lowerCAmelCase__ )
# construct lower levels patches
for key in submodules[i + 1 :]:
setattr(lowerCAmelCase__ , lowerCAmelCase__ , _PatchedModuleObj(getattr(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ) , attrs=self.attrs ) )
_UpperCAmelCase : Any = getattr(lowerCAmelCase__ , lowerCAmelCase__ )
# finally set the target attribute
setattr(lowerCAmelCase__ , lowerCAmelCase__ , self.new )
# Patch attribute itself:
# it's used for builtins like "open",
# and also to patch "os.path.join" we may also need to patch "join"
# itself if it was imported as "from os.path import join".
if submodules: # if it's an attribute of a submodule like "os.path.join"
try:
_UpperCAmelCase : Dict = getattr(import_module(".".join(lowerCAmelCase__ ) ) , lowerCAmelCase__ )
except (AttributeError, ModuleNotFoundError):
return
# We iterate over all the globals in self.obj in case we find "os.path.join"
for attr in self.obj.__dir__():
# We don't check for the name of the global, but rather if its value *is* "os.path.join".
# This allows to patch renamed attributes like "from os.path import join as pjoin".
if getattr(self.obj , lowerCAmelCase__ ) is attr_value:
_UpperCAmelCase : Optional[Any] = getattr(self.obj , lowerCAmelCase__ )
setattr(self.obj , lowerCAmelCase__ , self.new )
elif target_attr in globals()["__builtins__"]: # if it'a s builtin like "open"
_UpperCAmelCase : Dict = globals()["__builtins__"][target_attr]
setattr(self.obj , lowerCAmelCase__ , self.new )
else:
raise RuntimeError(F"""Tried to patch attribute {target_attr} instead of a submodule.""" )
def __exit__( self : Optional[int] , *lowerCAmelCase__ : List[str] ) -> Union[str, Any]:
"""simple docstring"""
for attr in list(self.original ):
setattr(self.obj , lowerCAmelCase__ , self.original.pop(lowerCAmelCase__ ) )
def _lowerCAmelCase ( self : Union[str, Any] ) -> Optional[Any]:
"""simple docstring"""
self.__enter__()
self._active_patches.append(self )
def _lowerCAmelCase ( self : Optional[int] ) -> Tuple:
"""simple docstring"""
try:
self._active_patches.remove(self )
except ValueError:
# If the patch hasn't been started this will fail
return None
return self.__exit__()
| 17
| 0
|
'''simple docstring'''
import torch
def __UpperCAmelCase ( ):
if torch.cuda.is_available():
_UpperCAmelCase : Any = torch.cuda.device_count()
else:
_UpperCAmelCase : Optional[Any] = 0
print(f"""Successfully ran on {num_gpus} GPUs""" )
if __name__ == "__main__":
main()
| 361
|
'''simple docstring'''
import itertools
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union
import pandas as pd
import pyarrow as pa
import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast
from datasets.utils.py_utils import Literal
__a = datasets.utils.logging.get_logger(__name__)
__a = ['names', 'prefix']
__a = ['warn_bad_lines', 'error_bad_lines', 'mangle_dupe_cols']
__a = ['encoding_errors', 'on_bad_lines']
__a = ['date_format']
@dataclass
class A__ ( datasets.BuilderConfig ):
"""simple docstring"""
UpperCamelCase_ : str = ","
UpperCamelCase_ : Optional[str] = None
UpperCamelCase_ : Optional[Union[int, List[int], str]] = "infer"
UpperCamelCase_ : Optional[List[str]] = None
UpperCamelCase_ : Optional[List[str]] = None
UpperCamelCase_ : Optional[Union[int, str, List[int], List[str]]] = None
UpperCamelCase_ : Optional[Union[List[int], List[str]]] = None
UpperCamelCase_ : Optional[str] = None
UpperCamelCase_ : bool = True
UpperCamelCase_ : Optional[Literal["c", "python", "pyarrow"]] = None
UpperCamelCase_ : Dict[Union[int, str], Callable[[Any], Any]] = None
UpperCamelCase_ : Optional[list] = None
UpperCamelCase_ : Optional[list] = None
UpperCamelCase_ : bool = False
UpperCamelCase_ : Optional[Union[int, List[int]]] = None
UpperCamelCase_ : Optional[int] = None
UpperCamelCase_ : Optional[Union[str, List[str]]] = None
UpperCamelCase_ : bool = True
UpperCamelCase_ : bool = True
UpperCamelCase_ : bool = False
UpperCamelCase_ : bool = True
UpperCamelCase_ : Optional[str] = None
UpperCamelCase_ : str = "."
UpperCamelCase_ : Optional[str] = None
UpperCamelCase_ : str = '"'
UpperCamelCase_ : int = 0
UpperCamelCase_ : Optional[str] = None
UpperCamelCase_ : Optional[str] = None
UpperCamelCase_ : Optional[str] = None
UpperCamelCase_ : Optional[str] = None
UpperCamelCase_ : bool = True
UpperCamelCase_ : bool = True
UpperCamelCase_ : int = 0
UpperCamelCase_ : bool = True
UpperCamelCase_ : bool = False
UpperCamelCase_ : Optional[str] = None
UpperCamelCase_ : int = 1_00_00
UpperCamelCase_ : Optional[datasets.Features] = None
UpperCamelCase_ : Optional[str] = "strict"
UpperCamelCase_ : Literal["error", "warn", "skip"] = "error"
UpperCamelCase_ : Optional[str] = None
def _lowerCAmelCase ( self : str ) -> Tuple:
"""simple docstring"""
if self.delimiter is not None:
_UpperCAmelCase : Any = self.delimiter
if self.column_names is not None:
_UpperCAmelCase : List[Any] = self.column_names
@property
def _lowerCAmelCase ( self : Optional[int] ) -> Optional[int]:
"""simple docstring"""
_UpperCAmelCase : Dict = {
"sep": self.sep,
"header": self.header,
"names": self.names,
"index_col": self.index_col,
"usecols": self.usecols,
"prefix": self.prefix,
"mangle_dupe_cols": self.mangle_dupe_cols,
"engine": self.engine,
"converters": self.converters,
"true_values": self.true_values,
"false_values": self.false_values,
"skipinitialspace": self.skipinitialspace,
"skiprows": self.skiprows,
"nrows": self.nrows,
"na_values": self.na_values,
"keep_default_na": self.keep_default_na,
"na_filter": self.na_filter,
"verbose": self.verbose,
"skip_blank_lines": self.skip_blank_lines,
"thousands": self.thousands,
"decimal": self.decimal,
"lineterminator": self.lineterminator,
"quotechar": self.quotechar,
"quoting": self.quoting,
"escapechar": self.escapechar,
"comment": self.comment,
"encoding": self.encoding,
"dialect": self.dialect,
"error_bad_lines": self.error_bad_lines,
"warn_bad_lines": self.warn_bad_lines,
"skipfooter": self.skipfooter,
"doublequote": self.doublequote,
"memory_map": self.memory_map,
"float_precision": self.float_precision,
"chunksize": self.chunksize,
"encoding_errors": self.encoding_errors,
"on_bad_lines": self.on_bad_lines,
"date_format": self.date_format,
}
# some kwargs must not be passed if they don't have a default value
# some others are deprecated and we can also not pass them if they are the default value
for pd_read_csv_parameter in _PANDAS_READ_CSV_NO_DEFAULT_PARAMETERS + _PANDAS_READ_CSV_DEPRECATED_PARAMETERS:
if pd_read_csv_kwargs[pd_read_csv_parameter] == getattr(CsvConfig() , lowerCAmelCase__ ):
del pd_read_csv_kwargs[pd_read_csv_parameter]
# Remove 2.0 new arguments
if not (datasets.config.PANDAS_VERSION.major >= 2):
for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_2_0_0_PARAMETERS:
del pd_read_csv_kwargs[pd_read_csv_parameter]
# Remove 1.3 new arguments
if not (datasets.config.PANDAS_VERSION.major >= 1 and datasets.config.PANDAS_VERSION.minor >= 3):
for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_1_3_0_PARAMETERS:
del pd_read_csv_kwargs[pd_read_csv_parameter]
return pd_read_csv_kwargs
class A__ ( datasets.ArrowBasedBuilder ):
"""simple docstring"""
UpperCamelCase_ : int = CsvConfig
def _lowerCAmelCase ( self : Optional[Any] ) -> Optional[int]:
"""simple docstring"""
return datasets.DatasetInfo(features=self.config.features )
def _lowerCAmelCase ( self : Tuple , lowerCAmelCase__ : str ) -> List[str]:
"""simple docstring"""
if not self.config.data_files:
raise ValueError(F"""At least one data file must be specified, but got data_files={self.config.data_files}""" )
_UpperCAmelCase : List[str] = dl_manager.download_and_extract(self.config.data_files )
if isinstance(lowerCAmelCase__ , (str, list, tuple) ):
_UpperCAmelCase : int = data_files
if isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
_UpperCAmelCase : Any = [files]
_UpperCAmelCase : List[Any] = [dl_manager.iter_files(lowerCAmelCase__ ) for file in files]
return [datasets.SplitGenerator(name=datasets.Split.TRAIN , gen_kwargs={"files": files} )]
_UpperCAmelCase : Optional[Any] = []
for split_name, files in data_files.items():
if isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
_UpperCAmelCase : str = [files]
_UpperCAmelCase : Any = [dl_manager.iter_files(lowerCAmelCase__ ) for file in files]
splits.append(datasets.SplitGenerator(name=lowerCAmelCase__ , gen_kwargs={"files": files} ) )
return splits
def _lowerCAmelCase ( self : List[Any] , lowerCAmelCase__ : pa.Table ) -> pa.Table:
"""simple docstring"""
if self.config.features is not None:
_UpperCAmelCase : Tuple = self.config.features.arrow_schema
if all(not require_storage_cast(lowerCAmelCase__ ) for feature in self.config.features.values() ):
# cheaper cast
_UpperCAmelCase : Any = pa.Table.from_arrays([pa_table[field.name] for field in schema] , schema=lowerCAmelCase__ )
else:
# more expensive cast; allows str <-> int/float or str to Audio for example
_UpperCAmelCase : int = table_cast(lowerCAmelCase__ , lowerCAmelCase__ )
return pa_table
def _lowerCAmelCase ( self : Dict , lowerCAmelCase__ : Dict ) -> Dict:
"""simple docstring"""
_UpperCAmelCase : int = self.config.features.arrow_schema if self.config.features else None
# dtype allows reading an int column as str
_UpperCAmelCase : Optional[Any] = (
{
name: dtype.to_pandas_dtype() if not require_storage_cast(lowerCAmelCase__ ) else object
for name, dtype, feature in zip(schema.names , schema.types , self.config.features.values() )
}
if schema is not None
else None
)
for file_idx, file in enumerate(itertools.chain.from_iterable(lowerCAmelCase__ ) ):
_UpperCAmelCase : Optional[Any] = pd.read_csv(lowerCAmelCase__ , iterator=lowerCAmelCase__ , dtype=lowerCAmelCase__ , **self.config.pd_read_csv_kwargs )
try:
for batch_idx, df in enumerate(lowerCAmelCase__ ):
_UpperCAmelCase : Optional[int] = pa.Table.from_pandas(lowerCAmelCase__ )
# Uncomment for debugging (will print the Arrow table size and elements)
# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
yield (file_idx, batch_idx), self._cast_table(lowerCAmelCase__ )
except ValueError as e:
logger.error(F"""Failed to read file '{file}' with error {type(lowerCAmelCase__ )}: {e}""" )
raise
| 17
| 0
|
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